DNA Methylation of Telomere-Related Genes and Cancer Risk...Kit (Zymo Research). In the NAS, this was done on blood collected between 1999 and 2007. DNA methylation was ... and 949

Research Article

DNA Methylation of Telomere-Related Genesand Cancer RiskBrian T. Joyce1, Yinan Zheng1, Drew Nannini1, Zhou Zhang1, Lei Liu2,Tao Gao1, Masha Kocherginsky3, Robert Murphy4, Hushan Yang5,Chad J. Achenbach6, Lewis R. Roberts7, Mirjam Hoxha8, Jincheng Shen9,Pantel Vokonas10,11, Joel Schwartz12, Andrea Baccarelli13, and Lifang Hou1

Abstract

Researchers hypothesized that telomere shorteningfacilitates carcinogenesis. Previous studies found incon-sistent associations between blood leukocyte telomerelength (LTL) and cancer. Epigenetic reprogramming oftelomere maintenance mechanisms may help explainthis inconsistency. We examined associations betweenDNAmethylation in telomere-related genes (TRG) andcancer. We analyzed 475 participants providing 889samples 1 to 3 times (median follow-up, 10.1 years)from 1999 to 2013 in the Normative Aging Study. Allparticipants were cancer-free at each visit and bloodleukocytes profiled using the Illumina 450K array. Of121 participants who developed cancer, 34 had prostatecancer, 10 melanoma, 34 unknown skin malignancies,and 43 another cancer. We examined 2,651 CpGs from80 TRGs and applied a combination of Cox and mixedmodels to identify CpGs prospectively associated with

cancer (at FDR < 0.05). We also explored trajectories ofDNAmethylation, logistic regression stratified by time todiagnosis/censoring, and cross-sectional models of LTLat first blood draw. We identified 30 CpGs on 23 TRGswhosemethylationwas positively associatedwith cancerincidence (b ¼ 1.0–6.93) and one protective CpG inMAD1L1 (b¼�0.65),ofwhich87%were located inTRGpromoters.Methylation trajectoriesof21CpGs increasedin cancer cases relative to controls; at 4 to 8 years pre-diagnosis/censoring, 17 CpGs were positively associatedwith cancer. ThreeCpGswere cross-sectionally associatedwithLTL. TRGmethylationmaybeamechanismthroughwhich LTL dynamics reflect cancer risk. Future researchshould confirm these findings and explore potentialmechanisms underlying these findings, includingtelomere maintenance and DNA repair dysfunction.Cancer Prev Res; 11(8); 511–22. �2018 AACR.

IntroductionTelomeres are tandem TTAGGG nucleotide repeats that

"cap" the ends of eukaryotic chromosomes and serve tomaintain genomic stability and limit cellular proliferation(1). Blood leukocyte telomere length (LTL) shortens withage, and this process can be accelerated by exposureto environmental risk factors (in particular those knownto cause oxidative stress and/or chronic inflammation,two major carcinogenic pathways; ref. 2). Prior studies

demonstrated that LTL shortening may reflect in situchanges in telomere length among precancerous andcancerous cells (2) and that cellular senescence inducedby critical telomere shortening and the Hayflick limit isgenerally thought tobe a tumor-suppressive process,whichcancer cells must overcome early in carcinogenesis (3).However, the exact role of LTL in cancer developmentremains uncertain. There are numerous studies reportingassociations between LTL and cancer risk (2), with largelyinconsistent results. These inconsistencies may be due to

1Center for Population Epigenetics, Robert H. Lurie Comprehensive CancerCenter and Department of Preventive Medicine, Northwestern UniversityFeinberg School of Medicine, Chicago, Illinois. 2Division of Biostatistics,Washington University in St. Louis, St. Louis, Missouri. 3Department ofPreventive Medicine, Northwestern University Feinberg School of Medicine,Chicago, Illinois. 4Center for Global Health, Feinberg School of Medicine,Northwestern University, Chicago, Illinois. 5Division of Population Science,Department of Medical Oncology, Sidney Kimmel Cancer Center, ThomasJefferson University, Philadelphia, Pennsylvania. 6Department of Medicine,Northwestern University Feinberg School of Medicine, Chicago, Illinois. 7Divisionof Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic,Rochester, Minnesota. 8Molecular Epidemiology and Environmental EpigeneticsLaboratory, Department of Clinical Sciences and Community Health, Universit�adegli Studi di Milano, Milan, Italy. 9Department of Population Health Sciences,University of Utah School of Medicine, Salt Lake City, Utah. 10VA Normative

Aging Study, VA Boston Healthcare System, Boston, Massachusetts.11Department of Medicine, Boston University School of Medicine, Boston,Massachusetts. 12Department of Environmental Health, Harvard School ofPublic Health, Boston, Massachusetts. 13Department of Environmental HealthScience, Mailman School of Public Health, Columbia University, New York,New York.

Note: Supplementary data for this article are available at Cancer PreventionResearch Online (http://cancerprevres.aacrjournals.org/).

Corresponding Author: Brian T. Joyce, Northwestern University, 680 N. LakeShore Drive, Suite 1400, Chicago, IL 60611. Phone: 312-503-5407; Fax: 312-908-9588; E-mail: [email protected]

doi: 10.1158/1940-6207.CAPR-17-0413

�2018 American Association for Cancer Research.

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differences in study design (e.g., variations in time betweenLTL measurement and cancer diagnosis) and relativelysparse data from prospective observational studies. Ourrecent prospective study found that incident cancer casesexperienced accelerated LTL shortening until around 4years prior to diagnosis, at which point their LTL stabilizedrelative to controls (4), suggesting a dynamic relationshipbetween LTL and cancer development.The underlying regulatory mechanisms responsible for

the telomere shortening-lengthening balance and its relat-ed cancer risk are only partially understood at present. Aprevious study of genetic mutations in telomere-relatedgenes (TRG) found limited associations with LTL (5). Thismay be because of the low genetic variability of these genesin human populations (6). Conversely, a genome-widemeta-analysis identified loci at TRGs associated with bothLTL and cancer (7). One possible alternative to a geneticmechanism is epigenetic control of TRGs. In human stud-ies, LTL has been associated with DNA methylation insubtelomeric regions and selected loci within TRGs (8)and repetitive elements Alu and LINE-1 (surrogates forglobalmethylation; ref. 9). The rate of telomere shorteningover time was also associated with LINE-1 methylation,suggesting a time-dependent association between DNAmethylation and telomere length (9).However, to our knowledge no prior population-based

studies have examined DNA methylation of TRGs inrelation to LTL dynamics and cancer risk, particularlyin a prospective, longitudinal setting. In light of ourprior finding of the shift from accelerated telomereshortening to telomere stabilization prior to cancer diag-nosis (4), a prospective examination of epigeneticchanges in TRGs may shed light on the involvement ofLTL dynamics in carcinogenesis. Thus, our primaryobjective is to assess whether blood DNA methylationin TRGs is prospectively associated with cancer risk.Our secondary objective is to explore whether DNAmethylation of cancer-associated CpG sites on TRGs isassociated with LTL.

Materials and MethodsStudy populationThe Normative Aging Study (NAS) was established in

1963 by the U.S. Department of Veterans Affairs to assessthe determinants of healthy aging in an initial cohort of2,280 men. Eligibility criteria included being between theages of 21 and 80, veteran status, living in the Boston area,and having no history of chronic health conditions (car-diovascular disease, cancer, etc.). Participants returned forclinical examinations every 3 to 5 years, and starting in1999, these examinations included a 7-mL blood draw forgenetic and epigenetic analysis. From enrollment to 1999,981 participants died and 470 were lost to follow-up(primarily by moving away from the Boston area);descriptive analysis previously found no differences in

characteristics between either of these subgroups and the829 participants remaining as of 1999 (4).Between January 1, 1999, and December 31, 2013, 802

of 829 (96.7%) active participants consented to blooddonation (median follow-up time, 10.1 years). Of these,686 were randomly selected for whole-epigenome profil-ing using the Illumina Infinium HumanMethylation450BeadChip array, and 491 were cancer-free at the time oftheir first methylation measurement. To minimize con-founding due to genetic ancestry, we excluded 16 partici-pants of non-white race, leaving 889 observations of 475participants for analysis. In total, 157 (33%) participantshad data from one blood draw, 222 (47%) participantsfrom two blood draws, and 96 (20%) subjects from threeblood draws. Among this final set, 121 cases developedcancer (34 prostate, 34 unspecified skin malignancies, 10melanomas, 8 lung, 5 bladder, 4 colorectal, 26 others) and354 participants remained cancer-free for our entire fol-low-up. Information on medical history obtained fromquestionnaires was confirmed via blinded medical recordreview and included cancer diagnoses and comorbidities.We identified TRGs using a PubMed literature search for

genes linked to telomere maintenance, elongation, andrepair (5–7, 10–29). This resulted in 80 TRGs (Table 1)containing 2,651 CpG sites available in our dataset, whichwe list with accompanying annotation information(and mean/SD methylation at the first blood draw) in

Table 1. Number of CpGs in each gene of interest by pathway

Helicase Repair OtherBLM 17 ATM 59 ACYP2 26DDX1 10 BTBD12 28 BHMT 15DDX11 19 DCLRE1C 20 BICD1 29PIF1 15 DDB1 17 C17orf68 21RECQL 14 FEN1 25 CLPTM1L 53RECQL4 19 HMBOX1 24 CXCR4 26RECQL5 54 MRE11A 21 DCAF4 24WRN 41 MSH2 14 DCLRE1B 16

Shelterin NBN 10 EHMT2 177ACD 31 PARP3 19 MAD1L1 731POT1 15 PCNA 26 MCM4 14RAP1A 17 PML 31 MEN1 24TERF1 12 RAD50 14 MPHOSPH6 15TERF2 15 RAD51 18 MTR 22TERF2IP 17 RAD51AP1 16 MTRR 20TINF2 15 RAD51C 15 MYC 37

Telomerase RAD51L1 78 NAF1 18DKC1 22 RAD51L3 15 OBFC1 18GAR1 14 RAD54L 13 PARP1 19NHP2 19 SIRT1 17 PARP2 9NOP10 11 SIRT6 17 PIK3C3 11TEP1 13 SMC5 9 PINX1 30TERC 9 SMC6 16 PRKDC 37TERT 100 TP53BP1 30 PRMT8 35WRAP53 34 XRCC6 20 PXK 21

RTEL1 33SIP1 9TNKS 28TNKS2 16UCP2 13ZNF208 9ZNF676 1

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Supplementary Table S1. For ease of presentation, we alsoclassified genes [based on Mirabello and colleagues' work(5) or literature review and GeneCard search] into one offive telomere-related pathways: Helicase, Shelterin, Telo-merase, Repair, or Other.

Telomere measurementLaboratory methods for measuring LTL in the NAS have

been described previously (4). In brief, LTL was measuredusing quantitative qPCR. Relative LTL was measured bytaking the ratio of the telomere (T) repeat copy number tosingle-copy gene (S) copy number (T:S ratio) in a givensample and reported as relative units expressing the ratiobetween test DNA LTL and reference pooled DNA LTL. Thelatter was created using DNA from 475 participants (400ng/sample) and used to generate a fresh standard curvefrom 0.25 to 20 ng/mL in every T and S qPCR run. Allsampleswere run in triplicate, and the average of the three Tmeasurements was divided by the average of the three Smeasurements to calculate the average T:S ratio. The intra-assay coefficient of variation for the T/S ratiowas 8.1%. Theaverage coefficient of variation for the T reaction was 8%,and for the S reaction 5.6%. When the coefficient ofvariation for the T or S reactions was higher than 15%,the measurement was repeated.

DNA methylation measurementBuffy coat DNA was isolated from each sample via the

QIAamp DNA Blood Kit (QIAGEN) and a 0.5 mg aliquotwas bisulfite converted with the EZ-96 DNA MethylationKit (Zymo Research). In the NAS, this was done on bloodcollected between 1999 and 2007. DNA methylation wassubsequently detected by the Infinium HumanMethyla-tion450 BeadChip platform (Northwestern University,Feinberg School of Medicine, Center for Genetic Medicine,Chicago, IL). Technical effects due to the plate/chip wereminimized by utilizing a two-stage age-stratified algorithmto randomize the samples, thereby ensuring comparableage distribution across plates/chips.Quality control samples consistedof replicate pairs anda

single sample that was run within and between plates/chips to help detect batch effects. Analytic plates were runconsecutively, by the same technician, and processed andread on the same scanner. Quality control approaches alsoincluded the detection and removal of 15 DNA samplesand 949 probes via the pfilter command in the Biocon-ductor wateRmelon package, which excluded DNA sam-ples containing >1% of probes with detection P values>0.05 and probes having >1% of samples with detection Pvalue >0.05 (after omitting samples excluded above).Furthermore, we also excluded probes with specific designand/or annotation, namely 65 with genotyping function,3,091 used for detecting CpH methylation, and 3,688containing an SNP in the last 10 bases with a minor allelefrequency greater than 0.01 in the CEU reference set. Anumber of these probeswere already excludedby thepfilter

command, so after these steps,wefinally obtained 477,927probes (i.e., �98.4% out of 485,512), which were used toobtain DNA methylation. Finally, we applied a 3-part,preprocessing pipeline to our data: (i) background correc-tion via the out-of-band (noob) method by Triche andcolleagues (30); (ii) dye-bias adjustment by the Biocon-ductor methylumi package (31); and (iii) probe-type cor-rection with BMIQ according to Teschendorff and collea-gues (2013; ref. 32), as provided by wateRmelon (33).

Statistical analysisFor descriptive analyses, we performed c2 or Kruskal–

Wallis tests to assess differences in participant character-istics at the first methylationmeasurement by cancer status(patients who would later develop cancer during the studyperiod vs. those who remained cancer-free throughout).We next used a joint model under the shared randomeffects model framework (reduced method by Liu andHang; ref. 34) to combine our repeated methylation mea-sures (linear mixed model) and time to cancer diagnosisdata (Cox proportional hazards model) and to examineassociations between cancer incidence and DNA methyl-ation of all 2,651 CpG sites of interest.This method was designed as an extension of the shared

random effects model and uses a Gaussian quadraturetechnique with a piecewise constant baseline hazard toapproximate the baseline hazard in a Cox model, whileincorporating repeatedmeasures as with amixedmodel. Atraditional approach to evaluating longitudinal biomar-kers with time to event data is to use observed values as atime-varying covariate in a Cox proportional hazardsmodel. However, this requires a complete set of repeatedmeasures in a time-continuous process, whereas in reality,our biomarkers of interest are measured only at discretetime points, generally not including the time of eventoccurrence (35). Although the value of the biomarker atevent time can be obtained by, for example, last observa-tion carried forward (LOCF), this practice could be crudeand lead to inappropriate inferences, especially when thetime interval betweenbiomarkermeasurement anddiseaseoutcome is long (35). Furthermore patient survival toevent occurrence might depend on the "underlying true"(or expected) values of biomarkers, rather than theobserved valueswithmeasurement errors; in this situation,a traditional model would be biased toward the null (35).Thus, we used a joint model of longitudinal biomarkers

and survival. Our model accounts for selection bias by therandom effects shared between the mixed model of meth-ylation markers and survival model for time to event.Rather than LOCF, the missing methylation measures atthe event time can be imputed by empirical Bayes estimate(posterior expected value of random effects conditional onthe observed data) from the mixed model, based on theobserved history of individuals who did not have an eventup to that time. Also, the "underlying true" (expected)biomarkers, rather than the observed values accompanying

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measurement errors, are incorporated in the survival mod-el, which address the "biased toward the null" concern.This model is designed to maximize statistical power andminimize bias in the analysis of correlated repeated mea-sures (e.g., DNAmethylation data) with time to an event asthe outcome, without making assumptions regarding thedata structure ormissingness. Themodel failed to convergefor 37 of the 2,651 CpG sites (1.4%), which were excludedfrom analysis. We used the Benjamini–Hochberg FDR tocorrect for all of the remaining 2,614 tests and report CpGsites with FDR 8 years).All methylation values were standardized to have a stan-dard deviation equal to 1 for this analysis. For participantswith multiple observations within the same stratum, weused the first observation from each subject only. We alsoexplored cross-sectional associations betweenmethylationat each of these significant CpG sites and LTL, both mea-sured at the first blood draw only and restricted to subjectswho were cancer-free for the entire follow-up to minimizepotential confounding by age- and cancer-related factors.All of the above analyses were conducted using SAS v. 9.4(SAS Institute) and adjusted for age, BMI, education,smoking status and pack-years, alcohol consumption,blood cell type abundances (CD8, CD4, natural killer, Bcells, and monocytes; ref. 36), and five principal compo-nents (previously calculated to represent 95% of DNAprocessing batch effects), all based on our prior workstudying DNA methylation in this cohort (37).

Bioinformatic analysisFinally, we performed a regulatory enrichment analysis

of the 31 cancer-associated CpG sites using R v. 3.4.0. Weused DNase I hypersensitivity sites (DNase), transcriptionfactor–binding sites (TFBS), and annotations of histonemodification ChIP peaks pooled across cell lines (dataavailable in the ENCODE Analysis Hub at the EuropeanBioinformatics Institute). For each regulatory element, we

then calculated the number of overlapping CpGs amongthe 31 significant CpGs (observed) and 10,000 sets ofrandomly selected CpGs across the genome (expected).We calculated the ratio of observed tomean expected as theenrichment fold and obtained an empirical P value fromthe distribution of the expected in the background.

ResultsTable 2 shows the characteristics of all participants at the

first blood draw by cancer status. Briefly, participants whowere cancer-free for the full follow-up were slightly olderthan those who later developed cancer. Our descriptiveanalysis identified no other significant differences in par-ticipant characteristics across cancer status. Table 3 showsthe results of the jointmodel,with31CpGsites on23TRGsassociated with cancer incidence at FDR

available upon request). Generally speaking, CpGs on thesame gene as our primary findings tended to be associatedwith cancer incidence in the same direction as the primaryfinding, albeit not to the same degree of statistical signif-icance. In the unadjusted correlation analysis, methylationof significantCpG sites tended tobe significantly correlatedwith one another (0.3–0.6 for almost all CpG sites; dataavailable upon request) despite their disparate locations inthe genome.For the trajectory analyses, overall we found significant

differences in methylation over time by cancer status in 21of 31 cancer-associated CpG sites including both of theCpGs in each of the Helicase, Shelterin, and Telomerasepathways (Supplementary Table S3). Figure 1 plots thetrajectory analyses of DNA methylation over time at selectnoteworthy CpG sites by cancer status. CpGs on TINF2 (animportant telomere-regulating gene) as well as PIF1 (in theHelicase pathway) and the DNA repair genes DDB1 andPARP3 (Fig. 1A–C and E, respectively) showed strongtrends with higher methylation in subjects developingcancer, generally beginning around 4 to 6 years prediag-nosis/censoring. Conversely, CpGs on DKC1 in the Telo-merase pathway as well as MYC (Fig. 1D and F, respec-tively) showed few differences between cancer cases andcancer-free subjects, and no clear temporal trend. FormanyCpG sites, methylation trajectories between cancer casesand cancer-free participants began to diverge as early as 6 to

8 years prior to diagnosis/censoring, with clear trendsvisible for most cancer-CpG sites beginning 4 years pre-diagnosis (see Supplementary Fig. S2 for correspondingfigures with 95% CIs added; Supplementary Fig. S3 con-tains figures for the remaining 25 CpG sites).Table 4 shows the results of the logistic regression

analysis of DNA methylation and later cancer status at 0to 4 and 4 to 8 years prediagnosis/censoring. In the stratumof 0 to 4 years prediagnosis/censoring, we found 11 CpGsites associated with cancer incidence: one CpG in each ofthe Shelterin (TINF2) and Telomerase (WRAP53) path-ways, one CpG on each of three TRGs in the DNA Repairpathway (PARP3, FEN1, and SIRT6) and four CpGs on afourth (DDB1), and one CpG on each of CLPTM1L andMAD1L1. In the stratum of 4 to 8 years prediagnosis/censoring, we found 17 CpG sites associated with cancerincidence: one CpG in each of the Helicase (RECQL4),Shelterin (ACD), and Telomerase (DKC1) pathways; eightCpGs on six TRGs in the DNA repair pathway (DCLRE1C,DDB1,MSH2, PARP3, RAD51L3, and SIRT6); andoneCpGon each of BICD1, CLPTM1L,MAD1L1,MTRR, RTEL1, andSIP1. Methylation at four CpG sites (on CLPTM1L, DDB1,PARP3, and SIRT6) was associated with incident cancer inboth time strata. Supplementary Table S4 shows the logis-tic regression results in samples collectedmore than 8 yearsprediagnosis/censoring; oneCpGonPARP3was associatedwith cancer incidence.

Table 3. Cancer-associated CpGs in TRGs by pathway at FDR < 0.05Pathway Gene CpG Region Island ba 95% CI FDR

Helicase PIF1 cg11013726 50UTR Island 1.00 0.57–1.43 0.02RECQL4 cg17368874 TSS200 Island 6.67 3.13–10.20 0.04

Shelterin ACD cg04265926 TSS1500 Island 6.67 3.09–10.25 0.04TINF2 cg02271180 1stExon Island 1.99 0.88–3.10 0.05

Telomerase DKC1 cg19944582 TSS200 Island 5.80 2.74–8.85 0.04WRAP53 cg25053252 TSS1500 Island 5.47 2.82–8.12 0.03

Repair BTBD12 cg04157159 TSS200 Island 4.25 1.92–6.58 0.04DCLRE1C cg14369264 TSS1500 Island 1.11 0.53–1.69 0.04DCLRE1C cg24866702 TSS200 Island 6.53 3.12–9.95 0.04DCLRE1C cg04785461 TSS200 Island 5.38 2.37–8.40 0.05DDB1 cg23053918 1stExon Island 5.45 2.75–8.15 0.03DDB1 cg20772347 TSS200 Island 5.68 2.65–8.72 0.04DDB1 cg24840365 TSS200 Island 5.49 2.55–8.43 0.04DDB1 cg25530631 1stExon Island 6.63 3.04–10.22 0.04DDB1 cg08724919 1stExon Island 1.45 0.64–2.26 0.05FEN1 cg25628257 TSS200 Island 3.95 2.03–5.87 0.03HMBOX1 cg14143435 TSS200 N_Shore 1.41 0.73–2.10 0.03MSH2 cg00547758 50UTR Island 6.23 2.97–9.48 0.04PARP3 cg14974841 TSS1500 Island 5.22 2.49–7.95 0.04PARP3 cg14262432 TSS200 Island 6.93 3.00–10.86 0.05RAD51L3 cg19223675 TSS200 S_Shore 5.16 2.27–8.05 0.05RAD54L cg24955114 TSS1500 OpenSea 6.16 2.67–9.66 0.05SIRT6 cg15034464 50UTR Island 5.11 2.78–7.44 0.03

Other BICD1 cg21587861 TSS200 Island 6.93 3.12–10.75 0.04CLPTM1L cg19739264 1stExon Island 4.89 2.37–7.41 0.04MAD1L1 cg09776772 Body OpenSea -0.65 �0.97 to �0.33 0.03MAD1L1 cg13247668 TSS200 Island 5.00 2.30–7.71 0.04MTRR cg26627933 1stExon Island 5.45 2.73–8.16 0.03MYC cg07871324 TSS1500 Island 5.81 2.91–8.71 0.03RTEL1 cg27236539 TSS200 Island 1.25 0.56–1.94 0.04SIP1 cg15533434 TSS200 Island 5.28 2.48–8.09 0.04

aBeta coefficients represent the average difference in methylation (M-value) between cases and controls.






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Figure 1.

DNA methylation by years to cancer diagnosis/censoring and cancer status for select CpG sites.

Table 4. Logistic regression results stratified by time interval between blood draw and diagnosis/censoring

0–

Table 5 shows the results of our cross-sectionalmodels ofLTL on DNAmethylation. DNA methylation of three CpGsites, all of them on DNA repair genes, was positivelyassociated with LTL at the first blood draw: cg24866702onDCLRE1C, cg00547758 onMSH2, and cg25530631 onDDB1.We found no other significant associations betweenDNA methylation of TRGs and LTL. Finally, Supplemen-tary Fig. S4 shows the results of our regulatory elementenrichment analysis. Five histone modifications (notablyH3K27ac, H3K4me2, H3K4me3, H3K79me2, andH3K9ac) were significantly enriched at the CpG sites sig-nificantly associated with cancer (all P < 0.001). Supple-mentary Table S5 contains more detailed tabular findings.

DiscussionTo our knowledge, this is the first study to identify DNA

methylation changes in TRGs that are prospectively asso-ciated with cancer. In this cohort, we identified positiveassociations between cancer incidence and methylation at30 CpG sites (and one negative association), most in genepromoter regions, on 23 genes related to telomere main-tenance and regulation. Over time, methylation of 21 CpGsites began to diverge by later cancer status several yearsprior to diagnosis/censoring. In general, cancer cases expe-rienced increased static methylation and cancer-free parti-

cipants experienced decreased methylation. Furthermore,our logistic regression identified 11 and17CpG siteswheremethylation at 0 to 4 years and 4 to 8 years prediagnosis/censoring, respectively, was associated with cancer inci-dence (including four CpGs in both strata). Finally, inparticipants who remained cancer free, at the first blooddraw, DNA methylation at three CpG sites was associatedwith telomere length. Few studies have examined thesegenes as potential blood-based cancer biomarkers; thus,our findings should be validated in other populations.Nonetheless, these findings suggest mechanisms throughwhich cancer cells may be able to alter telomere homeo-stasis, possibly as a precursor to clinical disease, thusindicating DNA methylation of TRGs as a potentiallyuseful biomarker of cancer.We identified methylation of CpG sites (cg19944582

and cg25053252) in the promoters of two genes (DKC1and WRAP53) involved with telomerase, a well-character-ized telomere maintenance pathway, as positively associ-ated with cancer. The two genes involved in the telomerasepathway,DKC1 andWRAP53, jointly promote telomeraseexpression and telomeremaintenance.Mutations ofDKC1were identified in cancer cells (38), as was promoterhypermethylation ofDKC1 (39). Further evidence suggeststhat reductions in DKC1 expression may increase cancersusceptibility through nontelomere mechanisms, such as

Table 5. Associations between cancer-associated CpG sites and telomere length at first blood draw (N ¼ 346)CpG Gene Pathway b 95% CI P

cg11013726 PIF1 Helicase 0.03 �0.07–0.12 0.56cg17368874 RECQL4 Helicase 0.06 �0.02–0.14 0.14cg04265926 ACD Shelterin 0.05 �0.10–0.19 0.52cg02271180 TINF2 Shelterin �0.02 �0.20–0.17 0.87cg19944582 DKC1 Telomerase �0.13 �0.33–0.08 0.23cg25053252 WRAP53 Telomerase 0.07 �0.20–0.34 0.60cg04157159 BTBD12 Repair 0.02 �0.13–0.18 0.76cg04785461 DCLRE1C Repair �0.01 �0.12–0.10 0.88cg14369264 DCLRE1C Repair 0.04 �0.04–0.13 0.31cg24866702 DCLRE1C Repair 0.25 0.08–0.41

reduced p53 expression (40), which may partially explainthe lack of association with telomere length in our cross-sectional analysis. DKC1 downregulation has also beenassociated with exposure to arsenic, a known carcinogen(41). Similarly, reduced WRAP53 expression was associ-ated with cancer prognosis (42). Methylation of thesegenes may thus be involved with cancer risk and/or pro-gression independent of telomere length.Our study also identified methylation of two CpGs

(cg04265926 and cg02271180) in the promoters of twogenes (ACD and TINF2) in the shelterin pathway, anotherwell-characterized telomere maintenance pathway, as pos-itively associated with cancer. Changes in shelterin com-plex expression have been implicated in a variety of cancertypes, including germlinemutations in bothACD (43) andTINF2 (44). However, limited evidence exists to supportthis hypothesis as previous DNA methylation studies oftelomerase-associated genes tended to focus on TERT.However, studies of TERTmethylation in blood leukocytesfound no associations with cancer (45), concordant withourfindings.One possible explanation is that the normallystrict regulatory control of TERTmay be preserved in bloodleukocytes even in participants experiencing carcinogene-sis, suggesting that future studies of blood DNA methyl-ation should focus on other shelterin complex genes.Among other DNA repair genes, we identified methyl-

ation at multiple loci within the promoters of three genes(PARP3, DCLRE1C, and DDB1) as positively associatedwith cancer. A prior study of cancer samples found down-regulation of bothPARP3 andDCLRE1C in cancer cells andwas additionally associated with telomerase reactivation(13). Downregulation of DCLRE1C has also been associ-ated with chronic exposure to ionizing radiation (46).Furthermore, methylation at one of the significant loci oneach ofDCLRE1C andDDB1was also positively associatedwith telomere length at the first blood draw. Thus, epige-netic repression of these DNA repair genes may be onemechanism through which cancer cells can activate telo-mere maintenance mechanisms.In our prior examination of LTL and cancer incidence in

this same cohort, we identified cancer-associated acceler-ated LTL shortening that stabilized starting approximately4 years prior to diagnosis (4). We observed that higherDNA methylation at CpG sites on 15 TRGs of interest inthis study was associated with cancer status 4 to 8 yearsprior to diagnosis (Table 4). In addition, we observedsignificantly different methylation trajectories betweencancer cases and cancer-free participants in 21 sites on17 genes. Examples of this divergence can be seenwith fourCpGs (cg02271180 in TINF2, cg11013726 in PIF1,cg08724919 in DDB1, and cg14974841 in PARP3)in Fig. 1. In all of these cases, DNA methylation began tosignificantly differ between cases and controls beginning atleast 4 years prediagnosis/censoring. Finally, three CpGs(cg24866702 on DCLRE1C, cg25530631 on DDB1, and

cg00547758 on MSH2) were positively associated withtelomere length in our cross-sectional analysis. These find-ings all occurred prior to (or at the same time as) the shift inLTL change that our previous study observed. Our trajec-tory analyses suggest that increased DNA methylation atthese and other sites may be an early event in the devel-opment of cancer, either reflecting constitutive exposuresthat also increase cancer risk or correlating with DNAmethylation changes occurring in cancer cells, that remainsdetectable for years. Also of note, methylation of bothcg24866702 and cg00547758 was associated with bothtelomere length at the first blood draw andwith cancer risk4 to 8 years prior to diagnosis (but not 0–4 years prior).This suggests that thesemethylation changes occur prior toour previously observed change in telomere length andmay be involved in driving this change via a DNA repair-related mechanism. As studies of the relationship betweenLTLmeasured at a single time point and cancer risk remainunclear (47), alterations in DNA methylation of theseTRGs may help explain the between-study differences(e.g., differences in the timing of LTL measurement relativeto cancer diagnosis). Together, our results suggest thatstudying DNA methylation in blood leukocytes is promis-ing for future research into the role of dynamic changes intelomere length during cancer development, and that incor-porating epigenetic data may help improve the utility oftelomere length in blood leukocytes as a cancer biomarker.Finally, although we lacked gene expression data, we

were able to identify enrichment of numerous importantregulatory elements in the set of CpGs associated withcancer. These includeH2A.Z, TFBS, andDNase, whichmayall point toward a role of methylation of these CpGs in cis-regulatory changes and potential transcriptional activation(consistent with most of the CpGs being located in genepromoter regions). We also identified five activating his-tones andone repressive histone in associationwith our setof CpGs at P < 0.001. The repressive histone, H3K9me1,has been previously found to have altered levels in somecancers (48). Similarly, the activating histone markerH3K27ac has been found to be dysregulated in cancer(49). Expression levels of some of these histones havepreviously been associated with DNA methylation (50).Taken together, these findings bolster our conclusion thatthe identifiedCpG sites in these important TRGsmay affectgene expression.This study is subject to limitations. Although the longi-

tudinal nature of our study design allowed us to exploreaspects of the temporal associations between DNA meth-ylation of TRGs, LTL, and current cancer risk, it remainschallenging to accommodate a formal mediation analysisof longitudinal mechanisms. Our conclusions regardingthe interplay of DNA methylation and LTL in carcinogen-esis over time thus require confirmation in additionalprospective studies. In addition, the study population ofthe NAS is not representative, and thus, more diverse

Joyce et al.





populations should be studied to validate our findings,although there is little reason to believe these mechanismswould substantially vary by gender or by race. Further-more, the sample sizes of most specific cancer types in theNAS were too small to permit a statistically rigorousexploration of these associations for individual cancers.Although we hypothesized that dysregulation of telomeremaintenance mechanisms are a general mechanism affect-ing many different cancer types, this should also be testedin larger studies. Similarly, the relatively small number ofcases coupled with our time stratification limited thesample size for each time stratum analyzed. This may haveresulted in false negatives, which may explain some asso-ciations that were significant 4 to 8 years prediagnosis butnot 0 to 4 years. Alternatively, this discrepancymay reflect asubset of cancer-free subjects who developed cancer afterthe end of our study period (and were thus effectivelymisclassified). Validation with a longer follow-up and/orlarger study population would be necessary to test thesepossible explanations. The dearth of significant associa-tions between DNA methylation and LTL in our cross-sectional model may also be a consequence of reducedsample size; the dynamic natures of LTL and methylation(and their potential relationships with one another andwith cancer) limited us to a cross-sectional model. Thus,our findings may represent a lack of a biological effect or alack of statistical power and should be interpreted withcaution until they can be validated. Similarly, the NASdataset lacked gene expression data to provide functionalverification of our findings posited above. Future researchshould verify the relationship between DNA methylationand expression of these specific genes.Nonetheless, this study provides an important, poten-

tiallymechanistic explanation for thedynamic relationshipbetween LTL and cancer that we previously observed.Future studies should confirm and explore these CpG sitesand genes as potential early detection biomarkers andtherapeutic targets; the strong correlations between mostCpG sites in our analysis (despite their disparate locationson the genome) further bolster the possibility of theseCpGs collectively making a biomarker in the future. Futureresearch in larger, more diverse populations should focuson examining changes in the DNA methylation of theseTRGs in termsof gene expression, LTL, and cancer to furtherelucidate the temporal sequence of these events and theirpotential role in mechanisms of carcinogenesis. DNAmethylation of TRGs could be an important early eventin carcinogenesis and, with appropriate confirmation,could have extremely valuable clinical applications forcancer. These DNA methylation changes in blood leuko-cytes may have been induced by environmental exposures(pollutants, nutrients, etc.) acting constitutionally; thus,our findings may provide important information on onepossible mechanism of action for previously identifiedcarcinogenic exposures. Future research should explore

this possibility by examining potential exposure–methyl-ation relationships in the geneswe identified. Furthermore,if epigenetic changes in these genes do influence the lengthof cancer cells' telomeres, therapeutically targeting thesechanges could theoretically induce cellular senescence incancer cells and thus provide a new effective, safe, andtargeted therapy for cancer. However, for this to happen,future studies will need to validate the epigenetic changeswe have identified in blood both in cancer and in normalhealthy tissue. Nonetheless, these findings provide impor-tant information for future cancer early detection, preven-tion, and treatment. This may be particularly true in popu-lations with underlying immune dysfunction or chronicinflammation (e.g., chronic HIV infection, autoimmunedisorders). Exploring these pathwaysmay also facilitate theuse of cancer immunotherapies to correct immune dys-function and cancer-specific immune responses.

Disclosure of Potential Conflicts of InterestM. Kocherginsky has provided expert testimony for The University

of Chicago. No potential conflicts of interest were disclosed by theother authors.

Authors' ContributionsConception and design: B.T. Joyce, Y. Zheng, J. Schwartz, L. HouDevelopment of methodology: B.T. Joyce, L. Liu, M. Hoxha, L. HouAcquisition of data (provided animals, acquired and managedpatients, provided facilities, etc.):M.Hoxha, P. Vokonas, J. Schwartz,A. BaccarelliAnalysis and interpretation of data (e.g., statistical analysis, bio-statistics, computational analysis): B.T. Joyce, Y. Zheng, Z. Zhang,L. Liu, M. Kocherginsky, R. Murphy, J. ShenWriting, review, and/or revisionof themanuscript:B.T. Joyce, Y. Zheng,D. Nannini, Z. Zhang, L. Liu, T. Gao, M. Kocherginsky, R. Murphy,H. Yang, C.J. Achenbach, L.R. Roberts, J. Shen, J. Schwartz, L. HouAdministrative, technical, or material support (i.e., reporting ororganizing data, constructing databases): B.T. Joyce, R. Murphy,J. Shen, P. VokonasStudy supervision: B.T. Joyce, L. Liu, P. Vokonas, L. Hou

AcknowledgmentsThe Normative Aging Study is supported by the Epidemiology

Research and Information Center of the U.S. Department of VeteransAffairs (NIEHS R01- ES015172) and is a research component of theMassachusetts Veterans EpidemiologyResearch and InformationCen-ter (MAVERIC). L. Hou received additional support from the North-western University Robert H. Lurie Comprehensive Cancer CenterRosenberg Research Fund. L. Hou, R. Murphy, and L.R. Roberts alsoreceived support from the NCI: 1U54CA221205-01 and D43TW009575. A. Baccarelli and J. Schwartz received additional supportfrom the National Institute of Environmental Health Sciences; NIEHSR01-ES021733,NIEHSR01-ES015172,NIEHS-R01ES025225,NIEHSP30-ES009089, and NIEHS P30-ES00002.

The costs of publication of this article were defrayed in part by thepayment of page charges. This articlemust therefore be herebymarkedadvertisement in accordance with 18 U.S.C. Section 1734 solely toindicate this fact.

Received December 15, 2017; revised April 3, 2018; accepted May22, 2018; published first June 12, 2018.






References1. Frias C, Pampalona J, Genesca A, Tusell L. Telomere dysfunction

and genome instability. Front Biosci (Landmark Ed) 2012;17:2181–96.

2. Hou L, Zhang X, Gawron AJ, Liu J. Surrogate tissue telomerelength and cancer risk: shorter or longer? Cancer Lett 2012;319:130–5.

3. Giaimo S, d'Adda di Fagagna F. Is cellular senescence an exampleof antagonistic pleiotropy? Aging Cell 2012;11:378–83.

4. Hou L, Joyce BT, Gao T, Liu L, Zheng Y, Penedo FJ, et al. Bloodtelomere length attrition and cancer development in the norma-tive aging study cohort. EBioMedicine 2015;2:591–6.

5. Mirabello L, Yu K, Kraft P, De Vivo I, Hunter DJ, Prescott J, et al.The association of telomere length and genetic variation intelomere biology genes. Hum Mutat 2010;31:1050–8.

6. Mirabello L, YeagerM, Chowdhury S, Qi L, Deng X,Wang Z, et al.Worldwide genetic structure in 37 genes important in telomerebiology. Heredity 2012;108:124–33.

7. Codd V, Nelson CP, Albrecht E, ManginoM, Deelen J, Buxton JL,et al. Identification of seven loci affecting mean telomere lengthand their association with disease. Nat Genet 2013;45:422–7.

8. Buxton JL, Suderman M, Pappas JJ, Borghol N, McArdle W,Blakemore AI, et al. Human leukocyte telomere length is associ-ated with DNA methylation levels in multiple subtelomeric andimprinted loci. Sci Rep 2014;4:4954.

9. Wong JY, De Vivo I, Lin X, Grashow R, Cavallari J, Christiani DC.The association between global DNA methylation and telomerelength in a longitudinal study of boilermakers. Genet Epidemiol2014;38:254–64.

10. Dilley RL, Verma P, Cho NW, Winters HD, Wondisford AR,Greenberg RA. Break-induced telomere synthesis underlies alter-native telomere maintenance. Nature 2016;539:54–8.

11. Dimitrova N, Chen YC, Spector DL, de Lange T. 53BP1 promotesnon-homologous end joining of telomeres by increasing chro-matin mobility. Nature 2008;456:524–8.

12. Feng X, Luo Z, Jiang S, Li F, Han X, Hu Y, et al. The telomere-associated homeobox-containing protein TAH1/HMBOX1 parti-cipates in telomere maintenance in ALT cells. J Cell Sci 2013;126(Pt 17):3982–9.

13. Frias C, Garcia-Aranda C, De Juan C, Moran A, Ortega P, GomezA, et al. Telomere shortening is associated with poor prognosisand telomerase activity correlateswithDNA repair impairment innon-small cell lung cancer. Lung Cancer 2008;60:416–25.

14. Jia G, Su L, Singhal S, Liu X. Emerging roles of SIRT6 on telomeremaintenance, DNA repair, metabolism and mammalian aging.Mol Cell Biochem 2012;364:345–50.

15. Karami S, Han Y, Pande M, Cheng I, Rudd J, Pierce BL, et al.Telomere structure andmaintenance gene variants and risk offivecancer types. Int J Cancer 2016;139:2655–70.

16. Kim S, Bi X, Czarny-Ratajczak M, Dai J, Welsh DA, Myers L, et al.Telomere maintenance genes SIRT1 and XRCC6 impact age-related decline in telomere length but only SIRT1 is associatedwith human longevity. Biogerontology 2012;13:119–31.

17. Kim S, Parks CG, Xu Z, Carswell G, DeRoo LA, Sandler DP, et al.Association between genetic variants in DNA and histone meth-ylation and telomere length. PLoS One 2012;7:e40504.

18. Lee JH, Cheng R, Honig LS, Feitosa M, Kammerer CM, Kang MS,et al. Genome wide association and linkage analyses identifiedthree loci-4q25, 17q23.2, and 10q11.21-associated with varia-tion in leukocyte telomere length: the Long Life Family Study.Front Genet 2013;4:310.

19. Levy D, Neuhausen SL, Hunt SC, Kimura M, Hwang SJ, Chen W,et al. Genome-wide association identifies OBFC1 as a locus

involved in human leukocyte telomere biology. Proc Natl AcadSci U S A 2010;107:9293–8.

20. Mangino M, Christiansen L, Stone R, Hunt SC, Horvath K,Eisenberg DT, et al. DCAF4, a novel gene associated with leuco-cyte telomere length. J Med Genet 2015;52:157–62.

21. MarchesiniM,Matocci R, Tasselli L, Cambiaghi V, Orleth A, FuriaL, et al. PML is required for telomere stability in non-neoplastichuman cells. Oncogene 2016;35:1811–21.

22. Mendez-Bermudez A, Royle NJ. Deficiency in DNA mismatchrepair increases the rate of telomere shortening in normal humancells. Hum Mutat 2011;32:939–46.

23. Mirabello L, Richards EG, Duong LM, Yu K, Wang Z, Cawthon R,et al. Telomere length and variation in telomere biology genes inindividuals with osteosarcoma. Int J Mol Epidemiol Genet2011;2:19–29.

24. Pooley KA, Bojesen SE, Weischer M, Nielsen SF, Thompson D,AminAlOlamaA, et al. A genome-wide association scan (GWAS)formean telomere lengthwithin theCOGSproject: identified locishow little association with hormone-related cancer risk. HumMol Genet 2013;22:5056–64.

25. Potts PR, Yu H. The SMC5/6 complex maintains telomere lengthin ALT cancer cells through SUMOylation of telomere-bindingproteins. Nat Struct Mol Biol 2007;14:581–90.

26. Saharia A, Teasley DC, Duxin JP, Dao B, Chiappinelli KB, StewartSA. FEN1ensures telomere stability by facilitating replication forkre-initiation. J Biol Chem 2010;285:27057–66.

27. Stern JL, Zyner KG, Pickett HA, Cohen SB, Bryan TM. Telomeraserecruitment requires both TCAB1 and Cajal bodies independent-ly. Mol Cell Biol 2012;32:2384–95.

28. Wilson JS, Tejera AM, Castor D, Toth R, Blasco MA, Rouse J.Localization-dependent and -independent roles of SLX4 in reg-ulating telomeres. Cell Rep 2013;4:853–60.

29. Zee RY, Ridker PM, Chasman DI. Genetic variants in eleventelomere-associated genes and the risk of incident cardio/cere-brovascular disease: The Women's Genome Health Study. ClinChim Acta 2011;412:199–202.

30. Triche TJ Jr, Weisenberger DJ, Van Den Berg D, Laird PW, Sieg-mund KD. Low-level processing of Illumina Infinium DNAMethylation BeadArrays. Nucleic Acids Res 2013;41:e90.

31. Davis S, Du P, Bilke S, Triche T Jr, Bootwalla M. methylumi:Handle Illuminamethylation data. 2015. Available from: https://rdrr.io/bioc/methylumi/.

32. Teschendorff AE, Marabita F, Lechner M, Bartlett T, Tegner J,Gomez-Cabrero D, et al. A beta-mixture quantile normaliza-tion method for correcting probe design bias in IlluminaInfinium 450 k DNA methylation data. Bioinformatics 2013;29:189–96.

33. Pidsley R, CC YW, Volta M, Lunnon K, Mill J, Schalkwyk LC. Adata-driven approach to preprocessing Illumina 450K methyla-tion array data. BMC Genomics 2013;14:293.

34. Liu L,HuangX. Joint analysis of correlated repeatedmeasures andrecurrent events processes in the presence of death, with appli-cation to a study on acquired immune deficiency syndrome. J RStat Soc Ser C Appl Stat 2009;58:65–81.

35. Liu Y, Liu L. Joint models for longitudinal data and time-to-eventoccurrence. Routledge International Handbook of AdvancedQuantitative Methods in Nursing Research. Taylor and FrancisInc., New York, NY; 2015.

36. Houseman EA, Accomando WP, Koestler DC, Christensen BC,Marsit CJ, NelsonHH, et al. DNAmethylation arrays as surrogatemeasures of cell mixture distribution. BMC Bioinformatics 2012;13:86.

Joyce et al.




https://rdrr.io/bioc/methylumi/https://rdrr.io/bioc/methylumi/http://cancerpreventionresearch.aacrjournals.org/

37. Zheng Y, Joyce BT, Colicino E, Liu L, ZhangW, Dai Q, et al. Bloodepigenetic age may predict cancer incidence and mortality. EBio-Medicine 2016;5:68–73.

38. Bellodi C, Krasnykh O, Haynes N, Theodoropoulou M, Peng G,Montanaro L, et al. Loss of function of the tumor suppressorDKC1 perturbs p27 translation control and contributes to pitu-itary tumorigenesis. Cancer Res 2010;70:6026–35.

39. Smith IM, Mithani SK, Mydlarz WK, Chang SS, Califano JA.Inactivation of the tumor suppressor genes causing the hereditarysyndromes predisposing to head and neck cancer via promoterhypermethylation in sporadic head and neck cancers. ORL JOtorhinolaryngol Relat Spec 2010;72:44–50.

40. Montanaro L. Dyskerin and cancer: more than telomerase. Thedefect in mRNA translation helps in explaining how a prolifer-ative defect leads to cancer. J Pathol 2010;222:345–9.

41. Gao J, RoyS, TongL,ArgosM, Jasmine F,RahamanR, et al. Arsenicexposure, telomere length, and expression of telomere-relatedgenes among Bangladeshi individuals. Environ Res 2015;136:462–9.

42. ZhangH,WangDW,Adell G, Sun XF.WRAP53 is an independentprognostic factor in rectal cancer- a study of Swedish clinical trialof preoperative radiotherapy in rectal cancer patients. BMCCancer 2012;12:294.

43. Aoude LG, Pritchard AL, Robles-Espinoza CD, Wadt K, HarlandM, Choi J, et al. Nonsense mutations in the shelterin complexgenes ACD and TERF2IP in familial melanoma. J Natl Cancer Inst2015;107:pii:dju408.

44. Hartmann K, Illing A, Leithauser F, Baisantry A, Quintanilla-Martinez L, Rudolph KL. Gene dosage reductions of Trf1 and/or Tin2 induce telomere DNA damage and lymphoma formationin aging mice. Leukemia 2016;30:749–53.

45. Hoxha M, Fabris S, Agnelli L, Bollati V, Cutrona G, Matis S, et al.Relevance of telomere/telomerase system impairment in earlystage chronic lymphocytic leukemia. Genes Chromosomes Can-cer 2014;53:612–21.

46. Fachin AL, Mello SS, Sandrin-Garcia P, Junta CM, Ghilardi-NettoT, Donadi EA, et al. Gene expression profiles in radiation workersoccupationally exposed to ionizing radiation. J Radiat Res2009;50:61–71.

47. Zhang X, Zhao Q, Zhu W, Liu T, Xie SH, Zhong LX, et al. Theassociation of telomere length in peripheral blood cells withcancer risk: a systematic review and meta-analysis of prospec-tive studies. Cancer Epidemiol Biomarkers Prev 2017;26:1381–90.

48. Ellinger J, Bachmann A, Goke F, Behbahani TE, Baumann C,Heukamp LC, et al. Alterations of global histone H3K9 andH3K27 methylation levels in bladder cancer. Urol Int 2014;93:113–8.

49. Song Y, Li ZX, Liu X, Wang R, Li LW, Zhang Q. The Wnt/beta-catenin and PI3K/Akt signaling pathways promote EMT in gastriccancer by epigenetic regulation via H3 lysine 27 acetylation.Tumour Biol 2017;39:1010428317712617.

50. Okitsu CY, Hsieh CL. DNA methylation dictates histone H3K4methylation. Mol Cell Biol 2007;27:2746–57.






2018;11:511-522. Published OnlineFirst June 12, 2018.Cancer Prev Res Brian T. Joyce, Yinan Zheng, Drew Nannini, et al. DNA Methylation of Telomere-Related Genes and Cancer Risk

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DNA Methylation of Telomere-Related Genes and Cancer Risk...Kit (Zymo Research). In the NAS, this was done on blood collected between 1999 and 2007. DNA methylation was ... and 949