Abstract
PURPOSE: This study aimed to identify molecular prognostic biomarkers for gastric cancer. METHODS: mRNA and miRNA expression profiles of eligible gastric cancer and control samples were downloaded from Gene Expression Omnibus to screen the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs), using MetaDE and limma packages, respectively. Target genes of the DEmiRs were also collected from both predictive and experimentally validated target databases of miRNAs. The overlapping genes between selected targets and DEGs were identified as risk genes, followed by functional enrichment analysis. Human pathways and their corresponding genes were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database for the expression analysis of each pathway in gastric cancer samples. Next, co-pathway pairs were selected according to the Pearson correlation coefficients. Finally, the co-pathway pairs, miRNA-target pairs, and risk gene-pathway pairs were merged into a complex interaction network, the most important nodes (miRNAs/target genes/co-pathway pairs) of which were selected by calculating their degrees. RESULTS: Totally, 1,260 DEGs and 144 DEmiRs were identified. There were 336 risk genes found in the 9,572 miRNA-target pairs. Judging from the pathway expression files, 45 co-pathway pairs were screened out. There were 1,389 interactive pairs and 480 nodes in the integrated network. Among all nodes in the network, focal adhesion/extracellular matrix-receptor interaction pathways, CALM2, miR-19b, and miR-181b were the hub nodes with higher degrees. CONCLUSION: CALM2, hsa-miR-19b, and hsa-miR-181b might be used as potential prognostic targets for gastric cancer.