Zhou Ye, Zhang Hengyan, Yan Heguo, Han Pingxing, Liu Yangwen
Department of Endocrinology, Zhaotong Hospital of Traditional Chinese Medicine, Zhaotong, China.
Department of Dermatology, Zhaotong Hospital of Traditional Chinese Medicine, Zhaotong, China.
Iran J Allergy Asthma Immunol. 2025 Jun 26;24(4):519-532.
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.
通过对癌症基因组图谱（TCGA）数据的综合分析，探索膀胱癌基因表达谱的免疫基础和预后潜力。我们使用TCGA数据来识别差异表达基因（DEG），并进行富集分析以揭示相关的生物学途径。同时，采用最小绝对收缩和选择算子（LASSO）算法建立预后模型。然后我们在TCGA和GSE13507数据集中评估了该模型的性能。此外，我们对模型构建中使用的特征基因进行了全面研究，包括基因表达谱分析和生存分析。最后，应用免疫浸润分析和药物敏感性分析来阐明该疾病的免疫基础，并提供潜在的治疗策略。我们共鉴定出837个DEG，重点关注免疫相关基因。使用LASSO算法，我们建立了一个包含七个关键基因（NXPH4、FAM110B、GPC2、STXBP6、CYP27B1、GARNL3和PTGER3）的预后模型，该模型在TCGA和GSE13507数据集中均显示出很强的预测准确性。此外，免疫浸润分析显示高危患者中M0和M2巨噬细胞的丰度更高，这表明巨噬细胞极化可能是调节免疫微环境的潜在治疗靶点。药物敏感性分析进一步表明，高危患者对几种化疗药物表现出不同的反应，具有潜在的治疗意义。本研究构建了一个有效的预后模型，为膀胱癌的个性化治疗提供了新的见解和潜在的治疗靶点，这需要进一步验证。
