Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach

Philibert, Robert A.; Dawes, Kelsey; Moody, Joanna; Hoffman, Richard M.; Sieren, Jessica C.; Long, Jeffrey D.

doi:10.1080/15592294.2022.2108082

Cited by 6 publications

(3 citation statements)

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“…Using DNAm biomarkers could provide a more precisely defined smoking status phenotype and further strengthen the evidence for genetic associations between smoking exposure and cancer risks. Recently, two independent studies have used the DNAm level at cg05575921- AHRR as part of an algorithm for guiding low dose computed tomography for lung cancer screening [ 85 , 86 ], and the improved specificity suggests a potential use of DNAm biomarkers in clinical use for cancer screening. Relying on a DNAm biomarker to evaluate smoking status rather than self-report would also provide medical personnel with a more accurate smoking assessment for individuals undergoing cancer treatment.…”

Section: Discussionmentioning

confidence: 99%

Epigenetic biomarkers for smoking cessation

Fang

Andersen

Philibert

et al. 2023

Addiction Neuroscience

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Section: Discussionmentioning

confidence: 99%

Epigenetic biomarkers for smoking cessation

Fang

Andersen

Philibert

et al. 2023

Addiction Neuroscience

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“…This signal is robustly observed between discordant MZ twins [ 273 ]. Hypomethylation of the top CpG, AHRR cg05575921, is a predictive biomarker of future lung cancer [ 274 ], with even proposed utility in population lung cancer screening [ 275 ].…”

Section: The Environmental Influence On Epigenomementioning

confidence: 99%

Epigenomic insights into common human disease pathology

Bell

2024

Cell. Mol. Life Sci.

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The epigenome—the chemical modifications and chromatin-related packaging of the genome—enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological ‘clocks’ constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

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“…For the self-report smoking validation, there were N = 3084 participants whose DNA was available to determine cg05575921 methylation [36]. Cg05575921 methylation was determined using the Smoke Signature assay from Behavioural Diagnostics (Coralville, IA, USA) [31,37].…”

Section: Participantsmentioning

confidence: 99%

Predictors of Smoking in Older Adults and an Epigenetic Validation of Self-Report

Long

Gehlsen

Moody

et al. 2022

Genes

Self Cite

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There are several established predictors of smoking, but it is unknown if these predictors operate similarly for young and old smokers. We examined clinical data from the National Lung Screening Trial (NLST) to determine the predictive ability of gender, body mass index (BMI), marital status, and race on smoking behavior, with emphasis on gender interactions. In addition, we validated the self-report of smoking behaviors for a subgroup that had available epigenetic data in the form of cg05575921 methylation. Participants were N=9572 current or former smokers from the NLST biofluids database, age 55–74, minimum of 30 pack years, and mostly White. A subgroup of N=3084 who had DNA were used for the self-report validation analysis. The predictor analysis was based on the larger group and used penalized logistic regression to predict the self-report of being a former or current smoker at baseline. Cg05575921 methylation showed a moderate ability to discriminate among former and current smokers, AUC = 0.85 (95% confidence interval = [0.83, 0.86]). The final selected variables for the prediction model were BMI, gender, BMI by gender, age, divorced (vs. married), education, and race. The gender by BMI interaction was such that males had a higher probability of current smoking for lower BMI, but this switched to females having higher current smoking for overweight to obese. There is evidence that the self-reported smoking behavior in NLST is moderately accurate. The results of the primary analysis are consistent with the general smoking literature, and our results provide additional specificity regarding the gender by BMI interaction. Body weight issues might play a role in smoking cessation for older established smokers in a similar manner as younger smokers. It could be that women have less success with cessation when their BMI increases.

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Using Cg05575921 methylation to predict lung cancer risk: a potentially bias-free precision epigenetics approach

Cited by 6 publications

References 50 publications

Epigenetic biomarkers for smoking cessation

Epigenetic biomarkers for smoking cessation

Epigenomic insights into common human disease pathology

Predictors of Smoking in Older Adults and an Epigenetic Validation of Self-Report

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