Genome-wide analysis of NGS data to compile cancer-specific panels of miRNA biomarkers

MicroRNAs are small non-coding RNAs that influence gene expression by
binding to the 3' UTR of target mRNAs in order to repress protein
synthesis. Soon after discovery, microRNA dysregulation has been
associated to several pathologies. In particular, they have often been
reported as differentially expressed in healthy and tumor samples. This
fact suggested that microRNAs are likely to be good candidate biomarkers
for cancer diagnosis and personalized medicine. With the advent of
Next-Generation Sequencing (NGS), measuring the expression level of the
whole miRNAome at once is now routine. Yet, the collaborative effort of
sharing data opens to the possibility of population analyses. This
context motivated us to perform an in-silico study to distill
cancer-specific panels of microRNAs that can serve as biomarkers. We
observed that the problem of finding biomarkers can be modeled as a
two-class classification task where, given the miRNAomes of a population
of healthy and cancerous samples, we want to find the subset of
microRNAs that leads to the highest classification accuracy. We fulfill
this task leveraging on a sensible combination of data mining tools. In
particular, we used: differential evolution for candidate selection,
component analysis to preserve the relationships among miRNAs, and SVM
for sample classification. We identified $10$ cancer-specific panels
whose classification accuracy is always higher than 92%. These panels
have a very little overlap suggesting that miRNAs are not only
predictive of the onset of cancer, but can be used for classification
purposes as well. We experimentally validated the contribution of each
of the employed tools to the selection of discriminating
miRNAs.Moreover, we tested the significance of each panel for the
corresponding cancer type. In particular, enrichment analysis showed
that the selected miRNAs are involved in oncogenesis pathways, while
survival analysis proved that miRNAs can be used to evaluate cancer
severity. Summarizing: results demonstrated that our method is able to
produce cancer-specific panels that are promising candidates for a
subsequent in vitro validation.