Associations Between Circulating Metabolites and Skin Cancer: A Bidirectional Two-Sample Mendelian Randomization...

Skin cancer is the most common type of cancer worldwide, categorized into melanoma and non-melanoma types. While genetics play a crucial role in the development of skin cancer, recent developments in metabolomics have revealed that circulating metabolites influence genetic risk profiles.

In this study, we utilized a bidirectional two-sample Mendelian Randomization (MR) approach, analyzing metabolite datasets from the Canadian Longitudinal Study of Aging (CLSA) and skin cancer datasets from the UK Biobank. Replication analyses were performed with the 10th round of the FinnGen project. Instrumental variables (IVs) were chosen according to established MR criteria and assessed through the Wald ratio, IVW, MR-Egger, and weighted median methods. Various sensitivity analyses were conducted utilizing Cochrane's Q, Egger's intercept, MR-PRESSO, and leave-one-out (LOO) techniques.

After meticulous analysis, we identified causal relationships between circulating metabolites and skin cancer risks. Specifically, levels of 1,2-dilinoleoyl-GPE (18:2/18:2), 1-palmitoyl-2-linoleoyl-gpc (16:0/18:2), 1-stearoyl-2-arachidonoyl-gpc (18:0/20:4), 1-stearoyl-2-linoleoyl-gpc (18:0/18:2), and Docosapentaenoate n3 DPA, the ratio of Arachidonate (20:4n6) to oleate to vaccenate (18:1), and Oleoyl-linoleoyl-glycerol (18:1 to 18:2) to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) were linked to non-melanoma skin cancer, whereas levels of trans-urocanate were causally linked to melanoma of skin, and S-adenosylhomocysteine (SAH) showed a putative association. It should be noted that docosapentaenoate n3 DPA and SAH were each supported by a single SNP and should be interpreted as hypothesis-generating rather than definitive causal evidence. Reverse MR analysis highlighted robust causal relationships with 18 and 10 metabolites and non-melanoma skin cancer and melanoma of skin, respectively.

These findings enhance our understanding of skin cancer etiology and risk factors, providing genetic evidence for hypothesis-generating associations that support the development of targeted therapies and aid in the identification of potential biomarkers.