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A comprehensive analysis identified the key differentially expressed circular RNAs and methylation-related function in pheochromocytomas and paragangliomas

ABSTRACT: Aim: To investigate the differentially expressed circular RNAs (circRNAs) and their potential functions in pheochromocytomas and paragangliomas (PCC/PGLs). Materials & Methods: We analyzed the expression levels of circRNAs through RNA sequencing in tumor and adjacent normal tissues from 7 PCC/PGL patients. Real-time qPCR was conducted to verify the key candidates identified in the sequencing data. GO and KEGG Pathway analyses were performed to predict the functions of these circRNAs. The circRNA-miRNA-mRNA coding-noncoding gene co-expression (CNC) networks were mapped to show the potential binding relationship between circRNAs and their targets in PCC/PGLs. Results: A total of 367 circRNAs were shown to have differential expression between tumor and normal samples. The top 3 histone methylation-related circRNAs (hsa_circ_0000567, hsa_circ_0002897, hsa_circ_0004473) and their target miRNAs were identified and validated. The top 5 mRNAs, 88 miRNAs, and 132 circRNAs related to the pathogenesis were utilized to map the CNC network, and we observed that the interactions of these candidates with their target miRNAs regulated the procedure of histone-methylation, and further controlled the pathogenesis of PCC/PGLs. Conclusions: Our study is the first to provide the whole profile of differentially expressed circRNAs in PCC/PGLs, and our data indicates that the altered circRNAs may control the pathogenesis of PCC/PGLs by regulating the histone-methylation processes and act as biomarkers.

ORGANISM(S): Homo sapiens

PROVIDER: GSE139518 | GEO | 2019/10/29

REPOSITORIES: GEO

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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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A comprehensive analysis identified the key differentially expressed circular RNAs and methylation-related function in pheochromocytomas and paragangliomas

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