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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.

To understand driving biological factors for 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, collective regulation, and groupwise effect of ceRNAs are not considered. In this study, we developed a computational pipeline named Crinet to infer cancer-associated ceRNA networks addressing critical drawbacks. Crinet considers lncRNAs, pseudogenes, and all mRNAs as potential ceRNAs and incorporates a network deconvolution method to exclude the amplifying effect of ceRNA pairs in terms of ceRNA regulation. We tested Crinet on breast cancer data in TCGA. Crinet inferred reproducible ceRNA interactions and groups, which were significantly enriched in cancer-related genes and processes. We validated our results using protein expression benchmarks. Crinet outperformed other tool predicting gene expression change in knockdown assays, and showed better performance for antibodies following miRNA transfection. Top high-degree genes in the inferred network included known suppressor/oncogene lncRNAs of breast cancer showing the importance of non-coding RNA's inclusion for ceRNA inference. Materials and MethodsCrinet is a computational tool to infer cancer-associated ceRNA network genome-wide with pairwise and groupwise interactions. Briefly, the first step is data preparation. The second step is computing miRNA-target interactions integrating with expression datasets and considering sufficient abundance. Starting with final miRNA-target 2/16

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