ArticleDiscover oncology2025
DNA methylation and transcription factor-driven immune subtypes in ovarian cancer.
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.
- A machine learning-based basement membrane gene signature model for predicting ovarian cancer survival.Translational cancer research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Ovarian cancer (OC) remains one of the deadliest gynecological malignancies. Immune checkpoint blockade (ICB) inhibitors efficacy in OC has been minimal, highlighting the need for a deeper understanding of the immune microenvironment in OC. Recent studies suggest that DNA methylation and transcription factors may influence the response to immunotherapy. This study aims to classify ovarian cancer into distinct immune subtypes by integrating DNA methylation and transcription factor data through comprehensive bioinformatics analysis. Using data from The Cancer Genome Atlas (TCGA), we identified twelve differentially methylated genes (DMGs) associated with transcription factors and categorized OC into two immune subtypes, C1 and C2.The C1 subtype exhibited higher levels of immune infiltration and better prognosis, characteristic of immune "hot" tumors, whereas the C2 subtype was associated with lower immune infiltration and poorer prognosis, indicative of immune "cold" tumors. A prognostic prediction model based on four key genes-KRT81, PAPPA2, FGF10, and FMO2-was developed using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. This model effectively stratified the TCGA OC cohort into high- and low-risk groups and was validated by predicting patient survival outcomes. Additionally, drug sensitivity analysis revealed potential therapeutic targets for different risk groups, offering new avenues for precision treatment in ovarian cancer. Immunohistochemical tests confirmed the potential of KRT81 as a prognostic marker for ovarian cancer. Our findings enhance the understanding of the molecular characteristics of the OC immune microenvironment, propose novel biomarkers for prognosis, which may potentially improve the prognosis of OC.
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.