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Cancer, a disease which has been a serious threaten to people's health, is of great concern to scientists. But the study on it has not made substantial progress attributed to its multi-factor incidents. Specimen information in the tissue, as one of the important data sources of the translational research, plays an indispensable role in promoting the diagnosis and treatment of cancer. With the advent of the era of “big data”, it becomes extremely significant to associate the clinical information with the basic research information and to manage the information of the bio-specimen efficaciously, so that some analysis such as data-mining can be done with the follow-up information obtained. However, the existing information management systems of tissue banks only focus on the storing, importing, and exporting of the bio-specimen, while the information of the area of basic research is overlooked. Therefore, we proposed and established a tumor tissue information management system, which can associate the clinical information with the molecular experimental information, so as to establish an information database of tissue samples and to avoid the miss of experimental information. A case study for colorectal cancer research was applied by using the proposed system, which demonstrated the feasibility of our patient-centered tumor tissue information management system.

Nowadays, a growing number of researchers devote themselves to re-excavation of existing biomedical knowledge discovery, focusing on how to establish associations between clinical and genomic data. However, quantitative analysis is still inadequate for a particular disease. Colorectal cancer is the one of malignant tumors whose molecular mechanism is relatively clear, making it a more appropriate object of study. This paper proposed a quantitative measurement of clinic-genomic associations for colorectal cancer based on Google Distance, using MEDLINE database as the corpus. Our method is engineered with several technologies, including mapping clinic and genomic data to MeSH terms, modifying Normalized Google Distance using year average. Data from Electronic Medical Records (EMR), Online Mendelian Inheritance in Man (OMIM), and Genetic Association Database (GAD) were used in this study. A total of 3795 clinic-genomic associations of colorectal cancer between 67 clinical concepts and 236 genes were obtained, of which 584 associations were identified for their gene is contained in the colorectal cancer pathway using KEGG pathway analysis. Assessment and interpretation were conducted using KEGG, GeneCards, and then getting new discoveries. This method is valid in quantitative analysis using biomedical literature and achieves a good performance in measuring the clinical data and genomic data, which can be transplanted to other disease research.