Abstract
Stomach adenocarcinoma (STAD) is the fifth most prevalent cancer and the third leading cause of cancer-related death in the world and is more common in Asia than in most Western countries. There is an urgent need to identify potential novel oncogenes and tumor suppressor genes, and biomarkers for STAD. 6652 differentially expressed genes were identified between STAD and normal samples based on the transcriptome data analysis of the TCGA and GEO databases. 13 key modules were identified in STAD by WGCNA analysis. 293 potential STAD associated genes were identified from intersection by Venn Diagram. The 293 intersected genes were enriched in cell cortex and infection by GO and KEGG analysis. 10 hub genes were identified from PPI and Cytoscape analyses of the intersected genes. KLF4/CGN low and SHH/LIF high expression were associated with short overall survival of Asian STAD patients. Bioinformatics analysis revealed potential novel tumor suppressors (KLF4/CGN), oncogenes (SHH/LIF) and biomarkers for diagnosis, therapy and prognosis of STAD, specifically for Asian patients.
Highlights
More than one million people worldwide are diagnosed with gastric adenocarcinoma or stomach adenocarcinoma (STAD) each year
There is an urgent need to identify potential novel oncogenes and tumor suppressor genes, and biomarkers for Stomach adenocarcinoma (STAD). 6652 differentially expressed genes were identified between STAD and normal samples based on the transcriptome data analysis of the TCGA and GEO databases. 13 key modules were identified in STAD by Weighted correlation network analysis (WGCNA) analysis. 293 potential STAD associated genes were identified from intersection by Venn Diagram
KLF4/CGN low and SHH/ LIF high expression were associated with short overall survival of Asian STAD patients
Summary
More than one million people worldwide are diagnosed with gastric adenocarcinoma or stomach adenocarcinoma (STAD) each year. Weighted correlation network analysis (WGCNA) [3] is a data reduction and unsupervised classification method It simplifies the interpretation of many gene responses to multiple synthetic genomes (or modules). Connectivity between genes is interpreted as distance, with which genes are grouped into modules This is the way to reduce many genes to several clusters, the expression of which is quantified by Eigengenes (the first principal component in the module). It assumes that highly related genes in the module are involved in a common biological process. We used WGCNA together with other bioinformatic tools to identify potential novel oncogenes, tumor suppressor genes, and biomarkers of diagnosis, therapy and prognosis for Asian STAD
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