ArticleBMC genomics2022
Combined PD-1/PD-L1 and tumor-infiltrating immune cells redefined a unique molecular subtype of high-grade serous ovarian carcinoma.
Article in BMC genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Letter to the Editor: Development of a prognostic immune cell-based model for ovarian cancer using multiplex immunofluorescence.Journal of translational medicine · 2025Article
- Therapeutic landscape of ovarian cancer: recent advances and emerging therapies.Biomarker research · 2025Review
- Prognostic Significance of Programmed Cell Death Ligand 1 Expression in High-Grade Serous Ovarian Carcinoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- The Prognostic Significance of Tumor-Infiltrating Lymphocytes, PD-L1, BRCA Mutation Status and Tumor Mutational Burden in Early-Stage High-Grade Serous Ovarian Carcinoma-A Study by the Spanish Group for Ovarian Cancer Research (GEICO).International journal of molecular sciences · 2023Article
- Recent Insight about HE4 Role in Ovarian Cancer Oncogenesis.International journal of molecular sciences · 2023Review
- Prognostic and Predictive Role of Tumor-Infiltrating Lymphocytes (TILs) in Ovarian Cancer.Cancers · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-grade serous ovarian carcinoma is highly heterogeneous, and although many studies have been conducted to identify high-grade serous ovarian carcinoma molecular subtypes that are sensitive to immunotherapy, no precise molecular subtype has been proposed to date. Immune cell infiltration and immune checkpoints are highly correlated with immunotherapy. Here, we investigated immune cell infiltration and immune checkpoint values for prognosis and precise immunotherapy for high-grade serous ovarian carcinoma based on molecular subtype classification.
results"High antigen-presenting cells infiltration molecular subtype of high-grade serous ovarian carcinoma" was identified in immune cell infiltration profiles. Each of the three immune cell infiltration clusters (A, B, and C) demonstrated distinct immune cell characterization, with immune cell infiltration cluster C exhibiting high antigen-presenting cell infiltration, improved prognosis, and higher sensitivity to immunotherapy. Programmed death-1/programmed death ligand 1 has a prognostic and predictive role that can help classify molecular subtypes.
conclusionsOur findings redefined a unique molecular subtype of high-grade serous ovarian carcinoma, suggesting that high-grade serous ovarian carcinoma patients with higher antigen-presenting cell infiltration and programmed death-1/programmed death ligand 1 expression can benefit from precise immunotherapy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.