4.6 Article

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

Journal

FRONTIERS IN ONCOLOGY
Volume 11, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.725181

Keywords

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

Categories

Funding

  1. National Natural Science Foundation [81904139, 81973819, 81904145]
  2. Collaborative Innovation Team Project of First-Rate Universities and Disciplines and High-level University Discipline of Guangzhou University of Chinese Medicine [2021xk47]
  3. Natural Science Foundation of Guangdong Province [2019A1515011145]
  4. Guangdong Medical Science and Technology Research Fund [B2021089, A2020186]
  5. Clinical Research Project of Innovation Hospital in the First Affiliated Hospital of Guangzhou University of Chinese Medicine [2019IIT19]
  6. High-level Hospital Construction project of the First Affiliated Hospital of Guangzhou University of Chinese Medicine [2019QN01]

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The study investigated the role of m(6)A-related lncRNAs in gastric adenocarcinoma and their prognostic value. Through analysis of gene expression and clinicopathological data, different patient clusters were identified based on m(6)A-related lncRNAs, and a prognostic signature was established. The prognostic signature showed significant predictive value for overall survival, clinicopathological characteristics, tumor microenvironment, and immune response in patients with gastric adenocarcinoma.
Objectives The purpose of this study was to investigate the role of m(6)A-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 m(6)A-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 m(6)A-related lncRNAs were identified, and a prognostic signature comprising 14 m(6)A-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 m(6)A-related lncRNAs in STAD. Together, our finding refreshed the understanding of m(6)A-related lncRNAs and provided novel insights to identify predictive biomarkers and immunotherapy targets for STAD.

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