Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms

Abstract

This study aims to determine hub genes related to the incidence and prognosis of EGFR-mutant (MT) lung adenocarcinoma (LUAD) with weighted gene coexpression network analysis (WGCNA). From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we used 253 EGFR-MT LUAD samples and 38 normal lung tissue samples. At the same time, GSE19188 was additionally included to verify the accuracy of the predicted gene. To discover differentially expressed genes (DEGs), the R package “limma” was used. The R packages “WGCNA” and “survival” were used to perform WGCNA and survival analyses, respectively. The functional analysis was carried out with the R package “clusterProfiler.” In total, 1450 EGFR-MT–specific DEGs were found, and 7 tumor-related modules were marked with WGCNA. We found 6 hub genes in DEGs that overlapped with the tumor-related modules, and the overexpression level of B3GNT3 was significantly associated with the worse OS (overall survival) of the EGFR-MT LUAD patients (p < 0.05). Functional analysis of the hub genes showed the metabolism and protein synthesis–related terms added value. In conclusion, we used WGCNA to identify hub genes in the development of EGFR-MT LUAD. The established prognostic factors could be used as clinical biomarkers. To confirm the mechanism of those genes in EGFR-MT LUAD, further molecular research is required.