Objective To analyze the effect of m6A regulators on the prognosis of bladder cancer (BC), and establish a prognostic prediction model.

Methods  The high-throughput sequencing data and clinicopathological characteristics from 397 BC tissues were obtained from The Cancer Genome Atlas (TCGA) database. Among the 26 m6A regulators, univariate Cox regression was used to screen for prognostic m6A regulators in BC. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to establish a BC prognosis risk model. The differences in overall survival (OS), and expression of immune checkpoint related gene and targeted therapy related gene between high and low-risk groups were compared. Gene set enrichment analysis was conducted to compare the enrichment of signaling pathways between high and low-risk groups. Single sample gene set enrichment analysis (ssGSEA) and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) methods were used to evaluate the differences in immune cell infiltration levels between high and low-risk groups.

Results  YTHDC1, IGF2BP3, LRPPRC, FTO, and ALKBH3 were independent prognostic factors for BC. The LASSO Cox regression method was used to establish a prognostic risk model for BC based on these 5 m6A regulators. The Kaplan-Meier analysis showed significant differences in OS between high and low-risk groups (P<0.001), with an area under the receiver operating characteristic (ROC) curve of 0.665. The high-risk group was enriched in signaling pathways such as chemokines, NOD-like receptors, purine metabolism, and pyruvate metabolism, and had rich immune cell infiltration characteristics. The expression of PD-L1, CTLA-4, EGFR, and KRAS genes were higher in the high-risk group.

Conclusion  The prognosis model constructed based on m6A regulators in this study has good accuracy, which is helpful for clinical prognosis and stratified individualized treatment.

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