Identification of the OA-related metabolism-related genes, corresponding transcription factors, relevant pathways, and specific bioactive small molecules

Metabolic alteration of articular cartilage is associated with the pathogenesis of Osteoarthritis (OA). This study aims to identify the metabolism-related genes, corresponding transcription factors (TFs), and relevant pathways. Overall, RNA sequencing profiles of articular cartilage were collected from the GEO database. Metabolism-related genes and OA-related hallmarks were collected from the MSigDB v7.1. Differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Variation Analysis (GSVA) were conducted to identify pathways or hallmarks that were related to the pathogenesis of OA. The Pearson correlation analysis was used to establish the regulatory network among transcription factors, metabolism-related genes, and hallmarks. To further confirm the regulation of the identified transcription factors, Chromatin Immunoprecipitation-sequencing (ChIP-seq) was conducted, and single-cell sequencing was used to locate the cell clusters. Connectivity Map (CM) analysis were also conducted to identify the potential specific bioactive small molecules targeting the metabolic alteration of osteoarthritis. scTPA database was used to detect activated signaling pathways. Collectively, a total of 74 and 38 differentially expressed metabolism-related genes and TFs were retrieved. Skeletal system development, extracellular matrix, and cell adhesion molecule binding were important pathways in GO analysis. Human papillomavirus infection, PI3K-Akt signaling pathway, and Human T-cell leukemia virus 1 infection were the top 3 pathways in KEGG. 7 and 12 hallmarks were down- and up-regulated in GSVA, respectively. Ten bioactive small molecules may be potential treatments of OA by regulating the metabolism of articular cartilage. ChIP-seq analysis showed high relativity between transcription factors and their target genes. Furthermore, single-cell sequencing confirms the high expression of identified transcription factors in chondrocytes. To conclude, we established a comprehensive network integrated with transcription factors, metabolism-related genes, and hallmarks.