Highly accurate diagnosis of pancreatic cancer by integrative modeling using gut microbiome and exposome data

Yuli Zhang; Haohong Zhang; Bingqiang Liu; Kang Ning

doi:10.1016/j.isci.2024.109294

Highly accurate diagnosis of pancreatic cancer by integrative modeling using gut microbiome and exposome data

iScience. 2024 Feb 21;27(3):109294. doi: 10.1016/j.isci.2024.109294. eCollection 2024 Mar 15.

Authors

Yuli Zhang¹, Haohong Zhang², Bingqiang Liu¹, Kang Ning²

Affiliations

¹ School of Mathematics, Shandong University, Jinan 250200, Shandong, China.
² Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

Abstract

The noninvasive detection of pancreatic ductal adenocarcinoma (PDAC) remains an immense challenge. In this study, we proposed a robust, accurate, and noninvasive classifier, namely Multi-Omics Co-training Graph Convolutional Networks (MOCO-GCN). It achieved high accuracy (0.9 $\pm$ 0.06), F1 score (0.9 $\pm$ 0.07), and AUROC (0.89 $\pm$ 0.08), surpassing contemporary approaches. The performance of model was validated on an external cohort of German PDAC patients. Additionally, we discovered that the exposome may impact PDAC development through its complex interplay with gut microbiome by mediation analysis. For example, Fusobacterium hwasookii nucleatum, known for its ability to induce inflammatory responses, may serve as a mediator for the impact of rheumatoid arthritis on PDAC. Overall, our study sheds light on how exposome and microbiome in concert could contribute to PDAC development, and enable PDAC diagnosis with high fidelity and interpretability.

Keywords: Cancer; Environment; Machine learning; Microbiome.