Identification of an Autophagy-Related Signature Predicting Overall Survival for Papillary Thyroid Carcinoma
Gang Hu, Hong-Fang Feng, Hui Zhan
Abstract
Background: Papillary thyroid carcinoma usually shows an excellent prognosis. However, its recurrence or persistence rate is high. In this study, we used bioinformatics to identify autophagy-related genes (ARGs) and establish a novel scoring system for papillary thyroid carcinoma. Methods: We collected ARGs sequencing data of patients with papillary thyroid carcinoma from The Cancer Genome Atlas database. Differentially expressed ARGs were identified by the “Limma” package in R language. After univariate and multivariate Cox regression analysis, an ARG signature was developed. The established prognostic signature was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic. Results: A sum of 26 differentially expressed ARGs were identified. Gene set enrichment analysis revealed that several significantly oncological signatures were enriched, such as autophagy, p53 signaling pathway, apoptosis, human cytomegalovirus infection, and platinum drug resistance. After univariate and multivariate analysis, 3 ARGs ( ITPR1, CCL2, and CDKN2A) were selected to develop autophagy-related signature. Patients with high risk had significantly shorter overall survival than those with low risk. The areas under the curve indicated that the signature showed good accuracy of prediction. Conclusions: We established a novel scoring system based on 3 ARGs, which provides a promising tool for the development of personalized therapy.