Construction of web-based miRNA database of cancer therapy11/13/2015

Background: miRNA and Cancer

AAPS J. 2010 Sep; 12(3): 309–317.

Small non-coding RNA molecule (containing about 22 nucleotides)

The main functions are RNA silencing and post-transcriptional regulation of gene expression.

Motivation

• It is important to classify the miRNA according to cancer effect and kinase signal pathway.

• The statistics of the whole miRNA database related to cancer could provide us useful information to select future target for cancer

Background: Database• What is a database? a collection of data that is logically coherent.• DBMS – Database Management System. Allows users controlled access to data in

the database.

• Why database? Just imagine when we want to check a gene and potential related miRNA. We have two choices: 1. Go to Pubmed and find all the related publications and read them. 2. Go to a relevant database and type the gene keyword.

Experimental design: Database ModelsHierarchical modelNetwork modelRelational model: Data are organized in two-dimensional tables called relations. The most popular model.

Database design of miRNA database(version Oct 2015)

Material and Methods: How to implement it?The best commercial DBMS is Oracle DB. However we cannot publish Oracle

Database project online unless we purchased the licence.Fortunately, we have our free open-source DBMS.

Better web environmentBetter at handling proceduce task. pl/pgsql.

PHP/MySQLBuild a dynamic website with database at no license fee.

Very easy to transplant to professional platform once project get more professional and bigger.

Raw Data From Pubmed

miRNA tableGene Table Pathway

Target table Pathway Target table

Querry Results

Construct a miRNA of Cancer Kinase function Database

• Since miRNA database is widely distributed. My project would focus on fucntion of miRNA on Cancer Kinase.

• Search Terms in Pubmed (10/20/2015):• ("phosphotransferases"[MeSH Terms] OR "phosphotransferases"[All Fields] OR

"kinase"[All Fields]) AND ("micrornas"[MeSH Terms] OR "micrornas"[All Fields] OR "mirna"[All Fields]) AND ("neoplasms"[MeSH Terms] OR "neoplasms"[All Fields] OR "cancer"[All Fields])

• Total 2255 articles: 2120 full text available.• 2205 in English.• 17Clinical Trial• 197 review.• 1969 Journal articles.

• Not a very big database.

Results

Website

Next Step(Statistics Analysis on the database)

• Categorical analysis to find the distribution and correlation of miRNAs in different kinase pathway.

• Build logit model to explain the effect of various miRNA expression related to cancer outcome.

Statistics datatissue cou_tissue

breast 61

liver 44

colorect 41

prostate 38

brain 33

lung 33

skin 25

blood 22

stomach 21

head and 18

NPA 18

ovary 15

bladder 9

esophagu 8

cervix 7

kidney 6

pancreas 6

uterus 5

bone and 5

nerve 4

eye 3

thyroid 2

pituitar 1

sum_effect num

NPA 67

oncogenic 104

oncogenic/tumor-s

1

tumor-suppressive

253

Logistic model of onc=tissue

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 80.8431 24 <.0001

Score 75.1702 24 <.0001

Wald 60.3267 24 <.0001

Type 3 Analysis of Effects

Effect DF WaldChi-Square

Pr > ChiSq

tissue 24 60.3267 <.0001

proc logistic data=new; class tissue / param=ref ; model onc = tissue;run;

Analysis of Maximum Likelihood Estimates

Parameter   DF Estimate StandardError

WaldChi-Square

Pr > ChiSq

Intercept   1 0.3747 0.3917 0.9152 0.3387

tissue NPA 1 0.6098 0.4402 1.9187 0.1660

tissue bladder 1 2.0232 0.6528 9.6057 0.0019

tissue blood 1 0.2708 0.4346 0.3884 0.5332

tissue bone 1 1.5712 0.6627 5.6219 0.0177

tissue brain 1 -0.2053 0.4264 0.2318 0.6302

tissue breast 1 0.3973 0.4186 0.9010 0.3425

tissue cervix 1 0.0848 0.5379 0.0249 0.8747

tissue colorect 1 0.4562 0.4313 1.1189 0.2902

tissue esophagu 1 -0.2028 0.5182 0.1532 0.6955

tissue eye 1 -14.8794 705.7 0.0004 0.9832

tissue head and 1 0.4070 0.4554 0.7987 0.3715

tissue kidney 1 0.9690 0.6030 2.5829 0.1080

tissue liver 1 0.0469 0.4167 0.0127 0.9104

tissue lung 1 0.7845 0.4410 3.1652 0.0752

tissue mesothel 1 14.0094 664.4 0.0004 0.9832

tissue nerve 1 0.8781 0.5605 2.4545 0.1172

tissue ovary 1 0.4520 0.5061 0.7977 0.3718

tissue pancreas 1 0.1108 0.5044 0.0483 0.8261

tissue pituitar 1 1.2347 1.1634 1.1265 0.2885

tissue prostate 1 0.8531 0.4450 3.6761 0.0552

tissue skin 1 0.7029 0.4870 2.0828 0.1490

tissue stomach 1 -0.0324 0.4339 0.0056 0.9405

tissue testis 1 -0.3747 1.0740 0.1217 0.7272

tissue thyroid 1 -0.2693 0.6038 0.1990 0.6555

We found 3 tissue have significance in the logistic model of onc = tissueBeta(bladder) = 2.0232 represents a miRNA in our data base if have increase express, the probability of oncogenic effect will be P. As P/(1-P) = e 2.0232 P = 0.88 Beta(bone) = 1.5712 similarly P = 0.83Beta(prostate) = 0.8531 P = 0.70

Next step

• More data

• More logistic models, such as onc= tissue, direct_target; onc = direct_target pathways.