Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Repeatability and comparability of microRNA microarray

ABSTRACT: We compared repeatability and comparability of microRNA microarray using 5 different platforms. and compared microarray data generated from five different microRNA microarray platforms with quantitative RT-PCR. Our data suggested that the most accurate and repeatable methods for microRNA expression profiling are Agilent and Toray, and the numbers of detected microRNA at Toray are more than at Agilent. Keywords: repeatability, comparability, microRNA, microarray We compared repeatability and comparability of microRNA microarray using 5 different platforms (Agilent, Ambion, Exiqon, Invitrogen, and Toray). In addition, we compared microarray data generated from five different microRNA microarray platforms with quantitative RT-PCR.

ORGANISM(S): Homo sapiens

SUBMITTER: Akira Myoumoto

PROVIDER: E-GEOD-13860 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Intra-platform repeatability and inter-platform comparability of microRNA microarray technology.

Sato Fumiaki F Tsuchiya Soken S Terasawa Kazuya K Tsujimoto Gozoh G

PloS one 20090514 5

Over the last decade, DNA microarray technology has provided a great contribution to the life sciences. The MicroArray Quality Control (MAQC) project demonstrated the way to analyze the expression microarray. Recently, microarray technology has been utilized to analyze a comprehensive microRNA expression profiling. Currently, several platforms of microRNA microarray chips are commercially available. Thus, we compared repeatability and comparability of five different microRNA microarray platforms ...[more]

PMID: 19436744

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Project description:Pheochromocytoma (PCC) and paraganglioma (PGL) are rare neuroendocrine neoplasias of neural crest origin. They can be part of several syndromes, and their mRNA profile is dependent on genetic background, but questions related to clinical behavior or even main location remain unanswered. MicroRNAs are key modulators of target genes through translational repression, mRNA degradation, or both, and therefore they could resolve some of these issues. To determine the role microRNAs play in tumorigenesis and progression of PCC/PGL, as well as to identify microRNA biomarkers specifically related to different PCC/PGL genetic classes known so far, we characterized microRNA profiles in a large series of frozen tumors with germline mutations in SDHD, SDHB, VHL, RET, NF1, and TMEM127 genes through microarray analysis. We identified microRNA signatures specific to, as well as common among, the genetic classes of PCC/PGLs, and the best candidate microRNAs (miR-122, miR-126*, miR-129*, miR-133b, miR-137, miR-183, miR-210, miR-382, miR-488, miR-885-5p, and miR-96) were validated in an independent series of formalin-fixed paraffin-embedded PCC/PGL samples by qRT-PCR. MicroRNA-137, -96/183, and -143/145 expression in PCC/PGLs correlated inversely with the differentiation status of tumor cells. MicroRNA-210, -382, and -380 could modulate pseudohypoxic cellular response in VHL-deficient PCC/PGL. MicroRNA-193b, -365, and -424 were commonly downregulated among all genetic classes, suggesting their involvement in cell cycle control and differentiation. Herein, we demonstrate that PCC/PGLs have different microRNA profiles according to the underlying primary mutation, suggesting they could be used as specific biomarkers and add information on the etiology of these tumors. For this study, we employed the Agilent-019118 Human miRNA Microarray 2.0 G4470B (with Labeling kit: Agilent miRNA labeling reagent and Hybridization Kit, Cat # 5190-0408) to perform microRNA expression profiling on a large series of 54 fresh-frozen tissue samples, including a total of 48 genetically characterized pheochromocytomas (n=37) and paragangliomas (n=11) (8 SDHB-related tumors, 4 SDHD-related tumors, 14 Ret-related tumors, 12 VHL-related tumors, 4 NF1-related tumors, 3 TMEM127-related tumors, and 3 familial pheochromocytoma (FPCC) samples), and 6 normal adrenal medulla. Normalization of array data was performed applying the robust multiarray average (RMA) method using the AgiMicroRna package in Bioconductor. RMA normalization, by default, merges replicates of each probe and produces an Eset with a single value for each microRNA.

Dataset Information

Repeatability and comparability of microRNA microarray

Publications

Intra-platform repeatability and inter-platform comparability of microRNA microarray technology.

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