Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China.
Taiyuan Central Hospital, Ninth Hospital of Shanxi Medical University, Southern Fendong Road 256, Taiyuan, ShanXi, 030009, China.
BMC Musculoskelet Disord. 2023 Sep 12;24(1):729. doi: 10.1186/s12891-023-06854-4.
Low back pain (LBP) has drawn much widespread attention and is a major global health concern. In this field, intervertebral disc degeneration (IVDD) is frequently the focus of classic studies. However, the mechanistic foundation of IVDD is unclear and has led to conflicting outcomes.
Gene expression profiles (GSE34095, GSE147383) of IVDD patients alongside control groups were analyzed to identify differentially expressed genes (DEGs) in the GEO database. GSE23130 and GSE70362 were applied to validate the yielded key genes from DEGs by means of a best subset selection regression. Four machine-learning models were established to assess their predictive ability. Single-sample gene set enrichment analysis (ssGSEA) was used to profile the correlation between overall immune infiltration levels with Thompson grades and key genes. The upstream targeting miRNAs of key genes (GSE63492) were also analyzed. A single-cell transcriptome sequencing data (GSE160756) was used to define several cell clusters of nucleus pulposus (NP), annulus fibrosus (AF), and cartilaginous endplate (CEP) of human intervertebral discs and the distribution of key genes in different cell clusters was yielded.
By developing appropriate p-values and logFC values, a total of 6 DEGs was obtained. 3 key genes (LRPPRC, GREM1, and SLC39A4) were validated by an externally validated predictive modeling method. The ssGSEA results indicated that key genes were correlated with the infiltration abundance of multiple immune cells, such as dendritic cells and macrophages. Accordingly, these 4 key miRNAs (miR-103a-3p, miR-484, miR-665, miR-107) were identified as upstream regulators targeting key genes using the miRNet database and external GEO datasets. Finally, the spatial distribution of key genes in AF, CEP, and NP was plotted. Pseudo-time series and GSEA analysis indicated that the expression level of GREM1 and the differentiation trajectory of NP chondrocytes are generally consistent. GREM1 may mainly exacerbate the degeneration of NP cells in IVDD.
Our study gives a novel perspective for identifying reliable and effective gene therapy targets in IVDD.
下腰痛（LBP）引起了广泛关注，是一个主要的全球健康问题。在这个领域，椎间盘退变（IVDD）经常是经典研究的焦点。然而，IVDD 的机械基础尚不清楚，导致结果相互矛盾。
分析 GEO 数据库中 IVDD 患者和对照组的基因表达谱（GSE34095、GSE147383），以鉴定差异表达基因（DEGs）。应用 GSE23130 和 GSE70362 通过最佳子集回归验证 DEGs 产生的关键基因。建立四个机器学习模型来评估它们的预测能力。使用单样本基因集富集分析（ssGSEA）来分析整体免疫浸润水平与 Thompson 分级和关键基因之间的相关性。还分析了关键基因的上游靶向 miRNA（GSE63492）。使用单细胞转录组测序数据（GSE160756）定义了人类椎间盘核髓核（NP）、纤维环（AF）和软骨终板（CEP）的几个细胞簇，并得出了关键基因在不同细胞簇中的分布。
通过制定适当的 p 值和 logFC 值，共获得 6 个 DEGs。通过外部验证预测模型方法验证了 3 个关键基因（LRPPRC、GREM1 和 SLC39A4）。ssGSEA 结果表明，关键基因与多种免疫细胞（如树突状细胞和巨噬细胞）的浸润丰度相关。因此，使用 miRNet 数据库和外部 GEO 数据集，鉴定了这 4 个关键 miRNA（miR-103a-3p、miR-484、miR-665、miR-107）作为靶向关键基因的上游调节剂。最后，绘制了关键基因在 AF、CEP 和 NP 中的空间分布。拟时序列和 GSEA 分析表明，GREM1 的表达水平和 NP 软骨细胞的分化轨迹大致一致。GREM1 可能主要加剧 IVDD 中 NP 细胞的退变。
我们的研究为鉴定 IVDD 中可靠有效的基因治疗靶点提供了新的视角。
