Literature DB >> 26208504

Integrated systems approach identifies risk regulatory pathways and key regulators in coronary artery disease.

Yan Zhang1, Dianming Liu1, Lihong Wang2, Shuyuan Wang1, Xuexin Yu1, Enyu Dai1, Xinyi Liu1, Shanshun Luo3, Wei Jiang4.   

Abstract

UNLABELLED: Coronary artery disease (CAD) is the most common type of heart disease. However, the molecular mechanisms of CAD remain elusive. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, inferring risk regulatory pathways is an important step toward elucidating the mechanisms underlying CAD. With advances in high-throughput data, we developed an integrated systems approach to identify CAD risk regulatory pathways and key regulators. Firstly, a CAD-related core subnetwork was identified from a curated transcription factor (TF) and microRNA (miRNA) regulatory network based on a random walk algorithm. Secondly, candidate risk regulatory pathways were extracted from the subnetwork by applying a breadth-first search (BFS) algorithm. Then, risk regulatory pathways were prioritized based on multiple CAD-associated data sources. Finally, we also proposed a new measure to prioritize upstream regulators. We inferred that phosphatase and tensin homolog (PTEN) may be a key regulator in the dysregulation of risk regulatory pathways. This study takes a closer step than the identification of disease subnetworks or modules. From the risk regulatory pathways, we could understand the flow of regulatory information in the initiation and progression of the disease. Our approach helps to uncover its potential etiology. KEY MESSAGES: We developed an integrated systems approach to identify risk regulatory pathways. We proposed a new measure to prioritize the key regulators in CAD. PTEN may be a key regulator in dysregulation of the risk regulatory pathways.

Entities:  

Keywords:  Coronary artery disease; Regulatory pathway; Systems biology; Transcription factor; miRNA

Mesh:

Substances:

Year:  2015        PMID: 26208504     DOI: 10.1007/s00109-015-1315-x

Source DB:  PubMed          Journal:  J Mol Med (Berl)        ISSN: 0946-2716            Impact factor:   4.599


  50 in total

1.  KEGG: kyoto encyclopedia of genes and genomes.

Authors:  M Kanehisa; S Goto
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Complexity in biological signaling systems.

Authors:  G Weng; U S Bhalla; R Iyengar
Journal:  Science       Date:  1999-04-02       Impact factor: 47.728

3.  Discovering regulatory and signalling circuits in molecular interaction networks.

Authors:  Trey Ideker; Owen Ozier; Benno Schwikowski; Andrew F Siegel
Journal:  Bioinformatics       Date:  2002       Impact factor: 6.937

4.  The human disease network.

Authors:  Kwang-Il Goh; Michael E Cusick; David Valle; Barton Childs; Marc Vidal; Albert-László Barabási
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-14       Impact factor: 11.205

5.  Walking the interactome for prioritization of candidate disease genes.

Authors:  Sebastian Köhler; Sebastian Bauer; Denise Horn; Peter N Robinson
Journal:  Am J Hum Genet       Date:  2008-03-27       Impact factor: 11.025

6.  GO-function: deriving biologically relevant functions from statistically significant functions.

Authors:  Jing Wang; Xianxiao Zhou; Jing Zhu; Yunyan Gu; Wenyuan Zhao; Jinfeng Zou; Zheng Guo
Journal:  Brief Bioinform       Date:  2011-06-24       Impact factor: 11.622

7.  Short communication: asymmetric dimethylarginine impairs angiogenic progenitor cell function in patients with coronary artery disease through a microRNA-21-dependent mechanism.

Authors:  Felix Fleissner; Virginija Jazbutyte; Jan Fiedler; Shashi K Gupta; Xiaoke Yin; Qingbo Xu; Paolo Galuppo; Susanne Kneitz; Manuel Mayr; Georg Ertl; Johann Bauersachs; Thomas Thum
Journal:  Circ Res       Date:  2010-05-20       Impact factor: 17.367

8.  miRecords: an integrated resource for microRNA-target interactions.

Authors:  Feifei Xiao; Zhixiang Zuo; Guoshuai Cai; Shuli Kang; Xiaolian Gao; Tongbin Li
Journal:  Nucleic Acids Res       Date:  2008-11-07       Impact factor: 16.971

9.  A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis.

Authors:  Andreas Keller; Christina Backes; Andreas Gerasch; Michael Kaufmann; Oliver Kohlbacher; Eckart Meese; Hans-Peter Lenhof
Journal:  Bioinformatics       Date:  2009-08-27       Impact factor: 6.937

10.  HIV Tat induces expression of ICAM-1 in HUVECs: implications for miR-221/-222 in HIV-associated cardiomyopathy.

Authors:  Ming Duan; Honghong Yao; Guoku Hu; XianMing Chen; Amie K Lund; Shilpa Buch
Journal:  PLoS One       Date:  2013-03-28       Impact factor: 3.240

View more
  6 in total

Review 1.  Translational Bioinformatics: Past, Present, and Future.

Authors:  Jessica D Tenenbaum
Journal:  Genomics Proteomics Bioinformatics       Date:  2016-02-11       Impact factor: 7.691

2.  Systematic Characterization of Circular RNA-Associated CeRNA Network Identified Novel circRNA Biomarkers in Alzheimer's Disease.

Authors:  Yan Zhang; Fulong Yu; Siqi Bao; Jie Sun
Journal:  Front Bioeng Biotechnol       Date:  2019-09-11

3.  A network-based analysis for mining the risk pathways in glioblastoma.

Authors:  Jing Li; Yujie Xie; Chi Zhang; Jianxiong Wang; Yong Wu; Yuan Yang; Yang Xie; Zhiyu Lv
Journal:  Oncol Lett       Date:  2019-07-09       Impact factor: 2.967

Review 4.  Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease.

Authors:  Teresa Infante; Luca Del Viscovo; Maria Luisa De Rimini; Sergio Padula; Pio Caso; Claudio Napoli
Journal:  J Atheroscler Thromb       Date:  2019-11-12       Impact factor: 4.928

5.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database.

Authors:  Chih-Hung Chou; Nai-Wen Chang; Sirjana Shrestha; Sheng-Da Hsu; Yu-Ling Lin; Wei-Hsiang Lee; Chi-Dung Yang; Hsiao-Chin Hong; Ting-Yen Wei; Siang-Jyun Tu; Tzi-Ren Tsai; Shu-Yi Ho; Ting-Yan Jian; Hsin-Yi Wu; Pin-Rong Chen; Nai-Chieh Lin; Hsin-Tzu Huang; Tzu-Ling Yang; Chung-Yuan Pai; Chun-San Tai; Wen-Liang Chen; Chia-Yen Huang; Chun-Chi Liu; Shun-Long Weng; Kuang-Wen Liao; Wen-Lian Hsu; Hsien-Da Huang
Journal:  Nucleic Acids Res       Date:  2015-11-20       Impact factor: 16.971

6.  MicroRNA regulatory pathway analysis identifies miR-142-5p as a negative regulator of TGF-β pathway via targeting SMAD3.

Authors:  Zhaowu Ma; Teng Liu; Wei Huang; Hui Liu; Hong-Mei Zhang; Qiubai Li; Zhichao Chen; An-Yuan Guo
Journal:  Oncotarget       Date:  2016-11-01
  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.