Exploring the common mechanisms and biomarker ST8SIA4 of atherosclerosis and ankylosing spondylitis through bioinformatics analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39131703", "kind": "science-record", "revision": 4, "content_hash": "sha256:8bfe11ca6d3416efe1ccf879f9a102c58c64852d3d58a2181492b156f8f04946", "created_at": "2026-11-01T20:29:13Z", "updated_at": "2026-11-01T22:54:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the common mechanisms and biomarker ST8SIA4 of atherosclerosis and ankylosing spondylitis through bioinformatics analysis and machine learning.", "doi": "10.3389/fcvm.2024.1421071", "pmid": "39131703", "authors": ["Yirong Ma", "Junyu Lai", "Qiang Wan", "Liqiang Sun", "Yang Wang", "Xingliang Li", "Qinhe Zhang", "Jianguang Wu"], "journal": "Frontiers in cardiovascular medicine", "year": 2024, "abstract": "Background: Atherosclerosis (AS) is a major contributor to cerebrovascular and cardiovascular events. There is growing evidence that ankylosing spondylitis is closely linked to AS, often co-occurring with it; however, the shared pathogenic mechanisms between the two conditions are not well understood. This study employs bioinformatics approaches to identify common biomarkers and pathways between AS and ankylosing spondylitis.\nMethods: Gene expression datasets for AS (GSE100927, GSE28829, GSE155512) and ankylosing spondylitis (GSE73754, GSE25101) were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes for AS and ankylosing spondylitis were identified using the Limma R package and weighted gene co-expression network analysis (WGCNA) techniques, respectively. The machine learning algorithm SVM-RFE was applied to pinpoint promising biomarkers, which were then validated in terms of their expression levels and diagnostic efficacy in AS and ankylosing spondylitis, using two separate GEO datasets. Furthermore, the interaction of the key biomarker with the immune microenvironment was investigated via the CIBERSORT algorithm, single-cell analysis was used to identify the locations of common diagnostic markers.\nResults: The dataset GSE100927 contains 524 DEGs associated with AS, whereas dataset GSE73754 includes 1,384 genes categorized into modules specific to ankylosing spondylitis. Analysis of these datasets revealed an overlap of 71 genes between the DEGs of AS and the modular genes of ankylosing spondylitis. Utilizing the SVM-RFE algorithm, 15 and 24 central diagnostic genes were identified in datasets GSE100927 and GSE73754, respectively. Further validation of six key genes using external datasets confirmed ST8SIA4 as a common diagnostic marker for both conditions. Notably, ST8SIA4 is upregulated in samples from both diseases. Additionally, ROC analysis confirmed the robust diagnostic utility of ST8SIA4. Moreover, analysis through CIBERSORT suggested an association of the ST8SIA4 gene with the immune microenvironment in both disease contexts. Single-cell analysis revealed that ST8SIA4 is primarily expressed in Macrophages, Monocytes, T cells, and CMPs.\nConclusion: This study investigates the role of ST8SIA4 as a common diagnostic gene and the involvement of the lysosomal pathway in both AS and ankylosing spondylitis. The findings may yield potential diagnostic biomarkers and offer new insights into the shared pathogenic mechanisms underlying these conditions.", "claims": [{"id": "claim-method", "statement": "To pinpoint potential diagnostic markers for AS with ankylosing spondylitis, we utilized the Support Vector Machine Recursive Feature Elimination (SVM-RFE) technique.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39131703", "locator": "sections[7] \"Identification and verification of diagnostic biomarkers\" paragraph 2", "quote": "To pinpoint potential diagnostic markers for AS with ankylosing spondylitis, we utilized the Support Vector Machine Recursive Feature Elimination (SVM-RFE) technique."}], "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:39131703", "locator": "sections[23] \"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": "Further validation of six key genes using external datasets confirmed ST8SIA4 as a common diagnostic marker for both conditions.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39131703", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Further validation of six key genes using external datasets confirmed ST8SIA4 as a common diagnostic marker for both conditions."}, {"asset": "source:pmid:39131703", "locator": "sections[15] \"Identification and validation of potential shared diagnostic biomarkers\" paragraph 2", "quote": "Furthermore, ROC analysis demonstrated significant diagnostic efficacy of ST8SIA4, with AUC values of 0.976 in GSE100927 and 0.773 in GSE73754 (Figures 6F,H). In further independent analyses, ST8SIA4 demonstrated significant diagnostic effectiveness in datasets GSE28829 and GSE25101, achieving AUC values of 0.846 and 0.812, respectively (Figures 6E,G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Further validation of six key genes using external datasets confirmed ST8SIA4 as a common diagnostic marker for both conditions.", "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:39131703"], "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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