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利用源自RNA测序数据的全局转录因子活性图谱对透明细胞肾细胞癌进行患者分层

Patient Stratification of Clear Cell Renal Cell Carcinoma Using the Global Transcription Factor Activity Landscape Derived From RNA-Seq Data.

作者信息

Zhu Yanyan, Cang Shundong, Chen Bowang, Gu Yue, Jiang Miaomiao, Yan Junya, Shao Fengmin, Huang Xiaoyun

机构信息

Department of Oncology, Henan Provincial People's Hospital; Zhengzhou University People's Hospital, Zhengzhou, China.

Research and Development, Zhiyu Inc, Shenzhen, China.

出版信息

Front Oncol. 2020 Dec 4;10:526577. doi: 10.3389/fonc.2020.526577. eCollection 2020.

DOI:10.3389/fonc.2020.526577
PMID:33344220
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7746882/
Abstract

Clear cell renal cell carcinoma represents the most common type of kidney cancer. Precision medicine approach to ccRCC requires an accurate stratification of patients that can predict prognosis and guide therapeutic decision. Transcription factors are implicated in the initiation and progression of human carcinogenesis. However, no comprehensive analysis of transcription factor activity has been proposed so far to realize patient stratification. Here we propose a novel approach to determine the subtypes of ccRCC patients based on global transcription factor activity landscape. Using the TCGA cohort dataset, we identified different subtypes that have distinct up-regulated biomarkers and altered biological pathways. More important, this subtype information can be used to predict the overall survival of ccRCC patients. Our results suggest that transcription factor activity can be harnessed to perform patient stratification.

摘要

透明细胞肾细胞癌是最常见的肾癌类型。对透明细胞肾细胞癌采用精准医学方法需要对患者进行准确分层，以预测预后并指导治疗决策。转录因子与人类致癌作用的起始和进展有关。然而，迄今为止尚未提出对转录因子活性进行全面分析以实现患者分层。在此，我们提出一种基于全局转录因子活性图谱确定透明细胞肾细胞癌患者亚型的新方法。利用TCGA队列数据集，我们识别出具有不同上调生物标志物和改变生物途径的不同亚型。更重要的是，这种亚型信息可用于预测透明细胞肾细胞癌患者的总生存期。我们的结果表明，转录因子活性可用于进行患者分层。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/ea2202a271b3/fonc-10-526577-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/88001fbd9e2e/fonc-10-526577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/2bcacfd9a6f2/fonc-10-526577-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/c4b3da97ddb2/fonc-10-526577-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/4186d1295585/fonc-10-526577-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/eaa9af782b82/fonc-10-526577-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/ea2202a271b3/fonc-10-526577-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/88001fbd9e2e/fonc-10-526577-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/2bcacfd9a6f2/fonc-10-526577-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/c4b3da97ddb2/fonc-10-526577-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/4186d1295585/fonc-10-526577-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/eaa9af782b82/fonc-10-526577-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9531/7746882/ea2202a271b3/fonc-10-526577-g006.jpg

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