Identification of the m6A/m5C/m1A methylation modification genes in Alzheimer's disease based on bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39485681", "kind": "science-record", "revision": 3, "content_hash": "sha256:be032e592002020248beb40d53b2addfb5c51faca15b1b0d210bd40e0a60a807", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T22:13:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the m6A/m5C/m1A methylation modification genes in Alzheimer's disease based on bioinformatic analysis.", "doi": "10.18632/aging.206146", "pmid": "39485681", "authors": ["Qifa Tan", "Desheng Zhou", "Yuan Guo", "Haijun Chen", "Peng Xie"], "journal": "Aging", "year": 2024, "abstract": "BACKGROUND: As a progressive neurodegenerative disease, the comprehensive understanding of the pathogenesis of Alzheimer's disease (AD) is yet to be clarified. Modifications in RNA, including m6A/m5C/m1A, affect the onset and progression of many diseases. Consequently, this study focuses on the role of methylation modification in the pathogenesis of AD.\nMATERIALS AND METHODS: Three AD-related datasets, namely GSE33000, GSE122063, and GSE44770, were acquired from GEO. Differential analysis of m6A/m5C/m1A regulator genes was conducted. Applying a consensus clustering approach, distinct subtypes within AD were identified as per the expression patterns of relevant differentially expressed genes. Machine learning models were constructed to identify five significant genes from the best model. The analysis of hub gene-based drug regulatory networks and ceRNA regulatory networks was conducted by Cytoscape.\nRESULTS: In comparison to non-AD patients, 24 genes were identified as dysregulated in AD patients, and these genes were associated with various immunological characteristics. Two distinct clusters were successfully identified through consensus clustering, with cluster 2 demonstrating higher immune characteristics compared to cluster 1. The performance of four machine learning models was determined by conducting a receiver operating characteristic (ROC) analysis. The analysis revealed that the SVM model achieved the highest AUC value of 0.947. Five genes (YTHDF1, METTL3, DNMT1, DNMT3A, ALKBH1) were selected as the predicted genes. Finally, a hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully developed.\nCONCLUSIONS: The findings offered fresh perspectives on the molecular patterns and immune mechanisms underlying AD, contributing valuable insights into our understanding of this complex neurodegenerative disorder.", "claims": [{"id": "claim-data-access", "statement": "The raw data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39485681", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The raw data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The SVM model achieved the highest AUC value of 0.947.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39485681", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The analysis revealed that the SVM model achieved the highest AUC value of 0.947."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The SVM model achieved the highest AUC value of 0.947.", "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:39485681"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122063"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122063"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39485681", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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