Native glycan fragments detected by MALDI mass spectrometry imaging are independent prognostic factors in pancreatic ductal adenocarcinoma

作者：Na Sun, Marija Trajkovic‐Arsic, Fengxia Li, Yin Wu, Corinna Münch, Thomas Kunzke, Annette Feuchtinger, Katja Steiger, Anna Melissa Schlitter, Wilko Weichert, Iréne Esposito, Jens T. Siveke, Axel Walch · 发表于：EJNMMI Research · 年份：2021 · DOI：10.1186/s13550-021-00862-y · 被引用次数：11 · 研究领域：Glycosylation and Glycoproteins Research、Mass Spectrometry Techniques and Applications、Advanced Proteomics Techniques and Applications

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies to date. The impressively developed stroma that surrounds and modulates the behavior of cancer cells is one of the main factors regulating the PDAC growth, metastasis and therapy resistance. Here, we postulate that stromal and cancer cell compartments differentiate in protein/lipid glycosylation patterns and analyze differences in glycan fragments in those compartments with clinicopathologic correlates. RESULTS: We analyzed native glycan fragments in 109 human FFPE PDAC samples using high mass resolution matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometric imaging (MALDI-FT-ICR-MSI). Our method allows detection of native glycan fragments without previous digestion with PNGase or any other biochemical reaction. With this method, 8 and 18 native glycans were identified as uniquely expressed in only stromal or only cancer cell compartment, respectively. Kaplan-Meier survival model identified glycan fragments that are expressed in cancer cell or stromal compartment and significantly associated with patient outcome. Among cancer cell region-specific glycans, 10 predicted better and 6 worse patient survival. In the stroma, 1 glycan predicted good and 4 poor patient survival. Using factor analysis as a dimension reduction method, we were able to group the identified glycans in 2 factors. Multivariate analysis revealed that these factors ca...