Clinical nomogram for predicting the survival of patients with cerebral anaplastic gliomas.

The present study aimed to develop an effective nomogram for predicting the overall survival (OS) of patients with cerebral anaplastic glioma (AG).This study included 1939 patients diagnosed with AG between 1973 and 2013 who were identified using the Surveillance, Epidemiology, and End Results database. A multivariate Cox regression analysis revealed that age, histology, tumor site, marital status, radiotherapy, and surgery were independent prognostic factors and, thus, these factors were selected to build a clinical nomogram. Harrell's concordance index (C-index) and a calibration curve were formulated to evaluate the discrimination and calibration of the nomogram using bootstrapping.A nomogram was developed to predict 5- and 9-year OS rates based on 6 independent prognostic factors identified in the training set: age, tumor site, marital status, histology, radiotherapy, and surgery (P < .05). The Harrell's concordance index values of the training and validation sets were 0.776 (0.759-0.793) and 0.766 (0.739-0.792), respectively. The calibration curve exhibited good consistency with the actual observation curve in both sets.Although the prognostic value of the World Health Organization (WHO) classification has been validated, we developed a novel nomogram based on readily available clinical variables in terms of demographic data, therapeutic modalities, and tumor characteristics to predict the survival of AG patients. When used in combination with the WHO classification system, this clinical nomogram can aid clinicians in making individualized predictions of AG patient survival and improving treatment strategies.

Fluid Shear Stress Induces Endothelial Cell Injury via Protein Kinase C Alpha-Mediated Repression of p120-Catenin and Vascular Endothelial Cadherin In Vitro.

OBJECTIVE: The present study aimed to characterize the mechanism of fluid shear stress (FSS)-induced endothelial cell (EC) injury via protein kinase C alpha (PKCα)-mediated vascular endothelial cadherin (VE-cadherin) and p120-catenin (p120ctn) expression. METHODS: We designed a T chamber system that produced stable FSS on ECs in vitro. Human umbilical vein endothelial cells (HUVECs) in which PKCα was knocked down and normal HUVECs were cultured on the coverslips. FSS was impinged on these 2 types of ECs for 0 hours and 6 hours. The morphology and density of HUVECs were evaluated, and expression levels of phosphorylated PKCα, p120-catenin (p120ctn), VE-cadherin, phosphorylated p120ctn at S879 (p-S879p120ctn), and nuclear factor kappa B (NF-κB) were analyzed by Western blot. RESULTS: HUVECs exposed to FSS were characterized by a polygonal shape and decreased cell density. The phosphorylated PKCα level was increased under FSS at 6 hours (P < 0.05). In normal HUVECs during FSS, p120ctn and VE-cadherin were decreased, whereas p-S879p120ctn and NF-κB were increased, at 6 hours (P < 0.05). In HUVECs after PKCα knockdown, p120ctn and VE-cadherin were not significantly changed (P > 0.05), p-S879p120ctn was undetectable, but NF-κB was decreased (P < 0.05) at 6 hours. CONCLUSIONS: The possible mechanism of FSS-induced EC injury may be as follows: 1) PKCα induces low expression of p120ctn, which leads to activation of NF-κB and degradation of VE-cadherin; 2) PKCα-mediated phosphorylation of p120ctn at S879 disrupts p120ctn binding to VE-cadherin.

A prognostic nomogram for distal bile duct cancer from Surveillance, Epidemiology, and End Results (SEER) database based on the STROBE compliant.

In this study, we aimed to develop a reliable nomogram to estimate individualized prognosis for patients with distal bile duct cancer (DBDC) and compare the predictive value with the American Joint Committee on Cancer staging system.Data of 1110 patients diagnosed with DBDC were recruited from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. All patients were randomly divided into the training (n = 777) and validation (n = 333) cohorts, respectively. Multivariate Cox regression was performed to identify the independent risk factors. The Akaike information criterion was used to select covariates for constructing a nomogram. The predictive ability of the nomogram was assessed by concordance index (C-index) and area under receiver operating characteristic curve (AUROC) compared to tumor-node-metastasis (TNM) staging system.A nomogram integrating 8 risk factors was developed with a higher C-index than that of the TNM staging system (training data set, 0.70 vs 0.61; validation data set, 0.71 vs 0.57). The AUROCs of the nomogram for 1-year and 3-year overall survival (OS) predication were 0.76 and 0.78 in the training cohort, 0.78 and 0.77 in the validation cohort. However, AUROCs of the TNM stage for predicting 1-year and 3-year OS were all below 0.60. Calibration curves showed the optimal agreement in predicating OS between nomogram and actual observation. In addition, this nomogram can effectively distinguish the OS between low and high-risk groups divided by the median score (P < .01).Present study was the first one to construct a prognostic nomogram of DBDC patients, which has the potential to provide individual prediction of OS.

A Nomogram for Predicting Individual Prognosis of Patients with Low-Grade Glioma.

OBJECTIVE: The present study aimed to develop and evaluate a nomogram for predicting the overall survival (OS) of patients with low-grade glioma (LGG). METHODS: Patients with LGG diagnosed from 1973 to 2013 were identified using the Surveillance, Epidemiology, and End Results (SEER) database. A total of 3732 patients were randomly divided into a training set (n = 2612) and a validation set (n = 1120). Univariate and multivariate Cox regression analyses of the clinical variables were performed to screen for significant prognostic factors. Next, a nomogram that included significant prognostic variables was formulated to predict for LGG. Harrell's concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram using bootstrapping according to the internal (training set) and external (validation set) validity. RESULTS: A nomogram was developed to predict the 5- and 9-year OS rates using 7 variables in the training set: age, tumor site, sex, marital status, histological type, tumor size, and surgery (P < 0.05). The C-index for internal validation, which the nomogram used to predict OS according to the training set, was 0.777 (range, 0.763-0.791), and the C-index for external validation (validation set) was 0.776 (range, 0.754-0.797). The results of the calibration plots showed that the actual observation and prediction values obtained by the nomogram had good consistency between the 2 sets. CONCLUSIONS: We have developed a ready-to-use nomogram model that includes clinical characteristics to predict OS. The nomogram might provide consultation and risk assessments for subsequent treatment of patients with LGG.