PURPOSE: To compare the landscape of tumor microenvironment (TME) of lung squamous carcinoma (LUSC) in different immune pattern and explore potential factors on immune therapy and prognosis. METHOD AND MATERIALS: We have obtained the LUSC data from TCGA, GEO, and our department and classified them into 2 TME clusters by random forest model based on the infiltration pattern of 24 immune cell populations. We systemically compared the genomic significance, clinical characteristics, and immune infiltration pattern in 2 TME clusters. RESULTS: Samples were divided into 2 TME clusters based on the relative abundance of 24 immune cells, and a random forest classifier model was constructed. TME cluster B was a higher immune infiltration group with lower mutation load, richer co-infiltrate immune cells, upregulated immune-related cytokines, immune checkpoint molecules, and higher active immune cells. TME cluster was also an independent predictor in prognosis (B vs. A, p < 0.05) in patients from TCGA, GEO, and our department. CONCLUSIONS: Our study has described the microenvironment landscape of LUSC in different immune infiltration patterns and systemically analyzed genomic and clinical characteristics with distinct immunophenotypes, thus partly revealed the interaction between tumors and the immune microenvironment, which may guide a more precise and personalized immune therapeutic strategy for LUSC patients.
PURPOSE: To compare the landscape of tumor microenvironment (TME) of lung squamous carcinoma (LUSC) in different immune pattern and explore potential factors on immune therapy and prognosis. METHOD AND MATERIALS: We have obtained the LUSC data from TCGA, GEO, and our department and classified them into 2 TME clusters by random forest model based on the infiltration pattern of 24 immune cell populations. We systemically compared the genomic significance, clinical characteristics, and immune infiltration pattern in 2 TME clusters. RESULTS: Samples were divided into 2 TME clusters based on the relative abundance of 24 immune cells, and a random forest classifier model was constructed. TME cluster B was a higher immune infiltration group with lower mutation load, richer co-infiltrate immune cells, upregulated immune-related cytokines, immune checkpoint molecules, and higher active immune cells. TME cluster was also an independent predictor in prognosis (B vs. A, p < 0.05) in patients from TCGA, GEO, and our department. CONCLUSIONS: Our study has described the microenvironment landscape of LUSC in different immune infiltration patterns and systemically analyzed genomic and clinical characteristics with distinct immunophenotypes, thus partly revealed the interaction between tumors and the immune microenvironment, which may guide a more precise and personalized immune therapeutic strategy for LUSC patients.