Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease.

Gender is an important risk factor in predicting chronic kidney disease (CKD); however, it is under-researched. The purpose of this study was to examine whether gender differences affect the risk factors of early CKD prediction. This study used data from 19,270 adult health screenings, including 5101 with CKD, to screen for 11 independent variables selected as risk factors and to test for the significant effects of statistical Chi-square test variables, using seven machine learning techniques to train the predictive models. Performance indicators included classification accuracy, sensitivity, specificity, and precision. Unbalanced category issues were addressed using three extraction methods: manual sampling, the synthetic minority oversampling technique, and SpreadSubsample. The Chi-square test revealed statistically significant results (p < 0.001) for gender, age, red blood cell count in urine, urine protein (PRO) content, and the PRO-to-urinary creatinine ratio. In terms of classifier prediction performance, the manual extraction method, logistic regression, exhibited the highest average prediction accuracy rate (0.8053) for men, whereas the manual extraction method, linear discriminant analysis, demonstrated the highest average prediction accuracy rate (0.8485) for women. The clinical features of a normal or abnormal PRO-to-urinary creatinine ratio indicated that PRO ratio, age, and urine red blood cell count are the most important risk factors with which to predict CKD in both genders. As a result, this study proposes a prediction model with acceptable prediction accuracy. The model supports doctors in diagnosis and treatment and achieves the goal of early detection and treatment. Based on the evidence-based medicine, machine learning methods are used to develop predictive model in this study. The model has proven to support the prediction of early clinical risk of CKD as much as possible to improve the efficacy and quality of clinical decision making.

LncRNA KCNQ1OT1 sponges miR-15a to promote immune evasion and malignant progression of prostate cancer via up-regulating PD-L1.

BACKGROUND: We focused on the KCNQ1OT1/miR-15a/PD-L1 axis and explored its significance in regulating immune evasion and malignant behaviors of prostate cancer (PC) cells. METHODS: The expression levels of KCNQ1OT1, miR-15a, PD-L1, and CD8 in cells or tissues were examined by RT-qPCR, western blot or immunohistochemistry (IHC) assays. The direct regulations between KCNQ1OT1, miR-15a and PD-L1 were validated by luciferase reporter assay. PC cells were co-cultured with CD8+ T cells to study the immune evasion. Proliferation, apoptosis, migration and invasion abilities were detected by MTT, flow cytometry, wound healing and Transwell assays, respectively. The cytotoxicity of CD8+ T cells was determined by LDH cytotoxicity Kit. Epithelial-mesenchymal transition (EMT) and Ras/ERK signaling markers were evaluated by western blot. RESULTS: KCNQ1OT1, PD-L1 and CD8 were increased, while miR-15a was decreased in PC tissues. MiR-15a directly bound to the 3'-UTR of PD-L1 and inhibited the expression of PD-L1. Overexpressing miR-15a in PC cells was sufficient to promote cytotoxicity and proliferation, while inhibit apoptosis of CD8+ T cells, and also suppressed viability, migration, invasion and EMT while promoted apoptosis of PC cells. The above anti-tumor effects of miR-15a were reversed by overexpressing PD-L1. KCNQ1OT1 sponged miR-15a and released its inhibition on PD-L1. Functionally, KCNQ1OT1 in PC cells was essential for suppressing the cytotoxicity of CD8+ T cells and maintaining multiple malignant phenotypes of PC cells. The Ras/ERK signaling was suppressed after overexpressing miR-15a or knocking down KCNQ1OT1. CONCLUSIONS: LncRNA KCNQ1OT1 sponges miR-15a to promote immune evasion and malignant progression of PC via up-regulating PD-L1.