Application of serum protein fingerprint model and support vector machine in diagnosis of thyroid cancer
Wang, J.-x.; Wang, L.; Fan, Y.-z.; Liu, Q.-l.; Zhang, J.; Yu, J.-k.; Zheng, S.
Zhonghua Yi Xue Za Zhi 86(14): 979-982
2006
ISSN/ISBN: 0376-2491 PMID: 16759540 Document Number: 600869
To detect new biomarkers and to establish a serum protein fingerprint model for early detection and diagnosis of thyroid cancer. The serum samples of 40 thyroid cancer patients, 9 thyroid adenoma patient, and 32 healthy individuals were randomly divided into 2 sets: training set (n = 66, including 32 thyroid cancer patients, 9 thyroid adenoma patients, and 25 healthy individuals) and test set (n = 15). The serum protein were bound to WCX2 chip and tested by surface enhanced laser desorption/ionization time of flight-mass spectrometry (SELDI-TOF-MS). The data of spectra were analyzed by support vector machine (SVM) to establish a diagnostic model. The detective model combined with 3 biomarkers could differentiate the serum of thyroid cancer from that of healthy individual with a specificity of 86% and a sensitivity of 100%. The diagnostic model combined with 3 biomarkers could differentiate thyroid cancer from thyroid adenoma with a specificity of 88.9% and a sensitivity of 96.9%. The positive predictive value to differentiate papillary thyroid carcinoma from the thyroid cancer of other types was 97%, and the positive predictive value of thyroid carcinoma of other pathological types was 71%. The combination of SELDI with bioinformatics tools is a novel, effective, and highly specific and sensitive method for thyroid cancer detection and diagnosis.