RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37042849", "kind": "science-record", "revision": 2, "content_hash": "sha256:8c5021d47a06922b9ed79eaad9da77c6478864351b0ff4ab428a174609f369e0", "created_at": "2026-11-01T19:01:41Z", "updated_at": "2026-11-01T21:30:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "RNA methylation-related genes of m6A, m5C, and m1A predict prognosis and immunotherapy response in cervical cancer.", "doi": "10.1080/07853890.2023.2190618", "pmid": "37042849", "authors": ["Yan Wang", "Yiwen Mao", "Caizhi Wang", "Xuefeng Jiang", "Qionglan Tang", "Lingling Wang", "Jialei Zhu", "Mengqiu Zhao"], "journal": "Annals of medicine", "year": 2023, "abstract": "PURPOSE: To investigate the prognostic value of N6-methyladenosine (m6A)-, 5-methylcytosine (m5C)-, and N1-methyladenosine (m1A)-related genes in cervical cancer (CESC) and predicting immunotherapy response.\nMETHODS: We downloaded cervical cancer mRNA expression profiles, clinical data, and m6A, m5C, m1A-related genes from public databases, and subjected them to serial bioinformatics analysis and clinical sample validation.\nRESULTS: Differential analysis revealed 106 methylation-related differential genes (MEDs), including 44 differentially downregulated and 62 upregulated genes. We then obtained methylation models containing 10 genes by univariate and multifactorial COX analysis. High risk genes with HR > 1 include IQGAP3, PTBP1, STAC3, CUX1, SLC2A1, and CA2, and low risk genes with HR < 1 include IGBP1, DUOX1, CHAF1A, and STAC3. We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729). K-M analysis showed shorter survival times in the High-risk group, and Immunocytic infiltration analysis showed model genes closely associated with six immune cells. The high-risk group may benefit more effectively from immunosuppressive therapy, especially anti-CTLA-4 therapy (p < .05). We also screened nine drugs for potential treatment and verified the expression of three key genes SLC2A1, CUX1, and CA2 using immunohistochemistry and RT-qPCR experiments with clinical samples.\nCONCLUSION: We identified a prognostic model using m6A/m5C/m1A-related genes in cervical cancer, which can predict survival time and correlate with immune cell infiltration. Additionally, anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer.KEY MESSAGESCervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival.The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer.We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2.", "claims": [{"id": "claim-method", "statement": "COX, LASSO, and K-M survival analyses were used to identify m6A/m1A/m5C regulatory genes associated with the prognosis of CESC patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37042849", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "COX, LASSO, and K-M survival analyses were used to identify m6A/m1A/m5C regulatory genes associated with the prognosis of CESC patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All authors confirm that all supporting data, figures, grants, and ethics for this study can be found in the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37042849", "locator": "sections[30] \"Data availability statement\" paragraph 2", "quote": "All authors confirm that all supporting data, figures, grants, and ethics for this study can be found in the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729).", "kind": "quantitative", "target": {"value": 0.729, "unit": "auc", "tolerance": null, "source": "We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729)."}, "evidence": [{"asset": "source:pmid:37042849", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729).", "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:37042849"], "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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