PO.CH02.01 · 化学
基于机器学习的血浆蛋白质组学特征用于胆管癌的诊断和预后
Machine learning-based plasma proteomic signatures for diagnosis and prognosis of cholangiocarcinoma
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：胆管癌（CCA）缺乏可靠的非侵入性生物标志物用于早期诊断和个体化预后评估。循环蛋白质组分析为开发临床可操作的分子特征提供了机遇。方法：跨两个中心共采集320份血浆样本，包括CCA患者、良性胆道疾病患者和健康对照。使用Olink Explore Oncology II panel对蛋白进行定量。采用整合LASSO特征选择和集成分类器的机器学习流程构建诊断和预后模型。在独立测试队列中评估模型的稳健性和判别能力。与bulk RNA测序和单细胞数据集整合，以确定候选蛋白的细胞来源和肿瘤微环境背景。结果：一个由五种蛋白构成的诊断分类器（5-PCM）能够准确区分CCA与非CCA对照，在各队列中AUC达到0.917–0.930，优于传统血清生物标志物。一个由七种蛋白构成的预后模型（7-PPC）对总生存进行了分层，在推导队列和验证队列中的一致性指数分别为0.726和0.853。多组学分析表明，这些诊断和预后蛋白富集于恶性上皮细胞、免疫亚群或基质区室中，支持其在CCA中的生物学相关性。结论：血浆蛋白质组学结合机器学习能够实现CCA准确、非侵入性的诊断和预后分层。这些蛋白特征在胆管癌的早期检测和精准临床管理方面显示出强大的转化潜力。
查看英文原文 English abstract
Background: Cholangiocarcinoma (CCA) lacks reliable non-invasive biomarkers for early diagnosis and individualized prognostic assessment. Circulating proteomic profiling offers an opportunity to develop clinically actionable molecular signatures.Methods: A total of 320 plasma samples were collected across two centers, including patients with CCA, benign biliary disease, and healthy controls. Proteins were quantified using the Olink Explore Oncology II panel. Machine-learning pipelines integrating LASSO feature selection and ensemble classifiers were used to construct diagnostic and prognostic models. Model robustness and discrimination were evaluated in independent test cohorts. Integration with bulk RNA sequencing and single-cell datasets was performed to determine the cellular origins and tumor microenvironment context of candidate proteins.Results: A five-protein diagnostic classifier (5-PCM) accurately distinguished CCA from non-CCA controls, achieving AUCs of 0.917-0.930 across cohorts and outperforming conventional serum biomarkers. A seven-protein prognostic model (7-PPC) stratified overall survival with concordance indices of 0.726 and 0.853 in the derivation and validation cohorts, respectively. Multi-omics analyses demonstrated that these diagnostic and prognostic proteins were enriched in malignant epithelial cells, immune subsets, or stromal compartments, supporting their biological relevance in CCA.Conclusions: Plasma proteomics combined with machine learning enables accurate, non-invasive diagnosis and prognostic stratification of CCA. These protein signatures show strong translational potential for early detection and precision clinical management of cholangiocarcinoma.
利益披露 Disclosure
C. Zheng, None.