{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T19:36:47Z","timestamp":1783021007977,"version":"3.54.6"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Osteoporosis (OP) is a systemic metabolic skeletal disorder characterized by diminished bone mineral density, deteriorated bone microarchitecture, and a consequently heightened susceptibility to fragility fractures. Bioinformatics approaches serve as a crucial bridge between genomic investigations and clinical translation, and have been extensively utilized in OP research. Nevertheless, the precise identification of core pathogenic genes and the subsequent development of robust and accurate diagnostic biomarkers remain urgent clinical imperatives.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Initially, we identified differentially expressed genes (DEGs) by comparing transcriptomic profiles between healthy controls and osteoporotic patients, followed by functional enrichment analyses of associated biological processes and signaling pathways. Weighted gene co-expression network analysis was subsequently applied to isolate disease-specific module genes. By intersecting these module genes with the DEGs, OP-related DEGs were precisely delineated. The LASSO regression algorithm was utilized to filter seven hub candidate genes. Subsequently, Support Vector Machine and Random Forest machine learning algorithms were employed to further optimize and cross-validate these potential diagnostic biomarkers. The intersection of these multi-algorithmic outputs ultimately designated the core biomarker. Furthermore, an ovariectomized (OVX) mouse model of OP was established to experimentally validate ZFYVE27 expression levels. A competitive endogenous RNA regulatory network was concurrently constructed to elucidate its post-transcriptional regulatory mechanisms underlying OP pathogenesis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ZFYVE27 emerged and was successfully validated as an optimal diagnostic biomarker for OP. Compared to the sham-operated group, both mRNA and protein expression levels of ZFYVE27 were significantly upregulated in the OVX model group (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&amp;lt;\u00a00.01), strongly suggesting that ZFYVE27 plays a critical role in the pathological progression of OP.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The present study identifies ZFYVE27 as a novel biomarker for the clinical diagnosis of OP. Furthermore, our findings provide a solid theoretical foundation for further elucidating the molecular pathogenesis of OP and developing targeted therapeutic interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1862140","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:50:56Z","timestamp":1782276656000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Screening and validation of ZFYVE27 as a potential diagnostic biomarker for osteoporosis via integrative bioinformatics and machine learning approaches"],"prefix":"10.3389","volume":"17","author":[{"given":"Libo","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou University","place":["Lanzhou, China"]},{"name":"Orthopaedic Clinical Research Center of Gansu","place":["Lanzhou, China"]},{"name":"Intelligent Orthopaedic Industry Technology Center of Gansu Province","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zirui","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou University","place":["Lanzhou, China"]},{"name":"Orthopaedic Clinical Research Center of Gansu","place":["Lanzhou, China"]},{"name":"Intelligent Orthopaedic Industry Technology Center of Gansu Province","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongcheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Orthopaedics, Lanzhou University Second Hospital, Lanzhou University","place":["Lanzhou, China"]},{"name":"Orthopaedic Clinical Research Center of Gansu","place":["Lanzhou, China"]},{"name":"Intelligent Orthopaedic Industry Technology Center of Gansu Province","place":["Lanzhou, 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