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This Article Full Text Full Text (PDF) Supplementary Data All Versions of this Article: ERC-09-0096v1 16/3/895    most recent Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Tömböl, Z. Articles by Igaz, P. PubMed PubMed Citation Articles by Tömböl, Z. Articles by Igaz, P.

Second Department of Medicine, Faculty of Medicine, Semmelweis University, Szentkirályi Street 46, H-1088 Budapest, Hungary
¹ Department of Genetics, Cell- and Immunobiology, Faculty of Medicine, Semmelweis University, Nagyvárad Square 4, H-1089 Budapest, Hungary
² First Department of Surgery
³ Department of Urology, Faculty of Medicine, Semmelweis University, Ülli Street 78./b, H-1082 Budapest, Hungary
⁴ Molecular Medicine Research Group, Hungarian Academy of Sciences and Semmelweis University, Szentkirályi Street 46, H-1088 Budapest, Hungary
⁵ Gedeon Richter Ltd, Gyömri Street 19-21, H-1103 Budapest, Hungary
⁶ Division of Endocrinology, Diabetology and Metabolism, University Hospital Lausanne, Rue du Bugnon 46, CH-1011 Lausanne, Switzerland
⁷ Research Group of Inflammation Biology and Immunogenomics, Hungarian Academy of Sciences and Semmelweis University, Nagyvárad Square 4, H-1089 Budapest, Hungary

(Correspondence should be addressed to P Igaz; Email: igapet{at}bel2.sote.hu)

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR–mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT_miR-511 and dCT_miR-503 (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.