Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets

• Main Authors: Fang Liu, Yaning Feng, Zhenye Li, Chao Pan, Yuncong Su, Rui Yang, Liying Song, Huilong Duan, Ning Deng
• Format: Article
• Language: English
• Published: Hindawi Limited 2014-01-01
• Series: BioMed Research International
• Online Access: http://dx.doi.org/10.1155/2014/170289
• ISSN: 2314-6133; 2314-6141

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 23517 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.