To find new biomarkers and establish serum protein fingerprint models for early diagnosis and preoperative staging of papillary thyroid carcinoma, we employed SELDI-TOF-MS and bioinformatics tools. A total of 116 samples were analyzed in this study. The first 80 samples were analyzed by SELDI-TOF-MS and two biomarker patterns were identified. Pattern 1 distinguishes patients with papillary thyroid carcinoma from healthy individuals. Pattern 2 distinguishes papillary thyroid carcinoma from benign thyroid nodes. The remaining 29 samples were analyzed on the second day and served as an independent test set. The analysis of this independent test set yielded a specificity of 80.0% and a sensitivity of 88.9% for pattern 1 and a specificity of 80.0% and a sensitivity of 80.0% for pattern 2. Two additional biomarker patterns were identified to distinguish different stages of the papillary thyroid carcinoma (pattern 3) with an accuracy of 77.1% and different pathological types of thyroid carcinoma (pattern 4) with an accuracy of 88.1%. Taken together, the SELDI-TOF-MS technique combined with bioinformatics approaches can not only facilitate the discovery of better biomarkers for papillary thyroid carcinoma but also provide a useful tool for molecular diagnosis in the future.
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