Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China.
Department of Acupuncture and Moxibustion, The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan, China.
J Gene Med. 2024 Jan;26(1):e3655. doi: 10.1002/jgm.3655.
A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets.
The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner.
This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response.
A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.
本研究基于共刺激分子构建了膀胱癌预后模型,并在不同数据集验证了其稳定性和准确性。
通过计算生物学分析癌症基因组图谱(TCGA)数据库中膀胱癌 RNA 的表达谱及其相应的临床数据,构建了共刺激分子相关基因的预后模型。将该模型应用于 TCGA 数据集和基因表达综合数据库中的 GSE160693、GSE176307、Xiangya_Cohort、GSE13507、GSE19423、GSE31684、GSE32894、GSE48075、GSE69795 和 GSE70691 中。同时,还探索了共刺激分子在膀胱癌肿瘤亚型中的作用。通过一致聚类分析,将 TCGA 数据集的膀胱癌分为两个亚型:C1 和 C2。C1 亚型预后不良,免疫细胞浸润水平高,C1 亚型中自然杀伤细胞、T 细胞和树突状细胞显著富集。此外,通过 ESTIMATE 算法计算的免疫评分在两个亚型之间差异显著,C1 亚型的免疫评分显著高于 C2 亚型。
本研究还评估了共刺激分子与免疫治疗反应之间的关系。高危组对免疫治疗反应不佳,两组之间大多数免疫细胞的数量存在显著差异。此外,ESTIMATE 算法的三个指数和 CIBERSORT 算法的 22 种免疫细胞与风险值显著相关。这些发现表明共刺激分子在预测免疫治疗反应方面具有潜在价值。
本研究构建并验证了基于共刺激分子的膀胱癌预后模型。该模型为每个膀胱癌患者制定个体化治疗方案提供了新的思路,为临床决策提供了有价值的信息。同时,本研究还深入探讨了共刺激分子在不同膀胱癌亚型中的意义,为提高膀胱癌免疫治疗策略提供了新的思路。