Functional and protein‑protein interaction network analysis of colorectal cancer induced by ulcerative colitis

A network approach to elucidate and prioritize microbial dark matter in microbial communities

Microbes compose most of the biomass on the planet, yet the majority of taxa remain uncharacterized. These unknown microbes, often referred to as “microbial dark matter,” represent a major challenge for biology. To understand the ecological contributions of these Unknown taxa, it is essential to first understand the relationship between unknown species, neighboring microbes, and their respective environment. Here, we establish a method to study the ecological significance of “microbial dark matter” by building microbial co-occurrence networks from publicly available 16S rRNA gene sequencing data of four extreme aquatic habitats. For each environment, we constructed networks including and excluding unknown organisms at multiple taxonomic levels and used network centrality measures to quantitatively compare networks. When the Unknown taxa were excluded from the networks, a significant reduction in degree and betweenness was observed for all environments. Strikingly, Unknown taxa occurred as top hubs in all environments, suggesting that “microbial dark matter” play necessary ecological roles within their respective communities. In addition, novel adaptation-related genes were detected after using 16S rRNA gene sequences from top-scoring hub taxa as probes to blast metagenome databases. This work demonstrates the broad applicability of network metrics to identify and prioritize key Unknown taxa and improve understanding of ecosystem structure across diverse habitats.

Expression of microRNA-99a-3p in Prostate Cancer Based on Bioinformatics Data and Meta-Analysis of a Literature Review of 965 Cases

BACKGROUND microRNAs (miRNAs) have a role as biomarkers in human cancer. The aim of this study was to use bioinformatics data, and review of cases identified from the literature, to investigate the role of microRNA-99a-3p (miR-99a-3p) in prostate cancer, including the identification of its target genes and signaling pathways. MATERIAL AND METHODS Meta-analysis from a literature review included 965 cases of prostate cancer. Bioinformatics databases interrogated for miR-99a-3p in prostate cancer included The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and ArrayExpress. Twelve computational predictive algorithms were developed to integrate miR-99a-3p target gene prediction data. Bioinformatics analysis data from Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analysis were used investigate the possible pathways and target genes for miR-99a-3p in prostate cancer. RESULTS TCGA data showed that miR-99a was down-regulated in prostate cancer when compared with normal prostate tissue. Receiver-operating characteristic (ROC) curve area under the curve (AUC) for miR-99a-3p was 0.660 (95% CI, 0.587-0.732) or a moderate level of discriminations. Pathway analysis showed that miR-99a-3p was associated with the Wnt and vascular endothelial growth factor (VEGF) signaling pathways. The PPP3CA and HYOU1 genes, selected from the PPI network, were highly expressed in prostate cancer tissue compared with normal prostate tissue, and negatively correlated with the expression of miR-99a-3p. CONCLUSIONS In prostate cancer, miR-99a-3p expression was associated with the Wnt and VEGF signaling pathways, which might inhibit the expression of PPP3CA or HYOU1.