Drug-Target Interaction Network Analysis of Gene-Phenotype Connectivity Maintained by Genistein

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Abstract

Genistein is a type of isoflavone, which has been widely described as an antitumor agent in many cancers. The present study aimed to provide information on the mechanisms of genistein's activity and thus enable a wider range of targeted therapies in hepatitis B virus (HBV)-related liver cancer. We searched the DrugBank database for direct targets of genistein, which were then analyzed through the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database to predict their secondary protein targets. Thirteen primary protein targets of genistein and 209 secondary protein targets-associated genes were identified. The data were integrated into the network of protein targets-associated genes and visualized with the Cytoscape software. We further carried out GO (Gene Ontology) analysis and KEGG (Kyoto Encyclopedia of Gene and Genome) pathway analysis using DAVID (database for annotation, visualization, and integrated discovery) tool. The top 14 KEGG pathways were further assessed, and 19 overlapping genes derived from pathways of hepatitis B and cancer were discovered. The overlapping targets were further mapped in the online tool UALCAN to evaluate the survival rate of hepatocellular carcinoma (HCC) patients. We found that the overexpression of Grb2 (growth factor receptor-binding protein 2) ( p < 0.0001) was linked to poor overall survival for liver HCC patients, followed by AKT1 ( p = 0.0015) and PIK3CA ( p = 0.0088). The present study analyzes the drug-target-disease network and may prove to be a useful tool in gene-phenotype connectivity for genistein in HBV-related liver cancer. Our data also pave the way for further research on Grb2 during the development of chronic HBV infection in liver cancer.

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DrugBank 5.0: a major update to the DrugBank database for 2018

David S Wishart, Yannick D Feunang, An C Guo … (2017)

Abstract DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year’s update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.

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UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses1

Darshan S. Chandrashekar, Bhuwan Bashel, Sai Balasubramanya … (2017)

Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu.

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STRING v10: protein–protein interaction networks, integrated over the tree of life

Damian Szklarczyk, Andrea Franceschini, Stefan Wyder … (2014)

The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.

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Author and article information

Journal

Journal ID (nlm-ta): J Comput Biol

Journal ID (iso-abbrev): J Comput Biol

Journal ID (publisher-id): cmb

Title: Journal of Computational Biology

Publisher: Mary Ann Liebert, Inc., publishers (140 Huguenot Street, 3rd FloorNew Rochelle, NY 10801USA )

ISSN (Print): 1066-5277

ISSN (Electronic): 1557-8666

Publication date (Print): December 2020

Publication date (Electronic): 04 December 2020

Publication date PMC-release: 04 December 2020

Volume: 27

Issue: 12

Pages: 1678-1687

Affiliations

[ ¹ ]Department of General Medicine and Geriatrics, Chongqing University Central Hospital/Chongqing Emergency Medical Center, Chongqing, China.

[ ² ]Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, Chongqing, China.

[ ³ ]Institute for Viral Hepatitis, Chongqing Medical University, Chongqing, China.

[ ⁴ ]Department of Infectious Diseases, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China.

Author notes

[*]Address correspondence to: Dr. Qian Zhou, Department of Infectious Diseases, The Second Affiliated Hospital, Chongqing Medical University, 1st Medical College Road, Yuzhong, Chongqing 400016, China zq895@ 123456163.com

Article

Publisher ID: 10.1089/cmb.2019.0443

DOI: 10.1089/cmb.2019.0443

PMC ID: 7757588

PubMed ID: 32298608

SO-VID: d762bde8-3ff4-4d29-9e95-d90aec1adb23

License:

This Open Access article is distributed under the terms of the Creative Commons License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.

Drug-Target Interaction Network Analysis of Gene-Phenotype Connectivity Maintained by Genistein

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Most cited references 23

DrugBank 5.0: a major update to the DrugBank database for 2018

UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses1

STRING v10: protein–protein interaction networks, integrated over the tree of life

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