Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Opposing Effects of HIF1M-NM-1 and HIF2M-NM-1 on Chromaffin Cell Phenotypic Features and Tumor Cell Proliferation: Insights from MAX [human]

ABSTRACT: Pheochromocytomas and paragangliomas (PPGLs) are catecholamine-producing tumors with diverse phenotypic features reflecting mutations in numerous genes, including MYC-associated factor X (MAX). To establish whether PPGL phenotypic differences reflect a MAX-mediated mechanism and opposing influences of HIF2M-NM-1 and HIF1M-NM-1, we combined observational investigations in PPGLs and gene-manipulation studies in two pheochromocytoma cell lines. In cell lines lacking Max, re-expression of the gene resulted in maturation of phenotypic features and decreased cell cycle progression. In cell lines lacking Hif2M-NM-1, overexpression of the gene led to immature phenotypic features, failure of dexamethasone to induce differentiation and increased proliferation. HIF1M-NM-1 has opposing actions to HIF2M-NM-1. These model systems explain the features observed in PPGLs due to mutations of MAX and other PPGL susceptibility genes. 88 samples (primary pheochromocytoma (PCC)/paraganglioma tumors) were hybridized onto a cDNA microarray in order to investigate possible heterogeneity within these tumors. This series of tumors is an expansion of GSE19422 that includes nine additional tumors to examine the role of MAX mutations in PCC/PGL.

ORGANISM(S): Homo sapiens

SUBMITTER: Mercedes Robledo

PROVIDER: E-GEOD-51081 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

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Opposing Effects of HIF1M-NM-1 and HIF2M-NM-1 on Chromaffin Cell Phenotypic Features and Tumor Cell Proliferation: Insights from MAX [human]

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