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Cancer heterogeneity arises from complex molecular interactions. Elucidatingsystems-level properties of gene interaction networks distinguishing cancerfrom normal cells is critical for understanding disease mechanisms anddeveloping targeted therapies. Previous works focused only on identifyingdifferences in network structures. In this study, we used graph frequencyanalysis of cancer genetic signals defined on a co-expression network todescribe the spectral properties of underlying cancer systems. We demonstratedthat cancer cells exhibit distinctive signatures in the graph frequency contentof their gene expression signals. Applying graph frequency filtering, graphFourier transforms, and its inverse to gene expression from different cancerstages resulted in significant improvements in average F-statistics of thegenes compared to using their unfiltered expression levels. We propose graphspectral properties of cancer genetic signals defined on gene co-expressionnetworks as cancer hallmarks with potential application for differentialco-expression analysis.

Graph Signal Processing For Cancer Gene Co-Expression Network Analysis