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

Background

Bladder cancer is a common malignant type in the world, and over 90% are transitional cell carcinoma. While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer still needs to be understood.

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 identify the differentially expressed IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. The intersection between them was displayed by Venn diagram. 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 in 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 that 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 is 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 that 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 in vitro was markedly upregulated for transitional bladder cancer.

Conclusions

A novel model based on the 10 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.

Highly sensitive and specific novel biomarkers for the diagnosis of transitional bladder carcinoma

Transitional bladder carcinoma (BCa) is prevalent in developed countries, particularly among men. Given that these tumors frequently recur or progress, the early detection and subsequent monitoring of BCa at different stages is critical. Current BCa diagnostic biomarkers are not sufficiently sensitive for substituting or complementing invasive cystoscopy. Here, we sought to identify a robust set of urine biomarkers for BCa detection. Using a high-resolution, mass spectrometry-based, quantitative proteomics approach, we measured, compared and validated protein variations in 451 voided urine samples from healthy subjects, non-bladder cancer patients and patients with non-invasive and invasive BCa. We identified five robust biomarkers: Coronin-1A, Apolipoprotein A4, Semenogelin-2, Gamma synuclein and DJ-1/PARK7. In diagnosing Ta/T1 BCa, these biomarkers achieved an AUC of 0.92 and 0.98, respectively, using ELISA and western blot data (sensitivity, 79.2% and 93.9%; specificity, 100% and 96.7%, respectively). In diagnosing T2/T3 BCa, an AUC of 0.94 and 1.0 was attained (sensitivity, 86.4% and 100%; specificity, 100%) using the same methods. Thus, our multiplex biomarker panel offers unprecedented accuracy for the diagnosis of BCa patients and provides the prospect for a non-invasive way to detect bladder cancer.