title: Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer.
abstract: Recently, microRNAs (miRNA), small noncoding RNAs, have taken center stage in the field of human molecular oncology. However, their roles in tumor biology remain largely unknown. According to the assumption that miRNAs implicated in a specific tumor phenotype will show aberrant regulation of their target genes, we introduce an approach based on the miRNA target-dysregulated network (MTDN) to prioritize novel disease miRNAs. Target genes have predicted binding sites for any miRNA. The MTDN is constructed by combining computational target prediction with miRNA and mRNA expression profiles in tumor and nontumor tissues. Application of the proposed method to prostate cancer reveals that known prostate cancer miRNAs are characterized by a greater number of dysregulations and coregulators and the tendency to coregulate with each other and that they share a higher proportion of targets with other prostate cancer miRNAs. Support vector machine classifier, based on these features and changes in miRNA expression, is constructed and gives an average overall prediction accuracy of 0.8872 in cross-validation tests. The classifier is then applied to miRNAs in the MTDN. Functions enriched by dysregulated targets of novel predicted miRNAs are closely associated with oncogenesis. In addition, predicted cancer miRNAs within families or from different families show combinatorial dysregulation of target genes, as revealed by analysis of the MTDN modular organization. Finally, 3 miRNA target regulations are verified to hold in prostate cancer cells by transfection assays. These results show that the network-centric method could prioritize novel disease miRNAs and model how oncogenic lesions are mediated by miRNAs, providing important insights into tumorigenesis.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
authors.forename: Juan
authors.initials: J
authors.lastname: Xu
authors.forename: Chuan-Xing
authors.initials: CX
authors.lastname: Li
authors.forename: Jun-Ying
authors.initials: JY
authors.lastname: Lv
authors.forename: Yong-Sheng
authors.initials: YS
authors.lastname: Li
authors.forename: Yun
authors.initials: Y
authors.lastname: Xiao
authors.forename: Ting-Ting
authors.initials: TT
authors.lastname: Shao
authors.forename: Xiao
authors.initials: X
authors.lastname: Huo
authors.forename: Xiang
authors.initials: X
authors.lastname: Li
authors.forename: Yan
authors.initials: Y
authors.lastname: Zou
authors.forename: Qing-Lian
authors.initials: QL
authors.lastname: Han
authors.forename: Xia
authors.initials: X
authors.lastname: Li
authors.forename: Li-Hua
authors.initials: LH
authors.lastname: Wang
authors.forename: Huan
authors.initials: H
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D045744:Cell Line, Tumor; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D058990:Molecular Targeted Therapy; D020411:Oligonucleotide Array Sequence Analysis; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000628:therapy; D017423:Sequence Analysis, RNA
chemical_list: D035683:MicroRNAs