We have located links that may give you full text access.
Identification of differentiated functional modules in papillary thyroid carcinoma by analyzing differential networks.
Journal of Cancer Research and Therapeutics 2018 December
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.
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.
Full text links
Trending Papers
A Personalized Approach to the Management of Congestion in Acute Heart Failure.Heart International 2023
Potential Mechanisms of the Protective Effects of the Cardiometabolic Drugs Type-2 Sodium-Glucose Transporter Inhibitors and Glucagon-like Peptide-1 Receptor Agonists in Heart Failure.International Journal of Molecular Sciences 2024 Februrary 21
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app