A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37152368", "kind": "science-record", "revision": 3, "content_hash": "sha256:f9226ea83f79041e8867c061e25cdd4f6c7dc56169387ecbf69cfe067f53b9c4", "created_at": "2026-11-01T19:01:49Z", "updated_at": "2026-11-01T22:09:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity.", "doi": "10.1155/2023/3220235", "pmid": "37152368", "authors": ["Wenxin Gao", "Ruirui Hou", "Yungang Chen", "Xiaoying Wang", "Guoyan Liu", "Wanli Hu", "Kang Yao", "Yanke Hao"], "journal": "Mediators of inflammation", "year": 2023, "abstract": "Background: The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency.\nMethods: To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis.\nResults: Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis.\nConclusion: The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy.", "claims": [{"id": "claim-method", "statement": "To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37152368", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73754 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25101.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37152368", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73754 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25101."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37152368", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression.", "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:37152368"], "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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