Systematic analysis of apoptosis-related genes in the prognosis of Lung squamous cell carcinoma: a combined single-cell RNA sequencing study

Purpose: Lung squamous cell carcinoma (LUSC) has a poor prognosis and lacks appropriate diagnostic and treatment strategies.Apoptosis dysregulation is associated with tumor occurrence and drug resistance, but the prognostic value of apoptosis-related genes (ARGs) in LUSC remains unclear. Methods We constructed an ARGs model that can predict LUSC through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs. We conducted correlation analysis of prognostic ARGs by combining the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. Then, we constructed a risk model and the predictive ability of the model was evaluated by using ROC (Receiver Operating Characteristic Curve) analysis. NSCLC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were clarified by establishing pseudotime analysis using Monocle. Results We identified four apoptosis prognostic genes and constructed a stable prognostic risk model. Kaplan-Meier curve analysis showed that the high-risk group had a poorer prognosis (P < 0.05). Furthermore, the ROC curve confirmed that the model had good predictive value for LUSC patients. Through analysis of single-cell sequencing data, apoptosis prognostic genes were found to be enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells. Conclusions This study identified apoptosis-related genes that are associated with prognosis in LUSC, and constructed a risk model based on these prognostic genes that accurately predicts the prognosis of LUSC. Single-cell sequencing analysis provided new insights into the cellular-level development of tumors. These findings provide more guidance for the diagnosis and treatment of LUSC patients.