A TP53-based immune prognostic model for muscle-invasive bladder cancer
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Published on: October 30, 2013
Hongyan Li1, Huayi Lu2, Wanli Cui1
1Jilin Key Laboratory of Urologic Oncology, Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, China.
Background:
Muscle-invasive bladder cancer (MIBC) patients are subject to unfavorable treatment options and a high recurrence rate. The status of TP53 mutations played an essential role in the progression and the prognosis of MIBC. The present study proposed to investigate the association between TP53 mutations and immunophenotype in MIBC.
Results:
We established an immune prognostic model (IPM) ground on the immune-associated genes derived from variation analysis between wild-type TP53 and mutated TP53 TCGA-MIBC patients, and validated in another cohort from GEO database. Based on IPM, we divided MIBC patients into low and high risk subgroups. The high risk MIBC patients had higher proportions of macrophages M1, and lower proportions of T cells regulatory (Tregs) and activated dendritic cells than the low risk MIBC patients. Moreover, the expression of immune checkpoints genes (PD1, CTLA4, LAG3, HAVCR2 and TIGIT) was higher in the high risk patients than the low risk patients. In clinical application, IPM exhibited better survival prediction than conventional clinical characteristics.
Conclusions:
Our investigation presented practical prognostic significance for MIBC patients and displayed the overarching landscape of the immune response in the MIBC microenvironment.
Methods:
Data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) analysis between the TP53 mutated and wild-type MIBC patients was conducted. The CIBERSORT algorithm was performed to evaluate the proportion of immune cell types. Gene expression profiles from the TCGA and GEO were used as training and testing cohorts to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors.
