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

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.

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。

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。本项目我们首先基于网络模块算法结合Bootstrap抽样，并拟和多个多态位点的连锁不平衡信息而开发出新优化模块化算法，并对中国前列腺癌协作组和我们完成的前列腺炎的全基因组扫描数据进行整合分析，选择Grid2和ALDH2进行动物敲除，研究它们的功能。同时我们建立了前列腺炎及癌前病变的小鼠模型，已经全部完成本项目的研究内容。另外，我们仍扩展在负反馈环关键点与雄激素抵抗前列腺癌的功能研究、多个基因重要多态位点的生物功能、CtBP2与前列腺癌新生血管、与前列腺癌和肾透明细胞癌相关的甲基化挖掘及功能研究以及多个数据库的构建等进行研究。本项目共发表8篇SCI论文，获2部软件著作权和2个专利，为前列腺癌的研究提供重要的调控网络和方法，为其治疗提供重要的靶点信息。