Accurate Discrimination of Benign Biliary Diseases and Cholangiocarcinoma with Serum Multiomics Revealed by High-Throughput Nanoassisted Laser Desorption Ionization Mass Spectrometry

Cholangiocarcinoma (CCA) is an aggressive malignant tumor with a poor prognosis. Carbohydrate antigen 19-9 is an essential biomarker for CCA diagnosis, but its low sensitivity (72%) makes the diagnosis unreliable. To explore potential biomarkers for the diagnosis of CCA, a high-throughput nanoassisted laser desorption ionization mass spectrometry technique was constructed. We performed serum lipidomics and peptidomics analyses from 112 patients with CCA and 123 patients with benign biliary diseases. Lipidomics analysis showed that various lipids, such as glycerophospholipids, glycerides, and sphingolipids, were perturbed. Peptidomics analysis revealed perturbations of multiple proteins involved in the coagulation cascade, lipid transport, and so on. After data mining, 25 characteristic molecules including 20 lipids and 5 peptides were identified as potential diagnostic biomarkers. After screening various machine learning algorithms, artificial neural network was selected to construct a multiomics model for CCA diagnosis with 96.5% sensitivity and 96.4% specificity. The sensitivity and specificity of the model in the independent test cohort were 93.8 and 87.5%, respectively. Furthermore, integrated analysis with transcriptomic data in the cancer genome atlas confirmed that genes altered in CCA significantly affected multiple lipid- and protein-related pathways. Data are available via MetaboLights with the identifier MTBLS6712.