ArticleBMC genomics2013
REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.
Article in BMC genomics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer.Molecular cancer research : MCR · 2015Pooled it
- Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data.Bioinformatics (Oxford, England) · 2021Article
- Identification of Pathogenic Genes and Transcription Factors in Osteosarcoma.Pathology oncology research : POR · 2020Article
- Genetic Programs Driving Oncogenic Transformation: Lessons from in Vitro Models.International journal of molecular sciences · 2019Article
- Computational STAT3 activity inference reveals its roles in the pancreatic tumor microenvironment.Scientific reports · 2019Article
- The E2F4 prognostic signature predicts pathological response to neoadjuvant chemotherapy in breast cancer patients.BMC cancer · 2017Article
- Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome.PLoS computational biology · 2017Article
- The pan-cancer pathological regulatory landscape.Scientific reports · 2016Article
- Targeting the RB-E2F pathway in breast cancer.Oncogene · 2016Review
- Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value.Molecular cancer research : MCR · 2016Article
- IDEA: Integrated Drug Expression Analysis-Integration of Gene Expression and Clinical Data for the Identification of Therapeutic Candidates.CPT: pharmacometrics & systems pharmacology · 2015Article
- Integrative analysis of survival-associated gene sets in breast cancer.BMC medical genomics · 2015Article
- E2F4 regulatory program predicts patient survival prognosis in breast cancer.Breast cancer research : BCR · 2014Article
- DPRP: a database of phenotype-specific regulatory programs derived from transcription factor binding data.Nucleic acids research · 2014Article
- Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity.Genome medicine · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.
resultsHere we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.
conclusionsREACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.