Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40034343", "kind": "science-record", "revision": 4, "content_hash": "sha256:5b19cd47b04d34fb2db60a050adfcf2bfe2f1b85937da7fab5a9b5c9233a13aa", "created_at": "2026-11-01T20:30:15Z", "updated_at": "2026-11-01T22:56:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.", "doi": "10.1177/25424823241289804", "pmid": "40034343", "authors": ["Wanning Zheng", "Dongdong Lin", "Shunan Shi", "Jiayi Ren", "Jiong Wu", "Ming Wang", "Shu Wan"], "journal": "Journal of Alzheimer's disease reports", "year": 2024, "abstract": "Background: Alzheimer's disease (AD) and vascular dementia (VaD) share overlapping pathophysiological characteristics, yet comparative genetic studies are rare. Understanding these overlaps may aid in identifying common diagnostic markers and therapeutic targets.\nObjective: This study identifies shared diagnostic genes and mechanisms linking AD and VaD.\nMethods: Datasets GSE5281 and GSE122063 from the GEO database were used to identify differentially expressed genes (DEGs). Intersection DEGs were analyzed using KEGG and GO enrichment to explore signaling pathways. A PPI network was constructed, and LASSO and SVM-RFE were applied to identify core genes. CIBERSORT assessed immune cell composition and their relationship with core genes. Diagnostic efficacy was evaluated using ROC curves, nomogram, and Decision Curve Analysis (DCA). Core genes were used to identify characteristic genes in various brain regions of AD patients.\nResults: The analysis identified 9021 DEGs for AD and 373 DEGs for VaD, with 74 co-expressed genes and 8 core genes. ROC curves, nomogram, and DCA indicated high diagnostic accuracy. Core gene analysis revealed differential expression of characteristic genes in various brain regions of AD patients.\nConclusions: This research identified 74 co-expressed genes and 8 pivotal diagnostic genes. These genes likely play roles in signal transduction, neuroinflammation, and autophagy in both AD and VaD. The findings offer potential targets for future research and clinical interventions. Further research should use larger, more diverse datasets and incorporate custom NGS panels to identify novel genetic variants, enhancing precise diagnostic and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "These analyses were conducted using the Limma package in R, which applies empirical Bayes methods to estimate gene expression changes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40034343", "locator": "sections[4] \"Differentially expressed gene screening\" paragraph 2", "quote": "These analyses were conducted using the Limma package in R, which applies empirical Bayes methods to estimate gene expression changes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this paper come from public databases: The original datasets were obtained from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). The specific sources of datasets GSE5281 and GSE122063 are: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5281 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122063. We extracted data from vascular dementia patients in GSE122063 for analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40034343", "locator": "sections[23] \"fn-group\" paragraph 1", "quote": "The data used in this paper come from public databases: The original datasets were obtained from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). The specific sources of datasets GSE5281 and GSE122063 are: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5281 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122063. We extracted data from vascular dementia patients in GSE122063 for analysis."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 74 co-expressed genes and 8 pivotal diagnostic genes shared by AD and VaD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40034343", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "This research identified 74 co-expressed genes and 8 pivotal diagnostic genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 74 co-expressed genes and 8 pivotal diagnostic genes shared by AD and VaD.", "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:40034343"], "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": "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:GSE122063"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40034343", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1177/25424823241289804", "sha256": "sha256:1865d45a8902c0cecfedd33c8cbf39886bd3f1e9601364711ad499802658a5dc", "bytes": 32447, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1177/25424823241289804", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11863729", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122063", "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=GSE122063", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122063", "version": null, "note": "The data used in this paper come from public databases: The original datasets were obtained from Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). 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