| Autor: |
Yousra Mohamed Abd-El-Halim, Abdessamad El Kaoutari, Françoise Silvy, Marion Rubis, Martin Bigonnet, Julie Roques, Jérôme Cros, Rémy Nicolle, Juan Iovanna, Nelson Dusetti, Eric Mas |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
EBioMedicine, Vol 71, Iss , Pp 103541- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2352-3964 |
| DOI: |
10.1016/j.ebiom.2021.103541 |
| Popis: |
Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis. Methods: Bioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets. Findings: New PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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