Integrated bioinformatics analysis identifies prognostic hub genes and the potential of GPIHBP1 as a therapeutic target in lung adenocarcinoma

Lung cancer (LC) is amongst the commonly diagnosed cancers and continues to be the major causality for cancer reported deaths worldwide. Despite major advances in our approach to uncovering disease biology, using predictive biomarkers, and improving treatment strategies, the five-year survival rate remains disappointingly low. This study aimed to identify key genes in Non-Small Cell Lung Cancer (NSCLC), a major type of LC. In-silico analysis was carried out using the dataset GSE116959, obtained from the open-source Gene Expression Omnibus (GEO). The chosen dataset included samples from cancerous and adjacent non-cancerous tissues, leading to the identification of 331 differentially expressed genes (DEGs). To identify and further elucidate the molecular mechanisms of these DEGs, a series of analyses were performed, including pathway enrichment, gene ontology (GO) analysis, construction of a protein interaction network (PPI) for module identification, and hub gene selection. Subsequently, survival analysis and hub gene validation were performed. Ultimately, 21 hub genes were identified and further examined through survival and mutation studies to evaluate their prognostic potential in lung adenocarcinoma. The lower mRNA expression levels of included genes RETN, IHH, CD36, GPIHBP1, ROBO4, OGN, CLDN18, SCGB3A2, SCGB1A1, A2M, TIMP3, ITGA8, SFTPD, COL6A6, CAV2 and FBLN5 and higher expression of MMP1 and MMP12 in tumor vs. adjacent normal tissue samples, were linked significantly with poor survival in Lung adenocarcinoma (LUAD) patients and warrants their investigation as potential candidates for prognostic biomarkers. cBioPortal analysis revealed that the highest mutation rate was observed in GPIHBP1 (7%). These identified DEGs, enriched pathways, and hub genes provide not only an invaluable understanding of the underlying molecular mechanism in LUAD but also potential biomarkers and therapeutic targets for future drug development. Notably, the 21 hub genes identified show significant potential as biomarkers for disease prognosis and as candidates for targeted therapies. Among these, GPIHBP1, which exhibited the highest mutation frequency, warrants further investigation as a potential therapeutic target for LUAD.