Diagnosing phenotypes of single-sample individuals by edge biomarkers

fox stephen;

Network or edge biomarkers area reliable form to characterize phenotypes or diseases.However, obtaining edges or correlations between molecules for an individual requires measurement ofmultiple samples of that individual,which are generally unavailable in clinical practice. Thus, it is strongly demanded to diagnose a disease by edge or network biomarkers in one-sample-for-one-individual context. Here,
we developed a new computational framework, Edge Biomarker, to integrate edge and node biomarkers to diagnose phenotype of each
single test sample. By applying the method to data sets of lung and breast cancer, it reveals new marker genes/gene-pairs and related
sub-networks for distinguishing earlier and advanced cancer stages. Our method shows advantages over traditional methods: (i) edge
biomarkers extracted from non-differentially expressed genes achieve better cross-validation accuracy of diagnosis than molecule or
node biomarkers from differentially expressed genes, suggesting that certain pathogenic information is only present at the level of
network and under-estimated by traditional methods; (ii) edge biomarkers categorize patients into low/high survival rate in a more reliablemanner; (iii) edge biomarkers are significantly enriched in relevant biological functions or pathways, implying that the association
changes ina network, rather thanexpressionchanges in individualmolecules, tendtobecausally related to cancer development. The new framework of edge biomarkers paves theway for diagnosing diseases and analyzing their molecular mechanisms by edges or networks in one-sample-for-one-individual basis. This also provides a powerful tool for precision medicine or big-data medicine.