Graduate Institute of Physiology, College of Medicine, National Taiwan University, Taipei, 10051, Taiwan.
Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, 10055, Taiwan.
BMC Cancer. 2019 Aug 13;19(1):796. doi: 10.1186/s12885-019-6023-4.
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.
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.
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.
In summary, we identified specific protein signatures for accurately classifying subtypes of thymoma, which could facilitate accurate diagnosis of thymoma patients.
组织学是一种传统的胸腺瘤亚型分类方法，因为成本低且方便。然而，由于胸腺瘤形态多样，这种方法增加了组织病理学分类的复杂性，需要有经验的专家进行正确的诊断。因此，在这项研究中，我们通过鉴定蛋白质生物标志物开发了一种替代方法，以帮助临床医生正确分类胸腺瘤亚型。
使用质谱法共鉴定出三种胸腺瘤亚型（AB、B2 和 B3）中的 204 个差异表达蛋白。通路分析显示，三种亚型中的差异表达蛋白参与了激活相关、信号转导相关和补体系统相关通路。为了使用鉴定的蛋白特征预测胸腺瘤的亚型，使用了支持向量机算法。采用留一法交叉验证方法和受试者工作特征分析来评估预测性能。
这三种亚型的平均准确率均>80%，曲线下面积均≧0.93。特别是，B3 亚型的准确率最高（96%），AB 亚型的曲线下面积最大（0.99）。进一步使用免疫组织化学法验证了其中一个差异表达蛋白 COL17A2。
总之，我们鉴定了用于准确分类胸腺瘤亚型的特定蛋白特征，这有助于对胸腺瘤患者进行准确诊断。
