Hu Kaibo, Ou Yanghuan, Xiao Leyang, Gu Ruonan, He Fei, Peng Jie, Shu Yuan, Li Ting, Hao Liang
Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China.
The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
J Inflamm Res. 2024 Jun 11;17:3753-3770. doi: 10.2147/JIR.S462179. eCollection 2024.
Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation.
In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset.
Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets.
Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.
骨关节炎（OA）是人类残疾的主要原因。尽管接受了治疗，但中晚期OA患者的生存结局仍较差。因此，在预测、预防和个性化医学（PPPM/3PM）框架内，OA的早期个性化诊断尤为突出。PPPM旨在通过整合多种组学技术准确识别疾病；然而，目前可用的方法和生物标志物在预测和诊断OA方面的效率仍有待提高。双硫死亡是一种新型的程序性细胞死亡机制，出现在特定的代谢状态中，在OA的发生和发展中具有神秘的特征，值得进一步研究。
在本研究中，我们整合了来自基因表达综合数据库（GEO）的三个公共数据集，包括26个OA样本和20个正常样本。通过一系列生物信息学分析和机器学习，我们鉴定了诊断生物标志物和OA的几种亚型。此外，这些生物标志物的表达在我们的内部队列和单细胞数据集中得到了验证。
通过差异表达分析和机器学习鉴定了三个双硫死亡的重要调节因子（NCKAP1、OXSM和SLC3A2）。基于这三个调节因子构建的列线图在预测早期和晚期OA方面表现出理想的效率。此外，基于这三个调节因子的表达，我们鉴定了两种与双硫死亡相关的OA亚型，它们具有不同的免疫细胞浸润和免疫检查点的个性化表达水平。值得注意的是，这三个调节因子的表达在单细胞RNA图谱中得到了证实，并在我们包括6名OA患者和6名正常人的内部队列的滑膜组织中得到了验证。最后，构建了一个高效的双硫死亡介导的OA诊断模型，在训练集中的AUC值为97.6923%，在两个验证集中分别为93.3333%和100%。
总体而言，关于PPPM，本研究为双硫死亡调节因子在OA个性化诊断和治疗中的作用提供了新的见解。
