Transcriptome and single-cell RNA sequencing analysis with 101 machine learning combinations and experimental verification reveals the mechanism of action of mannose metabolism in bladder cancer.

Background: Bladder cancer (BLCA) is a prevalent genitourinary malignancy characterized by high recurrence and mortality rates. While mannose metabolism has demonstrated anti-tumor potential across various cancers, its role in BLCA remains underexplored. This study examines the influence of mannose metabolism on BLCA prognosis. Methods: BLCA-related datasets and genes associated with mannose metabolism (MMRGs) were obtained from public databases. Candidate genes were identified by overlapping differentially expressed genes with MMRGs. Prognostic genes were pinpointed using ten machine learning algorithms and regression analysis to develop a risk model, which was subsequently validated. A nomogram was constructed by integrating the risk score with clinical features, and its predictive accuracy was assessed. We performed functional enrichment, drug sensitivity, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), Western blotting, immunohistochemistry, and immune infiltration analyses. Key cellular components were identified, and further analyses, including pathway enrichment, pseudo-temporal analysis, and cell communication, were performed. Results: CALR, SLMAP, PFKFB4, and TMTC1 were identified as prognostic genes in BLCA. Notably, the expression of SLMAP and TMTC1 was significantly downregulated in BLCA, whereas PFKFB4 and CALR were upregulated. These findings were consistently validated by RT-qPCR, Western blotting, and immunohistochemical analyses (p < 0.05). The risk model stratified patients into a high-risk group (HRG) and a low-risk group (LRG), with HRG patients exhibiting significantly poorer survival outcomes. The risk score was identified as an independent prognostic factor, and the nomogram demonstrated high diagnostic accuracy. Notable differences between HRG and LRG patients were observed in the "Ribosome" pathway. Additionally, 86 chemotherapeutic drugs exhibited significant differential responses between HRG and LRG, with 23 immune cell types showing differential abundances, including activated dendritic cells (p < 0.05). Single-cell analysis revealed macrophages as key cells in BLCA, which were classified into five subtypes, with CALR, SLMAP, and PFKFB4 influencing their expression. Conclusion: Four mannose metabolism-related prognostic genes were identified in BLCA, and macrophages were confirmed as critical cells. These findings provide valuable insights for improving prognostic assessment in BLCA.