Differential Expression and in Silico Functional Analysis of Plasma MicroRNAs in the Pathogenesis of Non-segmental Vitiligo

Vitiligo is a disease characterized by acquired depigmentation, white macules, and patches on the skin due to the dysfunction of epidermal melanocytes. In this study, we attempt to profile the microRNA (miRNA) expression patterns and predict the potential targets, assessing the biological functions of differentially expressed miRNAs in the blood of generalized vitiligo patients. Peripheral blood samples were taken from all participants, and the expression levels of 89 identified miRNAs were analyzed with real-time quantitative polymerase chain reaction (PCR). The results indicated significant upregulation of six miRNAs and downregulation of 19 miRNAs in the plasma of vitiligo patients. The top three upregulated miRNAs were hsa-miR-451a, hsa-miR-25-3p, and hsa-miR-19a-3p, and the top three downregulated miRNAs were hsa-miR-146a-5p, hsa-miR-940, and hsa-miR-142-3p. Moreover, the miRNA expression profiles of patients with Type 3 and Type 4 phototypes were substantially different in such a way that the patients with Type 3 phototype would be more prone to the emergence of melanoma and cancer. While significant variations in the expression patterns of miRNAs in male and female vitiligo patients were demonstrated, miR-let-7i-5p, miR-19a-3p, miR-25-3p, and miR-451a were commonly upregulated, and miR-142-3p and miR-146a-5p were commonly repressed in both sexes. This study may shed light on the roles of differentially expressed miRNAs in vitiligo patients by examining the miRNA expression patterns and the combined effects of miRNA and their predicted targets.

FunSet: an open-source software and web server for performing and displaying Gene Ontology enrichment analysis

Background

Gene Ontology enrichment analysis provides an effective way to extract meaningful information from complex biological datasets. By identifying terms that are significantly overrepresented in a gene set, researchers can uncover biological features shared by genes. In addition to extracting enriched terms, it is also important to visualize the results in a way that is conducive to biological interpretation.

Results

Here we present FunSet, a new web server to perform and visualize enrichment analysis. The web server identifies Gene Ontology terms that are statistically overrepresented in a target set with respect to a background set. The enriched terms are displayed in a 2D plot that captures the semantic similarity between terms, with the option to cluster terms via spectral clustering and identify a representative term for each cluster. FunSet can be used interactively or programmatically, and allows users to download the enrichment results both in tabular form and in graphical form as SVG files or in data format as JSON or csv. To enhance reproducibility of the analyses, users have access to historical data for the ontology and the annotations. The source code for the standalone program and the web server are made available with an open-source license.