Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer - Feng, Songwei; Xia, Tianyi; Ge, Yu; Zhang, Ke; Ji, Xuan; Luo, Shanhui; Shen, Yang (Frontiers in Immunology, 2022)
Purpose: The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a noninvasive radiogenomics approach to identify hypoxia pattern with potential application in patient prognostication. Methods: Specific hypoxia-related genes (sHRGs) were identified based on RNA-seq of OC cell lines cultured with different oxygen conditions. Meanwhile, multiple hypoxia-related subtypes were identified by unsupervised consensus analysis, and Lasso-Cox regression analysis. Subsequently, diversified bioinformatics algorithms were used to explore immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting risk status of patients were developed by multiple machine learning algorithms. Results: 140 sHRGs, and three types of hypoxia-related subtypes were identified. Among them, hypoxia-cluster-B, gene-cluster-B and high-risk subtypes had poor survival outcomes. The subtypes were closely related to each other, and hypoxia-cluster-B and gene-cluster-B have higher hypoxia risk score. Notably, low-risk subtype had a active immune microenvironment and may benefit from immunotherapy. Finally, four-features radiogenomic model were constructed to reveal hypoxia risk status, and the model achieved an area under curve (AUC) value of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion: As a non-invasive approach, the computed tomography-based radiogenomics biomarkers may enable the pre-treatment prediction of hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in OC patients.