The discovery of therapeutic miRNAs is one of the most exciting challenges for pharmaceutical companies. Since the first miRNA was discovered in 1993, our knowledge of miRNA biology has grown considerably. Many studies have demonstrated that miRNA expression is dysregulated in many diseases, making them appealing tools for novel therapeutic approaches. This review aims to discuss miRNA biogenesis and function, as well as highlight strategies for delivering miRNA agents, presenting viral, non-viral, and exosomic delivery as therapeutic approaches for different cancer types. We also consider the therapeutic role of microRNA-mediated drug repurposing in cancer therapy.

Background/Aim: Oncogenic processes are delineated by metabolic dysregulation. Drug likeness is pharmacokinetically tested through the CYP450 enzymatic system, whose genetic aberrations under epigenetic stress could shift male organisms into prostate cancer pathways. Our objective was to predict the susceptibility to prostate neoplasia, focused on benign prostatic hyperplasia (BPH) and prostate cancer (PCa), based on the pharmacoepigenetic and the metabolic profile of Caucasians. Materials and Methods: Two independent cohorts of 47,389 individuals in total were assessed to find risk associations of CYP450 genes with prostatic neoplasia. The metabolic profile of the first cohort was statistically evaluated and frequencies of absorption-distribution-metabolism-excretion-toxicity (ADMET) properties were calculated. Prediction of miRNA pharmacoepigenetic targeting was performed. Results: We found that prostate cancer and benign prostatic hyperplasia patients of the first cohort shared common cardiometabolic trends. Drug classes C08CA, C09AA, C09CA, C10AA, C10AX of the cardiovascular, and G04CA, G04CB of the genitourinary systems, were associated with increased prostate cancer risk, while C03CA and N06AB of the cardiovascular and nervous systems were associated with low-risk for PCa. CYP3A4*1B was the most related pharmacogenetic polymorphism associated with prostate cancer susceptibility. miRNA-200c-3p and miRNA-27b-3p seem to be associated with CYP3A4 targeting and prostate cancer predisposition. Metabolomic analysis indicated that 11β-OHT, 2β-OHT, 15β-OHT, 2α-OHT and 6β-OHT had a high risk, and 16α-OHT, and 16β-OHT had an intermediate disease-risk. Conclusion: These findings constitute a novel integrated signature for prostate cancer susceptibility. Further studies are required to assess their predictive value more fully.

The production of traditional feta cheese of protected designation of origin (feta PDO) involves the use of traditional wooden barrels or large tins. Increasingly, studies have highlighted the beneficial role of wooden dairy equipment in enriching milk microbiota and enhancing acidification processes. The present study aimed to assess the hygiene, processing conditions and microbiological quality of feta PDO produced traditionally. Samples were collected at various stages of ripening. These samples included pasteurised milk, biofilm, feta cheese and brine. The present study focused on the hygiene and safety aspects of the production process, including the microbial populations present on the internal surfaces of the wooden barrels used in cheese production. The analysis confirmed that the barrels met safety criteria, as no major pathogenic microorganisms were detected in compliance with legislative standards. The low microbial content of the cheese samples, particularly the absence of Salmonella spp., Listeria monocytogenes and Staphylococcus aureus, reflected excellent hygienic practices throughout the production process. Factors contributing to this outcome included effective heat treatment, proper refrigeration and rigorous hygiene during maturation and storage. On the whole, these findings suggest that feta PDO cheese production follows a well‑established, standardised process that is fully compliant with current national legislation.

Epilepsy is a global public health problem with increasing prevalence. However, the quality and accuracy of diagnostic models in clinical practice remain limited. In recent years, machine learning (ML) models applied to transcriptomic data have been explored as potential tools for improving diagnostic accuracy. This narrative review aimed to evaluate studies that applied ML algorithms to gene expression data, including both mRNA and miRNA, for the diagnosis of epilepsy. PubMed and Scopus were searched for studies published between 2020 and June 2026. Eligible studies included human epilepsy samples, transcriptomic data was used to train ML classifiers, and only articles reporting AUC were included. A total of 17 studies were included. To provide a clinically meaningful synthesis, studies were grouped according to the biological source of the biomarkers into peripheral blood-based biomarkers and brain tissue-derived biomarkers. Peripheral blood studies investigated whole-blood transcriptomic signatures, peripheral blood mononuclear cells, circulating miRNAs, or extracellular vesicle-derived miRNAs, whereas brain tissue studies mainly analyzed hippocampal or cortical samples. Across studies, commonly used models included random forests, support vector machines, artificial neural networks, logistic regression, and penalized regression methods. Reported diagnostic performance was frequently high, but study designs were heterogeneous, sample sizes were often small, and validation strategies were limited. Studies with external validation generally showed more conservative and likely more realistic performance estimates than those relying on internal validation only. Overall, gene expression-based machine learning models show promising exploratory potential for epilepsy biomarker discovery and diagnostic stratification. However, the current evidence does not yet support clinical implementation because most studies remain limited by small sample sizes, heterogeneous preprocessing strategies, limited independent external validation, and potential risk of overfitting. Future studies should prioritize prospective cohorts, predefined analysis plans, harmonized preprocessing, nested cross-validation, external multicenter testing, and transparent reporting according to TRIPOD-AI/PROBAST-AI principles.

Background/Objectives: Prostate cancer is the most frequent male malignancy. The incidence of disease varies among different ethnic groups. CYP3A polymorphisms are candidates for prostate cancer susceptibility studies. The aim of the present study is to investigate the ethnicity-related clinical impact of CYP3A4 variants on prostate cancer risk. Methods: A systematic literature search and meta-analysis were conducted according to PRISMA guidelines. A total of 10 eligible studies, including 3116 prostate cancer cases and 3008 healthy controls, were analyzed. We evaluated the association between the CYP3A4*1B (rs2740574, −392 A > G) variant and prostate cancer risk in European Caucasians. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using six genetic models. Data were analyzed using fixed and random-effects models based on the I2 value of heterogeneity magnitude. Funnel plots and Egger’s linear regression tests were used to assess publication bias. Results: CYP3A4*1B was associated with prostate cancer susceptibility in the allele (G vs. A: OR = 1.32, CI = 0.91–1.93), dominant (AG + GG vs. AA OR = 1.41, CI = 0.95–2.09), recessive (GG vs. AA + AG, OR = 1.82, CI = 1.26–2.63), homozygous (GG vs. AA, OR = 1.92, CI = 1.32–2.77), heterozygous model (AG vs. AA, OR = 1.31, CI = 0.89–1.93) and co-dominant model (AG vs. AA + GG; OR = 1.27, CI = 0.88–1.85). Significant heterogeneity characterized the allele, as well as the dominant model (I2 = 84.1%, I2 = 80.0%). Egger’s tests (p < 0.05) and funnel plots did not identify publication bias. Conclusions: The present meta-analysis indicates that the G allele and GG genotype might affect prostate cancer susceptibility in European Caucasians; however, the validity and reliability of the results need to be examined in future research.