Using proteomic profiling to characterize protein signatures of different thymoma subtypes

Abstract Background Histology is a traditional way to classify subtypes of thymoma, because of low cost and convenience. Yet, due to the diverse morphology of thymoma, this method increases the complexity of histopathologic classification, and requires experienced experts to perform correct diagnosi...

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Main Authors: Liang-Chuan Lai, Qiang-Ling Sun, Yu-An Chen, Yi-Wen Hsiao, Tzu-Pin Lu, Mong-Hsun Tsai, Lei Zhu, Eric Y. Chuang, Wentao Fang
Format: Article
Language:English
Published: BMC 2019-08-01
Series:BMC Cancer
Subjects:
Online Access:http://link.springer.com/article/10.1186/s12885-019-6023-4
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spelling doaj-0d3c737e60824e1c8f7c507add16bb6f2020-11-25T03:24:10ZengBMCBMC Cancer1471-24072019-08-011911810.1186/s12885-019-6023-4Using proteomic profiling to characterize protein signatures of different thymoma subtypesLiang-Chuan Lai0Qiang-Ling Sun1Yu-An Chen2Yi-Wen Hsiao3Tzu-Pin Lu4Mong-Hsun Tsai5Lei Zhu6Eric Y. Chuang7Wentao Fang8Graduate Institute of Physiology, College of Medicine, National Taiwan UniversityDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong UniversityBioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan UniversityBioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan UniversityDepartment of Public Health, National Taiwan UniversityBioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan UniversityDepartment of Pathology, Shanghai Chest Hospital, Shanghai Jiao Tong UniversityBioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan UniversityDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong UniversityAbstract Background Histology is a traditional way to classify subtypes of thymoma, because of low cost and convenience. Yet, due to the diverse morphology of thymoma, this method increases the complexity of histopathologic classification, and requires experienced experts to perform correct diagnosis. Therefore, in this study, we developed an alternative method by identifying protein biomarkers in order to assist clinical practitioners to make right classification of thymoma subtypes. Methods In total, 204 differentially expressed proteins in three subtypes of thymoma, AB, B2, and B3, were identified using mass spectrometry. Pathway analysis showed that the differentially expressed proteins in the three subtypes were involved in activation-related, signaling transduction-related and complement system-related pathways. To predict the subtypes of thymoma using the identified protein signatures, a support vector machine algorithm was used. Leave-one-out cross validation methods and receiver operating characteristic analysis were used to evaluate the predictive performance. Results The mean accuracy rates were > 80% and areas under the curve were ≧0.93 across these three subtypes. Especially, subtype B3 had the highest accuracy rate (96%) and subtype AB had the greatest area under the curve (0.99). One of the differentially expressed proteins COL17A2 was further validated using immunohistochemistry. Conclusions In summary, we identified specific protein signatures for accurately classifying subtypes of thymoma, which could facilitate accurate diagnosis of thymoma patients.http://link.springer.com/article/10.1186/s12885-019-6023-4Proteomic profilingThymomaSupport vector machineWHO classification
collection DOAJ
language English
format Article
sources DOAJ
author Liang-Chuan Lai
Qiang-Ling Sun
Yu-An Chen
Yi-Wen Hsiao
Tzu-Pin Lu
Mong-Hsun Tsai
Lei Zhu
Eric Y. Chuang
Wentao Fang
spellingShingle Liang-Chuan Lai
Qiang-Ling Sun
Yu-An Chen
Yi-Wen Hsiao
Tzu-Pin Lu
Mong-Hsun Tsai
Lei Zhu
Eric Y. Chuang
Wentao Fang
Using proteomic profiling to characterize protein signatures of different thymoma subtypes
BMC Cancer
Proteomic profiling
Thymoma
Support vector machine
WHO classification
author_facet Liang-Chuan Lai
Qiang-Ling Sun
Yu-An Chen
Yi-Wen Hsiao
Tzu-Pin Lu
Mong-Hsun Tsai
Lei Zhu
Eric Y. Chuang
Wentao Fang
author_sort Liang-Chuan Lai
title Using proteomic profiling to characterize protein signatures of different thymoma subtypes
title_short Using proteomic profiling to characterize protein signatures of different thymoma subtypes
title_full Using proteomic profiling to characterize protein signatures of different thymoma subtypes
title_fullStr Using proteomic profiling to characterize protein signatures of different thymoma subtypes
title_full_unstemmed Using proteomic profiling to characterize protein signatures of different thymoma subtypes
title_sort using proteomic profiling to characterize protein signatures of different thymoma subtypes
publisher BMC
series BMC Cancer
issn 1471-2407
publishDate 2019-08-01
description Abstract Background Histology is a traditional way to classify subtypes of thymoma, because of low cost and convenience. Yet, due to the diverse morphology of thymoma, this method increases the complexity of histopathologic classification, and requires experienced experts to perform correct diagnosis. Therefore, in this study, we developed an alternative method by identifying protein biomarkers in order to assist clinical practitioners to make right classification of thymoma subtypes. Methods In total, 204 differentially expressed proteins in three subtypes of thymoma, AB, B2, and B3, were identified using mass spectrometry. Pathway analysis showed that the differentially expressed proteins in the three subtypes were involved in activation-related, signaling transduction-related and complement system-related pathways. To predict the subtypes of thymoma using the identified protein signatures, a support vector machine algorithm was used. Leave-one-out cross validation methods and receiver operating characteristic analysis were used to evaluate the predictive performance. Results The mean accuracy rates were > 80% and areas under the curve were ≧0.93 across these three subtypes. Especially, subtype B3 had the highest accuracy rate (96%) and subtype AB had the greatest area under the curve (0.99). One of the differentially expressed proteins COL17A2 was further validated using immunohistochemistry. Conclusions In summary, we identified specific protein signatures for accurately classifying subtypes of thymoma, which could facilitate accurate diagnosis of thymoma patients.
topic Proteomic profiling
Thymoma
Support vector machine
WHO classification
url http://link.springer.com/article/10.1186/s12885-019-6023-4
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