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

Autor: Aisha Al-Dherasi, Qi-Tian Huang, Yuwei Liao, Sultan Al-Mosaib, Rulin Hua, Yichen Wang, Ying Yu, Yu Zhang, Xuehong Zhang, Chao Huang, Haithm Mousa, Dongcen Ge, Sufiyan Sufiyan, Wanting Bai, Ruimei Liu, Yanyan Shao, Yulong Li, Jingkai Zhang, Leming Shi, Dekang Lv, Zhiguang Li, Quentin Liu
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Cancer Cell International, Vol 21, Iss 1, Pp 1-16 (2021)
Druh dokumentu: article
ISSN: 1475-2867
DOI: 10.1186/s12935-021-01975-z
Popis: 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
Databáze: Directory of Open Access Journals
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