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POLYMORPHISMS IN DNA REGULATION -ASSOCIATED GENES AND GASTRIC CANCER DEVELOPMENT MSc dissertation in Medicine and Molecular Oncology submitted to the FACULTY OF MEDICINE, UNIVERSITY OF PORTO

Marco António Assunção das Neves

Molecular Oncology and Viral Pathology Group (CI-IPOP),

IPO Porto FG EPE, Porto, Portugal

SUPERVISION

Hugo Manuel Lopes de Sousa, MD, PhD

Virology Service & Molecular Oncology and Viral Pathology Group (CI-IPOP),


CO-SUPERVISION

Joana Patrícia Costa Ribeiro, MSc

Molecular Oncology and Viral Pathology Group (CI-IPOP),


Rui Manuel de Medeiros Melo Silva

Invited Professor, PharmD PhD

Virology Service & Molecular Oncology and Viral Pathology Group (CI-IPOP),


Department of Molecular Pathology and Immunology,

ICBAS - UP, Porto, Portugal

AKNOWLEDGMENTS

AKNOWLEDGMENTS

Polymorphisms in DNA regulation-associated genes and GC development |iii

Ao Professor Doutor Hugo Sousa, meu orientador, muito obrigado pelo apoio

e por tudo que me ensinou. Praticamente tudo o que aprendi nesta etapa de

aspirante a investigador foi graças ao Dr. Hugo. Se as coisas não estão a

funcionar então somos nós que temos de as resolver, há sempre solução

para um problema.

À Joana Ribeiro, minha co-orientadora, pela disponibilidade para ajudar em

tudo aquilo que eu precisei. Obrigado por partilhares a tua expêriencia no

laboratório comigo, aprendi imenso contigo.

Ao Professor Doutor Rui Medeiros, meu co-orientador, pelos conselhos e

sabedoria que me transmitiu. É sempre bom poder contar com a ajuda de

alguém com a experiência do Dr. Rui nos momentos em que as coisas não

correm como esperado.

Ao Professor Doutor Henrique Almeida, coordenador do Mestrado, pela

disponibilidade para resolver todos os problemas que foram surgindo ao

longo deste ano e pela oportunidade que me deu para poder alcançar um

dos objetivos da minha vida, realizar este mestrado que tanto me ensinou

nestes últimos dois anos.

Aos co-autores dos projetos de investigação: Dra. Áurea Lima e Mónica

Silva, pela ajuda crucial na realização de parte desta dissertação. Áurea,

obrigado por tudo o que disponibilizaste para que fosse possível realizar esta

dissertação. Agradeço imenso o apoio que me deste na fase mais

importante e que mais precisava. Mónica foste uma excelente colega de

laboratório, parte deste trabalho foi realizado graças à tua ajuda. Muito

obrigado pela disponibilidade e pela companhia. O trabalho é sempre mais

agradável quando temos alguém para conversar.

Ao Grupo de Oncologia Molecular, pela disponibilidade de todos para ajudar

quando foi necessário e pelo bom “ambiente científico” que proporcionam.

É mais fácil “fazer” ciência quando se trabalha num grupo com o vosso.

AKNOWLEDGMENTS

iv| Polymorphisms in DNA regulation-associated genes and GC development

Aos meus amigos, por todo apoio que me deram. O trabalho de investigação

não é fácil e é sempre bom poder contar com os nossos amigos para

desanuviar e melhorar um dia que não estava a correr tão bem. Um obrigado

especial Fernandes e Gonçalo, vocês sabem como me fazer sentir bem.

Muito obrigado pelo apoio meus meninos.

E por último, mas sempre em primeiro, à minha família e à minha namorada,

o meu maior apoio e a minha maior fonte de motivação. Paizinhos muito

obrigado por tudo o que fazem por mim. Eu sei o esforço que fizeram para

que eu pudesse chegar a este momento. Tudo se torna mais fácil com o

vosso apoio incondicional. Dedico-vos este trabalho como pequena forma

de agradecer por tudo.

Mariana, meu amor, tu sabes o quão importante és para mim e que sem ti

eu não encontraria forças para me superar. Obrigado por seres a minha

companheira e por saberes sempre o que é melhor para mim.

You are the love of my life.

Por último, quero agradecer à minha Avó, a minha maior inspiração. Eu sei

que a Avó é a pessoa que fica mais feliz por eu terminar esta etapa da minha

vida. Essa felicidade que me transmite é a minha maior fonte de força de

vontade para fazer mais e melhor. Obrigado pelo carinho, obrigado pelo

apoio, obrigado por tudo Avó.

ABBREVIATIONS LIST

ABBREVIATIONS LIST

Polymorphisms in DNA regulation-associated genes and GC development |vi

A

A – Adenine

Arg – Arginine

C

C – Cytosine

CI – Confidence Interval

CGI – CpG Island

CpG – 5'-C-phosphate-G-3'

D

DNA – Deoxyribonucleic Acid

DNMT – DNA Methyltransferase

DNMT1 – DNA (cytosine-5)-methyltransferase 1

DNMT2 – TRNA (cytosine38-C5)-methyltransferase

DNMT3 – DNA (cytosine-5)-methyltransferase 3

DNMT3A – DNA (cytosine-5)-methyltransferase 3 Alpha

DNMT3B – DNA (cytosine-5-)-methyltransferase 3 Beta

dNTP – deoxyribose Nucleoside Triphosphate

G

G – Guanine

GC – Gastric Cancer

GLOBOCAN – International Agency for Research on Cancer’s online database

GWAS – Genome-Wide Association Studies

H

His – Histidine

HP – Helicobacter pylori

HRM – High Resolution Melting

HWE – Hardy–Weinberg Equilibrium

I

IARC – International Agency for Research on Cancer

ABBREVIATIONS LIST

vii| Polymorphisms in DNA regulation-associated genes and GC development

M

MALDI-TOF – Matrix Assisted Laser Desorption/Ionization - Time of Flight

miRNA – micro Ribonucleic Acid

MGMT – O6-methylguanine-DNA Methyltransferase

MTHF – Methylenetetrahydrofolate

MTHFR – Methylenetetrahydrofolate Reductase

O

OR – Odds Ratio

P

PCR – Polymerase Chain Reaction

R

Real-Time PCR – Real-Time Polymerase Chain Reaction

RFC1 – Reduced Folate Carrier 1

RFLP – Restriction Fragment Length Polymorphism

RNA – Ribonucleic Acid

S

SAM – S-adenosylmethionine

SLC19A1 – Solute Carrier family 19, Member 1

SNP – Single Nucleotide Polymorphism

SPSS – Statistical Package for Social Sciences

T

T – Thymine

tRNA – transfer Ribonucleic Acid

Tyr – Tyrosine

OUTLINE OF THE THESIS


Polymorphisms in DNA regulation-associated genes and GC development |ix


In Chapter 1, the rational for this study will be presented focusing on explain the potential

impact of Single Nucleotide Polymorphisms (SNPs), in genes involved in DNA regulation,

on Gastric Cancer susceptibility.

In Chapter 2, it will be presented a systematic review and meta-analysis regarding the

association of SNPs of DNA methyltransferases (DNMTs) with gastric cancer. This

chapter is presented as the following article:

Neves M, Ribeiro J, Medeiros R, Sousa H. Genetic polymorphism in DNMTs and

gastric cancer: a systematic review and meta-analysis. (prepared to submit to

Porto Biomedical Journal)

In Chapter 3, it will be characterized the association of DNMT2 and DNMT3A SNPs with

the development of gastric lesions and gastric adenocarcinoma in a population from the

northern region of Portugal. This chapter is presented as the following article:

Neves M, Ribeiro J, Dinis-Ribeiro M, Medeiros R, Sousa H. Single Nucleotide

polymorphisms in DNA methyltransferases and gastric cancer. (prepared to

submit to Acta Medica)

In Chapter 4, it will be shown the association of SNPs in SLC19A1 and MTHFR genes

with the susceptibility to GC. This chapter is presented as the following article:

Neves M, Francisco M, Ribeiro J, Lima A, Dinis-Ribeiro M, Medeiros R, Sousa H.

SLC19A1 G80A and MTHFR C677T as risk factors for gastric cancer

development. (prepared to submit to Tumor Biology)

In Chapter 5, it will be summarized the importance of the results obtained in this thesis.

Up to our knowledge, this is the first study to evaluate the association of SNPs in

SLC19A1, MTHFR and DNMTs with GC, in the Portuguese population.

Finally, in Chapter 6, it will be presented the references of the thesis.

INDEX

Polymorphisms in DNA regulation-associated genes and GC development |xi

INDEX OF CONTENTS

AKNOWLEDGMENTS .................................................................................................. ii

ABBREVIATIONS LIST ................................................................................................ v

OUTLINE OF THE THESIS ........................................................................................ viii

INDEX ......................................................................................................................... xi

ABSTRACT ................................................................................................................ xiii

RESUMO .................................................................................................................... xiv

CHAPTER 1 ................................................................................................................. 1

CHAPTER 2 ............................................................................................................... 10

CHAPTER 3 ............................................................................................................... 29

CHAPTER 4 ............................................................................................................... 40

CHAPTER 5 ............................................................................................................... 55

CHAPTER 6 ............................................................................................................... 62

ATTACHMENTS ......................................................................................................... 66

ABSTRACT

ABSTRACT

Polymorphisms in DNA regulation-associated genes and GC development |xiii

Gastric cancer (GC) constitutes a serious public health problem, representing the fifth

most common diagnosed cancer with approximately 3 000 new diagnoses and 2 200

deaths per year, in Portugal. GC carcinogenesis is associated with several risk factors,

such as, environmental factors (Helicobacter pylori, diet, obesity, smoking), epigenetic

alterations and genetic susceptibility. Regarding epigenetic alterations, studies have

shown that aberrant DNA methylation have a role in GC carcinogenesis. Therefore,

genetic alterations in genes responsible for DNA methylation (DNA methyltransferases)

may have an impact on GC development. Other risk factor that has been associated with

GC is low folate intake. Folates are important for DNA regulation, thereby alterations in

genes important for folate metabolism might affect individual’s susceptibility for GC.

Genetic polymorphisms, such as single nucleotide polymorphisms (SNPs), have been

studied and associated with altered protein functions and, consequently, with the

development of cancer. Several studies suggest that SNPs in genes important for DNA

methylation might have an influence on the susceptibility for GC.

We developed a study to analyze the role of some SNPs in GC development. The study

was performed with a population from the northern region of Portugal with gastric lesions

(n=43), GC (n=137) and a cancer-free control group (n=250). SNPs were selected based

on literature and genetic characterization was performed with different methodologies.

Firstly, we performed a systematic review and meta-analysis to evaluate which SNPs in

DNA methyltransferases (DNMTs) may be associated to GC, according to literature. The

results showed that DNMT2 rs11254413, DNMT3A rs7560488, DNMT3A rs36012910

and, specially, DNMT1 rs16999593 seem to be good candidates to study the role of

SNPs in DNMTs in GC development. Then, we aimed to characterize DNMT2

rs11254413 and DNMT3A rs7560488 genotypes and their association with GC, in our

population. Our results showed that there is no association between both SNPs and GC

development. Finally, another aim of our study was to analyze the association between

SLC19A1 G80A and MTHFR C677T with GC development. Regarding the results

obtained for SLC19A1 G80A genotypes, we observed a protective effect for gastric

lesion (OR=0.373; 95%CI=0.189-0.736; p=0.006), but not with GC. On the other hand,

MTHFR C677T was associated with an increased risk for GC development (OR=2.046;

CI 95%=1.323-3.165; p=0.001), but it was not associated with gastric lesions.

ABSTRACT

Polymorphisms in DNA regulation-associated genes and GC development |xiv

This study revealed that DNMT2 rs11254413 and DNMT3A rs7560488 polymorphisms

are not associated with GC in our population, nevertheless SLC19A1 G80A may have a

protective role in gastric lesions development and MTHFR C677T may be associated

with an increased risk for GC development.

RESUMO

RESUMO

Polymorphisms in DNA regulation-associated genes and GC development |xv

O cancro gástrico (CG) constitui um grave problema de saúde pública, representando o

5º cancro mais diagnosticado com cerca de 3 000 novos diagnósticos e 2 200 mortes

por ano, em Portugal. A carcinogénese do CG está associada a vários fatores de risco,

tais como, fatores ambientais (Helicobacter pylori, dieta, obesidade, tabaco), alterações

epigenéticas e suscetibilidade genética. Em relação às alterações epigenéticas, estudos

têm mostrado que a metilação anormal do DNA tem um papel na carcinogénese do CG.

Portanto, alterações genéticas em genes responsáveis pela metilação do DNA (DNA

metiltransferases) podem ter um impacto no desenvolvimento do CG. Outro fator de

risco que tem sido associado ao CG é a baixa ingestão de folatos. Os folatos são

importantes para a regulação do DNA, pelo que alterações nos genes importantes para

o metabolismo dos folatos podem afetar a suscetibilidade individual para o CG. Os

polimorfismos genéticos, tais como single nucleotide polymorphisms (SNPs), têm sido

estudados e associados a alterações nas funções das proteínas e, consequentemente,

com o desenvolvimento de cancro. Muitos estudos sugerem que SNPs nos genes

responsáveis pela metilação podem ter influência na suscetibilidade para o CG.

Nós desenvolvemos um estudo para analisar o papel de alguns SNPs no

desenvolvimento de CG. O estudo foi realizado com uma população da região Norte de

Portugal com lesões gástricas (n=43), CG (n=137) e um grupo sem cancro (n=250). Os

SNPs foram selecionados com base na literatura e a caracterização genética foi feita

através de diferentes metodologias. Primeiramente, nós realizamos uma revisão

sistemática e uma meta-análise para avaliar que SNPs em DNA metiltransferases

(DNMTs) podem ser associados ao CG, de acordo com a literatura. Os resultados

mostraram que os DNMT2 rs11254413, DNMT3A rs7560488, DNMT3A rs36012910 e,

especialmente, o DNMT1 rs16999593 parecem ser bons candidatos para estudar o

papel de SNPs em DNMTs no desenvolvimento de CG. De seguida, o nosso objetivo foi

caracterizar os genótipos para DNMT2 rs11254413 e DNMT3A rs7560488 na nossa

população. Os nossos resultados mostraram que não existe associação entre os dois

SNPs e o desenvolvimento de CG. Por fim, outro objetivo do nosso estudo era analisar

a associação entre SLC19A1 G80A e MTHFR C677T com o desenvolvimento de CG.

De acordo com os resultados obtidos para os genótipos do SLC19A1 G80A,

observámos um efeito protetor para a lesões gástricas (OR = 0.373; 95%CI = 0.189-

0.736; p = 0.006), mas não para o CG. Por outro lado, MTHFR C677T foi associado a

um risco aumentado de desenvolvimento de CG (OR = 2.046; CI 95% = 1.323-3.165; p

= 0.001), mas não associado a lesões gástricas.

RESUMO

Polymorphisms in DNA regulation-associated genes and GC development |xvi

Este estudo revelou que os polimorfismos DNMT2 rs11254413 e DNMT3A rs7560488

não estão associados com CG na nossa população, porém o SLC19A1 G80A pode ter

um papel protetor no desenvolvimento de lesões gástricas e o MTHFR C677T pode

estar associado a um risco aumentado de desenvolvimento de CG.

CHAPTER 1

CHAPTER 1

Polymorphisms in DNA regulation-associated genes and GC development |2

I. CANCER

Cancer could be defined as a disease where cells, with “damaged” DNA, grow without

control and gain the potential to invade other tissues and spread to other parts of the

body. The emergence of conditions for the development of cancer is dependent on many

factors, such as genetic, epigenetic and environmental factors, which shows the

complexity of this disease (1).

According to the International Agency for Research on Cancer (IARC), cancer is one of

the leading causes of mortality worldwide, with 8.2 million deaths in 2012 (13% of all

deaths) (2,3). In the same year, 14 million new cases of cancer were reported and it is

expected a 70% increase of new cases of cancer in the next 20 years (3). The top 5

cancers worldwide, and in Portugal, are Breast, Prostate, Lung, Colorectal and Stomach,

being breast cancer the more incident and lung cancer the one with higher mortality –

figure 1.

Figure 1. Incidence and mortality of top 5 cancer sites (both sexes) in 2012. (Adapted from

GLOBOCAN 2012 (4))

CHAPTER 1


II. GASTRIC CANCER

i. Epidemiology

Gastric Cancer (GC) is the third leading cause of death by cancer, worldwide (4). It

represents 6.8% of all incident cancer cases and 8.8% of all cancer deaths. The highest

mortality rates are in eastern Asia (China is the country with higher mortality rates) and

the lowers in north America. Central and eastern Europe and central and south America

have also high mortality rates (4). In Portugal, GC represents 9.5% of all cancer deaths

(2265 deaths) and 6.1% of all incident cases (3018 new cases) (4).

The most frequent histological types of GC are adenocarcinomas and, according to

Lauren classification, are divided in two sub-types: diffuse (poorly differentiated) and

intestinal (well/moderately differentiated). Additionally, tumors that exhibit both intestinal

and diffuse characteristics, and undifferentiated tumors are classified as indeterminate

(5). The most sensitive and specific diagnostic test for GC is endoscopic screening,

nevertheless the majority of GC patients are diagnosed at an advanced stage because

of the lack of early symptoms (6). GC initiation and progression are a result of complex

genetic and environmental interactions. GC carcinogenesis differs according to the

histological type: the intestinal type carcinogenesis is a sequential step process,

characterized by a start with a premalignant gastric change, such as atrophic gastritis,

followed by intestinal metaplasia, dysplasia and, finally, gastric carcinoma; and the

diffuse type carcinogenesis is more often associated with some genetic changes and

there is not a consistent sequence of events, such as in the intestinal type. The diffuse

type is associated with different pathological characteristics, such as signet ring cells and

loss of cell cohesion (6–8).

ii. Risk factors

Tumorigenesis of GC is a multistep process and involve multiple factors (9,10).The major

risk factor for the development of gastric cancer (especially the intestinal type) is the

infection by Helicobacter pylori (HP), nevertheless, GC can also be the result of specific

genetic alterations (7). The majority of GC cases arise sporadically and only less than

3% are hereditary cancer syndromes (8). There are several sporadic genetic changes

that influences GC pathogenesis, such as chromosomal instability, microsatellite

instability, epigenetic changes, somatic gene mutations or single nucleotide

polymorphisms (SNPs) (8).

CHAPTER 1


The incidence of gastric cancer have been reduced since the identification of HP as a

key factor in carcinogenesis, but other factors associated to our modern lifestyle and

aging of population are also influencing the incidence of GC worldwide (10). The risk for

GC increases with age and incidence rates of GC are twice higher in men comparing to

women (4). Unhealthy habits, such as heavy alcohol drinking (four or more drinks per

day) and smoking increases risk for GC (10). Obesity and diet are two major factors

affecting GC incidence, nowadays. Literature shows that low consumption of fruits and

vegetables, intake of salty and smoked food and obesity are associated with higher risk

for GC (9–14) - figure 2.

Figure 2. Gastric cancer risk factors. Environmental and genetic/epigenetic factors are

associated with gastric carcinogenesis. (9–17).

GASTRIC CANCER RISK

FACTORS

Helicobacter Pylori Epstein-Barr

Virus

Age

Sex

(Male)

Smoking

Alcohol

Obesity

High intake of salty, nitrated and smoked

food

Low consumption of

fruits and vegetables

Hereditary cancer

syndromes

Pre-cancerous lesions

Chromosomal and

microsatellite instability

Somatic mutations

Genetic susceptibility

Epigenetic alterations

CHAPTER 1


III. DNA REGULATION AND CANCER

i. Folate intake – Folates and DNA regulation

Folates are found naturally in a wide variety of foods, specially vegetables like spinach,

kale, brussels sprouts and broccoli. Folates are water soluble B-vitamins essential for

intracellular transmethylation reactions including those involved in DNA methylation and

DNA synthesis (17). The active form of folate for cells is reduced folate 5-

methyltetrahydrofolate (5-MTHF). Reduced folates are transported into the cell by

Reduced Folate Carrier 1 (RFC1 or SLC19A1), or metabolized by Methylene

tetrahydrofolate reductase (MTHFR) – Figure 3 (18). MTHFR has a key role by

converting dietary folates and folic acid (artificially form of folate) to 5-MTHF (18,19). The

5-MTHF works as a single carbon donor used to convert homocysteine in to methionine,

by removing a methyl group from 5-MTHF, using vitamin B12 as coenzyme. Methionine

serves as a methyl group donor through conversion to S-Adenosyl methionine (SAM), a

key agent for DNA methylation (18,20).

Figure 3. Folate metabolism mechanism. (Adapted from Ulrich 2003 (21))

CHAPTER 1


Multiple studies have demonstrated that alterations in folate metabolism, such as folate

deficiency, low levels of folate in serum or genetic factors affecting genes in folate

metabolism are associated with cancer, including GC (22–25). Alterations in RFC1

activity affects the amount of reduced folates, leading to a lower concentration in the

cells (23). MTHFR alterations and consequent reduced enzymatic activity leads to higher

levels of homocysteine and the cycle homocysteine – methionine – SAM is disrupted,

affecting DNA methylation and leading in to changes in the epigenetic profile (24).

ii. Epigenetics and control of gene expression

Epigenetics is the study of heritable and transient/reversible changes in gene expression

that are not accompanied by a change in the DNA sequence (1). The most extensively

studied epigenetic modification is DNA methylation, which is characterized by an addition

of a methyl group in to the fifth carbon position of a cytosine residue in a CpG dinucleotide

(26). Clusters of CpG dinucleotides in GC rich regions of the genome called CpG islands

(CGI) frequently occur in the 5′-flanking promoter areas of genes, which are often

responsible for the control of protein translation (15). Epigenetic control of gene

expression by cytosine methylation is facilitated by the activity of DNA

methyltransferases (DNMTs). These enzymes recognize CpGs and catalyze the

transmethylation of cytosine by transferring methyl groups from S-adenosylmethionine

(SAM) (26,27).

DNA methylation was the first epigenetic mark shown to be critically involved in cancer

development. Alterations in DNA methylation are associated with silencing of tumor

suppressor genes and the activation of oncogenes involved in cancer (17,1). Literature

have reported that global hypomethylation has been associated to early stages of

carcinogenesis, causing genome-wide allelic instability (28). Paradoxically, the genomes

of cancer cells are also characterized by promoter genes hypermethylation, typically in

the CpG island of tumor suppressor genes and microRNA (miRNA). In fact, the

inactivation of tumor suppressor genes through the hypermethylation of CpG islands

within promoter methylation has been considered an important hallmark of cancer

(17,26,29,30).

CHAPTER 1


IV. GENETIC SUSCEPTIBILITY AND CANCER

The human genome sequence of two individuals is only 99% similar, and 90% of the

variations in the genome are Single Nucleotide Polymorphisms (31). SNPs are

differences in single bases of DNA that turned out to be stable through evolution and

where the minor allelic frequency is over 1% in the population. These variants are

present in almost all population but some are specific from one region or population (32).

Several studies have reported the association of SNPs with differential function of

proteins and therefore contributing for the development of several diseases, including

cancer (31,33,34).

A project called Genome-Wide Association Studies (GWAS) have emerged as a

powerful approach to identify risk alleles for common diseases (32,35). These studies

compare two large groups of individuals, one healthy control group and one case group

affected by a disease, including cancer (35). The enormous variety of data produced by

GWAS and other studies regarding potential genomic markers in cancer contributes to

population screening for genetic susceptibility to cancer. All this information may help to

select groups of individuals who are at increased risk of cancer development and provide

to them a preventive strategy (36).

Figure 4. Preventive strategy on increased risk groups may help to reduce mortality rates by

reducing the risk of cancer development or by proving better prognosis and personalized therapy

for the populations. (Source: National Cancer Institute, USA)

CHAPTER 1


V. AIM OF THE DISSERTATION

The main aim of the dissertation was to evaluate different SNPs on genes that are

important for cellular methylation and DNA regulation and study an association with

gastric cancer. SNPs in genes of folate metabolism and DNMTs have already been

described in GC, nevertheless these were not studied in our population, and there are

only few studies in eastern/Caucasian populations. By evaluating SNPs in gastric cancer

patients and comparing with healthy controls, we expected to find associations between

SNPs and gastric cancer risk.

Throughout the dissertation we tried to answer specific aims through different studies:

1) Systematic review and meta-analysis to understand witch SNPs in DNMTs may

be associated to GC;

2) Case-control study to understand the association of some SNPs in DNMTs with

GC in our population;

3) Case-control study to understand the association of SNPs in SLC19A1 and

MTHFR with GC in our population.

CHAPTER 2

CHAPTER 2


TITLE:

GENETIC POLYMORPHISM IN DNMTs AND GASTRIC CANCER: A SYSTEMATIC

REVIEW AND META-ANALYSIS

AUTHORS

Neves M1,2, Ribeiro J1,2,3, Medeiros R1,2,3,4,5, Sousa H1,2,5

Affiliations

1 Molecular Oncology and Viral Pathology Group, Research Centre (CI-IPOP),

Portuguese Oncology Institute of Porto (IPO-Porto), Rua Dr. António Bernardino de

Almeida, 4200-072 Porto, PORTUGAL

2 Faculty of Medicine of University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319

Porto, PORTUGAL

3 Research Department, Portuguese League Against Cancer (LPCC-NRNorte), Estrada

Interior da Circunvalação 6657, 4200 Porto, PORTUGAL

4 Abel Salazar Institute for the Biomedical Sciences (ICBAS), University of Porto, Rua de

Jorge Viterbo Ferreira n.º 228, 4050-313 Porto, PORTUGAL.

5 Virology Service, Portuguese Oncology Institute of Porto (IPO-Porto), Rua Dr. António

Bernardino de Almeida, 4200-072 Porto, PORTUGAL

Correspondence to:

Hugo Sousa, MD PhD

Serviço de Virologia, Laboratórios 4º Piso

Instituto Português de Oncologia do Porto FG EPE

Rua Dr. António Bernardino Almeida

4200-072 Porto, Portugal

Phone: +351 22 508 4000 (ext 5410)

Fax: +351 22 508 4001

E-mail: [email protected] or [email protected]

mailto:[email protected]


CHAPTER 2


ABSTRACT

Epigenetics alterations, such as aberrant DNA methylation, have been associated with

gastric carcinogenesis. Single Nucleotide Polymorphisms (SNPs) in DNA

methyltransferases (DNMTs) may influence protein expression and therefore affect DNA

regulation and susceptibility for Gastric Cancer (GC).

We performed a systematic review and meta-analysis involving 11 studies and a total of

24 SNPs in DNMTs were analysed. According to literature, only 4 SNPs, DNMT1

rs16999593, DNMT2 rs11254413 and DNMT3A rs7560488 and DNMT3A rs36012910,

were associated with GC. DNMT1 rs16999593 and DNMT3A rs7560488 C allele and

DNMT3A rs36012910 G allele showed an increased risk for GC. On the other hand,

DNMT2 rs11254413 G allele presented a protective effect for GC. Additionally, the meta-

analysis evaluated the SNPs analysed in more than one study (n=6). Results revealed

that only DNMT1 rs16999593 had a statistically significant association with GC

development (OR = 1.31; 95% CI = 1.08-1.60; p = 0.006 for TC+CC genotypes).

Our study suggests that DNMT2 rs11254413, DNMT3A rs7560488, DNMT3A

rs36012910 and, specially, rs16999593 may have an association with GC development

and more studies with different populations are needed to verify if this SNPs are related

with GC.

KEY WORDS

DNA methylation; DNA Methyltransferase; Single Nucleotide Polymorphism; Gastric

Cancer

CHAPTER 2


INTRODUCTION

Epigenetics, and more extensively DNA methylation, has been studied in the past

decades and associated with several diseases, including cancer. Literature shows that

altered epigenetic control of gene expression has an important role in carcinogenesis

(Ziogas and Roukos, 2009). DNA methylation is an important epigenetic mark in

tumorigenesis, in which a methyl group is added to the 5’ position of cytosine residue in

a CpG dinucleotide (CpG islands (CGI)), in the 5′-flanking promoter’s genes (Feinberg

and Vogelstein, 1983). Aberrant methylation of promoter’s genes is an important

hallmark of cancer cells and, in Gastric Cancer (GC), a large number of genes involved

in carcinogenesis and clinical outcome accumulate aberrant methylation. Promoter

methylation is an important mechanism of inactivation of tumor suppressor genes, in

cancer cells (Feinberg and Vogelstein, 1983).

The process of promoter methylation is catalysed by DNA Methyltransferases (DNMTs)

which organize, regulate and maintain properly mammalian genomes (Yang et al., 2012).

There are three types of DNMTs: DNMT1, over-expressed in human cancers including

GC, that catalyse post-replication DNA methylation and maintain the methylation

patterns during cell divisions (Kanai et al., 2003; Saito et al., 2001; Yang et al., 2011);

DNMT2 which is the most conserved and has a role in both DNA and RNA methylation

(Schaefer and Lyko, 2010); and DNMT3, which is divided in two types (DNMT3a and

DNMT3b) that are responsible for de novo methylation during gametogenesis and

embryogenesis, and that also seem to be over-expressed in human cancers, including

GC (Yang et al., 2011, 2012). It is well known that gene expression can be influenced by

single nucleotide polymorphisms (SNPs) and, in DNMTs genes, they may have

functional consequences affecting methylation and therefore individual’s susceptibility to

cancer development.

Several studies have recently explored the potential association of SNPs in DNMTs with

the susceptibility to GC, nevertheless, the observed associations of these studies are

inconsistent and some studies are not large enough to take conclusions on the effect of

DNMTs SNPs on GC. As a result, we have performed a meta-analysis of all eligible

studies to understand the possible association of DNMTs SNPs with GC.

CHAPTER 2


MATERIALS AND METHODS

Search strategy

The present systematic review and meta-analysis follows PRISMA guidelines for

Systematic Review and Meta-analyses. Pubmed was searched till the end of March 2016

and literature searching was performed by applying the following query: "dna

modification methylases"[MeSH Terms] OR ("dna"[All Fields] AND "modification"[All

Fields] AND "methylases"[All Fields]) OR "dna modification methylases"[All Fields] OR

("dna"[All Fields] AND "methyltransferase"[All Fields]) OR "dna methyltransferase"[All

Fields] OR DNMT[All Fields]) AND ("polymorphism, genetic"[MeSH Terms] OR

("polymorphism"[All Fields] AND "genetic"[All Fields]) OR "genetic polymorphism"[All

Fields] OR "polymorphism"[All Fields]) AND ("stomach neoplasms"[MeSH Terms] OR

("stomach"[All Fields] AND "neoplasms"[All Fields]) OR "stomach neoplasms"[All Fields]

OR ("gastric"[All Fields] AND "cancer"[All Fields]) OR "gastric cancer"[All Fields].

Inclusion and exclusion criteria

The literature search was limited to original studies performed in humans and no

publication year restriction was applied. Only case-control studies with histologically-

confirmed adenocarcinoma cases were included in this analysis. All DNMTs

polymorphisms were selected for this analysis. Regarding the language of the papers,

only studies written in English, Portuguese or Spanish were selected. Reviews, Meta-

analysis, Systematic Reviews and studies not related to gastric cancer were excluded

for this analysis.

Data extraction

Two authors performed the data extraction and all disagreements were resolved with the

opinion of a third author. For each study, the following items were collected: country, type

of study, ethnicity, matching criteria, age (years), polymorphisms in study, genotype

method, number of cases and controls. Data concerning genotype distribution and the

relative risk were also extracted for all polymorphisms included in the studies.

Statistical analysis

The evidence of Hardy Weinberg Equilibrium (HWE) in controls was recalculated in the

present meta-analysis through the application of the online software

(http://www.had2know.com/academics/hardy-weinberg-equilibrium-calculator-2-

alleles.html). A p–value less than 0.05 of HWE was considered significant.

CHAPTER 2


Meta-analysis for DNMTs SNPs was conducted by using the software Review Manager,

version 5.3.5 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration,

2014). The calculated OR (95% CI) was performed with Mantel-Haenszel statistical

method and Random effects analysis model. A p–value less than 0.05 was considered

significant.

RESULTS

Literature search

A total of 35 papers were evaluated from which only 11 matched with inclusion criteria.

Seventeen articles were excluded due to the exclusion criteria by screening the titles and

abstracts. After a comprehensive evaluation of the remaining 18 articles, 7 were

excluded: 4 articles didn’t evaluate polymorphisms of DNMTs, only O6-methylguanine-

DNA methyltransferase (MGMT) (Chikan et al., 2015; Imai et al., 1995; Lin et al., 2014;

Xiong et al., 2013); 2 articles only reported mutations on DNMTs (Kanai et al., 2003; Oh

et al., 2014); 1 study was about allelic loss in gastric cancer (Kim et al., 2013). Finally,

11 studies involving a total of 3049 cases and 5185 controls were included in this

analysis – figure 1.

Study characteristics

Table 1 describes the principal baseline characteristics of included studies. The majority

of the studies were performed in China (Cao et al., 2013; Fan et al., 2010; Hu et al.,

2010; Jiang et al., 2012; Wang et al., 2015; Y. Wang et al., 2005; Wu et al., 2014, 2012;

Yang et al., 2012), one study in Iran (Khatami et al., 2009) and one in Japan (Aung et

al., 2005). Almost all studies have selected the controls and cases using age and gender

as matching criteria, but some studies also use match with Helicobacter Pylori infection

and Ethnicity. The number of cases and controls varies between the studies. A total of

24 polymorphisms were evaluated, 10 of DNMT1, 1 of DNMT2, 6 of DNMT3A and 7 of

DNMT3B. The majority of studies were performed using Polymerase Chain Reaction –

Restriction Fragment Length Polymorphism (PCR-RFLP) methodology, but other

methods of genotyping were used like TaqMan assay, High Resolution Melting (HRM)

and Matrix Assisted Laser Desorption/Ionization - Time of Flight (MALDI-TOF).

Supplementary table 1 shows the characteristics of all polymorphisms included in the

studies. The re-calculated HWE for controls is also shown in supplementary table 2.

CHAPTER 2


Genotyping results

The genotyping results of all studies are described in table 2. In this study, we have

included data from 24 SNPs in DNMTs from the 11 studies.

A total of 10 SNPs in DNMT1 were studied by 4 different studies (Jiang et al., 2012;

Khatami et al., 2009; Yang et al., 2012) – table 2.1. Of these SNPs, only rs16999593,

rs8101866 and rs2228611 have data from more than one population. Regarding the risk

analysis, there was only one study that reported a significant risk association of one SNP

(rs16999593) with increased risk of GC development (OR=1.45; 95%CI = 1.00–2.11;

p = 0.05, for C allele). All other SNPs do not show statistical significance.

Only one study evaluated SNPs in DNMT2 (Yang et al., 2012) revealing an association

between rs112254413 SNP and protection for the development of GC, especially

associated with A allele (OR = 0.15; 95%CI = 0.08-0.27; p <0.01) – table 2.2.

Thirteen SNPs of DNMT3, 6 of DNMT3A and 7 of DNMT3B, were evaluated by 9 different

studies, with 2 SNPs of each gene with more than one study (Aung et al., 2005; Cao et

al., 2013; Fan et al., 2010; Hu et al., 2010; Wang et al., 2015, 2005, Wu et al., 2014,

2012; Yang et al., 2012). Regarding the risk analysis, DNMT3A rs7560488 (OR = 1.65;

95%CI = 1.19–2.29; p = 0.002, for C allele) and rs36012910 (OR = 2.36; 95%CI = 1.35–

4.14; p = 0.002, for allele G) seem to be associated with an increased risk for GC (Wu

et al., 2014, 2012), and rs1550117 and rs13420867 show contradictory findings between

the reported studies. In DNMT3B, only rs1569686 showed a statistically significant

association (p<0.01) with GC, nevertheless the results are contradictory among different

studies – tables 2.3/2.4.

Meta-analysis results

A meta-analysis was performed with data from all SNPs evaluated in more than one

study - Figures 2-4. A total of 6 SNPs were analysed: DNMT1 rs16999593, rs8101866

and rs2228611; DNMT3A rs1550117 and rs13420827; and DNMT3B rs1569686. For the

analysis, we have used the dominant genetic model, considering the risk genotype as

the presence of any copy of the less frequent allele. The majority of studies showed no

significant heterogeneity in the data reported (p>0.05).

DNMT1: The analysis revealed that there was no heterogeneity (p>0.05) amongst the

three SNPs tested. We found that only rs16999593 had a statistically significant

association (p = 0.006), revealing a 31% increased risk of GC development for TC+CC

CHAPTER 2


genotypes (OR = 1.31; 95%CI = 1.08-1.60). The rs8101866 and rs2228011 showed no

significant differences (p>0.05) and moreover no significant impact on the risk (OR =

1.08 and OR = 0.93, respectively) – figure 2.

DNMT3A: Data showed that in addition to differences in the heterogeneity of rs1550117

(p = 0.03), there is no impact on the risk association of GC development for either

rs1550117 and rs13420827 (p = 0.20 and p = 0.19, respectively) – figure 3.

DNMT3B: One SNP (rs2424913) was excluded from our meta-analysis because it was

not possible to estimate the OR value. The lack of data for all genotypes in the three

studies makes it impossible to evaluate any association (Aung et al., 2005; Hu et al.,

2010; Wang et al., 2005). The analysis revealed that the data from studies regarding

rs1569686 are very contradictory, which result in a high heterogeneity on studies

(p<0.001) and no risk association with GC – figure 4.

We have also tested all data considering the Recessive Model for each SNP, and no

significant change was observed in the reported data (data not shown).

DISCUSSION

Gastric carcinogenesis is a multistep process, where different factors are involved and

epigenetic alterations, including DNA methylation of CGI, seem to play an important role

in early mechanisms of carcinogenesis (Yang et al., 2011). The process of CpG

methylation is catalysed by DNMTs and modified DNA methylation has direct effect on

the regulation of gene expression, thus altered activity of DNMTs can affect DNA

methylation. Literature reveals that DNMTs are frequently overexpressed in GC (Lin et

al., 2014). Considering the significant impact of genetic susceptibility based on the

genetic sequences of individuals, functional SNPs may affect DNMTs expression, and

therefore, it is important to study SNPs in DNMTs and understand the effect that

functional SNPs can have on their activity.

Recently, several studies have been performed to evaluate the impact of DNMTs

polymorphisms in GC susceptibility (Aung et al., 2005; Cao et al., 2013; Fan et al., 2010;

Hu et al., 2010; Jiang et al., 2012; Khatami et al., 2009; Wang et al., 2015, 2005, Wu et

al., 2014, 2012; Yang et al., 2012). Hence, this systematic review intends to resume the

data published regarding the association of SNPs in DNMTs with GC. By searching

literature, we have found 11 studies matching our inclusion criteria. One of the first

CHAPTER 2


findings was that the majority of included studies were performed in Asiatic populations,

where GC is extremely common (Karimi et al., 2014). This constitutes a major limitation

for this systematic review since that is not possible to extrapolate the conclusions to

others populations.

There are very few studies considering DNMTs SNPs and GC susceptibility and very few

data on different SNPs. A total of 24 SNPs were found in this systematic review, 10 in

DNMT1, 1 in DNMT2 and 13 in DNMT3. Possible limitations of some of the included

studies are not only population bias but also the different genotyping methods. The

genotyping methods used were different and with different specificity: PCR-RFLP

method has some limitations to genotype correctly the three genotypes, specially

heterozygote genotypes (Davis et al., 2007); while TaqMan real-time PCR assays are

more accurate and have less limitations and are a better candidate for SNP genotyping

(Shen et al., 2009). Furthermore, the number of cases and controls included are

extremely different among studies and therefore more studies with bigger populations

and more precise genotyping methods are still required.

The literature report that DNMT1 rs16999593, DNMT2 rs11254413 and DNMT3A

rs7560488 and rs36012910 were the only associated with GC within published studies

(Wu et al., 2014, 2012; Yang et al., 2012). The DNMT1 rs16999593 is characterized by

a C>T variation, resulting in an Arg to His amino acid substitution at position 97 of the

protein, leading to missense mutation that may affect the structure and function of

DNMT1. Literature revealed that rs16999593 is significantly associated to some

malignancies: Tao et. al showed that individuals with TC genotype presented a 4-fold

increased risk for sporadic triple-negative breast carcinoma (Tao et al., 2015); Xiang et.

al also revealed that rs16999593 can provide protective effect for ductal breast

carcinoma (Xiang et al., 2010); and Yang et. al reported a 1.45-fold increased risk for

TC+CC carriers and gastric cancer (Yang et al., 2012). Although these evidences, other

studies do not show any association (Jiang et al., 2012; Ye et al., 2010). DNMT2

rs11254413 is characterized by a G>A substitution leading to an amino acid change, His

to Tyr at position 101 of the protein that may affect DNMT2 function (Yang et al., 2012).

The A-allele was associated with protection risk for GC by Yang et al, nevertheless, there

is no functional study that support the biological role of this SNP on DNMT2 function

(Yang et al., 2012). The DNMT3A rs7560488 (variation T>C) is a tagSNP that represents

SNPs of the DNMT3A1 promoter with high linkage disequilibrium and has been

associated with higher risk for GC in Asiatic population (Wu et al., 2014). Authors

suggested that T to C change influences the binding of transcriptional factors and

CHAPTER 2


consequently expression levels of DNMT3a (Wu et al., 2014). The DNMT3A

rs36012910, which is characterized by a A>G substitution located in the promoter region

of DNMT3A and the substitution has been associated with DNMT3A activity (Wu et al.,

2012). Wu et al reveals that AG+GG genotypes were associated with a higher risk for

GC (Wu et al., 2012).

Regarding the data from the meta-analysis, we only were able to compare the

information regarding 6 SNPs, which were described in more than one study. For DNMT1

analysis, 3 SNPs were studied (rs16999593, rs8101866 and rs2228611) and only

rs16999593 showed statistically significant data from the information of two different

studies (Khatami et al., 2009; Yang et al., 2012) – figure 2. The results showed an

increased risk for TC+CC genotypes of GC development. Nevertheless, the low number

of studies and the lack of studies from populations apart from China make it necessary

to perform more studies with different populations to confirm the association of

rs16999593 with GC. Concerning DNMT3A, 2 SNPs were included in meta-analysis

(rs1550117 and rs13420827) – figure 3. It was not found association of both SNPs with

GC in the analysis. The heterogeneity analysis revealed differences only in rs1550117

populations, because the ORs of Fan et al. were different comparing to the other studies

(Fan et al., 2010). By removing Fan et al. of analysis, it became homogeneously but we

did not find a significantly association with GC. The rs1550117 was the only SNP

evaluated in three studies but analysis didn’t show an association with GC. The results

in the studies were different, probably because of different genotype methods used in

the three studies – table 1.

Finally, we included one SNP in DNMT3B on the meta-analysis (rs1569686) – figure 4.

We found no association of rs1569686 with GC. The analysis revealed heterogeneity

because OR values of two studies were too different (0.43 in Hu et al. and 1.21 in Wang

et al.) (Hu et al., 2010; Wang et al., 2015).

CHAPTER 2


CONCLUSIONS

We found 11 articles studying SNPs in DNMTs but the variety of SNPs and the reduced

number of studies per each SNP made this analysis more complicated allowing us to

draw only some remarks and not strong conclusions. The data from studies revealed

that rs11254413, rs7560488, rs36012910 and, specially, rs16999593 seem to be good

candidates to study the role of SNPs in DNMTs and GC development. Nevertheless, our

meta-analysis revealed that only one SNP (rs16999593) in DNMT1 was statistically

associated with GC development, especially TC+CC carriers. More studies with different

populations are needed to prove if the SNPs selected in our analysis are associated with

GC. Nevertheless, studies regarding biological importance of functional SNPs in DNMTs

activity and expression are also need to fully understand the impact of SNPs in DNMTs

on GC.

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Table 1. Characteristics of all studies included in the analysis

Reference Country

(Ethnicity) Matching Criteria

Cases Controls Gene SNPS (rs)

Genotype method n Age n Age

Wang et al.,2015 China (Asian) Age, gender and H. pylori 447 NA 961 NA DNMT3B rs6119954, rs1569686,

rs4911107, rs4911259, rs8118663 TaqMan assay

Wu et al., 2014 China (Asian) Age and gender 405 NA 408 NA DNMT3A rs7560488 HRM

Cao et al., 2013 China (Asian) Age, gender and H. pylori 447 61.6 961 50.6 DNMT3A rs1550117, rs13420827, TaqMan assay

Wu et al., 2012 China (Asian) Age and gender 340 62.2* 251 62.7* DNMT3A rs36012910 PCR-RFLP

Jiang et al., 2012 China (Asian) Age, gender and H. Pylori 447 61.6 961 50.6 DNMT1 rs10420321, rs16999593,

rs8101866, rs8111085, rs2288349 TaqMan assay

Yang et al., 2012 China (Asian) Age and gender 242 54.9 ± 12.5

294 58.4 ± 16.4

DNMT3A DNMT3B DNMT1 DNMT2

rs16999593, rs8101866, rs2228611, rs2114724,

rs11254413, rs1550117, rs13420827, rs11887120, rs13428812, rs2424908

MALDI-TOF

Fan et al., 2010 China (Asian) Age and gender 208 65 346 71 DNMT3A rs1550117 PCR-RFLP

Hu et al., 2010 China (Asian) Age and gender 259 64.0 262 65.1 DNMT3B rs1569686, rs2424913 PCR-RFLP

Khatami et al., 2009

Iran (Turk and Fars)

Age, gender and ethnicity 200 50±13 200 58±13 DNMT1 rs721186, rs13784, rs2228611,

rs11488 PCR-RFLP

Aung et al., 2005 Japan (Asian) NA 152 64,1* 247 43,9* DNMT3B rs2424913 PCR-RFLP

Wang et al., 2005 China (Asian) Age and gender 212 60.4 294 59.1 DNMT3B rs2424913 PCR-RFLP

NA, Not Available; * Median (range); H. Pylori - Helicobacter Pylori; DNMT1 - DNA (cytosine-5)-methyltransferase 1; DNMT2 - TRNA (cytosine38-C5)-methyltransferase; DNMT3A - DNA (cytosine-5)-methyltransferase 3 alpha; DNMT3B - DNA (cytosine-5-)-methyltransferase 3 beta; MALDI-TOF - Matrix-Assisted Laser Desorption/Ionization- Time of Flight; PCR-RFLP - Polymerase Chain Reaction-Restriction Fragment Length Polymorphism; HRM - High Resolution Melting.

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Table 2.1 Genotyping results for DNMT1 of all studies included

Gene SNP Genotype Distribution n (%)

Analysis Model OR (95%CI) p value Reference Cases Controls

DN

MT

1

rs10420321 AA AG GG AA AG GG

141 (31.5) 203 (51.5) 76 (17) 328 (34.1) 454 (47.2) 179 (18.6) CD 1.17 (0.88-1.55) (GG) 0.29 Jiang 2012

rs16999593

TT TC CC TT TC CC

283 (63.3) 144 (32.2) 20 (4.5) 659 (68.6) 273 (28.4) 29 (3) CD 1.25(0.95-1.66) (TC) 0.18 Jiang 2012

141 (58.3) 89 (36.8) 12 (5) 196 (66.7) 83 (28.2) 15 (5.1) CD 1.47 (1.01-2.14) (TC) 0.11

Yang 2012 Dominant 1.45 (1.00-2.11) (TC+CC) 0.05

rs8101866

CC CT TT CC CT TT

238 (53.5) 177 (39.8) 30 (6.7) 489 (50.9) 402 (41.8) 70 (7.3) CD 0.81 (0.18-1.35) (CT) 0.11 Jiang 2012

13 (5.4) 98 (40.1) 130 (53.9) 26 (8.8) 102 (34.7) 166 (56.5) CD 1.20 (0.83-1.74) (CT) 0.17

Yang 2012 Dominant 1.27 (0.89-1.82) (CT + CC) 0.19

rs8111085 TT TC CC TT TC CC

143 (32) 214 (47.9) 90 (20.1) 330 (34.3) 447 (46.5) 184 (19.1) CD 1.18 (0.32-1.69) (CC) 0.38 Jiang 2012

rs2288349 GG GA AA GG GA AA

249 (56.1) 161 (36.3) 34 (7.7) 515 (53.6) 372 (38.7) 74 (7.7) CD 0.81(0.50-1.33) (AA) 0.81 Jiang 2012

rs2114724

CC CT TT CC CT TT

132 (54.5) 97 (40.1) 13 (5.4) 162 (56.2) 101 (35.1) 25 (8.7) CD 1,16 (0,81-1,68) (CT) 0,22

Yang 2012 Dominant 1.23 (0.86-1.76) (CT+TT) 0.27

rs2228611

GG GA AA GG GA AA

132 (54.5) 97 (40.1) 13 (5.4) 160 (56.1) 99 (34.7) 26 (9.1) CD 1.18 (0.81-1.71) (GA) 0.14

Yang 2012 Dominant 1.26 (0.87-1.80) (GA+GG) 0.22

34 (34) 50 (50) 16 (16) 32 (32) 62 (62) 18 (18) CD 1.13 (0.05-6.30) (GA) NA Khatami 2009

rs721186 CC CT TT CC CT TT

99 (99) 1 (1) - 200 (100) - - NA NA NA Khatami 2009

rs13784 CC CT TT CC CT TT

200 (100) - - 100 (100) - - NA NA NA Khatami 2009

rs11488 AA AT TT AA AT TT

200 (100) - - 200 (100) - - NA NA NA Khatami 2009

CHAPTER 2


Table 2.2 Genotyping results for DNMT2 of all studies included

Gene SNP Genotype Distribution n (%) Analysis

Model OR (95%CI) p value Reference

Cases Controls

DNMT2 rs11254413

GG GA AA GG GA AA

204 (84.3) 15 (6.2) 23 (9.5) 187 (63.8) 91 (31.1) 15 (5.1) CD 0.16 (0.09-0.28) (GA) <0.01

Yang 2012 Dominant 0.15 (0.08-0.27) (GA+AA) <0.01

Table 2.3 Genotyping results for DNMT3A of all studies included



Cases Controls

DN

MT

3A

rs7560488

TT TC CC TT TC CC

279 (68.9) 111 (27.4) 15 (3.7) 326 (79.9) 75 (18.4) 7 (1.7) CD

1.73(1.24-2.41) (TC); 2.50(1.01-6.23) (CC)

0.01; 0.04 Wu 2014

Dominant 1.65(1.19-2.29) (TC + CC) 0.002

rs1550117

GG GA AA GG GA AA

289 (64.7) 142 (31.8) 16 (3.6) 640 (66.6) 288 (30) 33 (3.4) CD 1.10 (0.83-1.47) (AA) 0.51

Cao 2013 Dominant 1.06 (0.83-1.32) (GA+AA) 0.51

157 (64.9) 74 (30.6) 11 (4.5) 191 (65) 93 (31.6) 10 (3.4) CD 1.34 (0.55-3.29) (AA) 0.74

Yang 2012 Dominant 0.92 (0.63-1.34) (GA+AA) 0.68

102 (49) 75 (36.1) 31 (14.9) 218(63) 118 (34.1) 10 (2.9) CD 6.63 (3.13-14.03) (AA) 0.00 Fan 2010

rs13420827

CC CG GG CC CG GG

295 (66) 133 (29.8) 19 (4.3) 618 (64.3) 304 (31.6) 39 (4.1) CD 1.06 (0.79-1.41) (GG) 0.71

Cao 2013 Dominant 1.05(0.83-1.33) (CG+GG) 0.64

167 (69) 61 (25.2) 14 (5.8) 183 (62.7) 99 (33.9) 10 (3.4) CD 0.68 (0.46-1.01) (CG) 0.05

Yang 2012 Dominant 0.66 (0.45-0.97) (CG+GG) 0.03

rs36012910

AA AG GG AA AG GG

288 (84.71) 51 (15) 1 (0.29) 234 (93.23) 17 (6.77) 0 CD 2.44 (1.37-4.33) (AG) 0.002

Wu 2012 Dominant 2.36 (1.35-4.14) (AG+GG) 0.002

rs11887120 TT TC CC TT TC CC

57 (23.6) 121 (50) 64 (26.4) 74 (25,3) 155 (53,1) 63 (21,6) CD 1.37 (0.88-2.13) (AA) 0.17 Wang 2015

rs13428812

AA AG GG AA AG GG

137 (56.6) 84 (34.7) 21 (8.7) 160 (55.4) 106 (36.7) 23 (8) CD 1.11 (0.58-2.12) (GG) 0.84

Yang 2012 Dominant 0.91 (0.64-1.31) (AG+GG) 0.63

CHAPTER 2


Table 2.4 Genotyping results for DNMT3A of all studies included



Cases Controls

DN

MT

3B

rs6119954 GG GA AA GG GA AA

200 (44.8) 199 (44.6) 4 (10.6) 433 (45.1) 445 (46.3) 83 (8.6) CD 1.37 (0.88-2.13) (AA) 0.17 Wang 2015

rs1569686

TT TG GG TT TG GG

360 (80.5) 82 (18.3) 5 (1.1) 801 (83.4) 150 (15.6) 10 (1) CD 1.33 (0.41-4.31) (GG) 0.64 Wang 2015

230 (88.8) 27 (10.43) 2 (0.77) 203 (77.48) 55 (20.99) 4 (1.53) Dominant 0.43 (0.26-0.72) (TG+GG) <0.01 Hu 2010


360 (80.5) 82 (18.3) 5 (1.1) 800 (83.2) 151 (15.7) 10 (1.1) CD 1.08 (0.13-8.86) (GG) 0.94 Wang 2015

rs4911259 GG GT TT GG GT TT

359 (80.3) 83 (18.6) 5 (1.1) 799 (83.1) 152 (15.8) 10 (1.1) CD 1.09(0.13-8.88) (TT) 0.94 Wang 2015


126 (28.2) 233 (52.1) 88 (19.7) 314 (32.7) 473 (49.2) 174 (18.1) CD 1.32 (0.91-1.91) (GG) 0.15 Wang 2015

rs2424908

TT TC CC TT TC CC

78 (32.2) 114 (47.1) 50 (20.7) 99 (33.7) 139 (47.3) 56 (19.1) CD 1.05 (0.64-1.71) (CC) 0.96

Yang 2012 Dominant 0.96 (0.68-1.36) (TC+CC) 0.82

rs2424913

CC CT TT CC CT TT

257 (99.2) 2 (0.8) - 259 (98.8) 3 (1.2) - CD 1.49 (0.17-17.94) (CT) >0,05 Hu 2010

152 (100) - - 247 (100) - - NA NA NA Aung 2005

205 (96.7) 7 (3.3) - 279 (94.9) 15 (5.1) - Dominant 0.66 (0.26-1.67) (CT) 0,38 Wang 2005

NA, Not Available; CD, Co-Dominant; NR, No Risk; DNMT1 – DNA (cytosine-5)-methyltransferase 1; DNMT2 – TRNA (cytosine38-C5)-methyltransferase; DNMT3a – DNA (cytosine-5)-methyltransferase 3 alpha; DNMT3b - DNA (cytosine-5-)-methyltransferase 3 beta; SNP – Single Nucleotide Polymorphism; Allele: A- Adenine, C- Cytosine, G- Guanine, T – Thymine; OR (95% CI) – Odds Ratio (95% Confidence Interval)

CHAPTER 2


Figure 1. Flow chart of studies identification, exclusion and inclusion. Adapted from: Moher D,

Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for

Systematic Reviews and Meta-analyses: The PRISMA Statement. PLoS Med 6(6): e1000097.

doi:10.1371/journal.pmed1000097

Records identified through

database searching

(n =35)

Sc

ree

nin

g

Inclu

de

d

Eli

gib

ilit

y

Iden

tifi

cati

on

Additional records identified

through other sources

(n =0)

Records after duplicates removed

(n =35)

Records screened

(n =35)

Full-text articles

assessed for eligibility

(n =18)

Full-text articles

excluded (n =7)

MGMT polymorphisms (n=4)

Only evaluate DNMTs mutations (n=2)

Allelic loss in gastric cancer (n=1) Studies included in

qualitative synthesis

(n = 11)

Records excluded (n =17)

Do not evaluate DNMTs polymorphisms (n=9)

Do not evaluate polymorphisms (n=3)

Not related with gastric cancer (n=3)

Review or meta-analysis (n=2)

CHAPTER 2


Figure 2. Forest-plot for DNMT1 SNPs

Figure 3. Forest-plot for DNMT3A SNPs

Figure 4. Forest-plot for DNMT3B SNPs

CHAPTER 2


Supplementary Table 1. Description of DNMTs SNPS included in the studies Gene

(Chromosome) SNP Location

Allele Change

Amino Acid Change

Function

DNMT1 (Chromosome 19)

(19p13.2)

rs11488 10133368 A/T NA 3' UTR

rs13784 10133520 C/T NA 3' UTR

rs2288349 10146569 G/A NA Intron

rs2114724 10154572 C/T NA Intron

rs721186 10154636 C/T Thr594 Synonymous codon

rs2228611 10156401 G/A Pro463 Synonymous codon

rs8111085 10162696 T/C Ile327Val Missense

rs8101866 10164984 C/T NA Intron

rs16999593 10180505 T/C His97Arg Missense

rs10420321 10189741 A/G NA Intron

DNMT2 (Chromosome 10)

(10p15.1) rs11254413 17162188 G/A His101Tyr Missense

DNMT3A (Chromosome 2)

(2p23)

rs7560488 25345952 T/C NA NA

rs1550117 25343038 G/A NA NearGene-5

rs13420827 25231099 C/G NA NA

rs36012910 25345310 A/G NA Promoter

rs11887120 25262866 T/C NA Intron

rs13428812 25269598 A/G NA Intron

DNMT3B (Chromosome 20)

(20q11.2)

rs6119954 32776360 G/A NA Intron

rs1569686 32779273 T/G NA Intron

rs4911107 32787185 A/G NA Intron

rs4911259 32788476 G/T NA Intron

rs8118663 32811070 A/G NA NA

rs2424908 32772577 T/C NA Intron

rs2424913 32786453 C/T NA Intron

NA, Not Available; DNMT1 – DNA (cytosine-5)-methyltransferase 1; DNMT2 – TRNA (cytosine38-C5)-methyltransferase; DNMT3a – DNA (cytosine-5)-methyltransferase 3 alpha; DNMT3b - DNA (cytosine-5-)-methyltransferase 3 beta; SNP – Single Nucleotide Polymorphism; Allele: A- Adenine, C- Cytosine, G- Guanine, T – Thymine; UTR – Untranslated Region

CHAPTER 2


Supplementary Table 2. HWE in controls in all studies

Gene SNP Reference HWE (p)

DNMT1

rs10420321 Jiang 2012 0.323

rs16999593 Jiang 2012 0.910

Yang 2012 0.120

rs8101866 Jiang 2012 0.306

Yang 2012 0.078

rs8111085 Jiang 2012 0.139

rs2288349 Jiang 2012 0.550

rs721186 Khatami 2009 0.550

rs13784 Khatami 2009 0.550

rs2228611 Khatami 2009 0.187

Yang 2012 0.068

rs11488 Khatami 2009 0.550

rs2114724 Yang 2012 0.113

DNMT2 rs11254413 Yang 2012 0.371

DNMT3A

rs7560488 Wu 2014 0.274

rs1550117

Cao 2013 0.932

Yang 2012 0.747

Fan 2010 0.206

rs13420827 Cao 2013 0.833

Yang 2012 0.444

rs36012910 Wu 2012 0.579

rs11887120 Yang 2012 0.280

rs13428812 Yang 2012 0.450

DNMT3B

rs6119954 Wang 2015 0.036

rs1569686 Wang 2015 0.321

Hu 2010 0.901

rs4911107 Wang 2015 0.341

rs4911259 Wang 2015 0.362

rs8118663 Wang 2015 0.859

rs2424908 Yang 2012 0.563

rs2424913

Hu 2010 0.926

Aung 2005 0.824

Wang 2005 0.654

DNMT1 – DNA (cytosine-5)-methyltransferase 1; DNMT2 – TRNA (cytosine38-C5)-methyltransferase; DNMT3A – DNA (cytosine-5)-methyltransferase 3 alpha; DNMT3B - DNA (cytosine-5-)-methyltransferase 3 beta; HWE - Hardy–Weinberg equilibrium (http://www.had2know.com/academics/hardy-weinberg-

equilibrium-calculator-2-alleles.html)

CHAPTER 3

CHAPTER 3


TITLE

SINGLE NUCLEOTIDE POLYMORPHISMS IN DNA METHYLTRANSFERASES AND

GASTRIC CANCER

AUTHORS

Neves M1,2, Ribeiro J1,2,3, Dinis-Ribeiro M,4,5, Medeiros R1,3,6,7, Sousa H1, 6

AFILIATIONS

1 Molecular Oncology and Viral Pathology Group, CI-IPOP, Portuguese Oncology

Institute of Porto (IPO-Porto), Porto, Portugal.

2 Faculty of Medicine of University of Porto, Porto, Portugal.

3 Research Department, Portuguese League Against Cancer, Porto, Portugal.

4 Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine,

University of Porto, Porto, Portugal.

5Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal.

6 Abel Salazar Institute for the Biomedical Sciences (ICBAS), University of Porto, Rua de

Jorge Viterbo Ferreira n.º 228, 4050-313 Porto, Portugal.

7Virology Service, Portuguese Oncology Institute of Porto, Porto, Portugal.

Correspondence to:

Hugo Sousa, MD PhD





Phone: +351 22 508 4000 (ext 5410)

Fax: +351 22 508 4001




CHAPTER 3


ABSTRACT

AIM: To investigate the relationship between the DNMT2 rs11254413 and DNMT3A1

rs7560488 and the development of gastric lesions and gastric adenocarcinoma.

METHODS: A case-control study was performed involving 380 individuals, 220 without

evidence of neoplastic disease (control), 43 with gastric lesion and 117 patients with

gastric adenocarcinomas. Genotyping was executed with TaqMan® SNP Genotyping

Assay.

RESULTS: The genotyping results for DNMT2 rs11254413 and DNMT3A1 rs7560488

revealed no association with gastric lesion and gastric cancer. DNMT2 rs11254413 GG,

GA and AA genotypes frequencies were 27.3%, 51.8% and 20.9%, respectively in

controls; 25.6%, 48.8% and 25.6% in patients with gastric lesions; and 28.4%, 45.7%

and 25.9% in patients with gastric adenocarcinoma. Regarding DNMT3A1 rs7560488

analysis the frequencies of TT, TC and CC genotypes were 29.3%, 51.4% and 19.3%,

respectively in controls, 35.7%, 47.6% and 16.7% in patients with gastric lesions and

28.2%, 50.9% and 20.9% in patients with gastric adenocarcinoma.

CONCLUSION: DNMT2 rs11254413 and DNMT3A1 rs7560488 may not be associated

with the development of gastric lesions and gastric cancer. More studies, with different

populations, are needed to confirm our findings.

KEY WORDS

DNA Methyltransferase; DNMT2; DNMT3A; Single Nucleotide polymorphism; Gastric

Cancer

CHAPTER 3


INTRODUTION

Gastric cancer (GC) is one of the most common type of malignancies worldwide. It was

the fifth common malignancy in 2012 worldwide (952,000 cases, 6.8% of the total) and

the third leading cause of cancer deaths worldwide (723,000 deaths, 8.8% of the total)

(1). According to Lauren’s classification, GC can be divided in two types: intestinal and

diffuse type (2). The intestinal type is characterized by a stepwise carcinogenesis from

normal mucosa through atrophic gastritis, atrophy, intestinal metaplasia and dysplasia

to invasive adenocarcinoma (3,4). On the other hand, diffuse type is characterized by a

less differentiated malignant neoplasia, without a well-marked multiple step process

(3,4).

The major risk factor for the development of GC is Helicobacter pylori infection

(especially the intestinal type), nevertheless, GC can also be the result of specific genetic

alterations. Another factor recently associated to CG has been epigenetic changes (5,6).

Alterations in DNA methyltransferases (DNMTs), more precisely, DNMT1, DNMT2 and

DNMT3A have been studied and have been suggested to have a role in carcinogenesis

of GC (7,8,6,9). DNMTs are enzymes that catalyze the process of promoter methylation.

There are three types of DNMTs: DNMT1, DNMT2 and DNMT3 (10). DNMT1 function is

catalyze DNA post-replication methylation and maintain DNA methylation patterns during

cell divisions (11,10,12). DNMT2 is the most conserved DNMT and their function is

catalyze DNA and aspartic acid tRNA methylation (13). DNMT3 is divided in two types:

DNMT3A and DNMT3B and they are responsible for de novo methylation during

gametogenesis and embryogenesis (8).

The most common form of genetic variants of the human genome are Single Nucleotide

Polymorphisms (SNPs) (14) . Some SNPs have functional effects on the susceptibility to

the development of human cancers by modifying the transcriptional expression

(5,15,16). Several SNPs in DNMTs have been identified and some have been associated

to GC risk (17,8,18). For example, SNP rs11254413 of DNMT2 leads to missense

mutation of His to Tyr, and may also alter the function of the protein encoded by the

DNMT2 gene. Yang 2012 demonstrated that GA genotype of rs11254413 has a

potentially protective effect on GC risk (8). A study produced by Wu 2014 concluded that

DNMT3A1 rs7560488 can be a promising biomarker to evaluate GC susceptibility (6).

Other studies didn’t find an association between DNMTs SNPs and susceptibility for GC

(17,18).

CHAPTER 3


The aim of this study was to evaluate the association of DNMT2 and DNMT3A SNPs

(rs11254413 and rs7560488, respectively) with the development of gastric lesions and

gastric adenocarcinoma, in a population from the northern region of Portugal.

METHODS

Study Participants

A retrospective hospital-based case-control study was developed with 43 patients with

gastric lesion, 117 histologically confirmed GC patients and 220 cancer-free controls

from the northern region of Portugal recruited at the Portuguese Institute of Oncology,

Porto. Controls are healthy individuals without any clinical evidence of cancer selected

from a database of blood donors. The characteristics of our population are presented in

Table 1. An adjustment of gender and age it was taken into account to select controls.

The study was submitted to the approval of Ethical Committee from host institution.

Sample Collection and Processing

In this study we used peripheral blood samples collected with standard venipuncture

techniques in EDTA-containing tubes during routine clinical visits and stored in the

institution archives. Total nucleic acids extraction was performed with QIAamp DNA

Blood Mini Kit (QIAGEN, Venlo, Netherlands) according to manufacturer's instructions.

DNA quality was assessed by measuring the absorbance at 260/280nm using an

UV/Visible spectrophotometer and its purity assessed by the ratio of the values of

absorbance at 260/280nm. The presence of amplifiable genomic DNA was tested with a

PCR protocol for amplification of beta-globin gene (19).

Genotyping of DNMTs SNPs

The DNMTs SNPs, rs11254413 and rs7560488, were genotyped using the TaqMan®

SNP Genotyping Assay C___2822597_10 and C__26104534_10, respectively (Applied

Biosystems, Foster City CA, USA).

Reactions were performed in a 5μL master containing 1x TaqMan Genotyping Master

Mix (Applied Biosystems, Foster City CA, USA), 1x TaqMan® SNP Genotyping Assay

(Applied Biosystems, Foster City CA, USA) and 10ng of DNA from each sample.

Amplification was performed on the Applied Biosystems 7300 Real Time PCR System

(Applied Biosystems, Foster City CA, USA) according to the manufacturer instructions.

CHAPTER 3


Allelic discrimination was performed by measuring end-point fluorescence and analyzed

by two authors. Negative controls were used using double distilled water and 10% of all

samples were randomly selected and re-submitted to genotyping.


The Hardy–Weinberg equilibrium (HWE) was tested by calculated through the

application of the online software (http://www.had2know.com/academics/hardy-

weinberg-equilibrium-calculator-2-alleles.html) to compare the observed versus the

expected genotype frequencies. A p–value less than 0.05 of HWE was considered

significant.

Data analysis was performed using the computer software IBM SPSS Statistics for

Windows, Version 22.0 (IBM Corp, Armonk NY, USA). Chi-square analysis was used to

compare categorical variables, with a 5% level of significance. Multivariate logistic

regression analysis was used to estimate odds ratio (OR) and its 95% confidence interval

(CI) as a measure of the association between variant allele carriers and the risk for the

development of gastric cancer. Potential confounding variables (age, gender) were

addressed either by being included as covariates in the multivariate analysis or/and

through data stratification. Homozygotes for the allele with the highest frequency was

used as the reference group for each OR estimation. Variant allele carriers were defined

as the heterozygous and minor allele homozygous genotype carriers pooled together

(dominant model).

RESULTS

Allelic distribution of DNMT2 and DNMT3A SNPs

The distribution of rs11254413 genotypes is shown in table 2. The frequency of the GG,

GA and AA genotypes were 27.3%, 51.8% and 20.9%, respectively in controls; and

25.6%, 48.8% and 25.6% in patients with gastric lesions and 28.4%, 45.7% and 25.9%

in patients with gastric adenocarcinoma. The controls genotypic distribution was in HWE

(p = 0.547).

Regarding the distribution of rs7560488 genotypes, the frequencies of TT, TC and CC

genotypes were 29.3%, 51.4% and 19.3%, respectively in controls, 35.7%, 47.6% and

16.7% in patients with gastric lesions and 28.2%, 50.9% and 20.9% in patients with

http://www.had2know.com/academics/hardy-weinberg-equilibrium-calculator-2-alleles.html


CHAPTER 3


gastric adenocarcinoma – table 3. The distribution of genetic variants in controls was in

HWE (p = 0.431).

Risk estimates for associated lesions and gastric adenocarcinoma

Considering DNMT2 rs11254413 status, our results revealed no association with the

development of gastric lesion and adenocarcinoma. We did not found statistically

significant association with the development of gastric lesions and cancer (p = 0.793 and

p = 0.488, respectively) – table 2.

DNMT3A rs7560488 results revealed no statistically significant association with the

development of gastric lesions or GC (p = 0.724 and p = 0.946, respectively) – table 3.

DISCUSION

DNA methylation was the first epigenetic mark linked directly with tumorigenesis.

Aberrant CpG island methylation is associated to gene silencing and to cancer risk,

including GC (16,20). DNMTs are responsible for DNA methylation and literature reveals

that DNMTs are overexpressed in GC (21). Functional SNPs could affect DNMTs

expression, therefore, it is important to study SNPs in DNMTs.

DNMT2 rs11254413, leads to an amino acid change, His to Tyr, that may affect the

function of DNMT2 and it has been described as associated with protection for GC (8).

The other SNP analysed in this study, rs7560488, is a tagSNP representing different

SNPs of DNMT3A promoter with high linkage disequilibrium (5). Nevertheless, this

polymorphism seems to affect transcriptional activity of DNMT3A, resulting in altered

expression of protein and TC+CC genotypes have been associated with increased risk

for GC (5). Both these SNPs have only been studied in GC in one study each (5,8), and

up to our knowledge, this is the first study within a southern European population from

the northern region of Portugal reporting data regarding their role in GC development.

Our results suggest that rs11254413 and rs7560488 have no relation with the

development of gastric lesion and GC. These results are not in concordance with the

results observed in the two studies regarding the two SNPs in study (5,8). The

differences in the allele frequency, between regions, may explain the discordance of our

results with the ones find in the literature, since the two studies were performed with

Asiatic populations. We have compared the genetic distribution in our population with

other populations as described in the HapMap Project. Table 4 describe the allele

CHAPTER 3


frequency, in control population, of the two SNPs in 3 different regions, Asia, Europe and

Africa. After analyse the data, it is possible to conclude that allele frequency of the two

SNPs is similar comparing Europe and Africa, but major differences are found when

comparing to Asiatic population: in rs11254413, the GA genotype is the most common

genotype in Europe while GG genotype is the most common in Asia; and in rs7560488,

the TC genotype is most common in Europe and the genotype with higher percentage in

Asia is TT. Thus we can conclude that the genotypic distribution in our population is

similar to those already described in Caucasian populations, which may represent a

major genetic difference between these populations.

CONCLUSION

In conclusion, contribution of genetic polymorphisms to the risk of GC may be dependent

on the population in study, as well on several environmental and dietary factors that

influence that population. Our findings suggest these two SNPs (rs11254413 and

rs7560488) do not seem to be associated with GC, nevertheless more studies, with

larger Caucasian populations, are needed to clarify its role in GC susceptibility.

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16. Brookes AJ. The essence of SNPs. Gene [Internet]. 1999 Jul [cited 2016 Aug 17];234(2):177–86. Available from: http://linkinghub.elsevier.com/retrieve/pii/S037811199900219X

17. Jiang J, Jia Z, Cao D, Jin M-S, Kong F, Suo J, et al. Polymorphisms of the DNA methyltransferase 1 associated with reduced risks of Helicobacter pylori infection and increased risks of gastric atrophy. PLoS One [Internet]. United States; 2012 Jan [cited 2016 Apr 19];7(9):e46058. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3457938&tool=pmcentrez&rendertype=abstract

18. Cao X-Y, Jia Z-F, Cao D-H, Kong F, Jin M-S, Suo J, et al. DNMT3a rs1550117 polymorphism association with increased risk of Helicobacter pylori infection. Asian Pac J Cancer Prev. Thailand; 2013;14(10):5713–8.

19. Ribeiro J, Teixeira D, Marinho-Dias J, Monteiro P, Loureiro J, Baldaque I, et al. Characterization of human papillomavirus genotypes and HPV-16 physical status in cervical neoplasias of women from northern Portugal. Int J Gynaecol Obstet. Ireland; 2014 May;125(2):107–10.

20. Guo M, Yan W. Epigenetics of gastric cancer. Methods Mol Biol [Internet]. 2015 [cited 2016 Jul 16];1238:783–99. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25421692

CHAPTER 3


21. Lin J, Zeng RM, Li RN, Cao WH. Aberrant DNA methylation of the P16, MGMT,and hMLH1 genes in combination with the methylenetetrahydrofolate reductase C677T genetic polymorphism and folate intake in gastric cancer. Genet Mol Res. Brazil; 2014;13(1):2060–8.

22. International HapMap Consortium. The International HapMap Project. Nature [Internet]. 2003 Dec 18 [cited 2016 Aug 29];426(6968):789–96. Available from: http://www.ncbi.nlm.nih.gov/pubmed/14685227

CHAPTER 3


Table 1. Characteristics of the participants: age and gender

Control

n=220

Gastric Atrophy

n=43

Gastric Adenocarcinoma

n=117

Age, years

Mean ± SD 50.57 ± 6.67 59.56 ± 10.49 56.41 ± 11.24

Median (Range) 51.00 (25 – 64) 59.00 (25 – 78) 59 (25 – 78)

Gender, n

Male 100 (45.5%) 19 (44.2%) 75 (64.1%)

Female 120 (54.5%) 24 (55.8%) 42 (35.9%)

SD- Standard Deviation

Table 2. Genotype frequencies for DNMT2 SNP (rs11254413)

DNMT2 rs11254413 GG GA AA p

Normal 60 (27.3%) 114 (51.8%) 46 (20.9%) Ref.

Lesion/Cancer 44 (27.7%) 74 (46.5%) 41 (25.8%) 0.477

Lesion 11 (25.6%) 21 (48.8%) 11 (25.6%) 0.793

Cancer 33 (28.4%) 53 (45.7%) 30 (25.9%) 0.488

DNMT2 - TRNA (cytosine38-C5)-methyltransferase; Ref.- Reference

Table 3. Genotype frequencies for DNMT3A SNP (rs7560488)

DNMT3A rs7560488 TT TC CC p

Normal 41 (29.3%) 76 (51.4%) 27 (19.3%) Ref.

Lesion/Cancer 46 (30.3%) 76 (50.0%) 30 (19.7%) 0.970

Lesion 15 (35.7%) 20 (47.6%) 7 (16.7%) 0.724

Cancer 31 (28.2%) 56 (50.9%) 23 (20.9%) 0.946

DNMT3A - DNA (cytosine-5)-methyltransferase 3A; Ref.- Reference

Table 4. Allele frequency of rs11254413 and rs7560488 in different countries. Adapted from

The International HapMap Project (22)

Population Region rs11254413 rs7560488

n GG GA AA n TT TC CC

HapMap-CEU Europe 224 25.88% 52.69% 21.43% 218 35.78% 53.21% 11.01%

HapMap-HCB Asia 86 51.16% 41.86% 6.98% 86 65.12% 30.23% 4.65%

HapMap-JPT Asia 172 58.14% 37.21% 4.65% 172 67.44% 27.91% 4.65%

HapMap-YRI Africa 226 34.51% 46.90% 18.58% 220 33.64% 47.27% 19.09%

CEU - Northern and Western European; HCB - Han Chinese in Beijing; JPT - Japanese in Tokyo; YRI -

Yoruba in Ibadan, Nigeria

https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewTable.cgi?pop=1409




CHAPTER 4

CHAPTER 4


TITLE

SLC19A1 G80A AND MTHFR C677T AS PREDICTORS OF GASTRIC CANCER

DEVELOPMENT

AUTHORS

Neves M1,2, Francisco M3,4, Ribeiro J1,2,5, Lima A1,4,6, Dinis-Ribeiro M7,8 Medeiros R1,5,6,9,

Sousa H1,9

AFILIATIONS

1 Molecular Oncology and Viral Pathology Group, CI-IPOP, Portuguese Oncology

Institute of Porto (IPO-Porto), Porto, Portugal.

2 Faculty of Medicine of University of Porto, Porto, Portugal.

3 Faculty of Medicine of University of Coimbra, Coimbra, Portugal.

4 CESPU Institute of Research and Advanced Training in Health Sciences and

Technologies, Department of Pharmaceutical Sciences, Gandra PRD, Portugal.

5 Research Department, Portuguese League Against Cancer, Porto, Portugal.

6 Abel Salazar Institute for the Biomedical Sciences (ICBAS), University of Porto, Porto,

Portugal.

7Center for Health Technology and Services Research (CINTESIS), Faculty of Medicine,

University of Porto, Porto, Portugal.

8Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal.

9 Virology Service, Portuguese Oncology Institute of Porto, Porto, Portugal.

Correspondence to:

Hugo Sousa, MD PhD





Phone: +351 22 508 4000 (ext 5410)

Fax: +351 22 508 4001




CHAPTER 4


ABSTRACT

AIM: To evaluate the relationship between SLC19A1 G80A and MTHFR C677T and the

development of gastric lesions and gastric cancer.

METHODS: We performed a case-control study including 427 Portuguese individuals:

250 without evidence of neoplastic disease, 43 with gastric lesion and 137 patients with

gastric adenocarcinomas). For genotyping proceeding was used a PCR-RFLP method.

RESULTS: SLC19A1 G80A genotyping results revealed a protective effect for gastric

lesion (OR = 0.373; 95%CI = 0.189-0.736; p = 0.006), but not for gastric cancer.

Regarding MTHFR C677T results, we found an increased risk for GC development (OR

= 2.046; CI 95% = 1.323-3.165; p = 0.001), but no association with gastric lesions.

CONCLUSION: SLC19A1 G80A may have a protective role in gastric lesion

development and MTHFR C677T may be associated with an increased risk for GC

development, but more studies with Caucasian populations are needed to prove our

findings.

KEY WORDS

Folates; SLC19A1; MTHFR; Single Nucleotide Polymorphism; Gastric Cancer

CHAPTER 4


INTRODUTION

Gastric cancer (GC) is one of the most common type of malignancies worldwide,

representing the fifth common malignancy and the third leading cause of cancer deaths

(1). GC can be divided in two types: intestinal type defined by a sequence of events from

normal mucosa through atrophic gastritis, atrophy, intestinal metaplasia and dysplasia

to invasive adenocarcinoma; and diffuse type characterized by a less differentiated

malignant neoplasia, without a well-marked multiple step process (2,3,4). The

development of GC is associated with several risk factors and the major one, especially

for the development of the intestinal type, is Helicobacter pylori infection. GC can also

be the result of specific genetic alterations, epigenetic alterations and other

environmental factors, such as the low consumption of fruits and vegetables and, more

specifically, folate deficiency. Literature reveals that low folate levels are correlated with

development and invasiveness of GC (5).

It is well known that gene expression can be influenced by a single nucleotide

polymorphism (SNP). Polymorphisms in genes involved in folate metabolism, like

SLC19A1 and MTHFR, may have functional consequences affecting levels of folate in

plasma and therefore individual’s susceptibility to cancer.

SLC19A1 80G>A is characterized by a substitution of a G by an A at position 80, leading

to the change of arginine (Arg) into histidine (His) at codon 27 in the first transmembrane

domain, a region implicated in carrier function (1,4). This polymorphism has been

associated with differential activity of SLC19A1 and literature shows that it may modulate

risk of gastric cancer (GC) (6,7).

MTHFR C677T variant is characterized by a substitution of a C by a T at position 677,

leading to a change of Alanine (Ala) into valine (Val) at codon 222, resulting in a

thermolabile enzyme with reduced specific activity. Several studies have explored the

potential association of this SNP with the susceptibility to GC (8,9).

The aim of this case-study was to evaluate the association of SLC19A1 and MTHFR

SNPs with the development of gastric lesions and gastric adenocarcinoma, in a

population from the northern region of Portugal.

CHAPTER 4


METHODS

Study Participants

A retrospective hospital-based case-control study was developed with 43 patients with

gastric lesion, 137 histologically confirmed GC patients and 250 cancer-free controls

from the northern region of Portugal recruited at the Portuguese Institute of Oncology,

Porto. Controls are healthy individuals without any clinical evidence of cancer selected

from a database of blood donors. The characteristics of our population are presented in

Table 1. An adjustment for gender and age it was taken into account to select controls.

The study was submitted to the approval of Ethical Committee from host institution.

Sample Collection and Processing

In this study, we used peripheral blood samples collected with standard venipuncture

techniques in EDTA-containing tubes during routine clinical visits and stored in the

institution archives. Total nucleic acids extraction was performed with QIAamp DNA

Blood Mini Kit (QIAGEN, Venlo, Netherlands) according to manufacturer's instructions.

DNA quality was assessed by measuring the absorbance at 260/280nm using an

UV/Visible spectrophotometer and its purity assessed by the ratio of the values of

absorbance at 260/280nm. The presence of amplifiable genomic DNA was tested with a

PCR protocol for amplification of beta-globin gene (10).

Polymorphisms genotyping

The SLC19A1 G80A polymorphism (rs1051266) was genotyped by PCR-RFLP as

previously described (11). PCR reaction was performed in a final volume of 50μl

containing 1× DreamTaq® Green master mix (Thermo Fisher Scientific, MA USA), 0.3μM

of each primer (forward 5´-AGTGTCACCTTCGTCCCCTC-3´ and reverse 5´-

CTCCCGCGTGAAGTTCTT-3´), 1 unit of DreamTaq® (Thermo Fisher Scientific, MA

USA) and 50–100ng of genomic DNA. The PCR amplification consisted of initial

denaturation at 95°C during 5min followed by 40 cycles with denaturation for 15s at 95°C,

annealing/extension for 1min at 58°C and a final extension at 72°C for 7min. PCR

product of 230bp was digested by CfoI (HhaI) (Thermo Fisher Scientific, MA USA)

restriction endonuclease at 37°C for 1h. Individuals with the SLC19A1 80GG genotype

presented two fragments (68 bp and 125 bp), individuals with the SLC19A1 80AA also

presented two fragments (68 bp and 162 bp), whereas individuals with the SLC19A1

80AG genotype presented three fragments (68 bp, 125 bp and 162 bp).

CHAPTER 4


The MTHFR C677T polymorphism (rs1801133) was genotyped by PCR-RFLP as

previously shown (12). PCR reaction was performed in a final volume of 50μl containing

1× DreamTaq® Green master mix (Thermo Fisher Scientific, MA USA), 0.3μM of each

primer (forward 5´-TGAAGGAGAAGGTGTCTGCGGGA-3´ and reverse 5´-

AGGACGGTGCGGTGAGAGTG-3´), 1 unit of DreamTaq® (Thermo Fisher Scientific,

MA USA) and 50–100ng of genomic DNA. The PCR amplification consisted of initial

denaturation at 94°C during 5min followed by 30 cycles with denaturation for 1min at

94°C, annealing for 1min at 57°C, extension for 15s at 72°C, and a final extension at

72°C for 10min. PCR product of 198bp was digested by HinfI (Thermo Fisher Scientific,

MA USA) restriction endonuclease at 37°C for 1h. Individuals with the MTHFR 677CC

genotype presented one fragment with 198bp, individuals with the MTHFR 677TT

presented one fragment with 175bp, whereas individuals with the MTHFR 677CT

genotype presented two fragments (198bp and 175bp).

Genotypes were accessed by the interpretation of two authors. Negative controls were

added to each PCR reaction using double distilled water to replace template DNA and

10% of all samples were randomly selected and re-submitted to genotyping.


The Hardy–Weinberg equilibrium was calculated through online software

(http://www.had2know.com/academics/hardy-weinberg-equilibrium-calculator-2-

alleles.html) to compare the observed versus the expected genotype frequencies and

was considered significant a p–value less than 0.05. Data analysis was performed using

the computer software IBM SPSS Statistics for Windows, Version 22.0 (IBM Corp,

Armonk NY, USA). Chi-square analysis was applied to compare categorical variables,

using a 5% level of significance. Multivariate logistic regression analysis was used to

estimate odds ratio (OR) and its 95% confidence interval (CI) as a measure of the

association between variant allele carriers and the risk for the development of GC.

Possible confounding variables, such as age and gender, were addressed either by

being included as covariates in the multivariate analysis or/and through data

stratification. Homozygotes for the allele with the highest frequency was used as the

reference group for each OR estimation. Variant allele carriers were defined as the

heterozygous and minor allele homozygous genotype carriers pooled together through

a statistically dominant model.



CHAPTER 4


RESULTS

Allelic distribution of SLC19A1 G80A polymorphism

The distribution of rs1051266 genotypes is shown in table 2. The frequency of the AA,

AG, GG and Gcarrier genotypes were 21.2%, 50.2%, 28.6% and 78.8%, respectively in

controls, 41.9%, 44.2%, 14.0% and 58.1% in patients with gastric lesions and 27.7%,

48.2%, 24.1% and 72.3% in patients with gastric adenocarcinoma. The controls

genotypic distribution was in HWE (p = 0.88).

Allelic distribution of MTHFR C677T polymorphism

Regarding the distribution of rs1801133 genotypes, the frequencies of CC, CT, TT and

Tcarrier genotypes were 50.8%, 33.2%, 16.0% and 49.2%, respectively in controls,

41.9%, 41.9%, 16.3% and 58.1% in patients with gastric lesions and 33.6%, 47.2%,

17.2% and 66.4% in patients with gastric adenocarcinoma – table 3. The HWE was

presented in controls genotypic distribution (p = 0.15).

Risk estimates for associated lesions and gastric adenocarcinoma

Table 2 describes the Odds Ratio (OR) for the development of gastric lesion and

adenocarcinoma considering SLC19A1 G80A status. We found statistically significant

risk for the development of gastric lesion (OR = 0.373; 95%CI = 0.189-0.736; p = 0.006).

We observed a possible protective role for 80G carriers, and when the same analysis

was adjusted for age and gender we also observed this protective role (OR = 0.347;

95%CI = 0.158-0.762; p = 0.008). In contrast, we found no statistically significant risk for

the development of GC (OR = 0.699; 95%CI = 0.430-1.136; p = 0.166). When the same

analysis was adjusted for age and gender we also found the same no statistically

significant results (OR = 0.692; 95%CI = 0.410-1.166; p = 0.167).

Table 3 describes the OR for the development of gastric lesion and GC considering

MTHFR C677T status. We found no statistically risk for the development of lesion (OR

= 1.436; CI 95% = 0.745-2.771; p = 0.322). When the same analysis was adjusted for

age and gender we also found the same no statistically significant results (OR = 1.155;

CI 95% = 0.556-2.400; p = 0.700). In contrast, we found statistically significant risk for

the development of GC (OR = 2.046; CI 95% = 1.323-3.165; p = 0.001). We observed a

2-fold increased risk for the development of GC, and when the same analysis was

adjusted for age and gender we also found a statistically significant risk for the

development of GC (OR = 1.918; CI 95% = 1.209-3.044; p = 0.006).

CHAPTER 4


DISCUSSION

Literature revealed that lower serum folate levels are associated with development and

invasiveness of gastric cancer (5). Folates are important for DNA methylation and

nucleotide biosynthesis, and low levels of folate may result in DNA global

hypomethylation, DNA damage, impaired DNA repair and altered oncogene and tumor

suppressor gene expressions (5,13). Functional SNPs, in genes involved in folate

metabolism, may affect folate levels, therefore, it is important to study SNPs in genes

important for folate metabolism, such as, SLC19A1 and MTHFR.

The SLC19A1 G80A polymorphism leads to an amino acid change of Arg into His at

codon 27 in the first transmembrane domain, a region implicated in carrier function (1,4).

In literature, we found only one study evaluating the association of SLC19A1 G80A

polymorphism with GC development (7). Wang and colleagues reported a 1.43-fold

increased risk for GC development (7).

Literature reports other SNP in a protein involved in the folates metabolism that is often

associated with other diseases (9,13–16). The MTHFR C677T, characterized by an

amino acid change of Ala into Val at codon 222, results in a thermolabile enzyme with

reduced specific activity (9). Indeed, this polymorphism has been shown to significantly

increase susceptibility to gastric cancer (9,13–16).

A study conducted by Chango et. al demonstrated that individuals with SLC19A1 80AA

and MTHFR 677TT genotypes had significantly lower plasma folate levels and

homocysteinemia (high level of homocysteine in blood) than individuals with wild-type

SLC19A1 80GG and MTHFR 677CC genotypes (17). This evidence suggests a role for

both SLC19A1 G80A and MTHFR C677T in the carcinogenesis of GC.

SLC19A1 G80A and development of gastric lesion and GC

SLC19A1 G allele seems to be extremely frequent (78.8%) in our population, which by

comparison with data reported from other studies and from HapMap project is different

among the different populations (Asia, Europe and Africa). The G allele is more frequent

in Europe than in Asia and Africa. Moreover, our results for the genotype frequencies are

in concordance with reported data in Caucasian populations – table 4.

Our results suggest that SLC19A1 G80A is associated with a protective effect for gastric

lesion (OR = 0.373; 95%CI = 0.189-0.736; p = 0.006), but not with GC. After an analysis

adjusted for age and gender, the results remain statistically significant for gastric lesion

CHAPTER 4


but not for GC – Table 1. Up to our knowledge, there is no study regarding the association

of this SNP with gastric lesion. A meta-analysis performed by Huang et al. revealed a

protective association with GC (GA+GG: OR = 0.89, 95%CI = 0.81-0.99, p = 0.030) (6).

Our results also demonstrated the protective effect of G allele with GC but the results

were not statically significant (OR = 0.699; 95%CI = 0.430-1.136; p = 0.166).

MTHFR C677T and development of gastric lesion and GC

Regarding MTHFR C677T allele frequency, our results shown that is no big difference

between allele frequencies. The frequencies of this variant seem to diverge among

different ethnic populations: C allele is more frequent in Europe and Africa, with the

particularity that in Africa, the TT genotype is not present; in Asia the frequencies differ

between countries, being the C allele more common in Japan and the T allele more

frequent in China – Table 4. Furthermore, our results are not in concordance with the

reported data in Caucasian populations, being more similar to those from Chinese allele

frequencies. This results may be one factor to explain different risk susceptibilities of

populations and may approximate us from those countries with higher incidences of GC

when comparing to the European average rates (18).

MTHFR C677T is associated with an increased risk for GC development (OR = 2.046;

CI 95% = 1.323-3.165; p = 0.001), but was not associated with gastric lesions. The

analysis adjusted for age and gender presented the same results – Table 2. Our results

are in concordance with the results observed in the literature concerning the association

of this SNP with GC (9,13–16). A meta-analysis presented by Yan et al. showed that,

after stratified analyses according to ethnicity, MTHFR C677T may be a risk factor for

gastric cancer among Asians and Caucasians (16). Regarding the association of MTHFR

C677T with gastric lesion, to our knowledge, there is no study demonstrating this

association, and despite we report a tendency for an increased risk for the development

of gastric lesion (OR = 1.436; 95%CI = 0.745-2.771; p = 0.322) results were not statically

significant.

CHAPTER 4


CONCLUSIONS

In conclusion, our findings suggest that SLC19A1 G80A may have a protective role in

gastric lesion development and MTHFR C677T may be associated with an increased

risk for GC development. Up to our knowledge, this is the first study within a southern

European population, from the north region of Portugal evaluating the association of

SLC19A1 G80A and MTHFR C677T polymorphisms. The contribution of genetic

polymorphisms to the risk of GC may be dependent on the population in study, as well

on several environmental and dietary factors that influence that population. More studies

are needed, with Caucasian populations, to understand the relation of both SNPs with

GC.

REFERENCES





5. Lee TY, Chiang EP, Shih YT, Lane HY, Lin JT, Wu CY. Lower serum folate is associated with development and invasiveness of gastric cancer. World J Gastroenterol. 2014;20(32):11313–20.

6. Huang X, Gao Y, He J, Cai J, Ta N, Jiang H, et al. The association between RFC1 G80A polymorphism and cancer susceptibility: Evidence from 33 studies. J Cancer [Internet]. 2016;7(2):144–52. Available from: http://www.jcancer.org/v07p0144.htm

7. Wang L, Chen W, Wang J, Tan Y, Zhou Y, Ding W, et al. Reduced folate carrier gene G80A polymorphism is associated with an increased risk of gastroesophageal cancers in a chinese population. Eur J Cancer. 2006;42(18):3206–11.

8. Gotze T, Rocken C, Rohl FW, Wex T, Hoffmann J, Westphal S, et al. Gene polymorphisms of folate metabolizing enzymes and the risk of gastric cancer. Cancer Lett. 2007;251(2):228–36.

9. Xiong H-L, Liu X-Q, Sun A-H, He Y, Li J, Xia Y. Aberrant DNA methylation of P16, MGMT, hMLH1 and hMSH2 genes in combination with the MTHFR C677T genetic polymorphism in gastric cancer. Asian Pac J Cancer Prev. Thailand; 2013;14(5):3139–42.

10. Ribeiro J, Teixeira D, Marinho-Dias J, Monteiro P, Loureiro J, Baldaque I, et al.

CHAPTER 4


Characterization of human papillomavirus genotypes and HPV-16 physical status in cervical neoplasias of women from northern Portugal. Int J Gynaecol Obstet. Ireland; 2014 May;125(2):107–10.

11. Lima A, Bernardes M, Sousa H, Azevedo R, Costa LL, Ventura F, et al. SLC19A1 80G allele as a biomarker of methotrexate-related gastrointestinal toxicity in Portuguese rheumatoid arthritis patients. Pharmacogenomics [Internet]. England; 2014 Apr;15(6):807–20. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24350725

12. Lima A, Monteiro J, Bernardes M, Sousa H, Azevedo R, Seabra V, et al. Prediction of methotrexate clinical response in Portuguese rheumatoid arthritis patients: Implication of MTHFR RS1801133 and ATIC RS4673993 polymorphisms. Biomed Res Int. Hindawi Publishing Corporation; 2014;2014.

13. Gao S, Ding L-H, Wang J-W, Li C-B, Wang Z-Y. Diet folate, DNA methylation and polymorphisms in methylenetetrahydrofolate reductase in association with the susceptibility to gastric cancer. Asian Pac J Cancer Prev. Thailand; 2013;14(1):299–302.

14. Xia L-Z, Liu Y, Xu X-Z, Jiang P-C, Ma G, Bu X-F, et al. Methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and gastric cancer susceptibility. World J Gastroenterol [Internet]. 2014 Aug 28 [cited 2016 Sep 9];20(32):11429–38. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25170232

15. Lv L, Wang P, Sun B, Chen G. The polymorphism of methylenetetrahydrofolate reductase C677T but not A1298C contributes to gastric cancer. Tumour Biol [Internet]. 2014 Jan [cited 2016 Sep 9];35(1):227–37. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23897558

16. Yan S, Xu D, Wang P, Wang P, Liu C, Hua C, et al. MTHFR C677T polymorphism contributes to the risk for gastric cancer. Tumour Biol [Internet]. 2014 Mar [cited 2016 Sep 9];35(3):2123–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24122207

17. Chango A, Emery-Fillon N, de Courcy GP, Lambert D, Pfister M, Rosenblatt DS, et al. A Polymorphism (80G->A) in the Reduced Folate Carrier Gene and Its Associations with Folate Status and Homocysteinemia. Mol Genet Metab [Internet]. 2000 Aug [cited 2016 Aug 10];70(4):310–5. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1096719200930348

18. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur J Cancer [Internet]. 2013 [cited 2016 Sep 10];49:1374–403. Available from: http://dx.doi.org/10.1016/j.ejca.2012.12.027

CHAPTER 4


Table 1. Characteristics of the participants: age and gender

Control

n=250

Gastric Atrophy

n=43

Gastric Adenocarcinoma

n=117

Age, years

Mean ± SD 50.80 ± 6.86 59.56 ± 10.49 56.91 ± 11.14

Median (Range) 51 (25 – 64) 59(25 – 78) 59 (25 – 78)

Gender, n

Male 124 (50.4%) 19 (44.2%) 84 (61.3%)

Female 126 (49.6%) 24 (55.8%) 53 (38.7%)

SD – Standard Deviation

CHAPTER 4


Table 2. Genotype frequencies for SLC19A1 G80A polymorphism

a adjusted for age and gender; OR- Odds Ratio; CI- Confidence Interval

Table 3. Genotype frequencies for MTHFR C677T polymorphism

MTHFR C677T CC CT TT Tcarrier OR CI (95%) p ORa CI (95%)a pa

Normal 121 (50.8%) 79 (33.2%) 38 (16.0%) 117 (49.2%) 1.000 Reference 1.000 Reference

Lesion/Cancer 64 (35.6%) 85 (47.2%) 31 (17.2%) 116 (64.4%) 1.874 1.260-2.788 0.002 1.693 1.108-2.587 0.015

Lesion 18 (41.9%) 18 (41.9%) 7 (16.3%) 25 (58.1%) 1.436 0.745-2.771 0.322 1.155 0.556-2.400 0.700

Cancer 46 (33.6%) 85 (47.2%) 31 (17.2%) 91 (66.4%) 2.046 1.323-3.165 0.001 1.918 1.209-3.044 0.006

a adjusted for age and gender; OR- Odds Ratio; CI- Confidence Interval

SLC19A1 G80A GG GA AA Gcarrier OR CI (95%) p ORa CI (95%)a pa

Normal 69 (28.6%) 121 (50.2%) 51 (21.2%) 190 (78.8%) 1.000 Reference 1.000 Reference

Lesion/Cancer 39 (21.7%) 85 (47.2%) 56 (31.1%) 124 (68.9%) 0.594 0.382-0.924 0.024 0.551 0.340-0.894 0.016

Lesion 6 (14.0%) 19 (44.2%) 18 (41.9%) 25 (58.1%) 0.373 0.189-0.736 0.006 0.347 0.158-0.762 0.008

Cancer 33 (24.1%) 66 (48.2%) 38 (27.7%) 99 (72.3%) 0.699 0.430-1.136 0.166 0.692 0.410-1.166 0.167

Table 4. Allele frequency of SLC19A1 G80A and MTHFR C677TT in different regions. Adapted from The International HapMap Project (1)

Population Region n SLC19A1 G80A MTHFR C677TT

GG AG AA Gcarrier CC CT TT Tcarrier

HapMap-CEU Europe 224 28.57% 55.36% 16.07% 56.25% 46.90% 44.25% 8.85% 30.97%

HapMap-HCB Asia 86 25.58% 46.51% 27.91% 48.84% 27.91% 41.86% 30.23% 51.16%

HapMap-JPT Asia 172 19.77% 48.84% 31.39% 44.19% 39.53% 48.84% 11.63% 36.05%

HapMap-YRI Africa 226 13.27% 35.40% 51.33% 30.97% 81.42% 18.58% - 9.29%

CEU - Northern and Western European; HCB - Han Chinese in Beijing; JPT - Japanese in Tokyo; YRI - Yoruba in Ibadan, Nigeria





CHAPTER 5

CHAPTER 5


FINAL DISCUSSION AND CONCLUSIONS

Gastric Cancer is a major cause of cancer-related deaths worldwide (4). In Portugal, GC

is the fifth cause of death by cancer (4). The majority of patients with GC are diagnosed

in advanced stages, leading to a poor prognosis (37).Therefore, it is important to

implement preventive strategies, such as population screening and genetic susceptibility

tests, to allow the detection of GC at early stages.

In this thesis we have been focused on genetic risk markers of GC susceptibility

associated with two of the risk factors for GC development: altered DNA methylation and

low folate intake. Literature revealed that genes involved in carcinogenesis accumulate

aberrant methylation patterns (26) and additionally, studies have also found an

association between low folate levels and development and invasiveness of GC (22).

Genetic alterations, such as SNPs, have been described as important predictors of

cancer development and a great number of them have the ability to modulate gene

expression. Therefore, the study of SNPs in genes responsible for DNA methylation and

folate metabolism may contribute for a better knowledge of genetic influence on

individual’s susceptibility to GC development.

DNA Methylation

We started by performing a systematic review and meta-analysis to understand what has

been described in literature regarding the role of SNPs in DNMTs and GC development,

in order to attempt to clarify which SNPs could be important to study. From a total of 24

SNPs reported, we found that only 4 SNPs seem to have some association with GC. The

DNMT2 rs11254413, DNMT3A rs7560488 and rs36012910 and, specially, DNMT1

rs16999593 seemed to be good candidates to study the role of SNPs in DNMTs with

GC – figure 5.

The DNMT1 rs16999593 C>T variation results in a missense mutation caused by a His

to Arg amino acid substitution at position 97 of DNMT1 promoter (33). A study performed

by Yang and colleagues revealed that DNMT1 rs16999593 C allele was associated with

an increased risk of GC (OR = 1.45; 95%CI = 1.00-2.11; p = 0.05) (38). Moreover, Jiang

et al. results showed an association of DNMT1 rs16999593 in GC but the results were

not statistically significant (33). Nevertheless, the influence of this SNP on DNMT1

function is still unknown. We hypothesized that DNMT1 rs16999593 variation may have

an effect on structure and function of the protein.

CHAPTER 5


Figure 5. SNPs in DNMTs candidates for association with GC.

DNMT1 is responsible for the methylation of newly biosynthesized DNA strands after

replication (39). Literature showed that DNMT1 expression levels are significantly higher

in GC patients (40–42). DNMT1 increased expression levels are associated with

hypermethylation of CGI and, therefore, tumor suppressor genes silencing (43). Genetic

variants in DNMT1 promoter, such as rs16999593, may have an impact in GC

carcinogenesis and contribute to genetic susceptibility to GC.

The literature systematic review also revealed that DNMT2 rs11254413 A allele was

associated to a protective effect against GC (OR = 0.15; 95%CI = 0.08-0.27); p < 0.01)

(38). DNMT2 rs11254413 is characterized by a G>A allele change leading to a His to

Tyr missense mutation in position 101. DNMT2 is responsible for aspartic acid tRNA and

residual DNA (cytosine-C5) methylation, nevertheless, there are needed future studies

to understand the impact of DNMT2 in cellular pathways (44). The results presented by

Yang et al. suggest a potentially protective effect of DNMT2 rs11254413 on GC risk, but

more studies are needed to prove this association, since this was the only study

analyzing an association of DNMT2 rs11254413 with GC.

Finally, regarding DNMT3A literature analysis, we found two SNPs with a statistically

significant association with GC: rs7560488 and rs36012910. The C allele of DNMT3A1

rs7560488 was associated with an increased risk for GC (OR = 1.65; 95%CI = 1.19-2.29;

p = 0.002) (45). DNMT3A rs7560488 T>C variation is a putative functional tagSNP, which

means that represents all SNPs from DNMT3A promoter with high linkage disequilibrium

(35,45). Wu and colleagues also performed assays to understand the impact of this

RISK Protection

CHAPTER 5


tagSNP in DNMT3A expression. The results revealed that the expression levels of

DNMT3A1 mRNA was lower in individuals with C allele (p<0.05) (45). DNMT3A

rs7560488 T>C variation have an effect on the binding of transcriptional factors, which

results in an altered transcriptional activity of DNMT3A1 gene (45). Therefore, these

findings suggest that DNMT3A rs7560488 may regulate DNMT3A expression and

contribute for GC susceptibility. Another SNP in DNMT3A statistically significant

associated with GC was rs36012910. Individuals with the G allele of DNMT3A

rs36012910 presented an increased risk for GC when compared to controls (OR = 2.36;

95%CI = 1.35-4.14; p = 0.002) (46). This SNP is localized on the transcription start site

in the promoter region of DNMT3A and is characterized by an A to G allele change (46).

By bioinformatics analyses, Wu and his colleagues hypothesized that DNMT3A is a

functional SNP with impact on the DNMT3A promoter activity, and, therefore, may be

associated with GC (46). Future studies are needed to confirm the biological impact of

DNMT3A rs rs36012910 and the association with GC.

Furthermore, we have attempted to summarize the data in literature with a meta-

analysis, and we found that from the 24 SNPs, only 6 SNPs were evaluated in more than

one study, which clearly reduces the data available for the meta-analysis. Our results

revealed that only rs16999593 was associated with GC, showing statistically significant

increased risk (OR = 1.31; 95% CI = 1.08-1.60; p=0.006) for TC+CC genotypes. Only

two studies evaluate this association, both in Asiatic populations. As described before,

more studies are needed to understand the functional impact of this SNP. Besides, this

results suggest that Chinese individuals carrying DNMT1 rs16999593 C allele have an

increased risk for GC, and more studies with different populations are needed to confirm

the association of DNMT1 rs16999593 with GC.

After the literature systematic review and meta-analysis, we decided to evaluate 3

different SNPs in DNMTs: DNMT1 rs16999593, DNMT2 rs11254413 and DNMT3A

rs7560488. We decided to study one SNP for each DNMT, according to systematic

review and meta-analysis results. All chosen SNPs presented promising associations

with GC and we aimed to characterize the genotyping distribution for the three SNPs in

our population. Unfortunately, we only were capable to studied DNMT2 rs11254413 and

DNMT3A rs7560488 because of technical issues with the assay for the other SNP that

did not allowed us to do a correct interpretation of genotypes and therefore we have

decided to exclude it from the study. Our results revealed no association of both SNPs

with gastric lesions and cancer development. Nevertheless, the findings of our study are

not in agreement with the literature available. Only two studies evaluate the association

CHAPTER 5


of DNMT2 rs11254413 and DNMT3A rs7560488 with GC (38,45). The results, also

explored before, showed that DNMT2 rs11254413 A allele may have a protective effect

for GC and DNMT3A rs7560488 C allele may be associated with an increased risk for

GC. The majority of studies regarding SNPs in DNMTs and association with GC were

performed with Asian populations and there are no studies with Caucasian populations.

We found no association with gastric lesion and GC in our population, nevertheless, this

was the first study evaluating the association of DNMT2 rs11254413 and DNMT3A

rs7560488 in a European population and more studies are needed to confirm our

findings.

Folate Metabolism

Finally, we also performed a study to understand the association of SNPs in genes

essential for folate metabolism with gastric lesions and GC. We selected two important

genes that have been studied: SLC19A1 and MTHFR (47,48). Our study demonstrated

an association between SLC19A1 G80A and gastric lesions development and MTHFR

C677T and GC development.

SLC19A1 G80 is associated with a protective effect for gastric lesions (OR = 0.373;

95%CI = 0.189-0.736; p = 0.006), but not with GC. According to literature, there is only

one study evaluating the association of SLC19A1 G80A with GC (49). The results

showed a protective effect of G allele for GC, which is in line with our results although

we do not find a statistically significant association. This is the first study to evaluate the

association of this SNP with gastric lesions.

On the other hand, MTHFR C677T is associated with an increased risk for GC

development (OR = 2.046; 95%CI = 1.323-3.165; p = 0.001 for T allele), but was not

associated with gastric lesions. Up to our knowledge, there is no studies to demonstrate

the association between MTHFR C677T and gastric lesions, still, several studies have

been performed concerning the association of this SNP with GC (50–54). All studies

suggest that T allele is associated with an increased risk for GC development. Therefore,

MTHFR C677T seem like a good biomarker for GC susceptibility.

Folates are B vitamins naturally present in many foods, important for DNA regulation,

including DNA methylation (51). In folate metabolism, SLC19A1 or RFC1 is responsible

for transport of active form of folate 5-MTHF and MTHFR has the capacity to convert

folates in to the reduced and active form 5-MTHF. Without 5-MTHF, there is no

conversion of homocysteine in methionine and, therefore, no formation of SAM, a methyl

group donor crucial for DNA methylation catalysed by DNMTs – figure 6.

CHAPTER 5


Figure 6. Homocysteine cycle. The 5-MTHF is a methyl donor for the conversion of homocysteine

into methionine and SAM is a methyl donor for DNA methylation. (5-MTHF – 5-Methylene

tetrahydrofolate; SAM - S-Adenosyl methionine)

Literature showed that individuals with SLC19A1 80AA and MTHFR 677TT genotypes

had significantly lower plasma folate levels and homocysteinemia (high level of

homocysteine in blood), comparing to individuals with wild-type SLC19A1 GG80 and

MTHFR CC677 genotypes (17). The results of Chango and his colleagues suggests that

SLC19A1 G80A and MTHFR C677T influence protein function and, therefore,

carcinogenesis of GC (17).

Conclusions

In conclusion, our study demonstrated that DNMT2 rs11254413 and DNMT3A

rs7560488 are not associated with gastric lesions and GC in our population, but,

otherwise, SLC19A1 G80A and MTHFR C677T seem to have an impact on susceptibility

for gastric lesions and GC, respectively. These results suggest that SLC19A1 G80A and

MTHFR C677T may have a role in gastric carcinogenesis, though this observation needs

to be complemented with further studies, including larger studies with different ethnic

populations and functional studies regarding the effect of both SNPs in gene expression.

Homocysteine

Methionine

SAM

DNA Methylation

5-MTFH

CHAPTER 6

CHAPTER 6


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