Abu-Khudir Rasha, Hafsa Noor, Badr Badr E
Chemistry Department, College of Science, King Faisal University, P.O. Box 380, Hofuf 31982, Al-Ahsa, Saudi Arabia.
Chemistry Department, Biochemistry Branch, Faculty of Science, Tanta University, Tanta 31527, Egypt.
Diagnostics (Basel). 2023 Sep 29;13(19):3091. doi: 10.3390/diagnostics13193091.
Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
胰腺癌（PC）在所有主要癌症类型中生存率极低。因此，它是全球主要死因之一。血清生物标志物历来与PC术后并发症的早期预后密切相关。然而，目前文献中几乎不存在为PC成功预后识别有效生物标志物组合的尝试。本研究调查了多种血清生物标志物的作用，包括糖类抗原19-9（CA19-9）、趋化因子（C-X-C基序）配体8（CXCL-8）、降钙素原（PCT）以及其他相关临床数据，以确定PC患者中PC进展情况，分为脓毒症、复发和其他术后并发症。使用随机森林驱动的特征消除方法确定了与PC预后最相关的生化和临床标志物。利用这个信息丰富的生物标志物组合，所选的机器学习（ML）分类模型在独立测试数据上对将PC患者分为三个并发症组的分类显示出高度准确的结果。在对PC进展进行分类的任务中，联合生物标志物组合（最大AUC-ROC = 100%）相对于仅使用CA19-9特征（最大AUC-ROC = 75%）的优越性得到了进一步证实。这项新研究证明了联合生物标志物组合在成功诊断埃及PC幸存者中的PC进展和其他相关并发症方面的有效性。
