title: Identification of polycystic ovary syndrome potential drug targets based on pathobiological similarity in the protein-protein interaction network.
abstract: Polycystic ovary syndrome (PCOS) is one of the most common endocrinological disorders in reproductive aged women. PCOS and Type 2 Diabetes (T2D) are closely linked in multiple levels and possess high pathobiological similarity. Here, we put forward a new computational approach based on the pathobiological similarity to identify PCOS potential drug target modules (PPDT-Modules) and PCOS potential drug targets in the protein-protein interaction network (PPIN). From the systems level and biological background, 1 PPDT-Module and 22 PCOS potential drug targets were identified, 21 of which were verified by literatures to be associated with the pathogenesis of PCOS. 42 drugs targeting to 13 PCOS potential drug targets were investigated experimentally or clinically for PCOS. Evaluated by independent datasets, the whole PPDT-Module and 22 PCOS potential drug targets could not only reveal the drug response, but also distinguish the statuses between normal and disease. Our identified PPDT-Module and PCOS potential drug targets would shed light on the treatment of PCOS. And our approach would provide valuable insights to research on the pathogenesis and drug response of other diseases.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Huang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Yuehan
authors.initials: Y
authors.lastname: He
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Wan
authors.initials: W
authors.lastname: Li
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Wenqing
authors.initials: W
authors.lastname: Wei
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Yiran
authors.initials: Y
authors.lastname: Li
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Ruiqiang
authors.initials: R
authors.lastname: Xie
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Shanshan
authors.initials: S
authors.lastname: Guo
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Yahui
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Jiang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Binbin
authors.initials: B
authors.lastname: Chen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Junjie
authors.initials: J
authors.lastname: Lv
authors.affiliation: Department of Endocrinology and Metabolism Laboratory, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Nana
authors.initials: N
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
authors.forename: Lina
authors.initials: L
authors.lastname: Chen
authors.affiliation: Institute of Opto-electronics, Harbin Institute of Technology, Harbin, Heilongjiang Province, China.
authors.forename: Weiming
authors.initials: W
authors.lastname: He
mesh_terms: D000328:Adult; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000150:complications / Q000378:metabolism*; D015195:Drug Design; D005260:Female; D006801:Humans; D007333:Insulin Resistance; D015233:Models, Statistical; D011085:Polycystic Ovary Syndrome / Q000175:diagnosis* / Q000188:drug therapy / Q000378:metabolism*; D025941:Protein Interaction Mapping; D060066:Protein Interaction Maps; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: drug target; module; pathobiological similarity; polycystic ovary syndrome; protein-protein interaction network