Molecular typing and prognostic risk models for ovarian cancer: a study based on cell differentiation trajectory

Frontiers in Cell and Developmental Biology

Cell and Developmental Biology

Zhou Ying¹  Zi Wang²  Tingting Ni²  Lan Mu²  Hanqun Zhang²  Tingfeng Chen³ 

[1] Department of Medical Records and Statistics, Guizhou Provincial People’s Hospital, Guiyang, China;Department of Oncology, Guizhou Provincial People’s Hospital, Guiyang, China;Department of Oncology, Guizhou Provincial People’s Hospital, Guiyang, China;State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China;

关键词: ovarian cancer;    molecular typing;    single cell;    cell differentiation trajectory;    bioinformatics;   

DOI  :  10.3389/fcell.2023.1131494

 received in 2022-12-25, accepted in 2023-08-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

Ovarian cancer is a heterogeneous disease with different molecular phenotypes. We performed molecular typing of ovarian cancer using cell differentiation trajectory analysis and proposed a prognostic risk scoring model. Using the copy number variation provided by inferCNV, we identified malignant tumor cells. Then, ovarian cancer samples were divided into four subtypes based on differentiation-related genes (DRGs). There were significant differences in survival rates, clinical features, tumor microenvironment scores, and the expression levels of ICGs among the subtypes. Based on nine DRGs, a prognostic risk score model was generated (AUC at 1 year: 0.749; 3 years: 0.651). Then we obtained a nomogram of the prognostic variable combination, including risk scores and clinicopathological characteristics, and predicted the 1-, 3- and 5-year overall survival. Finally, we explored some issues of immune escape using the established risk model. Our study demonstrates the significant influence of cell differentiation on predicting prognosis in OV patients and provides new insights for OV treatment and potential immunotherapeutic strategies.

【 授权许可】

Unknown   
Copyright © 2023 Chen, Ni, Mu, Ying, Zhang and Wang.