Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152-6429, USA.
Biol Direct. 2010 Apr 20;5:26. doi: 10.1186/1745-6150-5-26.
Studies by molecular biologists and geneticists have shown that tumors of human colon cancer are developed from colon stem cells through two mechanisms: The chromosomal instability and the micro-satellite instability. The purpose of this paper is therefore to develop a new stochastic and state space model for carcinogenesis of human colon cancer incorporating these biological mechanisms.
Based on recent biological studies, in this paper we have developed a state space model for human colon cancer. In this state space model, the stochastic system is represented by a stochastic model, involving 2 different pathways-the chromosomal instability pathway and the micro-satellite instability pathway; the observation, cancer incidence data, is represented by a statistical model. Based on this model we have developed a generalized Bayesian approach to estimate the parameters through the posterior modes of the parameters via Gibbs sampling procedures. We have applied this model to fit and analyze the SEER data of human colon cancers from NCI/NIH.
Our results indicate that the model not only provides a logical avenue to incorporate biological information but also fits the data much better than other models including the 4-stage single pathway model. This model not only would provide more insights into human colon cancer but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.
分子生物学家和遗传学家的研究表明，人类结肠癌肿瘤是通过两种机制从结肠干细胞发展而来的：染色体不稳定性和微卫星不稳定性。因此，本文旨在结合这些生物学机制，为人类结肠癌的癌变发展建立一个新的随机状态空间模型。
基于最近的生物学研究，本文建立了一个人类结肠癌的状态空间模型。在这个状态空间模型中，随机系统由一个随机模型表示，涉及 2 种不同的途径——染色体不稳定性途径和微卫星不稳定性途径；观察结果，即癌症发病率数据，由一个统计模型表示。基于该模型，我们通过 Gibbs 抽样过程通过参数的后验模式开发了一种广义贝叶斯方法来估计参数。我们已经将该模型应用于拟合和分析来自 NCI/NIH 的人类结肠癌 SEER 数据。
我们的结果表明，该模型不仅提供了一种逻辑途径来结合生物学信息，而且比包括 4 阶段单途径模型在内的其他模型更能很好地拟合数据。该模型不仅可以深入了解人类结肠癌，还可以为其预防和控制以及预测未来的癌症病例提供有用的指导。
