Journal article
Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach
Epigenetics, Vol.17(13), pp.2096-2108
2022
DOI: 10.1080/15592294.2022.2108082
PMCID: PMC9665144
PMID: 35920547
Abstract
The decision to engage in lung cancer screening (LCS) necessitates weighing benefits versus harms. Previously, clinicians in the United States have used the PLCOM
2012
algorithm to guide LCS decision-making. However, that formula contains race and gender-based variables. Previously, using data from a European study, Bojesen and colleagues have suggested that cg05575921 methylation could guide decision-making. To test this hypothesis in a more diverse American population, we examined DNA and clinical data from 3081 subjects from the National Lung Screening Trial (NLST) study. Using survival analysis, we found a simple linear predictor consisting of age, pack-year consumption and cg05575921, to have the best predictive power among several alternatives (AUC = 0.66). Results showed that the highest quartile of risk was more than 2-fold more likely to develop lung cancer than those in the lowest quartile. Race, ethnicity, and gender had no effect on prediction with both cg05575921 and pack years contributing equally (both p < 0.003) to risk prediction. Current smokers had considerably lower methylation than former smokers (46% vs 67%; p < 0.001) with the average methylation of those who quit approaching 80% after 25 years of cessation. Finally, current male smokers had lower mean cg05575921 percentage than female smokers (46% vs 49%; p < 0.001). We conclude that cg05575921 (along with age and pack years) can be used to guide LCS decision-making, and additional studies might focus on how best to use methylation to inform decision-making.
Details
- Title: Subtitle
- Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach
- Creators
- Rob Philibert - Behavioral DiagnosticsKelsey Dawes - University of IowaJoanna Moody - University of IowaRichard Hoffman - University of IowaJessica Sieren - University of IowaJeffrey Long - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Epigenetics, Vol.17(13), pp.2096-2108
- Publisher
- Taylor & Francis
- DOI
- 10.1080/15592294.2022.2108082
- PMID
- 35920547
- PMCID
- PMC9665144
- ISSN
- 1559-2294
- eISSN
- 1559-2308
- Grant note
- DOI: 10.13039/100000054, name: the National Cancer Institute, award: R43CA257372
- Language
- English
- Electronic publication date
- 08/04/2022
- Date published
- 2022
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Psychiatry; Epidemiology; Iowa Neuroscience Institute; Biostatistics; General Internal Medicine; Internal Medicine
- Record Identifier
- 9984280873002771
Metrics
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