Competing Endogenous RNAs As Biomarkers of MiRNA Activity

Competing Endogenous RNAs As Biomarkers of MiRNA Activity

Abstract
Cancer 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. The competing endogenous RNA hypothesis suggests that such microRNAs, 19-23 nucleotide long RNAs that can regulate hundreds of different genes, can be considered as a limited resource in competing endogenous RNA (ceRNA) networks. In these networks, transcripts with binding sites for the same shared miRNAs influence each other through sponging of miRNA copies and indirect de-repression.
We have previously developed the method SPONGE [1], which allows for inferring a genome-wide ceRNA network from matched gene and miRNA expression data. In the database SPONGEdb [2] (https://exbio.wzw.tum.de/sponge/), we captured the global gene-miRNA regulatory landscape in 22 different cancer types. However, the sample-specific contributions of ceRNAs have thus far eluded us. Assessing the sample-specific regulatory activity of a ceRNA is an important pre-requisite to investigate the potential of ceRNAs as cancer biomarkers.
To mitigate this, we developed spongEffects [3], a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. spongEffect scores hence shed light on ceRNAs as potential biomarkers and 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.
References:
1. List M, Dehghani Amirabad A, Kostka D, Schulz MH. Large-scale inference of competing endogenous RNA networks with sparse partial correlation. Bioinformatics. 2019;35: i596–i604. doi:10.1093/bioinformatics/btz314;
2. Hoffmann M, Pachl E, Hartung M, Stiegler V, Baumbach J, Schulz MH, et al. SPONGEdb: a pan-cancer resource for competing endogenous RNA interactions. NAR Cancer. 2021;3. doi:10.1093/narcan/zcaa042;
3. Boniolo F, Hoffmann M, Roggendorf N, Tercan B, Baumbach J, Castro MAA, et al. spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. bioRxiv. 2022. p. 2022.03.29.486212. doi:10.1101/2022.03.29.486212