Identification ferroptosis-related hub genes and diagnostic model in Alzheimer's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37965040", "kind": "science-record", "revision": 4, "content_hash": "sha256:5e6a9f0002b4d6558bc5c530050dadafd6e02902403e30bf96016110b14921e6", "created_at": "2026-11-01T19:02:52Z", "updated_at": "2026-11-01T22:10:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification ferroptosis-related hub genes and diagnostic model in Alzheimer's disease.", "doi": "10.3389/fnmol.2023.1280639", "pmid": "37965040", "authors": ["Huabin Zhao", "Jiawei Wang", "Zhongzheng Li", "Shenghui Wang", "Guoying Yu", "Lan Wang"], "journal": "Frontiers in molecular neuroscience", "year": 2023, "abstract": "Background: Ferroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer's disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).\nMethods: The microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R \"limma\" package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.\nResults: We identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.\nConclusion: These results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.", "claims": [{"id": "claim-method", "statement": "We performed model construction using 18 genes associated with ferroptosis and selected 1,000 as the number of decision trees in the Random Forest model (Figure 2A).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37965040", "locator": "sections[14] \"Machine-learning-based for constructing diagnostic model of AD\" paragraph 2", "quote": "We performed model construction using 18 genes associated with ferroptosis and selected 1,000 as the number of decision trees in the Random Forest model (Figure 2A)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37965040", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest diagnostic model achieved good prediction performance in both the training set (AUC = 0.824) and validation set (AUC = 0.734).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37965040", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The random forest diagnostic model achieved good prediction performance in both the training set (AUC = 0.824) and validation set (AUC = 0.734).", "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:37965040"], "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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