Chen Yueying, Nian Fulin, Chen Jia, Jiang Qiuyu, Yuan Tianli, Feng Haokang, Shen Xizhong, Dong Ling
Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Shanghai Institute of Liver Diseases, Shanghai 200032, China.
Biomedicines. 2025 Apr 21;13(4):1000. doi: 10.3390/biomedicines13041000.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with poor early detection rates owing to the limited sensitivity and specificity of the current biomarker CA19-9. Gut microbiota dysbiosis plays a key role in PDAC pathogenesis. This study aimed to evaluate the noninvasive approach we developed, combining metagenome-derived microbial signatures with CA19-9, to improve PDAC detection. This study included 50 treatment-naïve patients with PDAC and their matched controls. Fecal samples were analyzed using shotgun metagenomic sequencing. Machine learning algorithms were used to develop and validate a diagnostic panel integrating microbial signatures and CA19-9 levels. Subgroup analyses were used to confirm the robustness of the microbial markers. The combined models at both species and genus levels effectively distinguished patients with PDAC from healthy individuals, and their strong diagnostic efficacy and accuracy were demonstrated through rigorous validation. In conclusion, the combination of gut microbiome profiling and CA19-9 improves PDAC detection accuracy compared to the use of CA19-9 alone, showing promise for early and noninvasive diagnosis.
胰腺导管腺癌（PDAC）是一种侵袭性很强的恶性肿瘤，由于目前的生物标志物CA19-9的敏感性和特异性有限，其早期检测率较低。肠道微生物群失调在PDAC发病机制中起关键作用。本研究旨在评估我们开发的将宏基因组衍生的微生物特征与CA19-9相结合的非侵入性方法，以改善PDAC的检测。本研究纳入了50例未经治疗的PDAC患者及其匹配的对照组。使用鸟枪法宏基因组测序分析粪便样本。机器学习算法用于开发和验证整合微生物特征和CA19-9水平的诊断面板。亚组分析用于确认微生物标志物的稳健性。在物种和属水平上的联合模型有效地将PDAC患者与健康个体区分开来，并且通过严格验证证明了它们强大的诊断效力和准确性。总之，与单独使用CA19-9相比，肠道微生物群分析与CA19-9的结合提高了PDAC检测的准确性，显示出早期和非侵入性诊断的前景。
