ArticleJournal of cellular and molecular medicine2025
Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study.
Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning of pretreatment ascites cytopathology for platinum-resistance risk stratification in advanced epithelial ovarian cancer.Neoplasia (New York, N.Y.) · 2026Article
- An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides.NPJ precision oncology · 2026Article
- Development and Validation of a Multi-Omics Model Integrating US-Derived and WSI-Based Features to Predict Lymph Node Metastasis in Ovarian Cancer: A Multi-Center Retrospective Study.International journal of women's health · 2026Article
- Development of a PANoptosis-Related Pathomics Prognostic Model in Ovarian Cancer: A Multi-Omics Study.Journal of cellular and molecular medicine · 2025Article
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Authors and funding
10 authors.
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Abstract
Ovarian cancer (OC) is a high-mortality gynaecological malignancy, and the role of PANoptosis, a comprehensive cell death mechanism, in its prognosis remains unexplored. This study aims to clarify it, potentially guiding OC diagnosis and treatment. We analysed the ovarian data from TCGA and GTEx, and the GSE184880 scRNA-seq dataset from GEO. Spatial data and pathological images were sourced from the 10X Genomics website and GDC Portal. Features were extracted using CellProfiler and ResNet-50, and a PANoptosis-related pathomics prognostic model (PANPM) powered by deep learning was developed. The PANoptosis-related hub gene STAT4 potentially served as a protective factor for patients with OC. A better prognosis in OC was found linked to higher PANoptosis. The PANPM, manifesting distinct advantages for clinical application by accurately extracting pathological features, performed excellently in validation and the high-risk group indicated a poor prognosis. Additionally, STAT4
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