Crinet: A computational tool to infer genome-wide competing endogenous RNA (ceRNA) interactions
Ziynet Nesibe Kesimoglu1,2, Serdar Bozdag1,2
1Department of Computer Science and Engineering, University of North Texas, Denton, Texas, United States of America.
Abstract:
To understand driving biological factors for complex diseases like cancer, regulatory circuity of genes needs to be discovered. Recently, a new gene regulation mechanism called competing endogenous RNA (ceRNA) interactions has been discovered. Certain genes targeted by common microRNAs (miRNAs) "compete" for these miRNAs, thereby regulate each other by making others free from miRNA regulation. Several computational tools have been published to infer ceRNA networks. In most existing tools, however, expression abundance sufficiency, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational tool named Crinet to infer genome-wide ceRNA networks addressing critical drawbacks. Crinet considers all mRNAs, lncRNAs, and pseudogenes as potential ceRNAs and incorporates a network deconvolution method to exclude the spurious ceRNA pairs. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in the cancer-related genes and processes. We validated the selected miRNA-target interactions with the protein expression-based benchmarks and also evaluated the inferred ceRNA interactions predicting gene expression change in knockdown assays. The hub genes in the inferred ceRNA network included known suppressor/oncogene lncRNAs in breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Crinet-inferred ceRNA groups that were consistently involved in the immune system related processes could be important assets in the light of the studies confirming the relation between immunotherapy and cancer. The source code of Crinet is in R and available at https://github.com/bozdaglab/crinet.
Insights
A new tool, Crinet, infers genome-wide competing endogenous RNA (ceRNA) networks by considering all potential ceRNAs and excluding spurious interactions. This method identifies reproducible cancer-related ceRNA groups and highlights the importance of non-coding RNAs in cancer.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Complex diseases like cancer are driven by intricate gene regulatory networks.
- Competing endogenous RNA (ceRNA) interactions, where RNAs compete for microRNAs (miRNAs), represent a novel gene regulation mechanism.
- Existing computational tools for ceRNA network inference often overlook crucial factors like expression abundance, collective regulation, and groupwise effects.
Purpose of the Study:
- To develop a computational tool, Crinet, for inferring genome-wide ceRNA networks.
- To address limitations in existing methods by considering all potential ceRNAs and excluding spurious interactions.
- To identify reproducible and biologically relevant ceRNA networks in cancer.
Main Methods:
- Crinet considers messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and pseudogenes as potential ceRNAs.
- A network deconvolution method is incorporated to filter out false positive ceRNA pairs.
- The tool was applied to breast cancer data from The Cancer Genome Atlas (TCGA).
Main Results:
- Crinet successfully inferred reproducible ceRNA interactions and groups from TCGA breast cancer data.
- The inferred networks were significantly enriched in cancer-associated genes and biological processes.
- Validation using protein expression data and knockdown assays supported the accuracy of the inferred miRNA-target interactions and ceRNA effects.
- Key genes in the ceRNA network included known lncRNAs with suppressor or oncogenic roles in breast cancer.
- Identified ceRNA groups were consistently linked to immune system processes, suggesting relevance for immunotherapy.
Conclusions:
- Crinet provides a robust method for genome-wide ceRNA network inference, overcoming limitations of previous tools.
- The inclusion of non-coding RNAs is crucial for accurate ceRNA network analysis.
- The inferred ceRNA networks, particularly those involved in immune processes, offer potential insights for cancer immunotherapy.
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