Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites

Background: N6-methyladenosine (m⁶A), the most abundant post-transcriptional modification in eukaryotic mRNA, plays pivotal roles in diverse biological processes. Dysregulation of m⁶A levels has been implicated in numerous human diseases, particularly cancer. Although several computational tools exist for predicting putative m⁶A sites, none have specifically addressed the identification of cancer-associated (or pro-cancer) m⁶A residues at single-base resolution. Methods: To address this gap, we developed m6A-CAPred, a computational framework for accurate prediction of cancer-associated m⁶A sites at base resolution. Our model was trained on a comprehensive dataset comprising experimentally validated m⁶A sites from 25 cancer cell lines and 23 normal tissue samples, based on a hybrid feature extraction approach integrating both sequence- and curated genome-derived features. Results: Initial analysis revealed that sequence information alone only provided limited predictive performance. However, by incorporating genomic context features, m6A-CAPred significantly achieved improvement in prediction performance (an average AUROC of 0.885 tested on independent dataset), successfully capturing the distinct characteristics between cancer-associated and normal m⁶A sites. We then applied m6A-CAPred for transcriptome-wide prediction to screen for potential cancer-associated m⁶A sites. The somatic variants derived from 33 types of TCGA cancer projects were extracted for independent validation, and the results showed that cancer-somatic SNP density clearly differentiated the predicted pro-cancer and normal m⁶A sites, further confirming the model's biological relevance. Additionally, the cancer-associated m⁶A sites showed significant enrichment in functional important biological processes and cancer-related pathways. Conclusions: Overall, we hope that m6A-CAPred will serve as a valuable resource for cancer epitranscriptome research, with potential applications in cancer biomarker discovery. The web server implementing our model is freely available at www.rnamd.org/m6A-CAPred.