Literature DB >> 23590569

Integration of cancer gene co-expression network and metabolic network to uncover potential cancer drug targets.

Jingqi Chen1, Ming Ma, Ning Shen, Jianzhong Jeff Xi, Weidong Tian.   

Abstract

Cell metabolism is critical for cancer cell transformation and progression. In this study, we have developed a novel method, named Met-express, that integrates a cancer gene co-expression network with the metabolic network to predict key enzyme-coding genes and metabolites in cancer cell metabolism. Met-express successfully identified a group of key enzyme-coding genes and metabolites in lung, leukemia, and breast cancers. Literature reviews suggest that approximately 33-53% of the predicted genes are either known or suggested anti-cancer drug targets, while 22% of the predicted metabolites are known or high-potential drug compounds in therapeutic use. Furthermore, experimental validations prove that 90% of the selected genes and 70% of metabolites demonstrate the significant anti-cancer phenotypes in cancer cells, implying that they may play important roles in cancer metabolism. Therefore, Met-express is a powerful tool for uncovering novel therapeutic biomarkers.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23590569     DOI: 10.1021/pr400162t

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  11 in total

1.  Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis.

Authors:  Pengfei Liu; Weidong Tian
Journal:  PeerJ       Date:  2020-08-03       Impact factor: 2.984

Review 2.  Integrating omics technologies to study pulmonary physiology and pathology at the systems level.

Authors:  Ravi Ramesh Pathak; Vrushank Davé
Journal:  Cell Physiol Biochem       Date:  2014-04-28

3.  Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks.

Authors:  Tien-Dzung Tran; Duc-Tinh Pham
Journal:  Sci Rep       Date:  2021-07-08       Impact factor: 4.379

4.  GeneFriends: a human RNA-seq-based gene and transcript co-expression database.

Authors:  Sipko van Dam; Thomas Craig; João Pedro de Magalhães
Journal:  Nucleic Acids Res       Date:  2014-10-31       Impact factor: 19.160

5.  Prediction of Metabolic Gene Biomarkers for Neurodegenerative Disease by an Integrated Network-Based Approach.

Authors:  Qi Ni; Xianming Su; Jingqi Chen; Weidong Tian
Journal:  Biomed Res Int       Date:  2015-05-03       Impact factor: 3.411

6.  Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach.

Authors:  Yanfen Ma; Jian Hu; Ning Zhang; Xinran Dong; Ying Li; Bo Yang; Weidong Tian; Xiaoqin Wang
Journal:  PLoS One       Date:  2016-02-24       Impact factor: 3.240

7.  Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.

Authors:  Stephen P Ficklin; Leland J Dunwoodie; William L Poehlman; Christopher Watson; Kimberly E Roche; F Alex Feltus
Journal:  Sci Rep       Date:  2017-08-17       Impact factor: 4.379

8.  In-silico gene co-expression network analysis in Paracoccidioides brasiliensis with reference to haloacid dehalogenase superfamily hydrolase gene.

Authors:  Raghunath Satpathy; V B Konkimalla; Jagnyeswar Ratha
Journal:  J Pharm Bioallied Sci       Date:  2015 Jul-Sep

9.  Identification of relevant drugable targets in diffuse large B-cell lymphoma using a genome-wide unbiased CD20 guilt-by association approach.

Authors:  Mathilde R W de Jong; Lydia Visser; Gerwin Huls; Arjan Diepstra; Marcel van Vugt; Emanuele Ammatuna; Rozemarijn S van Rijn; Edo Vellenga; Anke van den Berg; Rudolf S N Fehrmann; Tom van Meerten
Journal:  PLoS One       Date:  2018-02-28       Impact factor: 3.240

10.  EdgeScaping: Mapping the spatial distribution of pairwise gene expression intensities.

Authors:  Benafsh Husain; F Alex Feltus
Journal:  PLoS One       Date:  2019-08-06       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.