Risk score based on three mRNA expression predicts the survival of bladder cancer
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Qingzuo Liu1,*, Ruigang Diao1,*, Guoyan Feng1, Xiaodong Mu1 and Aiqun Li2
1Yantai Yuhuangding Hospital, Zhifu District, Yantai 264000, China
2Yantai Affiliated Hospital of Binzhou Medical University, Muping District, Yantai 264003, China
*These authors have contributed equally to this work
Xiaodong Mu, email: firstname.lastname@example.org
Aiqun Li, email: email@example.com
Keywords: bladder cancer, prognosis, expression
Received: February 27, 2017 Accepted: May 23, 2017 Published: June 27, 2017
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.
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