BACKGROUND: Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy. OBJECTIVE: To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer. METHODS: Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses. RESULTS: Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction. CONCLUSIONS: This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.
BACKGROUND:Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy. OBJECTIVE: To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer. METHODS: Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses. RESULTS: Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction. CONCLUSIONS: This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.