ArticleFrontiers in cell and developmental biology2025
Identification of the key immune gene NR3C1 as a diagnostic biomarker in differentiating ovarian borderline tumors from benign tumors.
Article in Frontiers in cell and developmental biology, 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.
- Transformer-based classification and interpretability of NR3C1 expression patterns in OSCC: Metabolic adaptation insights.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to evaluate novel immune-related biomarkers for distinguishing borderline ovarian tumors (BOTs) from Benign ovarian tumors (BeOTs), addressing the diagnostic challenges posed by their intermediate biological behavior between benign and malignant neoplasms. Methods: We obtained the microarray expression profiles from the datasets (GSE4122 + GSE6822 + GSE36668) in the Gene Expression Omnibus (GEO) database and integrated them with the immune-related genes in the ImmPort database. Differentially immune-related genes (DIRGs) underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein-protein interaction (PPI) network was built to explore the connection. Candidate biomarkers were identified using the Least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), with their diagnostic ability evaluated using Receiver operating characteristic (ROC) curves. A nomogram was constructed to predict BOTs. To validate the diagnostic potential and expression profiles, immunohistochemistry (IHC) analysis was performed in conjunction with the evaluation of an independent test group. We characterized the infiltration profiles of 22 immune cell types in BOTs through the CIBERSORT algorithm. Results: We identified 26 DIRGs between BOTs and BeOTs. These DIRGs were primarily associated with the positive regulation of transferase activity, the positive regulation of epithelial cell proliferation, and the positive regulation of the MAPK cascade. KEGG analysis indicated enrichment of Rap1 and PI3K-Akt signaling pathways. FGFR3, GNAI1, NR3C1, and PDGFA were found to have potential diagnostic value for BOTs (AUC Conclusion: Our study findings suggested that NR3C1 may serve as an immune-related diagnostic biomarker for BOTs, offering a novel perspective for investigating the development and diagnosis of BOTs.
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.