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BACKGROUND: Tumour-promoting inflammation is a “hallmark” of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. METHODS: We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis -Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,162 cancer cases and up to 824,556 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant ( P < 5.0 x 10 (-8) ) cis -acting SNPs (i.e. in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r (2) < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P -value (“ q -value”) < 0.05 was used as a threshold to define “strong evidence” to support associations and 0.05 ≤ q -value < 0.20 to define “suggestive evidence”. A colocalisation posterior probability (PPH (4) ) > 70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. RESULTS: We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR 1.19, 95% CI 1.10-1.29, q -value=0.033, PPH (4) =84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR 1.42, 95% CI 1.20-1.69, q -value=0.055, PPH (4) =73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR 0.66, 95% CI 0.53-0.81, q -value=0.067, PPH (4) =81.8%), macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR 1.14, 95% CI 1.05-1.23, q -value=0.072, PPH (4) =76.1%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR 0.92, 95% CI 0.88-0.97, q -value=0.15), PPH (4) =85.6%). For 22 of 30 cancer outcomes examined, there was little evidence ( q -value ≥ 0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. CONCLUSION: Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 5 circulating inflammatory markers in risk of 5 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated.

Background Epidemiological and experimental evidence has linked chronic inflammation to cancer aetiology. It is unclear whether associations for specific inflammatory biomarkers are causal or due to bias. In order to examine whether altered genetically predicted concentration of circulating cytokines are associated with cancer development, we performed a two-sample Mendelian randomisation (MR) analysis. Methods Up to 31,112 individuals of European descent were included in genome-wide association study (GWAS) meta-analyses of 47 circulating cytokines. Single nucleotide polymorphisms (SNPs) robustly associated with the cytokines, located in or close to their coding gene (cis), were used as instrumental variables. Inverse-variance weighted MR was used as the primary analysis, and the MR assumptions were evaluated in sensitivity and colocalization analyses and a false discovery rate (FDR) correction for multiple comparisons was applied. Corresponding germline GWAS summary data for five cancer outcomes (breast, endometrial, lung, ovarian, and prostate), and their subtypes were selected from the largest cancer-specific GWASs available (cases ranging from 12,906 for endometrial to 133,384 for breast cancer). Results There was evidence of inverse associations of macrophage migration inhibitory factor with breast cancer (OR per SD = 0.88, 95% CI 0.83 to 0.94), interleukin-1 receptor antagonist with endometrial cancer (0.86, 0.80 to 0.93), interleukin-18 with lung cancer (0.87, 0.81 to 0.93), and beta-chemokine-RANTES with ovarian cancer (0.70, 0.57 to 0.85) and positive associations of monokine induced by gamma interferon with endometrial cancer (3.73, 1.86 to 7.47) and cutaneous T-cell attracting chemokine with lung cancer (1.51, 1.22 to 1.87). These associations were similar in sensitivity analyses and supported in colocalization analyses. Conclusions Our study adds to current knowledge on the role of specific inflammatory biomarker pathways in cancer aetiology. Further validation is needed to assess the potential of these cytokines as pharmacological or lifestyle targets for cancer prevention.

Additional file 4. (Supplemental Figures): Supplementary figure 1. Correlation of MR-IVW estimates (betas) using the two different instrument definitions. Y axis represents the MR IVW estimates using the cis-eQTL and X axis represents the MR IVW estimates using the cis-pQTL instrument definition. Supplementary figure 2. Associations that showed MR evidence for both causality and colocalization (posterior probability>0.8) are plotted, within ��500 kb of the gene locus of the exposure cytokine.

Additional file 1. Members from the PRACTICAL Consortium, CRUK, BPC3, CAPS and PEGASUS.

Additional file 3: Appendix S16. STROBE-MR checklist of recommended items to address in reports of Mendelian randomization studies.

Additional file 3. Supplemental Methods (Details on the Finnish GWAS, the meta-analysis with the SCALLOP or the INTERVAL GWAS and the Mendelian randomisation analyses).

Additional file 1: Appendix S1. Supplementary methods. Appendix S2. List of unique molecular traits identified from literature search. Appendix S3. PubMed search terms for literature review. Table S4. Additional information on GWAS, including covariates adjusted for. Table S6. Results of female BMI sensitivity analysis MR. Table S7. Results of female-specific SNP fasting insulin sensitivity analysis MR. Table S8. Results of female-specific SNP BMI sensitivity mediation analysis. Table S9. Results of female-specific SNP fasting insulin mediation analysis. Table S10. Results of post-hoc power analysis for MR of all molecular traits on endometrial cancer risk, and BMI on all traits confirmed to have a causal effect on endometrial cancer. Table S11. Conditional F-statistics with different levels of genetic correlation for SHBG and bioavailable testosterone and BMI in multivariable Mendelian randomization analyses. Table S12. Conditional F-statistics for fasting insulin (adjusted for BMI) and BMI with differing thresholds used to construct fasting insulin. Table S13. Conditional F-statistics for multivariable Mendelian randomization of BMI and mediators on endometrial cancer risk. Table S14. Conditional F-statistics for further multivariable Mendelian randomization analyses. Table S15. Sample overlap between GWAS. Figure S17. Leave-one-out analysis for MR examining the effect of adult BMI on overall endometrial cancer risk. Table S18. Results from sensitivity analyses examining the influence of Winner’s curse on GWAS with overlapping samples. Table S19. Results from sensitivity analyses examining the influence of Winner’s curse on GWAS with overlapping samples in analyses determining the mediating role of traits in the relationship between BMI and endometrial cancer risk. Figure S20. Leave-one-out analysis for MR examining the effect of adult BMI on endometrioid endometrial cancer risk. Figure S21. Leave-one-out analysis for MR examining the effect of adult BMI on non-endometrioid endometrial cancer risk. Figure S22. Leave-one-out analysis for MR examining the effect of total testosterone level on endometrial cancer risk. Figure S23. Leave-one-out analysis for MR examining the effect of bioavailable testosterone level on endometrial cancer risk. Figure S24. Leave-one-out analysis for MR examining the effect of fasting insulin level on endometrial cancer risk. Figure S25. Leave-one-out analysis for MR examining the effect of SHBG level on endometrial cancer risk. Figure S26. Leave-one-out analysis for MR examining the effect of total serum cholesterol level on endometrial cancer risk. Table S27. Results of female-specific SNP CRP sensitivity analysis MR. Figure S28. Leave-one-out analysis for MR examining the effect of total testosterone level on endometrioid endometrial cancer risk. Figure S29. Leave-one-out analysis for MR examining the effect of bioavailable testosterone level on endometrioid endometrial cancer risk. Figure S30. Leave-one-out analysis for MR examining the effect of fasting insulin level on endometrioid endometrial cancer risk. Figure S31. Leave-one-out analysis for MR examining the effect of SHBG level on endometrioid endometrial cancer risk. Figure S32. Leave-one-out analysis for MR examining the effect of adult BMI on fasting insulin level. Figure S33. Leave-one-out analysis for MR examining the effect of adult BMI on SHBG level. Figure S34. Leave-one-out analysis for MR examining the effect of adult BMI on bioavailable testosterone level. Figure S35. Leave-one-out analysis for MR examining the effect of adult BMI on total serum cholesterol level. Figure S36. Leave-one-out analysis for MR examining the effect of adult BMI on C-reactive protein level. Table S37. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with fasting insulin as a potential mediator with BMI-adjusted fasting insulin instrument and 100 SNPs from BMI instrument. Table S38. Results of sensitivity analysis examining the effect of only including 100 SNPs for the BMI instrument in MR analyses. Table S39. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with fasting insulin as a potential mediator with 100 SNPs from BMI instrument. Table S40. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with fasting insulin as a potential mediator with BMI-adjusted fasting insulin instrument. Table S41. Results of multivariable MR mediation analysis examining the effect of endometrial cancer with pairs of confirmed mediating molecular traits without BMI. Table S42. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with all confirmed mediating molecular traits with BMI-adjusted fasting insulin. Table S43. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with pairs of confirmed mediating molecular traits. Table S44. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with all confirmed mediating molecular traits including fasting insulin adjusted for BMI with 100 SNPs from BMI instrument. Table S45. Results of multivariable MR mediation analysis examining the effect of BMI and endometrial cancer with pairs of confirmed mediating molecular traits with 100 SNPs from BMI instrument. Table S46. Results of multivariable MR mediation analysis examining the effect of endometrial cancer with all confirmed mediating molecular traits without BMI. Table S47. Results from sensitivity analyses examining the influence of Winner’s curse on GWAS with overlapping samples in analyses determining the interdependent effects of mediators of the relationship between BMI and endometrial cancer risk. Table S48. Results of HEIDI test-filtered low-density lipoprotein (LDL) cholesterol and overall endometrial cancer MR.