Discover Oncology (Aug 2024)

Association between common chronic pulmonary diseases and lung cancer: Mendelian randomization analysis

  • Wenbin Zhang,
  • Xinnan Song,
  • Tianjun Song,
  • Dongyun Zeng

DOI
https://doi.org/10.1007/s12672-024-01274-9
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 10

Abstract

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Abstract Background Lung cancer is a leading public health concern worldwide. Previous evidence suggests that chronic obstructive pulmonary disease (COPD) and asthma may contribute to its development. However, whether these common chronic pulmonary diseases are causal factors of lung cancer remained unclear. Methods Summary statistics from genome-wide association studies (GWAS) were used for Mendelian randomization (MR) analysis. Genetic data for COPD were obtained from the Global Biobank Meta-Analysis Initiative, and asthma data were retrieved from the UK Biobank cohort. Suitable instrumental variables were selected based on quality control measures. GWAS summary data for lung cancer were obtained from a large study involved 85,716 participants. MR analysis was performed using various methods, and sensitivity analyses were conducted. Multivariable MR (MVMR) analysis was employed to account for potential confounding factors. Results Our MR analysis revealed a significant causal association between COPD and lung cancer, including its subtypes such as lung squamous cell carcinoma, lung adenocarcinoma, and small cell lung carcinoma. Genetically predicted COPD was associated with a 64% increased risk of lung cancer and a 2.3 to 2.8-fold increased risk of the different subtypes. However, in the MVMR analysis adjusting for smoking, alcohol drinking, and body mass index, the association between COPD and lung cancer became non-significant. No significant association was observed between asthma (childhood-onset and adult-onset) and lung cancer and its histological subtypes. Conclusions Our study suggests a potential causal association between COPD and lung cancer. However, this association became non-significant after adjusting for smoking in the multivariable analysis.

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