ArticleBiology direct2026
Integrative analysis of myeloid cell signatures identifies a prognostic risk model and potential mechanisms in bladder cancer.
Article in Biology direct, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundBladder cancer (BLC) is one of the most common malignancies of the urinary system and represents a major public health burden. Myeloid cells are key components of the tumor microenvironment and play critical roles in tumor progression and therapeutic response; however, their prognostic significance in BLC remains incompletely understood.
methodsThe prognostic value of individual myeloid cell markers was evaluated using immunohistochemistry and survival analyses. A myeloid-based prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The model was validated across multiple independent cohorts. Multiplex immunofluorescence staining and RNA sequencing were performed to investigate immune landscape alterations and signaling pathways associated with different risk groups.
resultsHigh CD68 expression within tumor regions was associated with favorable prognosis, whereas elevated expression of CD14, CD74, CD163, and S100A12 correlated with poor survival outcomes in BLC patients. A myeloid risk score (MRS) was subsequently established and demonstrated robust prognostic performance across validation cohorts. Transcriptomic analysis revealed significant activation of the PI3K–AKT signaling pathway in the MRS-high group. Furthermore, MRS-high tumors exhibited increased expression of PD-L1, FOXP3, and CD11b, along with reduced CD8⁺ T-cell infiltration, indicating a highly immunosuppressive tumor microenvironment. Potential therapeutic targets and candidate agents for MRS-high patients were also identified.
conclusionsWe developed a robust myeloid cell–based prognostic model that effectively stratifies BLC patients by risk and reveals distinct immunosuppressive mechanisms in high-risk tumors. This model may facilitate personalized prognostic assessment and guide precision therapeutic strategies for patients with bladder cancer.
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