ArticleDiscover oncology2025
Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework.
Article in Discover oncology, 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.
- MEGF8-driven metabolic reprogramming and immune evasion define a high-risk subtype of endometriosis-associated ovarian cancer.American journal of translational research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes and cancer progression, their specific roles in ovarian carcinoma remain poorly understood.
methodsWe conducted an integrated analysis of single-cell RNA sequencing and bulk transcriptome data from public databases. DUB genes were identified through Genecard database. Using the Seurat package, we performed cell clustering and differential expression analysis. Cell-cell communications were analyzed using CellChat. A DUB-related risk signature (DRS) was developed using machine learning approaches through integration of GEO and TCGA datasets. The prognostic value and immune characteristics of the signature were systematically evaluated.
resultsOur analysis revealed eight distinct cell subtypes in the tumor microenvironment, including epithelial, fibroblast, myeloid, and Treg cells. DUB-high cells were predominantly found in Treg and myeloid populations, exhibiting elevated expression of tumor-related pathways and enhanced cell-cell communication networks, particularly between fibroblasts and myeloid cells. Conversely, DUB-low cells were enriched in epithelial populations with reduced immune activity. The DRS model demonstrated robust prognostic value across multiple independent cohorts. High-risk patients, as classified by the DRS, showed significantly poorer survival outcomes and distinct immune infiltration patterns compared to low-risk patients.
conclusionThis study provides comprehensive insights into DUB gene expression patterns across different cell populations in ovarian carcinoma. The established DRS model offers a promising tool for risk stratification and may guide personalized therapeutic strategies. Our findings highlight the potential role of DUB genes in modulating the tumor immune microenvironment and patient outcomes in ovarian carcinoma.
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.