基于生物信息学的骨关节炎自噬关键基因鉴定及Eucommin A治疗潜力分析

doi:10.5246/jcps.2025.09.061

摘要/Abstract

摘要：

本研究旨在探讨自噬相关基因在骨关节炎(OA)中的作用, 并评估杜仲关键木脂素成分——Eucommin A的治疗潜力。从基因表达综合(GEO)数据库获取OA患者与健康对照的基因表达数据。筛选差异表达基因(DEGs), 并与自噬基因数据库(Human Autophagy Database)交集以确定OA相关自噬基因。通过GO/KEGG富集分析其生物学功能及信号通路。结合机器学习算法及蛋白质-蛋白质相互作用网络筛选核心基因, 并利用独立验证集评估诊断效能。采用分子对接与100 ns分子动力学模拟验证OA自噬核心靶点与Eucommin A的结合稳定性, 结合均方根偏差(RMSD) 、均方根波动(RMSF) 、回转半径(Rg)及MM/GBSA结合自由能计算评估互作机制。结果发现, 在2436个DEGs中, 56个为自噬相关基因, 主要富集于营养响应、细胞凋亡及PI3K-Akt/FoxO通路。机器学习鉴定出EGFR、MAPK3和MAPK8为核心基因, 其中EGFR与MAPK8的诊断效能显著(AUC > 0.5) 。Eucommin A可以通过氢键与疏水作用与EGFR 及MAPK8 表现出强结合亲和力。分子动力学模拟证实其结合稳定, 且自由能分布良好。EGFR与MAPK8可作为OA自噬的诊断标志物。Eucommin A通过稳定自噬相关蛋白发挥多靶点治疗作用, 为基于自噬调控的OA治疗提供新策略。

关键词: 生物信息学, 分子动力学模拟, 骨关节炎, 自噬, Eucommin A

Abstract:

This study aimed to investigate the role of autophagy-related genes in osteoarthritis (OA) and evaluate the therapeutic potential of Eucommin A, a key lignan component derived from Eucommia ulmoides. Gene expression profiles from OA patients and healthy controls were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and intersected with autophagy-related genes from the Human Autophagy Database to pinpoint OA-specific autophagy candidates. Functional enrichment analyses via GO and KEGG highlighted involvement in nutrient response, apoptosis, and PI3K-Akt/FoxO signaling pathways. Core genes were prioritized using machine learning algorithms combined with protein-protein interaction (PPI) network analysis, followed by diagnostic validation in an independent cohort. Molecular docking and 100-ns molecular dynamics simulations were conducted to assess the binding stability between Eucommin A and the core targets. Interaction mechanisms were characterized using root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), and MM/GBSA binding free energy calculations. Among 2436 DEGs, 56 were autophagy-related and significantly enriched in key biological processes. Machine learning identified EGFR, MAPK3, and MAPK8 as hub genes, with EGFR and MAPK8 exhibiting significant diagnostic value (AUC > 0.5). Eucommin A demonstrated strong binding affinity to EGFR and MAPK8 via hydrogen bonding and hydrophobic interactions. Molecular dynamics simulations confirmed stable ligand-target complexes and favorable binding free energy profiles. These findings suggested EGFR and MAPK8 as diagnostic biomarkers for OA-related autophagy. Moreover, Eucommin A exerted multi-target therapeutic effects by stabilizing these autophagy-related proteins, proposing a novel strategy for OA treatment through modulation of autophagy.

Key words: Bioinformatics, Molecular dynamics simulation, Osteoarthritis, Autophagy, Eucommin A

Supporting:

张雅歌, 彭紫凝, 周榆皖, 张锦芳. 基于生物信息学的骨关节炎自噬关键基因鉴定及Eucommin A治疗潜力分析[J]. 中国药学(英文版), 2025, 34(9): 831-849.

Yage Zhang, Zining Peng, Yuwan Zhou, Jinfang Zhang. Bioinformatics-based identification of autophagy-related key genes in osteoarthritis and therapeutic potential analysis of Eucommin A[J]. Journal of Chinese Pharmaceutical Sciences, 2025, 34(9): 831-849.

图/表 11

Figure 1. A workflow of this study.

Table 1. Basic information and grouping of GEO data sets.

Figure 2. Expression of the training set after batch correction. (A) Pre-batch correction gene expression Principal Component Analysis (PCA). (B) After-batch correction gene expression PCA plot. (C) Heatmap of DEGs. (D) Top 20 up-regulated and down-regulated DEGs. Blue represents down-regulation, while yellow represents up-regulation. (E) Venn diagram illustrating the overlap between DEGs and autophagy genes. (F) Expression levels of autophagy genes in OA, with blue indicating low expression and red indicating high expression.

Figure 3. GO enrichment analyses and KEGG pathway enrichment. (A–C) GO enrichment analysis. (D) KEGG pathway enrichment bubble plot. (E) Secondary pathway diagram for KEGG pathway enrichment analysis. (F) Disease distribution map for enrichment.

Figure 4. GSEA. (A) GSAE pathway analysis of the control group in the training dataset. (B) GSAE pathway analysis of the OA in the training dataset.

Figure 5. Screening of autophagy characteristic genes in OA. (A) LASSO regression analysis. (B) SVM-RFE analyses. (C) PPI network diagram. (D) Venn diagram of feature genes selected by the three methods.

Figure 6. (A–B) Expressions of EGFR, MAPK3, and MAPK8 in the training set. (C–D) Receiver operating characteristic curves of EGFR, MAPK3, and MAPK8 in the GSE82107 validation set.

Table 2. Information related to receptor proteins.

Figure 7. Visualization diagram of molecular docking. (A) Visualization diagram of Eucommin A binding to EGFR protein; (B) Visualization diagram of Eucommin A binding to MAPK8 protein.

Figure 8. Results of MD simulation. (A) RMSD curve: The lower the RMSD value, the smaller the structural change of the complex and the higher the stability; (B) RMSF curve: The lower the RMSF value, the smaller the fluctuation of amino acid residues during the binding process; (C) Rg curve: The smaller the Rg value, the more it indicates that the system maintained a compact conformation during the simulation process and no significant expansion occurred; (D) Hydrogen bond fluctuation curve: The greater the number of hydrogen bonds, the stronger the interaction and structural stability; (E) SASA curve: A low and stable SASA value indicates that the conformation is tightly folded and stable during the binding process. Structural comparison diagrams of the complex of (F–G) EGFR protein, MAPK8 protein and Eucommin A at five moments of 0, 25, 50, 75 and 100 ns in MD simulation. The red, green, blue, yellow, and orange small molecules in the figure correspond to the Eucommin A molecular structure at the five moments of 0, 25, 50, 75, and 100 ns, respectively.

Figure 9. (A–B) Free energy distribution map of the complex; (C–D) Average binding free energy. VDWAALS, EEL, EGB, ESURF, GGAS, GSOLV, and TOTAL respectively represent van der Waals force, electrostatic energy, polar solvation energy, non-polar solvation energy, molecular mechanical term, solvation energy term, and average binding free energy; (E–F) Energy contribution of combined amino acid residues.

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