Identification and validation of pyroptosis patterns with a novel quantification system for the prediction of prognosis in lung squamous cell carcinoma.

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC.