title: mRNA expression profiles show differential regulatory effects of microRNAs between estrogen receptor-positive and estrogen receptor-negative breast cancer.
abstract: BACKGROUND
  Recent studies have shown that the regulatory effect of microRNAs can be investigated by examining expression changes of their target genes. Given this, it is useful to define an overall metric of regulatory effect for a specific microRNA and see how this changes across different conditions.
  
  
  RESULTS
  Here, we define a regulatory effect score (RE-score) to measure the inhibitory effect of a microRNA in a sample, essentially the average difference in expression of its targets versus non-targets. Then we compare the RE-scores of various microRNAs between two breast cancer subtypes: estrogen receptor positive (ER+) and negative (ER-). We applied this approach to five microarray breast cancer datasets and found that the expression of target genes of most microRNAs was more repressed in ER- than ER+; that is, microRNAs appear to have higher RE-scores in ER- breast cancer. These results are robust to the microRNA target prediction method. To interpret these findings, we analyzed the level of microRNA expression in previous studies and found that higher microRNA expression was not always accompanied by higher inhibitory effects. However, several key microRNA processing genes, especially Ago2 and Dicer, were differentially expressed between ER- and ER+ breast cancer, which may explain the different regulatory effects of microRNAs in these two breast cancer subtypes.
  
  
  CONCLUSIONS
  The RE-score is a promising indicator to measure microRNAs' inhibitory effects. Most microRNAs exhibit higher RE-scores in ER- than in ER+ samples, suggesting that they have stronger inhibitory effects in ER- breast cancers.
authors.affiliation: Program in Computational Biology and Bioinformatics, Yale University, George Street, New Haven, CT 06511, USA. chao.cheng@yale.edu
authors.forename: Chao
authors.initials: C
authors.lastname: Cheng
authors.forename: Xuping
authors.initials: X
authors.lastname: Fu
authors.forename: Pedro
authors.initials: P
authors.lastname: Alves
authors.forename: Mark
authors.initials: M
authors.lastname: Gerstein
mesh_terms: D000465:Algorithms; D060565:Argonaute Proteins; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D015852:Eukaryotic Initiation Factor-2 / Q000235:genetics; D039642:Eukaryotic Initiation Factors / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012333:RNA, Messenger / Q000235:genetics*; D011960:Receptors, Estrogen / Q000378:metabolism*; D043244:Ribonuclease III / Q000235:genetics
chemical_list: C115848:AGO1 protein, human; C466422:AGO2 protein, human; D060565:Argonaute Proteins; D015852:Eukaryotic Initiation Factor-2; D039642:Eukaryotic Initiation Factors; D035683:MicroRNAs; D012333:RNA, Messenger; D011960:Receptors, Estrogen; D043244:Ribonuclease III