Landscape of Immune Microenvironment in Epithelial Ovarian Cancer and Establishing Risk Model by Machine Learning.

Abstract

BACKGROUND:

Epithelial ovarian cancer (EOC) is an extremely lethal gynecological malignancy and has the potential to benefit from the immune checkpoint blockade (ICB) therapy, whose efficacy highly depends on the complex tumor microenvironment (TME).

METHOD AND RESULT:

We comprehensively analyze the landscape of TME and its prognostic value through immune infiltration analysis, somatic mutation analysis, and survival analysis. The results showed that high infiltration of immune cells predicts favorable clinical outcomes in EOC. Then, the detailed TME landscape of the EOC had been investigated through "xCell" algorithm, Gene set variation analysis (GSVA), cytokines expression analysis, and correlation analysis. It is observed that EOC patients with high infiltrating immune cells have an antitumor phenotype and are highly correlated with immune checkpoints. We further found that dendritic cells (DCs) may play a dominant role in promoting the infiltration of immune cells into TME and forming an antitumor immune phenotype. Finally, we conducted machine-learning Lasso regression, support vector machines (SVMs), and random forest, identifying six DC-related prognostic genes (CXCL9, VSIG4, ALOX5AP, TGFBI, UBD, and CXCL11). And DC-related risk stratify model had been well established and validated.

CONCLUSION:

High infiltration of immune cells predicted a better outcome and an antitumor phenotype in EOC, and the DCs might play a dominant role in the initiation of antitumor immune cells. The well-established risk model can be used for prognostic prediction in EOC.

• Authors: Shi-Yi Liu, Rong-Hui Zhu, Zi-Tao Wang, Wei Tan, Li Zhang, Yan-Qing Wang, Fang-Fang Dai, Meng-Qin Yuan, Ya-Jing Zheng, Dong-Yong Yang, Fei-Yan Wang, Shu Xian, Juan He, Yu-Wei Zhang, Ma-Li Wu, Zhi-Min Deng, Min Hu, Yan-Xiang Cheng, Ye-Qiang Liu
• Journal: Journal of oncology (J Oncol) Vol. 2021 Pg. 5523749 ( 2021) ISSN: 1687-8450 [Print] Egypt
• PMID: 34484333 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Shi-yi Liu et al.