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Article(id=1198558165731210072, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558165093675863, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.0523.2023.1023, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1681142400000, receivedDateStr=2023-04-11, revisedDate=null, revisedDateStr=null, acceptedDate=1686672000000, acceptedDateStr=2023-06-14, onlineDate=1763688134843, onlineDateStr=2025-11-21, pubDate=1724774400000, pubDateStr=2024-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763688134843, onlineIssueDateStr=2025-11-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763688134843, creator=13701087609, updateTime=1763688134843, updator=13701087609, issue=Issue{id=1198558165093675863, tenantId=1146029695717560320, journalId=1189873630562394117, year='2024', volume='49', issue='8', pageStart='855', pageEnd='976', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763688134691, creator=13701087609, updateTime=1763689174168, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1198562525043327039, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558165093675863, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1198562525043327040, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1198558165093675863, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=905, endPage=913, ext={EN=ArticleExt(id=1198558165999645529, articleId=1198558165731210072, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Identification and validation of necroptosis key genes in spinal cord injury, columnId=1190310110212751762, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Basic Research, runingTitle=null, highlight=null, articleAbstract=

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

, correspAuthors=Hong-Bin Fan, authorNote=null, correspAuthorsNote=
E-mail:
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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。

, correspAuthors=范宏斌, authorNote=null, correspAuthorsNote=
范宏斌，E-mail：
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刘冬，硕士研究生，主治医师，主要从事骨科常见疾病的诊治及骨肿瘤综合外科治疗

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刘冬，硕士研究生，主治医师，主要从事骨科常见疾病的诊治及骨肿瘤综合外科治疗

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刘冬，硕士研究生，主治医师，主要从事骨科常见疾病的诊治及骨肿瘤综合外科治疗

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The Int J Neurosci, 2018, 128(8): 778-782., articleTitle=The expression of IL-1β can deteriorate the prognosis of nervous system after spinal cord injury, refAbstract=null), Reference(id=1198578983412531723, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, doi=null, pmid=null, pmcid=null, year=2016, volume=54, issue=6, pageStart=407, pageEnd=412, url=null, language=null, rfNumber=[34], rfOrder=33, authorNames=Mizumura K, Maruoka S, Gon Y, journalName=Respir Investig, refType=null, unstructuredReference=Mizumura K, Maruoka S, Gon Y, et al. The role of necroptosis in pulmonary diseases[J]. Respir Investig, 2016, 54(6): 407-412., articleTitle=The role of necroptosis in pulmonary diseases, refAbstract=null), Reference(id=1198578983496417805, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, doi=null, pmid=null, pmcid=null, year=2023, volume=213, issue=null, pageStart=115591, pageEnd=null, url=null, language=null, rfNumber=[35], rfOrder=34, authorNames=Prasad Panda S, Kesharwani A, Prasanna Mallick S, journalName=Biochem Pharmacol, refType=null, unstructuredReference=Prasad Panda S, Kesharwani A, Prasanna Mallick S, et al. Viral-induced neuronal necroptosis: detrimental to brain function and regulation by necroptosis inhibitors[J]. Biochem Pharmacol, 2023, 213: 115591., articleTitle=Viral-induced neuronal necroptosis: detrimental to brain function and regulation by necroptosis inhibitors, refAbstract=null), Reference(id=1198578983563526671, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, doi=null, pmid=null, pmcid=null, year=2022, volume=2022, issue=null, pageStart=9313559, pageEnd=null, url=null, language=null, rfNumber=[36], rfOrder=35, authorNames=Bao J, Ye B, Ren Y, journalName=Genet Res (Camb), refType=null, unstructuredReference=Bao J, Ye B, Ren Y. ABIN1 inhibits inflammation through necroptosis-dependent pathway in ulcerative colitis[J]. 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Autophagy, 2020, 16(3): 466-85., articleTitle=PLA2G4A/cPLA2-mediated lysosomal membrane damage leads to inhibition of autophagy and neurodegeneration after brain trauma, refAbstract=null)], funds=[Fund(id=1198578979209839051, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, awardId=31971272, language=EN, fundingSource=National Natural Science Foundation of China(31971272), fundOrder=null, country=null), Fund(id=1198578979302113740, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, awardId=31971272, language=CN, fundingSource=国家自然科学基金(31971272), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1198578974499635550, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, xref=null, ext=[AuthorCompanyExt(id=1198578974508024159, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, companyId=1198578974499635550, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Orthopedics, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi 710032, China), AuthorCompanyExt(id=1198578974512218464, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, companyId=1198578974499635550, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=空军军医大学西京医院骨科，陕西西安 710032)])], figs=[ArticleFig(id=1198578978035433901, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.1, caption=Differentially expressed gene analysis and functional analysis between spinal cord injury group and healthy control group, figureFileSmall=QNJqNskvv/6p5EB9/xnW3A==, figureFileBig=2RJcii9RJP7AJXfzYuIQFA==, tableContent=null), ArticleFig(id=1198578978131902895, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图1, caption=脊髓损伤组与健康对照组差异表达基因筛选与功能富集分析

FDR. 错误发现率；A. 火山图；B. 热图；C. 京都基因和基因组百科全书(KEGG)通路富集分析；D. 基因本体论(GO)富集分析

, figureFileSmall=QNJqNskvv/6p5EB9/xnW3A==, figureFileBig=2RJcii9RJP7AJXfzYuIQFA==, tableContent=null), ArticleFig(id=1198578978278703538, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.2, caption=Identification of necroptosis differentially expressed genes and functional analysis between spinal cord injury group and healthy control group, figureFileSmall=4tK+pHFMZ9zcf7q1GvaZ7Q==, figureFileBig=wICcBvbiupIVIiFBwPUsaw==, tableContent=null), ArticleFig(id=1198578978337423797, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图2, caption=脊髓损伤组与健康对照组坏死性凋亡差异表达基因筛选与功能富集分析

A. 韦恩图；B. 坏死性凋亡差异表达基因的热图；C. 京都基因和基因组百科全书(KEGG)通路富集分析；D. 基因本体论(GO)富集分析

, figureFileSmall=4tK+pHFMZ9zcf7q1GvaZ7Q==, figureFileBig=wICcBvbiupIVIiFBwPUsaw==, tableContent=null), ArticleFig(id=1198578978408726968, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.3, caption=Identification of necroptosis key differentially expressed genes based on machine learning algorithms and PPI analysis, figureFileSmall=5PB6/4zqLrgmqR9uZs031A==, figureFileBig=XLEwAjqZUjbb2D03qW9CmA==, tableContent=null), ArticleFig(id=1198578978521973179, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图3, caption=基于机器学习算法和PPI分析筛选坏死性凋亡关键基因

A. 随机森林算法；B、C. LASSO算法；D. 蛋白质互作网络(PPI)分析；E. 三种算法的交叉分析

, figureFileSmall=5PB6/4zqLrgmqR9uZs031A==, figureFileBig=XLEwAjqZUjbb2D03qW9CmA==, tableContent=null), ArticleFig(id=1198578978631025086, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.4, caption=Diagnostic performance evaluation of necroptosis key differen tially expressed genes, figureFileSmall=5BaPAbSDhKahiXC3057efA==, figureFileBig=Niyx6fNiAOLkUXyfkILr2Q==, tableContent=null), ArticleFig(id=1198578978702328256, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图4, caption=坏死性凋亡关键基因对脊髓损伤的诊断价值

A. GSE151371中IL1B、PLA2G4A mRNA表达水平；B. 坏死性凋亡关键基因诊断脊髓损伤的ROC曲线；***P<0.001

, figureFileSmall=5BaPAbSDhKahiXC3057efA==, figureFileBig=Niyx6fNiAOLkUXyfkILr2Q==, tableContent=null), ArticleFig(id=1198578978798797250, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.5, caption=Construction and validation of diagnostic nomogram of spinal cord Injury, figureFileSmall=gBc5lMeakuXCOBEToGY0Zg==, figureFileBig=4QEdqiAfPLCBw3HS2+z3eQ==, tableContent=null), ArticleFig(id=1198578978878489029, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图5, caption=脊髓损伤的诊断列线图构建及验证

A. 基于坏死性凋亡关键基因预测脊髓损伤的诊断列线图；B. 校准曲线图；C. 临床决策分析

, figureFileSmall=gBc5lMeakuXCOBEToGY0Zg==, figureFileBig=4QEdqiAfPLCBw3HS2+z3eQ==, tableContent=null), ArticleFig(id=1198578978966569415, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=EN, label=Fig.6, caption=Validation of necroptosis key genes in rat spinal cord injury model, figureFileSmall=/vQxAESQHXN1Xyd6UoF47Q==, figureFileBig=RFlu9VnlVrLh1gMwR/Wo0g==, tableContent=null), ArticleFig(id=1198578979037872584, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1198558165731210072, language=CN, label=图6, caption=大鼠脊髓损伤模型验证坏死性凋亡关键基因的表达

A. 大鼠脊髓损伤模型示意图；B. 坏死性凋亡关键基因在脊髓损伤后不同时间点的相对蛋白表达水平(n=5)；C、D. 免疫荧光染色检测坏死性凋亡关键基因在在脊髓损伤后7 d的表达情况(n=5)；***P<0.001

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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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刘冬 , 朱志杰 , 张昭 , 王臻 , 范宏斌 *
解放军医学杂志 | 基础研究 2024,49(8): 905-913
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解放军医学杂志 | 基础研究 2024, 49(8): 905-913
脊髓损伤中坏死性凋亡关键基因的筛选与验证
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刘冬, 朱志杰, 张昭, 王臻, 范宏斌*
作者信息
  • 空军军医大学西京医院骨科，陕西西安 710032

    • 刘冬，硕士研究生，主治医师，主要从事骨科常见疾病的诊治及骨肿瘤综合外科治疗

通讯作者:

范宏斌，E-mail：
Identification and validation of necroptosis key genes in spinal cord injury
Dong Liu, Zhi-Jie Zhu, Zhao Zhang, Zhen Wang, Hong-Bin Fan*
Affiliations
  • Department of Orthopedics, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi 710032, China
出版时间: 2024-08-28 doi: 10.11855/j.issn.0577-7402.0523.2023.1023
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摘要
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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。

脊髓损伤  /  坏死性凋亡  /  机器学习  /  诊断  /  生物标志物

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 injury  /  necroptosis  /  machine learning  /  diagnosis  /  biomarkers
刘冬, 朱志杰, 张昭, 王臻, 范宏斌. 脊髓损伤中坏死性凋亡关键基因的筛选与验证. 解放军医学杂志, 2024 , 49 (8) : 905 -913 . DOI: 10.11855/j.issn.0577-7402.0523.2023.1023
Dong Liu, Zhi-Jie Zhu, Zhao Zhang, Zhen Wang, Hong-Bin Fan. Identification and validation of necroptosis key genes in spinal cord injury[J]. Medical Journal of Chinese People’s Liberation Army, 2024 , 49 (8) : 905 -913 . DOI: 10.11855/j.issn.0577-7402.0523.2023.1023
正文
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脊髓损伤是一种创伤性的中枢神经系统疾病，能引起多种症状，包括严重的感觉、运动和自主神经功能障碍等[1]。脊髓损伤具有高致残率和高病死率等特点，严重威胁患者的生存质量和生存时间。目前，全球脊髓损伤发病率持续增高，我国年患病率达到(13~60)/100万[2-3]。脊髓损伤的病理生理过程包括原发性和继发性损伤两个阶段。原发性损伤不可逆，而继发性损伤主要是由脊髓损伤后数小时至数周发生的细胞凋亡引起的一系列级联反应，如炎症反应、免疫失调和微循环损伤的产生，进一步加剧脊髓损伤[4]。早期手术减压和药物干预是目前脊髓损伤的主要治疗方案，但由于大部分患者错过最佳治疗时间，获益往往有限[5]。因此，深入了解脊髓损伤的发病机制，将有助于探索新的早期诊断生物标志物和治疗靶点。
程序性细胞死亡在中枢神经系统的发育和稳态中发挥着重要作用[6]。坏死性凋亡是一种新发现的程序性细胞坏死方式，由两种受体相互作用蛋白激酶(RIPK1、RIPK3)和混合谱系激酶结构域样假激酶(mixed lineage kinase domain-like，MLKL)介导发生[7]。众多证据表明，坏死性凋亡在脊髓损伤后的细胞死亡和炎症反应调控中发挥着重要作用[8]。然而，坏死性凋亡相关基因在脊髓损伤中的作用和功能目前仍缺乏全面系统性的认知。因此，识别脊髓损伤后坏死性凋亡触发的关键基因，将有助于进一步了解脊髓损伤的病理生理过程。本研究采用生物信息学方法分析脊髓损伤患者坏死性凋亡相关基因的表达，并应用机器学习进一步筛选坏死性凋亡关键基因以用于脊髓损伤的早期诊断，以期从坏死性凋亡的角度为脊髓损伤的早期干预和治疗提供新的思路。
1 材料与方法
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1.1 主要试剂
白细胞介素-1β(interleukin-1β，IL-1β)一抗(ab25436，英国Abcam公司)；磷脂酶A2亚型IVA(phospholipase A2 group IVA，PLA2G4A)一抗(AF3329，美国Affinity公司)；GADPH一抗(60004-1-Ig，中国Proteintech公司)。
1.2 数据获取
从基因表达综合(Gene Expression Omnibus，GEO)数据库的GSE151371数据集中获取脊髓损伤样本(设为脊髓损伤组，n=38)和健康对照样本(设为健康对照组，n=10)的外周血转录组数据[9]。下载前，GSE151371的数据已经过严格标准化。此外，从京都基因和基因组百科全书(KEGG)数据库(https://www.kegg.jp/)中的坏死性凋亡通路(map04217)中获取159个坏死性凋亡相关基因[10]。
1.3 差异表达基因筛选
使用R软件中的“limma”包进行差异表达分析，阈值设为|log2 fold change|≥1，adj-P≤0.05。然后，应用韦恩图对差异表达基因与坏死性凋亡相关基因进行交叉分析，确认差异表达基因中的坏死性凋亡相关基因。
1.4 功能富集分析
使用R软件中的“clusterProfiler”包对差异表达基因进行基因本体论(GO)和KEGG富集分析，并使用Metascape(https://metascape.org)对结果进行可视化[11]。
1.5 机器学习和蛋白质互作网络(protein-protein interaction networks，PPI)分析筛选坏死性凋亡关键基因
为了进一步筛选脊髓损伤诊断的候选基因，使用随机森林算法、LASSO算法及PPI分析筛选坏死性凋亡关键基因[12-14]。随机森林算法是基于决策树的集成学习方法，可从大量数据中选择错误率最小的基因集来识别潜在的生物标志物。使用R软件中的“randomForest”包进行随机森林分析，将候选基因阈值设置为相对重要性得分>1分。LASSO算法是一种基于基因表达谱的线性回归算法，通过过滤基因减少过拟合的发生，从而帮助临床医师提高预测精度。使用R软件中的“glmnet”包进行LASSO回归分析，阈值设定为最小λ值。此外，通过STRING数据库建立坏死性凋亡差异表达基因的PPI网络，设定阈值为置信度得分>0.015分。随后，使用Cytoscape构建PPI可视化网络，并应用CytoHubba插件中的betweenness算法筛选枢纽基因。坏死性凋亡关键基因被定义为3种算法的交叉。使用ROC曲线评估这些关键基因的诊断效能。
1.6 诊断列线图构建
为了预测脊髓损伤的发生和进展，基于坏死性凋亡关键基因的表达谱利用R软件中的“rms”包构建诊断列线图。使用校准曲线和决策曲线评估所构建列线图的精准性和临床效益。
1.7 大鼠脊髓损伤模型制备
25只SD雄性大鼠(250~300 g)购自空军军医大学动物实验中心。本研究获得空军军医大学动物伦理委员会(IACUC-20220401)批准。
在标准条件下(温度22~25 ℃，相对湿度45%~65%，12 h光/12 h暗循环，充足食物和水)饲养SD大鼠，随机分为假手术组(n=10)与脊髓损伤组(n=15)。采用改良的双侧脊髓钳夹法[15]制备大鼠脊髓损伤模型：腹腔注射1%戊巴比妥钠麻醉大鼠，于后背皮肤行正中切口，暴露T10棘突，切除相应节段棘突及椎板，显露T10节段脊髓，用镊子钳夹脊髓40 s造成脊髓损伤。假手术组大鼠仅打开椎板而不损伤脊髓。术后进行常规的动物护理。
1.8 Western blotting检测IL-1β、PLA2G4A蛋白表达水平
造模后3、7 d，使用生理盐水灌注大鼠，随后从损伤的脊髓中切取1 cm节段，磨碎并加入裂解液裂解组织。13 000 r/min离心10 min收集上清液，用BCA法测定蛋白浓度。上样进行电泳、转膜、脱脂奶粉封闭后，加入抗IL-1β(1:2000)、抗PLA2G4A(1:2000)、抗GADPH(1:5000)一抗4 ℃孵育过夜；洗涤后，加入二抗(1:2000)室温下孵育1 h，进行发光显影。
1.9 免疫荧光染色检测IL-1β、PLA2G4A的表达
造模7 d后用多聚甲醛灌注大鼠，从损伤的脊髓中切取2 cm节段。矢状切片的厚度为7 mm。在室温下用0.03% Triton X-100处理切片20 min，然后用山羊血清封闭1 h；加入抗IL-1β(1:200)、抗PLA2G4A(1:100)一抗4 ℃孵育过夜。清洗后，加入二抗(1:400)室温下孵育1 h，PBS洗涤10 min后，细胞核用DAPI染色。
1.10 统计学处理
使用R软件(4.0.5版)、SPSS 21.0软件(IBM SPSS公司，美国)和GraphPad Prism 8软件(Graphpad公司，美国)进行统计分析。计量资料以$\bar{x}±s$表示，两组间比较采用独立样本t检验或Mann-Whitney U检验。P<0.05为差异有统计学意义。
2 结果
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2.1 差异表达基因筛选及功能富集分析
GSE151371数据集中共包括38个脊髓损伤样本和10个健康对照样本，两组间共鉴定得到2050个差异表达基因，其中1152个表达上调，898个表达下调(图1A)。热图展示了脊髓损伤组与健康对照组之间差异表达的前30个基因[图1B，附表1(https://dx.doi.org/10.11855/j.issn.0577-7402.0523.2023.1023FJ)]。
KEGG通路富集分析结果显示，差异表达基因参与涉及核苷酸结合寡聚化域(nucleotide-binding oligomerization domain，NOD)样受体信号通路、造血细胞谱系及坏死性凋亡等途径(图1C)。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(图1D)。
2.2 坏死性凋亡差异表达基因筛选及功能富集分析
鉴于坏死性凋亡密切参与脊髓损伤进展过程，本研究通过交叉分析确认了15个坏死性凋亡差异表达基因，其中6个表达上调(PYGL、PYCARD、FADD、PLA2G4A、IL1B和CHMP3)，9个表达下调(TRAF5、BCL2、CAMK2D、FASLG、STAT4、BIRC3、ALOX15、TLR3和IFNG，图2A、B)。
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样受体信号通路等途径(图2C)。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等功能中明显富集(图2D)。
2.3 机器学习和PPI分析识别坏死性凋亡关键基因
使用两种机器学习算法(随机森林和LASSO)和PPI分析进一步筛选参与脊髓损伤发生和进展的坏死性凋亡关键基因，结果显示，随机森林算法共确定了6个候选基因，包括BCL2、PYGL、FASLG、PLA2G4A、PYCARD和IL1B(图3A)；LASSO算法确认最小λ值为7，获得7个候选基因，包括PYGL、PLA2G4A、ALOX15、PYCARD、IL1B、CHMP3和FASLG(图3B、C)；PPI分析显示5个枢纽基因参与脊髓损伤的进展，包括IFNG、TRAF5、FADD、IL1B和PLA2G4A(图3D)。将上述3种算法的结果进行交叉分析，共得到2个坏死性凋亡相关基因(IL1B和PLA2G4A)，这两个基因被认为是参与脊髓损伤进展的坏死性凋亡关键基因(图3E)。
2.4 坏死性凋亡关键基因的诊断价值评估
GSE151371数据集中，脊髓损伤组IL1B、PLA2G4A mRNA表达水平明显高于健康对照组(图4A)。ROC曲线分析结果显示，IL1B、PLA2G4A单独及联合诊断脊髓损伤的AUC分别为0.950、0.953、0.992，提示IL1B和PLA2G4A是诊断脊髓损伤的有效生物标志物(图4B)。
2.5 列线图构建及验证结果
为了能早期预测脊髓损伤的发生，将两个坏死性凋亡关键基因(IL1B、PLA2G4A)整合起来，建立一个新型诊断列线图(图5A)。校准曲线显示，诊断列线图对脊髓损伤的预测概率与实际概率较为一致(图5B)。临床决策曲线显示，脊髓损伤患者可从该诊断列线图中获益(图5C)。
2.6 动物实验验证
大鼠脊髓损伤模型如图6A所示。Western blotting检测结果显示，与假手术组比较，脊髓损伤后3、7 d IL-1β和PLA2G4A蛋白表达水平明显升高(P<0.01或P<0.001，图6B)。免疫荧光染色检测结果显示，IL-1β和PLA2G4A在脊髓损伤组的表达明显高于假手术组(图6C、D，P<0.001)。提示这两个坏死性凋亡关键基因与脊髓损伤的进展密切相关。
3 讨论
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脊髓损伤作为一种严重的中枢神经系统疾病，常造成持续性的器官功能障碍和永久性的神经功能损伤[1]。目前，脊髓损伤的治疗方式包括手术干预、药物治疗和物理康复等，但大多数患者预后不佳[5,14]。而脊髓损伤的传统治疗方式主要集中于减压、抗炎和抑制水肿，与卒中治疗中对于神经细胞的保护和减少细胞死亡给予足够重视的模式明显不同[16]。一般认为，坏死作为脊髓损伤后急性细胞死亡的主要原因，是不可控的。近年来随着细胞死亡领域的研究进展，发现了一种新型的程序性细胞死亡方式—坏死性凋亡，并揭示了其由细胞内RIP1/3/MLKL信号级联介导的潜在分子调控机制，为加深理解脊髓损伤后的细胞死亡提供了新的思路[6-8]。坏死性凋亡的特征包括早期细胞质膜完整性丢失、细胞内容物泄漏及细胞器肿胀，在中枢神经系统细胞死亡的各种病理形式中均起着关键作用，如缺血性脑损伤、神经退行性疾病和脑病毒感染等[17]。Wang等[18]证实了Necrostatin-1(坏死性凋亡特异性抑制剂)可显著降低脊髓损伤后的炎性因子水平，如肿瘤坏死因子-α(tumor necrosis factor-α，TNF-α)、IL-1β和IL-6，改善BBB(Basso-Beattie-Bresnahan)评分。抑制坏死性凋亡信号同样可减轻脊髓损伤后的炎症反应和神经元细胞死亡，并促进运动功能的恢复[19]。Liu等[20]发现，脊髓损伤后溶酶体功能障碍可促进RIPK1和RIPK3在神经元中迅速聚集，从而加剧细胞坏死性凋亡的发生。Kanno等[21]在脊髓损伤后继发性神经组织损伤小鼠中发现，神经元、星形胶质细胞和少突胶质细胞中RIPK1的表达显著上调，并在3 d后达到峰值，同时RIPK1阳性细胞数明显多于TUNEL阳性细胞。虽然坏死性凋亡被证实是脊髓损伤后主要的细胞死亡方式，但其在脊髓损伤病理过程中的作用尚不明确[6-8]。
本研究在脊髓损伤患者和健康对照样本的外周血转录组数据中筛选得到2050个差异表达基因，并分析其功能富集情况，发现坏死性凋亡在脊髓损伤病理进展中发挥着重要作用。随后，全面分析坏死性凋亡相关基因在脊髓损伤中的表达，筛选得到15个坏死性凋亡差异表达基因，其中6个表达上调，9个表达下调。这些坏死性凋亡差异表达基因主要通过NOD样受体信号传导途径和细胞因子相关功能参与脊髓损伤的进展。NOD样受体是一类位于细胞质的模式识别受体，其信号传导参与了多种病原体、组织损伤和细胞应激触发的机体固有免疫应答[22]。NOD样受体在感知识别相应模式分子后，可通过自身寡聚化组装形成炎症小体，从而激活其下游通路，参与免疫炎症反应的调控[23]。脊髓损伤后，脊髓常驻细胞中炎症小体的形成促进了caspase-1的激活，并释放大量细胞炎性因子，参与了继发性损伤的进展[24]。Xiao等[25]证实，坏死性凋亡能通过激活NOD样受体信号传导途径，来触发损伤相关的分子模式(damage-associated molecular patterns，DAMPs)而加重病变部位的损伤和炎症反应。此外，坏死性凋亡还能与NOD样受体相互作用加速炎症小体的活化，调控下游炎症信号的传导[26]。总之，脊髓损伤后坏死性凋亡的激活能通过NOD样受体信号传导途径进一步激活下游的炎症反应，加剧组织损伤。
脊髓损伤后，多种炎性细胞因子在中枢神经系统和全身水平都发生显著变化，被视为免疫失调和细胞死亡的关键介质[27]。由于血脊髓屏障的破坏，病变部位和邻近部位暴露在促炎细胞因子下，如IL-1β和TNF-α等，同时多种常驻(星形胶质细胞和小胶质细胞)和外周(巨噬细胞、淋巴细胞)免疫细胞向损伤部位浸润和活化，进一步释放大量炎性细胞因子，显著影响脊髓损伤患者的功能恢复[28]。有研究发现，坏死性凋亡能通过RIPK1、RIPK3和MLKL来介导NF-κB和p38的激活，从而诱导细胞产生大量的细胞因子触发免疫炎症反应失调[29]。脊髓损伤后，坏死性凋亡的星形胶质细胞可上调HMGB1的表达，以此通过RAGE-NF-κB诱导促炎性小胶质细胞的活化，加剧炎症反应[30]。Wang等[31]发现，miRNA-223-3p能够下调RIP3介导的坏死性凋亡信号级联反应，从而减少IL-1β和TNF-α炎性因子的分泌。Fan等[32]发现，脊髓损伤后，抑制巨噬细胞/小胶质细胞向促炎表型的分化，有助于减轻少突胶质细胞的坏死性凋亡，减轻髓鞘和轴突的损失。因此，坏死性凋亡可能通过NOD样受体信号通路和调节细胞因子来参与脊髓损伤后的免疫炎症反应，从而影响患者的功能恢复。
由于不同的算法预测关键基因的结果往往大不相同，只选择一种算法很难得到可靠的结果[14]。因此，为了确认坏死性凋亡关键基因，本研究将两种类型的机器学习(随机森林和LASSO)和PPI分析的结果进行交叉分析，筛选得到两个坏死性凋亡关键基因IL1B和PLA2G4A。进一步分析发现，IL1B和PLA2G4A对脊髓损伤具有较高的诊断效能；由这两个坏死性凋亡关键基因构建的列线图可预测脊髓损伤的发生，这为脊髓损伤患者的早期诊断提供了新的思路。此外，利用大鼠脊髓损伤模型验证IL-1β和PLA2G4A的表达，结果显示，脊髓损伤后3、7 d脊髓损伤组IL-1β和PLA2G4A的表达明显高于假手术组，进一步验证了生物信息学分析的准确性。
IL-1β是炎症反应的关键介质，参与多种细胞活动，如细胞增殖、分化和凋亡等[33]。坏死性凋亡执行者MLKL的磷酸化和激活能介导细胞膜上形成孔，释放大量IL-1β加剧炎症[34]。在神经元坏死性凋亡的同时，通过NOD样受体信号级联形成炎症小体，能切割并激活胱天蛋白酶-1和IL-1β，触发复杂的细胞反应网络，引起局部和全身炎症[35]。靶向坏死性凋亡途径可明显抑制IL-1β的表达，从而减轻免疫炎症反应[36]。脊髓损伤后，损伤部位残余的胶质细胞和血管内皮细胞能产生并释放大量炎性趋化因子和细胞因子[27]。在损伤早期，IL-1β在损伤部位大量产生，进一步诱导活性氧自由基和弹性蛋白酶的释放，从而破坏血管内皮的完整性，加重局部水肿和炎症反应。同时，大量兴奋性氨基酸、自由基和其他神经毒性物质的产生和释放也加剧脊髓损伤[37]。
PLA2G4A是花生四烯酸产生的限速酶，其表达上调可介导花生四烯酸的释放以及由此产生的前列腺素E2的产生，从而促进炎症反应[38]。RIP1激酶的活化被鉴定为介导花生四烯酸诱导的少突胶质细胞前体坏死的关键[39]。而Xiao等[40]发现，花生四烯酸补充治疗可下调肠上皮细胞中RIP1和MLKL的表达，并降低RIP1、RIP3和MLKL的磷酸化水平，从而抑制坏死性凋亡信号通路介导的细胞炎症和损伤。López-Vales等[41]发现，脊髓损伤后，PLA2超家族的上调与预后不良密切相关。Sarkar等[42]发现，PLA2G4A介导的溶酶体膜损伤与创伤性脑损伤后的神经元细胞死亡密切相关。以上结果表明，PLA2G4A可能通过上调花生四烯酸来触发脊髓损伤后的坏死性凋亡，介导细胞死亡和炎症反应。
综上所述，本研究通过生物信息学分析和动物实验初步探讨了坏死性凋亡相关基因在脊髓损伤发病机制中的作用，并通过机器学习鉴定并验证了两个坏死性凋亡关键基因(IL1B和PLA2G4A)。该结果为脊髓损伤的诊断和治疗提供了新的思路。本研究存在一定局限性：仅基于公共数据库的数据，缺少相应的临床和预后信息，不能进一步评估患者的临床转归；样本量相对较少。因此后续研究仍需要收集更多的样本来进一步评估坏死性凋亡关键基因的预测诊断性能。坏死性凋亡关键基因参与脊髓损伤进展的具体调控机制尚不清楚，未来亟须开展大量的基础研究加以阐明。
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  • 国家自然科学基金(31971272)
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2024年第49卷第8期
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doi: 10.11855/j.issn.0577-7402.0523.2023.1023
  • 接收时间：2023-04-11
  • 首发时间：2025-11-21
  • 出版时间：2024-08-28
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  • 收稿日期：2023-04-11
  • 录用日期：2023-06-14
基金
National Natural Science Foundation of China(31971272)
国家自然科学基金(31971272)
作者信息
    空军军医大学西京医院骨科，陕西西安 710032

通讯作者:

范宏斌，E-mail：
参考文献
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https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/10.11855/j.issn.0577-7402.0523.2023.1023
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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