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Identification of differentiated functional modules in papillary thyroid carcinoma by analyzing differential networks

1 Department of General Surgery, Linzi District People's Hospital, Zibo 255 400, Shandong Province, China
2 Department of Ultrasound, Linzi District People's Hospital, Zibo 255 400, Shandong Province, China

Correspondence Address:
Yingluan Wang,
No. 139, Huan Gong Road, Linzi District People's Hospital, Zibo 255 400, Shandong Province
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_730_16

Purpose: The incidence of papillary thyroid carcinoma (PTC) has dramatically increased over the past two decades. This study aimed to investigate the disparity of gene expression between PTC and normal tissues. Materials and Methods: Gene chip data of E-GEOD-33630 and E-GEOD-60542 were acquired and downloaded from European Bioinformatics Institute Part of the European Molecular Biology Laboratory website. E-GEOD-33630 data contained 94 test samples (49 PTC and 45 normal tissues), and E-GEOD-60542 data contained 63 test samples (33 PTC and thirty normal tissues). The two sets of data were analyzed by screening differential co-expression network (DCN) and identifying M-differential module. Results: Three differential modules were gained after statistical comparison between the PTC and normal tissues (P < 0.05). Short coiled-coil protein (SCOC) gene was as the seed gene of module 1, which contained 7 nodes and 9 edges. Moreover, SYPL1 was the seed gene of module 2 with 10 nodes and 16 edges. THAP1 was the seed gene of module 3 that contained 9 nodes and 12 edges. Conclusion: Analysis and statistical comparison of the gene chip can effectively screen out differential expression genes between the PTC and normal tissues. Based on a large number of samples and gene chip detection, three seed genes of SCOC, SYPL1, and THAP1 are determined. These data provide novel insights into the pathogenesis of PTC. Significant changes in the expression levels between PTC and normal tissues suggest that SCOC, SYPL1, or THAP1 may play a vital role in the incidence and development of PTC, which serve as potential biomarkers for the diagnosis of PTC.

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