基于功能基因组数据整合方法研究前列腺癌风险基因

Research Risk Genes of Prostate Cancer based on Functional Genomics Data Integration Method

目的:利用生物信息学方法,将高通量基因表达数据与单核酸多肽(SNP)基因型数据进行整合分析,研究并注释前列腺癌风险基因。方法:本文基于EST、SAGE、基因芯片三类功能基因组数据整合的方法研究前列腺癌风险基因。首先,通过三类数据寻找前列腺癌中异常表达和差异表达基因。利用全基因组关联分析得到前列腺癌相关基因的SNPs。定位所得到的前例腺癌差异表达基因与SNPs到染色体,获取与SNPs在同一区段的差异表达基因,并通过各种数据库注释所得基因。结果:通过数据整合分析,最终得到前列腺癌风险基因84个,其中20多个基因已被证实与前列腺癌极其相关。结论:整合前列腺癌高通量表达数据与SNP基因型数据,能够快速有效的获得前列腺癌显著相关基因。此方法可以推广于其它癌症的研究。

Objective： We will research and annotate risk genes of prostate cancer on integrate high-throughput gene expression data and single nucleotide polymorphism （SNP） genotype data with bioinformatics methods. Methods： In this paper,we used function genomics data such as SAGE data,EST data and gene chip data integration method to research risk genes of prostate cancer. First, we used three types of data looking for abnormal expression genes and differentially expression genes in prostate cancer. Through SNPs genotype data, We obtained prostate cancer-related SNPs by genome-wide association studying .We Integrated the obtained differential expressed genes and prostate-related SNPs to study prostate cancer-related genes. We annotated the obtained prostate cancer-related genes by many databases. Results： We acquired 84 prostate cancer-related genes by analyzing differential expressed genes and SNPs. There were more than 20 genes that were already confirmed by biology experiments. Conclusions： Prostate cancer-related genes can be obtained by integrate differential expressed genes and prostate cancer-related SNPs .This method can be used to study other cancers.