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Background  Muscle-invasive bladder cancer (MIBC) is originated in the muscle wall of the bladder, and is the ninth most common malignancy worldwide. However, there are no reliable, accurate and robust gene signatures for MIBC prognosis prediction, which is of the importance in assisting oncologists to make a more accurate evaluation in clinical practice.    Methods  This study used univariable and multivariable Cox regression models to select gene signatures and build risk prediction model, respectively. The t-test and fold change methods were used to perform the differential expression analysis. The hypergeometric test was used to test the enrichment of the differentially expressed genes in GO terms or KEGG pathways.    Results  In the present study, we identified three prognostic genes,  KLK6 ,  TNS1 , and  TRIM56 , as the best subset of genes for muscle-invasive bladder cancer (MIBC) risk prediction. The validation of this stratification method on two datasets demonstrated that the stratified patients exhibited significant difference in overall survival, and our stratification was superior to three other stratifications. Consistently, the high-risk group exhibited worse prognosis than low-risk group in samples with and without lymph node metastasis, distant metastasis, and radiation treatment. Moreover, the upregulated genes in high-risk MIBC were significantly enriched in several cancer-related pathways. Notably,  PDGFRB , a receptor for platelet-derived growth factor of PI3K-Akt signaling pathway, and  TUBA1A  were identified as two targets of multiple drugs. In addition, the angiogenesis-related genes, as well as two marker genes of M2 macrophage,  CD163  and  MRC1 , were highly upregulated in high-risk MIBC.    Conclusions  In summary, this study investigated the underlying molecular mechanism and potential therapeutic targets associated with worse prognosis of high-risk MIBC, which could improve our understanding of progression of MIBC and provide new therapeutic strategies for the MIBC patients.

Systematic analysis of gene expression profiles reveals prognostic stratification and underlying mechanisms for muscle-invasive bladder cancer