Comprehensive analysis of the role of a four-gene signature based on EMT in the prognosis, immunity, and treatment of lung squamous cell carcinoma

Epithelial-mesenchymal transition (EMT), a biological process through which epithelial cells transform into mesenchymal cells, contributes to tumor progression and metastasis. However, a comprehensive analysis of the role of EMT-related genes in Lung squamous cell carcinoma (LUSC) is still lacking. In this study, data were downloaded from available databases, including The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The association between differentially expressed EMT-related genes (EMT-RDGs) and LUSC prognosis, drug sensitivity, mutation, and immunity was analyzed using bioinformatics methods. In the results, Lasso and univariate Cox regression analyses identified four EMT-RDGs that were differentially expressed, and used to establish a prognostic model capable of distinguishing between high- and low-risk groups. Then, prognostic factors were identified by multivariate Cox regression analysis and used to construct a nomogram. The high-risk group had a significantly poorer prognosis than the low-risk group. The tumor immune environment was significantly different between the two groups, with the low-risk group exhibiting a better response to immunotherapy. In addition, the half-maximal inhibitory concentration prediction indicating that the constructed model could effectively predict sensitivity to chemotherapy. This study provides new reference for further exploration of new clinical therapeutic strategies for LUSC.