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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

Other Title
의사결정트리 프로그램 개발 및 갑상선유두암에서 질량분석법을 이용한 단백질 패턴 분석
Authors
윤, 준기; 이, 준; 안, 영실; 박, 복남; 윤, 석남
Citation
Haek ŭihak punja yŏngsang, 41(4):299-308, 2007
Journal Title
Haek ŭihak punja yŏngsang; Nuclear medicine and molecular imaging; 핵의학분자영상
ISSN
1975-129X
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.
Keywords
biomarker discoverySELDI-TOF-MSdecision treepapillary thyroid cancer
Appears in Collections:
Journal Papers > School of Medicine / Graduate School of Medicine > Nuclear Medicine & Molecular Imaging
AJOU Authors
윤, 준기윤, 석남
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