College of Basic Medical Science, Zhejiang Chinese Medical University, 548 Binwen Rd, Hangzhou, 310053, China.
Key Laboratory of Chinese Medicine Rheumatology of Zhejiang Province, 548 Binwen Rd, Hangzhou, 310053, China.
Sci Rep. 2024 Nov 4;14(1):26705. doi: 10.1038/s41598-024-77494-4.
Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis and prognosis. However, how to eliminate the errors introduced by peptide precursor identification and protein identification for pathological diagnosis remains a major unresolved issue. Here, we develop a powerful end-to-end deep learning model, termed "MS1Former", that is able to classify hepatocellular carcinoma tumors and adjacent non-tumor (normal) tissues directly using raw MS1 spectra without peptide precursor identification. Our model provides accurate discrimination of subtle m/z differences in MS1 between tumor and adjacent non-tumor tissue, as well as more general performance predictions for data-dependent acquisition, data-independent acquisition, and full-scan data. Our model achieves the best performance on multiple external validation datasets. Additionally, we perform a detailed exploration of the model's interpretability. Prospectively, we expect that the advanced end-to-end framework will be more applicable to the classification of other tumors.
临床蛋白质组学分析对于分析病理机制和发现疾病相关生物标志物具有重要意义。使用计算方法准确预测疾病类型可以有效提高患者疾病诊断和预后。然而，如何消除肽前体鉴定和蛋白质鉴定为病理诊断带来的误差仍然是一个未解决的主要问题。在这里，我们开发了一种强大的端到端深度学习模型，称为“MS1Former”，它可以直接使用原始 MS1 谱图对肝癌肿瘤和相邻非肿瘤（正常）组织进行分类，而无需肽前体鉴定。我们的模型提供了对肿瘤和相邻非肿瘤组织之间 MS1 中细微 m/z 差异的准确区分，以及对数据依赖采集、数据独立采集和全扫描数据的更通用性能预测。我们的模型在多个外部验证数据集上取得了最佳性能。此外，我们还对模型的可解释性进行了详细的探讨。展望未来，我们预计先进的端到端框架将更适用于其他肿瘤的分类。
