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Identification of gene-phenotype connectivity associated with flavanone naringenin by functional network analysis.

ABSTRACT: Naringenin, extracted from grapefruits and citrus fruits, is a bioactive flavonoid with antioxidative, anti-inflammatory, antifibrogenic, and anticancer properties. In the past two decades, the growth of publications of naringenin in PubMed suggests that naringenin is quickly gaining interest. However, systematically regarding its biological functions connected to its direct and indirect target proteins remains difficult but necessary. Herein, we employed a set of bioinformatic platforms to integrate and dissect available published data of naringenin. Analysis based on DrugBank and the Search Tool for the Retrieval of Interacting Genes/Proteins revealed seven direct protein targets and 102 indirect protein targets. The protein-protein interaction (PPI) network of total 109 naringenin-mediated proteins was next visualized using Cytoscape. What's more, all naringenin-mediated proteins were subject to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by the Database for Annotation, Visualization and Integrated Discovery, which resulted in three ESR1-related signaling pathways and prostate cancer pathway. Refined analysis of PPI network and KEGG pathway identified four genes (ESR1, PIK3CA, AKT1, and MAPK1). Further genomic analysis of four genes using cBioPortal indicated that naringenin might exert biological effects via ESR1 signaling axis. In general, this work scrutinized naringenin-relevant knowledge and provided an insight into the regulation and mediation of naringenin on prostate cancer.

SUBMITTER: Fu S

PROVIDER: S-EPMC6430101 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of gene-phenotype connectivity associated with flavanone naringenin by functional network analysis.

Fu Suhong S Zhang Yongqun Y Shi Jing J Hao Doudou D Zhang Pengfei P

PeerJ 20190319

Naringenin, extracted from grapefruits and citrus fruits, is a bioactive flavonoid with antioxidative, anti-inflammatory, antifibrogenic, and anticancer properties. In the past two decades, the growth of publications of naringenin in PubMed suggests that naringenin is quickly gaining interest. However, systematically regarding its biological functions connected to its direct and indirect target proteins remains difficult but necessary. Herein, we employed a set of bioinformatic platforms to inte ...[more]

PMID: 30918758

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Project description:PurposeGenistein 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 methodsGenistein 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.ResultsA 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.ConclusionThe 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.

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Identification of gene-phenotype connectivity associated with flavanone naringenin by functional network analysis.

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Identification of gene-phenotype connectivity associated with flavanone naringenin by functional network analysis.

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