Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36605987", "kind": "science-record", "revision": 4, "content_hash": "sha256:e8f6741819b35ff1e6d1aaf59a80fe5df32f8899dc6ee55fd576070971d3ebd3", "created_at": "2026-11-01T19:01:09Z", "updated_at": "2026-11-01T22:50:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease.", "doi": "10.3389/fmolb.2022.991425", "pmid": "36605987", "authors": ["Shifu Li", "Ying Han", "Qian Zhang", "Dong Tang", "Jian Li", "Ling Weng"], "journal": "Frontiers in molecular biosciences", "year": 2022, "abstract": "Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using \"limma\" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.", "claims": [{"id": "claim-method", "statement": "Differentially expressed ARGs (DEARGs) were identified using “limma” R packages.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36605987", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Differentially expressed ARGs (DEARGs) were identified using “limma” R packages."}], "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:36605987", "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": "Using limma differential expression analysis on GEO dataset GSE157628, the study identified 260 differentially expressed autoimmune-related genes in moyamoya disease.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36605987", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases."}, {"asset": "source:pmid:36605987", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Differentially expressed ARGs (DEARGs) were identified using “limma” R packages."}, {"asset": "source:pmid:36605987", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 260 DEARGs were identified in GSE157628 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma differential expression analysis on GEO dataset GSE157628, the study identified 260 differentially expressed autoimmune-related genes in moyamoya disease.", "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:36605987"], "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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The detailed procedures of RT-qPCR were described in our previous studies (Li e", "ontrol, GAPDH. The primer sequences used for RT-qPCR are listed in Supplementary Table S1. Supple", "also selected. The detailed procedures of RT-qPCR were described in our previous studies (Li e", "rol, GAPDH. The primer sequences used for RT-qPCR are listed in Supplementary Table S1. Supple"], "order_basis": "paper_text_order:2907", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differentially expressed ARGs (DEARGs) were identified using “limma” R packages."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}