Wang Guangxi, Yao Hantao, Gong Yan, Lu Zipeng, Pang Ruifang, Li Yang, Yuan Yuyao, Song Huajie, Liu Jia, Jin Yan, Ma Yongsu, Yang Yinmo, Nie Honggang, Zhang Guangze, Meng Zhu, Zhou Zhe, Zhao Xuyang, Qiu Mantang, Zhao Zhicheng, Jiang Kuirong, Zeng Qiang, Guo Limei, Yin Yuxin
Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking-Tsinghua Center for Life Sciences, Peking University Health Science Center, Beijing 100191, China.
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Sci Adv. 2021 Dec 24;7(52):eabh2724. doi: 10.1126/sciadv.abh2724. Epub 2021 Dec 22.
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, characterized by rapid progression, metastasis, and difficulty in diagnosis. However, there are no effective liquid-based testing methods available for PDAC detection. Here we introduce a minimally invasive approach that uses machine learning (ML) and lipidomics to detect PDAC. Through greedy algorithm and mass spectrum feature selection, we optimized 17 characteristic metabolites as detection features and developed a liquid chromatography-mass spectrometry-based targeted assay. In this study, 1033 patients with PDAC at various stages were examined. This approach has achieved 86.74% accuracy with an area under curve (AUC) of 0.9351 in the large external validation cohort and 85.00% accuracy with 0.9389 AUC in the prospective clinical cohort. Accordingly, single-cell sequencing, proteomics, and mass spectrometry imaging were applied and revealed notable alterations of selected lipids in PDAC tissues. We propose that the ML-aided lipidomics approach be used for early detection of PDAC.
胰腺导管腺癌（PDAC）是最致命的癌症之一，其特点是进展迅速、易转移且诊断困难。然而，目前尚无有效的基于液体检测的方法用于检测PDAC。在此，我们介绍一种微创方法，该方法利用机器学习（ML）和脂质组学来检测PDAC。通过贪心算法和质谱特征选择，我们优化了17种特征代谢物作为检测特征，并开发了一种基于液相色谱-质谱的靶向检测方法。在本研究中，对1033例不同阶段的PDAC患者进行了检查。在大型外部验证队列中，该方法的准确率达到86.74%，曲线下面积（AUC）为0.9351；在前瞻性临床队列中，准确率为85.00%，AUC为0.9389。因此，应用了单细胞测序、蛋白质组学和质谱成像技术，揭示了PDAC组织中所选脂质的显著变化。我们建议将ML辅助脂质组学方法用于PDAC的早期检测。
