BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limited availability, and often difficult interpretation pose challenges. This study explores the utility of circulating microRNAs (miRNAs) and machine learning in distinguishing between unilateral and bilateral forms of primary aldosteronism. METHODS: MiRNA profiling was conducted on plasma samples from 18 patients with primary aldosteronism taken during adrenal venous sampling on an Illumina MiSeq platform. Bioinformatics and machine learning identified 9 miRNAs for validation by reverse transcription real-time quantitative polymerase chain reaction. Validation was performed on a cohort consisting of 108 patients with known subdifferentiation. A 30-patient subset of the validation cohort involved both adrenal venous sampling and peripheral, the rest only peripheral samples. A neural network model was used for feature selection and comparison between adrenal venous sampling and peripheral samples, while a deep-learning model was used for classification. RESULTS: Our model identified 10 miRNA combinations achieving >85% accuracy in distinguishing unilateral primary aldosteronism and bilateral adrenal hyperplasia on a 30-sample subset, while also confirming the suitability of peripheral samples for analysis. The best model, involving 6 miRNAs, achieved an area under curve of 87.1%. Deep learning resulted in 100% accuracy on the subset and 90.9% sensitivity and 81.8% specificity on all 108 samples, with an area under curve of 86.7%. CONCLUSIONS: Machine learning analysis of circulating miRNAs offers a minimally invasive alternative for primary aldosteronism lateralization. Early identification of bilateral adrenal hyperplasia could expedite treatment initiation without the need for further localization, benefiting both patients and health care providers.

Background: Recent oncology guidelines recommend BRCA1/2 testing for a wide range of prostate cancer (PCa) patients. In addition, PARP inhibitors are available for mutation-positive metastatic castration-resistant PCa (mCRPC) patients following prior treatment with abiraterone, enzalutamide or docetaxel. However, the question of which of these standard treatments is the most effective for BRCA1/2 positive mCRPC patients remains to be answered. The aim of this meta-analysis was to assess the efficacy of abiraterone, enzalutamide and docetaxel in BRCA1/2 mutation-positive mCRPC patients in terms of PSA-response (PSA50), progression-free survival (PFS) and overall survival (OS). Methods: As no interventional trials are available on this topic, we performed the data synthesis of BRCA1/2 positive mCRPC patients by using both proportional and individual patient data. For PSA50 evaluation, we pooled event rates with 95% confidence intervals (CI), while for time-to-event (PFS, OS) analyses we used individual patient data with random effect Cox regression calculations. Results: Our meta-analysis included 16 eligible studies with 348 BRCA1/2 positive mCRPC patients. In the first treatment line, response rates for abiraterone, enzalutamide and docetaxel were 52% (CI: 25–79%), 64% (CI: 43–80%) and 55% (CI: 36–73%), respectively. Analyses of individual patient data revealed a PFS (HR: 0.47, CI: 0.26–0.83, p = 0.010) but no OS (HR: 1.41, CI: 0.82–2.42, p = 0.210) benefit for enzalutamide compared to abiraterone-treated patients. Conclusions: Our PSA50 analyses revealed that all the three first-line treatments have therapeutic effect in BRCA1/2 positive mCRPC; although, based on the results of PSA50 and PFS analyses, BRCA positive mCRPC patients might better respond to enzalutamide treatment. However, molecular marker-driven interventional studies directly comparing these agents are crucial for providing higher-level evidence.

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.

The differentiation of benign and malignant adrenocortical tumors is of major clinical relevance. Circulating microRNAs (miRNAs) hold promise as blood-borne biomarkers of adrenocortical cancer (ACC). There are, however, many difficulties with their use, including technical and biological standardization challenges. Our aim was to evaluate the interchangeability of quantitative polymerase chain reaction (qPCR) and digital PCR (dPCR) for measuring circulating miRNAs and to investigate whether K2- and K3-EDTA as anticoagulants influence the measurements. Blood samples were drawn simultaneously from 20 participants into K2- and K3-EDTA tubes. Three miRNAs shown to be associated with ACC (miR-483-5p, miR-210-3p, miR-21-5p), together with two controls (miR-16-5p, cel-miR-39-3p), were analyzed using RT-qPCR and dPCR. qPCR and dPCR results showed different correlations in K2- and K3-EDTA samples, with K2 performing better regarding ΔCt values. Moreover, proportional biases related to low or high miRNA expressions between the two methods were observed. In qPCR measurements, K3-EDTA samples showed larger standard deviations, particularly for cel-miR-39. While raw Ct values differed between K2- and K3-EDTA only for miR-483-5p, ΔCt values showed statistically significant differences across all miRNAs except for miR-483-5p. dPCR results were not affected by the choice of anticoagulant. In conclusion, this is the first study demonstrating that dPCR and qPCR results are not easily interchangeable for circulating miRNA, particularly for abundant or rare miRNAs, making cross-validation studies challenging. K2- and K3-EDTA could potentially influence qPCR outcomes, underscoring the need for standardized protocols. A consensus-based methodology could improve reproducibility, enhancing miRNA-based biomarker utility in adrenocortical tumor diagnostics.

Plasma levels of glial cell line-derived neurotrophic factor (GDNF), a pivotal regulator of differentiation and survival of dopaminergic neurons, are reportedly decreased in schizophrenia. To explore the involvement of GDNF in the pathogenesis of the disease, a case–control association analysis was performed between five non-coding single nucleotide polymorphisms (SNP) across the GDNF gene and schizophrenia. Of them, the ‘G’ allele of the rs11111 SNP located in the 3′ untranslated region (3′-UTR) of the gene was found to associate with schizophrenia. In silico analysis revealed that the rs11111 ‘G’ allele might create binding sites for three microRNA (miRNA) species. To explore the significance of this polymorphism, transient co-transfection assays were performed in human embryonic kidney 293T (HEK293T) cells with a luciferase reporter construct harboring either the ‘A’ or ‘G’ allele of the 3′-UTR of GDNF in combination with the hsa-miR-1185-1-3p pre-miRNA. It was demonstrated that in the presence of the rs11111 ‘G’ (but not the ‘A’) allele, hsa-miR-1185-2-3p repressed luciferase activity in a dose-dependent manner. Deletion of the miRNA binding site or its substitution with the complementary sequence abrogated the modulatory effect. Our results imply that the rs11111 ‘G’ allele occurring more frequently in patients with schizophrenia might downregulate GDNF expression in a miRNA-dependent fashion.