[1]朱利娟,李 鑫,马悦章,等.基于生物信息学分析筛选脊髓损伤后缺血缺氧关键基因和免疫浸润模式分析[J].现代检验医学杂志,2024,39(05):120-124,151.[doi:10.3969/j.issn.1671-7414.2024.05.022]
　ZHU Lijuan,LI Xin,MA Yuezhang,et al.Bioinformatics-based Analysis to Screen Key Genes for Ischemia and Hypoxia after Spinal Cord Injury and Analysis of Immune Infiltration Patterns[J].Journal of Modern Laboratory Medicine,2024,39(05):120-124,151.[doi:10.3969/j.issn.1671-7414.2024.05.022]
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基于生物信息学分析筛选脊髓损伤后缺血缺氧关键基因和免疫浸润模式分析()

《现代检验医学杂志》[ISSN:/CN:]

卷:
第39卷
期数:
2024年05期
页码:
120-124,151
栏目:
论著
出版日期:
2024-09-15

文章信息/Info

Title:
Bioinformatics-based Analysis to Screen Key Genes for Ischemia and Hypoxia after Spinal Cord Injury and Analysis of Immune Infiltration Patterns
文章编号:
1671-7414（2024）05-120-06
作者:
朱利娟1a，李 鑫1a，马悦章2，朱 婧1a，朱智博1b，赵 瑞1c
（1. 陕西省人民医院a. 麻醉科；b. 设备科；c. 神经内一科，西安 710068；2. 陕西省疾病预防控制中心免疾规化所，西安 710054）
Author(s):
ZHU Lijuan1a, LI Xin1a, MA Yuezhang2, ZHU Jing1a, ZHU Zhibo1b, ZHAO Rui1c
（1a. Department of Anesthesiology； 1b. Department of Equipment; 1c. the First Department of Neurology, Shaanxi Provincial People’s Hospital, Xi’an 710068, China; 2. Immunization Planing Institute, Shaanxi; Provincial Centre of Disease Control and Preventi
关键词:
脊髓损伤; 缺血缺氧关键基因; 免疫浸润; 生物信息学分析
分类号:
R744.9；Q786
DOI:
10.3969/j.issn.1671-7414.2024.05.022
文献标志码:
A
摘要:
目的 通过生物信息学方法筛选脊髓损伤（spina cord injury，SCI）后缺血缺氧相关基因（ischemic andhypoxia related genes , IAHRGs），并分析其免疫浸润模式。方法 从Gene Expression Omnibus(GEO) 下载脊髓损伤相关GSE5296，GSE47681 和GSE217797 的基因表达谱，其中GSE5296，GSE47681 样本作为测试集，GSE217797 样本作为验证集，获取脊髓损伤与健康样本之间的差异表达基因(differentialy expressed genes DEGs)。在GeneCards 数据库和MSigDB 数据库筛选（IAHRGs）。DEGs 和IAHRGs 两部分取交集得到缺血缺氧相关的差异表达基因（ischemic andhypoxia related differentially expressed genes，IAHRDEGs）。基于IAHRDEGs 通过LASSO 模型和SVM 分析共同筛选得到的关键基因。将关键基因进行Logistics 回归分析并构建诊断模型。通过Nomogram 分析诊断模型的诊断能力并绘制Logistic 预测值的列线图。使用受试者工作特征(ROC) 曲线评估诊断模型和关键基因对脊髓损伤的诊断价值。利用CIBERSORT 工具分析疾病的免疫细胞浸润模式。结果 共筛选IAHRGs 388 个，脊髓损伤与健康样本间差异表达基因313 个，其中表达上调312 个，下调1 个。取两者交集得到27 个上调的IAHRDEGs。基于IAHRDEGs 经LASSO 模型及SVM 分析共筛选5 个脊髓损伤后缺血缺氧相关关键基因（Abca1，Casp1，Lpl，Procr，Tnfrsf1a ）。Nomogram 分析明确Logistics 诊断模型效果良好。ROC 曲线分析显示Casp1，Lpl，Tnfrsf1a 的诊断效果较高（AUC > 0.9），Abca1，Procr 诊断效果次之（AUC：0.7 ～ 0.9），而Logistics Linear Predictors 的诊断效果最佳（AUC = 0.964）。CIBERSORT分析显示5 个关键基因与8 种免疫细胞（中性粒细胞、B 淋巴细胞、浆细胞、M0 巨噬细胞、CD4 T 细胞、CD4 滤泡细胞、Th17 细胞、静止NK 细胞）浸润相关。结论 Abca1，Casp1，Lpl，Procr 和Tnfrsf1a 5 个关键基因可能与脊髓损伤后缺血缺氧发病密切相关，可以作为脊髓损伤后诊断、治疗的候选分子标志物。
Abstract:
Objective To screen ischemia and hypoxiarelated genes (IAHRGs) after spinal cord injury (SCI) and analyze their immune infiltration patterns by bioinformatics methods. Methods The expression profiles of SCI -related GSE5296, GSE47681 and GSE217797 were downloaded from the Gene Expression Omnibus (GEO) database, where GSE5296，GSE47681 samples were used as the test set and GSE217797 samples as the validation set, and the differentially expressed genes (DEGs) between SCI and healthy samples were obtained. IAHRGs were screened in GeneCards and MSigDB databases. The intersection of DEGs and IAHRGs yielded ischemic and hypoxia related differentially expressed genes (IAHRDEGs). Based on the IAHRDEGs, the key genes were jointly screened by LASSO model and SVM analysis．The key genes were subjected to logistic regression analysis and a diagnostic model was constructed. The diagnostic ability of the diagnostic model was analyzed by Nomogram and the column line graph of Logistic predictive values was plotted. The diagnostic value of the diagnostic model and key genes for SCI was evaluated using the receiver operating characteristics (ROC). Immune cell infiltration patterns of the disease were analyzed using the CIBERSORT tool. Results A total of 388 IAHRGs were screened, 313 differentially expressed genes were detected between SCI and healthy samples, among which 312 were up-regulated and 1 was down-regulated. A sum of 27 upregulated IAHRDEGs genes were obtained. Five key genes related to ischemia and hypoxia after SCI (Abca1，Casp1，Lpl， Procr，Tnfrsf1a) were screened by LASSO model and SVM analysis based on IAHRDEGs. Nomogram analysis confirms the effect of logistics diagnosis model. ROC curve analysis showed that Casp1, Lpl and Tnfrsf1a had higher diagnostic efficacy (AUC > 0.9), followed by Abca1 and Procr (AUC: 0.7 ~ 0.9), and the logistics linear predictors had the best diagnostic effect (AUC = 0.964). CIBERSORT analysis showed that five key genes were associated with the infiltration of eight types of immune cells (neutrophil cells，B cells naive，plasma cells，M0 macrophage, T cells CD4 naive, T cells CD4 follicular，Th17 cells，and NK resting). Conclusion The five key genes of Abca1, Casp1, Lpl, Procr, and Tnfrsf1a，may be closely related to ischemichypoxic pathogenesis after SCI，and can be used as candidate molecular markers for the diagnosis and treatment after SCI.

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备注/Memo

备注/Memo:
基金项目： 陕西省重点研发计划一般项目（2024SF-YBXM-401），陕西省人民医院菁英人才计划（2022JY-36,2023JY-31），陕西省人民医院科技发展孵化基金（2023YJY-77）。
作者简介：朱利娟（1987-），女，硕士研究生，主治医师，主要从事生物信息学，E-mail:413401682@qq.com。
通讯作者：赵瑞（1985-），女，副主任医师 , 主要从事分子生物学研究。
更新日期/Last Update: 2024-09-15