Bioinformatics analysis of potential common pathogenic mechanism for carotid atherosclerosis and Parkinson's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37649719", "kind": "science-record", "revision": 3, "content_hash": "sha256:11b14336ed021adce7776b938ebd2c01a18f9b6a2fc184114b74ec659836b798", "created_at": "2026-11-01T19:02:26Z", "updated_at": "2026-11-01T22:10:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of potential common pathogenic mechanism for carotid atherosclerosis and Parkinson's disease.", "doi": "10.3389/fnagi.2023.1202952", "pmid": "37649719", "authors": ["Quan Wang", "Qun Xue"], "journal": "Frontiers in aging neuroscience", "year": 2023, "abstract": "Background: Cerebrovascular disease (CVD) related to atherosclerosis and Parkinson's disease (PD) are two prevalent neurological disorders. They share common risk factors and frequently occur together. The aim of this study is to investigate the association between atherosclerosis and PD using genetic databases to gain a comprehensive understanding of underlying biological mechanisms.\nMethods: The gene expression profiles of atherosclerosis (GSE28829 and GSE100927) and PD (GSE7621 and GSE49036) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for these two disorders, we constructed protein-protein interaction (PPI) networks and functional modules, and further identified hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) regression. The diagnostic effectiveness of these hub genes was evaluated using Receiver Operator Characteristic Curve (ROC) analysis. Furthermore, we used single sample gene set enrichment analysis (ssGSEA) to analyze immune cell infiltration and explored the association of the identified hub genes with infiltrating immune cells through Spearman's rank correlation analysis in R software.\nResults: A total of 50 shared DEGs, with 36 up-regulated and 14 down-regulated genes, were identified through the intersection of DEGs of atherosclerosis and PD. Using LASSO regression, we identified six hub genes, namely C1QB, CD53, LY96, P2RX7, C3, and TNFSF13B, in the lambda.min model, and CD14, C1QB, CD53, P2RX7, C3, and TNFSF13B in the lambda.1se model. ROC analysis confirmed that both models had good diagnostic efficiency for atherosclerosis datasets GSE28829 (lambda.min AUC = 0.99, lambda.1se AUC = 0.986) and GSE100927 (lambda.min AUC = 0.922, lambda.1se AUC = 0.933), as well as for PD datasets GSE7621 (lambda.min AUC = 0.924, lambda.1se AUC = 0.944) and GSE49036 (lambda.min AUC = 0.894, lambda.1se AUC = 0.881). Furthermore, we found that activated B cells, effector memory CD8 + T cells, and macrophages were the shared correlated types of immune cells in both atherosclerosis and PD.\nConclusion: This study provided new sights into shared molecular mechanisms between these two disorders. These common hub genes and infiltrating immune cells offer promising clues for further experimental studies to explore the common pathogenesis of these disorders.", "claims": [{"id": "claim-method", "statement": "The hub genes obtained from the shared DEGs of the GSE49036 and GSE28829 datasets were further narrowed down to six genes using LASSO regression analysis through the GLMNET R package (version 4.1-7).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37649719", "locator": "sections[6] \"2.4. Identification of hub genes using least absolute shrinkage and selection operator (LASSO) regression\" paragraph 2", "quote": "The hub genes obtained from the shared DEGs of the GSE49036 and GSE28829 datasets were further narrowed down to six genes using LASSO regression analysis through the GLMNET R package (version 4.1-7)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: GSE7621, GSE49036, GSE28829, and GSE100927.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37649719", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: GSE7621, GSE49036, GSE28829, and GSE100927."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO regression identified six hub genes, and both models showed good diagnostic efficiency across the atherosclerosis and PD datasets, with AUCs ranging from 0.881 to 0.99.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37649719", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using LASSO regression, we identified six hub genes, namely C1QB, CD53, LY96, P2RX7, C3, and TNFSF13B, in the lambda.min model, and CD14, C1QB, CD53, P2RX7, C3, and TNFSF13B in the lambda.1se model."}, {"asset": "source:pmid:37649719", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "ROC analysis confirmed that both models had good diagnostic efficiency for atherosclerosis datasets GSE28829 (lambda.min AUC = 0.99, lambda.1se AUC = 0.986) and GSE100927 (lambda.min AUC = 0.922, lambda.1se AUC = 0.933), as well as for PD datasets GSE7621 (lambda.min AUC = 0.924, lambda.1se AUC = 0.944) and GSE49036 (lambda.min AUC = 0.894, lambda.1se AUC = 0.881)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO regression identified six hub genes, and both models showed good diagnostic efficiency across the atherosclerosis and PD datasets, with AUCs ranging from 0.881 to 0.99.", "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:37649719"], "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:GSE100927"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37649719", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2023.1202952", "sha256": "sha256:c269d34c4a4e3a27a6b8e4ec01b5ea5d2c1c9c7c2352b88b8dcaf52d47d00cc6", "bytes": 60420, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2023.1202952", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10464527", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100927", "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=GSE100927", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100927", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: GSE7621, GSE49036, GSE28829, and GSE100927.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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