Zheng Sikuan, Zhao Xiaokun, Wu Hui, Cuan Xuhui, Cheng Xigao, He Dingwen
Department of Orthopedics The Second Affiliated Hospital of Nanchang University Nanchang Jiangxi Province China.
Institute of Orthopedics of Jiangxi Province Nanchang Jiangxi Province China.
JOR Spine. 2025 Apr 7;8(2):e70064. doi: 10.1002/jsp2.70064. eCollection 2025 Jun.
This study aims to examine the role of translation factors (TF) in intervertebral disc degeneration (IVDD) and to evaluate their clinical relevance through unsupervised clustering methods.
Gene expression data were retrieved from the GEO database, and the expression levels of translation factor-related genes (TFGs) were extracted for analysis.
Two distinct molecular clusters were identified based on the differential expression of nine significantly altered TFGs. Immune infiltration was notably higher in Cluster C2 compared to Cluster C1. Subsequently, two gene clusters were identified based on the differentially expressed genes between the clusters. A Sankey diagram illustrated a high degree of consistency between the molecular clusters and the gene clusters. Additionally, four machine learning models were developed and evaluated, with the SVM model being utilized to construct a nomogram for predicting the incidence of IVDD. Validation using external datasets and clinical samples confirmed the low expression of EEF2K, which was further analyzed in a pan-cancer context.
The identification and comprehensive assessment of the two molecular clusters offer significant insights for the classification and treatment of individuals with IVDD.
本研究旨在探讨翻译因子（TF）在椎间盘退变（IVDD）中的作用，并通过无监督聚类方法评估其临床相关性。
从基因表达综合数据库（GEO数据库）检索基因表达数据，并提取翻译因子相关基因（TFG）的表达水平进行分析。
基于9个显著改变的TFG的差异表达，鉴定出两个不同的分子簇。与簇C1相比，簇C2中的免疫浸润明显更高。随后，根据簇间差异表达基因鉴定出两个基因簇。桑基图显示分子簇与基因簇之间具有高度一致性。此外，开发并评估了四种机器学习模型，利用支持向量机（SVM）模型构建预测IVDD发生率的列线图。使用外部数据集和临床样本进行验证，证实了EEF2K的低表达，并在泛癌背景下对其进行了进一步分析。
对这两个分子簇的鉴定和综合评估为IVDD个体的分类和治疗提供了重要见解。
