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  • 标题:Transcript Level Analysis Improves the Understanding of Bladder Cancer
  • 本地全文:下载
  • 作者:Xiang Ao ; Shuaicheng Li
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2020
  • 卷号:10
  • 期号:5
  • 页码:29-40
  • DOI:10.5121/csit.2020.100503
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Bladder cancer (BC) is one of the most globally prevalent diseases, attracting various studies on BC relevant topics. High-throughput sequencing renders it convenient to extensively explore genetic changes, like the variation in gene expression, in the development of BC. In this study, we did differential analysis on gene and transcript expression (DGE and DTE) and differential transcript usage (DTU) analysis in an RNA-seq dataset of 42 bladder cancer patients. DGE analysis reported 8543 significantly differentially expressed (DE) genes. In contrast, DTE analysis detected 14350 significantly DE transcripts from 8371 genes, and DTU analysis detected 27914 significantly differentially used (DU) transcripts from 8072 genes. Analysis of the top 5 DE genes demonstrated that DTE and DTU analysis provided the source of changes in gene expression at the transcript level. The transcript-level analysis also identified some DE and DU transcripts from previously reported mutated genes that related to BC, like ERBB2, ESPL1, and STAG2, suggesting an intrinsic connection between gene mutation and alternative splicing. Hence, the transcript-level analysis may help disclose the underlying pathological mechanism of BC and further guide the design of personal treatment.
  • 关键词:Bladder Cancer ;Differential Gene Expression ;Differential Transcript Expression ;Differential Transcript Usage
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