An Algorithm for Differential Gene Analysis of Breast Cancer Based on PPI Network

被引：0
|
作者：
Wang X.-Y. [1 ]
Feng Y. [1 ]
机构：
[1] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2020年 / 43卷 / 04期
关键词：
Chi-square test; EdgeR algorithm; Ks test; Protein interaction network; Survival analysis;
D O I：
10.13190/j.jbupt.2019-174
中图分类号：
学科分类号：
摘要：
In order to improve the accuracy of screening differential genes for breast cancer, the differential expression genes of breast cancer was analyzed from the molecular level, combined with the characteristics of copy number and gene expression, studied the pathogenesis of breast cancer and provided new research ideas for the diagnosis and treatment of breast cancer. The cancer genome atlas database was used to download copy number and gene expression data of breast cancer, chi square test was used to extract copy number difference genes of breast cancer. Through R software, edgeR differential gene analysis algorithm was used to screen differentially expressed genes in breast cancer, ks test was used to correlate two differentially expressed genes to analyze the relationship between CNV variation and gene expression, string database was used to construct protein interaction network to screen core genes, the accuracy of the results was verified by survival analysis and go enrichment analysis. According to the standard of FDR greater than 1, p value less than 0.05, 10 579 genes were screened, 7 543 genes were up-regulated and 3 036 genes were down-regulated. It was found that eight genes such as ATAD2B were closely related to the occurrence and development of breast cancer. © 2020, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：76 / 82
页数：6
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