DNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study

Abstract

Background: Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic-hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic-hematopoietic cancers.

Methods: Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic-hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms.

Results: Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic-hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic-hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic-hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic-hematopoietic cancers.

Conclusions: This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic-hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic-hematopoietic cancers.

Keywords: American Indians; DNA methylation; Epigenetics; Hematopoietic cancers; Lymphatic cancers.

Fig. 1

Flowchart of the included participants from the Strong Heart Study

Fig. 2

Protein–Protein interaction network of Differentially Methylated Positions in lymphatic–hematopoietic cancer. The circle nodes indicate Differentially Methylated Positions in the Strong Heart Study and the square nodes those replicated in the Framingham Heart Study. The red nodes indicate Differentially Methylated Positions for lymphatic–hematopoietic cancers, the blue nodes for solid cancers and the yellow nodes for all cancers. The size of the nodes is proportional to the number of connections. The edges indicate confidence scores for interactions from 0 to 1

Fig. 3

Differentially methylated region for lymphatic–hematopoietic cancer. Hazard ratios (95% confidence intervals) and genomic location of the top 2 differentially methylated region for lymphatic–hematopoietic cancers including 41 CpG sites. Orange bars represent overlapping promoters. Locations of CpGs of the differentially methylated region in the chromosome are represented by blue vertical bars above the overlapping promoters. The grey area in the plot represents the differentially methylated region

Fig. 4

Violin plots for lymphatic–hematopoietic cancer. Distribution of the methylation proportions of lymphatic–hematopoietic cancers versus non-cases for the top four Differentially Variable Positions