The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730030, Gansu, China.
Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030, Gansu, China.
BMC Med. 2023 Aug 8;21(1):294. doi: 10.1186/s12916-023-02990-9.
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.
胆管癌（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型胆汁标志物用于 CCA 诊断，并建立基于深度学习的诊断模型。
本研究共纳入来自两个独立中心的 644 例受试者（236 例 CCA 和 408 例非 CCA），分为发现、交叉验证和外部验证集。通过三种蛋白质组学数据鉴定候选胆汁标志物，并在 635 例临床体液标本和 121 例组织标本中进行验证。通过深度学习在交叉验证集中建立包含胆汁和血清生物标志物的诊断多分析物模型，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结果表明，胆汁簇蛋白（CLU）在 CCA 体液中显著升高。基于 376 例交叉验证集的受试者，ROC 分析表明胆汁 CLU 具有良好的诊断效能（AUC：0.852，灵敏度：73.6%，特异性：90.1%）。基于胆汁 CLU 和 63 种血清标志物，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，该模型具有较高的诊断效能（AUC：0.947，灵敏度：90.3%，特异性：84.9%）。在独立的 259 例外部验证队列中，该模型对 CCA 的检测也具有相似的准确性。最后，为了便于操作，我们构建了一个在线的易于使用的 CCA 预测平台。
这是一项最大和最全面的结合胆汁和血清生物标志物来区分 CCA 的研究。该诊断模型可能具有用于检测 CCA 的潜力。
