ABSTRACT
BACKGROUND: Exosomes are a promising tool in disease detection because they are noninvasive, cost-effective, sensitive and stable in body fluids. MicroRNAs (miRNAs) are the main exosomal component and participate in tumor development. However, the exosomal miRNA profile among Asian melanoma patients remains unclear. METHODS: Exosomal miRNAs from the plasma of melanoma patients (n = 20) and healthy individuals (n = 20) were isolated and subjected to small RNA sequencing. Real-time PCR was performed to identify the differential miRNAs and to determine the diagnostic efficiency. Proliferation, scratch and Transwell assays were performed to detect the biological behavior of melanoma cells. RESULTS: Exosomal miRNA profiling revealed decreased miR-1180-3p expression as a potential diagnostic marker of melanoma. The validation group of melanoma patients (n = 28) and controls (n = 28) confirmed the diagnostic efficiency of miR-1180-3p. The level of miR-1180-3p in melanoma cells was lower than that in melanocytes. Accordingly, the level of miR-1180-3p was negatively associated with the proliferation, migration and invasion of melanoma cells. Functional analysis and target gene prediction found that ST3GAL4 was a potential target and highly expressed in melanoma tissues and was negatively regulated by miR-1180-3p. Knockdown of ST3GAL4 hindered the malignant phenotype of melanoma cells. CONCLUSIONS: This study indicates that reduced exosomal miR-1180-3p in melanoma patient plasma is a promising diagnostic marker and provides novel insight into melanoma development.
ABSTRACT
To identify potential aberrantly differentially methylated genes (DMGs) correlated with chemotherapy response (CR) and establish a polygenic methylation prediction model of CR in epithelial ovarian cancer (EOC), we accessed 177 (47 chemo-sensitive and 130 chemo-resistant) samples corresponding to three DNA-methylation microarray datasets from the Gene Expression Omnibus and 306 (290 chemo-sensitive and 16 chemo-resistant) samples from The Cancer Genome Atlas (TCGA) database. DMGs associated with chemotherapy sensitivity and chemotherapy resistance were identified by several packages of R software. Pathway enrichment and protein-protein interaction (PPI) network analyses were constructed by Metascape software. The key genes containing mRNA expressions associated with methylation levels were validated from the expression dataset by the GEO2R platform. The determination of the prognostic significance of key genes was performed by the Kaplan-Meier plotter database. The key genes-based polygenic methylation prediction model was established by binary logistic regression. Among accessed 483 samples, 457 (182 hypermethylated and 275 hypomethylated) DMGs correlated with chemo resistance. Twenty-nine hub genes were identified and further validated. Three genes, anterior gradient 2 (AGR2), heat shock-related 70-kDa protein 2 (HSPA2), and acetyltransferase 2 (ACAT2), showed a significantly negative correlation between their methylation levels and mRNA expressions, which also corresponded to prognostic significance. A polygenic methylation prediction model (0.5253 cutoff value) was established and validated with 0.659 sensitivity and 0.911 specificity.
ABSTRACT
BACKGROUND: DNA methylation in the human genome is acknowledged to be widely associated with biological processes and complex diseases. The Illumina Infinium methylation arrays have been approved as one of the most efficient and universal technologies to investigate the whole genome changes of methylation patterns. As methylation arrays may still be the dominant method for detecting methylation in the anticipated future, it is crucial to develop a reliable workflow to analysis methylation array data. RESULTS: In this study, we develop a web service MADA for the whole process of methylation arrays data analysis, which includes the steps of a comprehensive differential methylation analysis pipeline: pre-processing (data loading, quality control, data filtering, and normalization), batch effect correction, differential methylation analysis, and downstream analysis. In addition, we provide the visualization of pre-processing, differentially methylated probes or regions, gene ontology, pathway and cluster analysis results. Moreover, a customization function for users to define their own workflow is also provided in MADA. CONCLUSIONS: With the analysis of two case studies, we have shown that MADA can complete the whole procedure of methylation array data analysis. MADA provides a graphical user interface and enables users with no computational skills and limited bioinformatics background to carry on complicated methylation array data analysis. The web server is available at: http://120.24.94.89:8080/MADA.
