%0 Journal Article %A JAMES SALLER %A DALEY WHITE %A BROOKE HOUGH %A SEAN YODER %A JUNMIN WHITING %A DUNG-TSA CHEN %A ANTHONY MAGLIOCCO %A DOMENICO COPPOLA %T An miRNA Signature Predicts Grading of Pancreatic Neuroendocrine Neoplasms %D 2023 %R 10.21873/cgp.20370 %J Cancer Genomics - Proteomics %P 154-164 %V 20 %N 2 %X Background/Aim: Grading pancreatic neuroendocrine neoplasms (PNENs) via mitotic rate and Ki-67 index score is complicated by interobserver variability. Differentially expressed miRNAs (DEMs) are useful for predicting tumour progression and may be useful for grading. Patients and Methods: Twelve PNENs were selected. Four patients had grade (G) 1 pancreatic neuroendocrine tumours (PNETs); 4 had G2 PNETs; and 4 had G3 PNENs (2 PNETs and 2 pancreatic neuroendocrine carcinomas). Samples were profiled using the miRNA NanoString Assay. Results: There were 6 statistically significant DEMs between different grades of PNENs. MiR1285-5p was the sole miRNA differentially expressed (p=0.03) between G1 and G2 PNETs. Six statistically significant DEMs (miR135a-5p, miR200a-3p, miR3151-5p, miR-345-5p, miR548d-5p and miR9-5p) (p<0.05) were identified between G1 PNETs and G3 PNENs. Finally, 5 DEMs (miR155-5p, miR15b-5p, miR222-3p, miR548d-5p and miR9-5p) (p<0.05) were identified between G2 PNETs and G3 PNENs. Conclusion: The identified miRNA candidates are concordant with their patterns of dysregulation in other tumour types. The reliability of these DEMs as discriminators of PNEN grades support further investigations using larger patient populations. %U https://cgp.iiarjournals.org/content/cgp/20/2/154.full.pdf