ArticleJournal of ovarian research2025
Multi-omics reveals the immune and metabolic characteristics and associations in non-mucinous ovarian cancer.
Article in Journal of ovarian research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe complex metabolic and immune characteristics of ovarian cancer and their interconnections can promote tumor immune evasion, ultimately leading to immunotherapy failure. A comprehensive understanding of these relationships can inform the development of diagnostic markers and more effective therapeutics.
methodsMendelian randomization analysis identified the metabolic and immune characteristics of non-mucinous ovarian cancer. Glycosphingolipid metabolic gene signature was selected using Lasso and univariate Cox regression. Single-cell transcriptomics, proteomics, and spatial transcriptomics were applied to elucidate the expression of these genes and their association with immunological features, while cell-cell communication analysis explored potential molecular mechanisms. Prognostic models were constructed using multiple machine learning algorithms.
results13 metabolic and immune characteristics showed causally associations with non-mucinous ovarian cancer, including CD20 + B cells and the glycosphingolipid metabolism. PPIA-BSG mediated the cell-cell communication between CD20 + B cells and SUMF1 + malignant cells, promoting immune evasion driven by extracellular matrix remodeling. Prognostic models based on extracellular matrix genes co-experessed with SUMF1 demonstrated good predictive accuracy and generalizability across multiple independent datasets (maximum AUC = 0.732).
conclusionsIn non-mucinous ovarian cancer, we identified two distinct cell subpopulations: CD20 + B cells and SUMF1 + malignant cells, which interact through the PPIA-BSG signaling axis. These findings may aid in identifying diagnostic markers and clinical therapeutic targets for this disease.
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.