Identification of immune-associated prognostic biomarkers in lung adenocarcinoma on the basis of gene co-expression network

Abstract

Objective: We aimed to explore immune-related prognosis genes of lung adenocarcinoma (LUAD). Materials and methods: TCGA-LUAD and GSE31210 data sets were accessed from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) respectively. By using “WGCNA” R package, we established a gene co-expression network and clustered genes into various modules. The correlation between immune scores and module eigengenes by using Pearson analysis. Screened hub genes and constructed prognostic model by using LASSO and Cox regression analysis. Evaluated model by survival analysis and receiver operating characteristic (ROC) curves. Hub genes expression in clinical tissues of LUAD patients by qRT-PCR analysis. ssGSEA and TIMER (a website tool for examination of different immune cells in different cancers) analyzed immune correlation of hub genes. Gene set variation analysis (GSVA) uncovered difference of signal pathway between high- and low-risk score group. Results: We found that brown module significantly correlated with the immune scores of immune cells. Therefore, we constructed a 7-gene prognostic model based on brown module genes, and indicated that this model possessed good predictive performance. Patients in training and validation sets were stratified into the high- and low-risk group using this model. Also, hub genes CDCP1, PLSCR1 and CD79A were highly expressed in clinical tissues of LUAD patients, while ID1, CLEC7A, KIAA1324 and CMTM7 were lowly expressed. Both ssGSEA and TIMER revealed a significant negative correlation between risk score and B cell infiltration. Additionally, some signal pathways were suppressed in the high-risk group. Conclusion: We identified 7 immune-associated prognostic markers, which may play vital roles in LUAD and could be used as hopeful targets for immunotherapy of LUAD.