anamiR: integrated analysis of MicroRNA and gene expression profiling

BackgroundWith advancements in high-throughput technologies, the cost of obtaining expression profiles of both mRNA and microRNA in the same individual has substantially decreased. Integrated analysis of these profiles can help to elucidate the functional effects of RNA expression in complex diseases, such as cancer. However, fundamental discrepancies are observed in the results from microRNA-mRNA target gene prediction algorithms, and few packages can be used to analyze microRNA and mRNA expression levels simultaneously.ResultsTo address these issues, an R package, anamiR, was developed. A total of 10 experimental/prediction databases were integrated. Two analytical functions are provided in anamiR, including the single marker test and functional gene set enrichment analysis, and several parameters can be changed by users. Here we demonstrate the potential application of the anamiR package to 2 publicly available microarray datasets.ConclusionThe anamiR package is effective for an integrated analysis of both RNA and microRNA profiles. By characterizing biological functions and signaling pathways, this package helps identify dysregulated genes/miRNAs from biological and medical experiments. The source code and manual of the anamiR package are freely available at https://bioconductor.org/packages/release/bioc/html/anamiR.html.

[1]  Y. Kondo,et al.  Histone Deacetylase Inhibition in Prostate Cancer Triggers miR-320-Mediated Suppression of the Androgen Receptor. , 2016, Cancer research.

[2]  Hong‐Tai Chang,et al.  Comprehensive microRNA profiling of prostate cancer cells after ionizing radiation treatment , 2014, Oncology reports.

[3]  C. K. Hsiao,et al.  miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets , 2012, PloS one.

[4]  Mihaela Zavolan,et al.  Inference of miRNA targets using evolutionary conservation and pathway analysis , 2007, BMC Bioinformatics.

[5]  T. Hong,et al.  MicroRNA-320 suppresses the stem cell-like characteristics of prostate cancer cells by downregulating the Wnt/beta-catenin signaling pathway. , 2013, Carcinogenesis.

[6]  Martin Reczko,et al.  DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows , 2013, Nucleic Acids Res..

[7]  Maria Vila-Casadesús,et al.  MiRComb: An R Package to Analyse miRNA-mRNA Interactions. Examples across Five Digestive Cancers , 2016, PloS one.

[8]  Xiong Guo,et al.  Let-7b-5p regulates proliferation and apoptosis in multiple myeloma by targeting IGF1R. , 2014, Acta biochimica et biophysica Sinica.

[9]  Fei Guo,et al.  MDA-SKF: Similarity Kernel Fusion for Accurately Discovering miRNA-Disease Association , 2018, Front. Genet..

[10]  B. Berger,et al.  Unusually effective microRNA targeting within repeat-rich coding regions of mammalian mRNAs. , 2011, Genome research.

[11]  Qianben Wang,et al.  Phospho‐MED1‐enhanced UBE2C locus looping drives castration‐resistant prostate cancer growth , 2011, The EMBO journal.

[12]  S. Rossi,et al.  MicroRNA-424 impairs ubiquitination to activate STAT3 and promote prostate tumor progression. , 2016, The Journal of clinical investigation.

[13]  Sam Griffiths-Jones,et al.  Bias in microRNA functional enrichment analysis , 2015, Bioinform..

[14]  P. Tassone,et al.  MicroRNAs in multiple myeloma and related bone disease. , 2015, Annals of translational medicine.

[15]  N. Gao,et al.  miR-186 inhibits cell proliferation in multiple myeloma by repressing Jagged1. , 2016, Biochemical and biophysical research communications.

[16]  Shuliang Song,et al.  Involvement of ubiquitin-conjugating enzyme E2C in proliferation and invasion of prostate carcinoma cells. , 2014, Oncology research.

[17]  Michael Kertesz,et al.  The role of site accessibility in microRNA target recognition , 2007, Nature Genetics.

[18]  Isidore Rigoutsos,et al.  Interactive exploration of RNA22 microRNA target predictions , 2012, Bioinform..

[19]  P. Tassone,et al.  MicroRNAs: Novel Crossroads between Myeloma Cells and the Bone Marrow Microenvironment , 2016, BioMed research international.

[20]  Stijn van Dongen,et al.  miRBase: tools for microRNA genomics , 2007, Nucleic Acids Res..

[21]  Ivo Grosse,et al.  Functional microRNA targets in protein coding sequences , 2012, Bioinform..

[22]  D. Lancet,et al.  GeneCards: integrating information about genes, proteins and diseases. , 1997, Trends in genetics : TIG.

[23]  Jijun Tang,et al.  FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association , 2018, BMC Genomics.

[24]  G. Nuovo,et al.  Experimental validation of miRNA targets. , 2008, Methods.

[25]  Rongguo Fu,et al.  Advances in the Techniques for the Prediction of microRNA Targets , 2013, International journal of molecular sciences.

[26]  Xiaowei Wang miRDB: a microRNA target prediction and functional annotation database with a wiki interface. , 2008, RNA.

[27]  Carol J Bult,et al.  MouseCyc: a curated biochemical pathways database for the laboratory mouse , 2009, Genome Biology.

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

[29]  Lincoln Stein,et al.  Reactome: a database of reactions, pathways and biological processes , 2010, Nucleic Acids Res..

[30]  Gordon K. Smyth,et al.  limma: Linear Models for Microarray Data , 2005 .

[31]  Peter J. Woolf,et al.  GAGE: generally applicable gene set enrichment for pathway analysis , 2009, BMC Bioinformatics.

[32]  M. Schmidt‐Hieber,et al.  Multiple myeloma cells alter the senescence phenotype of bone marrow mesenchymal stromal cells under participation of the DLK1-DIO3 genomic region , 2015, BMC Cancer.

[33]  Hsien-Da Huang,et al.  miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database , 2015, Nucleic Acids Res..

[34]  I. Riquelme,et al.  Tools for Sequence-Based miRNA Target Prediction: What to Choose? , 2016, International journal of molecular sciences.

[35]  Tongbin Li,et al.  miRecords: an integrated resource for microRNA–target interactions , 2008, Nucleic Acids Res..

[36]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[37]  Yoshihiro Yamanishi,et al.  KEGG for linking genomes to life and the environment , 2007, Nucleic Acids Res..

[38]  H. Klocker,et al.  Lamin A/C protein is overexpressed in tissue-invading prostate cancer and promotes prostate cancer cell growth, migration and invasion through the PI3K/AKT/PTEN pathway. , 2012, Carcinogenesis.

[39]  Jeffrey A. Thompson,et al.  Common features of microRNA target prediction tools , 2014, Front. Genet..

[40]  J. Rivas,et al.  Deregulation of microRNA expression in the different genetic subtypes of multiple myeloma and correlation with gene expression profiling , 2010, Leukemia.

[41]  J. Davis Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2007 .

[42]  Doron Betel,et al.  The microRNA.org resource: targets and expression , 2007, Nucleic Acids Res..

[43]  N. Munshi,et al.  Identification of novel antigens with induced immune response in monoclonal gammopathy of undetermined significance. , 2009, Blood.