| Literature DB >> 31332712 |
Tianyu Zhai1, Dilidaer Muhanhali1, Xi Jia2, Zhiyong Wu3, Zhenqin Cai1, Yan Ling4.
Abstract
Papillary thyroid cancer (PTC) is the most prevalent histological type among thyroid cancers, and some patients are at a high risk for recurrent disease or even death. Identification for the potential biomarkers of PTC may contribute to early discovery of recurrence and treatment. In The Cancer Genome Atlas (TCGA) database, we obtained the information of RNA sequence data and clinical characteristics of PTC. Weighted gene co-expression network analysis (WGCNA) was performed to construct gene co-expression networks and investigate the relationship between modules and clinical traits. Finally, we constructed 16 co-expression modules in 10,428 genes, and three key modules (darkturquoise, lightyellow, and red) associated with tumor N grade were identified. The results of functional annotation indicated that the darkturquoise module was primarily enriched in the regulation of the extracellular matrix (ECM), collagen metabolism, and cell adhesion, the lightyellow module was primarily enriched in the mitochondrial function regulation and energy synthesis, and the red module was primarily enriched in the process of cell junction, apoptosis, and inflammatory response, suggesting their significant role in the progression of PTC. In addition, the hub genes in the three modules were identified and screened for differentially expressed genes (DEGs). Relapse-free survival analyses found that 11 genes (KCNQ3, MET, FN1, ITGA3, RUNX1, ITGA2, PERP, GCSH, FAAH, NGFRAP1, and HSPA5) may play a pivotal role in PTC relapse. In general, our research revealed the key co-expression modules and identified several prognostic biomarkers, which provides some new insights into the lymph node metastasis of PTC.Entities:
Keywords: Hub genes; Lymph node metastasis; Papillary thyroid cancer; Weighted gene co-expression network analysis
Mesh:
Year: 2019 PMID: 31332712 DOI: 10.1007/s12020-019-02021-9
Source DB: PubMed Journal: Endocrine ISSN: 1355-008X Impact factor: 3.633