Dataset Information

MicroRNA expression profiling identifies molecular signatures associated with anaplastic large cell lymphoma.

ABSTRACT: Anaplastic large-cell lymphomas (ALCLs) encompass at least 2 systemic diseases distinguished by the presence or absence of anaplastic lymphoma kinase (ALK) expression. We performed genome-wide microRNA (miRNA) profiling on 33 ALK-positive (ALK[+]) ALCLs, 25 ALK-negative (ALK[-]) ALCLs, 9 angioimmunoblastic T-cell lymphomas, 11 peripheral T-cell lymphomas not otherwise specified (PTCLNOS), and normal T cells, and demonstrated that ALCLs express many of the miRNAs that are highly expressed in normal T cells with the prominent exception of miR-146a. Unsupervised hierarchical clustering demonstrated distinct clustering of ALCL, PTCL-NOS, and the AITL subtype of PTCL. Cases of ALK(+) ALCL and ALK(-) ALCL were interspersed in unsupervised analysis, suggesting a close relationship at the molecular level. We identified an miRNA signature of 7 miRNAs (5 upregulated: miR-512-3p, miR-886-5p, miR-886-3p, miR-708, miR-135b; 2 downregulated: miR-146a, miR-155) significantly associated with ALK(+) ALCL cases. In addition, we derived an 11-miRNA signature (4 upregulated: miR-210, miR-197, miR-191, miR-512-3p; 7 downregulated: miR-451, miR-146a, miR-22, miR-455-3p, miR-455-5p, miR-143, miR-494) that differentiates ALK(-) ALCL from other PTCLs. Our in vitro studies identified a set of 32 miRNAs associated with ALK expression. Of these, the miR-17?92 cluster and its paralogues were also highly expressed in ALK(+) ALCL and may represent important downstream effectors of the ALK oncogenic pathway.

SUBMITTER: Liu C

PROVIDER: S-EPMC3778551 | biostudies-other | 2013 Sep

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

MicroRNA expression profiling identifies molecular signatures associated with anaplastic large cell lymphoma.

Liu Cuiling C Iqbal Javeed J Teruya-Feldstein Julie J Shen Yulei Y Dabrowska Magdalena Julia MJ Dybkaer Karen K Lim Megan S MS Piva Roberto R Barreca Antonella A Pellegrino Elisa E Spaccarotella Elisa E Lachel Cynthia M CM Kucuk Can C Jiang Chun-Sun CS Hu Xiaozhou X Bhagavathi Sharathkumar S Greiner Timothy C TC Weisenburger Dennis D DD Aoun Patricia P Perkins Sherrie L SL McKeithan Timothy W TW Inghirami Giorgio G Chan Wing C WC

Blood 20130625 12

Anaplastic large-cell lymphomas (ALCLs) encompass at least 2 systemic diseases distinguished by the presence or absence of anaplastic lymphoma kinase (ALK) expression. We performed genome-wide microRNA (miRNA) profiling on 33 ALK-positive (ALK[+]) ALCLs, 25 ALK-negative (ALK[-]) ALCLs, 9 angioimmunoblastic T-cell lymphomas, 11 peripheral T-cell lymphomas not otherwise specified (PTCLNOS), and normal T cells, and demonstrated that ALCLs express many of the miRNAs that are highly expressed in norm ...[more]

PMID: 23801630

Similar Datasets

Project description:BackgroundGene expression profiling has revolutionized our ability to molecularly classify primary human tumors and significantly enhanced the development of novel tumor markers and therapies; however, progress in the diagnosis and treatment of melanoma over the past 3 decades has been limited, and there is currently no approved therapy that significantly extends lifespan in patients with advanced disease. Profiling studies of melanoma to date have been inconsistent due to the heterogeneous nature of this malignancy and the limited availability of informative tissue specimens from early stages of disease.Methodology/principle findingsIn order to gain an improved understanding of the molecular basis of melanoma progression, we have compared gene expression profiles from a series of melanoma cell lines representing discrete stages of malignant progression that recapitulate critical characteristics of the primary lesions from which they were derived. Here we describe the unsupervised hierarchical clustering of profiling data from melanoma cell lines and melanocytes. This clustering identifies two distinctive molecular subclasses of melanoma segregating aggressive metastatic tumor cell lines from less-aggressive primary tumor cell lines. Further analysis of expression signatures associated with melanoma progression using functional annotations categorized these transcripts into three classes of genes: 1) Upregulation of activators of cell cycle progression, DNA replication and repair (CDCA2, NCAPH, NCAPG, NCAPG2, PBK, NUSAP1, BIRC5, ESCO2, HELLS, MELK, GINS1, GINS4, RAD54L, TYMS, and DHFR), 2) Loss of genes associated with cellular adhesion and melanocyte differentiation (CDH3, CDH1, c-KIT, PAX3, CITED1/MSG-1, TYR, MELANA, MC1R, and OCA2), 3) Upregulation of genes associated with resistance to apoptosis (BIRC5/survivin). While these broad classes of transcripts have previously been implicated in the progression of melanoma and other malignancies, the specific genes identified within each class of transcripts are novel. In addition, the transcription factor NF-KB was specifically identified as being a potential "master regulator" of melanoma invasion since NF-KB binding sites were identified as consistent consensus sequences within promoters of progression-associated genes.Conclusions/significanceWe conclude that tumor cell lines are a valuable resource for the early identification of gene signatures associated with malignant progression in tumors with significant heterogeneity like melanoma. We further conclude that the development of novel data reduction algorithms for analysis of microarray studies is critical to allow for optimized mining of important, clinically-relevant datasets. It is expected that subsequent validation studies in primary human tissues using such an approach will lead to more rapid translation of such studies to the identification of novel tumor biomarkers and therapeutic targets.

Dataset Information

MicroRNA expression profiling identifies molecular signatures associated with anaplastic large cell lymphoma.

Publications

MicroRNA expression profiling identifies molecular signatures associated with anaplastic large cell lymphoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets