It is then evident that computing the Jensen-Shannon divergences, the Normalized Shannon Entropy, and the Statistical Complexity of samples reveal different global transcriptional changes. It is, however, not easy to infer if these changes would correlate with a gradual progression or sudden changes. However, one valid mathematical possibility is that the most important ''hallmark of cancer'', a unifying principle above all, is the existence of a measurable gradual ''progression'' from a well-differentiated gene expression profile (corresponding to a healthy tissue). This would reveal the timeline of a higher level process that is observable and measurable via a change of Normalized Shannon Entropy and an increment of Jensen-Shannon divergences from the originating tissue type. If this is the case, by correlating the changes in Information Theory quantifiers with the expression of the genes we would be able to not only uncover useful biomarkers to track this progression but to explain the ''hallmarks'' in an ordered timeline. The timeline also yields clinical and translational important outcomes. Such analytical methodology will naturally produce ''a continuous staging'' of the cancer samples, based on a solid foundations of Information Theory, based on the knowledge of transcriptional profile of healthy cells as reference to measure divergences. In addition, as a mathematical methodology, it can be applied to other high-throughput technologies for which a probability distribution function of observed abundances has been computed.