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Can Smoking-Related DNA Methylation Markers Help to Predict Urothelial Cancer Risk?

By: Gavin Calabretta, BS
Posted: Friday, November 12, 2021

A recent study published in Cancer Epidemiology, Biomarkers & Prevention found that the combination of smoking-related DNA methylation markers may help to predict the risk of urothelial cell carcinoma. According to Pierre-Antoine Dugué, PhD, of Monash Health, Australia, and colleagues, these methylation markers may contribute to some improvements in risk stratification for this type of cancer in the general population, although further evaluation using larger data sets is warranted.

“There are other aspects of smoking history such as age at starting or passive smoking that are typically not or inaccurately captured by questionnaires,” the study authors commented. “As DNA methylation in blood can capture lifetime exposure or different individual responses to smoking, we evaluated the association between smoking-associated methylation and risk of urothelial cell carcinoma.”

The investigators focused on two matched case-control samples. For participants in the Melbourne Collaborative Cohort Study cohort, DNA was extracted from prediagnostic peripheral blood taken at recruitment (1990–1994) or during a follow-up visit (2003–2007). Using these data, predictive models were created for disease risk estimation. Case-control pairs for which blood was taken at recruitment were used to train the algorithms, and those for which blood was taken at follow-up were used to test the algorithms. The study authors separately used the external data from the Women’s Health Initiative study as an independent testing set, using conditional logistic regression models to assess the proposed methylation-based smoking predictors.

The study identified significant associations (P < 4.7 × 10-5) for 29 of 1,061 methylation sites, but these associations were weakened when they adjusted for self-reported smoking. Additionally, nominally significant associations (P < .05) were found at 387 methylation sites without adjustment for self-reported smoking, but this number decreased to 86 with adjustment. The most successful of the prediction models achieved an AUC estimate of 0.66, compared with a value of 0.64 (without methylation information), indicating a modest association between disease risk and methylation independent of self-reported smoking history.

Disclosure: The study authors reported no conflicts of interest.

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