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Construction of web-based miRNA database of cancer therapy11/13/2015
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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.
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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
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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.
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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)
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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.
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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.
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Raw Data From Pubmed
miRNA tableGene Table Pathway
Target table Pathway Target table
Querry Results
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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.
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Results
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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.
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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
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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;
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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
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Next step
• More data
• More logistic models, such as onc= tissue, direct_target; onc = direct_target pathways.