Bioinformatics analysis reveals the landscape of immune cell infiltration and novel immune-related biomarkers in moyamoya disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37265961", "kind": "science-record", "revision": 3, "content_hash": "sha256:4dcc3d3f28c53fa31fabbf78ea1e3cfb978ea9c843746187eda3888453731cf4", "created_at": "2026-11-01T19:01:59Z", "updated_at": "2026-11-01T22:09:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis reveals the landscape of immune cell infiltration and novel immune-related biomarkers in moyamoya disease.", "doi": "10.3389/fgene.2023.1101612", "pmid": "37265961", "authors": ["Lei Cao", "Yunzheng Ai", "Yang Dong", "Dongpeng Li", "Hao Wang", "Kaiwen Sun", "Chenchao Wang", "Manxia Zhang", "Dongming Yan", "Hongwei Li", "Guobiao Liang", "Bo Yang"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Objective: This study aimed to identify immune infiltration characteristics and new immunological diagnostic biomarkers in the cerebrovascular tissue of moyamoya disease (MMD) using bioinformatics analysis. Methods: GSE189993 and GSE141022 were downloaded from the GEO database. Differentially expressed gene and PPI analysis were performed. After performing WGCNA, the most significant module associated with MMD was obtained. Next, functional pathways according to GSEA, GO, and KEGG were enriched for the aforementioned core genes obtained from PPI and WGCNA. Additionally, immune infiltration, using the CIBERSORT deconvolution algorithm, immune-related biomarkers, and the relationship between these genes, was further explored. Finally, diagnostic accuracy was verified with ROC curves in the validation dataset GSE157628. Results: A total of 348 DEGs were screened, including 89 downregulated and 259 upregulated genes. The thistlel module was detected as the most significant module associated with MMD. Functional analysis of the core genes was chiefly involved in the immune response, immune system process, protein tyrosine kinase activity, secretory granule, and so on. Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity. Meanwhile, a higher proportion of eosinophils, not T cells or B cells, was demonstrated in the specific immune infiltration landscape of MMD. Conclusion: Immune activities and immune cells were actively involved in the progression of MMD. BTK, FGR, PTPN11, and SYK were identified as potential immune diagnostic biomarkers. These immune-related genes and cells may provide novel insights for immunotherapy in the future.", "claims": [{"id": "claim-method", "statement": "To evaluate diagnostic accuracy, the receiver operating characteristic (ROC) curve was applied, and we calculated the area under the curve (AUC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37265961", "locator": "sections[8] \"Evaluation of immune-related biomarkers and ROC curves\" paragraph 3", "quote": "To evaluate diagnostic accuracy, the receiver operating characteristic (ROC) curve was applied, and we calculated the area under the curve (AUC)."}], "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 nos. GSE189993, GSE141022, and GSE157628.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37265961", "locator": "sections[22] \"Data availability statement\" 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 nos. GSE189993, GSE141022, and GSE157628."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37265961", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Among 13 immune-related overlapping genes, 4 genes (BTK, FGR, PTPN11, and SYK) were identified as potential diagnostic biomarkers, where PTPN11 showed the highest specificity and sensitivity.", "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:37265961"], "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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