Abstract

Observational studies have suggested that smoking may increase the risk of cutaneous squamous cell carcinoma (cSCC) while decreasing the risks of basal cell carcinoma (BCC), and melanoma. However, it remains possible that confounding by other factors may explain these associations. The aim of this investigation was to use Mendelian randomization (MR) to test whether smoking is associated with skin cancer, independently of other factors. Two-sample MR analyses were conducted to determine the causal effect of smoking measures on skin cancer risk using genome-wide association study (GWAS) summary statistics. We used the inverse-variance-weighted estimator to derive separate risk estimates across genetic instruments for all smoking measures. A genetic predisposition to smoking initiation was associated with lower risks of all skin cancer types, although none of the effect estimates reached statistical significance (OR 95% CI BCC 0.91, 0.82–1.01; cSCC 0.82, 0.66–1.01; melanoma 0.91, 0.82–1.01). Results for other measures were similar to smoking initiation with the exception of smoking intensity which was associated with a significantly reduced risk of melanoma (OR 0.67, 95% CI 0.51–0.89). Our findings support the findings of observational studies linking smoking to lower risks of melanoma and BCC. However, we found no evidence that smoking is associated with an elevated risk of cSCC; indeed, our results are most consistent with a decreased risk, similar to BCC and melanoma.

Details

Title
Genetic variants for smoking behaviour and risk of skin cancer
Author
Dusingize, Jean Claude 1 ; Law, Matthew H. 2 ; Seviiri, Mathias 3 ; Olsen, Catherine M. 4 ; Pandeya, Nirmala 4 ; Landi, Maria Teresa 5 ; Iles, Mark M. 6 ; Neale, Rachel E. 4 ; Ong, Jue-Sheng 1 ; MacGregor, Stuart 4 ; Whiteman, David C. 4 

 QIMR Berghofer Medical Research Institute, Departments of Population Health and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395) 
 QIMR Berghofer Medical Research Institute, Departments of Population Health and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395); Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Australia (GRID:grid.1024.7) (ISNI:0000 0000 8915 0953); The University of Queensland, Faculty of Medicine, Brisbane, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537) 
 QIMR Berghofer Medical Research Institute, Departments of Population Health and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395); Queensland University of Technology, School of Biomedical Sciences, Faculty of Health, Brisbane, Australia (GRID:grid.1024.7) (ISNI:0000 0000 8915 0953) 
 QIMR Berghofer Medical Research Institute, Departments of Population Health and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395); The University of Queensland, Faculty of Medicine, Brisbane, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537) 
 National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, USA (GRID:grid.94365.3d) (ISNI:0000 0001 2297 5165) 
 University of Leeds, Leeds Institute for Data Analytics, Leeds, UK (GRID:grid.9909.9) (ISNI:0000 0004 1936 8403) 
Pages
16873
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2873642655
Copyright
© Springer Nature Limited 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.