Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39881357", "kind": "science-record", "revision": 4, "content_hash": "sha256:4c4abced0916d76749ac00ea8f4fd0869e9b1d0c22d48ab0f6acb0c427a48825", "created_at": "2026-11-01T20:30:10Z", "updated_at": "2026-11-01T22:56:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight.", "doi": "10.1186/s40360-025-00844-z", "pmid": "39881357", "authors": ["Jinxiao Li", "Jing Li", "Man Zheng", "Jinxing Liu", "Xinyou Zhao"], "journal": "BMC pharmacology & toxicology", "year": 2025, "abstract": "BACKGROUND: Osteoporosis (OP), often termed the \"silent epidemic,\" poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.\nMETHODS: This study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository. Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE. Functional enrichment of FBXW4-associated genes was assessed via GSEA and GSVA, identifying significant immune pathway involvement. Immune-related biological processes linked to FBXW4 expression were further evaluated using CIBERSORT and ESTIMATE algorithms. Validation of FBXW4 expression was performed using GSE35956.\nRESULTS: A total of 13 hub genes were selected through LASSO and SVM-RFE analyses. Functional assays implicated FBXW4 in antiviral defense, cytokine production, and immune response modulation. Notably, FBXW4 expression correlated positively with several immune cell subsets, including memory B cells, activated memory CD4+ T cells, naive B cells, gamma delta T cells, M0 macrophages, follicular helper T cells, and naive CD4+ T cells, while showing a negative association with neutrophils.\nCONCLUSIONS: This study uncovers a complex interplay between FBXW4 and immune processes in osteoporosis, suggesting its potential utility as a biomarker for OP diagnosis and monitoring. These findings lay the groundwork for future investigations into the therapeutic and diagnostic potential of FBXW4 in OP.", "claims": [{"id": "claim-method", "statement": "Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39881357", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE."}], "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 support [55]. The citation guidelines: http://www.kegg.jp/kegg/kegg1.html.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39881357", "locator": "sections[27] \"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 support [55]. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE105149"], "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:GSE105149"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39881357", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s40360-025-00844-z", "sha256": "sha256:e96ad8b1b5bb8ffe9f89b5c273741d3467dd463c834105877a30c569fca58516", "bytes": 58368, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s40360-025-00844-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11781060", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE105149", "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=GSE105149", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE105149", "version": null, "note": "The datasets generated during and/or analyzed during the current study are available in the appendix. 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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 support [55]. The citation guidelines: http://www.kegg.jp/kegg/kegg1.html.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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 support [55]. 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