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Intervertebral disc degeneration (IVDD) has been considered a major cause of low back pain. Therefore, further molecular subtypes of IVDD and identification of potential critical genes are urgently needed. First, consensus clustering was used to classify patients with IVDD into two subtypes and key module genes for subtyping were identified using weighted gene co-expression network analysis (WGCNA). Then, key module genes for the disease were identified by WGCNA. Subsequently, SVM and GLM were used to identify hub genes. Based on the above genes, a nomogram was constructed to predict the subtypes of IVDD. Finally, we find that ROM1 is lowered in IVDD and is linked to various cancer prognoses. The present work offers innovative diagnostic and therapeutic biomarkers for molecular subtypes of IVDD.
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http://dx.doi.org/10.18632/aging.205653 | DOI Listing |
J Inflamm Res
May 2025
Orthopedics Department of Spine Surgery, Hebei Medical University Third Hospital, Shijiazhuang City, Hebei Province, People's Republic of China.
Background: Low back pain represents a major global health issue, with intervertebral disc degeneration (IVDD) being one of its primary causes. Disc degeneration involves complex processes such as inflammation, matrix degradation, and cell death, yet the underlying mechanisms remain poorly understood. Single-cell RNA sequencing offers a powerful approach to elucidate cellular heterogeneity and dynamic changes in IVDD, providing valuable insights for early diagnosis and targeted therapeutic strategies.
View Article and Find Full Text PDFSci Rep
May 2025
Department of Orthopedics, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, 401120, China.
Degeneration of intervertebral discs is a significant factor in chronic lower back pain, impacting millions annually. Existing studies propose a potential link between lipids and disc disease, though causal relationships remain unclear. The objective of this study is to explore the causal connections between lipids, lower back pain, disc degeneration, and the risk of sciatica In this research, we utilized a comprehensive GWAS dataset encompassing 179 lipid traits to explore the causal connections between lipids and the susceptibility to conditions such as lower back pain (LBP), intervertebral disc degeneration (IVDD), and sciatica.
View Article and Find Full Text PDFObjective: 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.
JOR Spine
March 2025
Department of Orthopedics, The Affiliated Hospital of Traditional Chinese Medicine Southwest Medical University Luzhou Sichuan China.
Background: Intervertebral disc degeneration (IVDD) is a complex age-related physiological process, with cellular senescence (CS) being a primary contributing factor. However, the precise role of CS and its associated genes in IVDD remains unclear.
Methods: In this study, we performed differential expression analysis on the GSE124272 and GSE150408 datasets from the GEO database and identified 53 differentially expressed cellular senescence-related genes (CSRGs).
J Transl Med
February 2025
Department of Orthopaedic Surgery, Changhai Hospital, Shanghai, China.
Background: Intervertebral disc degeneration (IVDD) is a significant cause of global disability, reducing labor productivity, increasing the burden on public health, and affecting socio-economic well-being. Currently, there is a lack of recognized clinical approaches for molecular classification and precision therapy.
Methods: Chondrocyte differentiation and prognosis-related genes were extracted from single-cell RNA sequencing and multi-omics data in the Gene Expression Omnibus (GEO) database through chondrocyte trajectory analysis and non-parametric tests.