Mathematical bridge between epidemiological and molecular data on cancer and beyond

Background: At least six different mathematical models of cancer and their countless variations and combinations have been published to date in the scientific literature that reasonably explain epidemiological prediction of multi-step carcinogenesis. Each one deals with a particular set of problems at a given organizational level ranging from populations to genes. Any of the models adopted in those articles so far do not account for both epidemiological and molecular levels of carcinogenesis.

We have developed a mathematically rigorous system to derive those equations satisfying the basic assumptions of both epidemiology and molecular biology without incorporating arbitrary numerical coefficients or constants devoid of any causal explanation just to fit the empirical data. The dataset we have used encompasses 21 major categories of cancer, 124 selected populations, 108 cancer registries, 5 continents, and 14,067,894 individual cases.

We generalized all the epidemiological and molecular data using our derived equations through linear and non-linear regression and found all the necessary coefficients to explain the data. We also tested our equations against non-neoplastic conditions satisfying equivalent mathematical assumptions.

Our findings show that the new mathematical framework effectively bridges epidemiological and molecular data on carcinogenesis. Validated across various cancer types and extended to non-neoplastic conditions, this unified approach lays a strong foundation for future integrative cancer research.