In particular, by using a data integration approach that combines experimental evidence for high throughput genome wide gene expression, a non-equilibrium thermodynamics analysis, nonlinear correlation networks as well as database mining, we were able to hypothesize about the role that transcription factors MEF2C and MNDA may have as master regulators in primary breast cancer phenomenology, as well as the possible interrelationship between malignancy and metabolic dysfunction. Here, MNDA is linked to breast cancer.