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Time to infer miRNA sponge modules

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Abstract Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer‐related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer‐associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan‐cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule‐RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs
Publications of ceRNA research. (a) PubMed trend of ceRNA research at both network and module levels in the past decade. The number of queried publications is obtained from PubMed library with keywords “ceRNA network” and “ceRNA module.” (b) Bubble plot of top 10 human cancers in ceRNA research. The abstracts are obtained from PubMed library with keyword “ceRNA AND cancer,” and the human cancer with higher frequency in the abstracts means more popular in ceRNA research
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Overall performance in terms of percentage of EMT‐related modules, percentage of disease‐related modules, percentage of diagnostic modules, and percentage of prognostic modules. (a) Performance of five representative module discovery methods in predicting lncRNA related miRNA sponge modules. (b) Performance of five representative module discovery methods in predicting pseudogene related miRNA sponge modules
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Tumor samples in pan‐cancer transcriptomics data. (a) Number of tumor samples in each type. (b) Number of tumor samples in each EMT type. (c) Number of tumor samples belonging to each EMT type across 31 tumor types. The 31 tumor types include adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical and endocervical cancers (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B‐cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM)
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Conventional means of functional gene module identification based on a biological network
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Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs
RNA Interactions with Proteins and Other Molecules > Small Molecule–RNA Interactions

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