Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40808948", "kind": "science-record", "revision": 4, "content_hash": "sha256:7f8ef9900ce91fbb358190ba96706fb3ac8f6ebd12f346fee40d33c6fef71bab", "created_at": "2026-11-01T20:30:54Z", "updated_at": "2026-11-01T22:57:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.", "doi": "10.3389/fimmu.2025.1615540", "pmid": "40808948", "authors": ["Wentong Li", "Yiyi Zhang", "Chuanhong Li", "Mingyang Jiang", "Dong Wang", "Luomeng Chao", "Yuxia Yang"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Introduction: Vascular dementia (VaD), the second most prevalent form of dementia globally, remains insufficiently understood in terms of its molecular mechanisms and diagnostic biomarkers. This study aims to elucidate the regulatory network and diagnostic potential of the molecular chaperone system in VaD through the integration of multi-omics data and machine learning algorithms.\nMethods: Transcriptomic data from frontal and temporal cortex (GSE122063, n=15)and white matter (GSE282111, n=8) samples were obtained from the GEO database. Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.\nResults: A total of 897 DEGs were identified, with functional enrichment analysis revealing significant involvement in T cell activation (p=2.84×10-3), neuroactive ligand-receptor interaction (p=6.01×10-4), and osteoclast differentiation (NES=2.83). PPI network analysis identified HSP90AA1, HSPA1B, and DNAJB1 as core hub genes (degree centrality >20). Machine learning validation demonstrated their combined exceptional diagnostic efficacy (AUC=0.963, F1 = 0.88). Immune analysis revealed that this molecular chaperone axis modulates neuroinflammation by suppressing naive B cell differentiation (61% reduction) and activating Tregs (55.53% increase). Single-cell resolution analysis showed HSP90AA1 to be specifically overexpressed in oligodendrocytes (72.23%), significantly correlating with glial depletion (4.56% decrease in oligodendrocytes, p<0.01) and aberrant neuronal proliferation (144.23% increase, p=0.0032). In vivo experiments utilized a bilateral common carotid artery stenosis (BCAS) mouse model to simulate human vascular dementia (VaD), with further validation through Morris water maze testing. The BCAS group exhibited significantly upregulated mRNA expression of HSP90AA1, HSPA1B, and DNAJB1, consistent with integrated bioinformatics analysis results.\nConclusion: This study elucidates the HSP90AA1-HSPA1B-DNAJB1 network as a key driver of VaD pathogenesis through dual mechanisms of protein homeostasis and immune reprogramming. The diagnostic performance of this network significantly surpasses traditional biomarkers (ΔAUC≥14.3%), offering novel targets for precision diagnostics and therapeutics. However, further validation with larger cohorts is necessary to assess its clinical translational potential.", "claims": [{"id": "claim-method", "statement": "Differentially Expressed Genes (DEGs) were identified using the limma package (v3.58.1) to construct linear models, with significance thresholds set at adjusted p<0.05 and log2FoldChange>0.656 (corresponding to a 1.6-fold expression difference) (21).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40808948", "locator": "sections[4] \"Systematic identification of differentially expressed genes\" paragraph 3", "quote": "Differentially Expressed Genes (DEGs) were identified using the limma package (v3.58.1) to construct linear models, with significance thresholds set at adjusted p<0.05 and log2FoldChange>0.656 (corresponding to a 1.6-fold expression difference) (21)."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40808948", "locator": "sections[26] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The three genes HSP90AA1, HSPA1B, and DNAJB1 achieved F1 scores of 0.88 for distinguishing vascular dementia patients from controls.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40808948", "locator": "sections[19] \"Clinical translation efficacy validation of diagnostic biomarkers\" paragraph 2", "quote": "Their accuracy and recall rates were exceptional, with notably high F1 scores of 0.88 for all three genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The three genes HSP90AA1, HSPA1B, and DNAJB1 achieved F1 scores of 0.88 for distinguishing vascular dementia patients from controls.", "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:40808948"], "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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