ArticleBiomarker research2024
Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer.
Article in Biomarker research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Biomimetic fusion nanosystem from ginger exosomes and tumor cell membranes: boosting PLK1-targeted therapy in BRCA-heterogeneous HGSOC.Journal of nanobiotechnology · 2026Article
- NKG2A inhibition promotes NK cell-CD8Nature communications · 2026Article
- SpaHE-Infil: A spatial heterogeneity framework for decoding TME infiltration from H&E-stained slides.iScience · 2026Article
- HOXB8 promotes invasion and metastasis of high-grade serous ovarian cancer via suppression of the KDM6B/C/EBPα signaling axis.Translational cancer research · 2026Article
- Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
- AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy.Frontiers in oncology · 2026Review
- Development and validation of a delta ultrasomics model for predicting treatment response to neoadjuvant chemotherapy in breast cancer.Translational cancer research · 2025Article
- RNA sequencing in ovarian cancer research: a comprehensive review.Journal of ovarian research · 2025Review
- Insights into spheroid formation: interaction of ovarian cancer cells with macrophage populations in the tumor microenvironment.Journal of translational medicine · 2025Article
- TGF-β-driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states.Frontiers in immunology · 2025Review
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6 authors.
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Abstract
backgroundHigh-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.
methodsOur study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.
resultsBy integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.
conclusionsThis study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.
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