Risk score based on three mRNA expression predicts the survival of bladder cancer

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.

Keywords: bladder cancer; expression; prognosis.

Figure 1. Performance of risk score in the training dataset (TCGA)

The overall survival (A) and recurrence-free survival (B) rate in low-risk group is significantly higher than high-risk group, and the survival details were shown in (C) the three-year survival receiving operating characteristic curve (ROC) plotted and area under curves (AUC) were calculated (D).

Figure 2. The performance of risk score in validation dataset

The overall survival difference of high/low-risk group were shown in GSE13507 and GSE31684 datasets (A, left and right, respectively). Profiles of Recurrence-free survival and overall survival rate of another two totally independent datasets (E-TABM-4321 and GSE40875) were similar (B). Detailed survival information was shown (C left for GSE13507, right for GSE31684, D left for E-TABM-4321 right for GSE40875).

Figure 3. Clinical information and risk score

The clinical significance of clinical information and risk score (A), and association between them (B). The performance of risk score on patients underwent radiation (C) and without radiation (D) was also plotted. A nomogram containing clinical information and risk score was plotted (E).

Figure 4. KEGG pathways associated with risk score

The high-risk score associated pathways were calculated (A) with GSEA, and JAK-STAT signaling pathway (B) and focal adhesion (C) were noted.