Cargando…

Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular m...

Descripción completa

Detalles Bibliográficos
Autores principales: Yan, Wenying, Liu, Xingyi, Wang, Yibo, Han, Shuqing, Wang, Fan, Liu, Xin, Xiao, Fei, Hu, Guang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204992/
https://www.ncbi.nlm.nih.gov/pubmed/32425783
http://dx.doi.org/10.3389/fphar.2020.00534
_version_ 1783530163526434816
author Yan, Wenying
Liu, Xingyi
Wang, Yibo
Han, Shuqing
Wang, Fan
Liu, Xin
Xiao, Fei
Hu, Guang
author_facet Yan, Wenying
Liu, Xingyi
Wang, Yibo
Han, Shuqing
Wang, Fan
Liu, Xin
Xiao, Fei
Hu, Guang
author_sort Yan, Wenying
collection PubMed
description Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.
format Online
Article
Text
id pubmed-7204992
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-72049922020-05-18 Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling Yan, Wenying Liu, Xingyi Wang, Yibo Han, Shuqing Wang, Fan Liu, Xin Xiao, Fei Hu, Guang Front Pharmacol Pharmacology Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types. Frontiers Media S.A. 2020-04-30 /pmc/articles/PMC7204992/ /pubmed/32425783 http://dx.doi.org/10.3389/fphar.2020.00534 Text en Copyright © 2020 Yan, Liu, Wang, Han, Wang, Liu, Xiao and Hu http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Pharmacology
Yan, Wenying
Liu, Xingyi
Wang, Yibo
Han, Shuqing
Wang, Fan
Liu, Xin
Xiao, Fei
Hu, Guang
Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title_full Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title_fullStr Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title_full_unstemmed Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title_short Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling
title_sort identifying drug targets in pancreatic ductal adenocarcinoma through machine learning, analyzing biomolecular networks, and structural modeling
topic Pharmacology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204992/
https://www.ncbi.nlm.nih.gov/pubmed/32425783
http://dx.doi.org/10.3389/fphar.2020.00534
work_keys_str_mv AT yanwenying identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT liuxingyi identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT wangyibo identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT hanshuqing identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT wangfan identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT liuxin identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT xiaofei identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling
AT huguang identifyingdrugtargetsinpancreaticductaladenocarcinomathroughmachinelearninganalyzingbiomolecularnetworksandstructuralmodeling