Integrative models of histopathological images and multi-omics data predict prognosis in endometrial carcinoma

PeerJ

article

Yueyi Li¹  Peixin Du²  Hao Zeng²  Yuhao Wei³  Haoxuan Fu⁴  Xi Zhong⁵  Xuelei Ma¹ 

[1] Department of Targeting Therapy & Immunology, Cancer Center, West China Hospital, Sichuan University;Laboratory of Integrative Medicine, Clinical Research Center for Breast, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University;West China School of Medicine, West China Hospital, Sichuan University;Department of Statistics and Data Science, Wharton School, University of Pennsylvania;Department of Critical Care Medicine, West China Hospital of Sichuan University

关键词: Histopathology;    Proteomics;    Transcriptomics;    Genomics;    Endometrial carcinoma;   

DOI  :  10.7717/peerj.15674

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Objective This study aimed to predict the molecular features of endometrial carcinoma (EC) and the overall survival (OS) of EC patients using histopathological imaging. Methods The patients from The Cancer Genome Atlas (TCGA) were separated into the training set (n = 215) and test set (n = 214) in proportion of 1:1. By analyzing quantitative histological image features and setting up random forest model verified by cross-validation, we constructed prognostic models for OS. The model performance is evaluated with the time-dependent receiver operating characteristics (AUC) over the test set. Results Prognostic models based on histopathological imaging features (HIF) predicted OS in the test set (5-year AUC = 0.803). The performance of combining histopathology and omics transcends that of genomics, transcriptomics, or proteomics alone. Additionally, multi-dimensional omics data, including HIF, genomics, transcriptomics, and proteomics, attained the largest AUCs of 0.866, 0.869, and 0.856 at years 1, 3, and 5, respectively, showcasing the highest discrepancy in survival (HR = 18.347, 95% CI [11.09–25.65], p < 0.001). Conclusions The results of this experiment indicated that the complementary features of HIF could improve the prognostic performance of EC patients. Moreover, the integration of HIF and multi-dimensional omics data might ameliorate survival prediction and risk stratification in clinical practice.

【 授权许可】

CC BY