RESUMO
UNASSIGNED: Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the "RIC" version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies. (Rev Invest Clin. 2024;76(2):116-31).
