Identification of N6-Methylandenosine-Related lncRNAs for Subtype Identification and Risk Stratification in Gastric Adenocarcinoma

Yuancheng Huang; Zehong Yang; Chaoyuan Huang; Xiaotao Jiang; Yanhua Yan; Kunhai Zhuang; Yi Wen; Fengbin Liu; Peiwu Li

doi:10.3389/fonc.2021.725181

Identification of N6-Methylandenosine-Related lncRNAs for Subtype Identification and Risk Stratification in Gastric Adenocarcinoma

Front Oncol. 2021 Sep 27:11:725181. doi: 10.3389/fonc.2021.725181. eCollection 2021.

Authors

Yuancheng Huang¹, Zehong Yang¹, Chaoyuan Huang¹, Xiaotao Jiang¹, Yanhua Yan¹, Kunhai Zhuang², Yi Wen³, Fengbin Liu², Peiwu Li³

Affiliations

¹ First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
² Department of Gastroenterology, Baiyun Branch of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
³ Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

Abstract

Objectives: The purpose of this study was to investigate the role of m⁶A-related lncRNAs in gastric adenocarcinoma (STAD) and to determine their prognostic value.

Methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis and univariate Cox regression analysis were conducted to identify m⁶A-related prognostic lncRNAs. Subsequently, different clusters of patients with STAD were identified via consensus clustering analysis, and a prognostic signature was established by least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The clinicopathological characteristics, tumor microenvironment (TME), immune checkpoint genes (ICGs) expression, and the response to immune checkpoint inhibitors (ICIs) in different clusters and subgroups were explored. The prognostic value of the prognostic signature was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Additionally, Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) analysis were performed for biological functional analysis.

Results: Two clusters based on 19 m⁶A-related lncRNAs were identified, and a prognostic signature comprising 14 m⁶A-related lncRNAs was constructed, which had significant value in predicting the OS of patients with STAD, clinicopathological characteristics, TME, ICGs expression, and the response to ICIs. Biological processes and pathways associated with cancer and immune response were identified.

Conclusions: We revealed the role and prognostic value of m⁶A-related lncRNAs in STAD. Together, our finding refreshed the understanding of m⁶A-related lncRNAs and provided novel insights to identify predictive biomarkers and immunotherapy targets for STAD.

Keywords: N6-methyladenosine; gastric adenocarcinoma; immunotherapy; long noncoding RNAs; prognostic signature; tumor microenvironment.