ArticleBMC bioinformatics2017
A novel statistical approach for identification of the master regulator transcription factor.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression.NPJ systems biology and applications · 2026Article
- Aggressive serous ovarian cancer subtype defined by high centrality lncRNA profiles and master transcription factors.Scientific reports · 2025Article
- Analysis of the CHS Gene Family Reveals Its Functional Responses to Hormones, Salinity, and Drought Stress in Moso Bamboo (Plants (Basel, Switzerland) · 2025Article
- Pharmacological inhibition of RUNX1 reduces infarct size after acute myocardial infarction in rats and underlying mechanism revealed by proteomics implicates repressed cathepsin levels.Functional & integrative genomics · 2024Article
- Comprehensive understanding of glioblastoma molecular phenotypes: classification, characteristics, and transition.Cancer biology & medicine · 2024Review
- Complexation of histone deacetylase inhibitor belinostat to Cu(II) prevents premature metabolic inactivation in vitro and demonstrates potent anti-cancer activity in vitro and ex vivo in colon cancer.Cellular oncology (Dordrecht, Netherlands) · 2024Article
- Gene regulatory network inference using mixed-norms regularized multivariate model with covariance selection.PLoS computational biology · 2023Article
- Validating methods for testing natural molecules on molecular pathways of interest in silico and in vitro.Journal of preventive medicine and hygiene · 2022Article
- MicroRNA-326 impairs chemotherapy resistance in non small cell lung cancer by suppressing histone deacetylase SIRT1-mediated HIF1α and elevating VEGFA.Bioengineered · 2022Article
- Computational Identification of Master Regulators Influencing Trypanotolerance in Cattle.International journal of molecular sciences · 2021Article
- Motifs enable communication efficiency and fault-tolerance in transcriptional networks.Scientific reports · 2020Article
- Discovering master regulators in hepatocellular carcinoma: one novel MR, SEC14L2 inhibits cancer cells.Aging · 2019Article
- Integrating gene regulatory pathways into differential network analysis of gene expression data.Scientific reports · 2019Article
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Authors and funding
2 authors.
Funding
Abstract
backgroundTranscription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option.
methodsWe present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one.
resultsIn the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software .
conclusionWe have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.
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