Zhu Yinhui, Zhu Yingqun, Chen Sirui, Cai Qian
Department of Respiratory and Critical Care Medicine, The Third Hospital of Changsha, Hunan, China.
Department of Emergency Medicine, The Third Hospital of Changsha, Hunan, China.
Comput Methods Biomech Biomed Engin. 2025 Feb;28(3):326-336. doi: 10.1080/10255842.2023.2287418. Epub 2023 Nov 28.
Cancer-associated fibroblasts (CAFs) are an important component of the tumor microenvironment that contribute toward the development of tumors. This study aimed to establish a new algorithm based on CAF scores to predict the prognosis and immunotherapy response in patients with lung squamous cell carcinoma (LUSC). The RNA-seq data of LUSC patients were obtained from two databases and merged after removing inter-batch differences. The CAF-related data for each sample were obtained through three different algorithms. Consistency cluster analysis was performed to obtain different CAF clusters, which were analyzed to identify differentially expressed genes. These were subjected to uniform cluster analysis to obtain different gene clusters. The Boruta algorithm was used to calculate the CAF score. Three CAF clusters and two gene clusters were obtained, all of which differed in their patient prognoses and the content of infiltrating immune cells. Patients with high CAF scores exhibited worse overall survival, higher expression of biomarkers related to immune checkpoints and immune activity, and lower tumor mutation burden. The CAF score could also predict the immunotherapy response of patients. This study suggests that the CAF score can accurately predict the prognosis and immunotherapy response of LUSC patients.
癌症相关成纤维细胞（CAFs）是肿瘤微环境的重要组成部分，对肿瘤的发展起作用。本研究旨在建立一种基于CAF评分的新算法，以预测肺鳞状细胞癌（LUSC）患者的预后和免疫治疗反应。LUSC患者的RNA测序数据从两个数据库中获取，并在消除批次间差异后进行合并。通过三种不同算法获得每个样本的CAF相关数据。进行一致性聚类分析以获得不同的CAF簇，并对其进行分析以鉴定差异表达基因。对这些基因进行统一聚类分析以获得不同的基因簇。使用Boruta算法计算CAF评分。获得了三个CAF簇和两个基因簇，所有这些在患者预后和浸润免疫细胞含量方面均有所不同。CAF评分高的患者总生存期较差，与免疫检查点和免疫活性相关的生物标志物表达较高，肿瘤突变负担较低。CAF评分还可以预测患者的免疫治疗反应。本研究表明，CAF评分可以准确预测LUSC患者的预后和免疫治疗反应。
