转录组和单细胞RNA测序结合101种机器学习组合揭示甘露糖代谢在膀胱癌中的作用机制

Abstract

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 immun