Prognostic Value of Genomic Instability of m6A-Related lncRNAs in Lung Adenocarcinoma

Rui Li; Jian-Ping Li; Ting-Ting Liu; Chen Huo; Jie Yao; Xiu-Li Ji; Yi-Qing Qu

doi:10.3389/fcell.2022.707405

Prognostic Value of Genomic Instability of m⁶A-Related lncRNAs in Lung Adenocarcinoma

Front Cell Dev Biol. 2022 Mar 3:10:707405. doi: 10.3389/fcell.2022.707405. eCollection 2022.

Authors

Rui Li¹, Jian-Ping Li¹, Ting-Ting Liu¹, Chen Huo¹, Jie Yao¹, Xiu-Li Ji², Yi-Qing Qu³

Affiliations

¹ Shandong Key Laboratory of Infectious Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
² Department of Pulmonary Disease, Traditional Chinese Medicine Hospital of Jinan, Jinan, China.
³ Shandong Key Laboratory of Infectious Respiratory Diseases, Department of Pulmonary and Critical Care Medicine, Qilu Hospital of Shandong University, Jinan, China.

Abstract

Background: Genomic instability of N6-methyladenosine (m⁶A)-related long noncoding RNAs (lncRNAs) plays a pivotal role in the tumorigenesis of lung adenocarcinoma (LUAD). Our study identified a signature of genomic instability of m⁶A-associated lncRNA signature and revealed its prognostic role in LUAD. Methods: We downloaded RNA-sequencing data and somatic mutation data for LUAD from The Cancer Genome Atlas (TCGA) and the GSE102287 dataset from the Gene Expression Omnibus (GEO) database. The "Limma" R package was used to identify a network of regulatory m⁶A-related lncRNAs. We used the Wilcoxon test method to identify genomic-instability-derived m⁶A-related lncRNAs. A competing endogenous RNA (ceRNA) network was constructed to identify the mechanism of the genomic instability of m⁶A-related lncRNAs. Univariate and multivariate Cox regression analyses were performed to construct a prognostic model for internal testing and validation of the prognostic m⁶A-related lncRNAs using the GEO dataset. Performance analysis was conducted to compare our prognostic model with the previously published lncRNA models. The CIBERSORT algorithm was used to explore the relationship of m⁶A-related lncRNAs and the immune microenvironment. Prognostic m⁶A-related lncRNAs in prognosis, the tumor microenvironment, stemness scores, and anticancer drug sensitivity were analyzed to explore the role of prognostic m⁶A-related lncRNAs in LUAD. Results: A total of 42 genomic instability-derived m⁶A-related lncRNAs were differentially expressed between the GS (genomic stable) and GU (genomic unstable) groups of LUAD patients. Four differentially expressed lncRNAs, 17 differentially expressed microRNAs, and 75 differentially expressed mRNAs were involved in the genomic-instability-derived m⁶A-related lncRNA-mediated ceRNA network. A prediction model based on 17 prognostic m⁶A-associated lncRNAs was constructed based on three TCGA datasets (all, training, and testing) and validated in the GSE102287 dataset. Performance comparison analysis showed that our prediction model (area under the curve [AUC] = 0.746) could better predict the survival of LUAD patients than the previously published lncRNA models (AUC = 0.577, AUC = 0.681). Prognostic m⁶A-related-lncRNAs have pivotal roles in the tumor microenvironment, stemness scores, and anticancer drug sensitivity of LUAD. Conclusion: A signature of genomic instability of m⁶A-associated lncRNAs to predict the survival of LUAD patients was validated. The prognostic, immune microenvironment and anticancer drug sensitivity analysis shed new light on the potential novel therapeutic targets in LUAD.

Keywords: anticancer drug sensitivity analysis; genomic instability–derived m6A-related lncRNA; lung adenocarcinoma; performance comparison analysis; prognostic risk model.