ArticleProceedings of the National Academy of Sciences of the United States of America2015
Inference of transcriptional regulation in cancers.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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Who cites it
57 citing papers in PubMed, 109 citations in OpenAlex.
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- A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data.NAR genomics and bioinformatics · 2023Article
- Comprehensive analysis of alternative splicing across multiple transcriptomic cohorts reveals prognostic signatures in prostate cancer.Human genomics · 2023Article
- Identifying genetic regulatory variants that affect transcription factor activity.Cell genomics · 2023Article
- Decoding key cell sub-populations and molecular alterations in glioblastoma at recurrence by single-cell analysis.Acta neuropathologica communications · 2023Article
- A Comprehensive Analysis Revealing FBXW9 as a Potential Prognostic and Immunological Biomarker in Breast Cancer.International journal of molecular sciences · 2023Article
- Increased FOXM1 Expression was Associated with the Prognosis and the Recruitment of Neutrophils in Endometrial Cancer.Journal of immunology research · 2023Article
- Exosomal miR-1304-3p promotes breast cancer progression in African Americans by activating cancer-associated adipocytes.Nature communications · 2022Article
- The Pan-Cancer Multi-Omics Landscape of FOXO Family Relevant to Clinical Outcome and Drug Resistance.International journal of molecular sciences · 2022Article
- Natural Coevolution of Tumor and Immunoenvironment in Glioblastoma.Cancer discovery · 2022Article
- Big data in basic and translational cancer research.Nature reviews. Cancer · 2022Review
- A T cell resilience model associated with response to immunotherapy in multiple tumor types.Nature medicine · 2022Article
- MIR503HG impeded ovarian cancer progression by interacting with SPI1 and preventing TMEFF1 transcription.Aging · 2022Article
- Transcription Elongation Machinery Is a Druggable Dependency and Potentiates Immunotherapy in Glioblastoma Stem Cells.Cancer discovery · 2022Article
- Predictive modeling of gene expression regulation.BMC bioinformatics · 2021Article
- Genetic fusions favor tumorigenesis through degron loss in oncogenes.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Despite the rapid accumulation of tumor-profiling data and transcription factor (TF) ChIP-seq profiles, efforts integrating TF binding with the tumor-profiling data to understand how TFs regulate tumor gene expression are still limited. To systematically search for cancer-associated TFs, we comprehensively integrated 686 ENCODE ChIP-seq profiles representing 150 TFs with 7484 TCGA tumor data in 18 cancer types. For efficient and accurate inference on gene regulatory rules across a large number and variety of datasets, we developed an algorithm, RABIT (regression analysis with background integration). In each tumor sample, RABIT tests whether the TF target genes from ChIP-seq show strong differential regulation after controlling for background effect from copy number alteration and DNA methylation. When multiple ChIP-seq profiles are available for a TF, RABIT prioritizes the most relevant ChIP-seq profile in each tumor. In each cancer type, RABIT further tests whether the TF expression and somatic mutation variations are correlated with differential expression patterns of its target genes across tumors. Our predicted TF impact on tumor gene expression is highly consistent with the knowledge from cancer-related gene databases and reveals many previously unidentified aspects of transcriptional regulation in tumor progression. We also applied RABIT on RNA-binding protein motifs and found that some alternative splicing factors could affect tumor-specific gene expression by binding to target gene 3'UTR regions. Thus, RABIT (rabit.dfci.harvard.edu) is a general platform for predicting the oncogenic role of gene expression regulators.
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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.