miRNA 特征可预测胰腺神经内分泌肿瘤的分级。
An miRNA Signature Predicts Grading of Pancreatic Neuroendocrine Neoplasms.
机构信息
Department of Anatomic Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, U.S.A.
Department of Biomedical Library, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, U.S.A.
出版信息
Cancer Genomics Proteomics. 2023 Mar-Apr;20(2):154-164. doi: 10.21873/cgp.20370.
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.
背景/目的：通过有丝分裂率和 Ki-67 指数评分对胰腺神经内分泌肿瘤 (PNENs) 进行分级比较复杂，存在观察者间的变异性。差异表达的 microRNAs (DEMs) 可用于预测肿瘤进展，并且可能对分级有用。
患者和方法
选择了 12 例 PNEN。4 例患者为 1 级胰腺神经内分泌肿瘤 (PNET)；4 例为 2 级 PNET；4 例为 3 级 PNEN（2 例 PNET 和 2 例胰腺神经内分泌癌）。使用 miRNA NanoString 分析对样本进行了分析。
结果
不同分级的 PNEN 之间存在 6 个统计学上显著的 DEM。miR1285-5p 是唯一在 G1 和 G2 PNET 之间差异表达的 miRNA（p=0.03）。在 G1 PNET 和 G3 PNEN 之间鉴定出 6 个统计学上显著的 DEM（miR135a-5p、miR200a-3p、miR3151-5p、miR-345-5p、miR548d-5p 和 miR9-5p）（p<0.05）。最后，在 G2 PNET 和 G3 PNEN 之间鉴定出 5 个 DEM（miR155-5p、miR15b-5p、miR222-3p、miR548d-5p 和 miR9-5p）（p<0.05）。
结论
鉴定出的 miRNA 候选物与其在其他肿瘤类型中的失调模式一致。这些 DEM 作为 PNEN 分级的鉴别标志物的可靠性支持使用更大的患者群体进行进一步研究。
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