Estimating the risk of oral cancer using protein expression in saliva

• Main Authors: Chang, Chao-Di, 張兆締
• Other Authors: Ho, Shinn-Ying
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/51992442881964957095

碩士 === 國立交通大學 === 生物資訊及系統生物研究所 === 103 === In Taiwan, oral cancer is the fourth of cancer leading cause of death in men which is second only to liver cancer, gastric cancer, and colon cancer. The main risk factors associated with oral cancer are the smoking habit, betel chewing habit, and alcohol drinking habit that make oral cancer becomes the youngest of overall age-standardized mortality rate for all cancer types and the highest cancer mortality increasing rate in the past two decades. Thus, finding potential oral cancer patients and make the treatments as early as possible becomes the best solution for decreasing oral cancer mortality rate. Cancer early detection has already reported as a good solution in cancer treatment enhancement. By a large scale screening using a simple detection technique, a good early detection system can significantly improve cancer prognostic quality. However, the mostly used oral cancer early detection method is the tissue sections and pathologic diagnosis. This method not only cost a lot in every detection, but also lacks an oral cancer risk estimating system for those who have precancerous lesions. In the last few years, establishing an oral cancer early detection platform by detecting specific gene or protein expression in blood, saliva, or tissue becomes a mainstream in oral cancer research. Most of these studies are aim to use a single gene or single protein for oral cancer early detection analysis which is prone to error in oral cancer risk estimating for precancerous patients. Therefore, the aim of this study is using the expression of Matrix Metalloproteinase, MMP family proteins, which play an important role in cancer progression, and their specific inhibitor Tissue inhibitor of metalloproteinase, TIMP family proteins, and also the consider the risk factor information of each sample to create an oral cancer risk estimating system for precancerous patients.