ArticleFrontiers in oncology2020
Patient Stratification of Clear Cell Renal Cell Carcinoma Using the Global Transcription Factor Activity Landscape Derived From RNA-Seq Data.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Identification of IGF2BP3 and CENPA as key regulators of immunophenotypes in renal clear cell carcinoma.Frontiers in genetics · 2025Article
- Pan-cancer analysis reveals TRA16 as a master regulator of human carcinogenesis.Frontiers in oncology · 2025Article
- DUSP5 regulated by YTHDF1-mediated m6A modification promotes epithelial-mesenchymal transition and EGFR-TKI resistance via the TGF-β/Smad signaling pathway in lung adenocarcinoma.Cancer cell international · 2024Article
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Prognostic value and model construction of preoperative inflammatory markers in patients with metastatic renal cell carcinoma.World journal of surgical oncology · 2023Article
- Identification of key genes of the ccRCC subtype with poor prognosis.Scientific reports · 2022Article
- BNIP3 Upregulation Characterizes Cancer Cell Subpopulation With Increased Fitness and Proliferation.Frontiers in oncology · 2022Article
- Survival Prognosis, Tumor Immune Landscape, and Immune Responses of ADAMTS14 in Clear Cell Renal Cell Carcinoma and Its Potential Mechanisms.Frontiers in immunology · 2022Article
- Single-Cell Sequencing to Identify Six Heat Shock Protein (HSP) Genes-Mediated Progression Subtypes of Clear Cell Renal Cell Carcinoma.International journal of general medicine · 2021Article
- Patient Stratification of Clear Cell Renal Cell Carcinoma Using the Global Transcription Factor Activity Landscape Derived From RNA-Seq Data.Frontiers in oncology · 2020Article
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Authors and funding
8 authors.
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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.
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