Comprehensive Analysis of N6-Methyladenosine (m⁶A) RNA Methylation Regulators and Tumour Microenvironment Cell...

Gastroesophageal adenocarcinoma (GEA) is a high deadly and heterogeneous cancer. RNA N6-methyladenosine (m⁶A) modification plays a non-negligible role in shaping individual tumour microenvironment (TME) characterizations. However, the landscape and relationship of m⁶A modification patterns and TME cell infiltration features remain unknown in GEA.

In this study, we examined the TME of GEA using assessments of the RNA-sequencing data focusing on the distinct m⁶A modification patterns from the public databases. Intrinsic patterns of m⁶A modification were evaluated for associations with clinicopathological characteristics, underlying biological pathways, tumour immune cell infiltration, oncological outcomes, and treatment responses. The expression of key m⁶A regulators and module genes was validated by qRT-PCR analysis.

We identified two distinct m⁶A modification patterns of GEA (cluster 1/2 subgroup), and correlated two subgroups with TME cell-infiltrating characteristics. Cluster 2 subgroup correlates with a poorer prognosis, downregulated PD-1 expression, higher risk scores, and distinct immune cell infiltration. In addition, PPI and WGCNA network analysis were integrated to identify key module genes closely related to immune infiltration of GEA to find immunotherapy markers. COL4A1 and COL5A2 in the brown module were significantly correlated to the prognosis, PD-1/L1 and CTLA-4 expression of GEA patients. Finally, a prognostic risk score was constructed using m⁶A regulator-associated signatures that represented an independent prognosis factor for GEA. Interestingly, COL5A2 expression was linked to the response to anti-PD-1 immunotherapy, m⁶A regulator expression, and risk score.

Our work identified m⁶A RNA methylation regulators as an important class of players in the malignant progression of GEA and were associated with the complexity of the TME. COL5A2 may be the potential biomarker which contributes to predicting the response to anti-PD-1 immunotherapy and patients' prognosis.