Construction of a prognostic model for lung adenocarcinoma tumor endothelial cells and prediction of immunotherapy based on single-cell transcriptome and Bulk transcriptome

Abstract

Abstract Background: Lung adenocarcinoma (LUAD) is a common histologic subtype of lung cancer with high morbidity and mortality. Tumor endothelial cells (TEC) are associated with tumor progression and metastasis. In this study, we explored the effect of TEC on prognosis and immunotherapy of LUAD based on single-cell transcriptome and Bulk transcriptome. To help lung adenocarcinoma patients obtain accurate clinical treatment strategies. Methods: We identified TEC marker genes by single-cell transcriptome in this study. LUAD data were downloaded from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, and prognostic models of TEC marker genes were constructed using Lasso-Cox analysis in the TCGA cohort and externally validated in the GEO cohort. Differences in the immune microenvironment between high and low-risk groups were analyzed using the ESTIMATE and six immune cell infiltration algorithms. Using the TIDE algorithm, the IMvigor210, GSE78220, and Whijae Roh et al. cohorts were used to predict the outcome of immunotherapy in patients in different risk groups. In addition, differences in functional enrichment analysis and genomic mutations between high and low-risk groups were investigated. Finally, core genes were screened using differential and survival analyses, and RT-qPCR verified their expression. Results: The results showed that the prognostic model constructed based on TEC marker genes could categorize LUAD patients into two groups, and there was a significant difference in survival time between the two groups. In addition, we found significant differences between the high- and low-risk groups in terms of biological functions, genomic mutations, immune cell infiltration, immune characteristics, and chemotherapeutic drug sensitivity. Notably, patients in the low-risk group showed better immunotherapy response. Finally, the results of RT-qPCR experiments were consistent with the bioinformatics analysis. Conclusion: In this study, we developed a new TEC marker gene-based signature that effectively stratifies LUAD patients and has a strong efficacy in predicting the prognosis of LUAD patients and immunotherapy.