Abstract
Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.com/exomirhub/), a user-friendly database designed for biologists. This database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. ExomiRHub also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. All the data in ExomiRHub were further standardized and curated with annotations. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. Moreover, ExomiRHub provides a web service that allows biologists to analyze their uploaded miRNA expression data. Four additional tools were developed to evaluate the functions and targets of miRNAs and miRNA variations. Through ExomiRHub, we identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating its potential to significantly accelerate the discovery of extracellular miRNA biomarkers.
Overview
- ExomiRHub is a user-friendly database designed for biologists to analyze extracellular miRNA expression data generated by different laboratories. The database incorporates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. It also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. The primary objective of ExomiRHub is to facilitate the discovery of extracellular miRNA biomarkers for various diseases and treatments. The study aims to demonstrate the potential of ExomiRHub in identifying extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression.
Comparative Analysis & Findings
- ExomiRHub provides a comprehensive analysis of extracellular miRNA expression data generated by different laboratories. The database integrates 191 human extracellular miRNA expression datasets associated with 112 disease phenotypes, 62 treatments, and 24 genotypes, encompassing 29,198 and 23 sample types. It also integrates 16,012 miRNA transcriptomes of 156 cancer subtypes from The Cancer Genome Atlas. The platform offers 25 analytical functions, including differential expression, co-expression, Weighted Gene Co-Expression Network Analysis (WGCNA), feature selection, and functional enrichment, enabling users to select samples, define groups, and customize parameters for analyses. The study identified extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression, demonstrating the potential of ExomiRHub in facilitating the discovery of extracellular miRNA biomarkers for various diseases and treatments.
Implications and Future Directions
- ExomiRHub is a valuable resource for biologists to analyze extracellular miRNA expression data generated by different laboratories. The database provides a comprehensive analysis of extracellular miRNA expression data, enabling biologists to identify potential biomarkers for various diseases and treatments. The study demonstrates the potential of ExomiRHub in identifying extracellular miRNA biomarkers associated with angiogenesis for monitoring glioma progression. Future research directions could include expanding the database to include more extracellular miRNA expression datasets and integrating additional genomic and clinical data. The platform could also be used to develop predictive models for disease progression and treatment response.