Prospective evidence regarding the associations between circulating fatty acids (FAs) and lung cancer risk-particularly regarding their interplay with exposome factors and genetic susceptibility-remains limited. Furthermore, the predictive utility of FAs as biomarkers for lung cancer has not been fully characterized. Therefore, this study aims to evaluate the associations of circulating FAs metabolism with lung cancer risk, focusing on their interactions with smoking and genetic risk profiles, while further assessing their potential as predictive biomarkers. A total of 107,954 participants from the UK Biobank study were included in analyses. The associations of circulating FAs, as well as their interactions with polygenic risk score (PRS), smoking status, and FA-related genetic variants, with lung cancer were examined. The differential expression of genes and hub gene involved in FAs metabolism in lung cancer were explored using RNA-sequencing data from The Cancer Genome Atlas. 10-year lung cancer risk prediction models were developed and evaluated incorporating FAs, PRS, and traditional risk factors. A total of 965 lung cancer cases occurred during a mean follow-up of 12.12 years. Circulating saturated FAs (SFAs), monounsaturated FAs (MUFAs) and n-6/n-3 PUFAs ratios were positively associated with lung cancer, whereas polyunsaturated FAs (PUFAs)/n-3 PUFAs were negatively associated with the outcome (all P for trend < 0.05). Moreover, specific FAs presented significant interaction effects with smoking history, lung cancer-PRS, and FAs-associated alleles on the risk of lung cancer. RNA-sequencing of tumor and normal tissue indicated differential gene expression on FAs metabolism (FDR < 0.05). Models incorporating FAs and traditional risk factors significantly improved lung cancer risk prediction, achieving an area under the curve of 0.788. Our study identifies significant associations between circulating FAs and lung cancer, characterized by interplays with smoking and genetic risk profiles. These metabolic markers provide incremental predictive value, offering new insights into risk stratification and individualized lung cancer prevention.
Liu et al. (Wed,) studied this question.