ArticleMedical science monitor : international medical journal of experimental and clinical research2026
Anoikis-Related Genes Signature Contributes to Predicting Prognosis and Response to Immunotherapy in Lung Squamous Cell Carcinoma.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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<0.05 was considered as statistically significant. RESULTS S100A7, S100A8, and SPP1 were identified from the LUSC tumor micro-environment and were used to construct a nomogram. The immune profiling revealed significant heterogeneity in immune-cell infiltration across LUSC samples, with T cells, macrophages, tregs, and dendritic cells being predominantly associated with immune suppression. The nomogram integrating clinical and immune markers demonstrated moderate predictive accuracy for overall survival. Calibration and decision curve analyses confirmed the clinical utility of the nomogram for survival prediction. CONCLUSIONS Our study presents a prognostic model of the interplay between anoikis resistance and immune-cell infiltration. Personalized immunotherapy strategies, including targeting the identified prognostic markers can improve treatment efficacy and overcome immune evasion mechanisms and can enhance clinical outcomes for LUSC patients.
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