Study on molecular mechanism of intervertebral disc degeneration by single cell hdWGCNA combined with transcriptome sequencing

Abstract

Background: Intervertebral disc degeneration (IVDD) is one of the important causes of lower back pain, seriously affecting people's health and quality of life. This research employs single-cell analysis to identify the specific cellular subtypes and key regulatory genes associated with IVDD. Methods: We analyzed the single-cell data and screened cells that closely associated with the development of IVDD. The differential expression of feature genes between IVDD and control groups was analyzed. Additionally, drugs and regulatory transcription factors that interact with feature genes were predicted and clinically validated by reverse transcription quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and enzyme-linked immunosorbent assay (ELISA). Results: Our study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A, through the high-dimensional weighted gene co-expression network analysis (hdWGCNA) analysis, least absolute shrinkage and selection operator (LASSO), and random forest (RF). Besides, compared to the MDD group, IGFBP3 and TMEM45A were significantly upregulated in the SDD group, while ACAN and VAPA showed no significant difference between the two groups. ELISA testing revealed a positive correlation between IGFBP3 concentration and the grading of IVDD. Furthermore, Celecoxib may be used to treat IVDD by inhibiting IGFBP3. Conclusion: Our study identified the Chond2 cell subtype associated with IVDD and selected four feature genes influencing the development of IVDD, namely IGFBP3, ACAN, VAPA and TMEM45A. Our findings establish a robust theoretical foundation for the clinical diagnosis and treatment of IVDD patients.

Keywords

• Intervertebral disc degeneration
• Single cell analysis
• hdWGCNA
• Machine learning
• Diagnostic markers