Comprehensive Analysis of Programmed Cell Death-Related Genes in Diagnosis and Synovitis During Osteoarthritis Development: Based on Bulk and Single-Cell RNA Sequencing Data

Abstract

JiangFei Zhou,1,* SongSong Jiao,1,* Jian Huang,2,* TianMing Dai,3,* YangYang Xu,1,* Dong Xia,4,* ZhenCheng Feng,1 JunJie Chen,1 ZhiWu Li,5 LiQiong Hu,4 QingQi Meng1,3 1Department of Orthopedics, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, 510220, People’s Republic of China; 2Qingdao Medical College, Qingdao University, Qingdao, 266071, People’s Republic of China; 3Guangzhou Institute of Traumatic Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, 510220, People’s Republic of China; 4Critical Care Medicine Department, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, 510220, People’s Republic of China; 5Department of Orthopedics, the 2nd People’s Hospital of Bijie, Guizhou, 551700, People’s Republic of China*These authors contributed equally to this workCorrespondence: LiQiong Hu; QingQi Meng, Email [email protected]; [email protected]: Synovitis is one of the key pathological feature driving osteoarthritis (OA) development. Diverse programmed cell death (PCD) pathways are closely linked to the pathogenesis of OA, but few studies have explored the relationship between PCD-related genes and synovitis.Methods: The transcriptome expression profiles of OA synovial samples were obtained from the Gene Expression Omnibus (GEO) database. Using machine learning algorithms, Hub PCD-related differentially expressed genes (Hub PCD-DEGs) were identified. The expression of Hub PCD-DEGs was validated in human OA samples by qRT-PCR. A diagnostic model for OA was constructed based on the expression levels of Hub PCD-DEGs. Unsupervised consensus clustering analysis and weighted correlation network analysis (WGCNA) were employed to identify differential clustering patterns of PCD-related genes in OA patients. The molecular characteristics of Hub PCD-DEGs, their role in synovial immune inflammation, and their association with the immune microenvironment were investigated through functional enrichment analysis and ssGSEA immune infiltration analysis. Single-cell RNA sequencing analysis provided insights into the characteristics of distinct cell clusters in OA synovial tissues and their interactions with Hub PCD-DEGs.Results: We identified five Hub PCD-DEGs: TNFAIP3, JUN, PPP1R15A, INHBB, and DDIT4. qRT-PCR analysis confirmed that all five genes were significantly downregulated in OA synovial tissue. The diagnostic model constructed based on these Hub PCD-DEGs demonstrated diagnostic efficiency in distinguishing OA tissues as well as progression of OA. Additionally, a correlation was observed between the expression levels of Hub PCD-DEGs, immune cell infiltration, and inflammatory cytokine levels. We identified two distinct PCD clusters, each exhibiting unique molecular and immunological characteristics. Single-cell RNA sequencing further revealed dynamic and complex cellular changes in OA synovial tissue, with differential expression of Hub PCD-DEGs across various immune cell types.Conclusion: Our study suggests that PCD-related genes may be involved in development of OA synovitis. The five screened Hub PCD-DEGs (TNFAIP3, JUN, PPP1R15A, INHBB and DDIT4) could be explored as candidate biomarkers or therapeutic targets for OA.Keywords: osteoarthritis, programmed cell death, bioinformatics, machine learning, immune infiltration, biomarkers

Keywords

• osteoarthritis
• programmed cell death
• bioinformatics
• machine learning
• immune infiltration
• biomarkers.