{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Nyiro G"],"funding":["National Research, Development and Innovation Fund, Hungary","National Research, Development and Innovation Office","National Academy of Scientist Education Program of the National Biomedical Foundation"],"pagination":["2528"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11275009"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(14)"],"pubmed_abstract":["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."],"journal":["Cancers"],"pubmed_title":["miRNA Expression Profiling in G1 and G2 Pancreatic Neuroendocrine Tumors."],"pmcid":["PMC11275009"],"funding_grant_id":["K146906","TKP2021-EGA-24","K134215"],"pubmed_authors":["Decmann A","Vekony B","Szeredas BK","Zalatnai A","Kovalszky I","Borka K","Igaz P","Herold Z","Dezso K","Nyiro G"],"additional_accession":[]},"is_claimable":false,"name":"miRNA Expression Profiling in G1 and G2 Pancreatic Neuroendocrine Tumors.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-04-08T18:44:35.749Z","creation":"2025-08-27T03:10:59.569Z"},"accession":"S-EPMC11275009","cross_references":{"pubmed":["39061169"],"doi":["10.3390/cancers16142528"]}}