Abstract
Bladder cancer (BLCA) is one of the most malignant cancers worldwide, and its prognosis varies. 1214 BLCA samples in five different datasets and 2 platforms were enrolled in this study. By utilizing the gene expression in The Cancer Genome Atlas (TCGA) dataset, and another two datasets, in GSE13507 and GSE31684, we constructed a risk score staging system with Cox multivariate regression to evaluate predict the outcome of BLCA patients. Three genes consist of RCOR1, ST3GAL5, and COL10A1 were used to predict the survival of BLCA patients. The patients with low risk score have a better survival rate than those with high risk score, significantly. The survival profiles of another two datasets (GSE13507 and GSE31684), which were used for candidate gene selection, were similar as the training dataset (TCGA). Furthermore, survival prediction effect of risk score staging system in another 2 independent datasets, GSE40875 and E-TABM-4321, were also validated. Compared with other clinical observations, and the risk score performs better in evaluating the survival of BLCA patients. Moreover, the correlation between radiation were also evaluated, and we found that patients have a poor survival in high risk group, regardless of radiation. Gene Set Enrichment Analysis was also implemented to find the difference between high-risk and low-risk groups on biological pathways, and focal adhesion and JAK signaling pathway were significantly enriched. In summary, we developed a risk staging model for BLCA patients with three gene expression. The model is independent from and performs better than other clinical information.
Highlights
Bladder cancer (BLCA) is one of the most malignant diseases worldwide, with 73,510 new cases and 14,880 deaths [1] in the United States, 2012
With univariate Cox regression model, genes were used to evaluate the correlation between gene expression and overall survival in three independent datasets (TCGABLCA, GSE13507 and GSE31684)
In order to improve the robustness of the candidate gens, mRNA levels significantly correlated with overall survival in all these three datasets (p
Summary
Bladder cancer (BLCA) is one of the most malignant diseases worldwide, with 73,510 new cases and 14,880 deaths [1] in the United States, 2012. As most frequently used prognostic indicators, clinical observations often fail to predict the survival of bladder cancer patients. The performance of single biomarkers in predicting the survival of BLCA patients across datasets are unstable, while combination of biomarkers enhances the performance [4]. In this vein, we implemented Cox multivariate regression model on gene expression of BLCA samples in TCGA dataset. According to cox multivariate hazard analyses, the risk score performs better than the other clinical information in prognosis of BLCA patients. Gene Set Enrichment Analysis (GSEA) showed that focal adhesion pathway was significantly altered between high and low risk group, suggesting that the risk score reflects the cell adhesion status of BLCA
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