Prajumwongs Piya, Titapun Attapol, Thanasukarn Vasin, Jareanrat Apiwat, Khuntikeo Natcha, Rattanarak Krit, Namwat Nisana, Klanrit Poramate, Wangwiwatsin Arporn, Chindaprasirt Jarin, Koonmee Supinda, Sa-Ngiamwibool Prakasit, Muangritdech Nattha, Charoenlappanit Sawanya, Jaresitthikunchai Janthima, Roytrakul Sittiruk, Loilome Watcharin
Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand.
Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Sci Rep. 2025 Aug 9;15(1):29169. doi: 10.1038/s41598-025-14451-9.
This study aimed to investigate the potential of peptide mass fingerprints (PMFs) of the serum peptidome using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), in combination with machine learning algorithms-support vector machine (SVM) and random forest (RF)-for the diagnosis and classification of hepato-pancreato-biliary (HPB) cancers. Serum samples collected from healthy individuals and patients with various HPB cancers were analyzed to generate PMF profiles. The resulting data were randomly split into training and testing sets. Feature selection on the training set identified 71 informative peptide mass fingerprints, which were then used to construct predictive models using SVM and RF algorithms. Visualization using heatmap, PLS-DA, and multiclass RF analysis showed clear separation between healthy individuals and HPB cancer patients, as well as among different HPB cancer subtypes. Both models achieved high classification performance, with accuracy, AUROC, and MCC values exceeding 0.90 in both training and testing datasets. Notably, the models also exhibited strong multiclass discrimination ability. These findings demonstrate that serum PMF profiling using MALDI-TOF MS, combined with SVM and RF models, enables high-performance, non-invasive detection and classification of HPB cancers, with strong potential to support early diagnosis and inform clinical decision-making.
本研究旨在探讨使用基质辅助激光解吸/电离飞行时间质谱（MALDI-TOF MS）结合机器学习算法——支持向量机（SVM）和随机森林（RF）——分析血清肽组的肽质量指纹（PMF），用于肝胰胆（HPB）癌诊断和分类的潜力。分析了从健康个体和患有各种HPB癌的患者收集的血清样本，以生成PMF图谱。所得数据被随机分为训练集和测试集。在训练集上进行特征选择，确定了71个信息丰富的肽质量指纹，然后使用SVM和RF算法构建预测模型。使用热图、偏最小二乘判别分析（PLS-DA）和多类RF分析进行可视化，结果显示健康个体与HPB癌患者之间以及不同HPB癌亚型之间有明显区分。两个模型均取得了较高的分类性能，在训练和测试数据集中，准确率、曲线下面积（AUROC）和马修斯相关系数（MCC）值均超过0.90。值得注意的是，这些模型还表现出很强的多类判别能力。这些发现表明，使用MALDI-TOF MS结合SVM和RF模型进行血清PMF分析，能够对HPB癌进行高性能、非侵入性的检测和分类，在支持早期诊断和为临床决策提供依据方面具有强大潜力。
