ArticleFrontiers in oncology2023
A chemotherapy response prediction model derived from tumor-promoting B and Tregs and proinflammatory macrophages in HGSOC.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Unraveling tumor associated macrophage biology in gynecological cancers for targeted therapy in the single cell omics era.iScience · 2025Review
- Cancer-metastasis-on-a-chip reveals efficacy of bevacizumab and siRNA in overcoming carboplatin resistance in SKOV3 ovarian cancer within a fibrotic metastatic microenvironment.Materials today. Bio · 2025Article
- Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.Scientific reports · 2025Article
- Single-cell RNA sequencing reveals the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the tumor microenvironment of high-grade serous ovarian carcinoma.Discover oncology · 2024Article
- B-cell performance in chemotherapy: Unravelling the mystery of B-cell therapeutic potential.Clinical and translational medicine · 2024Review
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 1 country.
Funding
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Abstract
Background: Most patients with high-grade serous ovarian cancer (HGSOC) experienced disease recurrence with cumulative chemoresistance, leading to treatment failure. However, few biomarkers are currently available in clinical practice that can accurately predict chemotherapy response. The tumor immune microenvironment is critical for cancer development, and its transcriptomic profile may be associated with treatment response and differential outcomes. The aim of this study was to develop a new predictive signature for chemotherapy in patients with HGSOC. Methods: Two HGSOC single-cell RNA sequencing datasets from patients receiving chemotherapy were reinvestigated. The subtypes of endoplasmic reticulum stress-related XBP1 Results: By combining cluster-specific genes for the aforementioned cell subtypes, we constructed a chemotherapy response prediction model containing 43 signature genes that achieved an area under the receiver operator curve (AUC) of 0.97 ( Conclusions: The developed chemotherapy response prediction model can be used as a valuable clinical decision tool to guide chemotherapy in HGSOC patients.
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