Analysis of miRNA expression profiles in breast cancer using biclustering

BackgroundMicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level.ResultsWe adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm.ConclusionsThe obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.

[1]  A. Nicholson,et al.  CDKN2A, NF2, and JUN are dysregulated among other genes by miRNAs in malignant mesothelioma—A miRNA microarray analysis , 2009, Genes, chromosomes & cancer.

[2]  Phillip D Zamore,et al.  microPrimer: the biogenesis and function of microRNA , 2005, Development.

[3]  M Schena,et al.  Microarrays: biotechnology's discovery platform for functional genomics. , 1998, Trends in biotechnology.

[4]  S. Lowe,et al.  A microRNA polycistron as a potential human oncogene , 2005, Nature.

[5]  Burton B. Yang,et al.  MicroRNA miR-199a-3p regulates cell proliferation and survival by targeting caveolin-2 , 2011, Journal of Cell Science.

[6]  Chun-Nan Hsu,et al.  MetaMirClust: discovery of miRNA cluster patterns using a data-mining approach. , 2012, Genomics.

[7]  R. Russell,et al.  Principles of MicroRNA–Target Recognition , 2005, PLoS biology.

[8]  Nitzan Rosenfeld,et al.  MicroRNAs as a potential prognostic factor in gastric cancer. , 2010, World journal of gastroenterology.

[9]  C. Croce,et al.  Frequent deletions and down-regulation of micro- RNA genes miR15 and miR16 at 13q14 in chronic lymphocytic leukemia , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[10]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[11]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem. , 2003 .

[12]  Roded Sharan,et al.  Discovering statistically significant biclusters in gene expression data , 2002, ISMB.

[13]  Robert A. Weinberg,et al.  Tumour invasion and metastasis initiated by microRNA-10b in breast cancer (Nature (2007) 449, (682-688)) , 2008 .

[14]  Sven Bergmann,et al.  Modular analysis of gene expression data with R , 2010, Bioinform..

[15]  Mehmet Deveci,et al.  A comparative analysis of biclustering algorithms for gene expression data , 2013, Briefings Bioinform..

[16]  Klaus Pantel,et al.  Diagnostic potential of PTEN-targeting miR-214 in the blood of breast cancer patients , 2012, Breast Cancer Research and Treatment.

[17]  Xuan Pan,et al.  The Potential Role of miR-451 in Cancer Diagnosis, Prognosis, and Therapy , 2013, Molecular Cancer Therapeutics.

[18]  L. Holmberg,et al.  Integrated genomic analysis of triple-negative breast cancers reveals novel microRNAs associated with clinical and molecular phenotypes and sheds light on the pathways they control , 2013, BMC Genomics.

[19]  Daya Luo,et al.  A systematic evaluation of miRNA:mRNA interactions involved in the migration and invasion of breast cancer cells , 2013, Journal of Translational Medicine.

[20]  G. Goodall,et al.  The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1 , 2008, Nature Cell Biology.

[21]  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.

[22]  Edmund J Crampin,et al.  Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence , 2013, BMC Genomics.

[23]  Naoto Tsuchiya,et al.  Tumor-suppressive miR-34a induces senescence-like growth arrest through modulation of the E2F pathway in human colon cancer cells , 2007, Proceedings of the National Academy of Sciences.

[24]  Yi Tie,et al.  miR-34a inhibits migration and invasion by down-regulation of c-Met expression in human hepatocellular carcinoma cells. , 2009, Cancer letters.

[25]  Jerry Pelletier,et al.  Short RNAs repress translation after initiation in mammalian cells. , 2006, Molecular cell.

[26]  Mary Goldman,et al.  The UCSC Genome Browser database: update 2011 , 2010, Nucleic Acids Res..

[27]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[28]  Lixu Yan,et al.  [Serum miR-103 as a potential diagnostic biomarker for breast cancer]. , 2012, Nan fang yi ke da xue xue bao = Journal of Southern Medical University.

[29]  David Haussler,et al.  The UCSC genome browser database: update 2007 , 2006, Nucleic Acids Res..

[30]  H. Lodish.  Molecular Cell Biology , 1986 .

[31]  J. Steitz,et al.  Switching from Repression to Activation: MicroRNAs Can Up-Regulate Translation , 2007, Science.

[32]  Xin Zhou,et al.  Altered expression of miR-152 and miR-148a in ovarian cancer is related to cell proliferation. , 2011, Oncology reports.

[33]  Richard M. Karp,et al.  Discovering local structure in gene expression data: the order-preserving submatrix problem , 2002, RECOMB '02.

[34]  M. Stephens,et al.  RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. , 2008, Genome research.

[35]  Qing Xu,et al.  A regulatory circuit of miR-148a/152 and DNMT1 in modulating cell transformation and tumor angiogenesis through IGF-IR and IRS1. , 2013, Journal of molecular cell biology.

[36]  Liang Chen,et al.  Role of Deregulated microRNAs in Breast Cancer Progression Using FFPE Tissue , 2013, PloS one.

[37]  V. Kim.  MicroRNA biogenesis: coordinated cropping and dicing , 2005, Nature Reviews Molecular Cell Biology.

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

[39]  Chengzhong Xing,et al.  Altered Expression of MiR-148a and MiR-152 in Gastrointestinal Cancers and Its Clinical Significance , 2010, Journal of Gastrointestinal Surgery.

[40]  Ignace Vergote,et al.  Expression profiling of cancerous and normal breast tissues identifies microRNAs that are differentially expressed in serum from patients with (metastatic) breast cancer and healthy volunteers , 2012, Breast Cancer Research.

[41]  Robert B. Russell,et al.  Principles of MicroRNATarget Recognition , 2005 .

[42]  S. Spivack,et al.  Overexpression of the microRNA hsa-miR-200c leads to reduced expression of transcription factor 8 and increased expression of E-cadherin. , 2007, Cancer research.

[43]  Hailong Wu,et al.  Suppression of cell growth and invasion by miR-205 in breast cancer , 2008, Cell Research.

[44]  M. Peter.  Let-7 and miR-200 microRNAs: Guardians against pluripotency and cancer progression , 2009, Cell cycle.

[45]  Iris Barshack,et al.  MiRNA Expression in Psoriatic Skin: Reciprocal Regulation of hsa-miR-99a and IGF-1R , 2011, PloS one.

[46]  Sven Bergmann,et al.  Iterative signature algorithm for the analysis of large-scale gene expression data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  C. Croce,et al.  microRNA-205 regulates HER3 in human breast cancer. , 2009, Cancer research.

[48]  Ranit Aharonov,et al.  MicroRNAs as a potential prognostic factor in gastric cancer. , 2010 .

[49]  Volker Hovestadt,et al.  Molecular and Cellular Pathobiology MicroRNA Sequence and Expression Analysis in Breast Tumors by Deep Sequencing , 2011 .

[50]  BMC Bioinformatics , 2005 .

[51]  Carme Camps,et al.  hsa-miR-210 Is Induced by Hypoxia and Is an Independent Prognostic Factor in Breast Cancer , 2008, Clinical Cancer Research.

[52]  Daniel B. Martin,et al.  Circulating microRNAs as stable blood-based markers for cancer detection , 2008, Proceedings of the National Academy of Sciences.

[53]  Megan F. Cole,et al.  Connecting microRNA Genes to the Core Transcriptional Regulatory Circuitry of Embryonic Stem Cells , 2008, Cell.

[54]  D. Bartel.  MicroRNAs Genomics, Biogenesis, Mechanism, and Function , 2004, Cell.

[55]  Chee Woon Wang,et al.  Potentiality of a triple microRNA classifier: miR-193a-3p, miR-23a and miR-338-5p for early detection of colorectal cancer , 2013, BMC Cancer.

[56]  Wei Shi,et al.  Comprehensive MicroRNA Profiling for Head and Neck Squamous Cell Carcinomas , 2010, Clinical Cancer Research.

[57]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[58]  George A Calin,et al.  MicroRNA expression profiles associated with prognosis and therapeutic outcome in colon adenocarcinoma. , 2008, JAMA.

[59]  F. Slack,et al.  Oncomirs — microRNAs with a role in cancer , 2006, Nature Reviews Cancer.

[60]  Lin He,et al.  MicroRNAs: small RNAs with a big role in gene regulation , 2004, Nature Reviews Genetics.

[61]  Hidetoshi Eguchi,et al.  MicroRNAs as Biomarkers in Colorectal Cancer , 2017, Cancers.

[62]  T. Brabletz,et al.  A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells , 2008, EMBO reports.

[63]  John W M Martens,et al.  Four miRNAs associated with aggressiveness of lymph node-negative, estrogen receptor-positive human breast cancer , 2008, Proceedings of the National Academy of Sciences.

[64]  P. Brown,et al.  A DNA microarray system for analyzing complex DNA samples using two-color fluorescent probe hybridization. , 1996, Genome research.

[65]  M Morgan,et al.  Clinical recall from the NHS Breast Screening Programme: is it worth doing? , 2012, Breast Cancer Research.

[66]  Li Li,et al.  A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data , 2012, BioData Mining.

[67]  Yaniv Ziv,et al.  Revealing modular organization in the yeast transcriptional network , 2002, Nature Genetics.

[68]  Yasunori Fujita,et al.  Effects of miR-34a on cell growth and chemoresistance in prostate cancer PC3 cells. , 2008, Biochemical and biophysical research communications.

[69]  H. Horvitz,et al.  MicroRNA expression profiles classify human cancers , 2005, Nature.

[70]  Sampsa Hautaniemi,et al.  Biclustering Methods: Biological Relevance and Application in Gene Expression Analysis , 2014, PloS one.