Abstract
The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.
Highlights
Bladder cancer (BCa) is the most common malignancy in urinary system
75% of BCa are diagnosed with non-muscle-invasive BCa (NMIBC).[2]
There are almost 20% NMIBC patients will progress to muscle-invasive BCa (MIBC), which still has a poor outcome after systemic therapy.[3]
Summary
Bladder cancer (BCa) is the most common malignancy in urinary system. In 2017, the number of deaths caused by BCa has reached 16 870 in US.[1]. In the seven datasets of BCa, Weighted gene correlation network analysis (WGCNA) and differentially expressed genes (DEGs) screening was applied to exploring the candidate genes. Based on the candidate genes, the prognostic model was constructed by LASSO cox regression in the discovery dataset of BCa. we validate the performance and accuracy of the prognostic model by several test sets and time-dependent ROC respectively. Univariate and multivariate cox regression analysis were performed and the nomogram was established to validate the clinical utility of the prognostic model. The performance of the model was validated in pan-cancer in TCGA data Taken together, these results show the three gene signatures can be applied as new independent prognostic biomarkers for predicting the survival of BCa and pan-cancer patients. The DEGs were screened by making comparisons between tumour tissues and paracancerous normal tissues. |log2FC| > 1 and FDR < 0.05 were used as the cut-off threshold for datasets GSE13507, GSE40355 and GSE7476; for GSE76211, we chose |log2FC| > 1 and P value
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