AUTHOR=Xie Zhiwen , Cai Jinming , Sun Wenlan , Hua Shan , Wang Xingjie , Li Anguo , Jiang Juntao 

TITLE=Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.740985

DOI=10.3389/fonc.2021.740985

ISSN=2234-943X

ABSTRACT=Background: While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer is still need to understand.
Methods: In this study, IRAGs were download from Molecular Signature Database (MSigDB). The transcriptional expression and matched clinicopathological data were separately obtained from public databases. The TCGA- BLCA cohort was used to identified the differentially expressed-IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. Based on Least absolute shrinkage and selection operator (LASSO) regression analysis method, the TCGA-BLCA cohort was used to construct a risk signature. Survival analysis was conducted to calculate the overall survival (OS) in TCGA and GSE13507 cohort between two groups. We then conducted univariate and multivariate survival analyses to identify independently significant indicators for prognosis. Relationships between the risk scores and age, grade, stage, immune cell infiltration, immune function, and drug sensitivity were demonstrated by correlation analysis. The expression level of prognostic genes in vivo and vitro were determined by qRT-PCR assay.
Results: Comparing with normal tissues, there were 49 differentially expressed IRAGs in cancer tissues and 12 of them were markedly related to the prognosis in TCGA cohort for transitional bladder cancer patients. Based on LASSO regression analysis, a risk model consists of 10 IRAGs was established. Comparing with high-risk groups, survival analysis showed patients in low-risk groups were more likely to have a better survival time in TCGA and GSE13507 cohorts. Besides, the accuracy of the model in predicting prognosis is acceptable, which demonstrated by receiver operating characteristic curve (ROC) analysis. Age, stage and risk scores variables were identified as the independently significant indicators for survival in transitional bladder cancer. Correlation analysis represented the risk score was identified to be significantly related to the above variables except gender variable. Moreover, the expression level of prognostic genes in vivo and vitro were markedly upregulated for transitional bladder cancer.
Conclusions: A novel model based on the ten IRAGs that can be used to predict survival time for transitional bladder cancer. In addition, this study may provide treatment strategies according to the drug sensitivity in the future.