Ding Qiang, Xiong Bo, Liu Jinfu, Rong Xiangbin, Tian Zhao, Chen Limin, Tao Hongcheng, Li Hao, Zeng Ping
The First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning, China.
Yulin Orthopedic Hospital of Integrated Traditional Chinese and Western Medicine, Yulin, China.
Medicine (Baltimore). 2024 May 3;103(18):e37837. doi: 10.1097/MD.0000000000037837.
In this study, we aimed to investigate the involvement of PANoptosis, a form of regulated cell death, in the development of steroid-induced osteonecrosis of the femoral head (SONFH). The underlying pathogenesis of PANoptosis in SONFH remains unclear. To address this, we employed bioinformatics approaches to analyze the key genes associated with PANoptosis. Our analysis was based on the GSE123568 dataset, allowing us to investigate both the expression profiles of PANoptosis-related genes (PRGs) and the immune profiles in SONFHallowing us to investigate the expression profiles of PRGs as well as the immune profiles in SONFH. We conducted cluster classification based on PRGs and assessed immune cell infiltration. Additionally, we used the weighted gene co-expression network analysis (WGCNA) algorithm to identify cluster-specific hub genes. Furthermore, we developed an optimal machine learning model to identify the key predictive genes responsible for SONFH progression. We also constructed a nomogram model with high predictive accuracy for assessing risk factors in SONFH patients, and validated the model using external data (area under the curve; AUC = 1.000). Furthermore, we identified potential drug targets for SONFH through the Coremine medical database. Using the optimal machine learning model, we found that 2 PRGs, CASP1 and MLKL, were significantly correlated with the key predictive genes and exhibited higher expression levels in SONFH. Our analysis revealed the existence of 2 distinct PANoptosis molecular subtypes (C1 and C2) within SONFH. Importantly, we observed significant variations in the distribution of immune cells across these subtypes, with C2 displaying higher levels of immune cell infiltration. Gene set variation analysis indicated that C2 was closely associated with multiple immune responses. In conclusion, our study sheds light on the intricate relationship between PANoptosis and SONFH. We successfully developed a risk predictive model for SONFH patients and different SONFH subtypes. These findings enhance our understanding of the pathogenesis of SONFH and offer potential insights into therapeutic strategies.
在本研究中，我们旨在探究一种程序性细胞死亡形式——全程序死亡（PANoptosis）在类固醇诱导的股骨头坏死（SONFH）发病过程中的作用。SONFH中PANoptosis的潜在发病机制尚不清楚。为解决这一问题，我们采用生物信息学方法分析与PANoptosis相关的关键基因。我们的分析基于GSE123568数据集，这使我们能够研究SONFH中PANoptosis相关基因（PRGs）的表达谱以及免疫谱。我们基于PRGs进行聚类分类并评估免疫细胞浸润。此外，我们使用加权基因共表达网络分析（WGCNA）算法来识别聚类特异性枢纽基因。此外，我们开发了一种优化的机器学习模型来识别导致SONFH进展的关键预测基因。我们还构建了一个预测准确性高的列线图模型，用于评估SONFH患者的风险因素，并使用外部数据对该模型进行验证（曲线下面积；AUC = 1.000）。此外，我们通过Coremine医学数据库确定了SONFH的潜在药物靶点。使用优化的机器学习模型，我们发现2个PRGs，即CASP1和MLKL，与关键预测基因显著相关，且在SONFH中表达水平更高。我们的分析揭示了SONFH中存在2种不同的PANoptosis分子亚型（C1和C2）。重要的是，我们观察到这些亚型之间免疫细胞分布存在显著差异，C2显示出更高水平的免疫细胞浸润。基因集变异分析表明C2与多种免疫反应密切相关。总之，我们的研究揭示了PANoptosis与SONFH之间的复杂关系。我们成功地为SONFH患者和不同的SONFH亚型开发了一种风险预测模型。这些发现加深了我们对SONFH发病机制的理解，并为治疗策略提供了潜在的见解。
