Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

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.

Automated summary

This study identified 11 hub genes associated with gastric cancer, with functions enriched in cellular senescence, cell cycle, viral carcinogenesis, and the p53 signaling pathway. Ten of these genes were found to be related to overall survival in GC patients, and seven were significantly correlated with GC, potentially serving as prognostic biomarkers.