Anoikis-Related Genes Signature Contributes to Predicting Prognosis and Response to Immunotherapy in Lung Squamous Cell Carcinoma

BACKGROUND Lung squamous cell carcinoma (LUSC) is a highly heterogeneous malignancy, with the immune micro-environment playing a critical role in tumor progression and response to therapy. However, stemness, endothelial-to-mesenchymal transition (EMT), and anoikis (a type of apoptosis) are not sufficiently studied in the LUSC immune micro-environment. This research aimed to explore the prognostic value of anoikis-related genes and tumor immune in the treatment of LUSC. MATERIAL AND METHODS Immune-cell fractions in LUSC samples were predicted using 3 computational algorithms: CIBERSORT, quanTseq, and SVR. The immune-cell infiltration patterns, including B cells, NK cells, neutrophils, macrophages, mast cells, and T cells, were analyzed. A prognostic nomogram was constructed using clinical variables and immune markers, and its predictive ability for overall survival at 1, 3, and 5 years was evaluated. Calibration plots, decision curve analysis, and receiver operating characteristic (ROC) curves were used to assess model performance. We used Python and R software to perform the analysis. P