Role of miR-18a and miR-25 disruption and its mechanistic pattern in progression of liver cancer

Sparse Independence Component Analysis for Competitive Endogenous RNA Co-Module Identification in Liver Hepatocellular Carcinoma

OBJECTIVE

Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood.

METHODS

In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements.

RESULTS

We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules.

CONCLUSION

It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement-The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.

Exosomes derived from microRNA-129-5p-modified tumor cells selectively enhanced suppressive effect in malignant behaviors of homologous colon cancer cells

Exosome-encapsulated microRNAs (miRNAs) are novel diagnostic and predictive markers in colon cancer. Hence, the study of serum exosomal miRNAs in patients with colon cancer may help its diagnosis and treatment. PKH26-labeled exosomal uptake analysis identified whether exosomes transfer miRNA-129-5p to target cells. Transmission electron microscopy and dynamic light scattering analysis were applied to determine exosome morphology and size distribution. The Cell Counting Kit-8, wound healing assay and Transwell assays were used to detect cell proliferation, migration, and invasion after treatment with engineered exosomes. Moreover, the Western blotting was used to quantify the expression of proteins involved in cell apoptosis. In our study, hepatocellular liver carcinoma, cervical cancer and colon cancer cells were selected as the target cells of miRNA-129-5p exosomes. Exosomes containing miRNA-129-5p were found to be significantly more easily absorbed by colon cancer cells, presenting a stronger inhibitory effect on colon cancer cell proliferation. MiRNA-129-5p exosomes induced apoptosis in colon cancer cells while inhibiting their proliferation, migration, and invasion. In conclusion, exosomes derived from miRNA-129-5p-modified tumor cells selectively inhibited colon cancer progression, shedding new insights to therapeutic efficacy of this cancer.

Robust Biomarker Screening Using Spares Learning Approach for Liver Cancer Prognosis

LncRNAs, miRNAs, mRNAs, methylation, and proteins exert profound biological functions and are widely applied as prognostic features in liver cancer. This study aims to identify prognostic biomarkers' signature for liver cancer. Samples with inadequate tumor purity were filtered out and the expression data from different resources were retrieved. The Spares learning approach was applied to select lncRNAs, miRNAs, mRNAs, methylation, and proteins' features based on their differentially expressed groups. The LASSO boosting technique was employed for the predictive model construction. A total of 200 lncRNAs, 200 miRNAs, 371 mRNAs, 371 methylations, and 184 proteins were observed to be differentially expressed. Five lncRNAs, 11 miRNAs, 30 mRNAs, 4 methylations, and 3 proteins were selected for further evaluation using the feature elimination technique. The highest accuracy of 89.32% is achieved as a result of training and learning by Spares learning methodology. Final outcomes revealed that 5 lncRNA, 11 miRNA, 30 mRNA, 4 methylation, and 3 protein signatures could be potential biomarkers for the prognosis of liver cancer patients.