Identification of immune-infiltrated hub genes as potential biomarkers of Moyamoya disease by bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35197088", "kind": "science-record", "revision": 5, "content_hash": "sha256:fac0e057df10e977ee6fe214e439227b199aab51b5f7b9aeda19a66048ba84fe", "created_at": "2026-11-01T17:36:57Z", "updated_at": "2026-11-01T22:05:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-infiltrated hub genes as potential biomarkers of Moyamoya disease by bioinformatics analysis.", "doi": "10.1186/s13023-022-02238-4", "pmid": "35197088", "authors": ["Fa Jin", "Chuanzhi Duan"], "journal": "Orphanet journal of rare diseases", "year": 2022, "abstract": "BACKGROUND: Moyamoya disease (MMD) is a rare chronic progressive cerebrovascular disease. Recent studies have shown that autoimmune inflammation may also be an important pathology in MMD but the molecular mechanisms of inflammation in this disease are still large unknown. This study was designed to identify key biomarkers and the immune infiltration in vessel tissue of MMD using bioinformatics analysis.\nMETHODS: Raw gene expression profiles (GSE157628, GSE141024) were downloaded from the Gene Expression Omnibus (GEO) database, identified differentially expressed genes (DEGs) and performed functional enrichment analysis. The CIBERSORT deconvolution algorithm was used to analyze the proportion of immune cells between MMD and an MMD-negative control group. We screened for neutrophil-associated DEGs, constructed a protein-protein interaction network (PPI) using STRING, and clarified the gene cluster using the Cytoscape plugin MCODE analysis. The receiver operating characteristic (ROC) curve was applied to test and filter the best gene signature.\nRESULTS: A total of 570 DEGs were detected, including 212 downregulated and 358 up-regulated genes. Reactome and KEGG enrichment revealed that DEGs were involved in the cell cycle, molecular transport, and metabolic pathways. The immune infiltration profile demonstrated that MMD cerebrovascular tissues contained a higher proportion of neutrophils, monocytes, and natural killer cells in MMD than in controls. The PPI network and MCODE cluster identified nine DEGs (UNC13D, AZU1, PYCARD, ELANE, SDCBP, CCL11, CCL15, CCL20, and CXCL5) associated with neutrophil infiltration. ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846).\nCONCLUSIONS: The characteristics of immune infiltration in the cerebrovascular tissues of MMD patients and abnormal expression of hub genes provide new insights for understanding MMD progression. UNC13D is shows promise as a candidate molecule to determine neutrophil infiltration characteristics in MMD.", "claims": [{"id": "claim-method", "statement": "We applied the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) to evaluate diagnostic accuracy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35197088", "locator": "sections[12] \"ROC analysis\" paragraph 2", "quote": "We applied the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) to evaluate diagnostic accuracy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study were downloaded and accessed from the Gene Expression Omnibus (GEO) database: https://www.ncbi.nlm.nih.gov/geo/, with accession No: GSE157628, GSE141024.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35197088", "locator": "sections[29] \"Availability of data and materials\" paragraph 2", "quote": "The datasets analyzed in this study were downloaded and accessed from the Gene Expression Omnibus (GEO) database: https://www.ncbi.nlm.nih.gov/geo/, with accession No: GSE157628, GSE141024."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846).", "kind": "quantitative", "target": {"value": 0.7846, "unit": "auc", "tolerance": null, "source": "ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846)."}, "evidence": [{"asset": "source:pmid:35197088", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846).", "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:35197088"], "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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