Cross-talk of m6A methylation modification and the tumor microenvironment composition in esophageal cancer

Background Esophageal cancer (EC) remains a significant clinical challenge, characterized by its aggressive nature and poor prognosis. Current therapeutic strategies, including targeted therapies, have limitations due to the complex interplay between tumor heterogeneity and the tumor microenvironment (TME). However, the specific contributions of N6-methyladenosine (m 6 A) methylation to the TME in EC are yet to be fully elucidated. Methods Through comprehensive bioinformatics analyses, a detailed examination of m 6 A regulators were conducted in EC using datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Single-cell RNA sequencing (scRNA-seq) and a consensus clustering algorithm was employed to classify m 6 A modification patterns and analyze their relationships with immune cell infiltration and clinical outcomes. Additionally, an m 6 A scoring system was developed based on principal component analysis to assess the prognostic value of identified m 6 A modification patterns. Results The findings revealed two distinct m 6 A modification clusters associated with divergent TME characteristics and immune infiltration profiles. Patients exhibiting the immune-inflamed phenotype (m 6 A cluster B) demonstrated significantly improved survival compared to those with the immune-excluded phenotype (m 6 A cluster A). Notably, m 6 A scores correlated positively with immune cell presence and related with adverse prognostic outcomes, indicating their potential as predictive biomarkers for immunotherapy responses. A low m 6 A score indicated a better response to immunotherapy. Conclusion This study highlights the critical role of m 6 A methylation in shaping the TME and influencing immune dynamics in EC. The m 6 A score developed herein provides a novel quantitative tool for predicting tumor behavior and treatment efficacy, paving the way for more personalized immunotherapeutic strategies in clinical practice. This scoring system illustrates a strong correlation of EC with TME immune cell composition, suggesting potential as a biomarker for targeted therapeutic strategies for EC.