| Title: |
Plasma extracellular vesicle messenger RNA profiling identifies prognostic EV signature for non-invasive risk stratification for survival prediction of patients with pancreatic ductal adenocarcinoma |
| Authors: |
Han, Yi; Drobisch, Pascal; Krüger, Alexander; William, Doreen; Grützmann, Konrad; Böthig, Lukas; Polster, Heike; Seifert, Lena; Seifert, Adrian M.; Distler, Marius; Pecqueux, Mathieu; Riediger, Carina; Plodeck, Verens; Nebelung, Heiner; Weber, Georg F.; Pilarsky, Christian; Kahlert, Ulf; Hinz, Ulf; Roth, Susanne; Hackert, Thilo; Weitz, Jürgen; Wong, Fang Cheng; Kahlert, Christoph |
| Publisher Information: |
Biomed Central |
| Publication Year: |
2023 |
| Collection: |
Dresden University of Technology: Qucosa |
| Subject Terms: |
Extracellular vesicles; Pancreatic ductal adenocarcinoma; Prognosis; Plasma extracellular vesicle; mRNA biomarkers; info:eu-repo/classification/ddc/610; ddc:610 |
| Description: |
Background: The prognosis of pancreatic ductal adenocarcinoma (PDAC) is one of the most dismal of all cancers and the median survival of PDAC patients is only 6–8 months after diagnosis. While decades of research effort have been focused on early diagnosis and understanding of molecular mechanisms, few clinically useful markers have been universally applied. To improve the treatment and management of PDAC, it is equally relevant to identify prognostic factors for optimal therapeutic decision-making and patient survival. Compelling evidence have suggested the potential use of extracellular vesicles (EVs) as non-invasive biomarkers for PDAC. The aim of this study was thus to identify non-invasive plasma-based EV biomarkers for the prediction of PDAC patient survival after surgery. Methods: Plasma EVs were isolated from a total of 258 PDAC patients divided into three independent cohorts (discovery, training and validation). RNA sequencing was first employed to identify differentially-expressed EV mRNA candidates from the discovery cohort (n = 65) by DESeq2 tool. The candidates were tested in a training cohort (n = 91) by digital droplet polymerase chain reaction (ddPCR). Cox regression models and Kaplan–Meier analyses were used to build an EV signature which was subsequently validated on a multicenter cohort (n = 83) by ddPCR. Results: Transcriptomic profiling of plasma EVs revealed differentially-expressed mRNAs between long-term and short-term PDAC survivors, which led to 10 of the top-ranked candidate EV mRNAs being tested on an independent training cohort with ddPCR. The results of ddPCR enabled an establishment of a novel prognostic EV mRNA signature consisting of PPP1R12A, SCN7A and SGCD for risk stratification of PDAC patients. Based on the EV mRNA signature, PDAC patients with high risk displayed reduced overall survival (OS) rates compared to those with low risk in the training cohort (p = 0.014), which was successfully validated on another independent cohort (p = 0.024). Interestingly, the combination of ... |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| ISSN: |
1756-8722 |
| Relation: |
https://tud.qucosa.de/id/qucosa%3A97738 |
| Availability: |
https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-977382; https://tud.qucosa.de/id/qucosa%3A97738; https://tud.qucosa.de/api/qucosa%3A97738/attachment/ATT-0/ |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.95B6F736 |
| Database: |
BASE |