Su Xiaochen, Wang Shenglong, Tian Ye, Teng Menghao, Wang Jiachen, Zhang Yulong, Ji Wenchen, Zhang Yingang
Department of Orthopedics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China.
Healthy Food Evaluation Research Center, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, P. R. China.
Mol Neurobiol. 2025 Mar;62(3):2674-2694. doi: 10.1007/s12035-024-04431-x. Epub 2024 Aug 16.
Autophagy has been implicated in the pathogenesis and progression of spinal cord injury (SCI); however, its specific mechanisms remain unclear. This study is aimed at identifying potential molecular biomarkers related to autophagy in SCI through bioinformatics analysis and exploring potential therapeutic targets. The mRNA expression profile dataset GSE151371 was obtained from the GEO database, and R software was used to screen for differentially expressed autophagy-related genes (DE-ARGs) in SCI. A total of 39 DE-ARGs were detected in this study. Enrichment analysis, protein-protein interaction (PPI) network, TF-mRNA-miRNA regulatory network analysis, and the DSigDB database were used to investigate the regulatory mechanisms between DE-ARGs and identify potential drugs for SCI. Enrichment analysis revealed associations with autophagy, apoptosis, and cell death. PPI analysis identified the highest-scoring module and selected 10 hub genes to construct the TF-mRNA-miRNA network, revealing regulatory mechanisms. Analysis of the DSigDB database indicated that 1,9-Pyrazoloanthrone may be a potential therapeutic drug. Machine learning algorithms identified 3 key genes as candidate biomarkers. Additionally, immune cell infiltration results revealed significant correlations between PINK1, NLRC4, VAMP3, and immune cell accumulation. Molecular docking simulations revealed that imatinib can exert relatively strong regulatory effects on the three key proteins. Finally, in vivo experimental data revealed that the overall biological process of autophagy was disrupted. In summary, this study successfully identified 39 DE-ARGs and discovered several promising biomarkers, significantly contributing to our understanding of the underlying mechanisms of autophagy in SCI. These findings offer valuable insights for the development of novel therapeutic strategies.
自噬与脊髓损伤（SCI）的发病机制和进展有关；然而，其具体机制仍不清楚。本研究旨在通过生物信息学分析确定与SCI中自噬相关的潜在分子生物标志物，并探索潜在的治疗靶点。从GEO数据库中获取mRNA表达谱数据集GSE151371，并使用R软件筛选SCI中差异表达的自噬相关基因（DE-ARGs）。本研究共检测到39个DE-ARGs。采用富集分析、蛋白质-蛋白质相互作用（PPI）网络、TF-mRNA-miRNA调控网络分析和DSigDB数据库来研究DE-ARGs之间的调控机制，并确定SCI的潜在药物。富集分析揭示了与自噬、细胞凋亡和细胞死亡的关联。PPI分析确定了得分最高的模块，并选择了10个枢纽基因构建TF-mRNA-miRNA网络，揭示了调控机制。DSigDB数据库分析表明1,9-吡唑并蒽酮可能是一种潜在的治疗药物。机器学习算法确定了3个关键基因作为候选生物标志物。此外，免疫细胞浸润结果显示PINK1、NLRC4、VAMP3与免疫细胞积累之间存在显著相关性。分子对接模拟表明伊马替尼对这三种关键蛋白可发挥相对较强的调控作用。最后，体内实验数据表明自噬的整体生物学过程被破坏。总之，本研究成功鉴定出39个DE-ARGs，并发现了几个有前景的生物标志物，显著有助于我们理解SCI中自噬的潜在机制。这些发现为开发新的治疗策略提供了有价值的见解。
