Current knowledge on the molecular landscape of pancreatic neuroendocrine tumors (PanNETs) has advanced significantly. Still, the cellular origin of PanNETs is uncertain and the associated mechanisms remain largely unknown. DAXX/ATRX and MEN1 are the three most frequently altered genes that drive PanNETs. They are recognized as a link between genetics and epigenetics. Moreover, the acknowledged impact on DNA methylation by somatic mutations in MEN1 is a valid hallmark of epigenetic mechanism. DAXX/ATRX and MEN1 can be studied at the immunohistochemical level as a reliable surrogate for sequencing. DAXX/ATRX mutations promote alternative lengthening of telomeres (ALT) activation, determined by specific fluorescence in situ hybridization (FISH) analysis. ALT phenotype is considered a significant predictor of worse prognosis and a marker of pancreatic origin. Additionally, ARX/PDX1 expression is linked to important epigenomic alterations and can be used as lineage associated immunohistochemical marker. Herein, ARX/PDX1 association with DAXX/ATRX/MEN1 and ALT can be studied through pathological assessment, as these biomarkers may provide important clues to the mechanism underlying disease pathogenesis. In this review, we present an overview of a new approach to tumor stratification based on genetic and epigenetic characteristics as well as cellular origin, with prognostic consequences.

Neuroendocrine neoplasms (NENs) are a heterogeneous group of rare tumors with different types of physiology and prognosis. Therefore, prognostic information, including morphological differentiation, grade, tumor stage and primary location, are invaluable and contribute to the formulation of treatment decisions. Biomarkers that are currently used, including chromogranin A (CgA), serotonin and neuron‑specific enolase, are singular parameters that cannot be used to accurately predict variables associated with tumor growth, including proliferation, metabolic rate and metastatic potential. In addition, site‑specific biomarkers, such as insulin and gastrin, cannot be applied to all types of NENs. The clinical application of broad‑spectrum markers, as it is the case for CgA, remains controversial despite being widely used. Due to limitations of the currently available mono‑analyte biomarkers, recent studies were conducted to explore novel parameters for NEN diagnosis, prognosis, therapy stratification and evaluation of treatment response. Identification of prognostic factors for predicting NEN outcome is a critical requirement for the planning of adequate clinical management. Advances in ‘liquid’ biopsies and genomic analysis techniques, including microRNA, circulating tumor DNA or circulating tumor cells and sophisticated biomathematical analysis techniques, such as NETest or molecular image‑based biomarkers, are currently under investigation as potentially novel tools for the management of NENs in the future. Despite these recent findings yielding promising observations, further research is necessary. The present review therefore summarizes the existing knowledge and recent advancements in the exploration of biochemical markers for NENs, with focus on gastroenteropancreatic‑neuroendocrine tumors.

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.