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

Yuancheng Huang; Yushan Zou; Yanhua Tian; Zehong Yang; Zhengkun Hou; Peiwu Li; Fengbin Liu; Jiasheng Ling; Yi Wen

doi:10.3389/fonc.2022.1009881

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

Front Oncol. 2022 Nov 29:12:1009881. doi: 10.3389/fonc.2022.1009881. eCollection 2022.

Authors

Yuancheng Huang¹, Yushan Zou¹, Yanhua Tian¹, Zehong Yang¹, Zhengkun Hou², Peiwu Li², Fengbin Liu^{2

3}, Jiasheng Ling⁴, Yi Wen²

Affiliations

¹ First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
² Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
³ Department of Gastroenterology, Baiyun Branch of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
⁴ Department of Gastroenterology, Huizhou Hospital of Traditional Chinese Medicine, Huizhou, Guangdong, China.

Abstract

Objectives: This study aimed to probe into the significance of N6-methyladenosine (m⁶A)-related immune genes (m⁶AIGs) 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 m⁶AIGs were acquired by univariate Cox regression analysis. GC patients were stratified into different clusters via consensus clustering analysis and different risk subgroups via m⁶AIGs 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 m⁶AIGs and two clusters based on 14 prognostic m⁶AIGs were identified. A prognostic signature based on 4 m⁶AIGs 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 m⁶AIGs in GC, which enhanced the understanding of m⁶AIGs and paved the way for developing predictive biomarkers and therapeutic targets for GC.

Keywords: N6-methyladenosine; gastric cancer; immune genes; prognostic signature; tumor microenvironment.