Elucidating the role of LGALS3BP in coronary atherosclerosis: integrating bioinformatics and machine learning for advanced insights.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40814096", "kind": "science-record", "revision": 4, "content_hash": "sha256:4015a82c25eb8aef3744603b3f0f3bc273abf0e8d2437a739b602258ec28cc15", "created_at": "2026-11-01T20:30:54Z", "updated_at": "2026-11-01T22:57:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Elucidating the role of LGALS3BP in coronary atherosclerosis: integrating bioinformatics and machine learning for advanced insights.", "doi": "10.1186/s13019-025-03462-2", "pmid": "40814096", "authors": ["Suzhen Zhao", "Chuanxin Zhao", "Linlin Wang", "Baoe Cheng", "Man Zheng", "Xiuyun Wang"], "journal": "Journal of cardiothoracic surgery", "year": 2025, "abstract": "BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly impacts vascular health and contributes to cardiovascular disorders. LGALS3BP, a well-studied molecule, plays key roles in various physiological and pathological processes.\nMETHODS: We identified LGALS3BP as a central focus through integrative analysis of DEGs across multiple datasets (GSE43292 and GSE9820) from the GEO database, combined with immune-related gene sets from the ImmPort database. Advanced analytical techniques, including Lasso regression and SVM-RFE, were used to refine gene selection. GSEA and GSVA revealed significant enrichment in immune-related pathways. The relationship between LGALS3BP expression and immune processes was explored using CIBERSORT and ESTIMATE algorithms, which indicated a positive association between higher LGALS3BP expression and increased immune cell infiltration.\nRESULTS: Among the 10 DEGs associated with LGALS3BP, 13 hub genes were identified via LASSO and SVM-RFE analyses. Functional assays highlighted LGALS3BP's involvement in viral defense, cytokine production, and immune cascades. Increased expression of LGALS3BP was positively correlated with activated dendritic cells, NK cells, memory CD4 T cells, naïve CD4 T cells, CD8 T cells, follicular helper T cells, gamma delta T cells, and regulatory T cells (Tregs), while negatively associated with resting mast cells, M0 macrophages, and eosinophils.\nCONCLUSIONS: Our research elucidates the complex relationship between LGALS3BP and AS, positioning it as a potential novel biomarker for diagnosis and disease monitoring. These findings lay the groundwork for future studies and suggest that targeting LGALS3BP-related pathways could enhance our understanding of AS.", "claims": [{"id": "claim-method", "statement": "Differential expression analyses between AS and control groups were conducted utilizing the Linear Models for Microarray Data (limma) package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40814096", "locator": "sections[3] \"Transcriptional profiling data and determination of differentially expressed genes (DEGs)\" paragraph 3", "quote": "Differential expression analyses between AS and control groups were conducted utilizing the Linear Models for Microarray Data (limma) package."}], "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/’.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40814096", "locator": "sections[26] \"notes\" paragraph 1", "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/’."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LGALS3BP was identified as a central focus through integrative differential-expression and machine-learning analysis of two GEO datasets and immune-related gene sets from ImmPort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40814096", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We identified LGALS3BP as a central focus through integrative analysis of DEGs across multiple datasets (GSE43292 and GSE9820) from the GEO database, combined with immune-related gene sets from the ImmPort database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LGALS3BP was identified as a central focus through integrative differential-expression and machine-learning analysis of two GEO datasets and immune-related gene sets from ImmPort.", "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:40814096"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE43292"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE43292"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40814096", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s13019-025-03462-2", "sha256": "sha256:5bbfe31a9bfb8b2cadba02e488c14fbf0cb850f3104948c0f2f5cc3d04e75246", "bytes": 41701, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s13019-025-03462-2", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12355831", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE43292", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE43292", "version": null, "note": "The datasets generated during and/or analyzed during the current study are available in the appendix. 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