Peng Liangju, Cai Tingting, Xu Peihang, Chen Cong, Xiang Qingzhi, Zhu Yiping, Ye Dingwei, Shen Yijun
Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
Oncol Res. 2025 Aug 28;33(9):2463-2489. doi: 10.32604/or.2025.064331. eCollection 2025.
Studies have reported the special value of PANoptosis in cancer, but there is no study on the prognostic and therapeutic effects of PANoptosis in bladder cancer (BLCA). This study aimed to explore the role of PANoptosis in BLCA heterogeneity and its impact on clinical outcomes and immunotherapy response while establishing a robust prognostic model based on PANoptosis-related features. Gene expression profiles and clinical data were collected from public databases. Spatial heterogeneity of cell death pathways in BLCA was evaluated. Consensus clustering was performed based on identified PANoptosis genes. Cell death pathway scores, molecular, and pathway activation differences between different groups were compared. Protein-protein interaction (PPI) network construction was constructed, and immune-related gene sets, tumor immune dysfunction and exclusion (TIDE) scores, and SubMap analysis were used to evaluate immunomodulator expression and immunotherapy efficacy. Ten machine learning algorithms were utilized to develop the most accurate predictive risk model, and a nomogram was created for clinical application. BLCA demonstrated a spatially heterogeneous distribution of pyroptosis, apoptosis, and necroptosis. Notably, T effector cells significantly colocalized with total apoptosis. Two PANoptosis modes were identified: high PANoptosis (high. PANO) and low PANoptosis (low. PANO). High. PANO was associated with worse clinical outcomes and advanced tumor stage, and increased activation of immune-related and cell death pathways. It also showed increased infiltration of immune cells, elevated expression of immunomodulatory factors, and enhanced responsiveness to the immunotherapy. The PANoptosis-related machine learning prognostic signature (PMLS) exhibited strong predictive power for outcomes in BLCA. CSPG4 was identified as a key gene underlying prognostic and therapeutic differences. PANoptosis shapes distinct prognostic and immunological phenotypes in BLCA. PMLS offers a reliable prognostic tool. CSPG4 may represent a potential therapeutic target in PANoptosis-driven BLCA.
研究报告了PAN细胞焦亡在癌症中的特殊价值，但尚无关于PAN细胞焦亡在膀胱癌（BLCA）中的预后和治疗效果的研究。本研究旨在探讨PAN细胞焦亡在BLCA异质性中的作用及其对临床结局和免疫治疗反应的影响，同时建立基于PAN细胞焦亡相关特征的稳健预后模型。从公共数据库收集基因表达谱和临床数据。评估BLCA中细胞死亡途径的空间异质性。基于鉴定出的PAN细胞焦亡基因进行一致性聚类。比较不同组之间的细胞死亡途径评分、分子和途径激活差异。构建蛋白质-蛋白质相互作用（PPI）网络，并使用免疫相关基因集、肿瘤免疫功能障碍和排除（TIDE）评分以及SubMap分析来评估免疫调节因子表达和免疫治疗效果。利用十种机器学习算法开发最准确的预测风险模型，并创建列线图用于临床应用。BLCA显示出细胞焦亡、凋亡和坏死性凋亡的空间异质性分布。值得注意的是，T效应细胞与总凋亡显著共定位。鉴定出两种PAN细胞焦亡模式：高PAN细胞焦亡（high.PANO）和低PAN细胞焦亡（low.PANO）。High.PANO与更差的临床结局和晚期肿瘤分期相关，并增加免疫相关和细胞死亡途径的激活。它还显示免疫细胞浸润增加、免疫调节因子表达升高以及对免疫治疗的反应增强。PAN细胞焦亡相关的机器学习预后特征（PMLS）对BLCA的结局具有强大的预测能力。CSPG4被确定为预后和治疗差异的关键基因。PAN细胞焦亡塑造了BLCA中不同的预后和免疫表型。PMLS提供了一种可靠的预后工具。CSPG4可能代表PAN细胞焦亡驱动的BLCA中的潜在治疗靶点。
