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...
Main Authors: | , , , , , , , , |
---|---|
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 |
id |
doaj-0d3c737e60824e1c8f7c507add16bb6f |
---|---|
record_format |
Article |
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 |
work_keys_str_mv |
AT liangchuanlai usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT qianglingsun usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT yuanchen usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT yiwenhsiao usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT tzupinlu usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT monghsuntsai usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT leizhu usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT ericychuang usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes AT wentaofang usingproteomicprofilingtocharacterizeproteinsignaturesofdifferentthymomasubtypes |
_version_ |
1724602883296460800 |