Huang Zeling, Cai Xuefeng, Shen Xiaofeng, Chen Zixuan, Zhang Qingtian, Liu Yujiang, Lu Binjie, Xu Bo, Li Yuwei
Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, 215009, China.
Orthopaedic Traumatology Institute, Suzhou Academy of Wumen Chinese Medicine, Suzhou, Jiangsu, 215009, China.
Heliyon. 2024 Jul 11;10(14):e34530. doi: 10.1016/j.heliyon.2024.e34530. eCollection 2024 Jul 30.
Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD.
Gene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified.
Combined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant.
The hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD.
炎症和免疫因子是椎间盘退变（IDD）的核心，但IDD的免疫环境和表观遗传调控过程仍不清楚。本研究旨在识别IDD的免疫相关诊断候选基因，并寻找IDD的潜在发病机制和治疗靶点。
从基因表达综合数据库（GEO）获取基因表达数据集。通过加权基因共表达网络分析（WGCNA）和微阵列数据分析的线性模型（Limma）识别差异表达免疫基因（Imm-DEGs）。使用LASSO算法识别与IDD相关的特征基因，并将其与蛋白质-蛋白质相互作用（PPI）网络中的核心节点基因进行比较以获得枢纽基因。基于枢纽基因的系数构建风险模型，并通过受试者工作特征（ROC）分析进一步评估枢纽基因的诊断价值。使用免疫细胞分析工具Xcell评估免疫细胞的浸润情况。最后，将髓核细胞与巨噬细胞共培养以创建M1巨噬细胞免疫炎症环境，并验证枢纽基因的变化。
结合WGCNA和Limma基因差异分析结果，共鉴定出30个Imm-DEGs。Imm-DEGs富集于多个与免疫和炎症相关的通路。LASSO算法从Imm-DEGs中识别出10个对IDD有显著影响的特征基因，在与Imm-DEGs的PPI网络中的核心节点基因比较后，确定了6个枢纽基因（NR1H3、SORT1、PTGDS、AGT、IRF1、TGFB2）。ROC曲线和外部数据集验证结果表明，由这6个枢纽基因构建的风险模型对IDD具有较高的诊断价值。免疫细胞浸润分析表明，退变的髓核组织中存在各种失调的免疫细胞。体外实验结果表明，免疫炎症环境中髓核细胞中NR1H3、SORT1、PTGDS、IRF1和TGFB2的基因表达上调，但AGT的变化不显著。
枢纽基因NR1H3、SORT1、PTGDS、IRF1和TGFB2可作为IDD的免疫相关生物标志物，可能是IDD免疫调节治疗的潜在靶点。
