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.
2. Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder Cancer Incidence and Mortality: A Global Overview and Recent Trends. Eur Urol. 2017;71(1):96-108.
3. Kamat AM, Hahn NM, Efstathiou JA, Lerner SP, Malmstrom PU, Choi W, et al. Bladder cancer. Lancet. 2016;388(10061):2796-810.
4. Babjuk M, Burger M, Comperat EM, Gontero P, Mostafid AH, Palou J, et al. European Association of Urology Guidelines on Non-muscle-invasive Bladder Cancer (TaT1 and Carcinoma In Situ) - 2019 Update. Eur Urol. 2019;76(5):639-57.
5. Powles T, Duran I, van der Heijden MS, Loriot Y, Vogelzang NJ, De Giorgi U, et al. Atezolizumab versus chemotherapy in patients with platinum-treated locally advanced or metastatic urothelial carcinoma (IMvigor211): a multicentre, open-label, phase 3 randomised controlled trial. Lancet. 2018;391(10122):748-57.
6. Hemingway H. Prognosis research: why is Dr. Lydgate still waiting? J Clin Epidemiol. 2006;59(12):1229-38.
7. Mallett S, Royston P, Waters R, Dutton S, Altman DG. Reporting performance of prognostic models in cancer: a review. Bmc Med. 2010;8:21.
8. Harrell FE: Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis., Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis, 2015.
9. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16(4):e173-80.
10. Ma Y, Sun Y, Guo H, Yang R. Tumor-associated macrophages in bladder cancer: roles and targeted therapeutic strategies. Front Immunol. 2024;15:1418131. Published 2024 Nov 13.
11. Rubio C, Munera-Maravilla E, Lodewijk I, et al. Macrophage polarization as a novel weapon in conditioning tumor microenvironment for bladder cancer: can we turn demons into gods? Clin Transl Oncol. 2019;21(4):391-403.
12. Love M, Anders S, Huber W. Differential analysis of count data–the deseq2 package. 2014;
13. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284-7.
14. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol Biol. 2018;1711:243-59.
15. Ye F, Wang L, Castillo-Martin M, McBride R, Galsky MD, Zhu J, et al. Biomarkers for bladder cancer management: present and future. Am J Clin Exp Urol. 2014;2(1):1-14.
16. Santoni G, Morelli MB, Amantini C, Battelli N. Urinary Markers in Bladder Cancer: An Update. Front Oncol. 2018;8:362.
17. Masson-Lecomte A, Rava M, Real FX, Hartmann A, Allory Y, Malats N. Inflammatory biomarkers and bladder cancer prognosis: a systematic review. Eur Urol. 2014;66(6):1078-91.
18. Mitra AP, Datar RH, Cote RJ. Molecular pathways in invasive bladder cancer: new insights into mechanisms, progression, and target identification. J Clin Oncol. 2006;24(35):5552-64.
19. Mitra AP, Hansel DE, Cote RJ. Prognostic value of cell-cycle regulation biomarkers in bladder cancer. Semin Oncol. 2012;39(5):524-33.
20. Shangary S, Wang S. Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy. Annu Rev Pharmacol. 2009;49(12):223-41.
21. Fassl A, Geng Y, Sicinski P. CDK4 and CDK6 kinases: From basic science to cancer therapy. Science. 2022;375(6577):eabc1495.
22. Piazza GA, Ward A, Chen X, Maxuitenko Y, Coley A, Aboelella NS, et al. PDE5 and PDE10 inhibition activates cGMP/PKG signaling to block Wnt/β-catenin transcription, cancer cell growth, and tumor immunity. Drug Discov Today. 2020;25(8):1521-1527.
23. Sun X, Xin S, Jin L, Zhang Y, Ye L. Neurexophilin 4 is a prognostic biomarker correlated with immune infiltration in bladder cancer. Bioengineered. 2022;13(5):13986-99.
24. Tang Q, Chen YM, Shen MM, Dai W, Liang H, Liu JN, et al. Increased Expression of NXPH4 Correlates with Immune Cell Infiltration and Unfavorable Prognosis in Hepatocellular Carcinoma. J Oncol. 2022;2022:5005747.
25. Sun Z, Wang H, Xu Y, Liu Y, Wang L, Zhou R, et al. High expression of NXPH4 correlates with poor prognosis, metabolic reprogramming, and immune infiltration in colon adenocarcinoma. J Gastrointest Oncol. 2024;15(2):641-67.
26. Lv Y, Jin P, Chen Z, Zhang P. Characterization of hazard infiltrating immune cells and relative risk genes in bladder urothelial carcinoma. Am J Transl Res. 2020;12(11):7510-7527.
27. Li J, Jiang Y, Ma M, et al. Epithelial cell diversity and immune remodeling in bladder cancer progression: insights from single-cell transcriptomics. J Transl Med. 2025;23(1):135.
28. Mantovani A, Biswas SK, Galdiero MR, Sica A, Locati M. Macrophage plasticity and polarization in tissue repair and remodelling. J Pathol. 2013;229(2):176-85.
29. Xu Y, Wang X, Liu L, Wang J, Wu J, Sun C. Role of macrophages in tumor progression and therapy (Review). Int J Oncol. 2022;60(5):1-19.
30. Sharifi L, Nowroozi MR, Amini E, Arami MK, Ayati M, Mohsenzadegan M. A review on the role of M2 macrophages in bladder cancer; pathophysiology and targeting. Int Immunopharmacol. 2019;76(18):105880.
31. Leblond MM, Zdimerova H, Desponds E, Verdeil G. Tumor-Associated Macrophages in Bladder Cancer: Biological Role, Impact on Therapeutic Response and Perspectives for Immunotherapy. Cancers. 2021;13(18):4712.
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Issue | Articles in Press | |
Section | Original Article(s) | |
Keywords | ||
Bladder cancer Immune cell infiltration LASSO Macrophage polarization Prognostic risk model |
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