ArticleFrontiers in pharmacology2025
Transcriptome analysis of ovarian cancer uncovers association between tumor-related inflammation/immunity and patient outcome.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Platelet-to-lymphocyte ratio for prognostication in immune checkpoint inhibitor-treated cancer patients: a meta-analysis of 13027 patients highlighting nivolumab-responsive renal cell carcinoma.Frontiers in immunology · 2026Pooled it
- Adenosine Pathway-Based Prognostic Signature for Predicting Clinical Outcomes and Immune Microenvironment Characteristics in Epithelial Ovarian Cancer.Journal of clinical practice and research · 2026Article
- Metformin-A Type 2 Diabetes Mellitus Drug-And Ovarian Cancer: Anticancer Mechanisms and Therapeutic Implications.Biomolecules · 2026Review
- HPV-driven inflammatory pathways in ovarian carcinogenesis: molecular mechanisms and emerging therapeutic interventions.Journal of ovarian research · 2025Review
- Patient-derived ovarian cancer models demonstrate the influence of tumor-associated macrophages on therapeutic response.Oncoimmunology · 2025Article
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9 authors.
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Abstract
Background: Epithelial ovarian cancer (EOC) is a cancer that affects the female reproductive system and is highly lethal. It poses significant challenges in terms of treatment and often has a poor prognosis. In recent years, with the advent of PARPi, the treatment of ovarian cancer has entered a new stage of full-process management. Although more and more drugs have been approved, the therapeutic effect of PARPi is still very limited. With the rapid development of PD-1/PD-L1, CTLA-4, oncolytic viruses, cancer vaccines, adoptive cell therapy, etc., tumor immunotherapy has provided new opportunities for the treatment of ovarian cancer. Methods: This study used comprehensive transcriptome analysis across multiple databases to gather gene transcripts and clinical features of normal ovarian samples and tissue samples from ovarian cancer. The aim was to explore the mechanisms underlying tumor immunotherapy resistance and to reveal the relationship between ovarian cancer's immune microenvironment and genes linked to inflammation. Various R packages were used for differential gene analysis, enrichment analysis, co-expression network construction, and prognostic model building. Results: It has been found that the prognosis of ovarian cancer patients is closely associated with sets of genes involved in inflammation. The immune infiltration microenvironment, clinicopathological features, and survival rates differed significantly between two inflammatory gene expression patterns identified using cluster and immune microenvironment analyses. Further analysis revealed that the high-risk group had a higher abundance of M2-type macrophage infiltration, more active anti-tumor immune response, higher tumor stemness score, potentially worse prognosis, and lower response rates to multiple chemotherapy drugs and immune checkpoint inhibitors. Conclusion: These findings provide new perspectives and potential targets for immunotherapy and prognostic evaluation of ovarian cancer and offer new strategies and directions for clinical treatment and patient management. This study provides crucial information to further our comprehension of drug response mechanisms and tumor immunotherapy. It offers new strategies and methods for the treatment and prognostic improvement of ovarian cancer.
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