ORIGINAL RESEARCH article
Front. Physiol.
Sec. Skeletal Physiology
Volume 16 - 2025 | doi: 10.3389/fphys.2025.1605473
Cuproptosis related genes in immune infiltration and treatment of osteoporosis by bioinformatic analysis and machine learning methods
Provisionally accepted
- Second People’s Hospital of Shanghai, Shanghai, China
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Cuproptosis, a copper-dependent form of cell death, has been implicated in immune function and osteoporosis. However, the specific roles of cuproptosis-related genes (CRGs) in osteoporosis remain unclear. The differentially expressed CRGs from the Gene Expression Omnibus datasets of persons with osteoporosis and healthy individuals were categorized using R software tools in this study. Following that, the CIBERSORT algorithm and the GSVA technique were used to investigate the relationships between the different clusters and immune infiltration characteristics. Based on four machine learning techniques (Random Forest, Support Vector Machine, XGBoost, and Generalized Linear Model), Support Vector Machine and WGCNA analysis was carried out to identify the main genes linked to cuproptosis in the pathological course of osteoporosis. Subsequently, a model was built using the core genes related to cuproptosis to forecast the disease and identify potential treatment targets. The model was validated using an external dataset. In the end, a nomogram and calibration curve were created to improve this model's clinical applicability. Additionally, to investigate the possible biological roles of the core genes related to cuproptosis, we enriched them along several pathways. This study represents the first identification of key CRGs and core genes associated with cuproptosis in osteoporosis patients, findings that will facilitate the development of novel therapeutic strategies.
Keywords: cuproptosis, Osteoporosis, Cell Death, Inflammation, machine learning
Received: 03 Apr 2025; Accepted: 04 Aug 2025.
Copyright: © 2025 Wu, Wu and Wen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Haiyang Wu, Second People’s Hospital of Shanghai, Shanghai, China
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