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=14
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=Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis.
Methods: We analyzed 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 the least absolute shrinkage and selection operator (LASSO) computational algorithm.
Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0ยท001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients.
Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.