Bioinformatics-based identification of autophagy-related key genes in osteoarthritis and therapeutic potential analysis of Eucommin A

doi:10.5246/jcps.2025.09.061

Abstract

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:

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.

Figures/Tables 11

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

Table 2. Information related to receptor proteins.

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