ArticleNPJ precision oncology2024
Artificial intelligence-based epigenomic, transcriptomic and histologic signatures of tobacco use in oral squamous cell carcinoma.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- GNG12 overexpression inhibits triple-negative breast cancer cell proliferation, migration, and invasion.Scientific reports · 2026Article
- Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.Scientific reports · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
- Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.European journal of cancer (Oxford, England : 1990) · 2025Article
- Recurrent and Metastatic Head and Neck Cancer: Mechanisms of Treatment Failure, Treatment Paradigms, and New Horizons.Cancers · 2025Review
- Recent advances in biomarker detection of oral squamous cell carcinoma.Frontiers in oncology · 2025Review
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
Corrections and comments
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Authors and funding
26 authors.
Funding
Abstract
Oral squamous cell carcinoma (OSCC) biomarker studies rarely employ multi-omic biomarker strategies and pertinent clinicopathologic characteristics to predict mortality. In this study we determine for the first time a combined epigenetic, gene expression, and histology signature that differentiates between patients with different tobacco use history (heavy tobacco use with ≥10 pack years vs. no tobacco use). Using The Cancer Genome Atlas (TCGA) cohort (n = 257) and an internal cohort (n = 40), we identify 3 epigenetic markers (GPR15, GNG12, GDNF) and 13 expression markers (IGHA2, SCG5, RPL3L, NTRK1, CD96, BMP6, TFPI2, EFEMP2, RYR3, DMTN, GPD2, BAALC, and FMO3), which are dysregulated in OSCC patients who were never smokers vs. those who have a ≥ 10 pack year history. While mortality risk prediction based on smoking status and clinicopathologic covariates alone is inaccurate (c-statistic = 0.57), the combined epigenetic/expression and histologic signature has a c-statistic = 0.9409 in predicting 5-year mortality in OSCC patients.
Identifiers
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Registered trials
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