Here we present a novel web tool for the statistical analysis of gene expression data in multiple tag sampling experiments. Differentially expressed genes are detected by using six different test statistics. Result tables, linked to the GenBank, UniGene, or LocusLink database, can be browsed or searched in different ways. Software is freely available at the site: http://telethon.bio.unipd.it/bioinfo/IDEG6_form/, together with additional information on statistical methodologies

To date, little evidence of miRNA expression/ deregulation in multiple myeloma has been reported. To characterize miRNA in the context of the major multiple myeloma molecular types, we generated miRNA expression profiles of highly purified malignant plasma cells from 40 primary tumors. Furthermore, transcriptional profiles, available for all patients, were used to investigate the occurrence of miRNA/predicted target mRNA pair anticorrelations, and the miRNA and genome-wide DNA data were integrated in a subset of patients to evaluate the influence of allelic imbalances on miRNA expression. Differential miRNA expression patterns were identified, which were mainly associated with the major IGH translocations; particularly, t(4;14) patients showed specific overexpression of let-7e, miR-125a-5p, and miR-99b belonging to a cluster at 19q13.33. The occurrence of other lesions (ie, 1q gain, 13q and 17p deletions, and hyperdiploidy) was slightly characterized by specific miRNA signatures. Furthermore, the occurrence of several allelic imbalances or loss of heterozygosity was found significantly associated with the altered expression of miRNAs located in the involved regions, such as let-7b at 22q13.31 or miR-140-3p at 16q22. Finally, the integrative analysis based on computational target prediction and miRNA/ mRNA profiling defined a network of putative functional miRNA-target regulatory relations supported by expression data. (Blood. 2009;114:e20-e26) IntroductionMultiple myeloma (MM) is a malignant proliferation of bone marrow (BM) plasma cells (PCs), characterized by a profound genomic instability involving both numerical and structural chromosomal aberrations of potential prognostic relevance. 1 Nearly half of MM tumors are hyperdiploid (HD) with multiple trisomies of nonrandom odd-numbered chromosomes and a low prevalence of chromosomal translocations involving the immunoglobulin heavy chain (IGH) locus at 14q32 and chromosome 13 deletion 1 ; the others are nonhyperdiploid (NHD) tumors often showing chromosome 13 deletion, 1q gain, and IGH translocations, 2 with the most frequent partners being 11q13, 4p16, 16q23, 20q11, and 6p21. The deregulation of at least one of the cyclin D genes is observed in almost all MM cases and, in combination with recurrent IGH translocations, has been proposed for a molecular classification of MM called translocation/cyclin (TC) classification. 3,4 The occurrence of specific transcriptional patterns associated with the molecular subgroups and major genetic lesions of MM has been extensively described in several studies by us and others. [2][3][4][5][6][7][8][9][10] miRNAs are endogenous approximately 22 nt RNAs that play important regulatory roles in animals and plants by targeting mRNAs for cleavage or translational repression. 11 It has been suggested that chromosomal abnormalities and other types of genetic or epigenetic alterations might contribute to miRNA deregulation in cancer. 12,13 There are still few published data concerning miRNA expression in MM. Loffler et al have shown t...

Key Points• Differential gene and miRNA expression analysis in PMF granulocytes identifies new biomarkers and putative therapeutic targets.• Activation of the miR-155/ JARID2 axis in PMF CD341 cells results in overproduction of MK precursors.Primary myelofibrosis (PMF) is a myeloproliferative neoplasm characterized by megakaryocyte (MK) hyperplasia, bone marrow fibrosis, and abnormal stem cell trafficking. PMF may be associated with somatic mutations in JAK2, MPL, or CALR. Previous studies have shown that abnormal MKs play a central role in the pathophysiology of PMF. In this work, we studied both gene and microRNA (miRNA) expression profiles in CD34 1 cells from PMF patients. We identified several biomarkers and putative molecular targets such as FGR, LCN2, and OLFM4. By means of miRNA-gene expression integrative analysis, we found different regulatory networks involved in the dysregulation of transcriptional control and chromatin remodeling. In particular, we identified a network gathering several miRNAs with oncogenic potential (eg, miR-155-5p) and targeted genes whose abnormal function has been previously associated with myeloid neoplasms, including JARID2, NR4A3, CDC42, and HMGB3. Because the validation of miRNA-target interactions unveiled JARID2/miR-155-5p as the strongest relationship in the network, we studied the function of this axis in normal and PMF CD34 1 cells. We showed that JARID2 downregulation mediated by miR-155-5p overexpression leads to increased in vitro formation of CD41 1 MK precursors. These findings suggest that overexpression of miR-155-5p and the resulting downregulation of JARID2 may contribute to MK hyperplasia in PMF. (Blood. 2014;124(13):e21-e32)

BackgroundQualitative alterations or abnormal expression of microRNAs (miRNAs) in colon cancer have mainly been demonstrated in primary tumors. Poorly overlapping sets of oncomiRs, tumor suppressor miRNAs and metastamiRs have been linked with distinct stages in the progression of colorectal cancer. To identify changes in both miRNA and gene expression levels among normal colon mucosa, primary tumor and liver metastasis samples, and to classify miRNAs into functional networks, in this work miRNA and gene expression profiles in 158 samples from 46 patients were analysed.ResultsMost changes in miRNA and gene expression levels had already manifested in the primary tumors while these levels were almost stably maintained in the subsequent primary tumor-to-metastasis transition. In addition, comparing normal tissue, tumor and metastasis, we did not observe general impairment or any rise in miRNA biogenesis. While only few mRNAs were found to be differentially expressed between primary colorectal carcinoma and liver metastases, miRNA expression profiles can classify primary tumors and metastases well, including differential expression of miR-10b, miR-210 and miR-708. Of 82 miRNAs that were modulated during tumor progression, 22 were involved in EMT. qRT-PCR confirmed the down-regulation of miR-150 and miR-10b in both primary tumor and metastasis compared to normal mucosa and of miR-146a in metastases compared to primary tumor. The upregulation of miR-201 in metastasis compared both with normal and primary tumour was also confirmed. A preliminary survival analysis considering differentially expressed miRNAs suggested a possible link between miR-10b expression in metastasis and patient survival. By integrating miRNA and target gene expression data, we identified a combination of interconnected miRNAs, which are organized into sub-networks, including several regulatory relationships with differentially expressed genes. Key regulatory interactions were validated experimentally. Specific mixed circuits involving miRNAs and transcription factors were identified and deserve further investigation. The suppressor activity of miR-182 on ENTPD5 gene was identified for the first time and confirmed in an independent set of samples.ConclusionsUsing a large dataset of CRC miRNA and gene expression profiles, we describe the interplay of miRNA groups in regulating gene expression, which in turn affects modulated pathways that are important for tumor development.

Cell states in hematopoiesis are controlled by master regulators and by complex circuits of a growing family of RNA species impacting cell phenotype maintenance and plasticity. Circular RNAs (circRNAs) are rapidly gaining the status of particularly stable transcriptome members with distinctive qualities. RNA-seq identified thousands of circRNAs with developmental stage- and tissue-specific expression corroborating earlier suggestions that circular isoforms are a natural feature of the cell expression program. CircRNAs are abundantly expressed also in the hematopoietic compartment. There are a number of studies on circRNAs in blood cells, a specific overview is however lacking. In this review we first present current insight in circRNA biogenesis discussing the relevance for hematopoiesis of the highly interleaved processes of splicing and circRNA biogenesis. Regarding molecular functions circRNAs modulate host gene expression, but also compete for binding of microRNAs, RNA-binding proteins or translation initiation and participate in regulatory circuits. We examine circRNA expression in the hematopoietic compartment and in hematologic malignancies and review the recent breakthrough study that identified pathogenic circRNAs derived from leukemia fusion genes. CircRNA high and regulated expression in blood cell types indicate that further studies are warranted to inform the position of these regulators in normal and malignant hematopoiesis.

Tumor progression is accompanied by an altered myelopoiesis causing the accumulation of immunosuppressive cells. Here, we showed that miR-142-3p downregulation promoted macrophage differentiation and determined the acquisition of their immunosuppressive function in tumor. Tumor-released cytokines signaling through gp130, the common subunit of the interleukin-6 cytokine receptor family, induced the LAP∗ isoform of C/EBPβ transcription factor, promoting macrophage generation. miR-142-3p downregulated gp130 by canonical binding to its messenger RNA (mRNA) 3' UTR and repressed C/EBPβ LAP∗ by noncanonical binding to its 5' mRNA coding sequence. Enforced miR expression impaired macrophage differentiation both in vitro and in vivo. Mice constitutively expressing miR-142-3p in the bone marrow showed a marked increase in survival following immunotherapy with tumor-specific T lymphocytes. By modulating a specific miR in bone marrow precursors, we thus demonstrated the feasibility of altering tumor-induced macrophage differentiation as a potent tool to improve the efficacy of cancer immunotherapy.

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia.

MAGIA2 (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA2 performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA2 tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target.