There is no available blood marker for the preoperative diagnosis of adrenocortical malignancy. The objective of this study was to investigate the expression of extracellular vesicle-associated microRNAs and their diagnostic potential in plasma samples of patients suffering from adrenocortical tumors. Extracellular vesicles were isolated either by using Total Exosome Isolation Kit or by differential centrifugation/ultracentrifugation. Preoperative plasma extracellular vesicle samples of 6 adrenocortical adenomas (ACA) and 6 histologically verified adrenocortical cancer (ACC) were first screened by Taqman Human Microarray A-cards. Based on the results of screening, two miRNAs were selected and validated by targeted quantitative real-time PCR. The validation cohort included 18 ACAs and 16 ACCs. Beside RNA analysis, extracellular vesicle preparations were also assessed by transmission electron microscopy, flow cytometry and dynamic light scattering. Significant overexpression of hsa-miR-101 and hsa-miR-483-5p in ACC relative to ACA samples has been validated. Receiver operator characteristics of data revealed dCT _{hsa-miR-483-5p} normalized to cel-miR-39 to have the highest diagnostic accuracy (area under curve 0.965), the sensitivity and the specifity were 87.5 and 94.44, respectively. Extracellular vesicle-associated hsa-miR-483-5p thus appears to be a promising minimally invasive biomarker in the preoperative diagnosis of ACC but needs further validation in larger cohorts of patients.

Introduction Adrenal myelolipoma (AML) is the second most common, and invariably benign primary adrenal neoplasm. Due to the variable proportion of fat and hematopoietic elements, and its often large size, it can cause differential diagnostic problems. Several reports confirmed the utility of microRNAs (miRNAs) in the diagnosis of tumors, but the miRNA expression in AML has not yet been investigated. Materials and methods Next-generation sequencing (NGS) was performed on 30 formalin-fixed paraffin-embedded (FFPE) archived tissue [AML, adrenocortical adenoma (ACA) and adrenocortical carcinoma (ACC) 10 each] samples. Validation was performed by real-time RT-qPCR on a cohort containing 41 further FFPE samples (15 AML, 14 ACA and 12 ACC). Circulating miRNA counterparts of significantly differentially expressed tissue miRNAs were studied in altogether 33 plasma samples (ACA, ACC, AML 11 each). Results By NGS, 256 significantly differentially expressed miRNAs were discovered, and 8 of these were chosen for validation. Significant overexpression of hsa-miR-451a, hsa-miR-486-5p, hsa-miR-363-3p and hsa-miR-150-5p was confirmed in AML relative to ACA and ACC. Hsa-miR-184, hsa-miR-483-5p and hsa-miR-183-5p were significantly overexpressed in ACC relative to ACA, but not to AML. Circulating hsa-miR-451a and hsa-miR-363-3p were significantly overexpressed in AML, whereas circulating hsa-miR-483-5p and hsa-miR-483-3p were only significantly overexpressed in ACC vs. ACA. Conclusions We have found significantly differentially expressed miRNAs in AML and adrenocortical tumors. Circulating hsa-miR-451a might be a promising minimally invasive biomarker of AML. The lack of significantly different expression of hsa-miR-483-3p and hsa-miR-483-5p between AML and ACC might limit their applicability as diagnostic miRNA markers for ACC.

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 of miR-130b-3p and miR-106b in 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 of miR-30d-5p in 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 combination miR-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.