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

Abstract

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. Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs, we constructed an ARG-related prognostic model for LUSC survival rates. We conducted correlation analysis of prognostic ARGs by incorporating the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. We then constructed a risk model, and the predictive ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Non-small cell lung cancer (NSCLC) single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, these data were subjected to single-cell analysis. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were identified via pseudotemporal analysis with the Monocle 2 algorithm. We identified four significantly prognostic ARGs 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 analysis of 3-, 5- and 7-year survival rates confirmed that the model had good predictive value for patients with LUSC. Single-cell RNA sequencing showed the prognostic ARGS were enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells. This study identified ARGs that are associated with prognosis in LUSC, and a risk model based on these prognostic genes was constructed that could accurately predict 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 patients with LUSC.