机器学习与实验揭示了与脊髓损伤中全程序性坏死相关的关键基因，这些基因与药物预测和免疫格局有关。
Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury.
作者信息
Li Bo, Li Tao, Cai Yibo, Cheng Junyao, Zhang Chuyue, Liu Jianheng, Song Keran, Wang Zheng, Ji Xinran
机构信息
Department of Orthopedics, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, 100048, China.
Department of Neurosurgery, Jinling Hospital, Nanjing University, School of Medicine, Nanjing, 210093, China.
出版信息
Mol Neurobiol. 2025 Jun;62(6):7364-7379. doi: 10.1007/s12035-025-04717-8. Epub 2025 Jan 31.
Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many diseases, including brain and spinal cord injuries. However, the biological functions and molecular mechanisms of PANoptosis-related genes in spinal cord injury remain unclear. In the bioinformatics analysis of public data of SCI, the differentially expressed genes (DEGs) identified by GSE151371 were hybridized with PANoptosis-related genes (PRGs) to obtain differentially expressed PANoptosis-related genes (DE-PRGs). Through three machine learning algorithms, we obtained the hub genes. Then, we constructed functional analysis, drug prediction, regulatory network construction, and immune infiltrating cell analysis. Finally, the expression of the hub gene was verified in GSE93561, GSE45376, and qRT-PCR analysis. Through the above analysis, 14 DE-PRGs were obtained by intersecting 3582 DEGs with 46 PRGs. Five key hub genes, CASP4, GSDMB, NAIP, NLRC4, and NLRP3, were obtained by 3 machine learning algorithms. All five hub genes were enriched in phagocytosis mediated by FC GAMMA R. The 11 immune cells were significantly different between spinal cord injury (SCI) group and human control (HC) group, such as mast cell and gamma delta T cell. The transcription factor (TF)-hub gene network contained 10-nodes (4 hub genes and 6 TFs) and 8-edges. The miRNA-hub gene network consisting of 5-nodes (3 hub genes and 2 miRNAs) and 3-edges was constructed. Moreover, the CASP4 predicted 1 small molecule drug and NLRP3 predicted 9 small molecule drugs. Finally, the expression of 5 hub genes were significantly different in GSE45376 and GSE93561 (SCI vs. HC) and mice SCI model (Sham vs. SCI). Collectively, we identified 5 hub genes (CASP4, GSDMB, NAIP, NLRC4, and NLRP3) associated with PANoptosis, providing potential directions for treating spinal cord injury.
脊髓损伤（SCI）是一种严重的中枢神经系统损伤，目前尚无有效的治疗方法。泛凋亡参与了包括脑和脊髓损伤在内的多种疾病的发展。然而，泛凋亡相关基因在脊髓损伤中的生物学功能和分子机制仍不清楚。在对SCI公共数据的生物信息学分析中，将GSE151371鉴定出的差异表达基因（DEGs）与泛凋亡相关基因（PRGs）进行杂交，以获得差异表达的泛凋亡相关基因（DE-PRGs）。通过三种机器学习算法，我们获得了核心基因。然后，我们进行了功能分析、药物预测、调控网络构建和免疫浸润细胞分析。最后，在GSE93561、GSE45376和qRT-PCR分析中验证了核心基因的表达。通过上述分析，将3582个DEGs与46个PRGs相交，得到14个DE-PRGs。通过三种机器学习算法获得了五个关键核心基因，即半胱天冬酶4（CASP4）、Gasdermin B（GSDMB）、神经元凋亡抑制蛋白（NAIP）、核苷酸结合寡聚化结构域样受体含CARD结构域4（NLRC4）和NLR家族含pyrin结构域蛋白3（NLRP3）。所有五个核心基因都富集于由FcγR介导的吞噬作用。脊髓损伤（SCI）组和人类对照组（HC）之间的11种免疫细胞存在显著差异，如肥大细胞和γδT细胞。转录因子（TF）-核心基因网络包含10个节点（4个核心基因和6个TFs）和8条边。构建了由5个节点（3个核心基因和2个miRNA）和3条边组成的miRNA-核心基因网络。此外，CASP4预测了1种小分子药物，NLRP3预测了9种小分子药物。最后，在GSE45376和GSE93561（SCI与HC）以及小鼠SCI模型（假手术与SCI）中，5个核心基因的表达存在显著差异。总体而言，我们鉴定出了5个与泛凋亡相关的核心基因（CASP4、GSDMB、NAIP、NLRC4和NLRP3），为治疗脊髓损伤提供了潜在的方向。
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