Prognostic Implication of the m6A RNA Methylation Regulators in Rectal Cancer.

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Although the dysregulated expression of m6A regulators is implicated in cancer progression, our understanding of the prognostic value of the m6A regulators in rectal cancer is still quite limited. In this study, we analyzed the RNA expression levels of the 17 m6A regulator genes of 95 rectal cancer and 10 normal rectal samples from the The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) dataset. Lasso regression analysis was conducted to build a prognostic model and calculate the risk score. The rectal cancer patients were then devided into the high-risk and low-risk groups according to the mean risk score. The prognostic value of the identified model was separately evaluated in the TCGA-READ and GSE87211 datasets. GSEA was conducted to analyze the functional difference of high-risk and low-risk rectal cancer patients. Our analysis revealed that rectal cancer patients with lower expression of YTHDC2 and METTL14 had a remarkable worse overall survival (P < 0.05). The prognostic value of the model was validated in GSE87211 datasets, with AUC = 0.612 for OS and AUC = 0.651 for RFS. Furthermore, the m6A modification-based risk score system is associated with activation of distinct signaling pathways, such as DNA repair, epithelial-mesenchymal transition, G2M checkpoint and the MYC pathway, that may contribute to the progression of rectal cancer. In conclusion, our findings demonstrated that the m6A RNA methylation regulators, specifically YTHDC2 and METTL14, were significantly down-regulated and might be potential prognostic biomarkers in rectal cancer.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection (lasso) regression method using the selection operator algorithm as described (Liu et al., 2020; Meng et al., 2018; Wang et al., 2020) was applied to construct a prognostic model with the highest efficiency and least redundancy based on the expression levels of the 17 genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34149792", "locator": "sections[4] \"Identification of the Survival-Related Signature\" paragraph 2", "quote": "The least absolute shrinkage and selection (lasso) regression method using the selection operator algorithm as described (Liu et al., 2020; Meng et al., 2018; Wang et al., 2020) was applied to construct a prognostic model with the highest efficiency and least redundancy based on the expression levels of the 17 genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["survival_km"], "assets": ["source:pmid:34149792", "data:geo:GSE119409"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE119409"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:31:10Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 27901, "completion_tokens": 1748, "cached_tokens": 776}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:34149792", "type": "paper", "label": "pmid:34149792"}, {"id": "source:pmid:34149792", "type": "manuscript", "label": "PMID 34149792 full text"}, {"id": "data:geo:GSE119409", "type": "dataset", "label": "GEO GSE119409"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34149792", "type": "paper", "label": "pmid:34149792"}, {"id": "source:pmid:34149792", "type": "manuscript", "label": "PMID 34149792 full text"}, {"id": "data:geo:GSE119409", "type": "dataset", "label": "GEO GSE119409"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34149792", "type": "extraction", "used": ["source:pmid:34149792"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE119409", "type": "accession_resolution", "used": ["data:geo:GSE119409"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:34149792", "parent_hash": "sha256:37ddab81ae83813a7b3e12fb90e998ab9e29abbeca7af1898dcfec04c51b490e", "fork_of": null, "generation": 5}, "gaps": ["asset:data:geo:GSE119409 licence not read", "asset:data:geo:GSE119409 not downloaded", "asset:data:geo:GSE150082 licence not read", "asset:data:geo:GSE150082 not downloaded", "asset:data:geo:GSE87211 licence not read", "asset:data:geo:GSE87211 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["d from the Agilent-026652 Whole Human Genome Microarray 4 × 44K v2 (GPL13497 platform). The expressi"], "order_basis": "paper_text_order:2821", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["survival_km"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Kaplan-Meier"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}