Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40059128", "kind": "science-record", "revision": 4, "content_hash": "sha256:42627fb2d703b0f5556bcf385b055babcd983827e78223ef19068dca4f9ceb68", "created_at": "2026-11-01T20:30:16Z", "updated_at": "2026-11-01T22:56:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.", "doi": "10.1038/s41598-025-91376-3", "pmid": "40059128", "authors": ["Xiaomei Hu", "Fanqi Liang", "Man Zheng", "Juying Xie", "Shanxi Wang"], "journal": "Scientific reports", "year": 2025, "abstract": "Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and serves as a major contributor to cardiovascular diseases. KCTD10, a protein implicated in a variety of biological processes, has garnered significant attention for its role in cardiovascular diseases and metabolic regulation. As a member of the KCTD protein family, KCTD10 is characterized by the presence of a T1 domain that interacts with voltage-gated potassium channels, a critical interaction for modulating channel activity and intracellular signal transduction. In our study, KCTD10 was identified as a focal point through an integrative analysis of differentially expressed genes (DEGs) across multiple datasets (GSE43292 and GSE9820) from the GEO database, aligned with immune-related gene sets from the ImmPort database. Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection. We further applied Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to these gene sets, revealing significant enrichment in immune-related pathways. The relationship between KCTD10 expression and immune processes was examined using CIBERSORT and ESTIMATE algorithms to assess tumor microenvironment characteristics, suggesting increased immune cell infiltration associated with higher KCTD10 expression. Validation of these findings was conducted using data from the GSE9820 dataset. Among 10 DEGs linked with KCTD10, 13 were identified as hub genes through LASSO and SVM-RFE analyses. Functional assays highlighted KCTD10's role in enhancing viral defense mechanisms, cytokine production, and immune cascades. Notably, KCTD10 expression correlated positively with several immune cells, including naive CD4 + T cells, eosinophils, resting NK cells, neutrophils, M0 macrophages, and particularly M1 macrophages, indicating a significant association. This research elucidates the complex relationship between KCTD10 and AS, underscoring its potential as a novel biomarker for diagnosing and monitoring the disease. Our findings provide a solid foundation for further investigations, suggesting that targeting KCTD10-related pathways could markedly advance our understanding and management of AS, offering new avenues for therapeutic intervention.", "claims": [{"id": "claim-method", "statement": "Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40059128", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Advanced analytical tools, including Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were employed to refine our gene selection."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available in the appendix. The datasets generated and/or analysed during the current study are available in the [GEO] repository. https://www.ncbi.nlm.nih.gov/geo/'. Thanks to the KEGG database for the great support41. The citation guidelines: www.kegg.jp/kegg/kegg1.html.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40059128", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are available in the appendix. The datasets generated and/or analysed during the current study are available in the [GEO] repository. https://www.ncbi.nlm.nih.gov/geo/'. Thanks to the KEGG database for the great support41. The citation guidelines: www.kegg.jp/kegg/kegg1.html."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Integrative transcriptomic and machine-learning analyses identified KCTD10 as a key gene associated with immune-related pathways and immune-cell infiltration in atherosclerosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40059128", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Notably, KCTD10 expression correlated positively with several immune cells, including naive CD4 + T cells, eosinophils, resting NK cells, neutrophils, M0 macrophages, and particularly M1 macrophages, indicating a significant association."}, {"asset": "source:pmid:40059128", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Among 10 DEGs linked with KCTD10, 13 were identified as hub genes through LASSO and SVM-RFE analyses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Integrative transcriptomic and machine-learning analyses identified KCTD10 as a key gene associated with immune-related pathways and immune-cell infiltration in atherosclerosis.", "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:40059128"], "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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