There is a strong body of evidence by several translational studies which demonstrate the potential of circulating miRNAs as a potential biomarker in oncology. However, recent reports documented varying stability of these small RNA molecules in serum samples. The aim of our pilot study was to evaluate the stability of miRNAs in serum in relation to food intake and sample storage. Serum miRNA expression levels of 16 different miRNAs from 8 healthy volunteers were quantified by real-time PCR. 4 samples from each donor were analysed—2 samples (fasting, in the morning and after food intake, at noon) were analysed within 24h and 2 samples (fasting and after food intake, at noon) were stored at -80°C for 14 days and subsequently analysed. Student´s t-test was used to determine significant differences. The detectability of the distinct miRNA as a surrogate for the stability of these small RNA molecules was slightly altered by the storage conditions, but only a miRNA 22-3p, out of the analysed 16 miRNAs, shows significant lower dCq expression (3.821 vs. 4.530; p<0,01) by qPCR dependent on storage conditions (-80°C vs. 4°C). However, miRNA levels were not affected by food intake. The difference between samples taken in the morning (fasting) and at noon (after a normal meal) did not show any significant differences. MiRNAs can be considered to be a relatively stable tool in laboratory diagnostics, but clearly every new assay needs thorough evaluation. The stability of miRNAs documented here in healthy volunteers shows their potential in the search for innovative biomarkers in oncology.

Background/Objectives: Endometriosis is a chronic gynecological disorder characterized by ectopic endometrial-like tissue. The symptoms of this disease negatively affect the patient’s quality of life, both physically and mentally. This study aims to identify key factors impacting health-related quality of life in endometriosis patients. Methods: A total of 175 patients from the Endometriosis Centre of the RWTH Aachen University Hospital were assessed using the German version of the EHP-30. The EHP is a patient self-report tool used to measure the wide range of impacts that endometriosis can have on women’s lives (affecting pain levels, leading to feelings of powerlessness and a loss of control, and affecting their emotional well-being, social support, and self-image). Multivariate linear regression and random forest analyses were performed to evaluate predictors of health-related quality of life, focusing on demographic characteristics, pain severity, endometriosis symptoms and planned procedures. Results: Key factors that have a significant negative impact on QoL include higher pain scores, dysuria, and persistent endometriosis. Higher pain scores negatively affect the EHP-30 pain (p < 0.0001), control and powerlessness (p < 0.0001) and emotional well-being (p < 0.01) scores. Dysuria has a negative effect on pain (p < 0.001), control and powerlessness (p < 0.001), emotional well-being (p < 0.05), and social support (p < 0.05). Persistent endometriosis was negatively associated with pain (p < 0.01), control and powerlessness (p < 0.01), and social support. Previous endometriosis surgery has a positive effect on the EHP-30 scores for pain, control and powerlessness, emotional well-being, and self-image (p < 0.05). Conclusions: Our study highlights the multifactorial impact of endometriosis on health-related QoL. Personalized treatments focusing on pain management, emotional support and social interventions are crucial to improve patient outcomes.

Purpose.The objective was to evaluate the usefulness of transperineal ultrasound in the assessment of the urethral length and urethral lumen by 3D/4D transperineal sonography to discriminate between female patients with subtypes of urinary incontinence.Methods.A total of 150 female patients underwent an examination because of urinary incontinence. 41 patients were diagnosed with urgency urinary incontinence (OAB), 67 patients were diagnosed with stress urinary incontinence (SUI), and 42 patients were in the control group (CTRL). Three diameters of the urethral lumen (proximal (U1), medial (U2), and distal (U3)) and the urethral length were measured. By the assessment of the urethral lumen, the presence of the urethral funneling was evaluated.Results.We found a significant difference in the urethral length and urethral lumen U2 of OAB and SUI versus CTRL. The urethral length was significantly greater (P<0.05) and the urethral lumen was significantly wider (P<0.05) in the patients with urinary incontinence. The incidence of the urethral funneling was significantly higher (P<0.05) in the study groups with urinary incontinence than in the control group.Conclusions.Our results have shown the urethral changes obtained by ultrasound in patients with urinary incontinence, but they are still insufficient to distinguish between subtypes of urinary incontinence.

Background: Endometriosis is a chronic gynecological condition marked by ectopic endometrial-like tissue, leading to inflammation, pain, and infertility. Diagnosis is often delayed by up to 10 years. Identifying non-invasive biomarkers could facilitate earlier detection. MicroRNAs, known for their stability in biological fluids and role in disease processes, have emerged as potential diagnostic tools. This pilot study investigated whether serum miRNA profiling can differentiate endometriosis from other causes of chronic pelvic pain. Methods: Serum samples from 52 patients (36 with laparoscopically confirmed endometriosis and 16 controls) treated for chronic pelvic pain at a University Endometriosis Centre were analyzed. High-throughput miRNA sequencing was performed. Feature selection reduced 4285 miRNAs to the 20 most informative MiRNAs. Machine learning models, including logistic regression, decision tree, random forest, and support vector machine, were trained and evaluated. Results: Among the tested machine learning models, support vector machine achieved the best overall performance (accuracy 0.71, precision 0.80), while logistic regression and random forest showed the highest AUC values (0.84 and 0.81, respectively), indicating strong diagnostic potential of serum miRNA profiling. Conclusions: This study demonstrates the feasibility of using serum miRNA profiling combined with machine learning for the non-invasive classification of endometriosis. The identified miRNA signature shows strong diagnostic potential and could contribute to earlier and more accurate detection of the disease.

Ovarian cancer is the second most fatal gynecological cancer. Early detection, which could be achieved through widespread screening, has not yet had an impact on mortality. The aim of our pilot study was to investigate the expression of miRNAs analyzed by a human miRNA microarray chip in urine and serum of patients with ovarian cancer. We analyzed three serum and three urine samples from healthy donors and five serum and five urine samples from patients with ovarian cancer taken at first diagnosis, before any treatment. We selected the seven miRNAs with the highest expression fold change in the microarray chip (cancer vs. control) in urine and serum, for validation by qPCR. We were able to validate two of the seven miRNAs in serum. In contrast to these findings, we were able to validate all of the top seven miRNAs identified in urine using qPCR. The top seven miRNAs in urine identified by microarray chip showed significantly greater differences in expression between patients with ovarian cancer and healthy donors compared to serum. Based on our finding, we can suggest that urine as a biomaterial is more suitable than serum for miRNA profiling by microarray chip in the search for new biomarkers in ovarian cancer.

Background: Endometriosis is a chronic gynecological disease associated with pain, infertility, and delayed diagnosis. Non-invasive biomarkers are urgently needed to facilitate earlier detection and reduce the reliance on diagnostic laparoscopy. MicroRNAs (miRNAs) are stable in body fluids and hold promise as diagnostic tools. Methods: In this pilot study, urine samples from 34 patients with histologically confirmed endometriosis and 18 control patients (laparoscopically confirmed absence of disease) were analyzed using next-generation miRNA sequencing. Differential expression analysis was performed with DESeq2. Feature selection applied variance filtering, univariate analysis, mutual information, and recursive feature elimination (RFE). The top 20 miRNAs were used to train four classification models: logistic regression, decision tree, random forest, and support vector machine (SVM). Model performance was evaluated by accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Results: Among all detected miRNAs, hsa-miR-10400-5p was significantly downregulated in endometriosis compared to controls (log₂ fold change − 2.70; adjusted p = 0.015). RFE identified 20 miRNAs, including hsa-mir-183, hsa-mir-500a, hsa-miR-3184-5p, hsa-miR-151b, and hsa-mir-196a-1, as the most informative for classification. The random forest model achieved the best performance (AUC = 0.91; accuracy and F1-score = 0.81), outperforming logistic regression (AUC = 0.83) and SVM (AUC = 0.81). Several identified miRNAs have been previously implicated in endometriosis pathogenesis, and we additionally identified hsa-miR-10400-5p as a significantly downregulated and previously unreported biomarker candidate, representing a novel finding with potential diagnostic relevance. Conclusions: Urinary miRNA profiling, combined with machine learning, shows promise as a completely non-invasive approach for the diagnosis of endometriosis. The identified miRNA signature, particularly the novel hsa-miR-10400-5p, warrants validation in larger, independent cohorts to confirm its clinical utility and potential to reduce diagnostic delays.