Electronic Theses and Dissertations




Yanan Wu



Document Type


Degree Name

Doctor of Philosophy


Mathematical Sciences



Committee Chair

Wai-Yuan Tan

Committee Member

Lih-Yuan Deng

Committee Member

Seok P. Wong


This dissertation is focused on the development of stochastic models for carcinogenesis of human kidney cancer. Based on recent biological studies, we have developed a multiple-pathway stochastic model for the human pediatric kidney cancer - Wilms' tumor. To account for hereditary cancer cases and the development of non-hereditary cancers through two different pathways in the stochastic model, we have also developed a generalized mixture model. In this mixture model, two mixing probability distributions were applied, which are a multinomial distribution to explain the genetic segregation of the stage-limiting tumor suppressor genes and a binomial distribution to account for the development of non-hereditary cancers through two pathways. We have applied this model to fit and analyze the SEER data of Wilms' tumor from NCI/NIH. Our results indicate that the proposed model involving hereditary and non-hereditary cancer cases fitted the data better than the single-pathway model with hereditary cancer cases. We have also derived a biologically supported stochastic model for human adult kidney cancer - renal cell carcinoma (RCC) involving three pathways. These pathways are: 3-stage pathway for pRCC , 4-stage pathway for ccRCC and 5-stage pathway for chRCC. To account for different individuals in the population at risk of developing renal cell carcinoma through different pathways, we have also presented a mixture model of three pathways. We have used this model to fit and analyze the SEER data of renal cell carcinoma from NCI/NIH. Our results indicate that the model not only provides a logical avenue to incorporate biological information but also fits the data well. These models not only would provide more insights into human kidney cancer but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.


Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.