发展新优化加权模式化算法构建前列腺炎易导前列腺癌模块的研究

项目名称： 发展新优化加权模式化算法构建前列腺炎易导前列腺癌模块的研究

项目编号： No.81472414

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 医药、卫生

项目作者： 胡艳玲

作者单位： 广西医科大学

项目金额： 72万元

中文摘要： 多个研究报道表明，前列腺炎与前列腺癌的发生存在显著关系。对比我们已完成的前列腺炎全基因组关联分析（GWAS）和中国前列腺癌协作组（ChinaPCA）完成的GWAS发现，前列腺炎与前列腺癌存在很多共同相关基因。进一步对比不同类型前列腺炎与前列腺癌GWAS，发现它们重叠基因存在较大差异。因此本项目总研究思路是：建立新优化加权模式化的群体模块分类算法，利用该算法构建基于全基因组扫描数据的前列腺炎群体模块分类，然后通过与ChinaPCA的GWAS对比，找出前列腺炎易导前列腺癌的关键模块群体。为进一步验证此方法的准确性，再通过构建易导模块基因互作网络，结合生物信息学分析及分子功能实验，找到前列腺炎易导前列腺癌模块潜在关键驱动基因。通过动物敲除实验研究关键基因生物功能。本项目研究为复杂疾病群体模块分类提供算法软件，为前列腺癌发生分子机制的阐明提供新的重要理论基础，以期更好地指导临床个体化医疗的实施。

中文关键词： 优化加权模式化算法；前列腺炎；前列腺癌；群体模块；生物功能

英文摘要： Several studies show that there is a significant association between prostatitis and prostate cancer. Through comparing the genome-wide associations (GWAS) of our prostatitis data and the data obtained by China Prostate Cancer Group (ChinaPCA), we found that there were many overlapping genes between prostatitis and prostate cancer, and there are significant differences among the overlapping genes of different types of prostatitis and prostate cancer. Therefore, we propose the following study: Develop a novel optimized weighted modularity algorithm, apply this new algorithm to construct prostatitis modules based on our comprehensive genome-wide data, and compare the resulted modules with the GWAS of ChinaPCA to obtain critical modules. Then construct the relevant gene network module， and combine bioinformatics analysis and molecular experiment to select the likely key driving genes that associated with the transformation from prostatitis to prostate cancer. In order to further verify the accuracy of the susceptible modularity, perform gene knockout experiments using animals to verify the impacts of the selected genes and to study their biological functions. The proposed research is expected to offer the available modularity algorithm and software to analyze complex disease, and provide new and important insight for the molecular mechanics of prostate cancer. Additionally, the project's outcome is expected to help in improving in clinic personal medical treatments.

英文关键词： Optimized weighted modularity algorithm;prostatitis;prostate cancer;group modules;biological function