Drug–target–disease network analysis of gene–phenotype connectivity for genistein in ovarian cancer
Authors Zhang C, Yang F, Ni S, Teng W, Ning Y
Received 9 August 2018
Accepted for publication 13 November 2018
Published 10 December 2018 Volume 2018:11 Pages 8901—8908
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Colin Mak
Peer reviewer comments 4
Editor who approved publication: Dr Sanjay Singh
Chen Zhang,1,* Fan Yang,2,* Suiqin Ni,1 Wenbing Teng,1 Yingxia Ning3
1Department of Clinical Pharmacy, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou 510180, China; 2Department of Health Examination, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China; 3Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
*These authors contributed equally to this work
Purpose: Genistein belongs to the group of isoflavones, which include powerful anticancer agents. Its antitumor properties have been intensively described in many cancers, but related studies assessing ovarian cancer are scarce. The aim of this study was to develop a new method of the underlying mechanisms of genistein’s effects and broaden the perspective of targeted therapies in ovarian carcinoma.
Materials and methods: Genistein targets were searched in the DrugBank database. Prediction of drug interactions with targets (including secondary targets) was performed with STRING database. Interaction pairs with overall score above 0.9 were recorded for protein–protein interaction (PPI) network generation based on the Cytoscape software. Genes with intense interconnections were grouped into a module. Then, PPI network modules with significance were assessed using Molecular Complex Detection (MCODE) analysis tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed for the critical genes. Furthermore, disease targets were searched in Comparative Toxicogenomics Database (CTD). The overlapping targets were studied using a Kaplan–Meier analysis to evaluate ovarian carcinoma survival.
Results: A total of 13 direct targets and 372 secondary targets were identified for genistein and further analyzed with the MCODE analysis tool to identify critical genes. The top 72 genes were further assessed with KEGG. Then, the term “ovarian cancer” was searched in CTD, and 123 genes associated only with the marker “T” or “M” were recorded. Next, seven overlapping genes (CDKN1B, PTEN, EGFR, MAPK1, MAPK3, PIK3C, and AKT1) resulting from the intersection of three pathways and 123 genes were obtained from CTD. Elevated CDKN1B amounts showed correlation with overall survival (log-rank P=0.021) according to Kaplan–Meier analysis.
Conclusion: The current findings indicated that drug–target–disease network analysis represents a useful tool in gene–phenotype connectivity for genistein in ovarian cancer. Our result also showed that CDKN1B is worthy of further research.
Keywords: protein–protein interaction, PPI, DrugBank, Comparative Toxicogenomics Database, CTD, CDKN1B, PI3K/AKT signaling pathway, FoxO signaling pathway
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