The biological association between programmed cell death function and osteoarthritis using multi-omic Mendelian Randomization

作者：Rong Lu, Kaibo Tang, Ruihong Pan, Shuzhen Shi, Xiao’ao Xue, Tingfang Hwang, Yang Song, Weijun Tang, Yue Yu, He Wang, Yao Lu, Ting Lin · 发表于：Biomedical Technology · 年份：2025 · DOI：10.1016/j.bmt.2025.100102 · 被引用次数：13 · 研究领域：Osteoarthritis Treatment and Mechanisms、Cytokine Signaling Pathways and Interactions、Inflammatory mediators and NSAID effects

Background Osteoarthritis (OA) is a degenerative joint disorder influenced by genetic, molecular, and environmental factors. Programmed cell death (PCD) pathways, including apoptosis, pyroptosis, necroptosis, ferroptosis, and autophagy, are linked to cartilage degradation, but their role in OA pathogenesis remains unclear. Methods Based on a large-scale GWAS database, this study employs a two-sample Mendelian randomization (MR) framework, integrating genomic data from 14 genes related to PCD at three levels (DNA methylation, gene expression, and protein abundance) to reveal causal relationships between these genes and OA. The MR analysis utilizes QTLs (mQTL, eQTL, and pQTL) as instrumental variables and employs five regression models (MR-Egger regression, Random-Effects Inverse Variance Weighted, Weighted Median, Weighted Mode, and Simple Mode) to assess causal effects. Furthermore, the reliability of causal inference is strengthened through FDR multiple testing correction, Steiger test, and colocalization analysis. Multi-omics evidence is integrated to identify key PCD genes causally related to OA. Finally, enrichment analysis, PPI analysis, and OA-related transcriptome analysis are used to explore the biological mechanisms of these key PCD genes. Findings Through MR analysis, we ultimately identified 103 PCD-related CpG sites, 170 PCD-related gene expressions, and 53 PCD-related protein levels that have significant causal relationships with OA. Multi-omics integration pinpo...