ArticleHeliyon2024
Predictive value of cuproptosis and disulfidptosis-related lncRNA in head and neck squamous cell carcinoma prognosis and treatment.
Article in Heliyon, 2024. 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
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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.
- Metabolic cell deaths in head and neck cancer: mechanisms, therapeutic potential, and challenges.Annals of medicine · 2025Review
- Ferroptosis and radiotherapy-related genes in head and neck squamous cell carcinoma: diagnostic and prognostic significance.Discover oncology · 2025Article
- Disulfidptosis-related gene signatures as prognostic biomarkers and predictors of immunotherapy response in HNSCC.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Head and neck squamous cell carcinoma (HNSCC) is a highly lethal and prevalent malignant tumor with a poor prognosis due to its high recurrence rate, This study aims to develop a prognostic index for HNSCC patients based on Cuproptosis and Disulfidptosis-related long noncoding RNA. Methods: Gene expression and clinical data for HNSCC were obtained from The Cancer Genome Atlas (TCGA). Using Lasso regression and multivariate Cox regression, we established a risk scoring model. The predictive ability of the nomogram, based on clinical features and risk scores, was verified using receiver operating characteristics and calibration curves. We compared independent prognostic parameters, risk score distribution, and survival between high-risk and low-risk groups, followed by preliminary validity evaluations of the model. Results: Our systematic evaluation of prognostic risk provides a new direction for improving the survival prognosis of HNSCC patients in clinical practice, The model effectively categorized patients into high- and low-risk groups with distinct outcomes, identifying numerous gene mutations in these groups, A low-risk score was associated with a better prognosis and higher survival rates. Conclusion: The risk score prognostic prediction system developed in this study shows potential efficacy in predicting the prognosis of HNSCC patients and has practical applications in clinical settings.
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Registered trials
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