关键词: 骨关节炎, 细胞自噬, 细胞衰老, 机器学习, 生物信息学, 分子靶点

Abstract: BACKGROUND: Autophagy and senescence are considered important factors in the pathogenesis of osteoarthritis, but their specific regulatory mechanisms remain unclear.  
OBJECTIVE: To screen autophagy- and senescence-related genes in osteoarthritis through bioinformatics analysis combined with machine learning methods, providing new molecular targets for early diagnosis and treatment of osteoarthritis.  
METHODS: Osteoarthritis-related datasets (including GSE51588, GSE169077, and GSE114007) were downloaded from the GEO database. Differential expression analysis, weighted gene co-expression network analysis, and functional enrichment analysis were performed to identify autophagy- and senescence-related genes in osteoarthritis. Subsequently, potential biomarkers were further screened using machine learning methods such as LASSO regression, Random Forest, and Support Vector Machine, and their diagnostic value was evaluated using receiver operating characteristic curves. Based on the GSE51588 dataset, the proportions of immune cell types such as T-cell subsets, B cells, and macrophages in osteoarthritis and healthy control knee cartilage samples were analyzed using the CIBERSORT algorithm. The expression of ubiquitin-conjugating enzyme E2I (UBE2I), ribosomal protein S6 kinase 1 (RPS6KB1), interleukin-2 receptor β chain (IL2RB), YEATS domain-containing protein 4 (YEATS4), histone H4 variant (H4C1), and Toll-like receptor 3 (TLR3) was detected in osteoarthritis samples and healthy controls in the external validation dataset GSE114007. Clinical samples, including five osteoarthritis knee cartilage specimens and five healthy control knee cartilage specimens, were collected. The mRNA expression of UBE2I, RPS6KB1, IL2RB, YEATS4, H4C1, and TLR3 was measured by RT-qPCR.  
RESULTS AND CONCLUSION: (1) A total of 26 differentially expressed genes related to autophagy and senescence in osteoarthritis were identified. Functional enrichment analysis showed that these genes were mainly involved in biological processes such as cellular homeostasis, immune regulation, and cell death, and played important roles in multiple signaling pathways. Six key genes were screened using machine learning methods: UBE2I, RPS6KB1, IL2RB, YEATS4, H4C1, and TLR3. The area under the receiver operating characteristic curve for these genes was all greater than 0.8, indicating high diagnostic performance. Immune infiltration analysis revealed that in the osteoarthritis group, the infiltration of plasma cells, resting CD4 memory T cells, resting NK cells, monocytes, M2 macrophages, eosinophils, and neutrophils was significantly reduced, while the infiltration of follicular helper T cells, γδ T cells, activated NK cells, M1 macrophages, and resting dendritic cells was significantly increased. (2) In the external validation dataset, the expression of UBE2I, IL2RB, and TLR3 was higher in the osteoarthritis group than the healthy control group (P < 0.05), while there was no significant difference in the expression of H4C1, YEATS4, and RPS6KB1 between the two groups (P > 0.05). In clinical samples, the mRNA expression of RPS6KB1, IL2RB, YEATS4, H4C1, and TLR3 was higher in the osteoarthritis group than the healthy control group (P < 0.05), while there was no significant difference in UBE2I mRNA expression between the two groups (P > 0.05). Overall, these findings indicate that TLR3 and IL2RB may serve as key genes for autophagy and senescence in osteoarthritis chondrocytes and potentially be used as diagnostic molecular markers and therapeutic targets for osteoarthritis.  

Key words: osteoarthritis, autophagy, cellular senescence, machine learning, bioinformatics, molecular targets