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.
Highlights
MicroRNAs are small RNA types that bind to other RNAs such as mRNA, long non-coding RNA, and circular RNA to regulate their expression post-transcriptionally
To evaluate the specificity of Crinet, we checked if our inferred competing endogenous RNA (ceRNA) pairs existed in different regulatory layers, namely protein-protein interactions (PPIs) and transcription factor (TF)-gene interactions
Since we built a ceRNA network relying on miRNA-target interactions, proper selection of these interactions is important; we evaluated each filtering step of miRNA-target interactions using protein expression-based benchmarks
Summary
MicroRNAs (miRNAs) are small RNA types that bind to other RNAs such as mRNA, long non-coding RNA (lncRNA), and circular RNA to regulate their expression post-transcriptionally. Crinet: A computational tool to infer genome-wide ceRNA interactions targeted by common miRNAs “compete” for these miRNAs and thereby regulate each other indirectly by making the other RNA(s) free from miRNA regulation. Such indirect interactions between RNAs are called competing endogenous RNA (ceRNA) interactions, which have important roles in diseases including cancer [2,3,4,5]. For the rest of the paper, “genes” refers to mRNAs, lncRNAs, and pseudogenes in our analysis
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