肌腱损伤免疫微环境稳态失调相关关键基因的识别和验证

Identification and validation of key genes associated with immune microenvironment homeostasis imbalance in tendon injury

摘要:

目的 识别肌腱损伤免疫微环境稳态失调关键基因。方法 GSE26051 数据集中，基于“wilcoxon”检验筛 选的差异表达基因谱构建加权基因共表达网络，以识别肌腱损伤相关基因模块。利用“ClusterProfiler”包对基因进行功 能注释和富集分析，并借助“igraph”包构建基因互作网络并衡量节点重要性，通过Q统计量综合排序确定关键基因。运 用“ssGSEA”包和“ConsensusClusterPlus”包对样本免疫微环境状态进行量化并聚类。分析不同状态下免疫细胞浸润的 差异性，并对关键基因和不同免疫微环境状态关系进行分析和验证。结果 基于 2 571 个差异表达基因谱的加权基因共 表达网络分析，确认蓝色、绿色和黄色模块为疾病相关基因模块，模块内 992 个基因显著富集在细胞外基质-受体相互作 用、细胞周期、胶原代谢等通路。Q统计量排序显示ASPM、CCNB1、CENPF、CDCA5 和C1QTNF6 为关键基因。聚类分 析表明肌腱损伤患者可以分为不同的免疫微环境状态。5 个关键基因在免疫激活状态具有更高的表达水平及更好的诊 断性能，其表达水平与免疫激活之间的关联性也得到验证。结论 ASPM、CCNB1、CENPF、CDCA5 和C1QTNF6 可能 是肌腱损伤免疫微环境稳态失调的关键基因。

Abstract:

Objective To identify key genes associated with the dysregulation of the immune microenvironment in tendon injury. Methods In the GSE26051 dataset, we constructed a weighted gene co-expression network based on differentially expressed gene profiles selected using the Wilcoxon test to identify gene modules related to tendon injury. We employed the “ClusterProfiler”package for functional annotation and enrichment analysis of the genes, and used the“igraph”package to construct a gene interaction network and assess node importance. Key genes were determined through comprehensive ranking using Q statistics. Additionally, the“ssGSEA”and“ConsensusClusterPlus”packages were utilized to quantify and cluster the immune microenvironment states of the samples. We analyzed the differences in immune cell infiltration under different states and examined the relationships between key genes and the immune microenvironment states, followed by validation. Results Based on the profiles of 2,571 differentially expressed genes, weighted gene co-expression network analysis identified the blue, green, and yellow modules as disease-associated gene modules. Within these modules, 992 genes were significantly enriched in pathways related to extracellular matrix-receptor interactions, the cell cycle, and collagen metabolism. The ranking of Q statistics indicated that ASPM, CCNB1, CENPF, CDCA5, and C1QTNF6 are key genes. Clustering analysis revealed that patients with tendon injuries can be classified into different immune microenvironment states. The five key genes exhibited higher expression levels and better diagnostic performance in the immune-activated state, and the association between their expression levels and immune activation was also validated. Conclusion ASPM, CCNB1, CENPF, CDCA5, and C1QTNF6 are key genes involved in the dysregulation of the immune microenvironment in tendon injuries.