Tianxiang Chen
Xianwen Ren
Zemin Zhang
aSchool of Life Sciences and BIOPIC, Peking University, Beijing, 100871, China
bBeijing Advanced Innovation Centre for Genomics, Peking-Tsinghua Centre for Life Sciences, Peking University, Beijing, 100871, China
More InformationCorresponding author: E-mail address: tangzefang@pku.edu.cn (Zefang Tang);E-mail address: zeminz@yahoo.com (Zemin Zhang)
Received Date: 2019-01-14
Accepted Date:2019-08-21
Rev Recd Date:2019-08-04
Available Online: 2019-09-25 Publish Date:2019-09-20
Abstract
Abstract
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 isoforms. In this study, we performed the first large-scale identification of transcriptional isoforms that are specifically associated with patient prognosis, even without gene-level association. These 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 isoforms. This dichotomy in the distribution of protein domains may indicate different patterns of TAPPs association with cancer. TAPPs in protein-coding genes, especially those with altered protein domains, are rich in known cancer driver genes. We further identified multiple types of cancer recurrent TAPPs, such asDCAF17-201, providing a new approach for the detection of cancer-associated events. In order to make the wide research community to study prognostic isoforms, we developed a portal named GESUR (http://gesur.cancer-pku.cn/), which illustrates the detailed prognostic characteristics of TAPPs and other isoforms. Overall, our integrated analysis of gene expression and clinical parameters provides a new perspective for understanding the applications of different gene isoforms in tumor progression.Keywords: Prognostic isoform,
Gene expression,
Survival analysis
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