MicroRNA Processing Gene Methylation and Cancer Risk

Abstract

Background

Dysregulation of microRNA (miRNA) and methylation levels are epigenetic hallmarks of cancer, potentially linked via miRNA processing genes. Studies have found genetic alterations to miRNA processing genes in cancer cells and human population studies. Our objective was to prospectively examine changes in DNA methylation of miRNA processing genes and their associations with cancer risk.

Methods

We examined cohort data from the Department of Veterans’ Affairs’ Normative Aging Study. Participants were assessed every 3-5 years starting in 1999 through 2013 including questionnaires, medical record review, and blood collection. Blood from 686 consenting participants was analyzed using the Illumina 450K BeadChip array to measure methylation at CpG sites throughout the genome. We selected 19 genes based on a literature review, with 519 corresponding CpG sites. We then used Cox Proportional Hazards models to examine associations with cancer incidence, and Generalized Estimating Equations to examine associations with cancer prevalence. Associations at false discovery rate (FDR) < 0.05 were considered statistically significant.

Results

Methylation of three CpGs (DROSHA: cg23230564, TNRC6B: cg06751583, and TNRC6B: cg21034183) was prospectively associated with time to cancer development (positively for cg06751583, inversely for cg23230564 and cg21034183) whereas methylation of one CpG site (DROSHA: cg16131300) was positively associated with cancer prevalence.

Conclusions

DNA methylation of DROSHA, a key miRNA processing gene, and TNRC6B may play a role in early carcinogenesis.

Impact

Changes in miRNA processing may exert multiple effects on cancer development, including protecting against it via altered global miRNAs, and may be a useful early detection biomarker of cancer.

Introduction

Over the past 20 years US cancer incidence and mortality have generally declined, but this trend has been driven to a great extent by changes in lifestyle and cancer screening behaviors that impact the four most common cancers in the US (lung, breast, prostate, and colorectal) (1). Death rates from other cancers have remained largely stable over the same period of time (1), emphasizing the need for new medical technologies to improve the diagnosis, prognostication, and treatment of these cancers. Studies of epigenetics, and other forms of posttranscriptional gene regulation, are believed to hold great promise for such development. MicroRNAs (miRNAs) are small (~22 nucleotide), noncoding RNAs that bind to messenger RNAs and regulate posttranscriptional gene expression in up to one-third of the human genome by inhibiting gene translation (2,3). In healthy individuals miRNAs play important roles in regulating cell proliferation, differentiation, and apoptosis while in cancer they exert both oncogenic and tumor suppressive effects (3). Global downregulation of miRNAs has also been found in most human tumors, which is generally thought to enhance neoplastic transformation and metastasis (4). One mechanism through which this may be accomplished is through altered expression of miRNA processing genes (5–7). Epigenetic alterations of miRNA processing genes (8,9), and in particular changes in the methylation of these genes (10–14), have been implicated in several types of cancer.

Dysregulation of DNA methylation is likewise ubiquitous across a variety of cancer types and considered a hallmark of cancer (15–17). Disease- and exposure-related methylation changes are detectable in blood, potentially allowing them to serve as biomarkers for cancer and/or the immune response to it (18–20). From a clinical standpoint, biomarkers measurable in blood are attractive for their minimally invasive, cost-effective method of collection and potential utility for screening, risk stratification, and personalized medicine (21). From a biological standpoint, epigenetic alterations of blood leukocyte DNA may be a precursor to cancer, for example, due to their involvement in inflammatory and immune response pathways (19,22–26). Additional evidence suggests that global miRNA levels in leukocytes may play a role in these pathways as well (27–29). However, few studies have measured epigenetic alterations of miRNA processing genes in more accessible surrogate tissues such as blood). Exploring epigenetic modifications of these genes over time may thus inform their use as an early detection biomarker of cancer and potentially provide important information on the role global miRNA dysregulation plays in carcinogenesis and/or cancer promotion.

The primary objective of this study is to prospectively examine the relationship between DNA methylation of miRNA processing genes and cancer incidence. We will also examine the cross-sectional relationship between miRNA processing gene methylation and cancer prevalence. Our primary hypothesis is that time to cancer incidence will be positively associated with increased DNA methylation in miRNA processing genes. We also hypothesize that cancer prevalence will be positively associated with increased DNA methylation of miRNA processing genes, after adjusting for time between diagnosis and blood draw in addition to other covariates.

Materials and Methods

Study Population

In 1963 the Normative Aging Study (NAS) was established by the US Department of Veterans Affairs with an initial cohort of 2280 healthy male veterans who were living in the Boston area; aged 21-80; and had no history of hypertension, cardiovascular disease, cancer, diabetes, or other chronic health conditions. From 1963 to 1999, 981 (43%) participants died and 470 (21%) were lost to follow up. Our previous study using NAS data (30) found no significant differences in subject characteristics (e.g., age, BMI) between participants lost to follow up and the 829 still active in the study as of 1999. Participants were recalled for in-person follow up every 3-5 years, and beginning in 1999 these visits included 7-ml blood samples for genetic and epigenetic analysis. All participants were asked to fast and abstain from smoking prior to their morning visit. From January 1^st, 1999 to December 31^st, 2013 802 of 829 participants (96.7%) consented to blood donation and 686 were randomly selected for whole-epigenome analysis (n=1379 samples) using the Illumina 450K BeadChip array; our prior analysis also found no differences in characteristics between this randomly selected subset and the full population of 802 participants (31). Median age at first blood draw for these participants was 72 (range: 5-100). Data including blood samples, physical examinations, and questionnaires were collected from these participants at each visit for a total of 1-4 visits separated by a median interval of 3.5 years (IQR: 3.1-5.7 years). The NAS was approved by the Institutional Review Boards of all participating institutions, and all participants provided written consent. This analysis excludes 18 participants of nonwhite race (to minimize potential confounding by genetic ancestry) as well as participants with a diagnosis of unspecified malignancy (24 pre-1999 diagnoses and 20 diagnoses occurring between 1999 and 2013), leaving 624 participants for analysis. Of these, 193 (31%) had methylation measured at one time point, 288 (46%) at two, 139 (22%) at three, and four (1%) at four.

Cancer Diagnosis

Information on medical history obtained from questionnaires was confirmed via blinded medical record review, and included cancer diagnoses and comorbidities. Of 624 participants included in this analysis 182 (29%) reported prevalent cancers (54 prostate, 82 skin, 16 colorectal, five lung, four bladder, 21 others) prior to their first blood sample collection and 132 (21%) developed incident cancer (38 prostate, 50 skin, 9 lung, five colorectal, five bladder, 25 others including leukemia, lymphoma, brain, etc.) during follow up. Median follow-up time from first blood draw to cancer diagnosis/censoring was 10.1 years (IQR: 5.8-12.7 years).

Methylation Measurement

We identified 19 candidate miRNA processing genes through: 1) a PubMed literature search for genes directly involved in miRNA biogenesis and degradation, 2) a GeneCard search for additional genes modifying the expression of those genes identified in step 1, and 3) a second PubMed search to identify associations between cancer and either up- or down-regulation of any of the genes identified in steps 1 and 2 (Figure 1: Role of Candidate Genes in miRNA Processing). We identified 30 genes as being involved in miRNA processing but without a link to cancer, which were excluded from subsequent analysis. Methylation of each CpG site within and up to 50kb upstream of each gene of interest was examined (to ensure proper characterization of promoter regions). We ultimately identified 519 CpGs on 19 genes as candidates for analysis; full details on these CpG sites can be found in our supplemental materials (Supplementary Table S1).

Figure 1.

Roles of candidate genes in miRNA biogenesis

Figure 1 diagrams the involvement of each candidate gene of interest in distinct step(s) of miRNA processing both within and without of the nucleus. It also labels distinct precursor miRNA products generated up to mRNA silencing in the RNA silencing complex (RISC). Critical miRNA processing genes/steps are bolded.

For methylation analysis, DNA was extracted from the buffy coat using the QIAamp DNA Blood Kit (QIAGEN, Valencia, CA, USA). A total of 500ng of DNA was used to perform bisulfite conversion using the EZ-96 DNA Methylation Kit (Zymo Research, Orange, CA, USA). To limit chip and plate effects a two-stage, age-stratified algorithm was used to randomize samples and ensure similar age distributions across chips and plates. We randomized 12 samples (sampled across all age quartiles) per chip, and then randomized chips to plates. We measured DNA methylation of CpG probes using Illumina Infinium HumanMethylation450 BeadChip (32). Quality control analysis was performed to remove samples with >1% of probes that had a detection p > 0.05, or probes with p-value > 5% samples with a bead count < 3 or with >1% samples with a detection p-value > 0.05. The remaining samples were preprocessed using Illumina-type background correction (33) and normalized with the dye-bias (34) and BMIQ (35) adjustments to generate methylation β-values, which we then converted to M-values for statistical analysis.(36)

Missing Data

Of the 481,462 CpG sites from 1379 samples (total 663,936,098) processed, 10,406,437 (1.6%) CpG measurements were missing after quality control and data preprocessing. Of the 519 CpG sites of interest from 1379 samples (725,354 values total), 11,094 values (1.5%) were missing. All 519 CpG sites of interest had less than 10% missing data, and 94.0% of them had less than 5% missing data across the 1379 samples. Using the full data set, we used the K Nearest Neighbors method to impute missing values (37). Assuming missingness completely at random, we took advantage of the correlated structure of methylation data to estimate the missing methylation values and impute them in the final data set. The univariate distributions of all significant cancer-associated CpG sites were examined for outliers via visual inspection and cross-checked against the methylation values imputed in the primary data set. For one CpG site (cg15827285) significantly associated with cancer incidence we identified six outliers that were the results of imputation (i.e., missing in the original data set). With these observations excluded this model was rerun, and the resulting non-significant results are not reported (data available upon request).

Statistical Methods

To evaluate the stability of our methylation markers of interest across individuals and across time we calculated the median and interquartile range (IQR) of all methylation values at the first blood draw as well as the three-year intraclass coefficient (ICC; Table S1). For the ICC calculation, we restricted our sample to the first two visits per each participant (excluding participants with only a single visit) and only to visits between two and four years apart (339/466 or 73% of all participants with at least two observations). ICCs were then calculated separately for each CpG site using a mixed linear regression model only adjusting for technical variations and cell type abundancies. A descriptive analysis using data at the first blood draw evaluated differences across subject characteristics by cancer status (cancer-free vs. prevalent cancer vs. incident cancer vs. unspecified) using chi-squared and Kruskal-Wallis tests for categorical and continuous variables, respectively. For the cancer incidence analysis, we examined the relationship between time-dependent miRNA processing gene methylation and time to cancer diagnosis via Cox proportional hazards models. Methylation measures obtained after a diagnosis of cancer were excluded from this analysis. As with our prior analyses, all multivariate models included three principal components that we previously showed could adjust for DNA CpG methylation processing batch, white blood cell counts (WBCs), and proportion neutrophils to account for technical variation and potential disease-related alterations in white blood cell types (31) based on the method by Houseman, et al (38). Based on our previous work with methylation and cancer in this cohort (19) we additionally included age, BMI, education, smoking status, smoking pack-years, and alcohol consumption (dichotomized as reporting 0-1 vs. 2+ drinks per day on average) as covariates.

For the cancer prevalence analysis, we used Generalized Estimating Equations (GEEs) to examine associations between cancer prevalence and DNA CpG methylation according to the following model equation:

$$Y_{\mathit{ijk}}={\beta }_{0k}+{\beta }_{1k}{t}_{ij}+{\beta }_{2k}I\left(t_{ij}>D_i\right)+\dots +e_{\mathit{ijk}}$$

where $Y_{\mathit{ijk}}$ is the the kth methylation marker of subject i at time $t_{ij}$ (i.e., each methylation marker was examined individually, in its own model), ${\beta }_{0k}$ is the intercept, ${\beta }_{1k}$ is the regression coefficient for temporal trend, ${\beta }_{2k}$ is the regression coefficient for $I\left(t_{ij}>D_i\right)$ to denote the effect of cancer diagnosis ( $D_i)$ on methylation marker (i.e., an indicator variable for whether the methylation marker was measured after cancer diagnosis), and $e_{\mathit{ijk}}$ the residual error term. Various linear combinations of coefficients in this model represent the average increases in methylation (as measured in standardized units) over time and for participants who are cancer-free vs. those who have already developed cancer. All cancer prevalence models adjusted for the same covariates as above. All incidence and prevalence analyses adjusted for multiple testing using the Benjamini-Hochberg false discovery rate (FDR) (39), based on the number of tests performed per each candidate gene.

Finally, we conducted several sensitivity analyses. First, for all analyses of DICER1 and DGCR8 methylation we examined the effects of including SNPs on those genes that are available in the NAS data set (rs1640299, rs3742330, and rs13078). Next, we performed two analyses using data from The Cancer Genome Atlas (TCGA). We first examined Pearson’s correlations between methylation at each of our significant CpG sites and RNA levels in both skin and prostate cancer tissue (the most common cancer types in the NAS). We then conducted a case-control analysis of each of our significant CpG sites using TCGA prostate tissue (due to lack of healthy skin tissue, skin cancer could not be analyzed) via unpaired t-test (so as to maximize sample size for comparison). We conducted all NAS analyses in SAS v.9.4 and all TCGA analyses in R v.3.4.2, and changes were considered statistically significant if the FDR-corrected p-value was less than 0.05 (40). Both FDR and Bonferroni-corrected p-values are reported in the complete results, available in our supplementary materials.

Results

Select participant characteristics are presented in Table 1. Briefly, age varied by cancer status (p<0.0001), with incident cancer cases younger and prevalent cases older than those remaining cancer-free. There were no other significant variations in participant characteristics. For the Cox proportional hazards models, results significant at p <0.05 are presented in Table 2; complete results are available in Supplementary Table S2. Prior to adjusting for multiple tests a total of 44 (19 hypermethylated, 25 hypomethylated) CpG sites on 16 genes were significantly associated with time to cancer diagnosis. After FDR adjustment a total of three CpG sites (1 hypermethylated, 2 hypomethylated) remained significantly associated with time to cancer diagnosis at FDR <0.05: cg23230564 on Drosha (HR= 0.66, FDR = 0.05), and cg06751583 and cg21034183 on TNRC6B (HR = 1.62 and 0.56, respectively, FDR = 0.04 for each). When using the more stringent Bonferroni correction for multiple testing, two of these three CpG sites remained significantly associated with time to cancer diagnosis while cg06751583 became non-significant (p=0.08).

Table 1.

Participant Characteristics at First Blood Draw by Cancer Status

	Total	Cancer-free	Prevalent Cancer	Incident Cancer	Unknown (excluded)	p
N		310	182	132	44
Age (years)	72.5+6.9	72.1+6.8	74.3+6.7	70.7+6.6	72.4+7.1	<0.0001
BMI	28.1+4.1	28.4+4.3	27.7+3.6	28.2+4.2	27.3+3.4	0.25
Education
≤ High School	174 (26%)	87 (28%)	44 (24%)	27 (21%)	16 (36%)	0.43
Some College	328 (49%)	149 (48%)	89 (49%)	71 (54%)	19 (43%)
College Grad	166 (25%)	74 (24%)	49 (27%)	34 (26%)	9 (21%)
Smoking
Never	193 (29%)	78 (25%)	58 (32%)	41 (31%)	16 (36%)	0.10
Current	27 (4%)	16 (5%)	2 (1%)	8 (6%)	1 (2%)
Former	448 (67%)	216 (70%)	122 (67%)	83 (63%)	27 (61%)
Pack-Years	21.1+25.2	21.3+24.5	21.6+26.8	21.8+25.6	15.0+21.7	0.30
Alcoholic Drinks
0-1/day	537 (80%)	257 (83%)	139 (76%)	110 (83%)	31 (71%)	0.09
2+/day	131 (20%)	53 (17%)	43 (24%)	22 (17%)	13 (30%)

Table 2.

Cox Model Results where p < 0.05 (n=442)

CpG	Gene	HR	p	FDR
cg23230564	DROSHA	0.66	<0.01	0.05*
cg06751583	TNRC6B	1.62	<0.01	0.04*
cg21034183	TNRC6B	0.56	<0.01	0.04*
cg01780585	AGO1	0.46	0.02	0.41
cg24492446	AGO2	2.16	0.01	0.48
cg11598062	AGO2	0.67	0.01	0.48
cg09815962	AGO2	1.50	0.02	0.48
cg11130692	AGO2	0.45	0.02	0.48
cg14650175	AGO2	1.74	0.02	0.48
cg15936375	AGO2	1.56	0.04	0.56
cg00405484	AGO2	0.62	0.04	0.56
cg10010182	AGO2	0.76	0.04	0.56
cg08444833	AGO2	0.72	0.04	0.56
cg15717330	AGO2	1.32	0.05	0.56
cg06007675	AGO2	1.26	0.05	0.56
cg03988279	AGO3	0.74	0.04	0.43
cg05930400	AGO4	0.71	0.04	0.44
cg15107202	BCDIN3D	0.78	0.03	0.42
cg03373091	DDX17	2.04	<0.01	0.06
cg02917540	DDX17	1.90	0.01	0.06
cg13604020	DDX17	0.67	0.03	0.18
cg17829984	DDX5	0.64	0.02	0.34
cg01901579	DICER1	1.35	0.03	0.33
cg23488578	DICER1	0.73	0.03	0.33
cg13997647	DICER1	0.55	0.04	0.33
cg00580354	PRKRA	0.70	<0.01	0.07
cg27480241	PRKRA	0.62	0.01	0.07
cg13151361	PRKRA	0.75	0.05	0.38
cg17629322	RAN	1.24	0.02	0.39
cg22265988	TARBP2	0.78	0.03	0.45
cg06420129	TNRC6A	0.77	0.03	0.36
cg08811227	TNRC6A	1.73	0.04	0.36
cg27407707	TNRC6A	0.58	0.04	0.36
cg01949902	TNRC6B	0.62	0.02	0.12
cg15462501	TNRC6B	0.68	0.03	0.19
cg14523804	TNRC6C	1.53	0.01	0.21
cg00142402	TNRC6C	1.64	0.01	0.21
cg12072376	TNRC6C	0.74	0.01	0.21
cg07787614	TNRC6C	1.29	0.02	0.21
cg26740249	TNRC6C	1.22	0.02	0.21
cg00423030	TNRC6C	1.28	0.03	0.22
cg18748888	TNRC6C	2.12	0.05	0.29
cg15646497	XPO5	0.51	0.01	0.11
cg01849007	XPO5	1.48	0.01	0.11

^*= Statistically significant at FDR < 0.05.

For the GEE models of prevalent cancers, results significant at p<0.05 are presented in Table 3; complete results can be found in Supplementary Table S3. Briefly, prior to FDR adjustment 30 (14 hypermethylated, 16 hypomethylated) CpG sites on 16 genes were associated with cancer prevalence. After adjusting for multiple tests, one CpG site remained significantly associated with cancer prevalence at FDR<0.05: cg16131300 on Drosha (β = 0.055, FDR=0.04). This CpG remained significantly associated with prevalent cancers after Bonferroni correction. For both the cancer incidence and the cancer prevalence analyses of DGCR8 and DICER1, we found no CpG sites associated with cancer regardless of whether data on SNPs were included in our models (results available upon request). Supplementary Table S4 shows the correlations between methylation of our significant CpG sites and RNA expression in TCGA cancer tissue. In skin cancer, methylation at cg16131300 in DROSHA (r=0.31, p<0.01) as well as cg06751583 (r=0.25, p=0.01) and cg21034183 (r=-0.21, p=0.03) in TNRC6B was significantly and positively correlated with RNA expression. In prostate cancer methylation of cg23230564 (r=0.27, p<0.01) and cg16131300 (r=0.12, p=0.01) in DROSHA was significantly and positively correlated with RNA expression, as was methylation of cg06751583 in TNRC6B (r=0.30, p<0.01). We also observed a marginally significant positive correlation between methylation at cg21034183 in TNRC6B and RNA expression (r=-0.08, p=0.08). Finally, our case-control analysis examining TCGA prostate cancer tissue was only able to validate one of our four significant findings. We found that methylation of cg23230564 in DROSHA was significantly lower in cancer tissue compared to normal (normal mean methylation M = 2.19 in tumor and 2.51 in normal tissue; p <0.01).

Table 3.

GEE Results where p < 0.05 (n=624)

CpG	Gene	β	p	FDR
cg16131300	DROSHA	0.05	<0.01	0.04*
cg09786420	ADAR	0.08	0.01	0.45
cg27026509	AGO1	0.04	0.04	0.57
cg00175844	AGO2	−0.05	0.01	0.25
cg00405484	AGO2	−0.08	<0.01	0.25
cg01980793	AGO2	−0.08	0.02	0.38
cg04476876	AGO2	0.04	0.03	0.38
cg07281647	AGO2	−0.04	0.02	0.38
cg11130692	AGO2	−0.04	0.03	0.38
cg14301764	AGO2	−0.07	<0.01	0.09
cg14484434	AGO2	−0.05	0.03	0.38
cg14835938	AGO2	−0.08	0.05	0.47
cg17050632	AGO2	−0.08	0.04	0.42
cg21208682	AGO2	−0.09	0.02	0.38
cg25071674	AGO2	0.06	0.03	0.38
cg02927292	AGO3	−0.05	0.04	0.67
cg07875848	BCDIN3D	−0.04	0.01	0.19
cg27416957	DDX17	0.04	0.04	0.68
cg01795697	DDX5	−0.03	0.05	0.81
cg26372423	DGCR8	−0.02	0.05	0.71
cg23126376	DICER1	0.08	0.03	0.67
cg00460704	PRKRA	−0.07	0.04	0.78
cg01060409	RAN	0.08	0.01	0.17
cg02785870	TNRC6A	0.05	0.05	0.62
cg03156546	TNRC6A	0.1	0.01	0.25
cg14311088	TNRC6B	0.04	0.02	0.38
cg00175441	TNRC6C	0.08	0.01	0.46
cg05736847	TNRC6C	0.06	0.03	0.46
cg18211066	TNRC6C	−0.11	0.03	0.46
cg14454015	ZCCHC6	0.04	0.04	0.31

^*= Statistically significant at FDR < 0.05.

Discussion

To our knowledge, this is the first study examining prospective relationships between alterations of DNA methylation of miRNA processing genes in blood leukocyte DNA and cancer risk. These results suggest relationships between DNA methylation of two miRNA processing genes (DROSHA and TNRC6B) and cancer. DNA methylation of three CpGs, one on Drosha and two on TNRC6B, was prospectively associated with time from sample collection to cancer diagnosis. Methylation of another CpG (also on Drosha) was associated with cancer prevalence in this cohort, suggesting that these genes may play a role in cancer and that these CpG sites may be useful for cancer detection.

cg23230564 is located in an mRNA-encoding area of the Drosha gene body. Both up- (10) and down-regulation (41,42) of Drosha have been found in various cancer cells. However, in humans SNPs in Drosha have been associated with risk of breast (43,44) and bladder (45) cancer as well as lung cancer recurrence. (46) Emerging evidence suggests that methylation of CpG sites in the gene body region can stimulate transcriptional elongation (47). Our analysis of TCGA data found that increased methylation at this CpG site was associated with increased DROSHA RNA expression, and found lower methylation at this CpG site in prostate cancer tissue compared to normal. If confirmed in other cohorts, this protective finding would be consistent with our hypothesized protective effect of greater miRNA processing gene expression on cancer. Future research should explore prospective cross-talk between cg23230564 methylation and miRNA expression levels in blood from cancer patients to explore the potential mechanistic explanations for this association outlined above. Paired blood-tissue studies could also examine the extent to which DNA methylation of cg23230564 correlates between blood and tumor tissue. In addition, this CpG site demonstrated only modest variation in methylation at the first blood draw (IQR=0.13) and strong ICC (0.68), suggesting that it is not highly variable in the population and is relatively stable over time, meaning it may be useful as a potential biomarker.

Another protective association was identified at cg21034183 on TNRC6B, located in an mRNA-encoding area of the 5′UTR of TNRC6B. Conversely DNA methylation of the other CpG site identified on TNRC6B, cg06751583, was positively associated with time to cancer incidence. This CpG site is within the TSS200 region, suggesting that it affects gene expression. The differential effects between these two CpG sites may be reflective of hydroxymethylation at one CpG site and methylcytosine methylation at the other, resulting in opposite effects on gene expression and therefore (potentially) disease risk. However this explanation is not supported by our TCGA analysis, which found DNA methylation at both sites to be positively associated with gene expression in cancer tissue. Our case-control analysis also could not validate our findings. One potential resolution to these contradictions comes from an in vivo study (48) that identified downregulated H3K36me3 around cg21034183 in cancers. H3K36 has been shown in vitro to inhibit gene expression (49–52). Since no significant histone modifications have been identified in the region of cg06751583, a mechanism through H3K36me3 may explain these contradictory results. With a low IQR (0.08) at the first blood draw and a high ICC (0.70), cg21034183 may have sufficient between- and intra-individual stability to be a useful biomarker, compared to cg06751583, which was stable between individuals at the first blood draw (IQR 0.04), but not over time (ICC=0.08). However the lack of validation in our TCGA case-control analysis suggests that our results should be interpreted with caution and that further studies of methylation at these loci in cancer are necessary.

The CpG site significantly associated with cancer prevalence, cg16131300, is located within an mRNA-encoding area of the 5′UTR region of Drosha 62kb away from cg23230564. Given that methylation at cg16131300 was associated with cancer only post-diagnostically, this may suggest a possible relationship to cancer prognosis or progression rather than development. However our TCGA validation analysis failed to validate this finding; this may reflect tissue-specific differences in methylation of cg16131300 or a relation to cancer progression. In addition the low inter-individual variation (IQR = 0.01) coupled with the low ICC (0.04) suggests that this CpG site may be dynamic over time. This may include a sensitivity to cancer development, reflected in its low consistency over time and the association with post-diagnosis cancer only. However the lack of validation in our TCGA case-control analysis suggests that this is not the case, thus our results should be interpreted with caution until they can be validated. Some studies have associated Drosha with cancer death in humans (57–59). Another possibility is that methylation of Drosha is a mechanism by which tumors evade the immune response, as demonstrated in vitro by miRNA-based reprogramming of dendritic cells by lung tumors (60). Other in vitro studies have also proposed a role for Drosha expression (29) and/or numerous miRNAs in immune system functioning (61–63). Drosha deletion in T-cells has also been found to produce severe inflammation in mice (64), raising the possibility that epigenetic silencing of Drosha in blood leukocytes may be a method by which cancer promotes inflammation. Future studies should also explore Drosha expression in relation to inflammation, the immune response, and cancer progression.

This analysis has several limitations of note. First, the NAS population is not representative. These subjects are older, white, and male; live in Boston; and have a history of military service, all of which may affect the results of the above analyses. Thus, validation in other, more diverse populations is necessary before these findings can be applicable to the general population. While we attempted a simple validation using prostate cancer tissue data from TCGA, the lack of available prospectively collected DNA methylation data from blood collected prior to cancer diagnosis is a serious limitation to our findings. As the two datasets are very heterogeneous in terms of data collection methods, population characteristics, etc. future research to replicate our findings is warranted. Furthermore, DNA methylation and gene expression changes in cancer tissue may not reflect the biological dysregulation measured by disease-related changes in blood DNA methylation (e.g., inflammation and immune response). Thus validation of our findings may be more appropriate in a cohort with available blood DNA data. Second, cancer data from the NAS are limited. Lack of information on mode of detection for prostate cancer, and lack of sufficient sample size for most other cancer types present, prevented a more detailed exploration of specific cancers. Due to the biological heterogeneity of different cancer types, these findings may not be generalizable to all cancers and should be verified in specific cancer types. However, this limitation was somewhat obviated by the use of blood leukocyte DNA for epigenetic analysis, as it reflects immune and inflammatory pathways and systemic exposures common to many different cancer types. Thus, all-cancer incidence may be an appropriate outcome for this analysis.

In conclusion, these findings suggest that methylation changes to two miRNA processing genes, Drosha and TNRC6B, may be important early events in cancer. Methylation changes at specific CpG sites on these genes may also reflect the alteration of miRNA biogenesis and/or inflammatory pathways that in turn affect cancer development. Alternatively, the proper functioning of these pathways in participants who did not develop cancer despite their advanced age and the lengthy follow up of this cohort may point to the important role of miRNAs in tumor suppression elucidated in other, prior research. Extensive additional research is necessary to validate these findings in other, more diverse cohorts (e.g., younger, female, and racial/ethnic minority populations, and with greater numbers of site-specific cancers). With sufficient validation and further study, these and other epigenetic events occurring early in cancer development could potentially be used in the future to develop an early detection biomarker for cancer. If confirmed these relationships could also reveal important information about a potential host of cancer-promoting (or suppressing) effects at the cellular level. Taken together, these findings indicate that epigenetic alterations of miRNA processing genes are a rich potential field of study, and future research should attempt to elucidate their additional potential clinical applications in cancer research and clinical care.