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脊髓损伤中坏死性凋亡关键基因的筛选与验证

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目的 筛选并验证脊髓损伤中坏死性凋亡的关键基因,为脊髓损伤的诊治提供新靶标。方法 从基因表达综合(GEO)数据库的GSE151371数据集获取脊髓损伤样本(脊髓损伤组,n=38)和健康对照样本(健康对照组,n=10)的外周血转录组数据。利用R软件筛选差异表达基因,并对差异表达基因进行功能富集分析。利用机器学习算法(随机森林和LASSO)和蛋白质互作网络(PPI)分析筛选坏死性凋亡关键基因,并构建脊髓损伤的诊断列线图。建立大鼠脊髓损伤模型,采用Western blotting和免疫荧光染色进一步验证坏死性凋亡关键基因的表达情况。结果 共筛选得到2050个差异表达基因。KEGG通路富集分析结果显示,差异表达基因涉及核苷酸结合寡聚化域(NOD)样受体信号通路、造血细胞谱系及坏死性凋亡等途径;GO富集分析结果显示,差异表达基因主要参与白细胞激活、三级颗粒、防御反应调节等方面。交叉分析筛选出15个坏死性凋亡差异表达基因。KEGG通路富集分析结果显示,坏死性凋亡差异表达基因参与坏死性凋亡、甲型流感和NOD样受体信号通路;GO富集分析结果显示,坏死性凋亡差异表达基因在细胞对细胞因子刺激的反应、细胞因子介导的信号通路和细胞因子反应中明显富集。整合两种机器学习算法和PPI分析,进一步筛选出两个特定的坏死性凋亡关键基因(IL1B和PLA2G4A)。利用IL1B和PLA2G4A建立的列线图可用于预测早期脊髓损伤的发生。大鼠脊髓损伤模型验证结果显示,脊髓损伤组IL-1β和PLA2G4A蛋白表达水平明显高于假手术组(P<0。05)。结论 IL1B和PLA2G4A作为参与脊髓损伤发生的坏死性凋亡关键基因,可用于预测脊髓损伤的发生,有望成为防治脊髓损伤的新靶点。
Identification and validation of necroptosis key genes in spinal cord injury
Objective To investigate the role of necroptosis key genes in spinal cord injury using bioinformatics methods to provide new targets for the diagnosis and treatment of spinal cord injury.Methods The peripheral blood transcriptome data of spinal cord injury samples(n=38)and healthy control samples(n=10)were obtained from GSE151371 data set in Gene Expression Omnibus(GEO)database.R software was used to identify differentially expressed genes and perform functional enrichment analysis.Machine learning algorithms(random forest and LASSO)and protein-protein interaction(PPI)networks are used to screen for necroptosis key genes and construct a diagnostic nomogram for spinal cord injury.Establish a rat spinal cord injury model to further verify the expression of necroptosis key genes by Western blotting and immunofluorescence staining.Results A total of 2050 differentially expressed genes were identified in the two groups.KEGG pathway enrichment analysis showed that the differentially expressed genes were involved in the nucleotide-binding oligomerization domain(NOD)-like receptor signaling pathway,hematopoietic cell lineage,and necroptosis;GO enrichment analysis showed that the differentially expressed genes were involved in the activation of leukocytes,tertiary granulation,and regulation of the defense response,and so on.Intersection analysis screened 15 necroptosis differentially expressed genes.KEGG pathway enrichment analysis showed that necroptosis differentially expressed genes were involved in necroptosis,influenza,and NOD-like receptor signaling pathways;GO enrichment analysis showed that necroptosis differentially expressed genes were significantly enriched in the cellular response to cytokine stimulation,cytokine-mediated signaling pathways,and response to cytokines.Integration of two machine learning algorithms and PPI analysis further screened two necroptosis key genes(IL1B and PLA2G4A).The nomogram established using IL1B and PLA2G4A can be used for early prediction of the occurrence of spinal cord injury.The validation results of the rat spinal cord injury model showed that the protein expression of IL-1β and PLA2G4A in the spinal cord injury group were significantly higher than those in the sham group(P<0.05).Conclusions IL1B and PLA2G4A as key genes of necroptosis involved in the development of spinal cord injury,can be used to predict the development of spinal cord injury with the promise of being new targets for the prevention and treatment of spinal cord injury.

spinal cord injurynecroptosismachine learningdiagnosisbiomarkers

刘冬、朱志杰、张昭、王臻、范宏斌

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空军军医大学西京医院骨科,陕西西安 710032

脊髓损伤 坏死性凋亡 机器学习 诊断 生物标志物

国家自然科学基金

31971272

2024

解放军医学杂志
人民军医出版社

解放军医学杂志

CSTPCD北大核心
影响因子:1.644
ISSN:0577-7402
年,卷(期):2024.49(8)