Cao Siyang, Wei Yihao, Yue Yaohang, Wang Deli, Xiong Ao, Yang Jun, Zeng Hui
National & Local Joint Engineering Research Centre of Orthopaedic Biomaterials, Peking University Shenzhen Hospital, Shenzhen 518036, China.
Shenzhen Key Laboratory of Orthopaedic Diseases and Biomaterials Research, Peking University Shenzhen Hospital, Shenzhen 518036, China.
Biomedicines. 2024 Aug 13;12(8):1840. doi: 10.3390/biomedicines12081840.
Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking.
This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments.
and were designated as hub genes, displaying excellent diagnostic performance. exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of and with chondrocyte inflammation.
Two hub genes, and , were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA.
骨关节炎(OA)是一种致残且全球数百万患者受影响的高度流行疾病。最近发现,二硫化物诱导的细胞死亡是由胱氨酸过度积累引发的一种新型细胞死亡形式。尽管其具有重要意义,但目前缺乏对OA中与二硫化物诱导的细胞死亡相关基因(DRGs)的系统探索。
本研究利用了三个与OA相关的数据集和DRGs。通过将来自GSE114007的差异表达基因(DEGs)与DRGs相交,得出差异表达的DRGs。通过三种机器学习算法对特征基因进行筛选。使用受试者工作特征曲线鉴定具有高诊断价值的基因。通过表达验证确认枢纽基因。然后利用这些枢纽基因构建列线图并进行富集、免疫和相关性分析。通过体外细胞实验对枢纽基因进行额外验证。
[具体基因名称1]和[具体基因名称2]被指定为枢纽基因,表现出优异的诊断性能。[具体基因名称1]在软骨细胞早期分化中表达较低,在后期显著升高,而[具体基因名称2]总体表达较高,在分化后期下降。细胞实验证实了[具体基因名称1]和[具体基因名称2]与软骨细胞炎症的相关性。
鉴定出与二硫化物诱导的细胞死亡相关的两个枢纽基因[具体基因名称1]和[具体基因名称2],为OA的诊断和治疗提供了潜在方向。