Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes

• Published in: Frontiers in genetics Vol. 11; p. 12
• Main Authors: Qiu, Huaide, Hu, Xiaorong, He, Chuan, Yu, Binbin, Li, Yongqiang, Li, Jianan
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 05-02-2020
• Subjects: bladder cancer; Genetics; immune related genes; nomogram; prognostic classifier; signature; prognostic classifier; nomogram; immune related genes; signature; bladder cancer
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2020.00012

There has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA).
RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified
Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA.
A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4
T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit.
The present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.