Abstract
BackgroundGastric cancer (GC) is one of the leading cancers associated with high mortality and poor prognosis mainly due to its relatively late diagnosis and the limited therapeutic options. Consequently, screening for prognostic GC biomarkers and novel molecular therapeutic targets is necessary to promote patient outcomes. Methods Weighted gene co-expression network analysis (WGCNA), a systems biology approach, was applied to analyze the mRNA sequencing data and clinical information of GC patients obtained from The Cancer Genome Atlas (TCGA). Gene modules and clinical traits were constructed according to the Pearson correlation analysis, and the gene ontology (GO) and functional enrichment analysis of meaningful modules were carried out. Hub genes from meaningful modules were screened out by two approaches: the intra-modular and protein-protein interaction (PPI) analysis methods. Next, through upstream regulatory analysis, hub genes with high connectivity degree were further validated with differential expression analysis, Kaplan-Meier survival analysis, and the Cox regression model. ResultsWe found that seven modules were associated with the following clinical traits: anatomical location of gastric adenocarcinoma, histological type, histological grade, and pathological stage. The hub gene ALDH1B1 was found to have potential as a biomarker for gastric cancer cells, the relationship between this hub gene and gastric cancer drug treatment is also worthy of attention.Conclusion These findings may contribute to understanding the GC tumourigenic mechanisms, as well as provide new potential prognostic factors and molecular therapeutic targets for GC. The ALDH1B1 hub gene also provides a new vantage point for further clinical experiments and large-scale cohort studies to validate its association with GC patient survival, and provide a new direction for the research of gastric cancer drug treatment.
PKPD modeling of acquired resistance to anti-cancer drug treatment
