TY - JOUR
T1 - Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action
AU - Sun, Jingchun
AU - Zhao, Min
AU - Jia, Peilin
AU - Wang, Lily
AU - Wu, Yonghui
AU - Iverson, Carissa
AU - Zhou, Yubo
AU - Bowton, Erica
AU - Roden, Dan M.
AU - Denny, Joshua C.
AU - Aldrich, Melinda C.
AU - Xu, Hua
AU - Zhao, Zhongming
N1 - Funding Information:
This project is partially supported by the National Institutes of Health (grant numbers R01LM011177, R01LM 010685, K07CA172294, P50CA90949, P50CA095103, P50CA098131, P30CA068485, RC2 GM092618, ULTR000445), the Cancer Prevention & Research Institute of Texas Rising Star Award (CPRIT R1307), 2013 NARSAD Young Investigator Award, and American Cancer Society Institutional Research Grant pilot project (#IRG-58-009-55) and Ingram Professorship Funds. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2015 Sun et al.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2015/6/17
Y1 - 2015/6/17
N2 - A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.
AB - A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin’s antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.
UR - http://www.scopus.com/inward/record.url?scp=84953272772&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84953272772&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1004202
DO - 10.1371/journal.pcbi.1004202
M3 - Article
C2 - 26083494
AN - SCOPUS:84953272772
VL - 11
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 6
M1 - e1004202
ER -