Shortened telomere length is associated with increased risk of cancer: a meta-analysis.

Shortened Telomere Length Is Associated with Increased
Risk of Cancer: A Meta-Analysis
Hongxia Ma1,2, Ziyuan Zhou1, Sheng Wei1, Zhensheng Liu1, Karen A. Pooley3, Alison M. Dunning4, Ulrika
Svenson5, Go¨ran Roos5, H. Dean Hosgood III6, Min Shen6, Qingyi Wei1*
1Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America, 2Department of Epidemiology and
Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China, 3Cancer Research UK Genetic Epidemiology Unit, Department of Public Health and
Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, United Kingdom, 4Department of Oncology, University of Cambridge, Strangeways
Research Laboratory, Cambridge, United Kingdom, 5Department of Medical Biosciences/Pathology, Umea˚ University, Umea˚, Sweden, 6Division of Cancer Epidemiology
and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, United States of America
Abstract
Background: Telomeres play a key role in the maintenance of chromosome integrity and stability, and telomere shortening
is involved in initiation and progression of malignancies. A series of epidemiological studies have examined the association
between shortened telomeres and risk of cancers, but the findings remain conflicting.
Methods: A dataset composed of 11,255 cases and 13,101 controls from 21 publications was included in a meta-analysis to
evaluate the association between overall cancer risk or cancer-specific risk and the relative telomere length. Heterogeneity
among studies and their publication bias were further assessed by the x2-based Q statistic test and Egger’s test,
respectively.
Results: The results showed that shorter telomeres were significantly associated with cancer risk (OR= 1.35, 95% CI = 1.14–
1.60), compared with longer telomeres. In the stratified analysis by tumor type, the association remained significant in
subgroups of bladder cancer (OR= 1.84, 95% CI = 1.38–2.44), lung cancer (OR= 2.39, 95% CI = 1.18–4.88), smoking-related
cancers (OR= 2.25, 95% CI = 1.83–2.78), cancers in the digestive system (OR= 1.69, 95% CI = 1.53–1.87) and the urogenital
system (OR = 1.73, 95% CI = 1.12–2.67). Furthermore, the results also indicated that the association between the relative
telomere length and overall cancer risk was statistically significant in studies of Caucasian subjects, Asian subjects,
retrospective designs, hospital-based controls and smaller sample sizes. Funnel plot and Egger’s test suggested that there
was no publication bias in the current meta-analysis (P= 0.532).
Conclusions: The results of this meta-analysis suggest that the presence of shortened telomeres may be a marker for
susceptibility to human cancer, but single larger, well-design prospective studies are warranted to confirm these
findings.
Citation: Ma H, Zhou Z, Wei S, Liu Z, Pooley KA, et al. (2011) Shortened Telomere Length Is Associated with Increased Risk of Cancer: A Meta-Analysis. PLoS
ONE 6(6): e20466. doi:10.1371/journal.pone.0020466
Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America
Received March 11, 2011; Accepted April 26, 2011; Published June 10, 2011
Copyright: � 2011 Ma et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by National Institute of Health grants R01 CA131274 and R01 ES011740 (Q. Wei) and P30 CA016672 (The University of Texas
M. D. Anderson Cancer Center). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: qwei@mdanderson.org
Introduction
Telomeres, a series of tandem repeats of TTAGGG nucleotides,
cap the ends of chromosomes in all eukaryotic cells [1] and
maintain genomic stability by prohibiting fatal events, such as
nucleolytic degradation, chromosomal end-to-end fusion and
irregular recombination [2]. Human telomeres are approximately
10–15 kb in somatic cells and progressively shortened by ,30 to
200 bp after each cycle of mitotic division, due to incomplete
replication of linear DNA molecules and the absence of a
mechanism for elongation of telomeres [3]. When the telomeres
reach a critical length, Rb and p53 signaling pathways are
triggered to initiate either cell senescence or apoptosis [4]. Thus,
telomere length has been suggested as a ‘‘cellular mitotic clock’’
that defines the number of cell divisions and cellular life span [1,5].
Several studies have documented correlations between short-
ened telomeres and multiple human diseases associated with age,
such as Alzheimer’s disease [6], myocardial infarction [7], vascular
dementia [8], liver cirrhosis [9], atherosclerosis [10], ulcerative
colitis [11] and premature aging syndromes [12]. Additionally,
telomere shortening is involved in initiation and progression of
malignancies in mouse models and functional studies [13,14]. For
example, short telomeres cause an increased risk of developing
epithelial cancers by the formation of complex non-reciprocal
translocations [15,16], and telomeres in tumor cells and their
precursor lesions are significantly shorter than that in surrounding
non-tumor cells [17,18].
Although evidence from functional studies and animal models
support the hypothesis that telomere shortening contributes
to tumor development, results from population studies remain
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conflicting rather than conclusive [19–44]. For instance, several
case-control studies have investigated the association between
telomere length in peripheral blood lymphocytes and breast cancer
risk [21,25,29,31,35,36,38]; some showed that shorter telomeres
were associated with increased risk of breast cancer [31,38], while
others indicated converse or insignificant associations [21,25,29,
35,36]. These findings suggest that any of these single studies may
have been underpowered to detect the association between telo-
mere length and cancer risk because of their limited sample sizes.
Furthermore, the underlying heterogeneity among different studies
can be explored in a meta-analysis. Thus, we conducted a syste-
matic meta-analysis on 21 relevant publications with 11,255 cases
and 13,101 controls to estimate the overall cancer risk or cancer-
specific risk associated with telomere length and to evaluate
potential between-study heterogeneity of these published studies.
Materials and Methods
Search strategy and selection criteria
We used two electronic databases (MEDLINE and EMBASE)
to identify all case-control studies published to date on an
association between telomere length and cancer risk (last search
update in November, 2010, using the search terms ‘‘telomere
length’’, ‘‘cancer’’ or ‘‘carcinoma’’, and ‘‘risk’’). Additional studies
were identified by a hands-on search of references of original
studies or reviews on this topic. Authors were also contacted
directly, if crucial data were not reported in original papers.
Studies included in the current meta-analysis had to meet the
following criteria: written in English; case-control design; sufficient
information needed to estimate odds ratios (ORs) and their 95%
confidence intervals (CIs); independent from other studies to avoid
double weighting in the estimates derived from the same study. In
addition, investigations in subjects with cancer-prone disposition
were excluded from the analysis.
Data extraction
Two authors (HM and ZZ) independently extracted data and
reached a consensus on all of the items. The following information
was extracted from each report: the first author, year of
publication, country of origin, ethnicity, cancer type, the number
of cases and controls grouped by median or quartiles of relative
telomere length (T/S ratio), study type, control source (population-
based and hospital-based), DNA source, and measurement
methods for telomere length. For studies including subjects of
different racial descent, data were extracted separately for each
ethnic group (categorized as Caucasian, Asian or others). When a
study did not state what ethnic groups were included or if it
was impossible to separate participants according to the data
presented, the sample was termed as ‘other populations’. Further-
more, references involved in different ethnic groups, different types
of cancer and different institutions were divided into multiple
study samples for subgroup analyses.
Quantitative data synthesis
The number of cases and controls grouped by the median of the
relative telomere length (T/S ratio) was collected from each study
to evaluate the risk of cancers (ORs and 95% CI). For each study,
a median value of the relative telomere length (T/S ratio) in
controls was considered as a cut-point dividing all subjects into two
groups: the longer telomere group and the shorter telomere group.
The association between the relative telomere length (T/S ratio)
and cancer risk was examined by ORs and 95% CIs with the
group of longer telomeres as the reference. The stratification
analyses were also conducted by cancer type (if one cancer type
was investigated in less than three studies, it would be merged into
the ‘other cancers’ group), study type (retrospective and prospec-
tive), ethnicity (Caucasian, Asian or others), control source
(hospital-based and population-based) and sample size (,500,
500–1000 and .1000). Smoking-related cancers were defined as
those of the lung, bladder, head and neck, kidney and pancreas;
and cancers of the digestive system included those of the stomach,
esophagus and colon. Additionally, cancers arising from the
bladder, kidney and prostate sites were considered cancers of the
urogenital system.
The x2-based Q test was performed to assess between-study
heterogeneity and considered significant if P,0.05 [45]. Hetero-
geneity was also quantified with the I2 statistic, a value that
indicates what proportion of the total variation across studies is
beyond chance, where 0% indicates no observed heterogeneity
and larger values show increasing heterogeneity [46]. The fixed-
effects model and the random-effects model, based on the Mantel-
Haenszel method [47] and the DerSimonian and Laird method
[48], respectively, were used to combine values from different
studies. When P value of the heterogeneity test was $0.05, the
fixed-effects model was used, which assumes the same homoge-
neity of effect size across all studies; otherwise, the random-effects
model was more appropriate, which tends to provide wider
confidence intervals, when the results of the constituent studies
differ among themselves. To evaluate the effect of individual
studies on the overall risk of cancers, sensitivity analyses were
performed by excluding each study individually and recalculating
the ORs and 95% CI. Furthermore, a sensitivity analysis was also
performed each by excluding three studies whose matching
information was unavailable [21,25,35], two studies whose DNA
were not from blood [20,34], and three studies that did not use
quantitative PCR to test relative telomere length(T/S ratio)
[19,22,36]. The inverted funnel plots and Egger’s test (linear
regression analysis) were used to investigate publication bias [49].
All analysis was conducted by using Review Manage (v.5.0) and
Stata 10.0. All P values were two-sided.
Figure 1. Flow chart for the process of selecting the final 21
publications.
doi:10.1371/journal.pone.0020466.g001
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Results
Characteristics of Studies
As shown in Fig. 1, a total of 146 published records were
retrieved by using the key words mentioned earlier in the
Methods, of which 26 examined the association between telomere
length and cancer risk. Among those 26 publications, five were
excluded either because they did not provide available data to
extract the ORs and 95% CI [40,41,43,44] or the subjects were of
cancer-prone predisposition [42]. The remaining 21 publications
of case-control studies contained 29 studies (Wu’s and Pooley’s
studies had datasets of four different cancers and McGrath’s
and Zheng’s studies had datasets of two different sources)
[19,23,36,38]. The essential information, including first author,
year of publication, country, ethnicity, cancer type, numbers of
cases and controls, study type, control source and DNA source for
all studies are listed in Table 1. Our meta-analysis included nine
breast cancer studies [21,29,31,35,36,38], four bladder studies
[19,20,23], three lung cancer studies [19,24,34], two renal cancer
studies [19,22], two gastric cancers [27,30], two colorectal cancers
[38] and seven studies of other cancers [19,26,28,32,33,37]
(Table 1). Because some controls in one publication [19] were
Table 1. Characteristics of studies included in the meta-analysis.
Author Year Country Ethnicity Cancer type
cases
/controls Study type Control source DNA source
Measurement
methods
Wu [19] 2003 USA Caucasian Head and neck
cancer
92/92 Retrospective Hospital-based Lymphocytes Southern Blot
Analysis
Wu [19]a 2003 USA Caucasian Bladder cancer 135/135 Retrospective Hospital-based Lymphocytes Q-FISHLSC
Wu [19]a 2003 USA Caucasian Lung cancer 54/54 Retrospective Hospital-based Lymphocytes Q-FISHLSC
Wu [19]a 2003 USA Caucasian Renal cell
carcinoma
32/32 Retrospective Hospital-based Lymphocytes Q-FISHLSC
Broberg [20] 2005 Sweden Caucasian Bladder cancer 63/93 Retrospective Population-based Buccal cells Quantitative PCR
Shen [21] 2007 USA Mixed Breast cancer 283/347 Retrospective Family-based White blood cells Quantitative PCR
Shao [22] 2007 USA Mixed Renal Cancer 65/65 Retrospective Hospital-based Lymphocytes Q-FISHLSC
McGrath [23] 2007 USA Not defined Bladder cancer
(NHS)
61/67 Prospective Population-based Buffy coat Quantitative PCR
McGrath [23] 2007 USA Not defined Bladder cancer
(HPFS)
123/125 Prospective Population-based Buffy coat Quantitative PCR
Jang [24] 2008 Korea Asian Lung cancer 243/243 Retrospective Hospital-based Whole blood Quantitative PCR
Svenson [25] 2008 Sweden European Breast cancer 265/446 Retrospective Population-based Buffy coat,
granulocyte
Quantitative PCR
Mirabello [26] 2009 USA Caucasian Prostate cancer 612/1049 Prospective Population-based Buffy coat Quantitative PCR
Liu [27] 2009 China Asian Gastric cancer 396/378 Retrospective Hospital-based Whole blood Quantitative PCR
Xing [28] 2009 USA Caucasian Esophageal cancer 94/92 Retrospective Hospital-based Whole blood Quantitative PCR
De Vivo [29] 2009 USA Caucasian Breast cancer 896/917 Prospective Population-based Lymphocytes Quantitative PCR
Hou [30] 2009 Poland Caucasian Gastric cancer 300/416 Retrospective Population-based Lymphocytes Quantitative PCR
Shen [31] 2009 USA Mixed Breast cancer 1026/1070 Retrospective Population-based Mononuclear cells Quantitative PCR
Lan [32] 2009 Finland Caucasian Non-Hodgkin
Lymphoma
107/107 Prospective Population-based Whole blood Quantitative PCR
Han [33] 2009 USA Caucasian Skin cancer 740/801 Prospective Population-based Buffy coat Quantitative PCR
Hosgood [34] 2009 China Asian Lung cancer 109/97 Retrospective Population-based Sputum Quantitative PCR
Gramatges [35] 2010 USA Mixed Breast cancer 102/50 Retrospective Population-based Whole blood Quantitative PCR
Zheng [36] 2010 USA Mixed Breast cancer
(RPC1)
152/176 Retrospective Hospital-based Buffy coat Quantitative PCR
Zheng [36] 2010 USA Mixed Breast cancer
(LCCC)
140/159 Retrospective Hospital-based Buffy coat Q-FISHLSC
Mirabello [37] 2010 Poland Caucasian Ovarian cancer 98/100 Retrospective Population-based Buffy coat Quantitative PCR
Pooley [38] 2010 UK Caucasian Breast cancer
(SEARCH)
2243/2181 Retrospective Population-based Blood Quantitative PCR
Pooley [38] 2010 UK Caucasian Breast cancer
(EPIC)
199/420 Prospective Population-based Blood Quantitative PCR
Pooley [38] 2010 UK Caucasian Colorectal cancer
(SEARCH)
2161/2249 Retrospective Population-based Blood Quantitative PCR
Pooley [38] 2010 UK Caucasian Colorectal cancer
(EPIC)
185/406 Prospective Population-based Blood Quantitative PCR
Prescott [39] 2010 USA Caucasian Endometrial cancer 279/791 Prospective Population-based Blood Quantitative PCR
aSome controls were shared. PCR, polymerase chain reaction; Q-FISHLSC, quantitative fluorescence in situ hybridization-based approaches.
doi:10.1371/journal.pone.0020466.t001
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shared by different cancers, it was defined as four studies (head and
neck cancer, bladder cancer, lung cancer and renal cell carcinoma)
in the analysis stratified by tumor type but defined as one study in
the overall analysis and stratification analysis by ethnicity, study
type, control source and sample size. Overall, 15 studies used
Caucasians, three used Asians, and eight used other ethnic groups;
in addition, nine studies were prospective and seventeen were
retrospective; 18 studies were population-based, seven were
hospital-based, and one was family-based [21]. Most of studies
provided matching information by age and/or other variables
except for three studies [21,25,35]. The quantitative PCR was the
most frequently used method to measure the relative telomere
length (T/S ratio), while three studies used other assays including
southern blot telomere restriction fragment (TRF) and quantitative
fluorescence in situ hybridization-based approaches (Q-FISH)
[19,22,36]. Additionally, the blood was the most common source
of DNA, although other sources were also applied, such as buccal
cells and sputum [20,34].
Meta-analysis results
We obtained the telomere genotyping data from 21 publications
consisting of 11,255 cases and 13,101 controls. When all eligible
studies were pooled into the meta-analysis, we found that shorter
telomeres were significantly associated with the overall cancer risk
(OR=1.35, 95% CI=1.14–1.60, P,0.001 for heterogeneity test,
I2 = 88%; Fig. 2). In the stratified analysis by tumor type
(Table 2), the comparisons showed that individuals with shorter
telomeres had an increased risk of bladder cancer (OR=1.84,
95% CI= 1.38–2.44, P=0.88 for heterogeneity test, I2 = 0%) and
lung cancer (OR=2.39, 95% CI= 1.18–4.88, P=0.009 for
heterogeneity test, I2 = 79%); but not breast cancer (OR=1.04,
95% CI= 0.77–1.40, P,0.001 for heterogeneity test, I2 = 92%).
We also found the association between the relative telomere length
and overall cancer risk was statistically significant in studies of
Caucasian subjects (OR=1.30, 95% CI= 1.06–1.61, P,0.001 for
heterogeneity test, I2 = 90%), Asian subjects (OR=2.08, 95%
CI= 1.31–3.30, P,0.001 for heterogeneity test, I2 = 75%),
retrospective design (OR=1.44, 95% CI=1.13–1.84, P,0.001
for heterogeneity test, I2 = 86%), hospital-based controls
(OR=2.01, 95% CI= 1.54–2.62, P=0.01 for heterogeneity test,
I2 = 62%), and sample sizes less than 500 (OR=1.51, 95%
CI= 1.06–2.16, P,0.001 for heterogeneity test, I2 = 83%).
Furthermore, when cancers were grouped into site-specific types
(Fig. 3), the results showed that the association remained
significant for smoking-related cancers (OR=2.25, 95%
CI= 1.83–2.78, P=0.07 for heterogeneity test, I2 = 54%), cancers
in the digestive system (OR=1.69, 95% CI=1.53–1.87, P=0.14
for heterogeneity test, I2 = 42%) and in the urogenital system
(OR=1.73, 95% CI= 1.12–2.67, P,0.001 for heterogeneity test,
I2 = 78%).
Figure 2. Odds ratios (ORs) and 95% confidence intervals (CIs) for overall cancer risk associated with relative telomere length
(shorter vs. longer, grouped by the median of telomere length ratio). a Some controls were shared in the study by Wu et al (2003) that
included a total of 313 cases and 256 controls.
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Heterogeneity and sensitivity analyses
Substantial heterogeneity was observed among all studies for the
relative telomere length and cancer risk (x2= 215.43, P,0.001,
Fig. 2). Therefore, we evaluated the source of heterogeneity by
tumor type, ethnicity, control source, study type and sample size, and
we found that tumor type and control source did contribute to
substantial heterogeneity (x2= 9.33, P=0.025 for tumor type and
x2= 9.88, P=0.002 for control source, respectively) but not from
ethnicity (x2= 3.90, P=0.143), study type (x2= 0.91, P=0.340) and
sample size (x2= 1.21, P=0.547). The leave-one-out sensitivity
analysis indicated that no single study changed the pooled ORs
qualitatively (data not shown). Furthermore, the sensitivity analysis
without three studies whose matching information was unavailable
[21,25,35], two studies whose DNA were not from blood [20,34], or
three studies without use of quantitative PCR to test relative telomere
length (T/S ratio) [19,22,36] did not alter the results of the meta-
analysis (OR=1.48, 95% CI=1.26–1.74, P,0.001 for heterogene-
ity test, I2 = 87%; OR=1.34, 95% CI=1.12–1.59, P,0.001 for
heterogeneity test, I2= 89%; and OR=1.30, 95% CI=1.08–1.55,
P,0.001 for heterogeneity test, I2= 89%; respectively).
Publication bias
As shown in Fig. 4, the shapes of the funnel plots seemed
symmetrical, and Egger’s test suggested that there was no pub-
lication bias in the current meta-analysis (P=0.532). These
indicated that bias from publications might not have a significant
influence on the results of our meta-analysis on the association
between telomere length and cancer risk.
Discussion
In this meta-analysis of 11,255 cancer cases and 13,101 controls
from 21 independent publications, we found that shorter telomeres
were significantly associated with risk of cancer, especially cancers
of the bladder and lung, smoking-related, the digestive system and
the urogenital system. Furthermore, the stratification analysis also
showed that the association was more prominent in studies of
Caucasian subjects, Asian subjects, retrospective design, hospital-
based controls, and smaller sample sizes.
Studies have showed that telomeres are critical for maintaining
genomic integrity and that telomere dysfunction or shortening is
an early, common genetic alteration acquired in the multistep
process of malignant transformation [12,50]. In addition, telomere
dysfunction has been found to be associated with decreased DNA
repair capacity and complex cytogenetic abnormalities [51]. Both
of animal studies and clinical observations have shown that shorter
telomeres were associated with increased risk of cancers, such as
epithelial cancers [52,53,54]. However, telomere shortening might
play conflicting roles in cancer development. For example, the
progressive loss of telomeric repeats with each cell division can
induce replicative senescence and limit the proliferative potential
of a cell, thus functioning as a tumor suppressor [12,55]. But, once
telomeres reach a critical length, it will result in chromosome
Table 2. Associations between relative telomere length and cancer risk stratified by selected factors.
Variables No of studies a Sample Shorter vs. longer P for Heterogeneity
Case/control OR(95%CI)b OR(95%CI)c
All 26 11,255/13,101 1.35 (1.14–1.60) 1.37 (1.30–1.44) ,0.00001
Tumor type
Breast cancer 9 5,306/5,766 1.04 (0.77–1.40) 1.29 (1.20–1.40) ,0.00001
Bladder cancer 4 382/420 1.83 (1.38–2.44) 1.84 (1.38–2.44) 0.88
Lung cancer 3 406/394 2.39 (1.18–4.88) 2.44 (1.82–3.27) 0.009
Other 13 5,161/6,578 1.47 (1.15–1.87) 1.37 (1.27–1.47) ,0.00001
Ethnicity
Caucasian 15 8,555/10,324 1.30 (1.06–1.61) 1.38 (1.30–1.46) ,0.00001
Asian 3 748/718 2.08 (1.31–3.30) 2.20 (1.78–2.72) ,0.00001
Other 8 1,952/2,059 1.21 (0.87–1.70) 1.11 (0.98–1.26) ,0.00001
Study type
Prospective 9 7,222/8,287 1.21 (0.93–1.57) 1.39 (1.30–1.48) ,0.00001
Retrospective 17 4,033/4,814 1.44 (1.13–1.84) 1.33 (1.22–1.45) ,0.00001
Control Source
Hospital 7 1,403/1,369 2.01 (1.54–2.62) 2.03 (1.74–2.36) 0.01
Population 18 9,569/11,385 1.18 (0.96–1.43) 1.30 (1.23–1.38) ,0.00001
Sample size
,500 13 1,670/1,630 1.51 (1.06–2.16) 1.61 (1.40–1.85) ,0.00001
500–1000 6 1,628/2,413 1.30 (0.91–1.86) 1.31 (1.15–1.49) ,0.00001
.1000 7 7,957/9,058 1.18 (0.91–1.53) 1.34 (1.26–1.42) ,0.00001
aSome controls in the publication by Wu (2003) et al were shared by different cancers; therefore, it was defined as four studies (head and neck cancer, bladder cancer,
lung cancer and renal cell carcinoma) in the analysis stratified by tumor type, but defined as one study in the analysis stratified by study type, ethnicity and source of
controls. In addition, the publication by Shen (2007) et al was family-based and excluded from the analysis for source of controls.
bRandom effects model.
cFixed effects model.
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