Construction and evaluation of Alzheimer's disease diagnostic prediction model based on genes involved in mitophagy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37032823", "kind": "science-record", "revision": 4, "content_hash": "sha256:8ef54080c4d5be6c395dd4aa516ae8b69cacffaf72d14825004ef320aa628f1a", "created_at": "2026-11-01T19:01:41Z", "updated_at": "2026-11-01T22:51:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and evaluation of Alzheimer's disease diagnostic prediction model based on genes involved in mitophagy.", "doi": "10.3389/fnagi.2023.1146660", "pmid": "37032823", "authors": ["Yongyan Pei", "Sijia Chen", "Fengling Zhou", "Tao Xie", "Hua Cao"], "journal": "Frontiers in aging neuroscience", "year": 2023, "abstract": "Introduction: Alzheimer's disease (AD) is a common neurodegenerative disease. The concealment of the disease is the difficulty of its prevention and treatment. Previous studies have shown that mitophagy is crucial to the development of AD. However, there is a lack of research on the identification and clinical significance of mitophagy-related genes in AD. Therefore, the purpose of this study was to identify the mitophagy-related genes with the diagnostic potential for AD and establish a diagnostic model for AD.\nMethods: Firstly, we download the AD gene expression profile from Gene Expression Omnibus (GEO). Limma, PPI, functional enrichment analysis and WGCNA were used to screen the differential expression of mitophagy-related AD gene. Then, machine learning methods (random forest, univariate analysis, support vector machine, LASSO regression and support vector machine classification) were used to identify diagnostic markers. Finally, the diagnostic model was established and evaluated by ROC, multiple regression analysis, nomogram, calibration curve and other methods. Moreover, multiple independent datasets, AD cell models and AD clinical samples were used to verify the expression level of characteristic genes in the diagnostic model.\nResults: In total, 72 differentially expressed mitophagy-related related genes were identified, which were mainly involved in biological functions such as autophagy, apoptosis and neurological diseases. Four mitophagy-related genes (OPTN, PTGS2, TOMM20, and VDAC1) were identified as biomarkers. A diagnostic prediction model was constructed, and the reliability of the model was verified by receiver operating characteristic (ROC) curve analysis of GSE122063 and GSE63061. Then, we combine four mitophagy-related genes with age to establish a nomogram model. The ROC, C index and calibration curve show that the model has good prediction performance. Finally, multiple independent datasets, AD cell model samples and clinical peripheral blood samples confirmed that the expression levels of four mitophagy-related genes were consistent with the results of bioinformatics analysis.\nDiscussion: The analysis results and diagnostic model of this study are helpful for the follow-up clinical work and mechanism research of AD.", "claims": [{"id": "claim-method", "statement": "Limma, PPI, functional enrichment analysis and WGCNA were used to screen the differential expression of mitophagy-related AD gene.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37032823", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Limma, PPI, functional enrichment analysis and WGCNA were used to screen the differential expression of mitophagy-related AD gene."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used for analysis in this study (GSE63060, GSE63061, GSE5281, and GSE122063) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37032823", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The datasets used for analysis in this study (GSE63060, GSE63061, GSE5281, and GSE122063) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC analysis results based on GSE122063 expression profile showed that the area under the curve of the diagnosis and prediction model was 0.965 (Figure 6D), indicating that the model had good prediction ability.", "kind": "quantitative", "target": {"value": 0.806, "unit": "auc", "tolerance": null, "source": "Subsequently, the model was used to verify the GSE63061 dataset, and the results showed that the AUC was 0.806 (Figure 6E), which further confirmed the prediction accuracy and stability of the diagnostic model."}, "evidence": [{"asset": "source:pmid:37032823", "locator": "sections[19] \"Establishment and evaluation of Alzheimer’s disease diagnostic prediction model\" paragraph 2", "quote": "ROC analysis results based on GSE122063 expression profile showed that the area under the curve of the diagnosis and prediction model was 0.965 (Figure 6D), indicating that the model had good prediction ability."}, {"asset": "source:pmid:37032823", "locator": "sections[19] \"Establishment and evaluation of Alzheimer’s disease diagnostic prediction model\" paragraph 2", "quote": "Subsequently, the model was used to verify the GSE63061 dataset, and the results showed that the AUC was 0.806 (Figure 6E), which further confirmed the prediction accuracy and stability of the diagnostic model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC analysis results based on GSE122063 expression profile showed that the area under the curve of the diagnosis and prediction model was 0.965 (Figure 6D), indicating that the model had good prediction ability.", "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:37032823"], "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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