Abstract
BackgroundBladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited.MethodIn this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan–Meier analysis, receiver operating characteristic curve, and univariate/multivariate Cox analysis were used to evaluate the prognostic model. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.ResultsA total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model’s predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.ConclusionThe proposed 11-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.
Highlights
Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited
A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1)
Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment
Summary
Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited. Bladder cancer is the tenth most common cancer in the world. It is more common in men than in women, and the morbidity and mortality rate in men is four times higher than that in women [1]. A significant risk factor for bladder cancer is smoking, with half of all cases are linked to smoking [2, 3]. About 75% of patients with nonmuscular invasive bladder cancer are treated by radical tumor resection, followed by intravesical instillation of. 25% of patients have muscular invasive or metastatic bladder cancer, and are treated with radical cystectomy and neoadjuvant chemotherapy [4]. Many clinical factors and molecular markers have been identified that can predict prognosis [5], these have low accuracy, and it does not have universal applicability
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