ArticleDiscover oncology2025
WGCNA and ferroptosis genes in OSCC: unraveling prognostic biomarkers and therapeutic targets.
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 3 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
3 citing papers in PubMed.
- Novel Disease-Specific Panel of Salivary microRNAs for the Detection of Oral Squamous Cell Carcinoma from Early Invasion to Stage IV Disease.International journal of molecular sciences · 2026Article
- Review
- Identification of KIF14 as a ferroptosis biomarker for predicting the prognosis and immunotherapy efficacy of bladder cancer.Discover oncology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOral squamous cell carcinoma (OSCC) is the predominant type of oral cancer, with over 370,000 new cases and approximately 170,000 deaths annually worldwide. Despite therapeutic advancements, OSCC mortality rates have been increasing, underscoring the need for improved prognostic models and therapeutic targets.
methodsWe integrated transcriptomic and clinical survival data from the TCGA-OSCC dataset to identify ferroptosis-related prognostic genes. Using weighted gene co-expression network analysis (WGCNA), we selected genes associated with OSCC prognosis and applied Lasso regression analysis to pinpoint key genes. A prognostic model was constructed and validated through survival analysis and receiver operating characteristic (ROC) curve analysis.
resultsWGCNA identified modules significantly correlated with ferroptosis, yielding 321 genes associated with OSCC prognosis. Univariate Cox analysis identified 13 genes affecting OSCC prognosis. Lasso regression and multivariate Cox regression narrowed down the gene set to a final set of 7 genes, which were used to construct the risk model. The model stratified patients into high- and low-risk groups with significant survival differences (P < 0.001). The model's predictive accuracy was validated, with AUC values ranging from 0.565 to 0.733 for 1-, 3-, and 5-year survival predictions. Immune-related analysis revealed that low-risk patients exhibited higher immune cell infiltration and were more likely to benefit from immunotherapy.
conclusionOur study presents a novel prognostic model for OSCC patients based on ferroptosis-related genes, which not only predicts survival but also identifies potential therapeutic targets. The model's predictive accuracy and clinical relevance were validated, offering a new strategy for OSCC treatment.
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.