OBJECTIVE: Our understanding of the pathophysiology of pancreatic neuroendocrine tumors (PanNETs) remains incomplete, largely due to their historically underestimated incidence and the perception of these tumors as rare and slow-growing cancers. Additionally, conventional reliance on histological examination alone is gradually being supplemented by the exploration and introduction of molecular biomarkers, such as microRNAs (miRNAs). As miRNAs modulate the expression of multiple genes and pathways involved in the tumorigenesis of PanNETs, these biomarkers hold considerable promise for diagnosis and prognosis applications. In this study, we aimed to identify miRNAs as tissue markers associated with the diagnosis of PanNETs.
METHODS: We conducted a case-control study including: 7 PanNETs and 19 nontumoral pancreatic tissues obtained from Romanian patients. The samples underwent miRNA profiling via quantitative RT-PCR to assess the expression of 84 miRNAs. Our results were compared with those obtained by reanalyzing a public dataset. Furthermore, we structured our miRNA expression data according to their targeted mRNAs and their roles in signaling pathways.
RESULTS: Fourteen miRNAs (miR-1, miR-133a-3p, miR-210-3p, miR-7-5p, miR-10a-5p, miR-92b-3p, miR-132-3p, miR-221-3p, miR-29b-3p, miR-107, miR-103a-3p, let-7b-5p, miR-148a-3p, and miR-202-3p) were identified as differentially expressed by comparing PanNETs with pancreatic nontumoral tissues, with six miRNAs (miR-7-5p, miR-92b-3p, miR-29b-3p, miR-107, miR-103a-3p, and miR-148a-3p) also found in the public dataset analyzed. Bioinformatic analysis revealed that the 14 identified miRNAs target 17 genes. Reanalyzing two public gene expression datasets, five of these genes have been found differentially expressed in PanNET compared to controls.
CONCLUSIONS: Our preliminary results, albeit limited by a small sample size, highlighted a specific miRNA expression pattern able to distinguish tumoral from normal pancreatic tissue. The diagnostic performance of these miRNAs, matching with circulating miRNAs and validated in more homogeneous and large cohorts, could represent a starting point for improving the diagnostic accuracy of PanNETs.
