Abstract
BackgroundLung 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.MethodsRaw 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.ResultsA 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.ConclusionOur 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.
Highlights
Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate
We identified 409 proteincoding genes associated with overall survival, and these genes were verified by the Kaplan–Meier plotter database consisting 719 patients with lung adenocarcinoma
Almost all studies used the training group to develop and build the molecular signatures depend on the selection of overlapping genes in most databases, and this could lead to the recurrence of some genes in the new signatures; this phenomenon may lead to similarity or convergence of the results, in addition to other concerns such as the absence of external independent verification, small sample size or effective verification that may hinder the efficiency and power of the prognostic model
Summary
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. Despite the advancements in lung cancer treatment, non-small lung cancer (NSCLC) remains one of the most common types and the leading cause of cancerassociated mortality among men and women worldwide [1]. NSCLC and small cell lung cancer (SCLC) are the two major types of lung cancer. The two main types of NSCLC are lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) [2]; these histological subtypes may determine the choice of treatment [2, 3]. A total of 235,760 new cases of lung cancer and 131,880 deaths from lung cancer were expected to occur in 2021 [9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
