Immune Landscape and Prognostic Significance of Gene Expression Profiles in Bladder Cancer: Insights from Immune Cell Infiltration and Risk Modeling
Abstract
To explore the immunological underpinnings and prognostic potential of gene expression profiles in bladder cancer through comprehensive analyses of The Cancer Genome Atlas (TCGA) data.
We used the TCGA data to identify differentially expressed genes (DEGs) and performed enrichment analysis to reveal the related biological pathways. Meanwhile, the least absolute shrinkage and selection operator (LASSO) algorithm was adopted to develop a prognostic model. Then we evaluated the performance of the model in both TCGA and GSE13507 datasets. Furthermore, we conducted a comprehensive investigation on the feature genes utilized in model construction, encompassing both gene expression profiling and survival analysis. Finally, immune infiltration analysis and drug sensitivity analysis were applied to elucidate the immunological basis of the disease and provide potential therapeutic strategies.
We identified a total of 837 DEGs, with a focus on immune-related genes. Using the LASSO algorithm, we developed a prognostic model incorporating seven key genes—NXPH4, FAM110B, GPC2, STXBP6, CYP27B1, GARNL3, and PTGER3—which demonstrated strong predictive accuracy in both TCGA and GSE13507 datasets. Moreover, immune infiltration analysis revealed a higher abundance of M0 and M2 macrophages in high-risk patients, suggesting that macrophage polarization could be a potential therapeutic target to modulate the immune microenvironment. Drug sensitivity analysis further suggested that high-risk patients exhibit differential responses to several chemotherapy agents, with potential therapeutic implications.
This study constructed an effective prognostic model, providing new insights and potential therapeutic targets for the personalized treatment of bladder cancer, which needs further validation.
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| Keywords | ||
| Bladder cancer Immune cell infiltration LASSO Macrophage polarization Prognostic risk model | ||
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