A risk scoring model for lung squamous cell carcinoma based on epithelial-mesenchymal transition-related genes: an integrative analysis of prognosis and immune infiltration characteristics

Background Despite expanding therapeutic options, the prognosis of lung squamous cell carcinoma (LUSC) remains poor. Immune checkpoint inhibitors benefit only a subset of patients, and epithelial–mesenchymal transition (EMT) has been implicated in invasion, metastasis, treatment resistance, and immune heterogeneity. Therefore, EMT-related biomarkers may offer improved risk stratification. Aim To identify differentially expressed EMT-related genes (DEEMTGs) in LUSC, construct an EMT-based prognostic signature, and evaluate its associations with the tumor microenvironment (TME), tumor mutational burden (TMB), and tissue-level expression patterns. Methods The Cancer Genome Atlas (TCGA) RNA-seq and clinical data were analyzed to obtain DEEMTGs. A prognostic model was built using LASSO and multivariable Cox regression. Survival performance was assessed via Kaplan–Meier, ROC, and Cox analyses. Immune infiltration (CIBERSORT), stromal/immune scores (ESTIMATE), and TMB were compared between risk groups. Exploratory immunohistochemistry (IHC; n = 8) provided orthogonal expression validation. Results A total of 1,651 DEEMTGs were identified, and a six-gene signature (GAB2, ALDOA, PCDHA3, TMEM92, ERH, IRS4) was established. The risk score independently predicted overall survival and corresponded to distinct TME patterns: low-risk tumors showed higher CD8+ T cells, activated CD4+ memory T cells, and naïve B cells, whereas high-risk tumors had more resting CD4+ memory T cells and M0 macrophages. TMB differences were nonsignificant. IHC provided directional protein-level support while acknowledging transcript-protein variability. Conclusion We developed a biologically interpretable EMT-based prognostic model that stratifies survival and reflects immune-microenvironment heterogeneity in LUSC. Larger, stage-balanced and immunotherapy-treated cohorts are needed to further validate its clinical utility.