Qin Xianyu, Wu Jiayan, Qin Fei, Zheng Yuzhen, Chen Junguo, Liu Zui, Tan Jian, Cai Weijie, He Shiyun, Jian Bozhu, Zheng Haosheng, Liao Hongying
Department of Thoracic Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Transl Lung Cancer Res. 2024 Dec 31;13(12):3657-3674. doi: 10.21037/tlcr-24-1003. Epub 2024 Dec 27.
The role of pyroptosis in lung squamous cell carcinoma (LUSC) remains unclear. This study aimed to screen pyroptosis-related genes (PRGs) and construct a model to investigate the immune infiltration, gene mutations, and immune response of patients of LUSC.
We conducted a comprehensive evaluation of pyroptosis patterns in patients with LUSC with 51 PRGs. Pyroptosis-related clusters were identified using consistency clustering algorithm. Differences in the biologic and clinical characteristics between the clusters were analyzed. Cox regression analysis was performed to screen for differentially expressed genes (DEGs) related to prognosis, and a principal component analysis (PCA) algorithm was used to construct a model based on these genes. The pyroptosis score was calculated for each tumor sample, and the samples were classified into high- and low-score groups based on the score. The disparities in survival, single-nucleotide variation (SNV), copy number variation (CNV), and immunotherapy response between high-score and low-score groups were analyzed.
A total of 51 PRGs were used to classify LUSC samples into three pyroptosis clusters with significant differences in survival (P=0.005). Based on the 390 DEGs between the three clusters, two distinct pyroptosis gene clusters were identified by secondary clustering, with significant differences in prognosis (P=0.005). A pyroptosis scoring model was established to evaluate the regulatory patterns of PRGs, and patients were stratified into two groups with high and low scores, using the median pyroptosis score as the cutoff. The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02).
The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC.
细胞焦亡在肺鳞状细胞癌（LUSC）中的作用仍不清楚。本研究旨在筛选细胞焦亡相关基因（PRG）并构建模型，以研究LUSC患者的免疫浸润、基因突变和免疫反应。
我们用51个PRG对LUSC患者的细胞焦亡模式进行了综合评估。使用一致性聚类算法鉴定细胞焦亡相关簇。分析了各簇之间生物学和临床特征的差异。进行Cox回归分析以筛选与预后相关的差异表达基因（DEG），并使用主成分分析（PCA）算法基于这些基因构建模型。计算每个肿瘤样本的细胞焦亡评分，并根据该评分将样本分为高分和低分两组。分析高分和低分两组在生存、单核苷酸变异（SNV）、拷贝数变异（CNV）和免疫治疗反应方面的差异。
共使用51个PRG将LUSC样本分为三个细胞焦亡簇，其生存存在显著差异（P = 0.005）。基于三个簇之间的390个DEG，通过二次聚类鉴定出两个不同的细胞焦亡基因簇，其预后存在显著差异（P = 0.005）。建立了细胞焦亡评分模型以评估PRG的调控模式，以细胞焦亡评分中位数为临界值将患者分为高分和低分两组。生存分析表明，高分患者在癌症基因组图谱（TCGA）-LUSC队列中的预后较差（P = 0.002），GSE37745（P = 0.006）和GSE135222数据集的分析进一步支持了这一点（P = 0.02）。
发现细胞焦亡模式的量化对于预测LUSC患者的预后和制定个性化治疗策略很重要。
