Abstract
BACKGROUND: This study aimed to explore the functions and potential mechanisms of PIWI-interacting RNA-related genes (piRPGs) in bladder cancer (BC) development and to identify potential prognostic genes. METHODS: This study used differential analysis and machine learning techniques to identify the differentially expressed piRPGs in BC. Consensus clustering was performed on The Cancer Genome Atlas-Urothelial Bladder Carcinoma dataset, and univariate and multivariate analyses were conducted to construct a BC prognostic model consisting of 3 piRPGs. Kaplan-Meier survival curves were used for survival analysis. Quantitative polymerase chain reaction was performed to validate the expression levels of piRPGs in BC cells and tissues. The functional roles and potential mechanisms of piRPGs in BC were investigated via single-cell sequencing and differentially methylated position sequencing. The DSigDB and CellMiner databases were used to screen for small-molecule drugs associated with piRPGs. RESULTS: This study identified 6 piRPGs that were significantly associated with BC: MAPK13, INHBA, LAMB2, DDX3X, TARBP2, and CDK2. A prognostic model comprising MAPK13, INHBA, and LAMB2 was constructed using consensus clustering technology. Kaplan-Meier curves demonstrated significantly prolonged survival in cluster 2 compared with cluster 1 (p < 0.01), validating the effectiveness of the prognostic model. Single-cell sequencing confirmed that MAPK13 expression was significantly upregulated in bladder tissues (p < 0.001). Methylation site sequencing and methylation-specific polymerase chain reaction revealed significantly decreased methylation levels of INHBA and MAPK13 in BC tissues, which were inversely correlated with their expression levels. CONCLUSIONS: This study effectively developed a 3-gene prognostic signature comprising MAPK13, INHBA, and LAMB2 using consensus clustering and multifactorial logistic regression. In addition, the functional roles and intrinsic mechanisms of piRPGs in bladder carcinogenesis were comprehensively explored using single-cell sequencing, methylation sequencing, and functional enrichment analysis.