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

Abstract: BACKGROUND: 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 5 087 genes, and 1 399 oxidative stress genes were identified, leading to the identification of 23 candidate hub genes. Gene ontology analysis revealed that these candidate hub genes are primarily involved in bacterial defense response, molecular response to bacteria, and other biological processes. In terms of cellular component, they are associated with secretion granule lumen and cytoplasmic vesicle lumen, among others. As for molecular function, they are related to endopeptidase activity and compound binding, including sulfur compounds. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these candidate hub genes are associated with neutrophil extracellular trap formation and the renin-angiotensin system pathway, among other signaling pathways. By applying three machine learning algorithms and conducting the receiver operator characteristic curve validation, two key genes, HSPA6 and PKD1, were determined. Immune infiltration analysis revealed a strong correlation between HSPA6 and activated dendritic cells (r=0.88, P < 0.001) as well as activated CD4+ T cells (r=-0.72, P < 0.01). Similarly, PKD1 showed close associations with effector memory CD8+ T cells (r=0.55, P < 0.05) and activated dendritic cells (r=-0.56, P < 0.05). qPCR experimental results indicated that the expression level of HSPA6 was lower in the intervertebral disc degeneration group compared with the control group (P < 0.000 1), while the expression level of PKD1 was higher in the intervertebral disc degeneration group (P < 0.000 1). These findings suggest that HSPA6 and PKD1 can serve as biomarkers for intervertebral disc degeneration accompanied by oxidative stress. Interventions targeting HSPA6 and PKD1 may hold promise for improving intervertebral disc degeneration.

Key words: intervertebral disc degeneration, oxidative stress, differential analysis, WGCNA analysis, LASSO regression, SVM-RFE analysis,  , random forest,  , feature gene, bioinformatics, immune cell infiltration analysis