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Τρίτη 19 Απριλίου 2016

Prospective validation of microRNA signatures for detecting pancreatic malignant transformation in endoscopic-ultrasound guided fine-needle aspiration biopsies.

Prospective validation of microRNA signatures for detecting pancreatic malignant transformation in endoscopic-ultrasound guided fine-needle aspiration biopsies.

Oncotarget. 2016 Apr 12;

Authors: Frampton AE, Krell J, Prado MM, Gall TM, Abbassi-Ghadi N, Del Vecchio Blanco G, Funel N, Giovannetti E, Castellano L, Basyouny M, Habib NA, Kaltsidis H, Vlavianos P, Stebbing J, Jiao LR

Abstract
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease. Novel biomarkers are required to aid treatment decisions and improve patient outcomes. MicroRNAs (miRNAs) are potentially ideal diagnostic biomarkers, as they are stable molecules, and tumour and tissue specific.
RESULTS: Logistic regression analysis revealed an endoscopic-ultrasound fine-needle aspiration (EUS-FNA) 2-miRNA classifier (miR-21 + miR-155) capable of distinguishing benign from malignant pancreatic lesions with a sensitivity of 81.5% and a specificity of 85.7% (AUC 0.930). Validation FNA cohorts confirmed both miRNAs were overexpressed in malignant disease, while circulating miRNAs performed poorly.
METHODS: Fifty-five patients with a suspicious pancreatic lesion on cross-sectional imaging were evaluated by EUS-FNA. At echo-endoscopy, the first part of the FNA was sent for cytological assessment and the second part was used for total RNA extraction. Candidate miRNAs were selected after careful review of the literature and expression was quantified by qRT-PCR. Validation was performed on an independent cohort of EUS-FNAs, as well as formalin-fixed paraffin embedded (FFPE) and plasma samples.
CONCLUSIONS: We provide further evidence for using miRNAs as diagnostic biomarkers for pancreatic malignancy. We demonstrate the feasibility of using fresh EUS-FNAs to establish miRNA-based signatures unique to pancreatic malignant transformation and the potential to enhance risk stratification and selection for surgery.

PMID: 27086919 [PubMed - as supplied by publisher]



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