Background: Chronic inflammation plays a key role in cancer etiology. DNA methylation modification, one of the epigenetic mechanisms regulating gene expression, is considered a hallmark of cancer. Human and animal models have identified numerous links between DNA methylation and inflammatory biomarkers. Our objective was to prospectively and longitudinally examine associations between methylation of four inflammatory genes and cancer risk.

Methods: We included 795 Normative Aging Study participants with blood drawn one to four times from 1999 to 2012 (median follow-up, 10.6 years). Promoter DNA methylation of IL6, ICAM-1, IFN, and TLR2 in blood leukocytes was measured using pyrosequencing at multiple CpG sites and averaged by gene for data analysis. We used Cox regression models to examine prospective associations of baseline and time-dependent methylation with cancer risk and compared mean methylation differences over time between cancer cases and cancer-free participants.

Results: Baseline IFN hypermethylation was associated with all-cancer (HR, 1.49; P = 0.04) and prostate cancer incidence (HR, 1.69; P = 0.02). Baseline ICAM-1 and IL6 hypermethylation were associated with prostate cancer incidence (HR, 1.43; P = 0.02; HR, 0.70; P = 0.03, respectively). In our time-dependent analyses, IFN hypermethylation was associated with all-cancer (HR, 1.79; P = 0.007) and prostate cancer (HR, 1.57; P = 0.03) incidence; and ICAM-1 and IL6 hypermethylation were associated with prostate cancer incidence (HR, 1.39; P = 0.02; HR, 0.69; P = 0.03, respectively). We detected significant ICAM-1 hypermethylation in cancer cases (P = 0.0003) 10 to 13 years prediagnosis.

Conclusion: Hypermethylation of IFN and ICAM-1 may play important roles in early carcinogenesis, particularly that of prostate cancer.

Impact: These methylation changes could inform the development of early detection biomarkers and potential treatments of inflammation-related carcinogenesis. Cancer Epidemiol Biomarkers Prev; 24(10); 1531–8. ©2015 AACR.

Chronic inflammation is a significant contributor to carcinogenesis. Drivers of inflammation are diverse, such as chronic or recurrent infection, autoimmune disease, obesity, and toxic exposures. Recent estimates suggest that inflammatory mechanisms directly contribute to roughly 25% of all cancers (1). Large-scale studies have found significant associations between circulating inflammatory factors and risk of multiple types of cancer (2, 3), as well as environmental and behavioral exposures previously linked to cancer (4–6). Dysregulation in the inflammatory immune response can potentially facilitate carcinogenesis through a number of mechanisms. Other studies have also suggested a potential field effect whereby chronic inflammation can induce epigenetic changes in blood leukocytes, which play a pivotal role in inflammation-related carcinogenesis (7–9). For example, inflammation induces characteristic aberrant methylation patterns associated with colorectal cancer (10–12), and inflammation-mediated formation of DNA damage byproducts has been linked to aberrant hypermethylation associated with glioblastoma (13). In prostate cancer, proinflammatory cytokines are reportedly susceptible to altered expression via aberrant DNA hypermethylation and in turn alter the regulation of other genes involved in cancer development (14–16). Genetic and epigenetic changes affecting genes regulating inflammation have been singled out as a potential cause of prostate cancer (17).

Aberrant methylation of DNA in inflammatory genes can be induced by environmental carcinogens (18–21), and this aberrant methylation is an important predictor of cancer incidence (22–24). This suggests DNA methylation as a promising candidate for specific mechanisms by which environmental carcinogens and chronic inflammation can contribute to cancer development. Methylation of blood leukocytes is a critical component governing immune response and inflammatory processes in the body (25–28), both of which have been linked to a wide variety of cancers (ref. 29; often through the induction of additional methylation aberrations; refs. 26, 30). In addition, the accessibility of blood leukocytes has led to increased interest in their use as potential epigenetic biomarkers for a variety of cancers (31).

However, most published human subjects studies in this area are limited by their case–control design, preventing researchers from establishing a temporal relationship between cancer and inflammatory factor expression. As blood samples taken postdiagnosis may be affected by the disease, or by treatment, this is an important issue for studies of cancer epigenetics. Longitudinal data to prospectively explore the viability of methylation measures as a biomarker of cancer are also greatly needed (32). Furthermore, most prospective studies of inflammatory factor methylation have methylation measurements at a single time point only. This means the relationship over time between changes in inflammatory factor methylation and carcinogenesis has yet to be established. The objective of our present study is to better understand this relationship by exploring the prospective associations between prediagnostic blood leukocyte DNA methylation measured at multiple time points, methylation rates of change over time, and risk of developing cancer.

Study population

The Normative Aging Study (NAS) was established by the U.S. Department of Veteran Affairs in 1963. The initial enrollment of the cohort consisted of 2,280 healthy men. Eligibility criteria included veteran status; living in or around Boston, MA; age 21–80 years; and no history of hypertension or other chronic conditions including heart disease, cancer, and diabetes. Since then, participants have been recalled periodically for clinical examinations every 3 to 5 years. From 1963 to 1999, 981 participants died and 470 were lost to follow-up. Statistical comparisons between the remaining 829 participants and those lost to follow-up revealed no significant differences in subject characteristics [age, body–mass index (BMI), etc.]. Beginning in 1999, follow-up examinations included a 7-mL blood sample for genetic and epigenetic analysis. From 1999 through 2012, 802 of 829 (96.7%) NAS participants who had been regularly attending study follow-up visits consented to the blood donation. This analysis focuses on 795 participants who had at least one methylation measurement of one or more of the following inflammatory genes (selected by the NAS for sequencing based on a literature review): IL6, ICAM-1, IFNγ, and TLR2. Circulating levels of IL6 and ICAM-1 proteins were measured from the same blood samples. Of the 795 total participants, 221 (28%) had one visit that included blood collection, 208 (26%) had 2 visits, 233 (29%) had 3 visits, and 133 (17%) had 4 visits. This study was approved by the Institutional Review Boards of all participating institutions and written consent forms obtained from all participants. Study baseline (defined as date of the first collection of a blood sample) ranged from 1999 to 2010 [median baseline, 2000; interquartile range (IQR), 1999–2001]. In total, 303 of 582 (52%) subjects had their baseline visit in 1999 or 2000, 245 of 582 (42%) from 2001 to 2003, and 34 of 582 (6%) from 2003 to 2010.

In addition to blood samples, the NAS consists of anthropometric measurements, standardized medical examinations, and questionnaires about medical history and lifestyle. For purposes of this analysis, we controlled for the following potential confounders: race (dichotomized as white or nonwhite), education (<13, 13–16, >16 years), 2 cigarette smoking variables (status of never/former/current and estimated cumulative pack-years), whether the respondent reported consuming 2 or more alcoholic drinks per day on average, BMI (calculated from weight and height measurements), and age. Because our methylation measures were obtained from blood DNA, we also adjusted for white blood cell count and proportion of neutrophils in the blood samples to account for the possibility that our results might reflect disease-related changes in the white blood cells rather than epigenetic alteration of their DNA.

Cancer diagnosis

NAS investigators obtained cancer diagnoses from questionnaires and confirmed them via medical records and histologic reports. Among the 795 participants included in the present study, a total of 213 participants had been diagnosed with cancer (64 prostate cancers, 95 skin cancers, 54 other) as of the study baseline. These participants were excluded. Among the 582 participants free of cancer at baseline, 137 (23.5%) developed cancer during a median of 10.6 years of follow-up including 47 prostate cancers, 43 skin cancers, and 47 other cancers. These 582 participants' median methylation levels were also determined at baseline to define cutoff values for variable categorization (see below). Initial analytical results of incident skin cancer as an outcome were not significant; therefore, it was dropped from our study and the incidence of all cancers and prostate cancers used as our primary outcomes of interest.

Methylation measurement

The full procedure for blood leukocyte DNA extraction and measurement has been reported previously (21). To maximize methylation measurement accuracy, we used a pyrosequencing-based assay to measure CpG sites at 2 positions each on IFN and IL6, 3 positions on ICAM-1, and 5 positions on TLR2. The CpG sites measured were selected to maximize assay coverage of the promoter region in each target gene, so as to provide the most accurate data on regional methylation. All methylation loci were selected as described in a previously reported protocol (33), on the basis of the reproducibility of primer sets and PCR products. All assays used built-in controls. Methylation measurements from each position were averaged via a simple mean (by gene) and then standardized by processing batch number to have a mean value of 0 and an SD of 1. In light of previous NAS findings (34) that gene-specific methylation can be affected by point mutations at the site of measurement, we searched for SNPs in these genes using the University of California Santa Cruz (UCSC; Santa Cruz, CA) genome browser (genome.ucsc.edu). Of all 12 CpG sites in the 4 inflammatory genes examined in our study, we only observed one SNP (C/T) for IL6 position 1 (rs2069831).

To determine whether to categorize methylation variables for purposes of our analysis, we performed simple scatter plots and fit trend lines (with R2 statistics) to assess the nature of the relationship between cancer incidence and the methylation measures present in the dataset in both continuous and categorical forms. For all cancers, methylation dichotomized about the median (as measured among all subjects free of cancer at baseline) fit the data better. For prostate cancer incidence, continuous methylation resulted in better fit with the data.

Statistical analysis

After descriptive analysis, correlations between mean DNA methylation level of IL6 and ICAM-1 and each of their corresponding blood protein levels were evaluated via Spearman rank correlation coefficients. To capture dynamic changes in DNA methylation, we used Cox proportional hazards models of time to cancer diagnosis on mean methylation as a time-dependent independent variable, the first using methylation data from baseline (first blood sample) only and the second using all available follow-up visits. For circulating proteins with significant Spearman rank correlations, we compared these model results to those using the corresponding protein level instead of methylation.

To examine the potential effects of the above-mentioned SNP and other potential variability by CpG position on our results, we also conducted a sensitivity analysis of standardized measurements of inflammatory factor methylation at each position separately to look for significant departures from our findings with mean methylation. We also conducted a second sensitivity analysis examining short-term time trends by creating variables for methylation values measured at 3-, 4-, and 5-year intervals from baseline minus baseline methylation values and using multiple Cox proportional hazards models to estimate prospective associations between these interval variables and risk of developing cancer.

We obtained change rate (in standardized units per year) as the slope of the repeated measures of DNA methylation to examine the relationship between increasing methylation change rate and cancer incidence. This involved using a linear regression model to estimate changes in methylation over time (slope) for all participants with more than one measurement and subsequently treating the slope value from this model as an independent variable in additional Cox regression models.

Finally, we compared the mean difference in methylation between cancer cases and cancer-free participants each year prior to cancer diagnosis to examine the difference in methylation trajectory between 2 groups. Because of low sample size, these 1-year intervals were collapsed into categories on the basis of 5-year intervals (<5, 5–<10, and 10+ years). Individual methylation measures were plotted, and statistical significance of the mean between-group difference between subjects who later developed cancer and those who did not was assessed via linear mixed-effects regression models of methylation on cancer status, time interval, and other independent variables as above. All analyses were performed using SAS version 9.3, with P ≤ 0.05 set as our threshold for statistical significance.

Participant characteristics by cancer status are similar to those reported previously for this cohort (35), although the number of incident cancer cases slightly increased as of the most recent follow-up. Overall, participants were older (mean age, 72 years), overwhelmingly Caucasian (96%), mostly (72%) college educated or more, and the majority (71%) were current or former smokers. Table 1 shows the results of our descriptive analysis of baseline inflammatory factor methylation by participant characteristics. Briefly, IFN methylation varied across both smoking variables (P = 0.05 for smoking status and P = 0.01 for pack-years of smoking), white blood cell count (P = 0.006), and percent neutrophils (P < 0.001). IL6 methylation varied across race (P = 0.01), and was significantly correlated with circulating IL6 protein level (ρ = -0.08, P = 0.02).

Table 1.

Participant characteristics and cancer risk factors by inflammatory factor methylation among cancer-free participants at baseline

IFNICAM-1IL6TLR2
Mean ± SD/n (%)LowHighLowHighLowHighLowHigh
Age 72.26 ± 6.84 72.1 ± 6.6 71.4 ± 6.8 71.6 ± 6.4 71.9 ± 7.1 71.8 ± 7.2 71.69 ± 6.3 71.5 ± 6.2 72.1 ± 7.0 
  P = 0.17 P = 0.67 P = 0.80 P = 0.33 
Race 
 White 766 (96%) 271 (96.8%) 266 (94.7%) 208 (97.2%) 204 (95.8%) 273 (97.9%) 262 (93.2%) 226 (95.8%) 212 (95.9%) 
 Non-White 29 (4%) 9 (3.2%) 15 (5.3%) 6 (2.8%) 9 (4.2%) 6 (2.1%) 19 (6.8%) 10 (4.2%) 9 (4.1%) 
  P = 0.30 P = 0.45 P = 0.01a P = 1.00 
Education 
 High school graduate or less 224 (28%) 77 (27.5%) 84 (29.9%) 58 (27.1%) 63 (29.6%) 76 (27.2%) 83 (29.5%) 74 (31.4%) 65 (29.4%) 
 Some college/college graduate 387 (49%) 135 (48.2%) 139 (49.5%) 111 (51.9%) 99 (46.5%) 144 (51.6%) 133 (47%) 109 (43.2%) 108 (48.9%) 
 Any professional/graduate school 184 (23%) 68 (24.3%) 58 (20.6%) 45 (21.0%) 51 (23.9%) 59 (21.1%) 65 (23.13%) 53 (22.5%) 48 (21.7%) 
  P = 0.56 P = 0.54 P = 0.61 P = 0.85 
BMI 28.3 ± 4.1 28.3 ± 3.9 28.3 ± 4.4 28.5 ± 4.1 28.3 ± 4.4 28.6 ± 4.3 28.1 ± 3.9 28.5 ± 4.4 28.2 ± 4.0 
  P = 0.92 P = 0.57 P = 0.15 P = 0.52 
Smoking status 
 Never smoked 227 (28.55%) 90 (32.1%) 66 (23.5%) 58 (27.1%) 60 (28.2%) 74 (26.5%) 80 (28.5%) 60 (25.4%) 65 (29.4%) 
 Current smoker 33 (4%) 10 (3.6%) 16 (5.7%) 10 (4.7%) 11 (5.2%) 12 (4.3%) 13 (4.6%) 12 (5.1%) 8 (3.6%) 
 Former smoker 535 (67%) 180 (64.3%) 199 (70.8%) 146 (68.2%) 142 (66.7%) 193 (69.2%) 188 (66.9%) 164 (69.5%) 148 (67.0%) 
  P = 0.05a P = 0.93 P = 0.87 P = 0.54 
Average pack-years 21.5 ± 26.7 18.2 ± 24.2 23.4 ± 24.2 21.87 ± 24.9 22.05 ± 24.4 21.6 ± 24.5 19.7 ± 23.8 21.9 ± 24.3 20.4 ± 25.2 
  P = 0.01a P = 0.94 P = 0.35 P = 0.51 
Alcohol consumption 
 0–1 average drinks/d 644 (81%) 237 (84.6%) 224 (79.7%) 170 (79.4%) 172 (80.8%) 232 (83.2%) 228 (81.1%) 195 (82.6%) 179 (81.0%) 
 2+ average drinks/d 151 (19%) 43 (15.4%) 57 (20.3%) 44 (20.6%) 41 (19.3%) 47 (16.9%) 53 (18.9%) 41 (17.4%) 42 (19.0%) 
  P = 0.15 P = 0.81 P = 0.58 P = 0.72 
White blood cell count 6.5 ± 3.25 6.1 ± 1.4 6.7 ± 2.8 6.5 ± 1.7 6.4 ± 2.9 6.5 ± 1.7 5.2 ± 1.5 6.5 ± 2.8 6.2 ± 1.6 
  P = 0.006a P = 0.78 P = 0.07 P = 0.12 
Proportion neutrophils 61.9 ± 8.7 59.3 ± 6.9 64.9 ± 8.3 62.5 ± 8.2 62.1 ± 8.6 62.3 ± 8.1 61.9 ± 7.9 62.8 ± 8.7 61.3 ± 7.7 
  P < 0.001a P = 0.64 P = 0.54 P = 0.06 
IFNICAM-1IL6TLR2
Mean ± SD/n (%)LowHighLowHighLowHighLowHigh
Age 72.26 ± 6.84 72.1 ± 6.6 71.4 ± 6.8 71.6 ± 6.4 71.9 ± 7.1 71.8 ± 7.2 71.69 ± 6.3 71.5 ± 6.2 72.1 ± 7.0 
  P = 0.17 P = 0.67 P = 0.80 P = 0.33 
Race 
 White 766 (96%) 271 (96.8%) 266 (94.7%) 208 (97.2%) 204 (95.8%) 273 (97.9%) 262 (93.2%) 226 (95.8%) 212 (95.9%) 
 Non-White 29 (4%) 9 (3.2%) 15 (5.3%) 6 (2.8%) 9 (4.2%) 6 (2.1%) 19 (6.8%) 10 (4.2%) 9 (4.1%) 
  P = 0.30 P = 0.45 P = 0.01a P = 1.00 
Education 
 High school graduate or less 224 (28%) 77 (27.5%) 84 (29.9%) 58 (27.1%) 63 (29.6%) 76 (27.2%) 83 (29.5%) 74 (31.4%) 65 (29.4%) 
 Some college/college graduate 387 (49%) 135 (48.2%) 139 (49.5%) 111 (51.9%) 99 (46.5%) 144 (51.6%) 133 (47%) 109 (43.2%) 108 (48.9%) 
 Any professional/graduate school 184 (23%) 68 (24.3%) 58 (20.6%) 45 (21.0%) 51 (23.9%) 59 (21.1%) 65 (23.13%) 53 (22.5%) 48 (21.7%) 
  P = 0.56 P = 0.54 P = 0.61 P = 0.85 
BMI 28.3 ± 4.1 28.3 ± 3.9 28.3 ± 4.4 28.5 ± 4.1 28.3 ± 4.4 28.6 ± 4.3 28.1 ± 3.9 28.5 ± 4.4 28.2 ± 4.0 
  P = 0.92 P = 0.57 P = 0.15 P = 0.52 
Smoking status 
 Never smoked 227 (28.55%) 90 (32.1%) 66 (23.5%) 58 (27.1%) 60 (28.2%) 74 (26.5%) 80 (28.5%) 60 (25.4%) 65 (29.4%) 
 Current smoker 33 (4%) 10 (3.6%) 16 (5.7%) 10 (4.7%) 11 (5.2%) 12 (4.3%) 13 (4.6%) 12 (5.1%) 8 (3.6%) 
 Former smoker 535 (67%) 180 (64.3%) 199 (70.8%) 146 (68.2%) 142 (66.7%) 193 (69.2%) 188 (66.9%) 164 (69.5%) 148 (67.0%) 
  P = 0.05a P = 0.93 P = 0.87 P = 0.54 
Average pack-years 21.5 ± 26.7 18.2 ± 24.2 23.4 ± 24.2 21.87 ± 24.9 22.05 ± 24.4 21.6 ± 24.5 19.7 ± 23.8 21.9 ± 24.3 20.4 ± 25.2 
  P = 0.01a P = 0.94 P = 0.35 P = 0.51 
Alcohol consumption 
 0–1 average drinks/d 644 (81%) 237 (84.6%) 224 (79.7%) 170 (79.4%) 172 (80.8%) 232 (83.2%) 228 (81.1%) 195 (82.6%) 179 (81.0%) 
 2+ average drinks/d 151 (19%) 43 (15.4%) 57 (20.3%) 44 (20.6%) 41 (19.3%) 47 (16.9%) 53 (18.9%) 41 (17.4%) 42 (19.0%) 
  P = 0.15 P = 0.81 P = 0.58 P = 0.72 
White blood cell count 6.5 ± 3.25 6.1 ± 1.4 6.7 ± 2.8 6.5 ± 1.7 6.4 ± 2.9 6.5 ± 1.7 5.2 ± 1.5 6.5 ± 2.8 6.2 ± 1.6 
  P = 0.006a P = 0.78 P = 0.07 P = 0.12 
Proportion neutrophils 61.9 ± 8.7 59.3 ± 6.9 64.9 ± 8.3 62.5 ± 8.2 62.1 ± 8.6 62.3 ± 8.1 61.9 ± 7.9 62.8 ± 8.7 61.3 ± 7.7 
  P < 0.001a P = 0.64 P = 0.54 P = 0.06 

aStatistically significant at P < 0.05. P values are shown for the Student t test and Fisher's exact test for continuous and categorical characteristics, respectively.

Table 2 shows the results of our analyses of baseline and time-dependent inflammatory factor methylation with risk of developing cancer. For methylation measured at baseline only, high IFN methylation was associated with all-cancer [HR, 1.49; 95% confidence interval (CI), 1.01–2.20] and prostate cancer incidence (HR, 1.69; 95% CI, 1.10–2.60). High baseline ICAM-1 (HR, 1.43; 95% CI, 1.07–1.92) and IL6 (HR, 0.70; 95% CI, 0.51–0.97) methylation were associated with prostate cancer incidence as well. For the time-dependent analyses, participants with high IFN methylation were significantly more likely to develop any cancer (HR, 1.71; 95% CI, 1.16–2.51), prostate cancer (HR, 1.57; 95% CI, 1.04–2.37), and other cancers (HR, 1.85; 95% CI, 1.15–2.99; data not shown). Participants with high time-dependent ICAM-1 methylation were more likely to develop prostate cancer (HR, 1.39; 95% CI, 1.02–1.89), whereas participants with high time-dependent methylation of IL6 were significantly less likely to develop prostate cancer (HR, 0.69; 95% CI, 0.50–0.95). There were no significant associations between TLR2 methylation and risk of developing cancer. When rerunning the above models using IL6 protein level instead of IL6 methylation level, we found no significant associations (data available upon request). Examining incident skin cancer as the outcome of interest likewise produced no noteworthy results (data available upon request).

Table 2.

Multivariable model results

Baseline methylation measureTime-dependent methylation
Cancer Dx (n)
NoYesHR (95% CI)PHR (95% CI)P
IFN: all cancer 
 Low 222 58 Ref.  Ref.  
 High 209 72 1.49 (1.01–2.20) 0.04a 1.71 (1.16–2.51) 0.007a 
 Prostate cancer 431 43 1.69 (1.10–2.60) 0.02a 1.57 (1.04–2.37) 0.03a 
ICAM-1: all cancer 
 Low 169 44 Ref.  Ref.  
 High 160 54 1.25 (0.83–1.88) 0.29 1.17 (0.79–1.74) 0.42 
 Prostate cancer 329 33 1.43 (1.07–1.92) 0.02a 1.39 (1.02–1.89) 0.04a 
IL6: all cancer 
 Low 213 66 Ref.  Ref.  
 High 219 62 0.93 (0.65–1.34) 0.7 0.87 (0.60–1.24) 0.43 
 Prostate cancer 432 44 0.70 (0.51–0.97) 0.03a 0.69 (0.50–0.95) 0.02a 
TLR2: all cancer 
 Low 178 49 Ref.  Ref.  
 High 179 51 0.96 (0.63–1.44) 0.83 0.88 (0.59–1.31) 0.53 
 Prostate cancer 357 35 0.88 (0.60–1.28) 0.49 0.95 (0.66–1.37) 0.77 
Baseline methylation measureTime-dependent methylation
Cancer Dx (n)
NoYesHR (95% CI)PHR (95% CI)P
IFN: all cancer 
 Low 222 58 Ref.  Ref.  
 High 209 72 1.49 (1.01–2.20) 0.04a 1.71 (1.16–2.51) 0.007a 
 Prostate cancer 431 43 1.69 (1.10–2.60) 0.02a 1.57 (1.04–2.37) 0.03a 
ICAM-1: all cancer 
 Low 169 44 Ref.  Ref.  
 High 160 54 1.25 (0.83–1.88) 0.29 1.17 (0.79–1.74) 0.42 
 Prostate cancer 329 33 1.43 (1.07–1.92) 0.02a 1.39 (1.02–1.89) 0.04a 
IL6: all cancer 
 Low 213 66 Ref.  Ref.  
 High 219 62 0.93 (0.65–1.34) 0.7 0.87 (0.60–1.24) 0.43 
 Prostate cancer 432 44 0.70 (0.51–0.97) 0.03a 0.69 (0.50–0.95) 0.02a 
TLR2: all cancer 
 Low 178 49 Ref.  Ref.  
 High 179 51 0.96 (0.63–1.44) 0.83 0.88 (0.59–1.31) 0.53 
 Prostate cancer 357 35 0.88 (0.60–1.28) 0.49 0.95 (0.66–1.37) 0.77 

aStatistically significant at P < 0.05.

Abbreviation: Dx, diagnosis.

Reanalyzing significant results by individual CpG position revealed no substantive deviations from mean methylation in directionality, magnitude, or statistical significance of the above associations (data available upon request). We also did not observe any significant associations between methylation change values over 3-, 4-, or 5-year intervals and risk of developing cancer. Increased rate of change of ICAM-1 methylation was significantly associated with prostate cancer incidence (HR, 25.1; 95% CI, 1.05–596, data not shown), whereas increased rate of IFN methylation was inversely associated with cancer incidence (HR, 0.58; 95% CI, 0.34–0.99, data not shown). Figure 1 plots the mean difference in ICAM-1 methylation between participants who ultimately developed cancer and cancer-free participants by time intervals prior to diagnosis. Notably, ICAM-1 methylation was significantly higher in participants who ultimately developed cancer than those who did not 10 or more years prior to cancer diagnosis (P = 0.0003).

Figure 1.

Mean ICAM-1 methylation in participants with and without an eventual cancer diagnosis by 5-year interval.

Figure 1.

Mean ICAM-1 methylation in participants with and without an eventual cancer diagnosis by 5-year interval.

Close modal

Our results show prospective relationships between IFN methylation levels over time and elevated risk of developing cancer. We also found a number of associations between risk of developing prostate cancer and 3 of the inflammatory factor genes studied (IFN, ICAM-1, and IL6). The consistent associations of both methylation at baseline and time-dependent inflammatory factor methylation (incorporating all follow-up visits) suggest that methylation of IFN, ICAM-1, and IL6 are epigenetic changes that occur early in prostate cancer development (and possibly other cancers as well, in the case of IFN), pointing to the involvement of inflammatory factor methylation in carcinogenesis. The methylation change rate analysis suggests that more rapid increases in ICAM-1 methylation are associated with risk of developing cancer, whereas a high rate of increase in IFN methylation is protective. Finally, a temporal relationship emerged with ICAM-1 methylation in cancer cases being significantly higher a decade or more prior to diagnosis. To our knowledge, this is the first study finding detectable differences in mean methylation level in cancer cases compared with cancer-free participants so many years prior to cancer diagnosis.

The positive association between IFN methylation and risk of developing cancer is consistent with its previously described role in an apoptosis pathway via DAP kinase (36). The role of IFN in promoting cell apoptosis can lead to it serving a tumor-suppressive function, one that has been previously shown to be reduced in colon cancer (37). IFN also serves to stimulate IFN8, which can exert tumor suppressive effects on a wide range of carcinomas (38). IFN methylation has been specifically shown to be a mechanism used by infiltrating tumor cells to induce immunosuppression (39). In our study, IFN methylation varied across both smoking variables (P = 0.05 for smoking status and P = 0.01 for pack-years of smoking), with heavier smokers tending to have higher methylation of IFN. Studies suggest that exposure to chemicals in cigarettes can affect gene-specific DNA methylation levels through pathways similar to those through which smoking can induce genetic changes (32). Animal studies have found that one mechanism through which smoking depresses the immune response is through reduced expression of IFN (40). Ouyang and colleagues reported the hypermethylation of the IFN promoter among workers with diisocyanate-induced occupational asthma due to exposure to inhaled carcinogens (41). This evidence suggests that hypermethylation of IFN may be one of the epigenetic mechanisms through which inhaled carcinogens induce carcinogenesis and, if confirmed, may lead to new interventions to reduce the impact of smoking, a significant public health concern worldwide.

In contrast to the results of our time-dependent analysis, where higher IFN methylation was associated with increased risk of developing cancer, we found that the speed of increase over time of IFN methylation was inversely associated with risk of developing prostate cancer. One possible explanation for this apparent contradiction with our baseline and time-dependent analyses (both of which found a significant, positive association) is that the mechanism by which IFN methylation influences the risk of developing prostate cancer may be cumulative in nature. In other words, early low-intensity IFN hypermethylation has a stronger cancer-promoting effect than later, high-intensity IFN hypermethylation. This may be a reflection of the indirect nature of the causal pathway, for example, a reduction in normal cellular apoptosis achieves an elevated risk of cancer that can only be fully realized over time or it may be related to other involved epigenetic mechanisms that we were unable to incorporate into our analysis. Although the tumor-suppressive functions of IFN have been found in blood and tissue of patients with cancer (42, 43), the possibility that accumulative IFN methylation over time helps drive cancer development has not been established. Under this theory, long-term IFN methylation aberration is necessary for prostate carcinogenesis, rather than severe, short-term methylation. If accurate, then epigenetic targets involved in the IFN pathway may be effective therapeutic or preventive targets for prostate cancer. One study already suggests that this may be the case in MYC-driven prostate cancer (44). Given the absence of genetic data from tumor tissue in our study, confirmation of our finding with regard to IFN methylation (particularly in conjunction with MYC expression in prostate cancer cells) is necessary. Future research incorporating IFN expression (e.g., through circulating protein levels unavailable in our data) can explore these hypotheses and help explain our complex findings for IFN methylation.

Similarly, ICAM-1 acts as a tumor suppressor primarily by modulating antitumor immunity (45). A previous study has detected reduced ICAM-1 expression in ovarian cancer cell lines (46), as well as some malignant melanomas (47). Recent work on targeted demethylation of genes in the ICAM-1 pathway (48) and upregulation of ICAM-1 expression (49) are promising avenues for enhancing the immune response to cancer. Our finding that ICAM-1 methylation level is, on average, significantly higher many years prior to conventional diagnosis of cancer is intriguing and may be reflected in our finding of elevated risk of developing cancer with greater ICAM-1 methylation rate of change. If replicated, this finding of hypermethylated ICAM-1 in patients with cancer many years before diagnosis may assist in the development, already underway (48), of enhanced diagnostic and therapeutic techniques to improve outcomes of a variety of cancers. The large lead time found in our analysis may explain why other studies with less prediagnostic follow-up do not consistently find associations between ICAM-1 methylation and cancer (47, 50), as well as the absence of other published findings relating ICAM-1 methylation to prostate cancer. Alternate methods of epigenetic silencing, such as histone modification (45), may also be involved in suppressing the expression of ICAM-1 (and through it the immune response) on tumor-conditioned endothelial cells in the time period closer to diagnosis. This is a plausible mechanism through which ICAM-1 methylation can affect cancer development independently of circulating ICAM protein levels and may be an explanation for the lack of any statistical associations between ICAM protein level and cancer incidence found in our study. As methylation is only one factor affecting gene transcription/expression as it relates to carcinogenesis (51), this may also explain the lack of a significant correlation between ICAM-1 methylation and circulating ICAM protein.

Elevated levels of the IL6 cytokine have been found repeatedly in both serum and local tissue samples taken from patients with prostate cancer (52, 53), and suppression of SOCS3 via expression of IL6 has been implicated in the development (54) and aggressiveness (55) of prostate cancer. These findings may point to an important causal factor for prostate cancer. Alternatively, IL6 hypomethylation in these cases may simply be a marker for more widespread (possibly even genome-wide) methylation in prostate cancer, a possibility that can be confirmed in other studies of the relationship between methylation and prostate cancer. Our significant finding despite the lack of an association between circulating IL6 and cancer incidence suggests that methylation of IL6 may potentially exert effects contributing to prostate carcinogenesis through mechanisms other than circulating protein expression (e.g., by affecting SOCS3 expression; ref. 56) and adds further evidence of its importance in that process and potential usefulness for further study. The significant inverse correlation between IL6 methylation and IL6 circulating protein found in our study may also facilitate the development of IL6 as a biomarker of prostate cancer.

DNA methylation is dynamic. However, it is still largely unknown whether the magnitude of methylation changes plays a role in cancer etiologically and, if they do, how much change is necessary to facilitate cancer initiation/development. The lack of significant findings for our interval analysis comparing the risk of developing cancer by 3-, 4-, and 5-year differences in inflammatory factor methylation may be due to an insufficiently large effect to be detectable across the interval chosen, if there is such an effect. Alternatively, it may be that lengthier follow-up or a larger sample than was available in this study is necessary to detect differences in inflammatory factor methylation related to early carcinogenesis.

This analysis has limitations. We were unable to consider other potential epigenetic mechanisms affecting inflammatory factor genes, such as histone modification or microRNAs. We were also unable to examine expression (e.g., circulating protein levels) for 2 of the inflammatory factor genes studied, preventing direct causal inference for these factors. Other data potentially relevant to the study of cancer (e.g., family history of cancer) were also unavailable in the source dataset, making unmeasured confounding of our results a potential concern. A strength of our study was the large quantity of data and multiple follow-up measurements available, offset by a relatively low sample size. This is particularly true for some of our subgroup analyses, such as those with large (>10 years) follow-up. Sample size also prevented us from examining many subtypes of cancer and even our prostate cancer sample was limited, resulting in wide confidence intervals for our rate-of-change analysis that should be confirmed in future studies with a larger sample size. This forced the use of all-cancer incidence as our outcome of interest. Given the heterogeneity of cancers and their biologic diversity, this aggregation may not reflect the biologic reality of the development and progression of all cancers. However, given that the majority of diagnoses in our study population were prostate (n = 47) or skin (n = 43) cancers, our findings suggest that inflammatory factor gene methylation may be an important factor involved in the development of these specific diseases. Our results will need to be verified for other specific cancer subtypes before viable interventions based on them can be developed. This also affected our interval analysis, as it was similarly restricted to participants whose visits were 3, 4, or 5 years apart. As our time-dependent analysis could use the full sample, it may have had sufficient statistical power to capture false-negatives missed in the interval analyses due to the lack of a specific short-term temporal mechanism relating inflammatory factor methylation and cancer. The variable number of follow-up visits also introduces potential information bias, as individuals who are less healthy (e.g., diagnosed with more aggressive metastatic disease) are less likely to be able to participate, and study participants are more likely to be cancer survivors. This combined with the fact that our sample was overwhelmingly older, Caucasian, and male warrants further studies in larger, more representative populations.

In conclusion, our results suggest several relationships between methylation of various inflammatory factor genes and cancer. The methylation of both IFN and ICAM-1 appears to play a role in the development of cancer, potentially during early stages of carcinogenesis, and these pathways should be investigated in larger studies with other populations including women, younger adults, and racial/ethnic minorities to confirm the mechanistic hypotheses discussed above. Our finding regarding ICAM-1 methylation and time to diagnosis in particular is novel and also warrants further investigation, but if true provides novel insight into when carcinogenetic methylation aberrations may occur relative to diagnosis, leading to new developments in the detection of a variety of cancers. The associations between prostate cancer and methylation in all 3 significant inflammatory factors also warrant further study, in particular with larger populations of African-Americans due to the well-documented racial disparities in prostate cancer incidence (57) and mortality (58) in the United States. Such studies can help elucidate the causal paths involved in prostate cancer and potentially explain part of this health disparity.

L. Liu is a consultant/advisory board member for Celladon, Outcome Research Solutions, and Zensun. No potential conflicts of interest were disclosed by the other authors.

Conception and design: L. Liu, A.A. Baccarelli, L. Hou

Development of methodology: B.T. Joyce, L. Liu, L. Hou

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Schwartz, L. Hou

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): B.T. Joyce, T. Gao, L. Liu, Y. Zheng, S. Liu, W. Zhang, F. Penedo, Q. Dai, J. Schwartz, L. Hou

Writing, review, and/or revision of the manuscript: B.T. Joyce, L. Liu, W. Zhang, F. Penedo, Q. Dai, J. Schwartz, A.A. Baccarelli, L. Hou

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): B.T. Joyce, L. Hou

Study supervision: A.A. Baccarelli, L. Hou

The VA Normative Aging Study is supported by the Cooperative Studies Program/Epidemiology Research and Information Center of the U.S. Department of Veterans Affairs and is a component of the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC).

The Normative Aging Study is supported by the Epidemiology Research and Information Center of U.S. Department of Veterans Affairs (NIEHS R01-ES015172). L. Hou received additional support from the Northwestern University Robert H. Lurie Comprehensive Cancer Center Rosenberg Research Fund. A. Baccarelli and J. Schwartz received additional support from the National Institute of Environmental Health Sciences (NIEHS R01-ES021733, NIEHS R01-ES015172, and NIEHS P30-ES00002).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hussain
SP
,
Harris
CC
. 
Inflammation and cancer: an ancient link with novel potentials
.
Int J Cancer
2007
;
121
:
2373
80
.
2.
Xu
B
,
Niu
XB
,
Wang
ZD
,
Cheng
W
,
Tong
N
,
Mi
YY
, et al
IL-6 -174G>C polymorphism and cancer risk: a meta-analysis involving 29,377 cases and 37,739 controls
.
Mol Biol Rep
2011
;
38
:
2589
96
.
3.
Borges
AH
,
Silverberg
MJ
,
Wentworth
D
,
Grulich
AE
,
Fatkenheuer
G
,
Mitsuyasu
R
, et al
Predicting risk of cancer during HIV infection: the role of inflammatory and coagulation biomarkers
.
Aids
2013
;
27
:
1433
41
.
4.
Zhang
FF
,
Santella
RM
,
Wolff
M
,
Kappil
MA
,
Markowitz
SB
,
Morabia
A
. 
White blood cell global methylation and IL-6 promoter methylation in association with diet and lifestyle risk factors in a cancer-free population
.
Epigenetics
2012
;
7
:
606
14
.
5.
Murata
M
,
Thanan
R
,
Ma
N
,
Kawanishi
S
. 
Role of nitrative and oxidative DNA damage in inflammation-related carcinogenesis
.
J Biomed Biotechnol
2012
;
2012
:
623019
.
6.
Bayarsaihan
D
. 
Epigenetic mechanisms in inflammation
.
J Dent Res
2011
;
90
:
9
17
.
7.
McAllister
F
,
Bailey
JM
,
Alsina
J
,
Nirschl
CJ
,
Sharma
R
,
Fan
H
, et al
Oncogenic Kras activates a hematopoietic-to-epithelial IL-17 signaling axis in preinvasive pancreatic neoplasia
.
Cancer Cell
2014
;
25
:
621
37
.
8.
Oberyszyn
TM
,
Conti
CJ
,
Ross
MS
,
Oberyszyn
AS
,
Tober
KL
,
Rackoff
AI
, et al
Beta2 integrin/ICAM-1 adhesion molecule interactions in cutaneous inflammation and tumor promotion
.
Carcinogenesis
1998
;
19
:
445
55
.
9.
Yongvanit
P
,
Thanan
R
,
Pinlaor
S
,
Sithithaworn
P
,
Loilome
W
,
Namwat
N
, et al
Increased expression of TLR-2, COX-2, and SOD-2 genes in the peripheral blood leukocytes of opisthorchiasis patients induced by Opisthorchis viverrini antigen
.
Parasitol Res
2012
;
110
:
1969
77
.
10.
Abu-Remaileh
M
,
Bender
S
,
Raddatz
G
,
Ansari
I
,
Cohen
D
,
Gutekunst
J
, et al
Chronic inflammation induces a novel epigenetic program that is conserved in intestinal adenomas and in colorectal cancer
.
Cancer Res
2015
;
75
:
2120
30
.
11.
Hahn
MA
,
Hahn
T
,
Lee
DH
,
Esworthy
RS
,
Kim
BW
,
Riggs
AD
, et al
Methylation of polycomb target genes in intestinal cancer is mediated by inflammation
.
Cancer Res
2008
;
68
:
10280
9
.
12.
Ueda
Y
,
Ando
T
,
Nanjo
S
,
Ushijima
T
,
Sugiyama
T
. 
DNA methylation of microRNA-124a is a potential risk marker of colitis-associated cancer in patients with ulcerative colitis
.
Dig Dis Sci
2014
;
59
:
2444
51
.
13.
Sowers
JL
,
Johnson
KM
,
Conrad
C
,
Patterson
JT
,
Sowers
LC
. 
The role of inflammation in brain cancer
.
Adv Exp Med Biol
2014
;
816
:
75
105
.
14.
Nelson
WG
,
De Marzo
AM
,
DeWeese
TL
,
Isaacs
WB
. 
The role of inflammation in the pathogenesis of prostate cancer
.
J Urol
2004
;
172
:
S6
11
;
discussion S-2
.
15.
Song
LN
,
Silva
J
,
Koller
A
,
Rosenthal
A
,
Chen
EI
,
Gelmann
EP
. 
The Tumor Suppressor NKX3.1 is Targeted for Degradation by DYRK1B Kinase
.
Mol Cancer Res
2015
;
13
:
913
22
.
16.
Yasmin
R
,
Siraj
S
,
Hassan
A
,
Khan
AR
,
Abbasi
R
,
Ahmad
N
. 
Epigenetic regulation of inflammatory cytokines and associated genes in human malignancies
.
Mediators Inflamm
2015
;
2015
:
201703
.
17.
Ianni
M
,
Porcellini
E
,
Carbone
I
,
Potenzoni
M
,
Pieri
AM
,
Pastizzaro
CD
, et al
Genetic factors regulating inflammation and DNA methylation associated with prostate cancer
.
Prostate Cancer Prostatic Dis
2013
;
16
:
56
61
.
18.
Stoccoro
A
,
Karlsson
HL
,
Coppede
F
,
Migliore
L
. 
Epigenetic effects of nano-sized materials
.
Toxicology
2013
;
313
:
3
14
.
19.
Jimenez-Garza
O
,
Baccarelli
A
,
Byun
HM
, Battista
Bartolucci
G
,
Carrieri
M
. 
Gene-specific DNA methylation as a valuable tool for risk assessment: the case of occupational exposure to different VOC's in Mexican workers
.
Occup Environ Med
2014
;
71
Suppl 1
:
A36
.
20.
Bollati
V
,
Baccarelli
A
,
Hou
L
,
Bonzini
M
,
Fustinoni
S
,
Cavallo
D
, et al
Changes in DNA methylation patterns in subjects exposed to low-dose benzene
.
Cancer Res
2007
;
67
:
876
80
.
21.
Hou
L
,
Zhang
X
,
Tarantini
L
,
Nordio
F
,
Bonzini
M
,
Angelici
L
, et al
Ambient PM exposure and DNA methylation in tumor suppressor genes: a cross-sectional study
.
Part Fibre Toxicol
2011
;
8
:
25
.
22.
Dubois
RN
. 
Role of inflammation and inflammatory mediators in colorectal cancer
.
Trans Am Clin Climatol Assoc
2014
;
125
:
358
72
; discussion 3
72
3
.
23.
Patel
SA
,
Bhambra
U
,
Charalambous
MP
,
David
RM
,
Edwards
RJ
,
Lightfoot
T
, et al
Interleukin-6 mediated upregulation of CYP1B1 and CYP2E1 in colorectal cancer involves DNA methylation, miR27b and STAT3
.
Br J Cancer
2014
;
111
:
2287
96
.
24.
Song
TY
,
Lim
J
,
Kim
B
,
Han
JW
,
Youn
HD
,
Cho
EJ
. 
The role of tumor suppressor menin in IL-6 regulation in mouse islet tumor cells
.
Biochem Biophys Res Commun
2014
;
451
:
308
13
.
25.
Wilson
AG
. 
Epigenetic regulation of gene expression in the inflammatory response and relevance to common diseases
.
J Periodontol
2008
;
79
:
1514
9
.
26.
Backdahl
L
,
Bushell
A
,
Beck
S
. 
Inflammatory signalling as mediator of epigenetic modulation in tissue-specific chronic inflammation
.
Int J Biochem Cell Biol
2009
;
41
:
176
84
.
27.
Campion
J
,
Milagro
FI
,
Goyenechea
E
,
Martinez
JA
. 
TNF-alpha promoter methylation as a predictive biomarker for weight-loss response
.
Obesity (Silver Spring)
2009
;
17
:
1293
7
.
28.
Nile
CJ
,
Read
RC
,
Akil
M
,
Duff
GW
,
Wilson
AG
. 
Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis
.
Arthritis Rheum
2008
;
58
:
2686
93
.
29.
Richardson
BC
. 
Role of DNA methylation in the regulation of cell function: autoimmunity, aging and cancer
.
J Nutr
2002
;
132
:
2401
s-5s.
30.
Franco
R
,
Schoneveld
O
,
Georgakilas
AG
,
Panayiotidis
MI
. 
Oxidative stress, DNA methylation and carcinogenesis
.
Cancer Lett
2008
;
266
:
6
11
.
31.
Hou
L
,
Zhang
X
,
Gawron
AJ
,
Liu
J
. 
Surrogate tissue telomere length and cancer risk: shorter or longer
?
Cancer letters
2012
;
319
:
130
5
.
32.
Terry
MB
,
Delgado-Cruzata
L
,
Vin-Raviv
N
,
Wu
HC
,
Santella
RM
. 
DNA methylation in white blood cells: association with risk factors in epidemiologic studies
.
Epigenetics
2011
;
6
:
828
37
.
33.
Madrigano
J
,
Baccarelli
A
,
Mittleman
MA
,
Sparrow
D
,
Vokonas
PS
,
Tarantini
L
, et al
Aging and epigenetics: longitudinal changes in gene-specific DNA methylation
.
Epigenetics
2012
;
7
:
63
70
.
34.
Lepeule
J
,
Baccarelli
A
,
Motta
V
,
Cantone
L
,
Litonjua
A
,
Sparrow
D
, et al
Gene promoter methylation is associated with lung function in the elderly: the Normative Aging Study
.
Epigenetics
2012
;
7
:
261
9
.
35.
Zhu
ZZ
,
Sparrow
D
,
Hou
L
,
Tarantini
L
,
Bollati
V
,
Litonjua
AA
, et al
Repetitive element hypomethylation in blood leukocyte DNA and cancer incidence, prevalence, and mortality in elderly individuals: the Normative Aging Study
.
Cancer Causes Control
2011
;
22
:
437
47
.
36.
Cohen
O
,
Kimchi
A
. 
DAP-kinase: from functional gene cloning to establishment of its role in apoptosis and cancer
.
Cell Death Differ
2001
;
8
:
6
15
.
37.
McGough
JM
,
Yang
D
,
Huang
S
,
Georgi
D
,
Hewitt
SM
,
Rocken
C
, et al
DNA methylation represses IFN-gamma-induced and signal transducer and activator of transcription 1-mediated IFN regulatory factor 8 activation in colon carcinoma cells
.
Mol Cancer Res
2008
;
6
:
1841
51
.
38.
Lee
KY
,
Geng
H
,
Ng
KM
,
Yu
J
,
van Hasselt
A
,
Cao
Y
, et al
Epigenetic disruption of interferon-gamma response through silencing the tumor suppressor interferon regulatory factor 8 in nasopharyngeal, esophageal and multiple other carcinomas
.
Oncogene
2008
;
27
:
5267
76
.
39.
Janson
PC
,
Marits
P
,
Thorn
M
,
Ohlsson
R
,
Winqvist
O
. 
CpG methylation of the IFNG gene as a mechanism to induce immunosuppression [correction of immunosupression] in tumor-infiltrating lymphocytes
.
J Immunol
2008
;
181
:
2878
86
.
40.
Lugade
AA
,
Bogner
PN
,
Thatcher
TH
,
Sime
PJ
,
Phipps
RP
,
Thanavala
Y
. 
Cigarette smoke exposure exacerbates lung inflammation and compromises immunity to bacterial infection
.
J Immunol
2014
;
192
:
5226
35
.
41.
Ouyang
B
,
Bernstein
DI
,
Lummus
ZL
,
Ying
J
,
Boulet
LP
,
Cartier
A
, et al
Interferon-gamma promoter is hypermethylated in blood DNA from workers with confirmed diisocyanate asthma
.
Toxicol Sci
2013
;
133
:
218
24
.
42.
Ganapathi
SK
,
Beggs
AD
,
Hodgson
SV
,
Kumar
D
. 
Expression and DNA methylation of TNF, IFNG and FOXP3 in colorectal cancer and their prognostic significance
.
Br J Cancer
2014
;
111
:
1581
9
.
43.
Ma
D
,
Jiang
C
,
Hu
X
,
Li
Q
,
Li
T
,
Yang
Y
, et al
Methylation patterns of the IFN-gamma gene in cervical cancer tissues
.
Sci Rep
2014
;
4
:
6331
.
44.
Wee
ZN
,
Li
Z
,
Lee
PL
,
Lee
ST
,
Lim
YP
,
Yu
Q
. 
EZH2-mediated inactivation of IFN-gamma-JAK-STAT1 signaling is an effective therapeutic target in MYC-driven prostate cancer
.
Cell Rep
2014
;
8
:
204
16
.
45.
Hellebrekers
DM
,
Castermans
K
,
Vire
E
,
Dings
RP
,
Hoebers
NT
,
Mayo
KH
, et al
Epigenetic regulation of tumor endothelial cell anergy: silencing of intercellular adhesion molecule-1 by histone modifications
.
Cancer Res
2006
;
66
:
10770
7
.
46.
Arnold
JM
,
Cummings
M
,
Purdie
D
,
Chenevix-Trench
G
. 
Reduced expression of intercellular adhesion molecule-1 in ovarian adenocarcinomas
.
Br J Cancer
2001
;
85
:
1351
8
.
47.
Pergoli
L
,
Favero
C
,
Pfeiffer
RM
,
Tarantini
L
,
Calista
D
,
Cavalleri
T
, et al
Blood DNA methylation, nevi number, and the risk of melanoma
.
Melanoma Res
2014
;
24
:
480
7
.
48.
Chen
H
,
Kazemier
HG
,
de Groote
ML
,
Ruiters
MH
,
Xu
GL
,
Rots
MG
. 
Induced DNA demethylation by targeting Ten-Eleven Translocation 2 to the human ICAM-1 promoter
.
Nucleic Acids Res
2014
;
42
:
1563
74
.
49.
Coral
S
,
Parisi
G
,
Nicolay
HJ
,
Colizzi
F
,
Danielli
R
,
Fratta
E
, et al
Immunomodulatory activity of SGI-110, a 5-aza-2′-deoxycytidine-containing demethylating dinucleotide
.
Cancer Immunol Immunother
2013
;
62
:
605
14
.
50.
Friedrich
MG
,
Chandrasoma
S
,
Siegmund
KD
,
Weisenberger
DJ
,
Cheng
JC
,
Toma
MI
, et al
Prognostic relevance of methylation markers in patients with non-muscle invasive bladder carcinoma
.
Eur J Cancer
2005
;
41
:
2769
78
.
51.
Zhang
X
,
Wallace
AD
,
Du
P
,
Lin
S
,
Baccarelli
AA
,
Jiang
H
, et al
Genome-wide study of DNA methylation alterations in response to diazinon exposure in vitro
.
Environ Toxicol Pharmacol
2012
;
34
:
959
68
.
52.
Giri
D
,
Ozen
M
,
Ittmann
M
. 
Interleukin-6 is an autocrine growth factor in human prostate cancer
.
Am J Pathol
2001
;
159
:
2159
65
.
53.
Hobisch
A
,
Eder
IE
,
Putz
T
,
Horninger
W
,
Bartsch
G
,
Klocker
H
, et al
Interleukin-6 regulates prostate-specific protein expression in prostate carcinoma cells by activation of the androgen receptor
.
Cancer Res
1998
;
58
:
4640
5
.
54.
Calarco
A
,
Pinto
F
,
Pierconti
F
,
Sacco
E
,
Marrucci
E
,
Totaro
A
, et al
Role of SOCS3 evaluated by immunohistochemical analysis in a cohort of patients affected by prostate cancer: preliminary results
.
Urologia
2012
;
79
Suppl 19
:
4
8
.
55.
Pierconti
F
,
Martini
M
,
Pinto
F
,
Cenci
T
,
Capodimonti
S
,
Calarco
A
, et al
Epigenetic silencing of SOCS3 identifies a subset of prostate cancer with an aggressive behavior
.
Prostate
2011
;
71
:
318
25
.
56.
Sommer
U
,
Schmid
C
,
Sobota
RM
,
Lehmann
U
,
Stevenson
NJ
,
Johnston
JA
, et al
Mechanisms of SOCS3 phosphorylation upon interleukin-6 stimulation. Contributions of Src- and receptor-tyrosine kinases
.
J Biol Chem
2005
;
280
:
31478
88
.
57.
Mordukhovich
I
,
Reiter
PL
,
Backes
DM
,
Family
L
,
McCullough
LE
,
O'Brien
KM
, et al
A review of African American-white differences in risk factors for cancer: prostate cancer
.
Cancer Causes Control
2011
;
22
:
341
57
.
58.
Graham-Steed
T
,
Uchio
E
,
Wells
CK
,
Aslan
M
,
Ko
J
,
Concato
J
. 
'Race' and prostate cancer mortality in equal-access healthcare systems
.
Am J Med
2013
;
126
:
1084
8
.