TY - JOUR
T1 - In silico model for miRNA-mediated regulatory network in cancer
AU - Ahmed, Khandakar Tanvir
AU - Sun, Jiao
AU - Chen, William
AU - Martinez, Irene
AU - Cheng, Sze
AU - Zhang, Wencai
AU - Yong, Jeongsik
AU - Zhang, Wei
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2021/11/1
Y1 - 2021/11/1
N2 - Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet
AB - Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet
KW - 3'-UTR APA
KW - graph-based learning model
KW - miRNA regulation
KW - protein expression prediction
UR - http://www.scopus.com/inward/record.url?scp=85114381345&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85114381345&partnerID=8YFLogxK
U2 - 10.1093/bib/bbab264
DO - 10.1093/bib/bbab264
M3 - Article
C2 - 34279571
AN - SCOPUS:85114381345
SN - 1467-5463
VL - 22
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 6
M1 - bbab264
ER -