Identification of upstream regulators for synovial expression signature genes in osteoarthritis

Abstract

Objectives: The detection of transcription factors (TFs) for OA signature genes provides better clues to the underlying regulatory mechanisms and therapeutic applications.

Methods: We searched GEO database for synovial expression profiling from different OA microarray studies to perform a systematic analysis. Functional annotation of DEGs was conducted, including gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Based on motif databases and the results from integrated analysis of current gene expression data, a global transcriptional regulatory network was constructed, and the upstream TFs were identified for OA signature genes.

Results: Six GEO datasets were obtained. Totally, 805 genes across the studies were consistently differentially expressed in OA (469 up-regulated and 336 down-regulated genes) with FDR≤0.01. Supporting an involvement of ECM in the development of OA, we showed that ECM-receptor interaction was the most significant pathway in our KEGG analysis (P=5.92E-12). Sixty-one differentially expressed TFs were identified with FDR≤0.05. The constructed OA-specific regulatory networks consisted of 648 TF-target interactions between 51 TFs and 429 DEGs in the context of OA. The top 10 TFs covering the most downstream DEGs were identified as crucial TFs involved in the development of OA, including ARID3A, NFIC, ZNF354C, NR4A2, BRCA1, EHF, FOXL1, FOXC1, EGR1, and HOXA5.

Conclusion: This integrated analysis has identified the OA signature, providing clues to pathogenesis of OA at the molecular level, which may be also used as diagnostic markers for OA. Some crucial upstream regulators, such as NR4A2, EHF, and EGR1 may be considered as potential new therapeutic targets for OA.

Keywords: Differentially expressed genes; Gene expression data; Integrated analysis; Microarray; Osteoarthritis.