ArticleBMC bioinformatics2011
RegNetB: predicting relevant regulator-gene relationships in localized prostate tumor samples.
Article in BMC bioinformatics, 2011. 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.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- MORE interpretable multi-omic regulatory networks to characterise phenotypes.Briefings in bioinformatics · 2025Article
- Regulatory mechanisms of heme regulatory protein BACH1: a potential therapeutic target for cancer.Medical oncology (Northwood, London, England) · 2021Review
- Prognostic Value Estimation of BRIP1 in Breast Cancer by Exploiting Transcriptomics Data Through Bioinformatics Approaches.Bioinformatics and biology insights · 2021Article
- BACH family members regulate angiogenesis and lymphangiogenesis by modulating VEGFC expression.Life science alliance · 2020Article
- Article
- Novel RNA biomarkers of prostate cancer revealed by RNA-seq analysis of formalin-fixed samples obtained from Russian patients.Oncotarget · 2017Article
- Elevated expression of ZNF217 promotes prostate cancer growth by restraining ferroportin-conducted iron egress.Oncotarget · 2016Article
- Transcriptional network analysis identifies BACH1 as a master regulator of breast cancer bone metastasis.The Journal of biological chemistry · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundA central question in cancer biology is what changes cause a healthy cell to form a tumor. Gene expression data could provide insight into this question, but it is difficult to distinguish between a gene that causes a change in gene expression from a gene that is affected by this change. Furthermore, the proteins that regulate gene expression are often themselves not regulated at the transcriptional level. Here we propose a Bayesian modeling framework we term RegNetB that uses mechanistic information about the gene regulatory network to distinguish between factors that cause a change in expression and genes that are affected by the change. We test this framework using human gene expression data describing localized prostate cancer progression.
resultsThe top regulatory relationships identified by RegNetB include the regulation of RLN1, RLN2, by PAX4, the regulation of ACPP (PAP) by JUN, BACH1 and BACH2, and the co-regulation of PGC and GDF15 by MAZ and TAF8. These target genes are known to participate in tumor progression, but the suggested regulatory roles of PAX4, BACH1, BACH2, MAZ and TAF8 in the process is new.
conclusionIntegrating gene expression data and regulatory topologies can aid in identifying potentially causal mechanisms for observed changes in gene expression.
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.