A novel multi-omics approach for identifying key genes in intervertebral disc degeneration

IF 2.5 4区 医学 Q3 BIOCHEMICAL RESEARCH METHODS
Xuan Zhao , Qijun Wang , Shuaikang Wang , Wei Wang , Xiaolong Chen , Shibao Lu
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引用次数: 0

Abstract

Many different cell types and complex molecular pathways are involved in intervertebral disc degeneration (IDD). We used a multi-omics approach combining single-cell RNA sequencing (scRNA-seq), differential gene expression analysis, and Mendelian randomization (MR) to clarify the underlying genetic architecture of IDD. We identified 1,164 differentially expressed genes (DEGs) across four important cell types associated with IDD using publicly available single-cell datasets. A thorough gene network analysis identified 122 genes that may be connected to programmed cell death (PCD), a crucial route in the etiology of IDD. SLC40A1, PTGS2, and GABARAPL1 have been identified as noteworthy regulatory genes that may impede the advancement of IDD. Furthermore, distinct cellular subpopulations and dynamic gene expression patterns were revealed by functional enrichment analysis and pseudo-temporal ordering of chondrocytes. Our results highlight the therapeutic potential of GABARAPL1, PTGS2, and SLC40A1 targeting in the treatment of IDD.
识别椎间盘退变关键基因的新型多指标方法
椎间盘变性(IDD)涉及许多不同的细胞类型和复杂的分子通路。我们采用了一种结合单细胞 RNA 测序、差异基因表达分析和孟德尔随机化的多组学方法来阐明 IDD 的潜在遗传结构。我们利用公开的单细胞数据集,在与 IDD 相关的四种重要细胞类型中发现了 1,164 个差异表达基因 (DEG)。通过全面的基因网络分析,我们发现了 122 个可能与程序性细胞死亡有关的基因,而程序性细胞死亡是 IDD 病因学中的一个重要途径。SLC40A1、PTGS2 和 GABARAPL1 被确定为可能阻碍 IDD 进展的值得注意的调控基因。此外,通过对软骨细胞进行功能富集分析和伪时序排序,还发现了不同的细胞亚群和动态基因表达模式。我们的研究结果凸显了 GABARAPL1、PTGS2 和 SLC40A1 靶向治疗 IDD 的治疗潜力。
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来源期刊
SLAS Technology
SLAS Technology Computer Science-Computer Science Applications
CiteScore
6.30
自引率
7.40%
发文量
47
审稿时长
106 days
期刊介绍: SLAS Technology emphasizes scientific and technical advances that enable and improve life sciences research and development; drug-delivery; diagnostics; biomedical and molecular imaging; and personalized and precision medicine. This includes high-throughput and other laboratory automation technologies; micro/nanotechnologies; analytical, separation and quantitative techniques; synthetic chemistry and biology; informatics (data analysis, statistics, bio, genomic and chemoinformatics); and more.
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