Identification of osteoarthritis-related genes and potential drugs based on single cell RNA-seq data
摘要
Osteoarthritis (OA) is a global problem that seriously affects human health. At present, there is still a lack of effective drugs to treat OA. Therefore, we need to find more drugs with preventive and therapeutic effects on OA. In this study, we obtained single-cell RNA sequencing (scRNA-seq) and bulk-RNA seq datasets from Gene Expression Omnibus (GEO). By using high-dimensional weighted correlation network analysis (hdWGCNA), random forest method and protein–protein interaction (PPI) network analyses, five key genes (CXCL8, CCL20, MMP3, BIRC3 and ICAM1) related to OA were identified and the RT-qPCR experiments verified the differential expression of CXCL8, CCL20 and BIRC3 between Triclocarban (TCC) treated zebrafishes and controls. The SAVERUNNER algorithm predicted 42 candidate drugs. Mendelian randomization (MR) of the candidate drugs showed that the increased expression of TUBB1 led to a reduced risk of OA (β = -0.08, P-value = 4.56E-04), while Cabazitaxel (a microtubule dynamics inhibitor commonly used in the treatment of advanced prostate cancer) inhibits the expression of TUBB1, thus increases the risk of OA. Pitavastatin (a statin lipid-lowering drug that can reduce blood lipid levels and the risk of cardiovascular diseases) target genes expression (for HMGCR