Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.
Author Summary MicroRNAs regulate the expression of genes in cells and are important in cancer development and progression. Designing new microRNA-based treatments requires the understanding of their mechanisms of action. Previous biological studies lack in depth since only a few genes are confirmed as microRNA targets. Additionally, key biological systems perturbed by altered microRNA functions in the context of cancer remain to be identified. Here, we demonstrate for the first time how genetic knowledge about the inheritance of cancer can be utilized jointly with data about the expression of genes in cancer samples to model deregulated microRNAs and their functions at multiple scales of biology. Our approach further uncovers previously unknown connections between microRNAs, their regulated genes, and their dynamics. Using head and neck cancer as a model, we predict the presence, functions, and gene targets of a new tumor suppressor microRNA in a cancer-associated chromosomal region where a candidate gene has not been identified. We then confirm their validity with extensive and thorough biological characterization and show attenuation of lung metastasis in mice. The discovery of molecular networks regulated by microRNAs could be exploited for the design of new treatments as an alternative to the single-gene target paradigm.