Application of data mining techniques to explore predictors of upper urinary tract damage in patients with neurogenic bladder

Prognostic value of plasma microRNAs for non-small cell lung cancer based on data mining models

BACKGROUND

As biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers.

METHODS

A total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients.

RESULTS

We identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively.

CONCLUSION

The results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.

ABT-263 exerts a protective effect on upper urinary tract damage by alleviating neurogenic bladder fibrosis

This study investigated the mechanism of action of ABT-263 in the treatment of neurogenic bladder fibrosis (NBF)and its protective effects against upper urinary tract damage (UUTD). Sixty 12-week-old Sprague-Dawley (SD) rats were randomly divided into sham, sham + ABT-263 (50 mg/kg), NBF, NBF + ABT-263 (25 mg/kg, oral gavage), and NBF + ABT-263 (50 mg/kg, oral gavage) groups. After cystometry, bladder and kidney tissue samples were collected for hematoxylin and eosin (HE), Masson, and Sirius red staining, and Western Blotting (WB) and qPCR detection. Primary rat bladder fibroblasts were isolated, extracted, and cultured. After co-stimulation with TGF-β1 (10 ng/mL) and ABT-263 (concentrations of 0, 0.1, 1, 10, and 100 µmol/L) for 24 h, cells were collected. Cell apoptosis was detected using CCK8, WB, immunofluorescence, and annexin/PI assays. Compared with the sham group, there was no significant difference in any physical parameters in the sham + ABT-263 (50 mg/kg) group. Compared with the NBF group, most of the markers involved in fibrosis were improved in the NBF + ABT-263 (25 mg/kg) and NBF + ABT-263 (50 mg/kg) groups, while the NBF + ABT-263 (50 mg/kg) group showed a significant improvement. When the concentration of ABT-263 was increased to 10 µmol/L, the apoptosis rate of primary bladder fibroblasts increased, and the expression of the anti-apoptotic protein BCL-xL began to decrease.ABT-263 plays an important role in relieving NBF and protecting against UUTD, which may be due to the promotion of myofibroblast apoptosis through the mitochondrial apoptosis pathway.

Diagnostic Value of Plasma MicroRNAs for Lung Cancer Using Support Vector Machine Model

Aim: Small single-stranded non-coding RNAs (miRNAs) play an important role in carcinogenesis through degrading target mRNAs. However, the diagnostic value of miRNAs was not explored in lung cancers. In this study, a support-vector-machine (SVM) model for diagnosis of lung cancer was established based on plasma miRNAs biomarkers, clinical symptoms and epidemiology material.

Methods: The expressions of plasma miRNA were examined with SYBR Green-based quantitative real-time PCR.

Results: We identified that the expressions of 10 plasma miRNAs (miR-21, miR-20a, miR-210, miR-145, miR-126, miR-223, miR-197, miR-30a, miR-30d, miR-25), smoking status, fever, cough, chest pain or tightness, bloody phlegm, haemoptysis, were significantly different between lung cancer and control groups (P<0.05). The accuracies of the combined SVM, miRNAs SVM, symptom SVM, combined Fisher, miRNAs Fisher and symptom Fisher were 96.34%, 80.49%, 84.15%, 84.15%, 75.61%, and 80.49%, respectively; AUC of these six model were 0.976, 0.841, 0.838, 0.865, 0.750, and 0.801, respectively. The accuracy and AUC of combined SVM were higher than the other 5 models (P<0.05).

Conclusions: Our findings indicate that SVM model based on plasma miRNAs biomarkers may serve as a novel, accurate, noninvasive method for auxiliary diagnosis of lung cancer.