ArticleAvicenna journal of medical biotechnology
Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.
Article in Avicenna journal of medical biotechnology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.Nucleic acids research · 2025Article
- DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein-protein interaction network.bioRxiv : the preprint server for biology · 2025Article
- DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data.Biomolecules · 2024Article
- Computational identification of mutually exclusive transcriptional drivers dysregulating metastatic microRNAs in prostate cancer.Nature communications · 2017Article
- Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.BioMed research international · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes.
methodsIn our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other.
resultsIn this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors.
conclusionThe results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.
Indexed as
Identifiers
25926947PMC4388891W2128341708What Socratic 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 Socratic graph.