ArticleBioinformatics (Oxford, England)2019
Exploring drivers of gene expression in the Cancer Genome Atlas.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 41 citations in OpenAlex.
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- Strategies for the drug development of cancer therapeutics.Frontiers in pharmacology · 2025Review
- Evolutionary Mechanism Based Conserved Gene Expression Biclustering Module Analysis for Breast Cancer Genomics.Biomedicines · 2024Article
- PSAT1 enhances the efficacy of the prognosis estimation nomogram model in stage-based clear cell renal cell carcinoma.BMC cancer · 2024Article
- The Concordant Disruption of B7/CD28 Immune Regulators Predicts the Prognosis of Oral Carcinomas.International journal of molecular sciences · 2023Article
- Expressional and prognostic value of HPCAL1 in cholangiocarcinoma via integrated bioinformatics analyses and experiments.Cancer medicine · 2023Article
- Individualized multi-omic pathway deviation scores using multiple factor analysis.Biostatistics (Oxford, England) · 2022Article
- Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma.Frontiers in oncology · 2022Article
- Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer.World journal of gastrointestinal oncology · 2020Article
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- Open Data for Differential Network Analysis in Glioma.International journal of molecular sciences · 2020Article
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- Identification of a Rat Mammary Tumor Risk Locus That Is Syntenic with the Commonly Amplified 8q12.1 and 8q22.1 Regions in Human Breast Cancer Patients.G3 (Bethesda, Md.) · 2019Article
- Weighted correlation network and differential expression analyses identify candidate genes associated with BRAF gene in melanoma.BMC medical genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Motivation: The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. Results: To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. Availability and implementation: We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga. All TCGA Open Access tier data are available at the Broad Institute GDAC Firehose and were downloaded using the TCGA2STAT R package. TCGA Controlled Access tier data are available via controlled access through the Genomic Data Commons (GDC). R scripts used to download, format and analyze the data and produce the interactive R/Shiny web app have been made available on GitHub at https://github.com/andreamrau/EDGE-in-TCGA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.