Department of Urology, the Affiliated Hospital of Yangzhou University, Yangzhou 225000, Jiangsu Province, China.
School of Nursing, School of Public Health, Yangzhou University, Yangzhou 225000, Jiangsu Province, China.
Zhejiang Da Xue Xue Bao Yi Xue Ban. 2023 Dec 24;53(1):47-57. doi: 10.3724/zdxbyxb-2023-0343.
To classify bladder cancer based on immune cell infiltration score and to construct a prognosis assessment model of patients with bladder cancer.
The transcriptome data and clinical data of breast cancer patients were obtained from the The Cancer Genome Atlas (TCGA) database. Single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was achieved by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were identified. A risk scoring model and a nomogram for prognosis assessment of bladder cancer patients were constructed and verified.
B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. The patients were clustered into two groups (Cluster 1 ´ and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1 ´, patients with Cluster 2 were more likely to benefit from immunotherapy (<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (<0.05). 35 genes related to key immune cells were screened out by WGCNA and 4 genes (, , and ) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.
According to the immune cell infiltration score, bladder cancer patients can be classified. Furthermore, bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.
基于免疫细胞浸润评分对膀胱癌进行分类，并构建膀胱癌患者的预后评估模型。
从癌症基因组图谱（TCGA）数据库中获取乳腺癌患者的转录组数据和临床数据。采用单样本基因集富集分析计算 16 种免疫细胞的浸润评分。通过无监督聚类对乳腺癌患者进行分类，并分析不同类型患者对免疫治疗和化疗的敏感性。通过加权相关网络分析（WGCNA）识别与关键免疫细胞浸润显著相关的关键模块，并鉴定模块中的关键基因。构建并验证膀胱癌患者的风险评分模型和预后评估列线图。
确定 B 细胞、肥大细胞、中性粒细胞、辅助性 T 细胞和肿瘤浸润淋巴细胞为膀胱癌的关键免疫细胞。根据免疫细胞浸润评分，将患者聚类为两个组（Cluster 1´和 Cluster 2）。与 Cluster 1´患者相比，Cluster 2 患者更有可能从免疫治疗中获益（<0.05），且 Cluster 2 患者对 Enbevaten、Docetaxel、Cyclopamine 和 Akadixin 更为敏感（<0.05）。通过 WGCNA 筛选出与关键免疫细胞相关的 35 个基因，并通过 LASSO Cox 回归进一步筛选出与膀胱癌预后相关的 4 个基因（、、和）。基于这 4 个基因构建的膀胱癌预后风险评分模型预测患者 1、3 和 5 年生存率的 ROC 曲线下面积（AUC）分别为 0.735、0.765 和 0.799。结合风险评分和临床参数构建的列线图在预测膀胱癌患者 1、3 和 5 年总生存率方面具有较高的准确性。
根据免疫细胞浸润评分可以对膀胱癌患者进行分类。此外，基于关键免疫细胞相关基因的膀胱癌预后风险评分模型和列线图在预测膀胱癌患者预后方面具有较高的准确性。
