In silico analysis revealed the prognostic potential of a miRNA panel in lung carcinoma

Abstract

Background. This research aims to identify potential prognostic biomarkers for squamous cell lung carcinoma (LUSC) by implementing bioinformatics tools to unveil the relationship between microRNAs (miRNAs) and LUSC, specifically by identifying the miRNAs and their critical target genes. Methods. We employed the Cancer Genome Atlas (TCGA)-LUSC dataset to identify differentially expressed miRNAs (DEmiRs) and genes (DEGs) utilizing R software. Subsequently, a lasso Cox regression survival model was developed to predict key prognostic DEmiRs. Their target genes were predicted using miRDB repository. Venn diagram was employed to identify the consensus genes shared between these target genes and DEGs in the TCGA-LUSC dataset. ClusetrProfiler analyzed Gene Ontology (GO) and KEGG pathways to comprehend these genes' biological functions. The STRING database and Cytoscape applications constructed the consensus gene protein-protein interactions network. Results. Lasso model predicted 6 prognostic DEmiRs (hsa-miR1270, hsa-miR-1291, hsa-19b-2, hsa-miR-2277, hsa-miR-4791, hsa-miR-485) for LUSC with 96.67% sensitivity and 68.54% specificity. Venn diagram retrieved 906 consensus genes shared between DEGs and these 6 prognostic DEmiRs. These genes were mainly related to protein-binding, neuroactive ligand receptor interaction, retinoid metabolism, carcinogenesis, cell cycle, and system development. Network analysis in Cytoscape STRING applications identified 19 crucial genes (SDC4, VCAN, BCAN, XYLT2, GPC1, GPC5, EGFR, EDNRA, EDN1, EDNRB, ERBB4, GNA14, GNAQ, CACNA1C, KCNQ1, KCND2, KCNH5, KCNB2, KCNQ5) linked to lung carcinogenesis. Conclusion. We developed a prognostic model reliant on 6 miRNAs that accurately predicted LUSC survival. These findings provide novel insights into lung carcinogenesis' underlying molecular mechanisms and potential biomarkers for prognosis and treatment.