Identification of a Four-Gene Signature Based on Metal Metabolism for Alzheimer's Disease Diagnosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41300737", "kind": "science-record", "revision": 3, "content_hash": "sha256:fefbcd1abc9ea99996dedad41ff2ec5ffb72bcba02d25b64c119baf6851687b0", "created_at": "2026-11-01T20:31:19Z", "updated_at": "2026-11-01T22:15:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Four-Gene Signature Based on Metal Metabolism for Alzheimer's Disease Diagnosis.", "doi": "10.3390/genes16111287", "pmid": "41300737", "authors": ["Dandan Huang", "Shasha Huang", "Yunhan Gao", "Linxi Yin", "Lijun Pan", "Wei Xu"], "journal": "Genes", "year": 2025, "abstract": "Background/Objectives: Alzheimer's disease (AD) is a progressive neurodegenerative disorder, and dysregulated metal metabolism in the brain is closely linked to its pathogenesis. Methods: In this study, we identified differentially expressed metal metabolism-related genes (DEMGs) associated with AD by integrating data from the GEO dataset GSE132903 and the GeneCards database. Protein-protein interaction (PPI) network analysis was used to identify hub genes, followed by receiver operating characteristic (ROC) curve analysis to assess their diagnostic potential as AD biomarkers. To validate these findings, we performed qRT-PCR experiments on a cellular model and further verified the results using an independent external dataset. Finally, we developed a multigene diagnostic model and constructed a nomogram to predict AD risk. Results: The results demonstrated that six out of the ten hub genes achieved an area under the curve (AUC) greater than 0.75, and four genes (GAD1, GFAP, SYP, and UQCRC2) showed significant potential as candidate biomarkers for AD after further validation. A multigene diagnostic model based on these genes demonstrated strong predictive performance (AUC = 0.861), and a nomogram with high predictive accuracy (C-index = 0.861) was developed to facilitate individualized AD risk assessment. Conclusions: This study identifies four metal metabolism-related genes as promising diagnostic biomarkers for AD and provides a validated multigene model along with a clinically applicable nomogram for individualized risk assessment.", "claims": [{"id": "claim-method", "statement": "A cross-sectional analysis was performed for our study, as all samples were collected and measured at a single time point.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41300737", "locator": "sections[4] \"2.2. Identification of DEGs and DEMGs\" paragraph 2", "quote": "A cross-sectional analysis was performed for our study, as all samples were collected and measured at a single time point."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41300737", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The original contributions presented in the study are included in the article/Supplementary Materials; further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results demonstrated that six out of the ten hub genes achieved an area under the curve (AUC) greater than 0.75, and four genes (GAD1, GFAP, SYP, and UQCRC2) showed significant potential as candidate biomarkers for AD after further validation.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41300737", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The results demonstrated that six out of the ten hub genes achieved an area under the curve (AUC) greater than 0.75, and four genes (GAD1, GFAP, SYP, and UQCRC2) showed significant potential as candidate biomarkers for AD after further validation."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The results demonstrated that six out of the ten hub genes achieved an area under the curve (AUC) greater than 0.75, and four genes (GAD1, GFAP, SYP, and UQCRC2) showed significant potential as candidate biomarkers for AD after further validation.", "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:41300737"], "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:GSE118553"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE118553"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41300737", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/genes16111287", "sha256": 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