Peritoneal effluent MicroRNA profile for detection of encapsulating peritoneal sclerosis.

BACKGROUND: Encapsulating peritoneal sclerosis (EPS) is a catastrophic complication of peritoneal dialysis (PD) with high mortality. Our aim is to develop a novel noninvasive microRNA (miRNA) test for EPS. METHODS: We collected 142 PD effluents (EPS: 62 and non-EPS:80). MiRNA profiles of PD effluents were examined by a high-throughput real-time polymerase chain reaction (PCR) array to first screen. Candidate miRNAs were verified by single real-time PCR. The model for EPS prediction was evaluated by multiple logistic regression and machine learning. RESULTS: Seven candidate miRNAs were identified from the screening of PCR-array of 377 miRNAs. The top five area under the curve (AUC) values with 5 miRNA-ratios were selected using 127 samples (EPS: 56 vs non-EPS: 71) to produce a receiver operating characteristic curve. After considering clinical characteristics and 5 miRNA-ratios, the accuracies of the machine learning model of Random Forest and multiple logistic regression were boosted to AUC 0.97 and 0.99, respectively. Furthermore, the pathway analysis of miRNA associated targeting genes and miRNA-compound interaction network revealed that these five miRNAs played the roles in TGF-ß signaling pathway. CONCLUSION: The model-based miRNA expressions in PD effluents may help determine the probability of EPS and provide further therapeutic opinion for EPS.

miRNA as a Modulator of Immunotherapy and Immune Response in Melanoma.

Immune checkpoint inhibitors are a promising therapy for the treatment of cancers, including melanoma, that improved benefit clinical outcomes. However, a subset of melanoma patients do not respond or acquire resistance to immunotherapy, which limits their clinical applicability. Recent studies have explored the reasons related to the resistance of melanoma to immune checkpoint inhibitors. Of note, miRNAs are the regulators of not only cancer progression but also of the response between cancer cells and immune cells. Investigation of miRNA functions within the tumor microenvironment have suggested that miRNAs could be considered as key partners in immunotherapy. Here, we reviewed the known mechanism by which melanoma induces resistance to immunotherapy and the role of miRNAs in immune responses and the microenvironment.

The MicroRNA Prediction Models as Ancillary Diagnosis Biomarkers for Urothelial Carcinoma in Patients With Chronic Kidney Disease.

Urothelial carcinoma is a common urological cancer in chronic kidney disease patients. Cystoscopy and urine cytology are the clinical diagnostic tools for UC. However, cystoscopy is an invasive procedure, while urine cytology showed low sensitivity for low-grade urothelial tumors. High accuracy with non-invasive tools for UC is needed for CKD patients. Our study collected a total of 272 urine and 138 plasma samples to detect the miRNA expression levels for establishing UC signatures from CKD patients. Seventeen candidate miRNAs of biofluids were selected and confirmed by qRT-PCR. Our results showed that urinary miR-1274a and miR-30a-5p expression levels were significantly lower but miR-19a-5p expression levels were higher in UC when compared with CKD. In plasma samples, miR-155-5p, miR-19b-1-5p, miR-378, and miR-636 showed significantly lower expression in UC compared to those with CKD. The Kaplan-Meier curve showed that lower expression of miR-19a, miR-19b, miR-636 and miR-378, and higher expression of miR-708-5p were associated with poor prognosis in patients with bladder cancer. In addition, we produced classifiers for predicting UC by multiple logistic regression. The urine signature was developed with four miRNAs, and the AUC was 0.8211. Eight miRNA expression levels from both urine and plasma samples were examined, and the AUC was 0.8595. Two miRNA classifiers and the nomograms could improve the drawbacks of current UC biomarker screenings for patients with CKD.

Plasma miRNA profile is a biomarker associated with urothelial carcinoma in chronic hemodialysis patients.

The incidence of urothelial carcinoma (UC) is higher in patients undergoing chronic dialysis than in the general population. This study investigated plasma miRNA profiling as the ancillary diagnosis biomarker associated with UC in patients undergoing chronic hemodialysis. We successfully screened out and detected miRNA expression from plasma in eight patients undergoing dialysis through quantitative real-time PCR array analysis and identified eight candidate miRNAs. The candidate miRNAs were then validated using single quantitative RT-PCR assays from 52 plasma samples. The miRNA classifier for ancillary UC detection was developed by multiple logistic regression analyses. Moreover, we validated the classifier by testing another nine samples. Expression levels of miR-150-5p, miR-150-5p/miR-155-5p, miR-378a-3p/miR-150-5p, miR-636/miR-150-5p, miR-150-5p/miR-210-3p, and miR-19b-1-5p/miR-378a-3p were shown to be significantly different between UC and non-UC samples (P = 0.035, 0.0048, 0.016, 0.024, 0.038, and 0.048). Kaplan-Meier curve analysis also showed that low miR-19b-1-5p expression was associated with a worse prognosis (P = 0.0382). We also developed a miRNA classifier based on five miRNA expression levels to predict UC and found that the area under curve was 0.882. The classifier had a sensitivity of 80% (95% confidence interval: 0.5191% to 0.9567%) and a specificity of 83.7% (95% confidence interval: 0.6799% to 0.9381%). This classifier was tested by nine samples with 100% accuracy. The miRNA classifier offers higher sensitivity and specificity than the existing makers. Thus, this approach will improve the prospective diagnosis of UC in patients undergoing chronic hemodialysis.

microRNA expression pattern as an ancillary prognostic signature for radiotherapy.

BACKGROUND: In view of the limited knowledge of plasma biomarkers relating to cancer resistance to radiotherapy, we have set up screening, training and testing stages to investigate the microRNAs (miRNAs) expression profile in plasma to predict between the poor responsive and responsive groups after 6 months of radiotherapy. METHODS: Plasma was collected prior to and after radiotherapy, and the microRNA profiles were analyzed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) arrays. Candidate miRNAs were validated by single qRT-PCR assays from the training and testing set. The classifier for ancillary prognosis was developed by multiple logistic regression analysis to correlate the ratios of miRNAs expression levels with clinical data. RESULTS: We revealed that eight miRNAs expressions had significant changes after radiotherapy and the expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p showed significant differences between the responsive and poor responsive groups in the pre-radiotherapy samples. The Kaplan-Meier curve analysis also showed that low miR-342-5p and miR-519d-3p expressions were associated with worse prognosis. Our results revealed two miRNA classifiers from the pre- and post-radiotherapy samples to predict radiotherapy response with area under curve values of 0.8923 and 0.9405. CONCLUSIONS: The expression levels of miR-374a-5p, miR-342-5p and miR-519d-3p in plasma are associated with radiotherapy responses. Two miRNA classifiers could be developed as a potential non-invasive ancillary tool for predicting patient response to radiotherapy.

DEVELOPMENT, VALIDATION AND DEMONSTRATION OF THE NTHU FLIGHT DOSE CALCULATOR.

Galactic cosmic-ray-induced air showers constitute the largest source of radiation exposure for aircrew and passengers. To facilitate the evaluation of effective doses received by this population, a database containing dose rates of various radiation components in the atmosphere was established; the parameterization accounted for altitude, geomagnetic rigidity and solar modulation. Energy spectra of secondary cosmic rays and effective dose rates were calculated using the FLUKA Monte Carlo code. An auxiliary MATLAB program with a graphical user interface was developed to estimate the aviation route dose through interpolation and integration. The dose calculator was benchmarked by comparing its predictions with data from other assessments. Finally, a set of representative flight routes of substantial interest in Taiwan was selected and evaluated.