MALDI-MSI

• Verfasst von: Gonçalves, Juliana P. L. [VerfasserIn]
• Verfasst von: Bollwein, Christine [VerfasserIn]
• Verfasst von: Schlitter, Anna Melissa [VerfasserIn]
• Verfasst von: Kriegsmann, Mark [VerfasserIn]
• Verfasst von: Jacob, Anne [VerfasserIn]
• Verfasst von: Weichert, Wilko [VerfasserIn]
• Verfasst von: Schwamborn, Kristina [VerfasserIn]
• Titel: MALDI-MSI
• Titelzusatz: a powerful approach to understand primary pancreatic ductal adenocarcinoma and metastases
• Verf.angabe: Juliana Pereira Lopes Gonçalves, Christine Bollwein, Anna Melissa Schlitter, Mark Kriegsmann, Anne Jacob, Wilko Weichert and Kristina Schwamborn
• E-Jahr: 2022
• Jahr: 27 July 2022
• Umfang: 15 S.
• Fussnoten: Gesehen am 06.12.2022
• Titel Quelle: Enthalten in: Molecules
• Ort Quelle: Basel : MDPI, 1996
• Jahr Quelle: 2022
• Band/Heft Quelle: 27(2022), 15, Artikel-ID 4811, Seite 1-15
• ISSN Quelle: 1420-3049
• DOI: doi:10.3390/molecules27154811
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: mass spectrometry imaging
• Sach-SW: metastasis
• Sach-SW: pancreatic ductal adenocarcinoma
• Sach-SW: prognosis
• Sach-SW: proteomics
• Sach-SW: tumor development
• K10plus-PPN: 1824541570

Abstract

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.