Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Reconstruction of microRNA/genes transcriptional regulatory networks of multiple myeloma through in silico integrative genomics analysis

ABSTRACT: This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

ORGANISM(S): Homo sapiens

SUBMITTER: Luca Agnelli

PROVIDER: E-GEOD-70323 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Disentangling the microRNA regulatory milieu in multiple myeloma: integrative genomics analysis outlines mixed miRNA-TF circuits and pathway-derived networks modulated in t(4;14) patients.

Calura Enrica E Bisognin Andrea A Manzoni Martina M Todoerti Katia K Taiana Elisa E Sales Gabriele G Morgan Gareth J GJ Tonon Giovanni G Amodio Nicola N Tassone Pierfrancesco P Neri Antonino A Agnelli Luca L Romualdi Chiara C Bortoluzzi Stefania S

Oncotarget 20160101 3

The identification of overexpressed miRNAs in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. miRNA and gene expression profiles of two large representative MM datasets, available from retrospective and prospective series and encompassing a total of 249 patients at diagnosis, were analyzed by means of in silico integrative genomics methods, based on MAGIA2 and Micrographite computational procedures. We first identified relevant miRNA/transcription facto ...[more]

PMID: 26496024

Similar Datasets

Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al, 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNAâtarget interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al, 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al, 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al, 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al, 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. âpathsâ, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.

Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al., 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNA-target interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al., 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al., 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al., 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al., 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. paths, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE73452 and GSE73454 Samples with the same patient number represent the same sample, profiled using two different Platforms.

Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al, 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNAâtarget interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al, 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al, 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al, 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al, 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. âpathsâ, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.

Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al., 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNA-target interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al., 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al., 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al., 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al., 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. paths, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE73452 and GSE73454 Samples with the same patient number represent the same sample, profiled using two different Platforms.

Project description:Small nucleolar RNAs (snoRNAs) and small Cajal body-specific RNAs (scaRNAs) are non-coding RNAs involved in the maturation of other RNA molecules and generally located in the introns of host genes. It is now emerging that altered sno/scaRNAs expression may play a pathological role in cancer. This study elucidates the patterns of sno/scaRNAs expression in multiple myeloma (MM), by profiling puri?ed malignant plasma cells from 55 MMs, 8 secondary plasma cell leukemias (sPCL) and 4 normal controls. Overall, a global sno/scaRNAs down-regulation was found in MMs and at more extent in sPCLs compared to normal plasma cells. Whereas SCARNA22 resulted the only sno/scaRNA characterizing the TC4 MM, TC2 group displayed a distinct sno/scaRNA signature overexpressing members of SNORD115 and SNORD116 families located in a region finely regulated by imprinting mechanism at 15q11. However, the imprinting center resulted overall hypomethylated in MMs independently of the SNORD115 and SNORD116 expression levels. Finally, integrative analyses with available gene expression and genome-wide data revealed the occurrence of significant sno/scaRNAs/host genes co-expression and the putative influence of allelic imbalances on specific snoRNAs expression. Our data extend the current view of sno/scaRNAs deregulation in cancer and add novel information into the bio-molecular complexity of plasma cell dyscrasias. This series of microarray experiments contains the gene expression profiles of purified plasma cells (PCs) obtained from 55 multiple myeloma (MM) and 8 secondary plasma cell leukemia (sPCL) at diagnosis. PCs were purified from bone marrow samples using CD138 immunomagnetic microbeads according to the manufacturer's instructions (MidiMACS system, Miltenyi Biotec); the purity of the positively selected PCs was assessed by morphology and flow cytometry and was > 90% in all cases. 5.5 micrograms of single-stranded DNA target obtained from 100 ng of purified total RNA was fragmented and then labeled using the WT Terminal Labeling Kit according to the standard Affymetrix protocol (GeneChipÂ® Whole Transcript (WT) Sense Target Labeling Assay Manual). The fragmented labeled single-stranded DNA target was hybridized for 16 hours and 30 minutes at 45Â°C on GeneChipÂ® Gene 1.0 ST array according to the standard Affymetrix protocol. Washing and scanning were performed using GeneChip System of Affymetrix (GeneChip Hybridization Oven 640, GeneChip Fluidics Station 450 and GeneChip Scanner 7G). The raw intensity expression values were processed by Robust Multi-array Average (RMA) complete procedure with the re-mapped Chip Definition Files (CDF) from BrainArray libraries version 15.0.0 available at http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/15.0.0/entrezg.asp.

Project description:Small nucleolar RNAs (snoRNAs) and small Cajal body-specific RNAs are non-coding RNAs involved in the maturation of other RNA molecules. Alterations of sno/scaRNA expression may play a role in cancerogenesis. This study elucidates the patterns of sno/scaRNA expression in highly purified cells from 211 chronic lymphocytic leukemia (CLL) patients (Binet stage A) also in comparison with those of different normal B-cell subsets. CLLs display a sno/scaRNAs expression profile similar to normal memory, naÃ¯ve and marginal-zone B-cells, with the exception of a few down-regulated transcripts (SNORA31, -6, -62, and -71C). Our analyses also suggest some heterogeneity in the pattern of sno/scaRNAs expression which is apparently unrelated to the major biological (ZAP-70 and CD38), molecular (IGHV mutation) and cytogenetic markers. Moreover, we found that SNORA70F was significantly down-regulated in poor prognostic subgroups and this phenomenon was associated with the down-regulation of its host gene COBLL1. Finally, we generated an independent model based on SNORA74A and SNORD116-18 expression, which appears to distinguish two different prognostic CLL groups. These data extend the view of sno/scaRNAs deregulation in cancer and may contribute to discover novel biomarkers associated with the disease and potentially useful to predict the clinical outcome of early stage CLL patients. This series of microarray experiments contains the gene expression profiles of purified B-cell chronic lymphocytic leukemia (B-CLL) cells obtained from 211 patients (Binet stage A), 6 normal controls from peripheral blood mononuclear cells (BC), and B-cell sub-populations from tonsils (3 naÃ¯ve B-cells (N), 2 marginal zone (MZ)-like, 3 switched memory (SM) B-cells and 4 germinal center (GC) ). For gene and miRNA expression profiling experiments CLL cells were enriched by negative selection with the EasySep-Human B cell enrichment kit without CD43 depletion (Stem Cell Technologies) using the fully automated protocol of immunomagnetic cell separation with RoboSepTM (Stem Cell Technologies).

Project description:Multiple myeloma (MM) is a malignant disorder characterized by the clonal proliferation of plasma cells (PCs) in the bone marrow (BM). The genetic background and clinical course of the disease are largely heterogeneous, and MM pathophysiology ranges from the premalignant condition of monoclonal gammopathy of undetermined significance (MGUS) to smoldering MM, symptomatic MM, and extramedullary MM/plasma cell leukemia (PCL). Recent genome-wide sequencing efforts have provided the rationale for molecularly aimed treatment approaches, identifying mutations that can be specifically targeted, such as those in the mitogen-activated protein kinase (MAPK) pathway, which represent the most prevalent mutations in MM. Among these, mutations affecting BRAF gene, detected in 4-15% of patients, are of potential immediate clinical relevance due to the availability of effective inhibitors of this serine-threonine kinase which are in fact being explored also in myeloma. In this study, we screened by next generation sequencing (NGS) a large and representative series of intramedullary and extramedullary MM patients, including primary and secondary plasma cell leukemia (pPCL and sPCL, respectively), for mutations in BRAF, NRAS and KRAS genes. We evaluated the relationship of identified variants with other clinical and biological features and determined the transcriptional signature associated with MAPK pathway activation in MM. To further elucidate the transcriptional programs modulated by BRAF activation in MM, we used the PLX4032 drug to inhibit BRAF activity in U266 human myeloma cell line (HMCL), carrying K601N mutation and showing constitutive activation of MEK/ERK signaling. After confirming its ability to suppress MAPK pathway and myeloma cell proliferation in culture in the U266 cell line, we investigated the specific modulation of gene expression induced by the drug. U266 cells were treated with PLX4032 (30 µM) or DMSO for 12 hours and subjected to gene expression profiling (GEP) analysis by using Affymetrix GeneChip Human Gene 1.0ST arrays.

Project description:Multiple myeloma (MM) is a malignant disorder characterized by the clonal proliferation of plasma cells (PCs) in the bone marrow (BM). The genetic background and clinical course of the disease are largely heterogeneous, and MM pathophysiology ranges from the premalignant condition of monoclonal gammopathy of undetermined significance (MGUS) to smoldering MM, symptomatic MM, and extramedullary MM/plasma cell leukemia (PCL). Recent genome-wide sequencing efforts have provided the rationale for molecularly aimed treatment approaches, identifying mutations that can be specifically targeted, such as those in the mitogen-activated protein kinase (MAPK) pathway, which represent the most prevalent mutations in MM. Among these, mutations affecting BRAF gene, detected in 4-15% of patients, are of potential immediate clinical relevance due to the availability of effective inhibitors of this serine-threonine kinase which are in fact being explored also in myeloma. In this study, we screened by next generation sequencing (NGS) a large and representative series of intramedullary and extramedullary MM patients, including primary and secondary plasma cell leukemia (pPCL and sPCL, respectively), for mutations in BRAF, NRAS and KRAS genes. We evaluated the relationship of identified variants with other clinical and biological features and determined the transcriptional signature associated with MAPK pathway activation in MM. To identify transcriptomic profiles possibly related to BRAF, NRAS and KRAS mutations found in our study, we investigated by Affymetrix microarray technology a large fraction (n=142) of the samples analyzed by NGS, including wild-type patients for all the three genes and cases carrying mutations in at least one of them. Assuming that alterations present in a very limited number of malignant PCs might not appreciably affect gene expression, we chose to arbitrarily establish 20% as the lower variant allele frequency cut-off to perform supervised analyses.

Dataset Information

Reconstruction of microRNA/genes transcriptional regulatory networks of multiple myeloma through in silico integrative genomics analysis

Publications

Disentangling the microRNA regulatory milieu in multiple myeloma: integrative genomics analysis outlines mixed miRNA-TF circuits and pathway-derived networks modulated in t(4;14) patients.

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