Wang Yuhan, Wang Shuang, Ding Ran, Zhang Zequn, Kong Jing, Xie Tian, Xu Bin, Fu Liming, Zhang Erli
First Hospital of Jilin University, Changchun, Jilin, 130021, People's Republic of China.
Onco Targets Ther. 2025 Apr 11;18:521-537. doi: 10.2147/OTT.S503419. eCollection 2025.
The objective of this study was to identify biomarkers associated with immunogenic cell death (ICD) in lung squamous cell carcinoma (LUSC), focusing on subtypes with distinct immunological characteristics and prognosis. Given the heterogeneous nature of LUSC, understanding ICD's role is crucial for developing tailored therapeutic strategies.
RNA sequencing data from 504 LUSC samples were analyzed using unsupervised clustering to identify ICD-related gene expression patterns. These patterns were linked to immune scores, immune cell infiltration, and clinical outcomes. A separate dataset was used to validate the association between ICD-related subtypes and immunotherapy efficacy.
Unsupervised clustering revealed two distinct ICD-related subtypes with significantly different immune scores, immune cell infiltration levels, and prognosis. A prognostic model was developed based on differentially expressed ICD-related genes, which demonstrated strong predictive power for patient survival and immune response. This model may offer valuable insights for clinical decision-making, particularly for immunotherapy strategies.
This study identified key ICD-related biomarkers and developed a prognostic model that can enhance our understanding of ICD in LUSC, ultimately guiding personalized treatment approaches.
本研究的目的是确定与肺鳞状细胞癌(LUSC)中免疫原性细胞死亡(ICD)相关的生物标志物,重点关注具有不同免疫特征和预后的亚型。鉴于LUSC的异质性,了解ICD的作用对于制定个性化治疗策略至关重要。
使用无监督聚类分析来自504例LUSC样本的RNA测序数据,以识别与ICD相关的基因表达模式。这些模式与免疫评分、免疫细胞浸润和临床结果相关联。使用一个单独的数据集来验证与ICD相关的亚型与免疫治疗疗效之间的关联。
无监督聚类揭示了两种不同的与ICD相关的亚型,它们在免疫评分、免疫细胞浸润水平和预后方面存在显著差异。基于差异表达的与ICD相关的基因建立了一个预后模型,该模型对患者生存和免疫反应具有强大的预测能力。该模型可能为临床决策提供有价值的见解,特别是对于免疫治疗策略。
本研究确定了关键的与ICD相关的生物标志物,并开发了一个预后模型,该模型可以增强我们对LUSC中ICD的理解,最终指导个性化治疗方法。