 |
 |
GEO help: Mouse over screen elements for information. |
|
| Status |
Public on Dec 08, 2015 |
| Title |
Reconstruction of microRNA/genes transcriptional regulatory networks of multiple myeloma through in silico integrative genomics analysis [MM, miRNA] |
| Platform organism |
synthetic construct |
| Sample organism |
Homo sapiens |
| Experiment type |
Non-coding RNA profiling by array
|
| Summary |
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.
|
| |
|
| Overall design |
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
GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.
|
| |
|
| Contributor(s) |
Calura E, Bisognin A, Manzoni M, Todoerti K, Sales G, Morgan GJ, Neri A, Agnelli L, Romualdi C, Bortoluzzi S, Taiana E, Amodio N, Tassone P, Tonon G |
| Citation(s) |
26496024 |
| |
| Submission date |
Jun 24, 2015 |
| Last update date |
Oct 11, 2016 |
| Contact name |
Luca Agnelli |
| E-mail(s) |
luca.agnelli@istitutotumori.mi.it, luca.agnelli@gmail.com
|
| Phone |
+390223903581
|
| Organization name |
IRCCS Istituto Nazionale dei Tumori
|
| Department |
Department of Advanced Diagnostics
|
| Street address |
Venezian 1
|
| City |
MILAN |
| ZIP/Postal code |
20133 |
| Country |
Italy |
| |
|
| Platforms (1) |
| GPL20614 |
[miRNA-3_0] Affymetrix Multispecies miRNA-3 Array [Hs_MIRBASEG] |
|
| Samples (96)
|
|
| This SubSeries is part of SuperSeries: |
| GSE70323 |
Reconstruction of microRNA/genes transcriptional regulatory networks of multiple myeloma through in silico integrative genomics analysis |
|
| Relations |
| BioProject |
PRJNA288307 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE70254_RAW.tar |
77.9 Mb |
(http)(custom) |
TAR (of CEL) |
| Processed data included within Sample table |
|
|
|
|
 |
Less...
More...