Identification and Validation of Key Genes Related to Lipophagy in Osteoporosis - Hu YX; Zuo ML; Wu Y; Yang Y; Shi XB; Zhang Q; Wu J; Xie RQ; Bi Y; Lin B; Mo C (Orthopedic Research and Reviews, 2025)
Yixin Hu,* Mingliang Zuo,* Yu Wu,* Yu Yang, Xiaobing Shi, Qian Zhang, Ji Wu, Runqi Xie, Yu Bi, Bo Lin, Chou Mo Department of Orthopedics, The Second People’s Hospital of Guiyang, Guiyang, Guizhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yu Wu, Department of Orthopedics, The Second People’s Hospital of Guiyang, No. 547, Jinyang South Road, Guiyang, Guizhou Province, People’s Republic of China, Email 948782069@qq.comBackground: Lipid droplet autophagy (lipophagy) is the breakdown and recycling of lipids within cells via autophagy. Some research suggests that enhancing lipophagy could have potential benefits for bone health. This study aimed to determine the key genes linked to lipophagy in osteoporosis (OP) and provided a reference for the treatment of OP.Methods: The study analyzed OP-related datasets (GSE56815, GSE62402) and lipophagy-related genes (LRGs). Candidate genes associated with lipophagocytosis were identified through differential expression (DE) analysis and weighted gene co-expression network analysis (WGCNA). The minimum absolute contraction selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and Boruta algorithm are used to identify candidate genes for OP-related feature genes, and the expression of key genes is analyzed. In addition, we constructed a nomogram to predict the incidence of OP patients. Subsequently, multiple bioinformatics tools were used to reveal the associations between key genes and OP. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of key genes.Results: Eight signature genes were identified by machine learning. Only EIF3K and SHMT2 had consistent, significantly different expression trends between OP and control in GSE56815 and GSE62402, being up-regulated in OP. Thus, they were recognized as lipophagy-related key genes. Enrichment analysis showed that EIF3K is related to “Mitochondrial cell assembly”, etc., and SHMT2 to “Arf-3 pathway”, etc. Both genes negatively linked to activated dendritic cells and mast cells. In regulatory networks, hsa-let-7 family miRNAs were upstream of these genes. Clindamycin and SCHEMBL14520730 targeted them. SHMT2 and EIF3K expression trends matched bioinformatic results.Conclusion: This study identified lipophagy-related key genes (EIF 3K and SHMT2) in OP, which contributed to the early diagnosis and clinical treatment of OP.Keywords: lipophagy, osteoporosis, EIF3K, SHMT2, key genes