A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Abstract

Background Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients. Methods Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature. Results A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis. Conclusion Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Comprehensive analysis reveals the potential value of inflammatory response genes in the prognosis, immunity, and drug sensitivity of lung adenocarcinoma

“…IRGS = β1 × expressionG1 + β2 × expressionG2 + …β n × expressionG n . To validate the predictive performance of the scoring system constructed in this study, we assessed survival differences between subgroups using the Kaplan–Meier survival analysis (log-rank test), and plotted the receiver operating characteristic (ROC) curves compared with previously developed signatures [ 27 – 30 ]. Furthermore, to validate the predictive performance of the IRGS in the different LUAD subgroups, we also performed a subgroup analysis.…”

The Risk Model Based on the Three Oxidative Stress-Related Genes Evaluates the Prognosis of LAC Patients

“…Decreased SUMOylation of Nrf2 and increased ROS stimulate the JNK/c-Jun signaling axis to enhance cell migration and cell adhesion, as well as promote LAC cell invasion [7]. At present, risk score models are utilized to evaluate the prognosis of cancer patients [9][10][11]. Herein, the oxidative stress-related genes (OSRGs) were downloaded from the official website of Gene Set Enrichment Analysis (GSEA).…”

One Ferroptosis-Related Gene-Pair Signature Serves as an Original Prognostic Biomarker in Lung Adenocarcinoma

“…Moreover, we explored the differences of the biological pathways and tumor immune microenvironment between high- and low-FRGPI groups. Previously, based on candidate prognostic genes, Li et al (2019) , Al-Dherasi et al (2021) , and Liu et al (2021) identified prognostic signatures in lung adenocarcinoma. These models included 16 genes, 7 genes, and 4 genes, respectively.…”