ReviewCurrent drug discovery technologies2013
Network insights into the genes regulated by hepatocyte nuclear factor 4 in response to drug induced perturbations: a review.
Review in Current drug discovery technologies, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed, 14 citations in OpenAlex.
- Integrated multi-omics analysis reveals clinical significance of hepatocyte nuclear factor-1β in tumor immune microenvironment, immunotherapy and prognostic prediction for colon adenocarcinoma.Cancer immunology, immunotherapy : CII · 2024Article
- Review
- Therapeutic Potential of HNF4α in End-stage Liver Disease.Organogenesis · 2021Review
- Long noncoding RNA H19 - a new player in the pathogenesis of liver diseases.Translational research : the journal of laboratory and clinical medicine · 2021Review
- Hepatocyte nuclear factor 1 beta: A perspective in cancer.Cancer medicine · 2021Review
- Molecular Dynamics Simulations Predict That rSNP Located in theBiomolecules · 2020Article
- A review on hepatocyte nuclear factor-1beta and tumor.Cell & bioscience · 2015Review
- Chromatin profiling reveals regulatory network shifts and a protective role for hepatocyte nuclear factor 4α during colitis.Molecular and cellular biology · 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
5 authors at 1 institution in 1 country.
Funding
Abstract
Transcription factors (TFs) play central role in normal cellular physiology and their aberrant expression is linked to different diseases. Hepatocyte Nuclear Factors (HNFs) are TFs that have been recognized to play multiple roles in liver physiology. Emerging research has highlighted their function in the sustenance of solid tumors, indicating that HNFs could serve as possible therapeutic targets in cancer. Although, there have been many attempts to develop HNF targeted drugs, the myriad downstream targets associated with these transcription factors, some of which are critical for normal cell homeostasis, led to the realization that HNFs are not easily druggable. Therefore, identifying and optimizing drugs that can selectively inactivate HNFs is a challenge to the pharmaceutical industry. To achieve this, a more in-depth understanding is required of the HNFs binding partners, the protein interaction networks it regulates and the resulting phenotype. This calls for network analysis of the pathways regulated by HNFs and how chemical perturbations can selectively activate or suppress their functions. Network biology is an emerging field of research that is finding applications in cancer drug discovery. Specifically, network pharmacology is cementing its position in cancer research and has various applications such as biomarker identification, in determining synergistic drug pairs and in drug repurposing. Developing a network understanding of HNFs, the target it hits and responses thereof can enhance our ability to design drugs against these TFs. This article reviews how network pharmacology can help in the identification of druggable avenues in TFs and also allow the selection of drugs and their synergistic pairs against HNFs for cancer therapy.
Indexed as
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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.