椎间盘退变伴氧化应激关键生物标志物:生物信息学和机器学习算法的识别  

Key biomarkers for the diagnosis of intervertebral disc degeneration associated with oxidative stress:identification based on bioinformatics and machine learning

作　　者：兰垚 陈浏阳 宋文慧[1] Lan Yao;Chen Liuyang;Song Wenhui(Department of Orthopaedic Spinal Surgery,The Second Hospital of Shanxi Medical University,Taiyuan 030001,Shanxi Province,China)

机构地区：[1]山西医科大学第二医院脊柱外科,山西省太原市030001

出　　处：《中国组织工程研究》2024年第35期5591-5597,共7页Chinese Journal of Tissue Engineering Research

摘　　要：背景:氧化应激与椎间盘退变的发生发展息息相关,但其发病机制和有效治疗方法仍不明确。目的:运用生物信息学及3种机器学习算法识别与椎间盘退变伴氧化应激相关的关键基因及免疫浸润分析,并进行实验验证。方法:从GEO数据库获得椎间盘退变基因表达谱以及从GeneCards数据库获得氧化应激相关基因,对椎间盘退变数据集进行差异分析及加权基因共表达网络(WGCNA)分析,两者取交集并与氧化应激相关基因取交集得到候选hub基因,对候选hub基因进行GO和KEGG分析;运用机器学习(LASSO回归、SVM-RFE及随机森林)筛选最佳特征基因并进行受试者特征曲线验证,同时行相关免疫浸润分析。收集2023年7-11月就诊于山西医科大学第二医院的颈椎病患者的椎间盘样本作为椎间盘退变组,颈椎脊髓损伤患者的椎间盘样本作为对照组,采用qPCR方法验证特征基因在椎间盘退变组织中的相对表达量。结果与结论:(1)经过差异基因分析获取424个差异表达基因,WGCNA分析得到5087个基因,同时获得氧化应激基因1399个,进而得到23个候选hub基因;(2)GO分析结果显示,主要参与细菌防御反应、细菌来源分子反应等生物过程;涉及分泌颗粒腔、细胞质囊泡腔等细胞组成;涉及内肽酶活性和硫化合物结合等分子功能;(3)KEGG分析结果显示,候选hub基因与中性粒细胞胞外诱捕网形成、肾素-血管紧张素系统通路等信号通路有关;(4)运用3种机器学习和ROC验证后得到关键基因HSPA6和PKD1;(5)免疫浸润分析显示HSPA6与活化树突状细胞(r=0.88,P<0.001)、活化CD4^(+)T细胞(r=-0.72,P<0.01)等密切相关,同时PKD1与效应型记忆CD8^(+)T细胞(r=0.55,P<0.05)、活化树突状细胞(r=-0.56,P<0.05)等密切相关;(6)q PCR实验结果表明椎间盘退变组中HSPA6基因低于对照组(P<0.0001),而PKD1基因高于对照组(P<0.0001);(7)结果表明运用生物信息学及机器学习算法证实HSPA6和PKDBACKGROUND:Oxidative stress is closely associated with the occurrence and progression of intervertebral disc degeneration,but its underlying mechanisms and effective treatment methods remain unclear.OBJECTIVE:To identify key genes associated with intervertebral disc degeneration accompanied by oxidative stress based on bioinformatics and three machine learning algorithms,as well as to conduct an immune infiltration analysis,followed by experimental validation.METHODS:Gene expression profiles related to intervertebral disc degeneration were obtained from the GEO database and oxidative stress-related genes obtained from the GeneCards database.Differential analysis and weighted gene co-expression networks analysis were performed on the intervertebral disc degeneration dataset.The intersection of the two analyses and the intersection with the oxidative stress-related genes were taken to obtain candidate hub genes.Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses on the candidate hub genes were performed.Machine learning algorithms(LASSO regression,SVM-RFE,and random forest)were used to select the optimal feature genes and perform the receiver operator characteristic curve validation.Simultaneously,immune infiltration analysis was conducted.Nucleus pulposus samples from patients with cervical spondylosis who were treated at the Second Hospital of Shanxi Medical University from July to November 2023 were enrolled as the intervertebral disc degeneration group and nucleus pulposus samples from patients with cervical spinal cord injury as the control group.The relative expression of feature genes in the degenerated intervertebral disc was validated using qPCR method.RESULTS AND CONCLUSION:After differential gene analysis,424 differentially expressed genes were obtained.Weighted gene co-expression networks analysis yielded 5087 genes,and 1399 oxidative stress genes were identified,leading to the identification of 23 candidate hub genes.Gene ontology analysis revealed that these candidate hub genes are prim

关 键 词：椎间盘退变 氧化应激 差异分析 WGCNA分析 LASSO回归 SVM-RFE分析 随机森林 特征基因 生物信息学 免疫浸润分析 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R363