Kim Young-Gon, Kim Sang-Mi, Lee Soo-Youn
Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Department of Laboratory Medicine, Chosun University Hospital, Chosun University School of Medicine, Gwangju, Korea.
Ann Lab Med. 2025 Jul 1;45(4):399-409. doi: 10.3343/alm.2024.0492. Epub 2025 Apr 2.
Pancreatic cancer (PC)-screening methods have limited accuracy despite their high clinical demand. Differential diagnosis of chronic pancreatitis (CP) poses another challenge for PC diagnosis. Therefore, we aimed to identify blood protein biomarkers for PC diagnosis and differential diagnosis of CP using high-throughput multiplex proteomic analysis.
Two independent cohorts (N=88 and 80) were included, and residual serum samples were collected from all individuals (N=168). Each cohort consisted of four groups: healthy (H) individuals and those with CP, stage I/II PC (PC1), or stage III/IV PC (PC2). Protein expression in the first cohort was quantified using the Olink Immuno-Oncology and Oncology 3 proximity extension assay (PEA) panels and was analyzed using machine-learning (ML)-based analyses. Samples in the second cohort were utilized to verify candidate biomarkers in immunoassays.
Both the PEA and immunoassay results confirmed that previously recognized biomarkers, such as the mucin-16 and interleukin-6 proteins, were more highly expressed in the PC (PC1 and PC2) groups than in the non-PC (CP and H) groups. Several novel biomarkers for PC diagnosis were identified via ML-based feature extraction, including C1QA and CDHR2, whereas pro-neuropeptide Y (NPY) appeared to be a promising biomarker for the differential diagnosis of CP. Applying XGBoost classification incorporating the selected features resulted in an area under the curve of 0.92 (0.85-0.98) for differentiating the PC group from the CP and H groups.
Promising blood biomarkers for PC diagnosis and differential diagnosis of CP were identified using a PEA platform and ML techniques.
尽管胰腺癌（PC）筛查方法的临床需求很高，但其准确性有限。慢性胰腺炎（CP）的鉴别诊断对PC诊断构成了另一项挑战。因此，我们旨在通过高通量多重蛋白质组学分析确定用于PC诊断和CP鉴别诊断的血液蛋白质生物标志物。
纳入两个独立队列（n = 88和80），收集所有个体（n = 168）的剩余血清样本。每个队列由四组组成：健康（H）个体以及患有CP、I/II期PC（PC1）或III/IV期PC（PC2）的个体。使用Olink免疫肿瘤学和肿瘤学3邻近延伸分析（PEA）面板对第一个队列中的蛋白质表达进行定量，并使用基于机器学习（ML）的分析方法进行分析。第二个队列中的样本用于在免疫测定中验证候选生物标志物。
PEA和免疫测定结果均证实，先前公认的生物标志物，如粘蛋白-16和白细胞介素-6蛋白，在PC（PC1和PC2）组中的表达高于非PC（CP和H）组。通过基于ML的特征提取确定了几种用于PC诊断的新型生物标志物，包括C1QA和CDHR2，而前神经肽Y（NPY）似乎是CP鉴别诊断的一种有前景的生物标志物。应用纳入所选特征的XGBoost分类法，区分PC组与CP和H组的曲线下面积为0.92（0.85 - 0.98）。
使用PEA平台和ML技术确定了用于PC诊断和CP鉴别诊断的有前景的血液生物标志物。
