Muscle-invasive bladder carcinoma (MIBC) accounts for 25% of newly diagnosed bladder carcinomas (BCs) and presents a high risk of progression and metastasis. This study aimed to identify reliable biomarkers associated with muscle invasion and prognosis to identify potential therapeutic targets for MIBC. Four gene datasets were downloaded from the Gene Expression Omnibus, and the integrated differentially expressed genes (DEGs) were then subjected to gene ontology (GO) terms and pathway enrichment analyses. Correlation analysis between the expression of the top-ranking DEGs and pathological T stages was performed to identify the genes associated with early muscle invasion. The corresponding prognostic values were evaluated, and co-expressed genes mined in the cBioPortal database were loaded into ClueGo in Cytoscape for pathway enrichment analysis. Using data mining from the STRING and TCGA databases, protein-protein interaction and competitive endogenous RNA networks were constructed. In total, 645 integrated DEGs were identified and these were mainly enriched in 26 pathways, including cell cycle, bladder cancer, DNA replication, and PPAR signaling pathway. S100A7 expression was significantly increased from the T2 stage and showed significantly worse overall survival and disease-specific survival in patients with BC. In total, 144 genes co-expressed with S100A7 in BC were significantly enriched in the IL-17 pathway. S100A7 was predicted to directly interact with LYZ, which potentially shows competitive binding with hsa-mir-140 to affect the expression of six lncRNAs in MIBC. In conclusion, high S100A7 expression was predicted to be associated with early muscle invasion and poor survival in patients with BC.
Keywords: Bioinformatics; Bladder carcinoma; Muscle invasion; Prognosis; S100A7.
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