Cancer is a disease of subverted regulatory pathways. In this paper, we reconstruct the regulatory network around E2F, a family of transcription factors whose deregulation has been associated to cancer progression, chemoresistance, invasiveness, and metastasis. We integrate gene expression profiles of cancer cell lines from two E2F1-driven highly aggressive bladder and breast tumors, and use network analysis methods to identify the tumor type-specific core of the network. By combining logic-based network modeling, in vitro experimentation, and gene expression profiles from patient cohorts displaying tumor aggressiveness, we identify and experimentally validate distinctive, tumor type-specific signatures of receptor proteins associated to epithelial–mesenchymal transition in bladder and breast cancer. Our integrative network-based methodology, exemplified in the case of E2F1-induced aggressive tumors, has the potential to support the design of cohort- as well as tumor type-specific treatments and ultimately, to fight metastasis and therapy resistance.

MicroRNAs (miRNAs) are an integral part of gene regulation at the post-transcriptional level. Recently, it has been shown that pairs of miRNAs can repress the translation of a target mRNA in a cooperative manner, which leads to an enhanced effectiveness and specificity in target repression. However, it remains unclear which miRNA pairs can synergize and which genes are target of cooperative miRNA regulation. In this paper, we present a computational workflow for the prediction and analysis of cooperating miRNAs and their mutual target genes, which we refer to as RNA triplexes. The workflow integrates methods of miRNA target prediction; triplex structure analysis; molecular dynamics simulations and mathematical modeling for a reliable prediction of functional RNA triplexes and target repression efficiency. In a case study we analyzed the human genome and identified several thousand targets of cooperative gene regulation. Our results suggest that miRNA cooperativity is a frequent mechanism for an enhanced target repression by pairs of miRNAs facilitating distinctive and fine-tuned target gene expression patterns. Human RNA triplexes predicted and characterized in this study are organized in a web resource at www.sbi.uni-rostock.de/triplexrna/.

MicroRNA (miRNA) target hubs are genes that can be simultaneously targeted by a comparatively large number of miRNAs, a class of non-coding RNAs that mediate post-transcriptional gene repression. Although the details of target hub regulation remain poorly understood, recent experiments suggest that pairs of miRNAs can cooperate if their binding sites reside in close proximity. To test this and other hypotheses, we established a novel approach to investigate mechanisms of collective miRNA repression. The approach presented here combines miRNA target prediction and transcription factor prediction with data from the literature and databases to generate a regulatory map for a chosen target hub. We then show how a kinetic model can be derived from the regulatory map. To validate our approach, we present a case study for p21, one of the first experimentally proved miRNA target hubs. Our analysis indicates that distinctive expression patterns for miRNAs, some of which interact cooperatively, fine-tune the features of transient and long-term regulation of target genes. With respect to p21, our model successfully predicts its protein levels for nine different cellular functions. In addition, we find that high abundance of miRNAs, in combination with cooperativity, can enhance noise buffering for the transcription of target hubs.

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