Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37396662", "kind": "science-record", "revision": 3, "content_hash": "sha256:3cd54c5d24ed4cbcbdddc30ea0676b5cdf39bab4cb521f68f3c583e778867118", "created_at": "2026-11-01T19:02:09Z", "updated_at": "2026-11-01T22:09:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and prediction of m7G-related Alzheimer's disease subtypes: insights from immune infiltration and machine learning models.", "doi": "10.3389/fnagi.2023.1161068", "pmid": "37396662", "authors": ["Chao Ma", "Jian Li", "Yuhua Chi", "Xuan Sun", "Maoquan Yang", "Xueqin Sui"], "journal": "Frontiers in aging neuroscience", "year": 2023, "abstract": "Introduction: Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder that primarily affects older individuals. N7-methylguanosine (m7G) is a common RNA chemical modification that impacts the development of numerous diseases. Thus, our work investigated m7G-related AD subtypes and established a predictive model.\nMethods: The datasets for AD patients, including GSE33000 and GSE44770, were obtained from the Gene Expression Omnibus (GEO) database, which were derived from the prefrontal cortex of the brain. We performed differential analysis of m7G regulators and examined the immune signatures differences between AD and matched-normal samples. Consensus clustering was employed to identify AD subtypes based on m7G-related differentially expressed genes (DEGs), and immune signatures were explored among different clusters. Furthermore, we developed four machine learning models based on the expression profiles of m7G-related DEGs and identified five important genes from the optimal model. We evaluated the predictive power of the 5-gene-based model using an external AD dataset (GSE44770).\nResults: A total of 15 genes related to m7G were found to be dysregulated in patients with AD compared to non-AD patients. This finding suggests that there are differences in immune characteristics between these two groups. Based on the differentially expressed m7G regulators, we categorized AD patients into two clusters and calculated the ESTIMATE score for each cluster. Cluster 2 exhibited a higher ImmuneScore than Cluster 1. We performed the receiver operating characteristic (ROC) analysis to compare the performance of four models, and we found that the Random Forest (RF) model had the highest AUC value of 1.000. Furthermore, we tested the predictive efficacy of a 5-gene-based RF model on an external AD dataset and obtained an AUC value of 0.968. The nomogram, calibration curve, and decision curve analysis (DCA) confirmed the accuracy of our model in predicting AD subtypes.\nConclusion: The present study systematically examines the biological significance of m7G methylation modification in AD and investigates its association with immune infiltration characteristics. Furthermore, the study develops potential predictive models to assess the risk of m7G subtypes and the pathological outcomes of patients with AD, which can facilitate risk classification and clinical management of AD patients.", "claims": [{"id": "claim-method", "statement": "From this analysis, we identified 15 differentially expressed M7RGs, with 4 of them (NUDT3, CYFIP1, NCBP1, and IFIT5) being upregulated in AD patients, while 11 of them (METTL1, DCPS, NUDT10, NUDT11, EIF4E, EIF4E2, EIF4E3, LARP1, EIF4G3, LSM1, and NCBP2L) were downregulated compared to non-AD patients (Figures 2A, B, E).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37396662", "locator": "sections[11] \"Identification of differentially expressed M7RGs\" paragraph 2", "quote": "From this analysis, we identified 15 differentially expressed M7RGs, with 4 of them (NUDT3, CYFIP1, NCBP1, and IFIT5) being upregulated in AD patients, while 11 of them (METTL1, DCPS, NUDT10, NUDT11, EIF4E, EIF4E2, EIF4E3, LARP1, EIF4G3, LSM1, and NCBP2L) were downregulated compared to non-AD patients (Figures 2A, B, E)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data presented in this study are deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository, accession numbers: GSE33000 and GSE44770.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37396662", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The data presented in this study are deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository, accession numbers: GSE33000 and GSE44770."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 5-gene-based Random Forest model achieved an AUC of 0.968 in the external GSE44770 AD dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37396662", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Furthermore, we tested the predictive efficacy of a 5-gene-based RF model on an external AD dataset and obtained an AUC value of 0.968."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The 5-gene-based Random Forest model achieved an AUC of 0.968 in the external GSE44770 AD dataset.", "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:37396662"], "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:GSE33000"], "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:GSE33000"], "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:37396662", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2023.1161068", "sha256": "sha256:d675fa27c0119db54b888adaec87a71ea474fa65556759b7d63084c15690ce3c", "bytes": 38212, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2023.1161068", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10312082", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE33000", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33000", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE33000", "version": null, "note": "The data presented in this study are deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository, accession numbers: GSE33000 and GSE44770.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data presented in this study are deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository, accession numbers: GSE33000 and GSE44770.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1423780", "GSM1423781", "GSM1423782", "GSM1423783", "GSM1423784", "GSM1423785", "GSM1423786", "GSM1423787", "GSM1423788", "GSM1423789", "GSM1423790", "GSM1423791", "GSM1423792", "GSM1423793", "GSM1423794", "GSM1423795", "GSM1423796", "GSM1423797", "GSM1423798", "GSM1423799", "GSM1423800", "GSM1423801", "GSM1423802", "GSM1423803", "GSM1423804", "GSM1423805", "GSM1423806", "GSM1423807", "GSM1423808", "GSM1423809", "GSM1423810", "GSM1423811", "GSM1423812", "GSM1423813", "GSM1423814", "GSM1423815", "GSM1423816", "GSM1423817", 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