Construction of a lung adenocarcinoma prognostic model based on N6-methyl-adenosine-related long noncoding RNA and screening of potential drugs based on this model. Issue 4 (24th February 2022)

• Record Type: Journal Article
• Authors: Hou, Qinghua; Zhong, Yanfeng; Liu, Linzhuang; Wu, Liusheng; Liu, Jixian
• Is Part Of: Anti-cancer drugs. Volume 33:Issue 4(2022)
• Page Start: 371
• Page End: 383
• Publication Date: 2022-02-24
• Subjects: long noncoding RNA -- lung adenocarcinoma -- N6-methyl-adenosine -- potential drugs -- prognostic model; Antineoplastic agents -- Periodicals; Cancer -- Chemotherapy -- Periodicals; Antineoplastic Agents -- therapeutic use -- Periodicals; Drug Therapy -- Periodicals; 616.994061
• DOI: 10.1097/CAD.0000000000001277
• Languages: English
• ISSNs: 0959-4973

Abstract

Abstract : Supplemental Digital Content is available in the text. Abstract : Lung adenocarcinoma (LUAD) has a high mortality rate. N6-methyl-adenosine (m6A)-related long noncoding RNA (lncRNA) is associated with tumor prognosis. Our objective was to construct an m6A-related lncRNA prognostic model and screen potential drugs for the treatment of LUAD. The LUAD sequencing data were randomly divided into Train and Test cohorts. In the Train group, the LASSO Cox regression was used to construct the m6A-related lncRNA prognostic model. The LUAD tumor immune dysfunction and exclusion model was used to evaluate immunotherapy efficacy in LUAD. The 'pRRophetic' package was utilized to screen potential drugs for the treatment of LUAD. Eleven m6A-related lncRNAs were identified by LASSO Cox regression and were used to construct the risk model to calculate sample risk scores. Patients were divided into high- and low-risk groups based on their median risk scores. The LUAD data of The Cancer Genome Atlas database showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group in both cohorts. Multivariate Cox regression analysis showed that this risk model could serve as an independent prognostic factor of LUAD, and receiver operating characteristic curves suggested that m6A-related lncRNA prognostic signature has a good ability in predicting OS. Finally, nine potential drugs for LUAD treatment were screened based on this prognostic model. The prognostic model constructed based on the m6A-related lncRNAs facilitated prognosis prediction in LUAD patients. The screened therapeutic agents have potential application values and provide a reference for the clinical treatment of LUAD.