Detail
Experiment ID:
EXP00076
Expression Profile:
- Description:MicroRNAs in Myeloma
- Organism:Homo sapiens
- Source:GEO
- Source ID:GSE16558
- Platform: GPL8695
- Number of samples:65
- Overall design:PatientsSixty patients with symptomatic newly diagnosed MM were included in the study. Five healthy controls of bone marrow (BM) samples were obtained from subjects undergoing BM harvest for allogeneic transplantation. In all the BM samples a CD138 positive plasma cell (PC) isolation using the AutoMACs automated separation system (Miltenyi-Biotec, Auburn, CA) was performed (purity was above 90%). All patients as well as healthy donors provided written informed consent in accordance with the Helsinki Declaration, and the research ethics committee of the University Hospital of Salamanca approved the study. The total 65 samples were analyzed from both miRNAs and mRNA gene expression profiling.Cytogenetic AnalysisThe selection of patients was based on cytogenetic features in order to include a representative number of samples with the most relevant and recurrent genetic abnormalities. The systematic screening for genomic aberrations in our institution includes interphase FISH studies for the detection of IGH rearrangements, RB1 and P53 deletions (Abbott Molecular/Vysis, Des Plaines, IL, USA) as previously described,18 and 1q gains (ON 1q21/SRD 1p36, Kreatech Diagnostics, Amsterdam). Furthermore, only patients with more than 80% of PC exhibiting genetic abnormalities were considered for the analysis, with the exception of gains on 1q and P53 deletions, which were present in a median of 77% (range, 28-100%) and 72% (range, 46-88%) of the PC, respectively. The distribution of cytogenetic abnormalities in the 60 MM patients is summarized in Table 1.RNA ExtractionTotal RNA was extracted from normal and tumor plasma cells using miRNEasy Mini Kit (Qiagen, Valencia, USA) following manufacturer's protocol. The RNA integrity was assessed using Agilent 2100 Bioanalyzer (Agilent Tech.Inc., Palo Alto, CA, USA).MicroRNA profilingcDNA was synthesized from total RNA using the so-called hair-pin RT-primer according to the TaqMan MicroRNA Reverse Transcription Kit (PE Applied Biosystems, Foster City, CA). Reverse transcriptase reactions contained: 20ng of RNA, 1.5 μl 10x RT buffer, 0.15 μl dNTP mix (100 mM total), 1 μl MultiScribe Reverse Transcriptase (50 U/μl), 0.19 μl AB RNase Inhibitor (20 U/μl), 1 μl multiplex RT primers and 4.16 μl H2O (final volume 10 μl). Reactions were incubated in an Applied Biosystems GeneAmp PCR System 9700 for 30 min at 16°C, 30 min at 42°C, 5 min at 85°C and then held at 4°C. A total of eight independent RT reactions must be run per sample. Diluted RT reaction product is mixed with TaqMan® Universal PCR Mastermix (No AmpErase UNG) and loaded into the corresponding TaqMan® Low Density Arrays fill ports (Applied Biosystems, Part number: 4384792). This panel contains 368 TaqMan® MicroRNA Assays enabling accurate quantification of 365 human miRNAs and three endogenous controls (RNU48, RNU48 and RNU6B) to aid in data normalization. Real-time PCR was performed using an Applied Biosystems 7900 HT Fast Real Time PCR Sequence Detection system. The reactions were incubated at 94.5 ºC for 10 min, followed by 50 cycles of 97 ºC for 30 s and 59.7 ºC for 1 min. The threshold cycle (Ct) data was determined using 0.3 as a threshold. The Ct is defined as the fractional cycle number at which the fluorescence passes the fixed threshold.MiRNAs with Ct values higher than 35 were excluded from the analysis, leaving a set of 192 miRNAs. Normalization was performed with the mean of RNU44 and RNU48, as they were uniformly expressed across the patient dataset. Relative quantification of miRNA expression was calculated with the 2-deltaCt and 2-deltadeltaCt methods, where deltaCtdeltaCt(miRNA)-Ct(miRNA control) and deltadeltaCt deltaCt(MM)-deltaCt(average normal PC), depending upon whether comparisons were made between MM samples and normal PC or between MM samples, respectively.19 The data was presented as log10 of the relative quantity of each miRNA.20 We used hierarchical clustering (Cluster and TreeView software) based on the average-linkage method with the centered correlation metric for unsupervised analysis.21 Differentially expressed miRNAs were identified using Significant Analysis of Microarrays (SAM) algorithm, by using the two-class (unpaired) format, not considering equal variances.22 Significant genes were selected based on false discovery ratio (FDR) and controlling the q-value for the gene list.Target prediction was performed using miRecords, an integrated resource for animal miRNA–target interactions.23 The Predicted Targets component of miRecords integrates the predicted targets of the following miRNA target prediction tools: DIANA-microT, MicroInspector, miRanda, MirTarget2, miTarget, NBmiRTar, PicTar, PITA, RNA22, RNAhybrid, and TargetScan.mRNA gene expression profilingRNA labeling and microarray hybridization have been previously reported.24 Total RNA was extracted from purified cell populations using RNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. Labelling and hybridizations were performed according to protocols from Affymetrix. Briefly, 100-300 ng of total RNA were amplified and labeled using the WT Sense Target labelling and control reagents kit (Affymetrix Inc., Santa Clara, CA, USA), and then hybridized to Human Gene 1.0 ST Array (Affymetrix). Washing and scanning were performed using GeneChip System of Affymetrix (GeneChip Hybridization Oven 640, GeneChip Fluidics Station 450 and GeneChip Scanner 7G). Expression value for each probe set was calculated using RMAExpress program that uses RMA (Robust Multi-Array Average) algorithm.25 A differential expression analysis was carried out on the data using SAM algorithm to identify genes with statistically significant changes in expression between different classes.To identify the putative mRNA targets of the significant miRNAs, which at the same time were selected as significantly deregulated genes in the class comparison analysis of gene expression data, a Pearson correlation analysis was performed using a cutoff p-value <0.05 (2-tailed). Calculation of the p-values assigned to the Pearson's correlation coefficients was done assuming that the variables follow a bivariate normal distribution. The functional analysis to identify the most relevant functional categories in the datasets of miRNA target genes selected by statistical analysis, was generated through the use of Ingenuity Pathways Analysis (Ingenuity Systems, Mountain View, CA).
- Instrument:Applied Biosystems Human TaqMan® Low Density Array
Design and Sample:
- Cancer Type:lymphoma
- Cancer SubType:multiple myeloma
- Cell Line:N/A
- Experimental Design:cancer vs normal
- Case Sample:multiple myeloma
- Control Sample:normal bone marrow
- Num of Case:60
- Num of Control:5
- Quantification Software:Limma
- Num of miRNAs:351
Identification:
- Num of Up:0
- Num of Down:99
Time Info:
- Create Time2016-03-14
- Update Time:2021-05-27
Status:
| miRNA ID | Cancer Type | Design | logFC | AveExpr | T value | P value | adj Pvalue | Status | Plot |
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