Hub Gene Screening and Prognostic Modeling of Lung Cancer: An Integrated Bioinformatics Study.

Background: One of the most frequent malignancies is lung carcinoma which poses heavy burden on the global health. The link among differentially expressed genes (DEGs) and lung cancer patients' clinical outcomes was still missing. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in lung carcinoma patients. Methods: To begin, DEGs were identified using the Gene Expression Omnibus (GEO) gene expression pattern (GSE180347). Then, these DEGs are being searched in the TCGA database using the DEGs collected in the preceding phase. The Kaplan-Meier plotter was then used to assess the predictive value of these DEGs in patients with lung cancer. Results: Our study revealed a total of 45 DEGs, 15 of which were up-regulated and 30 of which were down-regulated. These DEGs were mostly enriched in cytokine receptor binding and cytokine activity, according to GO enrichment analysis. These DEGs were mostly enriched in cytokine-cytokine receptor interaction, according to KEGG enrichment analysis. Based on the PPI network, which comprises of 12 DEGs, a major module was discovered. They are mostly interested in cytotoxicity mediated by natural killer cells. Among all 45 DEGs, the mutations of NCAM1 account for the most cases in TCGA database with a percentage above 15%. Among the 12 DEGs in the significant module, higher expression of FAS, GPR29, HAVCR2, and NCAM1 exhibits longer survival time with hazard ratio and 95% confident interval of 0.79 (0.69-0.89), 0.80 (0.70-0.90), 0.71 (0.60-0.84), and 0.73 (0.62-0.86), respectively. However, higher expression of FCGR3A and IFNG exhibits shorter survival time with hazard ratio and 95% confident interval of 1.50 (1.32-1.71) and 1.15 (1.02-1.31), respectively. Conclusion: Our results demonstrate significant correlation between some DEGs and the survival outcome in lung adenocarcinomas patients, providing a comprehensive bioinformatics study in anticipation of future molecular mechanisms and biomarker studies.