Yu Weitao, Yao Dongnuan, Ma Xueming, Hou Juanjuan, Tian Junqiang
Department of Urology, The Second Hospital of Lanzhou University, No.82 Cuiyingmen, Lanzhou, 730030, China.
Gansu Province Clinical Research Center for urinary system disease, Lanzhou, China.
Sci Rep. 2025 Jun 6;15(1):19912. doi: 10.1038/s41598-025-04037-w.
Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/ ). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs-SERPINE2, DPYSL3, CTSE, and KRT16-was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes-SERPINE2, DPYSL3, CTSE, and KRT16-that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer.
胞葬作用是巨噬细胞和树突状细胞等吞噬细胞清除凋亡细胞的过程，对维持组织稳态至关重要。然而，其在膀胱癌（BLCA）中的作用仍不清楚，值得进一步探索。本研究旨在基于胞葬作用相关基因（EFRGs）为膀胱癌患者建立一个预后和治疗反应特征。BLCA相关数据集来自癌症基因组图谱（TCGA，https://portal.gdc.cancer.gov/ ）和基因表达综合数据库（GEO，https://www.ncbi.nlm.nih.gov/geo/ ）。对28个预后EFRGs进行了综合分析。使用ConsensusClusterPlus进行聚类分析。基于各亚型间的表达差异鉴定预后差异表达基因（DEGs）。随后使用最小绝对收缩和选择算子（LASSO）和多变量Cox回归建立预后模型。最后，进行了深入分析，以探讨风险评分与肿瘤免疫微环境、体细胞突变以及免疫治疗和化疗反应之间的关系。共识聚类揭示了两种胞葬作用亚型，A簇和B簇，并确定了它们之间的61个预后DEGs。构建并验证了一个包含四个关键DEGs（丝氨酸蛋白酶抑制剂E2、二氢嘧啶酶样蛋白3、组织蛋白酶E和角蛋白16）的风险评分模型。该模型成功地将患者分为高风险和低风险组，高风险患者预后较差，免疫浸润增加，免疫检查点基因表达更高。风险评分还为患者的治疗反应提供了见解。总之，我们鉴定出四个关键基因——丝氨酸蛋白酶抑制剂E2、二氢嘧啶酶样蛋白3、组织蛋白酶E和角蛋白16——可用于建立膀胱癌的预后模型。这些发现可能为膀胱癌的临床诊断和治疗策略提供有价值的分子靶点。
