Matyasovska N, Valkova N, Gala M, Bendikova S, Abdulhamed A, Palicka V, Renwick Neil, Čekan Pavol, Paul Evan
MultiplexDX, s.r.o, Comenius University Science Park, Bratislava, Slovakia.
MultiplexDX, Inc, Rockville, MD, USA.
BMC Cancer. 2025 Apr 11;25(1):669. doi: 10.1186/s12885-025-14043-w.
Only a limited number of biomarkers guide personalized management of pancreatic neuroendocrine tumors (PanNETs). Transcriptome profiling of microRNA (miRs) and mRNA has shown value in segregating PanNETs and identifying patients more likely to respond to treatment. Because miRs are key regulators of mRNA expression, we sought to integrate expression data from both RNA species into miR-mRNA interaction networks to advance our understanding of PanNET biology.
We used deep miR/mRNA sequencing on six low-grade/high-risk, well-differentiated PanNETs compared with seven non-diseased tissues to identify differentially expressed miRs/mRNAs. Then we crossed a list of differentially expressed mRNAs with a list of in silico predicted mRNA targets of the most and least abundant miRs to generate high probability miR-mRNA interaction networks.
Gene ontology and pathway analyses revealed several miR-mRNA pairs implicated in cellular processes and pathways suggesting perturbed neuroendocrine function (miR-7 and Reg family genes), cell adhesion (miR-216 family and NLGN1, NCAM1, and CNTN1; miR-670 and the claudins, CLDN1 and CLDN2), and metabolic processes (miR-670 and BCAT1/MPST; miR-129 and CTH).
These novel miR-mRNA interaction networks identified dysregulated pathways not observed when assessing mRNA alone and provide a foundation for further investigation of their utility as diagnostic and predictive biomarkers.
仅有少数生物标志物可指导胰腺神经内分泌肿瘤(PanNETs)的个性化管理。微小RNA(miRs)和信使核糖核酸(mRNA)的转录组分析已显示出在区分PanNETs以及识别更可能对治疗产生反应的患者方面的价值。由于miRs是mRNA表达的关键调节因子,我们试图将来自这两种RNA的表达数据整合到miR-mRNA相互作用网络中,以加深我们对PanNET生物学的理解。
我们对6个低级别/高风险、高分化的PanNETs与7个非病变组织进行了深度miR/mRNA测序,以鉴定差异表达的miRs/mRNAs。然后,我们将差异表达的mRNA列表与计算机预测的最丰富和最不丰富miRs的mRNA靶标列表进行交叉,以生成高概率的miR-mRNA相互作用网络。
基因本体论和通路分析揭示了几对miR-mRNA,它们涉及细胞过程和通路,提示神经内分泌功能紊乱(miR-7与Reg家族基因)、细胞黏附(miR-216家族与NLGN1、NCAM1和CNTN1;miR-670与紧密连接蛋白CLDN1和CLDN2)以及代谢过程(miR-670与BCAT1/MPST;miR-129与CTH)。
这些新的miR-mRNA相互作用网络识别出了单独评估mRNA时未观察到的失调通路,并为进一步研究它们作为诊断和预测生物标志物的效用提供了基础。