Identification of osteoarthritis-related genes and potential drugs based on single cell RNA-seq data

Ning Wang1, Kun Liu1, Jia-Li Li1

  • 1Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, 410081, Hunan, China.

PubMed

Insights

This study identifies Pitavastatin as a potential treatment for osteoarthritis (OA) by analyzing gene expression data and zebrafish models. Conversely, Cabazitaxel was found to increase OA risk, highlighting distinct therapeutic potentials.

Area of Science:

  • Genomics and Bioinformatics
  • Pharmacology
  • Translational Medicine

Background:

  • Osteoarthritis (OA) poses a significant global health challenge with limited effective treatments.
  • There is an unmet need for novel therapeutic agents for OA prevention and treatment.

Purpose of the Study:

  • To identify key genes associated with OA using multi-omics data.
  • To screen for potential drug candidates for OA treatment.
  • To evaluate the therapeutic effects of candidate drugs in preclinical models.

Main Methods:

  • Single-cell and bulk RNA sequencing (scRNA-seq, bulk-RNA seq) data analysis.
  • High-dimensional weighted correlation network analysis (hdWGCNA), random forest, and protein-protein interaction (PPI) network analyses.
  • Mendelian randomization (MR), drug prediction algorithms (SAVERUNNER), and zebrafish OA models.

Main Results:

  • Five key OA-associated genes (CXCL8, CCL20, MMP3, BIRC3, ICAM1) were identified.
  • Mendelian randomization indicated that increased TUBB1 expression reduces OA risk, and Cabazitaxel, by inhibiting TUBB1, increases OA risk.
  • Zebrafish experiments demonstrated Pitavastatin's therapeutic effect in OA by increasing joint space, while Cabazitaxel worsened OA.
  • RT-qPCR confirmed Pitavastatin inhibited HMGCR and Cabazitaxel inhibited TUBB1 expression in zebrafish.

Conclusions:

  • Pitavastatin exhibits therapeutic potential for osteoarthritis.
  • Cabazitaxel may increase osteoarthritis risk.
  • This study provides a multi-omics and experimental validation approach for identifying OA therapeutics.

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