Department of Immunology, Jilin University, Changchun, China.
Department of Pharmacology, Jilin University, Changchun, China.
Microvasc Res. 2024 Jan;151:104614. doi: 10.1016/j.mvr.2023.104614. Epub 2023 Oct 5.
Bladder cancer (BLCA) is a common type of urogenital malignancy worldwide. The recurrence and metastasis of bladder cancer are closely related to angiogenesis, but the underlying mechanisms are unclear. In this study, we developed a method to predict survival outcomes among BLCA patients, which could be used to guide immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and identified angiogenesis-related genes from the GeneCards database. First, we used differential expression analysis and univariate Cox analysis to identify angiogenesis-related genes and used correlation analysis to generate molecular subtypes based on M2 macrophages. Next, we constructed a prognostic signature consisting of four genes (ECM1, EFEMP1, SLIT2, and PDGFRΑ), which was found to be an independent prognostic factor. Higher risk scores were associated with worse overall survival and higher expression of immune checkpoints. We also evaluated immune cell infiltration using the CIBERSORT and ssGSEA algorithms. Additionally, we performed stratification analyses, constructed a nomogram, and predicted chemotherapeutic responses based on the risk signature. Finally, we validated our findings by using qRT-PCR as well as IHC data to detect the expression levels of the four genes at mRNA and protein levels in BLCA patients and obtained results that were consistent with our predictions. Our study demonstrates the utility of a four-gene prognostic signature for prognostication in bladder cancer patients and designing personalized treatments, which could provide new avenues for personalized management of these patients.
膀胱癌（BLCA）是全球常见的泌尿生殖系统恶性肿瘤之一。膀胱癌的复发和转移与血管生成密切相关，但潜在机制尚不清楚。在这项研究中，我们开发了一种预测 BLCA 患者生存结局的方法，可用于指导免疫治疗和化疗。我们从癌症基因组图谱（TCGA）获得患者数据，并从基因卡片数据库中识别出与血管生成相关的基因。首先，我们使用差异表达分析和单因素 Cox 分析来识别与血管生成相关的基因，并使用相关性分析根据 M2 巨噬细胞生成分子亚型。接下来，我们构建了一个由四个基因（ECM1、EFEMP1、SLIT2 和 PDGFRΑ）组成的预后特征，该特征被发现是一个独立的预后因素。较高的风险评分与总生存率较差和免疫检查点表达较高相关。我们还使用 CIBERSORT 和 ssGSEA 算法评估了免疫细胞浸润。此外，我们进行了分层分析，构建了诺莫图，并根据风险特征预测化疗反应。最后，我们通过 qRT-PCR 和 IHC 数据验证了我们的发现，以检测 BLCA 患者中四个基因在 mRNA 和蛋白水平上的表达水平，得到的结果与我们的预测一致。我们的研究表明，四个基因预后特征可用于预测膀胱癌患者的预后，并设计个性化治疗方案，为这些患者的个体化管理提供新途径。
