Identification of potential functional genes in papillary thyroid cancer by co‑expression network analysis

Ao,Zeng‑Xin; Chen,Yuan‑Cheng; Lu,Jun‑Min; Shen,Jie; Peng,Lin‑Ping; Lin,Xu; Peng,Cheng; Zeng,Chun‑Ping; Wang,Xia‑Fang; Zhou,Rou; Chen,Zhi; Xiao,Hong‑Mei; Deng,Hong‑Wen

doi:10.3892/ol.2018.9306

Identification of potential functional genes in papillary thyroid cancer by co‑expression network analysis

Authors:
- Zeng‑Xin Ao
- Yuan‑Cheng Chen
- Jun‑Min Lu
- Jie Shen
- Lin‑Ping Peng
- Xu Lin
- Cheng Peng
- Chun‑Ping Zeng
- Xia‑Fang Wang
- Rou Zhou
- Zhi Chen
- Hong‑Mei Xiao
- Hong‑Wen Deng
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Affiliations: Department of Endocrinology and Metabolism, The Third Affiliated Hospital, Southern Medical University, Guangzhou, Guangdong 510630, P.R. China, School of Basic Medical Sciences, Central South University, Changsha, Hunan 410000, P.R. China
Published online on: August 14, 2018 https://doi.org/10.3892/ol.2018.9306
Pages: 4871-4878
Copyright: © Ao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co‑expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co‑expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co‑expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC‑related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.

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