AUTHOR=Chen Shuzhao , Zhang Limei , Huang Mayan , Liang Yang , Wang Yun 

TITLE=A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1190660

DOI=10.3389/fphar.2023.1190660

ISSN=1663-9812

ABSTRACT=Tumor-associated endothelial cells (TAEs) component plays a vital role in tumor immunity. However, systematic Tumor-associated endothelial related genes assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers has not been explored.Here, we investigated a tumor-associated endothelial genes risk model (TAEs) to predict CIT responses and patient survival in a pan-cancer analysis.We analysis publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer and renal cell carcinoma. We further established a binary classification model to predict CIT responses using least absolute shrinkage and selection operator (LASSO) computational algorithm.The model demonstrated high predictive accuracy in both training and validation cohorts. The response rate of the high-risk score group to immunotherapy in training cohort was significantly higher than that of the low-risk score group, with CIT response rates of 51% and 27%, respectively. Survival analysis showed that the prognosis of the high scores group was significantly better than that of the low scores group (all P < 0•001). Tumor-associated endothelial gene signatures scores correlated positively with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients with high scores group. Analysis of TAEs scores across 33 human cancers revealed that the TAEs model could reflect immune cell infiltration and predict cancer patient survival.The TAEs signature model could represent a CIT response prediction model with prognostic value in multiple cancer types.