A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Abstract

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (−0.2491 × EXPRRAGB) + (−0.0679 × EXPRSPH9) + (−0.2317 × EXPRPS6KL1) + (−0.1035 × EXPRXFP1) + 0.1571 × EXPRRM2 + 0.1104 × EXPRTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma

“…6C-6E. We also compared our results with the two published gene signature studies (Xie & Xie, 2019;Zhao, Li & Tian, 2018) and found that the C-index results were better than their signatures (Table 2). Taken together, these results indicated a better predictive performance about our prognostic signature.…”

“…Bioinformatics has contributed to determining the prognosis and treatment of LC (Parikh, 2019). There have been numerous studies on gene prognosis models that could contribute to the selection of LC treatment methods and the prediction of survival after LC surgery; for example, a prognostic signature containing six genes (RRAGB, RSPH9, RPS6KL1, RXFP1, RRM2, and RTL) to evaluate the prognosis of NSCLC patients (Xie & Xie, 2019). In another article on prognostic characteristics of LUAD, a prognostic model based on 20 genes was developed to predict patient overall survival (OS) (Zhao, Li & Tian, 2018).…”

Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma

“…The high-throughput sequencing technology has made it possible a comprehensive interrogation of whole transcriptome and genome of tumor tissues at an increasingly reasonable cost [4,5]. Previous studies focused on finding prognostic signatures based on gene expressio n [6][7][8][9] or mutation [10][11][12] for LUAD patients. For example, Li et al [7] reported gene expression-based models with an average C-index of 0.604 on testing datasets from TCGA-LUAD in predicting overall survival (OS).…”

Development of a Novel Prognostic Score Combining Clinicopathologic Variables, Gene Expression and Mutationprofiles for Lung Adenocarcinoma

“…The high-throughput sequencing technology has made it possible a comprehensive interrogation of whole transcriptome and genome of tumor tissues at an increasingly reasonable cost [4,5]. Previous studies focused on finding prognostic signatures based on gene expression [6][7][8][9] or mutation [10][11][12] for LUAD patients. For example, Li et al [7] reported gene expression-based models with an average C-index of 0.604 on testing datasets from TCGA-LUAD in predicting overall survival (OS).…”