Background We aimed to develop and validate a nomogram for predicting the disease-specific survival of Ewing sarcoma (ES) patients. Methods The Surveillance, Epidemiology, and End Results (SEER) program database was used to identify ES from 1990 to 2015, in which the data was extracted from 18 registries in the US. Multivariate analysis performed using Cox proportional hazards regression models was performed on the training set to identify independent prognostic factors and construct a nomogram for the prediction of the 3-, 5-, and 10-year survival rates of patients with ES. The predictive values were compared by using concordance indexes (C-indexes), calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results A total of 2,643 patients were identified. After multivariate Cox regression, a nomogram was established based on a new model containing the predictive variables of age, race, extent of disease, tumor size, and therapy of surgery. The new model provided better C-indexes (0.684 and 0.704 in the training and validation cohorts, respectively) than the model without therapy of surgery (0.661 and 0.668 in the training and validation cohorts, respectively). The good discrimination and calibration of the nomogram were demonstrated for both the training and validation cohorts. NRI and IDI were also improved. Finally, DCA demonstrated that the nomogram was clinically useful. Conclusion We developed a reliable nomogram for determining the prognosis and treatment outcomes of patients with ES in the US. However, the proposed nomogram still requires external data verification in future applications, especially for regions outside the US.

This study aimed to establish a comprehensive prognostic system for osteosarcoma based on a large population database with high quality. The Surveillance, Epidemiology, and End Results (SEER) Program database was used to identify patients with osteosarcoma from 1973 to 2015. Multivariate analysis was performed to screen statistically significant variables. A nomogram was constructed by R software to predict the 3-, 5- and 10-year survival rates. Predictive abilities were compared by C-indexes, calibration plots, integrated discrimination improvement (IDI), net reclassification improvement (NRI), as well as decision curve analysis (DCA). In total, 4505 osteosarcoma patients were identified. They were divided into training (70%, n = 3153) and validating (30%, n = 1352) groups. Multivariate analyses identified independent predictors. Subsequently, the nomogram system of a new model was established, which comprised 7 variables as age, sex, site, decade of diagnosis (DOD), extent of disease (EOD), tumor size and patients undergoing tri-modality therapy (surgery, radiotherapy and chemotherapy). It provided better C-indexes than the model without therapies (0.727, 0.712 vs 0.705, 0.668) in the 2 cohort, respectively. As well, the new model had good performances in the calibration plots. Moreover, both IDI and NRI improved for 3-, 5- and 10-year follow-up of C-indexes. Finally, DCA demonstrated that the nomogram of new model was clinically meaningful. We developed a reliable nomogram for prognostic determinants and treatment outcome analysis of osteosarcoma, thus helping better choose medical examinations and optimize therapeutic regimen under the cooperation among oncologists and surgeons.

BackgroundThe relationship between non-fasting remnant cholesterol and cardiovascular outcome in the era of potent statin therapy remained to be elucidated.MethodsA cohort study of three hundred and twenty eight diabetics diagnosed with new-onset stable coronary artery disease (CAD) by coronary angiography were enrolled. All cases were followed up for an average duration of twelve months. The association between baseline remnant cholesterol levels and major cardiovascular outcomes were evaluated using the receivers operating characteristic (ROC) curves and Cox proportional hazards regression analysis.ResultsDuring a period of 12-month’s follow-up, 14.3% patients (47/328) underwent pre-specified adverse outcomes. The remnant cholesterol associated with high sensitivity C-reactive protein, neutrophil count and fibrinogen (R 2 = 0.20, 0.12 and 0.14; P = 0.000, 0.036 and 0.010 respectively). Area under the ROC curves (AUC) indicated discriminatory power of the remnant cholesterol to predict the adverse outcomes for this population (AUC = 0.64, P < 0.005). Kaplan-Meier curve suggested that the lower levels of remnant cholesterol showed relatively lower cardiac events for diabetic patients with stable CAD (Log rank X 2 = 8.94, P = 0.04). However, according to multivariate Cox proportional hazards regression, apart from hemoglobin A1C (Hazard ratio [H.R.] =1.38, 95% CI: 1.14–1.66, P = 0.001) and Gensini scores (H.R. = 1.00, 95% CI: 1.00–1.02; P = 0.035), remnant cholesterol did not qualify as an independent predictor of adverse prognosis in these settings (H.R. = 1.05, 95% CI: 0.46–2.37, P = 0.909).ConclusionsNon-fasting remnant cholesterol was associated with inflammatory biomarkers and high incidence of revascularization, but not qualified as an independent predictor for short-term prognosis of diabetics with new-onset stable coronary artery disease.

Background Total leukocyte and differential Leukocyte counts are prognostic indictors in patients with coronary artery disease (CAD). However, there is no data available regarding their prognostic utility in very old patients with acute myocardial infarction (AMI). The aim of this study is to investigate the potential role of different leukocyte parameters in predicting the mortality among very old patients with AMI. Methods A total of 523 patients aged over 80 years with AMI were consecutively enrolled into this study. Leukocyte and its subtypes were obtained at admission in each patient. The primary study endpoint was cardiovascular mortality. Patients were followed up for an average of 2.2 years and 153 patients died. The associations of leukocyte parameters with mortality were assessed using Cox regression analyses. The concordance index was calculated to test the model efficiency. Results In multivariable regression analysis, neutrophils-plus-monocytes-to-lymphocytes ratio (NMLR) and neutrophils-to-lymphocytes ratio (NLR) were two most significant predictors of mortality among all the leukocyte parameters (HR = 3.21, 95% CI 1.75–5.35; HR = 2.79, 95% CI 1.59–4.88, respectively, all p < 0.001, adjusted for age, male gender, body mass index, family history of CAD, smoking, hypertension, diabetes mellitus, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, high sensitivity C-reactive protein, creatinine, left ventricular ejection fraction, troponin I, use of statin, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and percutaneous coronary intervention). Furthermore, adding NMLR and NLR into the Cox model increased the C-statistic by 0.038 and 0.037 respectively, which were more significant than that of other leukocyte parameters. Besides, addition of NMLR and NLR to the Canada Acute Coronary Syndrome Risk Score model also increased the C-statistic by 0.079 and 0.077 respectively. Conclusion Our data firstly indicated that most leukocyte subtypes were independent markers for the mortality in very old patients with AMI, while NMLR and NLR appeared to be more effective.

No abstract

Epithelial-mesenchymal transition (EMT) is one of the most important mechanisms in the metastasis of various cancers, including gastric cancer (GC). In this study, we explored the putative significance of miR-644a and its role in EMT-mediated metastasis of GC. We first detected the expression of miR-644a in a cohort of 107 GC tissues using quantitative RT-PCR. The expression of miR-644a was suppressed in GC tissues and was associated with a later clinical stage and tumor metastasis. Restoring the expression of miR-644a could significantly suppress the migration and invasion of HGC-27 and SGC-7901 cells, which might be correlated to its suppressive effect on the EMT process. We also found that carboxyl-terminal-binding protein 1 (CtBP1) was a putative target gene of miR-644a in GC and might be involved in the suppressive effect. Collectively, through targeting CtBP1-mediated suppression of the EMT process, miR-644a might suppress the tumor metastasis of GC cells.

No abstract

No abstract