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J Cancer Res Ther. 2018;14(4):833-837. doi: 10.4103/0973-1482.188294.

A differential expression network method identifies ankylosing spondylitis-related genes.

Author information

Department of Traumatology, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China.
Department of Rehabilitation, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China.
Department of Orthopaedics, Linyi People's Hospital, Linyi 276000, Shandong Province, P.R. China.



The exact pathogenic mechanism of ankylosing spondylitis (AS) is still unclear.


we aimed to screen key genes associated with AS using differential expression network (DEN), and further to reveal the molecular mechanism of AS.

Materials and Methods:

First, the gene expression data of AS were recruited and preprocessed. Meanwhile, differentially expressed genes (DEGs) were identified. Then, the DEN including the differential interactions and the nondifferential interactions were constructed, and the hub genes were determined according to degree centrality analysis of nodes. Finally, pathway enrichment analysis was conducted on these genes contained in the DEN to further to determine the importance of the hub genes.


A total of 20,102 genes were obtained and 145 DEGs which including 99 upregulated genes and 46 downregulated genes were identified. Then, a DEN which contained 434 differential interactions and 2 nondifferential interactions were constructed. In the following, four hub genes which were USP7, hepatoma-derived growth factor, EP300, and split hand/foot malformation type 1 (SHFM1) were screened out. None of them was DEGs. Finally, the hub genes of EP300 and SHFM1 were enriched in the pathways of prostate cancer and adherens junction and proteasome pathway, respectively.


Compared to the traditional differential genes methods, DEN is a more useful and comprehensive method to conduct on the AS. We predict that these genes (such as EP300 and SHFM1) could be chosen as novel predictive markers for AS.


Ankylosing spondylitis; centrality analysis; differential expression network; hub genes

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