Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

MicroRNAs play a key role in the regulation of gene expression. A majority of microRNA–mRNA interactions remain unidentified. Despite extensive research, our ability to predict human microRNA-mRNA interactions using computational algorithms remains limited by a complexity of the models for non-canonical interactions, and an abundance of false-positive results. Here, we present the landscape of human microRNA–mRNA interactions derived from comprehensive analysis of HEK293 and Huh7.5 datasets, along with publicly available microRNA and mRNA expression data. We show that, while only 1–2% of human genes were the most regulated by microRNAs, few cell line–specific RNAs, including EEF1A1 and HSPA1B in HEK293 and AFP, APOB, and MALAT1 genes in Huh7.5, display substantial “sponge-like” properties. We revealed a group of microRNAs that are expressed at a very high level, while interacting with only a few mRNAs, which, indeed, serve as their specific expression regulators. In order to establish reliable microRNA-binding regions, we collected and systematically analyzed the data from 79 CLIP datasets of microRNA-binding sites. We report 46,805 experimentally confirmed mRNA–miRNA duplex regions. Resulting dataset is available at http://score.generesearch.ru/services/mirna/. Our study provides initial insight into the complexity of human microRNA–mRNA interactions.