What question did this study set out to answer?

The aim is to understand the role of m6A RNA methylation in human cancers and its implications for gene expression and therapy.

April 5, 2026

Abstract 62: Comprehensive characterization of m6A RNA methylation across human cancers.

Key Points

The aim is to understand the role of m6A RNA methylation in human cancers and its implications for gene expression and therapy.
Conducted m6A-seq profiling across 226 tumor samples from 23 cancer types,
Integrated transcriptome-wide m6A modifications with multi-omics data,
Characterized protein expression of m6A regulators in 7482 TCGA samples,
Developed a deep-learning model to predict m6A intensity based on local DNA context and gene features.
Identified five major clusters of m6A patterns driven by cancer lineages,
Found approximately 10% of protein-coding genes have significant m6A level associations with expression,
Observed strong influence of m6A on key tumor states like epithelial-mesenchymal transition and hypoxia,
Uncovered frequent dysregulation of m6A regulators across various cancers,
Achieved high accuracy in predicting m6A intensity with deep-learning model.

Abstract

Abstract N6-methyladenosine (m6A) is the most abundant internal mRNA modification and plays essential roles in gene regulation, tumor progression, and therapeutic response. However, the biomedical and clinical significance of m6A RNA methylation in human cancer remains incompletely understood. Here, using m6A-seq profiling, we generated a comprehensive transcriptome-wide atlas of m6A modifications across 15,812 sites from 226 tumor samples spanning 23 cancer types in The Cancer Genome Atlas (TCGA). To elucidate the regulatory determinants and downstream consequences of m6A variation, we integrated these data with a broad spectrum of multi-omics features. We found that global m6A patterns segregate into five major clusters largely driven by cancer lineages. Somatic mutations exert widespread yet diverse effects on local m6A levels through alteration of DRACH motifs. Approximately 10% of protein-coding genes showed consistent positive or negative associations between m6A abundance and mRNA or protein expression. These genes are enriched for transcription factors, and their m6A levels strongly influence key tumor cell states such as epithelial-mesenchymal transition (EMT) and hypoxia. We further characterized the protein expression landscape of 15 m6A regulators in 7482 TCGA samples and uncovered frequent dysregulation across cancers arising from multiple distinct genetic and epigenetic mechanisms. Finally, we developed a deep-learning model that integrates local DNA sequence context, gene-level features, m6A regulator states, and tumor-specific context to predict m6A intensity. The model achieved high accuracy for a substantial fraction of m6A sites, enabling large-scale inference of m6A variation and facilitating biomarker discovery in extensive patient cohorts. Together, this study provides a key resource for understanding the genomic landscape and regulatory architecture of m6A methylation in cancer and establishes a foundation for leveraging m6A as a new class of biomarkers and therapeutic targets. Citation Format: Yining Zhao, Ke Chen, Hu Chen, Yizhe Song, Kamalika Mojumdar, Wei Liu, Stephanie H Ting, Ayush Semwal, Hui Shen, Li Ding, Genomic Data Analysis Network, Katherine Hoadley, Han Liang. Comprehensive characterization of m6A RNA methylation across human cancers abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 62.

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