Development of Decision Tree Software and Protein Profiling using Surface Enhanced Laser Desorption/Ionization - Time of Flight - Mass Spectrometry (SELDI-TOF-MS) in Papillary Thyroid Cancer.
- Author:
Joon Kee YOON
1
;
Jun LEE
;
Young Sil AN
;
Bok Nam PARK
;
Seok Nam YOON
Author Information
1. Department of Nuclear Medicine & Molecular Imaging, Ajou University School of Medicine, Suwon, Korea. snyoon@ajou.ac.kr
- Publication Type:Original Article
- Keywords:
biomarker discovery;
SELDI-TOF-MS;
decision tree;
papillary thyroid cancer
- MeSH:
Classification;
Computational Biology;
Decision Trees*;
Mass Spectrometry*;
Protein Array Analysis;
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization;
Thyroid Gland*;
Thyroid Neoplasms*;
Thyroiditis, Autoimmune
- From:Nuclear Medicine and Molecular Imaging
2007;41(4):299-308
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). MATERIALS AND METHODS: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. RESULTS: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). CONCLUSION: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.
