The aim of this study was to investigate miRNA profiles of clarified urine supernatant and combined urine vesicle fractions of healthy donors and patients with benign prostatic hyperplasia and prostate cancer (PCa). The comparative analysis of miRNA expression was conducted with a custom miRCURY LNA miRNA qPCR panel. Significant combinations of miRNA pairs were selected by the RandomForest-based feature selection algorithm Boruta; the difference of the medians between the groups and a 95% confidence interval was built using the bootstrap approach. The Asymptotic Wilcoxon-Mann-Whitney Test was performed for miRNA combinations to compare different groups of donors. Benjamini-Hochberg correction was used to adjust the statistical significance for multiple comparisons. The most diagnostically significant miRNAs pairs were miR-107-miR-26b.5p and miR-375.3p-miR-26b.5p in the urine supernatant fraction that discriminated the group of healthy patients and PCa patients, as well as miR-31.5p-miR-16.5p, miR-31.5p-miR-200b, miR-31.5p-miR-30e.3p and miR-31.5p-miR-660.5p in the fraction extracellular vesicles that were different between healthy men and benign prostate hyperplasia patients. Such statistical criteria as the occurrence of individual significant miRNA pairs in the total number of comparisons, median ΔC_t difference, and confidence interval can be useful tools for determining reliable markers of PCa.

Lung cancer (LC) remains a leading cause of global cancer mortality, driving the need for novel timely detection strategies, i.e., stages I–II detection when tumor curation is efficient. Circulating microRNA (miRNAs), with their unique stability in biofluids, offer a powerful approach for non-invasive detection. This review compiles validated miRNAs implicated in the early stages of non-small-cell lung cancer (NSCLC), elucidating their roles in key oncogenic pathways such as epithelial-mesenchymal transition (EMT), PI3K/AKT/mTOR, and JAK-STAT, which regulate proliferation, apoptosis, and metastasis. Furthermore, we critically evaluate developed miRNA panels with a specific focus on advanced quantification and normalization strategies, including exogenous spike-in controls and data-driven methods like pairwise normalization, to enhance diagnostic accuracy. Consequently, we identify and rank the most viable miRNA candidates according to key analytical and clinical metrics, providing a clear roadmap for translating these biomarkers into effective panels for the timely detection of NSCLC.

Aim: Radical prostatectomy (RP) is the most frequent frontline PCa treatment. Biochemical recurrence (BCR) after radical prostatectomy occurs in 20%–40% of patients, but only 30% of these patients dem...

Prostate cancer (PCa) is a common malignancy in men, necessitating accurate diagnosis and monitoring to ensure effective treatment and prevent early relapse. Radiation therapy (RT) is a standard treat...

The stability of miRNAs in biological fluids and their association with pathological conditions make miRNAs promising biomarkers for non-invasive disease diagnostics, including non-small cell lung cancer (NSCLC). However, variability in miRNA expression and technical aspects of quantitative reverse transcription PCR (RT-qPCR) necessitate effective normalization methods to ensure accurate evaluation of miRNA levels and identification of biological differences. In this study, we performed a comparative analysis of several miRNA normalization approaches applied to extracellular vesicles isolated from plasma of NSCLC patients, including pairwise normalization, “Tres” and “Quadro” normalization strategies, normalization to the mean, and normalization considering miRNA functional groups. Method effectiveness was evaluated using quality metrics of diagnostic models. The most robust results were observed with normalization methods utilizing miRNA pairs, triplets, and quadruplets, which provided high accuracy, model stability, and minimal overfitting. In contrast, normalization to the general or exclusive mean and functional group-based normalization showed lower efficiency in terms of classification performance and feature selection stability. These findings highlight the critical importance of normalization strategy choice to improve the accuracy and interpretability of RT-qPCR-based diagnostic models, particularly in the development of biomarker panels for NSCLC diagnostics. Pairwise normalization, combining computational simplicity and high efficiency, appears optimal for practical applications.