modelling of mirna-mrna network to identify gene signatures with diagnostic and prognostic value in gastric cancer: evidence from in-silico and in-vitro studies

Background: gastric cancer (gc) is a prevalent malignancy with high recurrence. advances in systems biology have identified molecular pathways and biomarkers. this study focuses on discovering gene and mirna biomarkers for diagnosing and predicting survival in gc patients.methods: three sets of genes (gse19826, gse81948, and gse112369) and two sets of mirna expression (gse26595, gse78775) were obtained from the gene expression omnibus (geo), and subsequently, differentially expressed genes (degs) and mirnas (dems) were identified. functional pathway enrichment, deg-mir-tf-protein–protein interaction network, dem-mrna network, roc curve, and survival analyses were performed. finally, qrt-pcr was applied to validate our results.results: from the high-throughput profiling studies of gc, we investigated 10 candidate mrna and 7 candidate mirnas as potential biomarkers. expression analysis of these hubs revealed that 5 mirnas (including mir-141-3p, mir-204-5p, mir-338-3p, mir-609, and mir-369-5p) were significantly upregulated compared to the controls. the genes with the highest degree included 6 upregulated and 4 downregulated genes in tumor samples compared to controls. the expression of mir-141-3p, mir-204-5p, sestd1, and antxr1 were verified in vitro from these hub dems and degs. the findings indicated a decrease in the expression of mir-141-3p and mir-204-5p and increased expression of sestd1 and antxr1 in gc cell lines compared to the ges-1 cell line.conclusions: the current investigation successfully recognized a set of prospective mirnas and genes that may serve as potential biomarkers for gc’s early diagnosis and prognosis.