Background

The abnormal expression patterns of genes associated with cell cycle control are essential contributors to the initiation and progression of bladder cancer (BC).

Methods

In this research, transcriptomic profiles sourced from The Cancer Genome Atlas (TCGA) were examined to pinpoint cell cycle-related genes with significant expression differences between early-stage (I–II) and advanced-stage (III–IV) BC. The identified BC samples were then randomly split into training and validation groups. A prognostic model containing nine key genes was subsequently constructed from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression algorithm, and its predictive reliability was independently confirmed in the validation dataset. Based on individual risk scores derived from this gene signature, patients were separated into high- and low-risk groups. Functional enrichment analyses (GO and KEGG), immune cell infiltration (CIBERSORT), and gene set variation analysis (GSVA) were performed to explore the biological functions and immune landscape associated with the risk signature.

Results

Survival analysis revealed substantially worse overall survival (OS) in patients categorized as high-risk. The prognostic efficacy of the constructed gene signature was further corroborated by receiver operating characteristic (ROC) curve analyses. Functional enrichment analyses revealed that these genes were chiefly associated with critical biological processes such as cellular differentiation, proliferation, and migration, involving key signaling pathways like PI3K-Akt signaling, positive modulation of the MAPK cascade, and cell-matrix interactions. Immune infiltration analysis showed that tumors from patients in the high-risk group had increased infiltration by M2 macrophages. Furthermore, GSVA results indicated that stromal and angiogenic functions were enriched in the high-risk group.

Conclusions

These results highlight the potential clinical utility of the newly developed cell cycle-associated gene signature, providing effective biomarkers to facilitate personalized therapeutic decision-making for BC patients.