Bladder cancer (BC) is one of the most common malignant tumors in the urinary system. The discovery of prognostic biomarkers is still one of the major challenges to improve clinical treatment of BC patients. In order to assist biologists and clinicians in easily evaluating the prognostic potency of genes in BC patients, we developed a user-friendly Online consensus Survival tool for bladder cancer (OSblca), to analyze the prognostic value of genes. The OSblca includes gene expression profiles of 1,075 BC patients and their respective clinical follow-up information. The clinical follow-up data include overall survival (OS), disease specific survival (DSS), disease free interval (DFI), and progression free interval (PFI). To analyze the prognostic value of a gene, users only need to input the official gene symbol and then click the “Kaplan-Meier plot” button, and Kaplan-Meier curve with the hazard ratio, 95% confidence intervals and log-rank P -value are generated and graphically displayed on the website using default options. For advanced analysis, users could limit their analysis by confounding factors including data source, survival type, TNM stage, histological type, smoking history, gender, lymph invasion, and race, which are set up as optional parameters to meet the specific needs of different researchers. To test the performance of the web server, we have tested and validated its reliability using previously reported prognostic biomarkers, including KPNA2, TP53 , and MYC etc ., which had their prognostic values validated as reported in OSblca. In conclusion, OSblca is a useful tool to evaluate and discover novel prognostic biomarkers in BC. The web server can be accessed at http://bioinfo.henu.edu.cn/BLCA/BLCAList.jsp .

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Pancreatic carcinoma (PC) is a type of highly lethal malignant tumor that has unfavorable outcomes. One major challenge in improving clinical outcomes is to identify novel biomarkers for prognosis. In this study, we developed an online consensus survival tool for pancreatic adenocarcinoma (OSpaad), which allows researchers and clinicians to analyze the prognostic value of selected genes in PC. OSpaad contains 1319 unique PC cases that have both gene expression data and correspondent clinical data from seven individual cohorts and provides four survival terms including overall survival, disease‐specific survival, disease‐free interval, progression‐free interval for prognosis evaluation. To meet the different research needs, OSpaad allows users to limit survival analysis in subgroups by selecting different terms of clinical confounding factors such as TNM stage, sex, smoking time, lymph invasion, and race. Moreover, we showed that 97% (116 out of 120) previously reported prognostic biomarkers, including ERBB2, TP53, EGFR and so forth, were validated and confirmed their prognostic significance in OSpaad, demonstrating the well performance of survival analysis in OSpaad. OSpaad is a user‐friendly online tool with a straightforward interface allowing clinicians and basic research scientists with even a limited bioinformatics background to easily screen and evaluate the prognostic value of genes in a large PC cohort. This online tool can be accessed at http://bioinfo.henu.edu.cn/PAAD/PAADList.jsp.

Uveal melanoma (UM) is a rare, aggressive, but the most frequent primary intraocular malignancy in adults, and up to 50% of patients develop a tendency of liver metastases. Great efforts have been made to develop biomarkers that facilitate diagnosis, prediction of the risk, and response to treatment of UM. However, a biologically informative and highly accurate gold standard system for prognostic evaluation of UM remains to be established. To facilitate assessment of the prognosis of UM patients, we established a user-friendly Online consensus Survival tool for uveal melanoma, named OSuvm, by which users can easily estimate the prognostic values of genes of interest by the Kaplan-Meier survival plot with hazard ratio and log-rank test. OSuvm comprises four independent cohorts including 229 patients with both gene expression profiles and relevant clinical follow-up information, and it has shown great performance in evaluating the prognostic roles of previously reported biomarkers. Using OSuvm enables researchers and clinicians to rapidly and conveniently explore the prognostic value of genes of interest and develop new potential molecular biomarkers for UM. OSuvm can be accessed at

The outcome of patients with acute type B aortic dissection (BAAD) is largely dictated by whether or not the case is “complicated.” The purpose of this study was to investigate the risk factors leading to in-hospital death among patients with BAAD and then to develop a predictive model to estimate individual risk of in-hospital death. A total of 188 patients with BAAD were enrolled. Risk factors for in-hospital death were investigated with univariate and multivariable logistic regression analysis. Significant risk factors were used to develop a predictive model. The in-hospital mortality rate was 9% (17 of 188 patients). Univariate analysis revealed 7 risk factors to be statistically significant predictors of in-hospital death ( P < .1). In multivariable analysis, the following variables at admission were independently associated with increased in-hospital mortality: hypotension (odds ratio [OR], 4.85; 95% confidence interval [CI], 1.12–18.90; P = .04), ischemic complications (OR, 8.24; 95% CI, 1.25–33.85; P < .001), renal dysfunction (OR, 12.32; 95% CI, 10.63–76.66; P < .001), and neutrophil percentage ≥80% (OR, 5.76; 95% CI, 2.58–12.56; P = .03). Based on these multivariable results, a reliable and simple prediction model was developed, a total score of 4 offered the best point value. Independent risk factors associated with in-hospital death can be predicted in BAAD patients. The prediction model could be used to identify the prognosis for BAAD patients and assist physicians in their choice of management.

Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of non‐Hodgkin lymphoma (NHL) and is a clinical, pathological, and molecular heterogeneous disease with highly variable clinical outcomes. Currently, valid prognostic biomarkers in DLBCL are still lacking. To optimize targeted therapy and improve the prognosis of DLBCL, the performance of proposed biomarkers needs to be evaluated in multiple cohorts, and new biomarkers need to be investigated in large datasets. Here, we developed a consensus Online Survival analysis web server for Diffuse Large B‐Cell Lymphoma, abbreviated OSdlbcl, to assess the prognostic value of individual gene. To build OSdlbcl, we collected 1100 samples with gene expression profiles and clinical follow‐up information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. In addition, DNA mutation data were also collected from the TCGA database. Overall survival (OS), progression‐free survival (PFS), disease‐specific survival (DSS), disease‐free interval (DFI), and progression‐free interval (PFI) are important endpoints to reflect the survival rate in OSdlbcl. Moreover, clinical features were integrated into OSdlbcl to allow data stratifications according to the user's special needs. By inputting an official gene symbol and selecting desired criteria, the survival analysis results can be graphically presented by the Kaplan‐Meier (KM) plot with hazard ratio (HR) and log‐rank p value. As a proof‐of‐concept demonstration, the prognostic value of 23 previously reported survival associated biomarkers, such as transcription factors FOXP1 and BCL2, was evaluated in OSdlbcl and found to be significantly associated with survival as reported (HR = 1.73, P < .01; HR = 1.47, P = .03, respectively). In conclusion, OSdlbcl is a new web server that integrates public gene expression, gene mutation data, and clinical follow‐up information to provide prognosis evaluations for biomarker development for DLBCL. The OSdlbcl web server is available at https://bioinfo.henu.edu.cn/DLBCL/DLBCLList.jsp.

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