Inferring coregulation of transcription factors and microRNAs in breast cancer.

Both transcription factors (TFs) and microRNAs (miRNAs) regulate gene expression. TFs activate or suppress the initiation of the transcription process and miRNAs regulate mRNAs post-transcriptionally, thus forming a temporally ordered regulatory event. Ectopic expression of key transcriptional regulators and/or miRNAs has been shown to be involved in various tumors. Therefore, uncovering the coregulation of TFs and miRNAs in human cancers may lead to the discovery of novel therapeutics. We introduced a two-stage learning fuzzy method to model TF-miRNA coregulation using both genomic data and verified regulatory relationships. In Stage 1, a learning (adaptive) fuzzy inference system (ANFIS) combines two sequence alignment features of TF and target by learning from verified TF-target pairs into a sequence matching score. Next, a non-learning FIS incorporates a sequence alignment score and a correlation score from paired TF-target gene expression to output a Stage 1 fuzzy score to infer whether a TF-target regulation exists. For significant TF-target pairs, in Stage 2, similar to Stage 1, we first infer whether a miRNA regulates each common target by an ANFIS, which incorporates their sequences and known miRNA-target relationships to output a sequence score. Next, an FIS incorporates the Stage 1 fuzzy score, Stage 2 sequence score and gene expression correlation score of a miRNA-target pair to determine whether TF-miRNA coregulation exists. We collected 54 (8) TF-miRNA-target triples validated in ER-positive (ER-negative) breast cancer cell lines in the same article, and they were used as positives. Negative examples were constructed for Stage 1 (Stage 2) by pairing TFs (miRNAs) with human housekeeping genes found in the literature; both positives and negatives were used to train ANFISs in the training step. This two-stage fizzy algorithm was applied to predict 54 (8) TF-miRNA coregulation triples in ER-positive (ER-negative) human breast cancer cell lines, and resulted in true-positive rates of 0.55 (0.74) and 0.57 (0.75) using 3-fold and n-fold cross validations (CVs), respectively. False-positive rate bound was 0.07 (0.13) for ER-positive (ER-negative) breast cancer using both 3-fold and n-fold CVs. Interestingly, among the 62 coregulatroy triples from ER-positive/negative breast cancer cells, about 72% have TF- and coregulatory miRNA expression simultaneously greater or less than the corresponding medians, while in the remaining 28% of TFs and their coregulatory miRNAs are conversely expressed. The proposed fuzzy algorithm performed well in identification of TF-miRNA coregulation triples in human breast cancer. After being trained by the corresponding verified coregulatory triples and genomic data, this algorithm can be applied to uncover novel coregulation in other cancers in the future.

[1]  Jack Y. Yang,et al.  Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells , 2008, BMC Genomics.

[2]  Hsuan-Cheng Huang,et al.  Coregulation of transcription factors and microRNAs in human transcriptional regulatory network , 2011, BMC Bioinformatics.

[3]  Grace S. Shieh,et al.  Inferring Genetic Interactions via a Data-Driven Second Order Model , 2012, Front. Gene..

[4]  Wei Keat Lim,et al.  The transcriptional network for mesenchymal transformation of brain tumors , 2009, Nature.

[5]  E. Levanon,et al.  Human housekeeping genes are compact. , 2003, Trends in genetics : TIG.

[6]  Grace S. Shieh,et al.  Inferring genetic interactions via a nonlinear model and an optimization algorithm , 2010, BMC Systems Biology.

[7]  Christian J Stoeckert,et al.  Clustering of genes into regulons using integrated modeling-COGRIM , 2007, Genome Biology.

[8]  John Quackenbush,et al.  Identification of Novel Kinase Targets for the Treatment of Estrogen Receptor–Negative Breast Cancer , 2009, Clinical Cancer Research.

[9]  Grace S. Shieh,et al.  A pattern recognition approach to infer time-lagged genetic interactions , 2008, Bioinform..

[10]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[11]  A. Lal,et al.  MicroRNAs and their target gene networks in breast cancer , 2010, Breast Cancer Research.

[12]  Yuval Kluger,et al.  Inter- and intra-combinatorial regulation by transcription factors and microRNAs , 2007, BMC Genomics.

[13]  Y. Pekarsky,et al.  Reprogramming of miRNA networks in cancer and leukemia. , 2010, Genome research.

[14]  Jan Krüger,et al.  RNAhybrid: microRNA target prediction easy, fast and flexible , 2006, Nucleic Acids Res..

[15]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[16]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  R. Weigel,et al.  Transcriptional regulation of estrogen receptor in breast carcinomas , 1995, Molecular and cellular biology.

[18]  Carme Camps,et al.  microRNA-associated progression pathways and potential therapeutic targets identified by integrated mRNA and microRNA expression profiling in breast cancer. , 2011, Cancer research.

[19]  Anton J. Enright,et al.  Human MicroRNA Targets , 2004, PLoS biology.

[20]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Grace S. Shieh,et al.  Uncovering transcriptional interactions via an adaptive fuzzy logic approach , 2009, BMC Bioinformatics.

[22]  Nicola J. Rinaldi,et al.  Computational discovery of gene modules and regulatory networks , 2003, Nature Biotechnology.

[23]  O. Hobert.  Gene Regulation by Transcription Factors and MicroRNAs , 2008, Science.