Regulatory feedback loops bridge the human gene regulatory network and regulate carcinogenesis

miR-186 regulates epithelial-mesenchymal transformation to promote nasopharyngeal carcinoma metastasis by targeting ZEB1

OBJECTIVES

Nasopharyngeal carcinoma (NPC) is an aggressive epithelial cancer. The expression of miR-186 is decreased in a variety of malignancies and can promote the invasion and metastasis of cancer cells. This study aimed to explore the role and possible mechanism of miR-186 in the metastasis and epithelial-mesenchymal transformation (EMT) of NPC.

METHODS

The expression of miR-186 in NPC tissues and cells was detected by RT-PCR. Then, miR-186 mimic was used to transfect NPC cell lines C666-1 and CNE-2, and cell activity, invasion and migration were detected by CCK8, transwell and scratch assay, respectively. The expression of EMT-related proteins was analyzed by western blotting analysis. The binding relationship between miR-186 and target gene Zinc Finger E-Box Binding Homeobox 1 (ZEB1) was confirmed by double luciferase assay.

RESULTS

The expression of miR-186 in NPC was significantly decreased, and transfection of miR-186 mimic could significantly inhibit the cell activity, invasion, and migration, and regulate the protein expressions of E-cadherin, N-cadherin and vimentin in C666-1 and CNE-2 cells. Further experiments confirmed that miR-186 could directly target ZEB1 and negatively regulate its expression. In addition, ZEB1 has been confirmed to be highly expressed in NPC, and inhibition of ZEB1 could inhibit the activity, invasion, metastasis and EMT of NPC cells. And co-transfection of miR-186 mimic and si-ZEB1 could further inhibit the proliferation and metastasis of NPC.

CONCLUSION

miR-186 may inhibit the proliferation, metastasis and EMT of NPC by targeting ZEB1, and the miR-186/ZEB1 axis plays an important role in NPC.

Comparing biological information contained in mRNA and non-coding RNAs for classification of lung cancer patients

BACKGROUND

Deciphering the meaning of the human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention and shown to be functional in part. Yet the importance of these RNAs especially for higher biological functions remains under investigation.

METHODS

In this paper, we analyze RNA-seq data, including non-coding and protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype of non-small-cell lung cancer, with deep learning neural networks and other state-of-the-art classification methods. The purpose of our paper is three-fold. First, we compare the classification performance of different versions of deep belief networks with SVMs, decision trees and random forests. Second, we compare the classification capabilities of protein coding and non-coding RNAs. Third, we study the influence of feature selection on the classification performance.

RESULTS

As a result, we find that deep belief networks perform at least competitively to other state-of-the-art classifiers. Second, data from non-coding RNAs perform better than coding RNAs across a number of different classification methods. This demonstrates the equivalence of predictive information as captured by non-coding RNAs compared to protein coding RNAs, conventionally used in computational diagnostics tasks. Third, we find that feature selection has in general a negative effect on the classification performance which means that unfiltered data with all features give the best classification results.

CONCLUSIONS

Our study is the first to use ncRNAs beyond miRNAs for the computational classification of cancer and for performing a direct comparison of the classification capabilities of protein coding RNAs and non-coding RNAs.