Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis

Abstract

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 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10^-8) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r² < 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 (PPH₄) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis.

Findings: 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, PPH₄ = 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, PPH₄ = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH₄ = 81.8%), 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, PPH₄ = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH₄ = 76.1%), this finding was not replicated when pooled with the FinnGen study. 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.

Interpretation: Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 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.

Funding: Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).

Keywords: Cancer; Genetic epidemiology; Inflammation; Mendelian randomization.

Fig. 1

A step-by-step overview of genome-wide association study selection, instrument construction, and statistical analysis stages. GWAS = genome-wide association study, QC = quality control, SNP = single-nucleotide polymorphism, MAF = minor allele frequency, pQTL = protein quantitative trait locus, LD = linkage disequilibrium, eQTL = expression quantitative trait locus.

Fig. 2

Scatter plot of associations of single-nucleotide polymorphisms with pro-adrenomedullin concentrations and breast cancer risk. SNP = single-nucleotide polymorphism.

Fig. 3

Scatter plot of associations of single-nucleotide polymorphisms with interleukin-23 receptor concentrations and pancreatic cancer risk. SNP = single-nucleotide polymorphism.

Fig. 4

Scatter plot of associations of single-nucleotide polymorphisms with interleukin-1 receptor-like 1 concentrations and triple-negative breast cancer risk. SNP = single-nucleotide polymorphism.

Fig. 5

Forest plot of associations of pro-adrenomedullin concentrations and breast cancer risk across Zhang et al. and FinnGen. RE = random effects. Fixed-effects model OR 1.19 (95% CI 1.10–1.28).

Fig. 6

Forest plot of associations of interleukin-23 receptor concentrations and pancreatic risk across Klein et al. and FinnGen. RE = random effects. Fixed-effects model OR 1.35 (95% CI 1.16–1.57).

Fig. 7

Forest plot of associations of prothrombin concentrations and basal cell carcinoma risk across Adolphe et al. and FinnGen. RE = random effects. Fixed-effects model OR 0.73 (95% CI 0.62–0.85).

Fig. 8

Forest plot of associations of macrophage migration inhibitory factor concentrations and bladder cancer risk across Galesloot et al. and FinnGen. RE = random effects. Fixed-effects model OR 1.50 (95% CI 1.08–2.07).