ArticleBioinformatics (Oxford, England)2023
spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Detection of Candidate Circular RNAs to Monitor Anti-Hormonal Response in the Mammary Gland.bioRxiv : the preprint server for biology · 2026Article
- CircRNAs in Colorectal Cancer: Unveiling Their Roles and Exploring Therapeutic Potential.Biochemical genetics · 2025Review
- Whole transcriptome analysis in oviduct provides insight into microRNAs and ceRNA regulative networks that targeted reproduction of goat (Capra hircus).BMC genomics · 2025Article
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Exploring the Regulatory Landscape of Dementia: Insights from Non-Coding RNAs.International journal of molecular sciences · 2024Review
- Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer.BMC genomics · 2024Article
- Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers.PLoS computational biology · 2023Article
- circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging.bioRxiv : the preprint server for biology · 2023Article
- circRNA-sponging: a pipeline for extensive analysis of circRNA expression and their role in miRNA sponging.Bioinformatics advances · 2023Article
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9 authors.
Funding
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Abstract
motivationCancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene-miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity.
resultsWe show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/devel/bioc/html/SPONGE.html.
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