title: Computational prediction of microRNA networks incorporating environmental toxicity and disease etiology.
abstract: MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Their expression can be altered by environmental factors (EFs), which are associated with many diseases. Identification of the phenotype-genotype relationships among miRNAs, EFs, and diseases at the network level will help us to better understand toxicology mechanisms and disease etiologies. In this study, we developed a computational systems toxicology framework to predict new associations among EFs, miRNAs and diseases by integrating EF structure similarity and disease phenotypic similarity. Specifically, three comprehensive bipartite networks: EF-miRNA, EF-disease and miRNA-disease associations, were constructed to build predictive models. The areas under the receiver operating characteristic curves using 10-fold cross validation ranged from 0.686 to 0.910. Furthermore, we successfully inferred novel EF-miRNA-disease networks in two case studies for breast cancer and cigarette smoke. Collectively, our methods provide a reliable and useful tool for the study of chemical risk assessment and disease etiology involving miRNAs.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Zengrui
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: 1] Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China [2] Key Laboratory of Cigarette Smoke, Technical Center, Shanghai Tobacco Group Co. Ltd., Shanghai 200082, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D003198:Computer Simulation; D004781:Environmental Exposure; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D059647:Gene-Environment Interaction; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology* / Q000235:genetics / Q000378:metabolism; D035683:MicroRNAs / Q000502:physiology*; D034622:RNA Interference; D012372:ROC Curve; D012307:Risk Factors; D014028:Tobacco Smoke Pollution / Q000009:adverse effects*
chemical_list: D035683:MicroRNAs; D014028:Tobacco Smoke Pollution