Background: With the advancements in the fields of science, technology, and medical therapy, there is an increasing awareness among the general public regarding tumor-infiltrating immune cells. These immune cells have a close association with the prognosis of clinical patients with lung cancer.
Methods: The research used a comprehensive analysis and assessed tumor-infiltrating immune cells in advanced lung squamous cell carcinoma (LUSC) using The Cancer Genome Atlas (TCGA) database and the CIBERSORT algorithm. The research examined 22 types of tumor-infiltrating immune cells and observed notable differences in the infiltration patterns of immune cells between normal tissue and advanced LUSC.
Results: Univariate Cox regression analyses revealed a positive correlation between macrophages M2 and patient prognoses, as well as potential influences on patient prognosis by natural killer (NK) cells resting, monocytes, and activated mast cells. Multivariate Cox regression models were developed, incorporating three types of immune cells. The efficacy of the model was evaluated using a receiver operating characteristic (ROC) curve. Furthermore, the research constructed a nomogram model to individually predict the mortality risk in patients with advanced LUSC. This prediction model serves as a valuable tool for clinicians, enabling them to provide effective guidance based on tumor-infiltrating immune cells for advanced LUSC patients.
Conclusions: The research comprehensively analyzed and evaluated 22 types of tumor-infiltrating immune cells from advanced LUSC, revealing the correlation between immune cell infiltration and overall survival (OS) in clinical patients. Based on the nomogram of NK cells resting, monocytes, and macrophages M2, it can make specific prognostic predictions for advanced LUSC patients.
Keywords: Advanced lung squamous cell carcinoma (advanced LUSC); The Cancer Genome Atlas (TCGA); prognosis; tumor-infiltrating immune cells.
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