Identification and verification of a novel signature that combines cuproptosis-related genes with ferroptosis-related genes in osteoarthritis using bioinformatics analysis and experimental validation

• Published in: Arthritis research & therapy Vol. 26; no. 1; pp. 100 - 19
• Main Authors: He, Baoqiang, Liao, Yehui, Tian, Minghao, Tang, Chao, Tang, Qiang, Ma, Fei, Zhou, Wenyang, Leng, Yebo, Zhong, Dejun
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.05.2024; BioMed Central; BMC
• Subjects: Analysis; Animals; Antiarthritic agents; Apoptosis; Bioinformatics; Biomarkers - analysis; Biomarkers - metabolism; Computational biology; Computational Biology - methods; Cuproptosis; Dosage and administration; Drug therapy; Ferroptosis; Ferroptosis - genetics; Gene Expression Profiling - methods; Gene Regulatory Networks - genetics; Genes; Health aspects; Humans; Inflammation; Information management; Machine Learning; Osteoarthritis; Osteoarthritis - genetics; Osteoarthritis - metabolism; Patient outcomes; Protein Interaction Maps - genetics; Protein-protein interactions; China; Ferroptosis; Bioinformatics; Osteoarthritis; Cuproptosis; Machine learning
• ISSN: 1478-6362; 1478-6354; 1478-6362
• DOI: 10.1186/s13075-024-03328-3

Exploring the pathogenesis of osteoarthritis (OA) is important for its prevention, diagnosis, and treatment. Therefore, we aimed to construct novel signature genes (c-FRGs) combining cuproptosis-related genes (CRGs) with ferroptosis-related genes (FRGs) to explore the pathogenesis of OA and aid in its treatment. Differentially expressed c-FRGs (c-FDEGs) were obtained using R software. Enrichment analysis was performed and a protein-protein interaction (PPI) network was constructed based on these c-FDEGs. Then, seven hub genes were screened. Three machine learning methods and verification experiments were used to identify four signature biomarkers from c-FDEGs, after which gene set enrichment analysis, gene set variation analysis, single-sample gene set enrichment analysis, immune function analysis, drug prediction, and ceRNA network analysis were performed based on these signature biomarkers. Subsequently, a disease model of OA was constructed using these biomarkers and validated on the GSE82107 dataset. Finally, we analyzed the distribution of the expression of these c-FDEGs in various cell populations. A total of 63 FRGs were found to be closely associated with 11 CRGs, and 40 c-FDEGs were identified. Bioenrichment analysis showed that they were mainly associated with inflammation, external cellular stimulation, and autophagy. CDKN1A, FZD7, GABARAPL2, and SLC39A14 were identified as OA signature biomarkers, and their corresponding miRNAs and lncRNAs were predicted. Finally, scRNA-seq data analysis showed that the differentially expressed c-FRGs had significantly different expression distributions across the cell populations. Four genes, namely CDKN1A, FZD7, GABARAPL2, and SLC39A14, are excellent biomarkers and prospective therapeutic targets for OA.