biostudies-literatureNyiro GNational Research, Development and Innovation Fund, HungaryNational Research, Development and Innovation OfficeNational Academy of Scientist Education Program of the National Biomedical Foundation2528https://www.ebi.ac.uk/biostudies/studies/S-EPMC11275009biostudies-literatureUnknown16(14)Pancreatic neuroendocrine neoplasms pose a growing clinical challenge due to their rising incidence and variable prognosis. The current study aims to investigate microRNAs (miRNA; miR) as potential biomarkers for distinguishing between grade 1 (G1) and grade 2 (G2) pancreatic neuroendocrine tumors (PanNET). A total of 33 formalin-fixed, paraffin-embedded samples were analyzed, comprising 17 G1 and 16 G2 tumors. Initially, literature-based miRNAs were validated via real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR), confirming significant downregulation of <i>miR-130b-3p</i> and <i>miR-106b</i> in G2 samples. Through next-generation sequencing, we have identified and selected the top six miRNAs showing the highest difference between G1 and G2 tumors, which were further validated. RT-qPCR validation confirmed the downregulation of <i>miR-30d-5p</i> in G2 tumors. miRNA combinations were created to distinguish between the two PanNET grades. The highest diagnostic performance in distinguishing between G1 and G2 PanNETs by a machine learning algorithm was achieved when using the combination <i>miR-106b + miR-130b-3p + miR-127-3p + miR-129-5p + miR-30d-5p</i>. The ROC analysis resulted in a sensitivity of 83.33% and a specificity of 87.5%. The findings underscore the potential use of miRNAs as biomarkers for stratifying PanNET grades, though further research is warranted to enhance diagnostic accuracy and clinical utility.CancersmiRNA Expression Profiling in G1 and G2 Pancreatic Neuroendocrine Tumors.PMC11275009K146906TKP2021-EGA-24K134215Decmann AVekony BSzeredas BKZalatnai AKovalszky IBorka KIgaz PHerold ZDezso KNyiro GfalsemiRNA Expression Profiling in G1 and G2 Pancreatic Neuroendocrine Tumors.Pancreatic neuroendocrine neoplasms pose a growing clinical challenge due to their rising incidence and variable prognosis. The current study aims to investigate microRNAs (miRNA; miR) as potential biomarkers for distinguishing between grade 1 (G1) and grade 2 (G2) pancreatic neuroendocrine tumors (PanNET). A total of 33 formalin-fixed, paraffin-embedded samples were analyzed, comprising 17 G1 and 16 G2 tumors. Initially, literature-based miRNAs were validated via real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR), confirming significant downregulation of <i>miR-130b-3p</i> and <i>miR-106b</i> in G2 samples. Through next-generation sequencing, we have identified and selected the top six miRNAs showing the highest difference between G1 and G2 tumors, which were further validated. RT-qPCR validation confirmed the downregulation of <i>miR-30d-5p</i> in G2 tumors. miRNA combinations were created to distinguish between the two PanNET grades. The highest diagnostic performance in distinguishing between G1 and G2 PanNETs by a machine learning algorithm was achieved when using the combination <i>miR-106b + miR-130b-3p + miR-127-3p + miR-129-5p + miR-30d-5p</i>. The ROC analysis resulted in a sensitivity of 83.33% and a specificity of 87.5%. The findings underscore the potential use of miRNAs as biomarkers for stratifying PanNET grades, though further research is warranted to enhance diagnostic accuracy and clinical utility.2024-01-01T00:00:00Z2024 Jul2026-04-08T18:44:35.749Z2025-08-27T03:10:59.569ZS-EPMC112750093906116910.3390/cancers16142528