ArticleScientific reports2015
Integrated network model provides new insights into castration-resistant prostate cancer.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Personalized Boolean network dynamic profiling and machine learning approaches to identify combinatorial treatments for prostate cancer.Frontiers in pharmacology · 2026Article
- Article
- Chromatin and Epigenetic Dysregulation of Prostate Cancer Development, Progression, and Therapeutic Response.Cancers · 2021Review
- A network pharmacology perspective for deciphering potential mechanisms of action of Solanum nigrum L. in bladder cancer.BMC complementary medicine and therapies · 2021Article
- Phase I dose-escalation trial of the oral AKT inhibitor uprosertib in combination with the oral MEK1/MEK2 inhibitor trametinib in patients with solid tumors.Cancer chemotherapy and pharmacology · 2020Article
- Key Genes And Pathways Controlled By E2F1 In Human Castration-Resistant Prostate Cancer Cells.OncoTargets and therapy · 2019Article
- Reversible lysine-specific demethylase 1 antagonist HCI-2509 inhibits growth and decreases c-MYC in castration- and docetaxel-resistant prostate cancer cells.Prostate cancer and prostatic diseases · 2016Article
- Effects of Arf6 downregulation on biological characteristics of human prostate cancer cells.International braz j urol : official journal of the Brazilian Society of UrologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Castration-resistant prostate cancer (CRPC) is the main challenge for prostate cancer treatment. Recent studies have indicated that extending the treatments to simultaneously targeting different pathways could provide better approaches. To better understand the regulatory functions of different pathways, a system-wide study of CRPC regulation is necessary. For this purpose, we constructed a comprehensive CRPC regulatory network by integrating multiple pathways such as the MEK/ERK and the PI3K/AKT pathways. We studied the feedback loops of this network and found that AKT was involved in all detected negative feedback loops. We translated the network into a predictive Boolean model and analyzed the stable states and the control effects of genes using novel methods. We found that the stable states naturally divide into two obvious groups characterizing PC3 and DU145 cells respectively. Stable state analysis further revealed that several critical genes, such as PTEN, AKT, RAF, and CDKN2A, had distinct expression behaviors in different clusters. Our model predicted the control effects of many genes. We used several public datasets as well as FHL2 overexpression to verify our finding. The results of this study can help in identifying potential therapeutic targets, especially simultaneous targets of multiple pathways, for CRPC.
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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.