Abstract
Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.
Highlights
Gastric cancer (GC) is one of the most common types of malignancy
Zhu et al found that CDK1 overexpression was a prognostic factor for hepatocellular carcinoma (HCC), which makes it a potential therapeutic target and biomarker for HCC diagnosis, through analysis data from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA)[5]
The graph was divided into three parts, representing biological processes (BP), molecular functions (MF) and cell components (CC)
Summary
Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. Bioinformatics analysis methods, are powerful tools for identifying potential biomarkers related to diagnosis and treatment, including the analysis of gene interaction networks, gene annotation and microarray expression profiles[3]. Hao et al explored 10 genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1 and COL11A1) as potential biomarkers and therapeutic targets for GC, through analysis data from the Gene Expression Omnibus (GEO) d atabase[4]. Zhu et al found that CDK1 overexpression was a prognostic factor for hepatocellular carcinoma (HCC), which makes it a potential therapeutic target and biomarker for HCC diagnosis, through analysis data from GEO and the Cancer Genome Atlas (TCGA)[5]. The methods of hub gene selection in the above literatures was single and the potential molecular mechanism of gastric cancer was still unclear, which needs further exploration
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