ObjectivesThe aim of this study was to establish comprehensive and practical nomograms, based on significant clinicopathological parameters, for predicting the overall survival (OS) and the disease-specific survival (DSS) of patients with clear cell renal cell carcinoma (ccRCC).Patients and methodsThe data of 35,151 ccRCC patients, diagnosed between 2004 and 2014, were obtained from the database of the Surveillance, Epidemiology, and End Results (SEER) program. The Kaplan–Meier method and Cox proportional hazards regression model were used to evaluate the prognostic effects of multiple clinicopathological variables on survival. Based on Cox models, a nomogram was constructed to predict the probabilities of OS and DSS for an individual patient. The predictive performance of nomograms was evaluated using the concordance index (C-index) and calibration curves.ResultsAccording to univariate and multivariate analyses, age at diagnosis, sex, race, marital status, surgical approach, tumor node metastasis (TNM) stage, and Fuhrman grade significantly correlated with the survival outcomes. These characteristics were used to establish nomograms. The nomograms showed good accuracy in predicting 3-, 5-, and 10-year OS and DSS, with a C-index of 0.79 (95% CI, 0.79–0.80) for OS and 0.87 (95% CI, 0.86–0.88) for DSS. All calibration curves revealed excellent consistency between predicted and actual survival.ConclusionNomograms were developed to predict death from ccRCC treated with nephrectomy. These new prognostic tools could aid in improving the predictive accuracy of survival outcomes, thus leading to reasonable individualized treatment.

Heart failure (HF) is known as the final manifestation of cardiovascular diseases. Although cellular heterogeneity of the heart is well understood, the phenotypic transformation of cardiac cells in progress of HF remains obscure. This study aimed to analyze phenotypic transformation of cardiac cells in HF through human single-cell RNA transcriptome profile. Here, phenotypic transformation of cardiomyocytes (CMs), endothelial cells (ECs), and fibroblasts was identified by data analysis and animal experiments. Abnormal myosin subunits including the decrease in Myosin Heavy Chain 6, Myosin Light Chain 7 and the increase in Myosin Heavy Chain 7 were found in CMs. Two disease phenotypes of ECs named inflammatory ECs and muscularized ECs were identified. In addition, myofibroblast was increased in HF and highly associated with abnormal extracellular matrix. Our study proposed an integrated map of phenotypic transformation of cardiac cells and highlighted the intercellular communication in HF. This detailed definition of cellular transformation will facilitate cell-based mapping of novel interventional targets for the treatment of HF.