Huang Kai, Shen Lingling, Guan Huiqin, Dai Lei, Huang Xiaogang, Zhang Xinjun, Xu Xiaojun, Liu Chao
Department of Orthopedics, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, P.R. China.
Department of Nursing, Shanghai Jiao Tong University School of Medicine Affiliated Shanghai General Hospital, Shanghai, P.R. China.
J Musculoskelet Neuronal Interact. 2025 Sep 1;25(3):316-327. doi: 10.22540/JMNI-25-316.
To identify pivotal gene markers and pathways involved in intervertebral disc degeneration (IDD) through the construction of a competing endogenous RNA (ceRNA) network.
A ceRNA network was constructed using mRNAs associated with clinical IDD phenotypes (age, MRI grade), identified through Weighted Gene Co-expression Network Analysis (WGCNA). From the core mRNAs within the ceRNA network, potential marker genes were identified using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms. A sub-network was then constructed, and the candidate marker genes were further validated using the mouse IDD dataset GSE134955.
A total of 119 differentially expressed long non-coding RNAs (DELs), 1,267 differentially expressed mRNAs (DEMs), and 37 differentially expressed microRNAs (DEMis) were identified in IDD samples compared to controls. WGCNA identified 1,190 DEMs significantly associated with MRI grade. Based on these MRI grade-associated DEMs, a hub ceRNA network comprising 4 DEMis, 90 DELs, and 18 DEMs was established. Among these, three DEMs-BTG2, MDM4, and ACOX1-were consistently identified as marker genes by LASSO, SVM, and Random Forest. These three genes also demonstrated high accuracy in distinguishing IDD from control samples in the independent mouse dataset.
This study identified key mRNAs implicated in IDD progression and provides new insights into the regulatory roles of ceRNA networks in the disease. These findings may contribute to the development of novel diagnostic biomarkers and therapeutic targets for IDD.
通过构建竞争性内源性RNA(ceRNA)网络来识别参与椎间盘退变(IDD)的关键基因标志物和信号通路。
使用通过加权基因共表达网络分析(WGCNA)鉴定出的与临床IDD表型(年龄、MRI分级)相关的mRNA构建ceRNA网络。从ceRNA网络中的核心mRNA中,使用套索回归、支持向量机(SVM)和随机森林算法识别潜在的标志物基因。然后构建一个子网络,并使用小鼠IDD数据集GSE134955进一步验证候选标志物基因。
与对照组相比,在IDD样本中总共鉴定出119个差异表达的长链非编码RNA(DEL)、1267个差异表达的mRNA(DEM)和37个差异表达的微小RNA(DEMi)。WGCNA鉴定出1190个与MRI分级显著相关的DEM。基于这些与MRI分级相关的DEM,建立了一个包含4个DEMi、90个DEL和18个DEM的核心ceRNA网络。其中,三个DEM——BTG2、MDM4和ACOX1——被套索回归、SVM和随机森林一致鉴定为标志物基因。在独立小鼠数据集中,这三个基因在区分IDD和对照样本方面也表现出高准确性。
本研究确定了与IDD进展相关的关键mRNA,并为ceRNA网络在该疾病中的调控作用提供了新见解。这些发现可能有助于开发用于IDD的新型诊断生物标志物和治疗靶点。