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N6-methylandenosine-related immune genes correlate with prognosis and immune landscapes in gastric cancer

OBJECTIVES: This study aimed to probe into the significance of N6-methyladenosine (m(6)A)-related immune genes (m(6)AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC). METHODS: The clinical data and transcriptomic matrix of GC patients were acquired from The Can...

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Detalles Bibliográficos
Autores principales: Huang, Yuancheng, Zou, Yushan, Tian, Yanhua, Yang, Zehong, Hou, Zhengkun, Li, Peiwu, Liu, Fengbin, Ling, Jiasheng, Wen, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9745091/
https://www.ncbi.nlm.nih.gov/pubmed/36523987
http://dx.doi.org/10.3389/fonc.2022.1009881
Descripción
Sumario:OBJECTIVES: This study aimed to probe into the significance of N6-methyladenosine (m(6)A)-related immune genes (m(6)AIGs) in predicting prognoses and immune landscapes of patients with gastric cancer (GC). METHODS: The clinical data and transcriptomic matrix of GC patients were acquired from The Cancer Genome Atlas database. The clinically meaningful m(6)AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m(6)AIGs prognostic signature. The clinicopathological features and tumor microenvironment (TME) in the different clusters and different risk subgroups were explored. The predictive performance was evaluated using the KM method, ROC curves, and univariate and multivariate regression analyses. Moreover, we fabricated a nomogram based on risk scores and clinical risk characteristics. Biological functional analysis was performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. The connectivity map was used to screen out potential small molecule drugs for GC patients. RESULTS: A total of 14 prognostic m(6)AIGs and two clusters based on 14 prognostic m(6)AIGs were identified. A prognostic signature based on 4 m(6)AIGs and a nomogram based on independent prognostic factors was constructed and validated. Different clusters and different risk subgroups were significantly correlated with TME scores, the distribution of immune cells, and the expression of immune checkpoint genes. Some malignant and immune biological processes and pathways were correlated with the patients with poor prognosis. Ten small molecular drugs with potential therapeutic effect were screened out. CONCLUSIONS: This study revealed the prognostic role and significant values of m(6)AIGs in GC, which enhanced the understanding of m(6)AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.