Author:
Zhong Lin-Kun,Deng Xing-Yan,Shen Fei,Cai Wen-Song,Feng Jian-Hua,Gan Xiao-Xiong,Jiang Shan,Liu Chi-Zhuai,Zhang Ming-Guang,Deng Jiang-Wei,Zheng Bing-Xing,Xie Xiao-Zhang,Ning Li-Qing,Huang Hui,Chen Shan-Shan,Miao Jian-Hang,Xu Bo
Abstract
The accurate determination of the risk of cancer recurrence is a critical unmet need in managing thyroid cancer (TC). Although numerous studies have successfully demonstrated the use of high throughput molecular diagnostics in TC prediction, it has not been successfully applied in routine clinical use, particularly in Chinese patients. In our study, we objective to screen for characteristic genes specific to PTC and establish an accurate model for diagnosis and prognostic evaluation of PTC. We screen the differentially expressed genes by Python 3.6 in The Cancer Genome Atlas (TCGA) database. We discovered a three-gene signature Gap junction protein beta 4 (GJB4), Ripply transcriptional repressor 3 (RIPPLY3), and Adrenoceptor alpha 1B (ADRA1B) that had a statistically significant difference. Then we used Gene Expression Omnibus (GEO) database to establish a diagnostic and prognostic model to verify the three-gene signature. For experimental validation, immunohistochemistry in tissue microarrays showed that thyroid samples’ proteins expressed by this three-gene are differentially expressed. Our protocol discovered a robust three-gene signature that can distinguish prognosis, which will have daily clinical application.
Subject
Biochemistry, Genetics and Molecular Biology (miscellaneous),Molecular Biology,Biochemistry
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献