ArticleFrontiers in immunology2022
Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 52 citations in OpenAlex.
- Radiogenomic landscape of the hallmarks of cancer.Biomarker research · 2026Review
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- SummArIzeR: simplifying cross-database enrichment result clustering and annotation via large language models.Bioinformatics (Oxford, England) · 2026Article
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Use of Radiomics in Characterizing Tumor Hypoxia.International journal of molecular sciences · 2025Review
- Artificial intelligence radiomics in the diagnosis, treatment, and prognosis of gynecological cancer: a literature review.Translational cancer research · 2025Review
- From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study.Journal for immunotherapy of cancer · 2023Article
- Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers.European radiology experimental · 2023Review
- Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer.European radiology · 2023Article
- A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility.Insights into imaging · 2023Review
- CRISPR-cas9 screening identified lethal genes enriched in Hippo kinase pathway and of predictive significance in primary low-grade glioma.Molecular medicine (Cambridge, Mass.) · 2023Article
- Machine learning-based mRNA signature in early acute myocardial infarction patients: the perspective toward immunological, predictive, and personalized.Functional & integrative genomics · 2023Article
- Molecular subtypes based on PANoptosis-related genes and tumor microenvironment infiltration characteristics in lower-grade glioma.Functional & integrative genomics · 2023Review
- Machine learning-based neddylation landscape indicates different prognosis and immune microenvironment in endometrial cancer.Frontiers in oncology · 2023Article
- Comprehensive analysis and molecular map of Hippo signaling pathway in lower grade glioma: the perspective toward immune microenvironment and prognosis.Frontiers in oncology · 2023Article
- Development of a machine learning-based signature utilizing inflammatory response genes for predicting prognosis and immune microenvironment in ovarian cancer.Open medicine (Warsaw, Poland) · 2023Article
- Diagnostics of Ovarian Tumors in Postmenopausal Patients.Diagnostics (Basel, Switzerland) · 2022Review
- Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets.Journal of oncology · 2022Article
- A Novel Defined RAS-Related Gene Signature for Predicting the Prognosis and Characterization of Biological Function in Osteosarcoma.Journal of oncology · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: The hypoxic microenvironment is involved in the tumorigenesis of ovarian cancer (OC). Therefore, we aim to develop a non-invasive radiogenomics approach to identify a 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 the immune microenvironment, prognosis, biological pathway alteration, and drug sensitivity among different subtypes. Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms. Results: One hundred forty 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 had higher hypoxia risk scores. Notably, the low-risk subtype had an active immune microenvironment and may benefit from immunotherapy. Finally, a four-feature radiogenomics model was constructed to reveal hypoxia risk status, and the model achieved area under the curve (AUC) values of 0.900 and 0.703 for the training and testing cohorts, respectively. Conclusion: As a non-invasive approach, computed tomography-based radiogenomics biomarkers may enable the pretreatment prediction of the hypoxia pattern, prognosis, therapeutic effect, and immune microenvironment in patients with OC.
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