Liu Fang, Feng Yaning, Li Zhenye, Pan Chao, Su Yuncong, Yang Rui, Song Liying, Duan Huilong, Deng Ning
Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.
General Hospital of Ningxia Medical University, Yinchuan 750004, China.
Biomed Res Int. 2014;2014:170289. doi: 10.1155/2014/170289. Epub 2014 Jun 2.
In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.
近年来，越来越多的研究人员开始关注如何建立临床数据与基因组数据之间的关联。然而，截至目前，缺乏通过全面分析单一疾病的可用基因表达数据来挖掘临床-基因组关联的研究。结直肠癌是恶性肿瘤之一。许多遗传综合征已被证明与结直肠癌有关。本文介绍了我们在生物医学大数据环境下挖掘结直肠癌临床-基因组关联的研究。所提出的方法采用了多种技术构建而成，包括使用统一医学语言系统（UMLS）提取临床概念、通过文献挖掘提取基因以及通过统计分析挖掘临床-基因组关联。我们将此方法应用于从基因表达综合数据库（GEO）和遗传关联数据库（GAD）中提取的数据集。共获得了139个临床概念与7914个基因之间的23517个临床-基因组关联，其中31个临床概念与1689个基因之间的3474个关联被确定为高度可靠的关联。使用UMLS、KEGG和Gephi进行了评估和解读，并探索了潜在的新发现。所提出的方法在从可用的生物医学大数据中挖掘有价值的知识方面是有效的，并且在将临床数据与结直肠癌的基因组数据相连接方面取得了良好的效果。
