In human history, finding a cure for the devastating disease of cancer has been a long, ongoing battle. Recently, scientific research has shown several feasible bio-medicinal ways for the treatment of cancer. The first method is through predictive regenerative medicine, which converts (breast) cancer cells back into phenotypically benign cells using continually updated predictions from Bayesian inference models. Another common method is the rewriting of damaged coordinating genes, but this has underlying moral issues — who can authorise the right to alter human genes? To investigate these methods, this paper will discuss elementary point-set topologies, physical gene mapping, and gene sequencing. The main purpose of this paper is to establish a statistical method with the help of mathematics in our biological life science. The proposed method can be applied in the case of manipulating genetic transcription and expression with a suitable Bayesian inference model (for genetic reprogramming). The peak value of cancer cell conversion is its Bayesian regression (based on the model’s parameters) from the corresponding Bayesian inference (for the normal posterior distribution, prior and likelihood). The relevant predictive conditions can be determined from the respective model, which could be used to reveal elements that help cancer cell conversion maximisation/minimisation. Practically, we may apply both of the Bayesian optimisation and the related attribute adjustment (to the Oct4, SoX2, Klf4 and c-Myc transcription factors) that can induce the reprogramming of cancer cells. The expected outcome is that we can biomedical-engine the cancer cells conversion. To go ahead a step, one may apply my proposed Net-Seizing Theory to capture the genetic mutations that underly cancer disease. It is hope that we can design the corresponding universal strategy to heal cancer.