| Title: | Genetic, clinical, lifestyle and sociodemographic risk factors for head and neck cancer: A UK Biobank study |
| Journal: | PLOS ONE |
| Published: | 4 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40184367/ |
| DOI: | https://doi.org/10.1371/journal.pone.0318889 |
Publication 14760
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Abstract
INTRODUCTION: Despite a steady decline in tobacco smoking, head and neck cancer (HNC) incidence rates are on the rise. Therefore, novel risk factors for HNC are needed to identify at-risk patients at an early stage. Here, we used genetic, clinical, lifestyle, and sociodemographic data from UK Biobank (UKB) to evaluate the relative importance of known risk factors for HNC and identify novel predictors of HNC risk.</p>
METHODS: All participants in the UKB between 2006 and 2021 were stratified into HNC cases and controls at baseline (cases: n = 534; controls: n = 501833) or during follow-up (cases: n = 1587; controls: n = 500246). A cross-sectional description of risk factors (clinical characteristics, lifestyle and sociodemographic) for HNC at baseline was performed, followed by multivariate Cox regression analysis (adjusted for age and sex) and gradient boosting machine learning to determine the relative importance of predictors (phenotypic predictors and SNPs) of HNC development after baseline.</p>
RESULTS: In addition to known risk factors for HNC (age, male sex, smoking and alcohol consumption habits, occupation), we show that smoking cessation at ≤ 40 years of age is the strongest predictor of HNC risk. Although SNPs may play a role in HNC development, a predictive model containing phenotypic variables and SNPs (C-index 0.75) did not significantly outperform a model containing the phenotypic predictors alone (C-index 0.73).</p>
CONCLUSION: Taken together, this study demonstrates that phenotypic variables such as past tobacco smoking habits, occupation, facial pain, education, pulmonary function, and anthropometric measures can be used to predict HNC risk.</p>
18 Keywords
- Adult
- Aged
- Alcohol Drinking
- Biological Specimen Banks
- Case-Control Studies
- Cross-Sectional Studies
- Female
- Head and Neck Neoplasms
- Humans
- Life Style
- Male
- Middle Aged
- Polymorphism, Single Nucleotide
- Risk Factors
- Smoking
- Sociodemographic Factors
- UK Biobank
- United Kingdom
6 Authors
- Lisa Tuomi
- Toshima Z. Parris
- Araz Rawshani
- Erik Andersson
- Alina Orozco
- Caterina Finizia
1 Application
| Application ID | Title |
|---|---|
| 76314 | Identification of gene-environment interactions that predict head and neck cancer (HNC) - a machine learning approach. |
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