Fibrosis to carcinogenesis: unveiling the causal dynamics between pulmonary fibrosis and lung cancer - Huang, Yiming; Lin, Zhi; Huang, Ting; Zhou, Heran (Frontiers in Oncology, 2024)
Background Previous clinical evidence has shown a correlation between pulmonary fibrosis (PF) and lung cancer (LC), but their causal relationship remains unknown.This study utilized a bidirectional two-sample Mendelian randomization (MR) approach to explore the causal relationship between PF and LC, including its subtypes.Genetic data were obtained from the IEU and FinnGen Genome-Wide Association Studies (GWAS). SNPs with genome-wide significance were selected, and analyses were conducted using Inverse-Variance Weighted (IVW), MR Egger, and Weighted Median methods. The IVW results for various subtypes of lung cancer and PF were used in a meta-analysis to investigate the overall causal effect between PF and lung cancer. Sensitivity analysis was used for both MR and meta-analysis to investigate the robustness of the results.The bidirectional MR analysis showed no significant causal relationship between PF and overall LC or its subtypes, except for SCLC, which had a significant positive association (OR = 1.29, 95% CI 1.07-1.57, p = 0.009). The meta-analysis results indicated no overall causal effect (OR = 1.067, 95% CI: 0.952-1.195, P = 0.265, I² = 57.3%). In the reverse MR analysis, NSCLC and LUSC showed significant associations with PF (OR = 1.12, 95% CI 1.01-1.23, p = 0.028 and OR = 1.04, 95% CI 1.01-1.08, p = 0.012, respectively), while the meta-analysis results indicated no significant causal effect (OR = 1.006, 95% CI: 0.973-1.040, P = 0.734, I² = 55.9%).Sensitivity analyses indicated no evidence of horizontal pleiotropy or significant heterogeneity.This study suggests a potential causal relationship between PF and SCLC, as well as between NSCLC and LUSC with PF. However, the overall causal relationship between PF and LC was not statistically significant, possibly due to individual variability and other influencing factors. Further research using data from diverse populations is needed to validate these findings.