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 ofmiR-130b-3pandmiR-106bin 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 ofmiR-30d-5pin 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 combinationmiR-106b + miR-130b-3p + miR-127-3p + miR-129-5p + miR-30d-5p. 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.
胰腺神经内分泌肿瘤因其发病率上升和预后多变，正成为日益严峻的临床挑战。本研究旨在探讨微小RNA作为区分胰腺神经内分泌肿瘤1级与2级的潜在生物标志物。研究共分析了33例福尔马林固定石蜡包埋样本，包括17例1级和16例2级肿瘤。首先通过实时定量逆转录聚合酶链反应验证文献报道的miRNA，确认miR-130b-3p和miR-106b在2级样本中显著下调。通过新一代测序技术，我们筛选出在1级与2级肿瘤间表达差异最显著的六种miRNA并进行验证。RT-qPCR验证证实miR-30d-5p在2级肿瘤中表达下调。研究构建了多种miRNA组合以区分两种肿瘤分级，其中机器学习算法显示miR-106b + miR-130b-3p + miR-127-3p + miR-129-5p + miR-30d-5p组合具有最佳诊断效能。ROC分析显示该组合灵敏度达83.33%，特异度为87.5%。研究结果凸显了miRNA作为胰腺神经内分泌肿瘤分级生物标志物的潜力，但需进一步研究以提升诊断准确性和临床适用性。
miRNA Expression Profiling in G1 and G2 Pancreatic Neuroendocrine Tumors
肿瘤(癌症)患者之家