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Bladder urothelial carcinoma (BC) has been identified as one of the most common malignant neoplasm worldwide. High-grade bladder urothelial carcinoma (HGBC) is aggressive with a high risk of recurrence, progression, metastasis, and poor prognosis. Therefore, HGBC clinical management is still a challenge. We performed the present study to seek new urine biomarkers for HGBC and investigate how they promote HGBC progression and thus affect the prognosis based on large-scale sequencing data. We identified the overlapped differentially expressed genes (DEGs) by combining GSE68020 and The Cancer Genome Atlas (TCGA) datasets. Subsequent receiver operating characteristic (ROC) curves, Kaplan-Meier (KM) curves, and Cox regression were conducted to test the diagnostic and prognostic role of the hub genes. Chi-square test and logistic regression were carried out to analyze the associations between clinicopathologic characteristics and the hub genes. Ultimately, we performed gene set enrichment analysis (GSEA), protein-protein interaction (PPI) networks, and Bayesian networks (BNs) to explore the underlying mechanisms by which ECM1, CRYAB, CGNL1, and GPX3 are involved in tumor progression. Immunohistochemistry based on The Human Protein Atlas and quantitative real-time polymerase chain reaction based on urine samples confirmed the downregulation and diagnostic values of the hub genes in HGBC. In conclusion, our study indicated that CRYAB, CGNL1, ECM1, and GPX3 are potential urine biomarkers of HGBC. These four novel urine biomarkers will have attractive applications to provide new diagnostic methods, prognostic predictors and treatment targets for HGBC, which could improve the prognosis of HGBC patients, if validated by further experiments and larger prospective clinical trials.

Gene Expression Profiles Identified Novel Urine Biomarkers for Diagnosis and Prognosis of High-Grade Bladder Urothelial Carcinoma