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Identifying microRNA/mRNA dysregulations in ovarian cancer.

ABSTRACT: BACKGROUND: MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In previous work, we developed a method to identify direct mRNA targets of microRNA using patient matched microRNA/mRNA expression data using an anti-correlation signature. This method, applied to clear cell Renal Cell Carcinoma (ccRCC), revealed many new regulatory pathways compromised in ccRCC. In the present paper, we apply this method to identify dysregulated microRNA/mRNA mechanisms in ovarian cancer using data from The Cancer Genome Atlas (TCGA). METHODS: TCGA Microarray data was normalized and samples whose class labels (tumour or normal) were ambiguous with respect to consensus ensemble K-Means clustering were removed. Significantly anti-correlated and correlated genes/microRNA differentially expressed between tumour and normal samples were identified. TargetScan was used to identify gene targets of microRNA. RESULTS: We identified novel microRNA/mRNA mechanisms in ovarian cancer. For example, the expression level of RAD51AP1 was found to be strongly anti-correlated with the expression of hsa-miR-140-3p, which was significantly down-regulated in the tumour samples. The anti-correlation signature was present separately in the tumour and normal samples, suggesting a direct causal dysregulation of RAD51AP1 by hsa-miR-140-3p in the ovary. Other pairs of potentially biological relevance include: hsa-miR-145/E2F3, hsa-miR-139-5p/TOP2A, and hsa-miR-133a/GCLC. We also identified sets of positively correlated microRNA/mRNA pairs that are most likely result from indirect regulatory mechanisms. CONCLUSIONS: Our findings identify novel microRNA/mRNA relationships that can be verified experimentally. We identify both generic microRNA/mRNA regulation mechanisms in the ovary as well as specific microRNA/mRNA controls which are turned on or off in ovarian tumours. Our results suggest that the disease process uses specific mechanisms which may be significant for their utility as early detection biomarkers or in the development of microRNA therapies in treating ovarian cancers. The positively correlated microRNA/mRNA pairs suggest the existence of novel regulatory mechanisms that proceed via intermediate states (indirect regulation) in ovarian tumorigenesis.

SUBMITTER: Miles GD

PROVIDER: S-EPMC3342161 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Identifying microRNA/mRNA dysregulations in ovarian cancer.

Miles Gregory D GD Seiler Michael M Rodriguez Lorna L Rajagopal Gunaretnam G Bhanot Gyan G

BMC research notes 20120327

<h4>Background</h4>MicroRNAs are a class of noncoding RNA molecules that co-regulate the expression of multiple genes via mRNA transcript degradation or translation inhibition. Since they often target entire pathways, they may be better drug targets than genes or proteins. MicroRNAs are known to be dysregulated in many tumours and associated with aggressive or poor prognosis phenotypes. Since they regulate mRNA in a tissue specific manner, their functional mRNA targets are poorly understood. In ...[more]

PMID: 22452920

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Project description:Ovarian cancer (OC), the eighth-leading cause of cancer-related death among females worldwide, is mainly represented by epithelial OC (EOC) that can be further subdivided into four subtypes: serous (75%), endometrioid (10%), clear cell (10%), and mucinous (3%). Major reasons for high mortality are the poor biological understanding of the OC mechanisms and a lack of reliable markers defining each EOC subtype. MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression primarily by targeting messenger RNA (mRNA) transcripts. Their aberrant expression patterns have been associated with cancer development, including OC. However, the role of miRNAs in tumorigenesis is still to be determined, mainly due to the lack of consensus regarding optimal methodologies for identification and validation of miRNAs and their targets. Several tools for computational target prediction exist, but false interpretations remain a problem. The experimental validation of every potential miRNA-mRNA pair is not feasible, as it is laborious and expensive. In this study, we analyzed the correlation between global miRNA and mRNA expression patterns derived from microarray profiling of 197 EOC patients to identify the signatures of miRNA-mRNA interactions associated with overall survival (OS). The aim was to investigate whether these miRNA-mRNA signatures might have a prognostic value for OS in different subtypes of EOC. The content of our cohort (162 serous carcinomas, 15 endometrioid carcinomas, 11 mucinous carcinomas, and 9 clear cell carcinomas) reflects a real-world scenario of EOC. Several interaction pairs between 6 miRNAs (hsa-miR-126-3p, hsa-miR-223-3p, hsa-miR-23a-5p, hsa-miR-27a-5p, hsa-miR-486-5p, and hsa-miR-506-3p) and 8 mRNAs (ATF3, CH25H, EMP1, HBB, HBEGF, NAMPT, POSTN, and PROCR) were identified and the findings appear to be well supported by the literature. This indicates that our study has a potential to reveal miRNA-mRNA signatures relevant for EOC. Thus, the evaluation on independent cohorts will further evaluate the performance of such findings.

Project description:BackgroundThe rate of ovarian cancer (OC) is one of the highest in women's reproductive systems. An improperly expressed microRNA (miRNA) has been discovered to have a vital role in the pathophysiology of OC. However, more research into OC's miRNA-message RNA (mRNA) gene interaction network is required.MethodsFirstly, the microarray data sets GSE25405 and GSE119055 from the GEO (Gene Expression Omnibus) database were downloaded and then analyzed with the GEO2R tool aiming at identifying DEMs (differential expressed miRNAs) between ovarian malignant tissue and ovarian normal tissue. The whole consistently changed miRNAs were then screened out to be candidate DEMs. For estimating underlying upstream transcription factors, FunRich was employed. miRNet was utilized to determine putative DEMs' downstream target genes. The R program was then used to do the GO annotation as well as the analysis of KEGG pathway enrichment for target genes. The PPI (protein-protein interaction), as well as the DEM-hub gene networks, were created by the Cytoscape software and STRING database. Finally, we chose the GSE74448 dataset to test the precision of hub gene expressions.ResultsWe have screened out six (five upregulated and one downregulated) DEMs. The majority of upregulated and downregulated DEMs are likely regulated by SP1 (specificity protein 1). SP4 (s protein 4), POU2F1 (POU class 2 homeobox 1), MEF2A (myocyte-specific enhancer factor 2A), ARID3A (AT-rich interaction domain 3A), and EGR1 (early growth response 1) can regulate upregulated and downregulated DEMs. We have found 807 target genes (656 upregulated and 151 downregulated DEM), being generally enriched in focal adhesion and proteoglycans in cancer, gastric cancer, hepatocellular carcinoma, as well as breast cancer. The majority of hub genes are projected to be controlled by hsa-miR-429, hsa-miR-140-5p, hsa-miR-199a-5p, and hsa-miR-199a-3p after the DEM-hub gene network was built. VEGFA (vascular endothelial growth factor A), EZH2 (enhancer of zeste 2 polycomb repressive complex 2 subunit), and HIF1A (hypoxia inducible factor 1 subunit alpha) expressions are consistent with the GSE74448 dataset in the first 18 hub genes.ConclusionWe have built an underlying miRNA-mRNA interacting network in OC, giving us unparalleled insight into the disease's diagnosis and treatment.

Project description:BACKGROUND: Malignant ovarian disease is characterised by high rates of mortality due to high rates of recurrent chemoresistant disease. Anecdotal evidence indicates this may be due to chemoresistant properties of cancer stem cells (CSCs). However, our understanding of the role of CSCs in recurrent ovarian disease remains sparse. In this study we used gene microarrays and meta-analysis of our previously published microRNA (miRNA) data to assess the involvement of cancer stemness signatures in recurrent ovarian disease. METHODS: Microarray analysis was used to characterise early regulation events in an embryonal carcinoma (EC) model of cancer stemness. This was then compared to our previously published microarray data from a study of primary versus recurrent ovarian disease. In parallel, meta-analysis was used to identify cancer stemness miRNA signatures in tumor patient samples. RESULTS: Microarray analysis demonstrated a 90% difference between gene expression events involved in early regulation of differentiation in murine EC (mEC) and embryonic stem (mES) cells. This contrasts the known parallels between mEC and mES cells in the undifferentiated and well-differentiated states. Genelist comparisons identified a cancer stemness signature set of genes in primary versus recurrent data, a subset of which are known p53-p21 regulators. This signature is present in primary and recurrent or in primary alone but essentially never in recurrent tumors specifically. Meta-analysis of miRNA expression showed a much stronger cancer stemness signature within tumor samples. This miRNA signature again related to p53-p21 regulation and was expressed prominently in recurrent tumors. Our data indicate that the regulation of p53-p21 in ovarian cancer involves, at least partially, a cancer stemness component. CONCLUSION: We present a p53-p21 cancer stemness signature model for ovarian cancer. We propose that this may, at least partially, differentially regulate the p53-p21 mechanism in ovarian disease. Targeting CSCs within ovarian cancer represents a potential therapeutic avenue.

Project description:BackgroundMicroRNA regulate mRNA levels in a tissue specific way, either by inducing degradation of the transcript or by inhibiting translation or transcription. Putative mRNA targets of microRNA identified from seed sequence matches are available in many databases. However, such matches have a high false positive rate and cannot identify tissue specificity of regulation.ResultsWe describe a simple method to identify direct mRNA targets of microRNA dysregulated in cancers from expression level measurements in patient matched tumor/normal samples. The word "direct" is used here in a strict sense to: a) represent mRNA which have an exact seed sequence match to the microRNA in their 3'UTR, b) the seed sequence match is strictly conserved across mouse, human, rat and dog genomes, c) the mRNA and microRNA expression levels can distinguish tumor from normal with high significance and d) the microRNA/mRNA expression levels are strongly and significantly anti-correlated in tumor and/or normal samples. We apply and validate the method using clear cell Renal Cell Carcinoma (ccRCC) and matched normal kidney samples, limiting our analysis to mRNA targets which undergo degradation of the mRNA transcript because of a perfect seed sequence match. Dysregulated microRNA and mRNA are first identified by comparing their expression levels in tumor vs normal samples. Putative dysregulated microRNA/mRNA pairs are identified from these using seed sequence matches, requiring that the seed sequence be conserved in human/dog/rat/mouse genomes. These are further pruned by requiring a strong anti-correlation signature in tumor and/or normal samples. The method revealed many new regulations in ccRCC. For instance, loss of miR-149, miR-200c and mir-141 causes gain of function of oncogenes (KCNMA1, LOX), VEGFA and SEMA6A respectively and increased levels of miR-142-3p, miR-185, mir-34a, miR-224, miR-21 cause loss of function of tumor suppressors LRRC2, PTPN13, SFRP1, ERBB4, and (SLC12A1, TCF21) respectively. We also found strong anti-correlation between VEGFA and the miR-200 family of microRNA: miR-200a*, 200b, 200c and miR-141. Several identified microRNA/mRNA pairs were validated on an independent set of matched ccRCC/normal samples. The regulation of SEMA6A by miR-141 was verified by a transfection assay.ConclusionsWe describe a simple and reliable method to identify direct gene targets of microRNA in any cancer. The constraints we impose (strong dysregulation signature for microRNA and mRNA levels between tumor/normal samples, evolutionary conservation of seed sequence and strong anti-correlation of expression levels) remove spurious matches and identify a subset of robust, tissue specific, functional mRNA targets of dysregulated microRNA.

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Identifying microRNA/mRNA dysregulations in ovarian cancer.

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Identifying microRNA/mRNA dysregulations in ovarian cancer.

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