Cui Xiangyu, Li Xiaodong, Qi Xin, Wang Dawang, Kang Boyuan, Li FengJiu, Xu Xilin
Department of Orthopaedic, The Third Affiliated Hospital of Heilongjiang University of Chinese Medicine, No.2 Xiangjiang Road, Xiangfang District, Harbin City, Heilongjiang Province, China.
Heilongjiang University of Chinese Medicine, Harbin, China.
BMC Pharmacol Toxicol. 2025 Sep 1;26(1):155. doi: 10.1186/s40360-025-00978-0.
Sarcopenia, characterized by progressive muscle loss and functional decline in aging, poses significant health challenges. Sex steroids, such as estradiol and testosterone, have potential therapeutic roles in mitigating muscle degeneration. This study explores the molecular mechanisms and targets of sex steroids in the treatment of sarcopenia using network pharmacology, enrichment analysis, machine learning, molecular docking, and molecular dynamics simulations.
We identified potential anti-sarcopenia targets by analyzing the interaction network between sex steroids and their targets, intersecting these with differentially expressed genes (DEGs) from the GSE1428. Enrichment analysis was conducted to determine the functional relevance of these targets. Gene set variation analysis (GSVA) was employed to explore pathway-level differences between age groups. Machine learning algorithms (RF, SVM, XGBoost) identified crucial biomarker genes. A nomogram for predicting sarcopenia was constructed and validated. Molecular docking and molecular dynamics (MD) simulations evaluated the binding interactions and stability of steroid-target complexes.
Intersection analysis revealed 69 potential anti-sarcopenia targets. Enrichment analysis highlighted pathways related to muscle function, such as calcium signaling and synaptic transmission. GSVA indicated significant upregulation of DNA damage response and immune response pathways in the older group. Machine learning algorithms pinpointed CFTR, FYN, and PRKCA as top biomarkers. The nomogram demonstrated high predictive accuracy with an AUC of 0.925. Molecular docking showed significant binding affinities of sex steroids with target proteins, further supported by stable RMSD values in MD simulations.
Sex steroids, specifically estradiol and testosterone, demonstrate promising interactions with key targets implicated in sarcopenia in silico. These computational findings offer preliminary mechanistic insights into the potential therapeutic role of sex steroids in modulating muscle-related pathways. Further experimental and clinical validation is warranted to assess their translational applicability for sarcopenia treatment.
Not applicable.
肌肉减少症以衰老过程中肌肉逐渐流失和功能衰退为特征，对健康构成重大挑战。雌二醇和睾酮等性类固醇在减轻肌肉退化方面具有潜在的治疗作用。本研究利用网络药理学、富集分析、机器学习、分子对接和分子动力学模拟，探索性类固醇治疗肌肉减少症的分子机制和靶点。
通过分析性类固醇与其靶点之间的相互作用网络，将这些靶点与来自GSE1428的差异表达基因（DEG）进行交叉分析，确定潜在的抗肌肉减少症靶点。进行富集分析以确定这些靶点的功能相关性。采用基因集变异分析（GSVA）来探索不同年龄组之间的通路水平差异。机器学习算法（随机森林、支持向量机、极端梯度提升）识别关键的生物标志物基因。构建并验证了预测肌肉减少症的列线图。分子对接和分子动力学（MD）模拟评估类固醇-靶点复合物的结合相互作用和稳定性。
交叉分析揭示了69个潜在的抗肌肉减少症靶点。富集分析突出了与肌肉功能相关的通路，如钙信号传导和突触传递。GSVA表明老年组中DNA损伤反应和免疫反应通路显著上调。机器学习算法确定囊性纤维化跨膜传导调节因子（CFTR）、FYN原癌基因（FYN）和蛋白激酶Cα（PRKCA）为顶级生物标志物。列线图显示出较高的预测准确性，曲线下面积（AUC）为0.925。分子对接显示性类固醇与靶蛋白具有显著的结合亲和力，MD模拟中稳定的均方根偏差（RMSD）值进一步支持了这一点。
性类固醇，特别是雌二醇和睾酮，在计算机模拟中显示出与肌肉减少症相关的关键靶点有良好的相互作用。这些计算结果为性类固醇在调节肌肉相关通路中的潜在治疗作用提供了初步的机制见解。有必要进行进一步的实验和临床验证，以评估它们在肌肉减少症治疗中的转化适用性。
不适用。
