Identification of transcriptional isoforms associated with survival in cancer patient

作     者：Zefang Tang Tianxiang Chen Xianwen Ren Zemin Zhang 

作者机构：School of Life Sciences and BIOPICPeking UniversityBeijing100871China Beijing Advanced Innovation Centre for GenomicsPeking-Tsinghua Centre for Life SciencesPeking UniversityBeijing100871China 

出 版 物：《Journal of Genetics and Genomics》 (遗传学报（英文版）)

年 卷 期：2019年第46卷第9期

页      面：413-421页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：supported by Beijing Advanced Innovation Centre for Genomics at Peking University,Key Technologies R&D Program(2016YFC0900100) National Natural Science Foundation of China(81573022,31530036,91742203 and 31601063) 

主　　题：Prognostic isoform Gene expression Survival analysis 

摘      要：The Cancer Genome Atlas(TCGA) project produced RNA-Seq data for tens of thousands of cancer and non-cancer samples with clinical survival information,providing an unprecedented opportunity for analyzing prognostic genes and their *** this study,we performed the first large-scale identification of transcriptional isoforms that are specifically associated with patient prognosis,even without gene-level *** specific isoforms are defined as Transcripts Associated with Patient Prognosis(TAPPs).Although a group of TAPPs are the principal isoforms of their genes with intact functional protein domains,another group of TAPPs lack important protein domains found in their canonical gene *** dichotomy in the distribution of protein domains may indicate different patterns of TAPPs association with *** in protein-coding genes,especially those with altered protein domains,are rich in known cancer driver *** further identified multiple types of cancer recurrent TAPPs,such as DCAF17-201,providing a new approach for the detection of cancer-associated *** order to make the wide research community to study prognostic isoforms,we developed a portal named GESUR(http://***/),which illustrates the detailed prognostic characteristics of TAPPs and other ***,our integrated analysis of gene expression and clinical parameters provides a new perspective for understanding the applications of different gene isoforms in tumor progression.