Dataset Information

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.

ABSTRACT:

Background

The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall functions from the construction to analysis of ceRNA networks.

Results

To fill this gap, we introduce CeNet Omnibus, an R/Shiny application, which provides a unified framework for the construction and analysis of ceRNA network. CeNet Omnibus enables users to select multiple measurements, such as Pearson correlation coefficient (PCC), mutual information (MI), and liquid association (LA), to identify ceRNA pairs and construct ceRNA networks. Furthermore, CeNet Omnibus provides a one-stop solution to analyze the topological properties of ceRNA networks, detect modules, and perform gene enrichment analysis and survival analysis. CeNet Omnibus intends to cover comprehensiveness, high efficiency, high expandability, and user customizability, and it also offers a web-based user-friendly interface to users to obtain the output intuitionally.

Conclusion

CeNet Omnibus is a comprehensive platform for the construction and analysis of ceRNA networks. It is highly customizable and outputs the results in intuitive and interactive. We expect that CeNet Omnibus will assist researchers to understand the property of ceRNA networks and associated biological phenomena. CeNet Omnibus is an R/Shiny application based on the Shiny framework developed by RStudio. The R package and detailed tutorial are available on our GitHub page with the URL https://github.com/GaoLabXDU/CeNetOmnibus .

SUBMITTER: Wen X

PROVIDER: S-EPMC7890952 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.

Wen Xiao X Gao Lin L Song Tuo T Jiang Chaoqun C

BMC bioinformatics 20210218 1

<h4>Background</h4>The competing endogenous RNA (ceRNA) regulation is a newly discovered post-transcriptional regulation mechanism and plays significant roles in physiological and pathological progress. CeRNA networks provide global views to help understand the regulation of ceRNAs. CeRNA networks have been widely used to detect survival biomarkers, select candidate regulators of disease genes, and predict long noncoding RNA functions. However, there is no software platform to provide overall fu ...[more]

PMID: 33602117

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Project description:Acute ischemic stroke (AIS) is a common neurological disease that seriously endangers both the physical and mental health of human. After AIS, activated immune cells are recruited to the stroke site, where inflammatory mediators are released locally, and severe immune inflammatory reactions occur within a short time, which affects the progress and prognosis of IS. Circular RNA (circRNA) is a type of non-coding RNA (ncRNA) with a closed-loop structure and high stability. Studies have found that circRNA can affect the course of IS. However, there is no report on ceRNA's pathogenesis in AIS that is mediated by circRNA. In this study, the CIBERSORT algorithm was used to analyze the distribution of immune cells in patients with AIS. mRNA dataset was downloaded from the GEO database, and the weighted gene co-expression network analysis (WGCNA) method was used to construct weighted gene co-expression to determine 668 target genes, using GO, KEGG enrichment analysis, construction of protein-protein interaction (PPI) network analysis, and molecular complex detection (MCODE) plug-in analysis. The results showed that the biological function of the target gene was in line with the activation and immune regulation of neutrophils; signal pathways were mostly enriched in immune inflammation-related pathways. A Venn diagram was used to obtain 52 intersection genes between target genes and disease genes. By analyzing the correlation between the intersection genes and immune cells, we found that the top 5 hub genes were TOM1, STAT3, RAB3D, MDM2, and FOS, which were all significantly positively correlated with neutrophils and significantly negatively correlated with eosinophils. A total of 52 intersection genes and the related circRNA and miRNA were used as input for Cytoscape software to construct a circRNA-mediated ceRNA competition endogenous network, where a total of 18 circRNAs were found. Further analysis of the correlation between circRNA and immune cells found that 4 circRNAs are positively correlated with neutrophils. Therefore, we speculate that there may be a regulatory relationship between circRNA-mediated ceRNA and the immune mechanism in AIS. This study has important guiding significance for the progress, outcome of AIS, and the development of new medicine.

Project description:Malignant melanoma (MM) is a malignant tumor originating from melanocytes, with high aggressiveness, high metastasis and extremely poor prognosis. MM accounts for 4% of skin cancers and 80% of mortality, and the median survival of patients with metastatic melanoma is only about 6 months, with a five-year survival rate of less than 10%. In recent years, the incidence of melanoma has gradually increased and has become one of the serious diseases that endanger human health. Competitive endogenous RNA (ceRNA) is the main model of the mechanism by which long chain non-coding RNAs (lncRNAs) play a regulatory role in the disease. LncRNAs can act as a "sponge", competitively attracting small RNAs (micoRNAs; miRNAs), thus interfering with miRNA function, and affect the expression of target gene messenger RNAs (mRNAs), ultimately promoting tumorigenesis and progression. Bioinformatics analysis can identify potentially prognostic and therapeutically relevant differentially expressed genes in MM, finding lncRNAs, miRNAs and mRNAs that are interconnected through the ceRNA network, providing further insight into gene regulation and prognosis of metastatic melanoma. Weighted co-expression networks were used to identify lncRNA and mRNA modules associated with the metastatic phenotype, as well as the co-expression genes contained in the modules. A total of 17 lncRNAs, six miRNAs, and 11 mRNAs were used to construct a ceRNA interaction network that plays a regulatory role in metastatic melanoma patients. The prognostic risk model was used as a sorter to classify the survival prognosis of melanoma patients. Four groups of ceRNA interaction triplets were finally obtained, which miR-3662 might has potential implication for the treatment of metaststic melanoma patients, and futher experiments confirmed the regulating relationship and phenotype of this assumption. This study provides new targets to regulate metastatic process, predict metastatic potential and indicates that the miR-3662 can be used in the treatment of melanoma.

Dataset Information

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.

Background

Results

Conclusion

Publications

CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network.

Similar Datasets

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