Identification of potential immune-related prognostic biomarkers of lung cancer using gene co-expression network analysis*

Abstract

Abstract Objective The objective of this study was to identify new carcinogenetic hub genes and develop the integration of differentially expressed genes to predict the prognosis of lung cancer. Methods GSE139032 microarray data packages were downloaded from the Gene Expression Omnibus for planning, testing, and review of data. We identified KRT6C, LAMC2, LAMB3, KRT6A, and MYEOV from a key module for validation. Results We found that the five genes were related to a poor prognosis, and the expression levels of these genes were associated with tumor stage. Furthermore, Kaplan-Meier plotter showed that the five hub genes had better prognostic values. The mean levels of methylation in lung adenocarcinoma (LUAD) were significantly lower than those in healthy lung tissues for the hub genes. However, gene set enrichment analysis (GSEA) for single hub genes showed that all of them were immune-related. Conclusion Our findings demonstrated that KRT6C, LAMC2, LAMB3, KRT6A, and MYEOV are all candidate diagnostic and prognostic biomarkers for LUAD. They may have clinical implications in LUAD patients not only for the improvement of risk stratification but also for therapeutic decisions and prognosis prediction. Abbreviations: LUAD, lung adenocarcinoma; GSEA, gene set enrichment analysis; NSCLC, non-small-cell lung cancer; WGCNA, weighted gene co-expression network analysis; MEs, module eigengenes; GS, gene significance; MS, module significance; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, gene ontology; CC, cellular component; MF, molecular function; BP, biological process; GEPIA, gene expression profiling interactive analysis; HPA, Human Protein Atlas; TIMER, Tumor Immune Estimation Resource; TCGA, The Cancer Genome Atlas; OS, overall survival; PF, first progression; PPS, post-progression survival; IHC, immunohistochemical