- 作者：Qi Fei ; JiSheng Lin ; Hai Meng ; BingQiang Wang ; Yong Yang ; Qi Wang ; Nan Su ; Jinjun Li ; Dong Li ; spinefei@163.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Integrated analysis ; Gene expression data ; Osteoarthritis ; Microarray ; Differentially expressed genes
- 刊名：Joint Bone Spine
- 出版年：2016
- 出版时间：October 2016
- 年：2016
- 卷：83
- 期：5
- 页码：545-551
- 全文大小：1894 K
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.