Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39292326", "kind": "science-record", "revision": 3, "content_hash": "sha256:90e5e425666aef7c6664a5af497cb34bdca9622258db42ae44727c33629c7700", "created_at": "2026-11-01T20:29:28Z", "updated_at": "2026-11-01T22:12:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterizing m6A modification factors and their interactions in colorectal cancer: implications for tumor subtypes and clinical outcomes.", "doi": "10.1007/s12672-024-01298-1", "pmid": "39292326", "authors": ["Weidong Sun", "Yingchao Su", "Zhiqiang Zhang"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: The study aims to comprehensively combine colorectal cancer data cohorts in order to analyze the effects of various DNA methylation-coding genes on colorectal cancer patients. The annual incidence and mortality of colorectal cancer are very high, and there are no effective treatments for advanced colorectal cancer. DNA methylation is a method widely used to regulate epigenetics in the molecular mechanism study of tumors.\nMETHOD: Three single-cell cohorts GSE166555, GSE146771, and EMTAB8107, and five transcriptome cohorts GSE17536, GSE39582, GSE72970, and TCGA-CRC (TCGA-COAD and TCGA-READ) were applied in this study. 2 erasers (ALKBH5 and FTO), There are 7 writers (METTL3, METTL14, WTAP, VIRMA, RBM15, RBM15B, and ZC3H13) and 11 readers (YTHDC1, IGF2BP1, IGF2BP2, IGF2BP3, YTHDF1, YTHDF3, YTHDC2, and HNRNPA2B1, YTHDF2, HNRNPC and RBMX), a total of 20 M6A regulators, were used as the basis of the dataset in this study and were applied to the construction of molecular typing and prognostic models. Drugs that are differentially sensitive in methylation-regulated gene-related prognostic models were identified using the ConsensusClusterPlus package, which was also used to identify distinct methylation regulatory expression patterns in colorectal cancer and to model the relationship between tissue gene expression profiles and drug IC50 values. Finally, TISCH2 assessed which immune cells were significantly expressed with M6A scores. The immunosuppression of M6A methylation is spatially explained.\nRESULTS: This study used data from 583 CRC patients in the TCGA-CRC cohort. Firstly, the mutation frequency and CNV variation frequency of 20 m6A modification-related factors were analyzed, and the corresponding histogram and heat map were drawn. The study next analyzed the expression variations between mutant and wild forms of the VIRMA gene and explored differences in the expression of these variables in tumor and normal tissues. In addition, the samples were divided into different subgroups by molecular clustering method based on m6A modification, and each subgroup's expression and clinicopathological characteristics were analyzed. Finally, we compared prognostic differences, tumor microenvironment (TME) characteristics, immune cell infiltration, and gene function enrichment among different subpopulations. We also developed a colorectal cancer m6A-associated gene signature and validated its prognostic effects across multiple cohorts. Finally, using single-cell RNA sequencing data, we confirmed that tumor cells show elevated expression of m6A-related gene signatures.\nDISCUSSION: This study explored the mutation frequency, expression differences, interactions, molecular clustering, prognostic effect, and association with tumor characteristics of m6A modification-related factors in CRC and validated them at the single-cell level. These results clarify the association between m6A alteration and colorectal cancer (CRC) and offer important insights into the molecular recognition and management of cancer.", "claims": [{"id": "claim-method", "statement": "Using LASSO + multivariate Cox regression analysis, we generated a m6A-related signature and the m6A score based on the prognostic DEGs between the two m6A gene clusters for each CRC patient (Fig. 6a and b).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39292326", "locator": "sections[16] \"Generation of m6A score\" paragraph 2", "quote": "Using LASSO + multivariate Cox regression analysis, we generated a m6A-related signature and the m6A score based on the prognostic DEGs between the two m6A gene clusters for each CRC patient (Fig. 6a and b)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in the present study are openly accessible in the GTEx, GEO, and TCGA databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39292326", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The data used in the present study are openly accessible in the GTEx, GEO, and TCGA databases."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39292326"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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