Identification of Gene Coexpression Modules and Prognostic Genes Associated with Papillary Thyroid Cancer

Abstract

Thyroid cancer is a great part of the endocrine tumor with an increasing incidence. Papillary thyroid carcinoma (PTC) is the most common subtype. With the enormous pace taken in the microarray technology, bioinformatics is applied in data mining more frequently. Weighted gene coexpression network analysis (WGCNA) can perform analysis combining clinic information. We performed WGCNA for prognostic genes associated with PTC. From the GEO profile, we got ten modules. We identified a key module that was closest to patients’ survival time. Then, we screened five hub genes (ATRX, BOD1L1, CEP290, DCAF16, and NEK1) from the key module based on the clinical information from TCGA. These five genes not only significantly differ between the normal and tumor groups but have prognostic value. The receiver operating characteristic (ROC) curve indicated that they had the potential to serve as prognostic genes. We performed next-generation sequencing using the PTC tissue to get more convincing evidence. Besides, we established a new signature and verified it through K-M plots and ROC. The signature could be an independent factor for the prognosis of PTC, and we built a nomogram to carry out a quantitative study. In a word, the hub genes we explored in the study deserved more basic and clinical research.