通过深度学习，发现了一种新的胆汁标志物簇素及基于此的胆管癌公共在线预测平台。
Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma.
发表日期：2023 Aug 08
作者：
Long Gao, Yanyan Lin, Ping Yue, Shuyan Li, Yong Zhang, Ningning Mi, Mingzhen Bai, Wenkang Fu, Zhili Xia, Ningzu Jiang, Jie Cao, Man Yang, Yanni Ma, Fanxiang Zhang, Chao Zhang, Joseph W Leung, Shun He, Jinqiu Yuan, Wenbo Meng, Xun Li
来源：
BMC Medicine
摘要：
胆管细胞癌（CCA）是一种高度侵袭性的恶性肿瘤，其诊断仍然是一个挑战。本研究旨在基于蛋白质组学鉴定一种新的胆汁标记物用于CCA诊断，并利用深度学习建立诊断模型。来自两个独立中心的共644名受试者（236例CCA和408例非CCA）被分为发现、交叉验证和外部验证组进行研究。候选的胆汁标记物经过三个蛋白质组学数据的验证，并在635例临床体液标本和121例组织标本上进行验证。利用深度学习在交叉验证组中建立了一个含有胆汁和血清生物标志物的诊断多分析模型，并在独立的外部队列中进行验证。蛋白质组学分析和临床标本验证的结果表明，胆汁簇素（CLU）在CCA体液中显著升高。基于交叉验证组中的376名受试者，ROC分析显示胆汁CLU具有令人满意的诊断能力（AUC：0.852，灵敏度：73.6％，特异度：90.1％）。在胆汁CLU和63个血清标志物的基础上，深度学习建立了一个包含七个因素（CLU，CA19-9，IBIL，GGT，LDL-C，TG和TBA）的诊断模型，显示出较高的诊断效能（AUC：0.947，灵敏度：90.3％，特异度：84.9％）。在一个独立队列（n = 259）中进行的外部验证结果显示了相似的CCA检测准确性。最后，为了方便操作，建立了一个用户友好的线上预测平台用于CCA。这是迄今最大、最全面的将胆汁和血清生物标志物结合起来区分CCA的研究。该诊断模型可能有潜力用于检测CCA。 © 2023 BioMed Central Ltd., part of Springer Nature.
Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning.A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort.The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power (AUC: 0.852, sensitivity: 73.6%, specificity: 90.1%). Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors (CLU, CA19-9, IBIL, GGT, LDL-C, TG, and TBA), which showed a high diagnostic utility (AUC: 0.947, sensitivity: 90.3%, specificity: 84.9%). External validation in an independent cohort (n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA.This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA.© 2023. BioMed Central Ltd., part of Springer Nature.