MiR-15a-3p suppresses the growth and metastasis of ovarian cancer cell by targeting Twist1

OBJECTIVE

To investigate the roles of miR-15a-3p in ovarian cancer cell growth and metastasis.

PATIENTS AND METHODS

A key role of miR-15a-3p was identified via gene profiling and bioinformatics analysis. The impact of miR-15a-3p on ovarian cancer cell growth, migration and invasion was measured by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT), wound-healing and transwell invasion assays. Bioinformatics and luciferase reporter assays were applied to identify that twist family BHLH transcription factor 1 (Twist1) was the target gene of miR-15a-3p. The miR-15a-3p level and the expression of Twist1 were detected using quantitative Real-time polymerase chain reaction (qRT-PCR) assay. The expressions of N-cadherin and E-cadherin were measured by immunofluorescence staining. Small interfering RNA targeting Twist1 and pCDNA3.1 containing Twist1 were applied to decrease and increase the expression of Twist1, respectively.

RESULTS

miR-15a-3p was markedly down-regulated in ovarian cancer. Exogenous up-regulation of miR-15a-3p inhibited the growth, colony formation, migration and invasion of ovarian cancer cell in vitro. Furthermore, a xenograft model indicated that miR-15a-3p inhibited tumour growth and the metastatic potential of ovarian cancer cell in vivo. We found that Twist1 was the direct target of miR-15a-3p in ovarian cancer and that its expression was negatively correlated with the level of miR-15a-3p in ovarian cancer tissues. Up-regulation of miR-15a-3p rescued the inhibitory impact of miR-15a-3p on ovarian cancer cell growth, migration and invasion. Finally, down-regulation of Twist1 mimicked the suppressive effects of miR-15a-3p on ovarian cancer cell.

CONCLUSIONS

We demonstrated that miR-15a-3p is down-regulated in ovarian cancer. Up-regulation of miR-15a-3p restrains the growth and metastasis of ovarian cancer cell by regulating Twist1.

IPCS: an integrated process control system for enhanced in-situ bioremediation

To date, there has been little or no research related to process control of subsurface remediation systems. In this study, a framework to develop an integrated process control system for improving remediation efficiencies and reducing operating costs was proposed based on physical and numerical models, stepwise cluster analysis, non-linear optimization and artificial neural networks. Process control for enhanced in-situ bioremediation was accomplished through incorporating the developed forecasters and optimizers with methods of genetic algorithm and neural networks modeling. Application of the proposed approach to a bioremediation process in a pilot-scale system indicated that it was effective in dynamic optimization and real-time process control of the sophisticated bioremediation systems.