OBJECTIVE: 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. METHODS: Gene expression data were retrieved from the GEO database, and the expression levels of translation factor-related genes (TFGs) were extracted for analysis. RESULTS: 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. CONCLUSION: The identification and comprehensive assessment of the two molecular clusters offer significant insights for the classification and treatment of individuals with IVDD.
Molecular Subtypes and Immune Microenvironment Characterization of the Annulus Fibrosus in Intervertebral Disc Degeneration: Insights From Translation Factor-Related Gene Analysis.
椎间盘退变中纤维环的分子亚型和免疫微环境特征:来自转化因子相关基因分析的见解
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作者:Zheng Sikuan, Zhao Xiaokun, Wu Hui, Cuan Xuhui, Cheng Xigao, He Dingwen
| 期刊: | JOR Spine | 影响因子: | 3.900 |
| 时间: | 2025 | 起止号: | 2025 Apr 7; 8(2):e70064 |
| doi: | 10.1002/jsp2.70064 | 研究方向: | 其它 |
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