Identification of biomarkers associated with histological grade and prognosis of gastric cancer by co-expression network analysis.

The biological characteristics and clinical outcomes of gastric cancer (GC) are largely dependent on the histopathological type and degree of differentiation. The identification of the molecular mechanisms underlying the histological grade of GC may provide information about tumorigenesis and tumor progression, and may subsequently be used to develop novel therapeutic agents. The present study obtained the RNA sequencing data and clinical characteristics of patients with GC from The Cancer Genome Atlas. A total of 1,400 differentially expressed genes (DEGs) were screened between two histological grades. Weighted gene co-expression network analysis (WGCNA) was subsequently used to identify nine co-expressed gene modules, and the black module was found to be the most significant for prognosis prediction of tumor. Additionally, the black module was associated with overall survival time, death event, N stage and tumor-node-metastasis (TNM) stage. Functional enrichment analysis revealed that the biological processes of the genes in the black module included 'Wnt signaling pathway' and 'structural molecule activity'. Additionally, 10 network hub genes that were significantly associated with the progression of GC were identified from the black module, and the significance of each hub gene was determined across different TNM stages. Kaplan-Meier survival curves revealed that keratin 40 and glycine decarboxylase were significantly associated with patient prognosis (P<0.05), suggesting that these genes may serve as potential progression and prognosis biomarkers in GC. The present study identified molecular markers that correlated with histological grade in GC. Therefore, the results obtained in the present study may have important clinical implications on treatment selection, risk stratification and prognosis prediction in patients with GC.