MicroRNA-Gene Association As a Prognostic Biomarker in Cancer Exposes Disease Mechanisms

The transcriptional networks that regulate gene expression and modifications to this network are at the core of the cancer phenotype. MicroRNAs, a well-studied species of small non-coding RNA molecules, have been shown to have a central role in regulating gene expression as part of this transcriptional network. Further, microRNA deregulation is associated with cancer development and with tumor progression. Glioblastoma Multiform (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. To study the transcriptional network and its modifications in GBM, we utilized gene expression, microRNA sequencing, whole genome sequencing and clinical data from hundreds of patients from different datasets. Using these data and a novel microRNA-gene association approach we introduce, we have identified unique microRNAs and their associated genes. This unique behavior is composed of the ability of the quantifiable association of the microRNA and the gene expression levels, which we show stratify patients into clinical subgroups of high statistical significance. Importantly, this stratification goes unobserved by other methods and is not affiliated by other subsets or phenotypes within the data. To investigate the robustness of the introduced approach, we demonstrate, in unrelated datasets, robustness of findings. Among the set of identified microRNA-gene associations, we closely study the example of MAF and hsa-miR-330-3p, and show how their co-behavior stratifies patients into prognosis clinical groups and how whole genome sequences tells us more about a specific genomic variation as a possible basis for patient variances. We argue that these identified associations may indicate previously unexplored specific disease control mechanisms and may be used as basis for further study and for possible therapeutic intervention.Author Summary: Despite major progress and improved understanding of Glioblastoma Multiforme, the disease is still associated with poor prognosis. The identification of genomic regulatory mechanisms, their affiliation with clinical outcome and the association between specific modifications in genome sequence that can explain gain and loss of such regulatory activity, combine to suggest specific disease mechanisms and possible means of intervention in the course of the disease. We report here a method and its implementation in exposing possible regulatory mechanisms in GBM. At the core of this method is the employment of associations between micro RNAs and genes as a quantifiable metric. Identification of these associations and their affiliation with clinical features, combined with the availability of whole genome sequences, brings forward specific micro RNAs and their associated genes. Affiliation of specific genomic sequences with clinical outcome thus translates personal genomics into tumor relevant decision-making.