Zeng Xiangju, Lu Zhijie, Dai Caixia, Su Hao, Liu Ziqi, Cheng Shunhua
Department of Outpatient, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
Department of Urology, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
Discov Oncol. 2024 Aug 26;15(1):368. doi: 10.1007/s12672-024-01187-7.
Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging.
We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients.
The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations.
This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.
膀胱癌是一种常见的恶性肿瘤，具有高度异质性。目前的治疗方法，如经尿道膀胱肿瘤切除术（TURBT）和膀胱内卡介苗（BCG）治疗，仍存在局限性，约30%的非肌层浸润性膀胱癌（NMIBC）会进展为肌层浸润性膀胱癌（MIBC），且大量MIBC患者术后会复发。免疫疗法已显示出潜在益处，但准确预测其预后效果仍具有挑战性。
我们分析了来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的膀胱癌RNA测序数据和临床信息，并使用各种机器学习算法从单细胞数据中筛选与肿瘤浸润免疫细胞（TIIC）相关的特征RNA。基于这些RNA，我们建立了TIIC特征评分，并评估其与膀胱癌患者总生存期（OS）和免疫治疗反应的关系。
该研究鉴定出171个TIIC-RNA，并通过生存分析选择了11个具有预后价值的TIIC-RNA。使用机器学习融合方法建立的TIIC特征评分与OS显著相关，且在不同数据集中表现出良好的预测性能。此外，该特征评分与免疫治疗反应呈负相关，TIIC特征评分低的患者在免疫治疗后显示出更好的生存结果。进一步的生物学功能分析揭示了TIIC特征评分与免疫调节过程、细胞代谢和基因变异之间的密切关联。
本研究成功构建并验证了一种基于肿瘤浸润免疫细胞（TIIC）特征的RNA特征评分系统，该系统可有效预测膀胱癌患者的OS和免疫治疗效果。
