Dataset Information

Perturbation of 14q32 miRNAs-cMYC gene network in osteosarcoma.

ABSTRACT: Osteosarcoma (OS) is the common histological form of primary bone cancer and one of the leading aggressive cancers in children under age fifteen. Although several genetic predisposing conditions have been associated with OS the understanding of its molecular etiology is limited. Here, we show that microRNAs (miRNAs) at the chr.14q32 locus are significantly downregulated in osteosarcoma compared to normal bone tissues. Bioinformatic predictions identified that a subset of 14q32 miRNAs (miR-382, miR-369-3p, miR-544 and miR-134) could potentially target cMYC transcript. The physical interaction between these 14q32 miRNAs and cMYC was validated using reporter assays. Further, restoring expression of these four 14q32 miRNAs decreased cMYC levels and induced apoptosis in Saos2 cells. We also show that exogenous expression of 14q32 miRNAs in Saos2 cells significantly downregulated miR-17-92, a transcriptional target of cMYC. The pro-apoptotic effect of 14q32 miRNAs in Saos2 cells was rescued either by overexpression of cMYC cDNA without the 3'UTR or with miR-17-92 cluster. Further, array comparative genomic hybridization studies showed no DNA copy number changes at 14q32 locus in OS patient samples suggesting that downregulation of 14q32 miRNAs are not due to deletion at this locus. Together, our data support a model where the deregulation of a network involving 14q32 miRNAs, cMYC and miR-17-92 miRNAs could contribute to osteosarcoma pathogenesis.

SUBMITTER: Thayanithy V

PROVIDER: S-EPMC3755949 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Perturbation of 14q32 miRNAs-cMYC gene network in osteosarcoma.

Thayanithy Venugopal V Sarver Aaron L AL Kartha Reena V RV Li Lihua L Angstadt Andrea Y AY Breen Matthew M Steer Clifford J CJ Modiano Jaime F JF Subramanian Subbaya S

Bone 20111018 1

Osteosarcoma (OS) is the common histological form of primary bone cancer and one of the leading aggressive cancers in children under age fifteen. Although several genetic predisposing conditions have been associated with OS the understanding of its molecular etiology is limited. Here, we show that microRNAs (miRNAs) at the chr.14q32 locus are significantly downregulated in osteosarcoma compared to normal bone tissues. Bioinformatic predictions identified that a subset of 14q32 miRNAs (miR-382, m ...[more]

PMID: 22037351

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Project description:Although microRNAs (miRNAs) are implicated in osteosarcoma biology and chemoresponse, miRNA prognostic models are still needed, particularly because prognosis is imperfectly correlated with chemoresponse. Formalin-fixed, paraffin-embedded tissue is a necessary resource for biomarker studies in this malignancy with limited frozen tissue availability.We performed miRNA and mRNA microarray formalin-fixed, paraffin-embedded assays in 65 osteosarcoma biopsy and 26 paired post-chemotherapy resection specimens and used the only publicly available miRNA dataset, generated independently by another group, to externally validate our strongest findings (n = 29). We used supervised principal components analysis and logistic regression for survival and chemoresponse, and miRNA activity and target gene set analysis to study miRNA regulatory activity.Several miRNA-based models with as few as five miRNAs were prognostic independently of pathologically assessed chemoresponse (median recurrence-free survival: 59 months versus not-yet-reached; adjusted hazards ratio = 2.90; P = 0.036). The independent dataset supported the reproducibility of recurrence and survival findings. The prognostic value of the profile was independent of confounding by known prognostic variables, including chemoresponse, tumor location and metastasis at diagnosis. Model performance improved when chemoresponse was added as a covariate (median recurrence-free survival: 59 months versus not-yet-reached; hazard ratio = 3.91; P = 0.002). Most prognostic miRNAs were located at 14q32 - a locus already linked to osteosarcoma - and their gene targets display deregulation patterns associated with outcome. We also identified miRNA profiles predictive of chemoresponse (75% to 80% accuracy), which did not overlap with prognostic profiles.Formalin-fixed, paraffin-embedded tissue-derived miRNA patterns are a powerful prognostic tool for risk-stratified osteosarcoma management strategies. Combined miRNA and mRNA analysis supports a possible role of the 14q32 locus in osteosarcoma progression and outcome. Our study creates a paradigm for formalin-fixed, paraffin-embedded-based miRNA biomarker studies in cancer.

Project description:BACKGROUND: Inverse modelling of gene regulatory networks (GRNs) capable of simulating continuous spatio-temporal biological processes requires accurate data and a good description of the system. If quantitative relations between genes cannot be extracted from direct measurements, an efficient method to estimate the unknown parameters is mandatory. A model that has been proposed to simulate spatio-temporal gene expression patterns is the connectionist model. This method describes the quantitative dynamics of a regulatory network in space. The model parameters are estimated by means of model-fitting algorithms. The gene interactions are identified without making any prior assumptions concerning the network connectivity. As a result, the inverse modelling might lead to multiple circuits showing the same quantitative behaviour and it is not possible to identify one optimal circuit. Consequently, it is important to address the quality of the circuits in terms of model robustness. RESULTS: Here we investigate the sensitivity and robustness of circuits obtained from reverse engineering a model capable of simulating measured gene expression patterns. As a case study we use the early gap gene segmentation mechanism in Drosophila melanogaster. We consider the limitations of the connectionist model used to describe GRN Inferred from spatio-temporal gene expression. We address the problem of circuit discrimination, where the selection criterion within the optimization technique is based of the least square minimization on the error between data and simulated results. CONCLUSION: Parameter sensitivity analysis allows one to discriminate between circuits having significant parameter and qualitative differences but exhibiting the same quantitative pattern. Furthermore, we show that using a stochastic model derived from a deterministic solution, one can introduce fluctuations within the model to analyze the circuits' robustness. Ultimately, we show that there is a close relation between circuit sensitivity and robustness to fluctuation, and that circuit robustness is rather modular than global. The current study shows that reverse engineering of GRNs should not only focus on estimating parameters by minimizing the difference between observation and simulation but also on other model properties. Our study suggests that multi-objective optimization based on robustness and sensitivity analysis has to be considered.

Dataset Information

Perturbation of 14q32 miRNAs-cMYC gene network in osteosarcoma.

Publications

Perturbation of 14q32 miRNAs-cMYC gene network in osteosarcoma.

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