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RESEARCH ARTICLE
Differences in the risk association of TERT-
CLPTM1L rs4975616 (A>G) with lung cancer
between Caucasian and Asian populations: A
meta-analysis
Xiaozheng WuID, Wen Li ID, Yunzhi ChenID*
Department of Preclinical medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou,
China
*chenyunzhi270@gzy.edu.cn
Abstract
Background
Although the G allele variant of TERT-CLPTM1L rs4975616 has been confirmed to be nega-
tively associated to the risk of lung cancer (LC), some other studies haven’t found this nega-
tive association. The purpose of this study is to clarify the association of the rs4975616 with
the risk of developing LC and the differences of this association among patients with differ-
ent ethnicities (Caucasians and Asians), different subtypes of LC, and different smoking
status.
Methods
Relevant literatures published before July 20, 2023 in PubMed, EMbase, Web of Science,
MEDLINE databases were searched through the Internet. Statistical analysis of data was
performed in Revman5.3, including drawing forest plots, funnel plots and so on. Sensitivity
and publication bias were performed in Stata 14.0. The stability of the results was assessed
using Test Sequence Analysis (TSA) software. Registration number: CRD42024568348.
Results
The G allele variant of rs4975616 was negatively associated with the risk of LC ([OR] =
0.86, 95%CI [0.84, 0.88]), and that this negative association was present in both Cauca-
sians ([OR] = 0.85, 95%CI [0.83, 0.87]) and Asians ([OR] = 0.91, 95%CI [0.86, 0.95]), and
the strength of the negative association was higher in Caucasians than in Asians (subgroup
differences: P = 0.02, I
2
= 80.3%). Across LC subtypes, rs4975616[G] was negatively asso-
ciated with the risk of NSCLC (LUAD, LUSC) in both Caucasians and Asians (P<0.05) and
the strength of the association with NSCLC (LUAD) was higher in Caucasians than in Asians
(Subgroup differences: I
2
>50%). In Caucasians, rs4975616[G] was negatively associated
with the risk of LC in both smokers and non-smokers (P<0.05), and the strength of the asso-
ciation did not differ between smokers and non-smokers (Subgroup differences: P = 0.18, I
2
= 45.0%). In Asians, rs4975616[G] was mainly negatively associated with the risk of LC in
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OPEN ACCESS
Citation: Wu X, Li W, Chen Y (2024) Differences in
the risk association of TERT-CLPTM1L rs4975616
(A>G) with lung cancer between Caucasian and
Asian populations: A meta-analysis. PLoS ONE
19(9): e0309747. https://doi.org/10.1371/journal.
pone.0309747
Editor: Peyman Tabnak, Tabriz University of
Medical Sciences, ISLAMIC REPUBLIC OF IRAN
Received: May 3, 2024
Accepted: August 17, 2024
Published: September 10, 2024
Copyright: ©2024 Wu 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.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: 1. Xiaozheng Wu:Guizhou Provincial
Basic Research Program(Natural Science)
(Qiankehe Foundation-ZK[2023]General 411). 2.
Xiaozheng Wu:Academic New Seedling Project of
Guizhou University of Traditional Chinese Medicine
(Guike Cooperative Academic New Seedling
[2023]-22). The funders had role in study design,
smokers (P<0.05) but not in non-smokers ([OR] = 0.97, 95%CI [0.78, 1.20]). Comparisons
between the two populations showed that the strength of this negative association was
higher in Caucasian non-smokers than in Asian non-smokers (Subgroup differences: P =
0.04, I
2
= 75.3%), whereas the strength of this negative association was the same for Cau-
casian smokers as for Asian smokers (Subgroup differences: P = 0.42, I
2
= 0%). Among the
different LC subtypes, rs4975616[G] was negatively associated with the risk of NSCLC
(LUAD) incidence in both Asian smokers and Caucasian non-smokers (P<0.05), whereas it
was not associated with the risk of NSCLC development in Asian non-smokers (P>0.05).
Comparisons between the two populations showed that the strength of the association was
higher in Caucasian non-smokers than in Asian non-smokers (Subgroup differences:
I
2
>50%).
Conclusion
The G allele variant of rs4975616 is negatively associated with the risk of LC and NSCLC
(LUAD, LUSC). Compared with Asians, Caucasians are more likely to have a higher risk of
LC and NSCLC (LUAD) due to the rs4975616 variant. In Caucasians, smoking and other
factors like non-smoking contribute to rs4975616 variations leading to LC, and other factors
like non-smoking also induce rs4975616 variations leading to NSCLC (LUAD). In Asians,
smoking is the major risk factor for the induction of rs4975616 variations leading to LC and
NSCLC(LUAD).
1. Introduction
Lung cancer (LC) was one of the cancers with a high mortality rate in the world, accounting
for about one quarter of all cancer deaths [1]. Smoking had been recognized as a major risk
factor for the development of LC [2]. However, not only smoking, but also genetic variability
was an important cause of LC. Previous genome-wide association studies (GWAS) in various
populations had identified dozens of risk loci for LC [3,4], and most of these loci were clus-
tered in the TERT-CLPTM1L region of chromosome 5p15.33 [5–11].
Cleft lip and cleft palate transmembrane protein 1 (CLPTM1L, alias CRR9) was responsible
for encoding cleft lip and palate-associated transmembrane 1-like proteins. Over-expression of
CLPTM1L had been observed in LC cells, and its over-expression promoted LC cell growth
and survival and was required for KRAS (kirsten rat sarcoma viral oncogene)-driven LC [12,
13]. However, the function of CLPTM1L and its role in LC remain unclear. Another study had
already reported that CLPTM1L, an anti-apoptotic factor commonly over-expressed in LC,
protected cells from genotoxic stress-induced apoptosis by regulating Bcl-xL, suggesting its
inhibitory role in genotoxic stress-induced apoptosis [13]. Later studies had also already
reported that CLPTM1L was over-expressed in human ovarian tumor cell lines and was resis-
tant to cisplatin-induced apoptosis [14–16]. All these evidences suggested that it had anti-apo-
ptotic function on tumor cells. In addition, CLPTM1L would function not only in relation to
its own biological activity, but also because it was in a region of high linkage disequilibrium
(LD) with the telomerase reverse transcriptase (TERT): the entire CLPTM1L gene was located
in a 62 kb LD region that contained the promoter at the 5’ end of TERT [17]. Genetic polymor-
phisms in TERT had been reported to associate with telomere length [18–20], and longer telo-
mere length contributed to an increased risk of LC [21–23]. And genetic polymorphisms in
CLPTM1L were associated with shorter telomere length [24], suggesting that CLPTM1L
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The risk association of TERT-CLPTM1L rs4975616 (A>G) with lung cancer
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data collection and analysis, decision to publish, or
preparation of the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
Abbreviations: LC, Lung cancer; GWAS, Genome-
wide association studies; CLPTM1L, Cleft lip and
cleft palate transmembrane protein 1; TERT,
Telomerase reverse transcriptase; OR, Odds ratio;
95% CI, 95% Confidence interval; HWE, Hardy-
Weinberg equilibrium; NOS, Newcastle Ottawa
scale; TSA, Trial sequential analysis; NSCLC, Non-
small-cell lung carcinoma; LUAD, Lung
adenocarcinoma; LUSC, Lung squamous cell
carcinoma; SCLC, Small cell lung carcinoma.
would be involved in telomere biology and even LC development together with TERT. Thus,
the polymorphisms in CLPTM1L may be in cascade disequilibrium with some causal motifs in
the TERT promoter, but these motifs have not been characterized so far.
The variant rs4975616 (A>G) located in the TERT-CLPTM1L region had been found to be
associated with the risk of developing LC in a previous large GWAS [25]. Several later studies
in Caucasian populations [11,26,27] and Asian populations [28,29] had also found the signif-
icant association with the risk of developing LC. These studies collectively showed that the
minor G allele variant of rs4975616 was negatively associated with the risk of LC, meaning that
its minor G allele frequency was reduced in LC patients compared to healthy individuals,
which implies that the G allele variant reduces the risk of developing LC. On the contrary, the
major A allele variant of rs4975616 was positively associated with the risk of LC development,
which indicates that the A allele is a risk allele for LC development. However, several other
studies in Asian populations did not find the G allele of rs4975616 to be negatively associated
with the risk of developing LC [30–34]. The study by Wang et al. (Texas-GWA) also did not
find the association between rs4975616 and the risk of NSCLC incidence in a white American
population [25]. The reasons for these different results may be related to different ethnicities,
countries, study methods, sample sizes, types of LC, smoking status of LC patients, and pat-
terns of genetic linkage disequilibrium. In addition, no meta-analysis has been conducted to
investigate the association between rs4975616 variants and the risk of developing LC. There-
fore, the results of the association between rs4975616 polymorphisms and the risk of LC devel-
opment currently lack a unified and definitive conclusion.
The present meta-analysis included data from genome-wide association studies and case-
control studies reporting the association of the TERT-CLPTM1L rs4975616 (A >G) polymor-
phism with LC to date, with the aim of clarifying the association of the rs4975616 polymor-
phism with the risk of developing LC and the differences of this association among patients
with different ethnicities (Caucasians and Asians), different subtypes of LC, and different
smoking status.
2. Data and methods
This study has been registered in PROSPERO(https://www.crd.york.ac.uk/prospero/), regis-
tration number: CRD42024568348.
2.1 Inclusion and exclusion criteria
2.1.1 Inclusion criteria. ①The type of studies should be genome-wide association studies
(GWAS) or case-control studies of the TERT-CLPTM1L rs4975616 (A>G) polymorphism
and susceptibility to LC. The language of these studies should be English, the ethnic groups
should be Caucasians or Asians, and the assay for the gene should be accurately described; ②
Allele frequency data were used to calculate Odds ratios (OR) and 95% confidence intervals
(95% CI); ③The distribution of genotype frequency of controls conforms to Hardy-Weinberg
(HWE) [35]; ④The score of Newcastle Ottawa scale (NOS) [36] should be no less than 7(≧7).
2.1.2 Exclusion criteria. ①Studies without allele data; ②Studies of the types of reviews,
meta-analyses, conference reports and case reports; ③Studies with pedigree as the reporting
object; ④Same studies have published for multiple times, only the one with the most complete
data will be included.
2.2 Outcomes
The pre-specified primary outcomes were to investigate whether the rs4975616 (A >G) vari-
ant was associated with LC risk in the overall populations. The secondary outcomes were to
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determine whether there were differences in the strength of the association between the two
populations for rs4975616 (A >G) and LC (including the various subtypes).
2.3 Retrieval strategy
Relevant literatures in PubMed, EMbase, Web of Science, MEDLINE databases published
before July 20, 2023 were searched by theme words and keywords. The language was limited to
English. Search terms (S1A Table): “Lung cancer” OR “LC” AND “rs4975616” OR
“CLPTM1L” OR “TRET” AND “polymorphism”. At the same time, manual retrieval and liter-
ature tracing methods were also used to expand the search scope.
2.4 Literature screening and data extraction
Two relatively independent researchers (X-ZW and WL) completed literature searching and
screening according to the inclusion criteria, and they cross checked and discussed them after-
wards. For the literatures with different opinions, the third party (Y-ZC) made the decision.
For some literatures with incomplete data, they tried to contact the author by e-mail to obtain
the complete data. Finally, data extraction was carried out for the literatures being chosen after
the final decision. These data include: author, year of publication, country, ethnicity, smoking
status of subjects, type of LC, number of cases in case and control groups, frequency of each
genotype in case and control groups, and the OR and 95% CI of each genotype.
2.5 Literature quality evaluation
The quality of the included literature was evaluated in the NOS [36] (X-ZW and WL), and
those with a score of no less than 7 were considered as literatures with high-quality.
2.6 Statistical methods
The HWE of the genotypes of the controls was detected by Pearson’s chi-square test in SPSS
24.0. All results were statistically counted and analyzed in Revman 5.3, including drawing for-
est plots and funnel plots. Because the included eligible studies were conducted in genetically
diverse populations, all results of this meta-analysis were statistically analyzed using the ran-
dom effects models. The effect size and effect value of the statistical results were presented by
OR value and 95% CI.
We used Q-tests to test for heterogeneity, which was quantified by I
2
. When P>0.1 or
I
2
<50% indicated that there was no significant heterogeneity in all studies or in all subgroups,
and the Comprehensive Meta-Analysis (CMA) v4 software was used to calculate the 95% Pre-
diction Interval (95% PI) to assess the dispersion of effects across included studies. Meta-
regression using any relevant clinical or biological characteristics of LC patients as indepen-
dent variables was performed to further explore sources of heterogeneity. Begg’s Test and
Egger’s Test were performed in Stata 14.0 to assess publication bias among studies, and sensi-
tivity analysis was performed to assess the results of statistical analysis with greater heterogene-
ity. TSA 0.9.5.10 software was performed for the TSA tests to evaluate the stability of the
conclusion ([Type I error] probability = 5%, statistical test power = 80%, relative risk
reduction = 20%).
2.7 Ethics and dissemination
This review does not require ethical approval because the included studies are published data
and do not involve the patients’ privacy. The results of this review will be reported in accor-
dance with the PRISMA extension statement and disseminated to a peer-reviewed journal.
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3. Results
3.1 Literature search results
4 databases initially examined 556 literatures. After screening, 20 studies of 16 literatures were
finally included (S1B Table), including 12 studies of Caucasians [5,11,25–27,37–40], 8 studies
of Asians [28–34,40] (S2 and S3 Tables). These studies included 90360 LC patients and
122140 healthy controls, including 79081 patients in Caucasians and 11279 patients in Asians,
and included 25314 smoking and 5061 non-smoking LC patients. The flow diagram was made
according to the PRISMA statement (Fig 1).
3.2 Quality evaluation
All 20 studies had high NOS [36] assessment scores (�7), indicating that they were all at low
risk of bias (S4 Table).
Fig 1. PRISMA literature screening flow diagram.
https://doi.org/10.1371/journal.pone.0309747.g001
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3.3 Meta-analysis
The results showed that the G allele variant of rs4975616 (allelic model) was negatively associ-
ated with the risk of developing LC in the overall population ([OR] = 0.86, 95%CI [0.84, 0.88]),
and that this negative association was present in both Caucasian populations ([OR] = 0.85,
95%CI [0.83, 0.87]) and in Asian populations ([OR] = 0.91, 95%CI [0.86, 0.95]), and the
strength of the negative association was higher in Caucasians than in Asians (Caucasians:
[OR] = 0.85 /Asians: [OR] = 0.91, subgroup differences: P = 0.02, I
2
= 80.3%) (Table 1,Fig 2).
In additive (GG vs. AA), heterozygous (GA vs. AA), dominant (GG+GA vs. AA), and recessive
(GG vs. GA+AA) models, the variant of rs4975616 was negatively associated with the risk of
developing LC in the overall population (GG vs. AA: [OR] = 0.74, 95%CI [0.66, 0.83]; GA vs.
AA: [OR] = 0.86, 95%CI [0.80, 0.93]; GG+GA vs. AA: [OR] = 0.84, 95%CI [0.77, 0.91]; GG vs.
GA+AA: [OR] = 0.81, 95%CI [0.73, 0.90]), and these negative associations were predominantly
found in Caucasian populations (p <0.05) rather than in Asian populations (P >0.05) (S5
Table and S1–S4 Figs).
Table 1. The results of meta-analysis and publication bias (rs4975616: Allele genetic model, G vs. A).
LC
subtypes
Subgroups Studies (n) Heterogeneity
test
Sample Model OR[95% CI] Effect
p value
Subgroup
differences
Publication
bias
OR[95% PI]
P values I
2
(%) Cases
(n)
Controls
(n)
P values I
2
(%) P
Begg
P
Egger
LC Caucasians 12 0.04 46 158162 217606 Random 0.85 [0.83,
0.87]
<0.00001 —— —— 0.732 0.113 0.85 [0.80,
0.91]
Asians 8 0.44 0 22558 26674 Random 0.91 [0.86,
0.95]
0.0001 —— —— 0.805 0.235 0.91 [0.86,
0.95]
Overall 20 0.04 39 180720 244280 Random 0.86 [0.84,
0.88]
<0.00001 0.02 80.3 0.675 0.031 0.86 [0.80,
0.91]
NSCLC
a
Caucasians 8 0.19 30 78174 341156 Random 0.85 [0.83,
0.87]
<0.00001 —— —— 0.216 0.154 0.85 [0.81,
0.89]
Asians 7 0.43 0 17480 36014 Random 0.90 [0.86,
0.95]
0.0001 —— —— 0.453 0.471 0.90 [0.86,
0.95]
Overall 15 0.12 32 95654 377170 Random 0.85 [0.83,
0.88]
<0.00001 0.03 78.4 0.152 0.089 0.86 [0.81,
0.90]
SCLC Overall 2 0.54 0 314 2312 Random 0.91 [0.68,
1.23]
0.54 —— —— —— —— 0.91 [0.68,
1.23]
LUAD
b
Caucasians 4 0.005 77 46032 169850 Random 0.83 [0.78,
0.88]
<0.00001 —— —— 0.174 0.014 0.83 [0.66,
1.05]
Asians 5 0.36 8 14088 24064 Random 0.92 [0.86,
0.98]
0.01 —— —— 0.624 0.827 0.92 [0.80,
1.05]
Overall 9 0.004 64 61020 193914 Random 0.86 [0.82,
0.90]
<0.00001 0.03 79.8 0.233 0.000 0.86 [0.75,
0.97]
LUSC Caucasians 4 0.97 0 29754 170138 Random 0.84 [0.82,
0.87]
<0.00001 —— —— 0.497 0.578 0.84 [0.82,
0.87]
Asians 3 0.75 0 3392 12350 Random 0.86 [0.77,
0.96]
0.006 —— —— 0.602 0.628 0.86 [0.77,
0.96]
Overall 7 0.99 0 33146 182488 Random 0.84 [0.82,
0.87]
<0.00001 0.75 0 0.393 0.383 0.84 [0.82,
0.87]
LC: Lung cancer; NSCLC: non-small-cell lung carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma.
a: NSCLC(Overall) vs. SCLC(Overall): Test for subgroup differences: Chi
2
= 0.18, df = 1 (P = 0.67), I
2
= 0%.
b: LUAD(Overall) vs. LUSC(Overall): Test for subgroup differences: Chi
2
= 1.18, df = 1 (P = 0.61), I
2
= 0%; LUAD(Caucasians) vs. LUSC(Caucasians): Test for subgroup
differences: Chi
2
= 0.29, df = 1 (P = 0.59), I
2
= 0%
LUAD(Asians) vs. LUSC(Asians): Test for subgroup differences: Chi
2
= 1.03, df = 1 (P = 0.31), I
2
= 2.9%.
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Across LC subtypes, the G allele variant of rs4975616 was negatively associated with the risk
of NSCLC development in the overall population ([OR] = 0.85, 95%CI [0.83, 0.88]), and this
negative association was present in both Caucasian populations ([OR] = 0.85, 95%CI [0.83,
0.87]) and Asian populations ([OR] = 0.90, 95%CI [0.86, 0.95]), and the strength of the nega-
tive association was higher in Caucasians than in Asians (Caucasians: [OR] = 0.85 /Asians:
[OR] = 0.90, subgroup differences: P = 0.03, I
2
= 78.4%) (Table 1,S5 Fig). In addition, the G
allele variant of rs4975616 was not associated with the risk of SCLC development in the overall
population ([OR] = 0.91, 95% CI [0.68, 1.23]) (Table 1,S6 Fig). The G allele variant of
rs4975616 was negatively associated with the risk of developing LUAD in the overall popula-
tion ([OR] = 0.86, 95%CI [0.82, 0.90]), and this negative association was present in both Cau-
casians ([OR] = 0.83, 95%CI [0.78, 0.88]) and Asians ([OR] = 0.92, 95%CI [0.86, 0.98]), and
the strength of the negative association was stronger in Caucasians than in Asians (Caucasians:
[OR] = 0.83 /Asians: [OR] = 0.92, subgroup differences: P = 0.03, I
2
= 79.8%) (Table 1,S7 Fig).
The G allele variant of rs4975616 was negatively associated with the risk of developing LUSC
in the overall population ([OR] = 0.84, 95%CI [0.82, 0.87]), and this negative association was
present in both Caucasians ([OR] = 0.84, 95%CI [0.82, 0.87]) and Asians ([OR] = 0.86, 95%CI
Fig 2. Forest plot of rs4975616 (G vs. A) for LC.
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Table 2. Meta-analysis results of smoking status (rs4975616: Allele genetic model, G vs. A).
LC subtypes Subgroups Smoking status Studies (n) Heterogeneity
test
Sample Model OR[95% CI] Effect
p value
Subgroup
differences
P values I
2
(%) Cases
(n)
Controls
(n)
P values I
2
(%)
LC
a
Overall Smoking 4 0.12 49 50628 36770 Random 0.83 [0.75, 0.92] 0.0005 —— ——
Non-smoking 5 0.23 29 10122 16160 Random 0.78 [0.71, 0.86] <0.00001 —— ——
Total 9 0.0009 70 60750 52930 Random 0.81 [0.74, 0.88] <0.00001 0.41 0
Caucasians Smoking 2 0.05 73 48884 35382 Random 0.85 [0.74, 0.96] 0.01 —— ——
Non-smoking 3 0.49 0 8674 14712 Random 0.77 [0.72, 0.81] <0.00001 —— ——
Total 5 <0.0001 83 57558 50094 Random 0.79 [0.72, 0.87] <0.00001 0.18 45.0
Asians Smoking 2 0.63 0 1744 1388 Random 0.77 [0.62, 0.94] 0.01 —— ——
Non-smoking 2 0.69 0 1448 1448 Random 0.97 [0.78, 1.20] 0.75 —— ——
Total 4 0.44 0 3192 2836 Random 0.86 [0.74, 0.99] 0.04 0.13 56.9
NSCLC
b
Overall Smoking (Asians) 2 0.67 0 1116 2412 Random 0.75 [0.60, 0.93] 0.008 —— ——
Non-smoking 3 0.06 65 1530 2554 Random 0.83 [0.62, 1.11] 0.2 —— ——
Total 5 0.19 34 2646 4966 Random 0.79 [0.67, 0.93] 0.004 0.59 0
Caucasians Non-smoking 1 —— —— 400 1106 Random 0.65 [0.51, 0.83] 0.0005 —— ——
Asians Smoking 2 0.67 0 1116 2412 Random 0.75 [0.60, 0.93] 0.008 —— ——
Non-smoking 2 0.67 0 1130 1448 Random 0.97 [0.77, 1.22] 0.78 —— ——
Total 4 0.39 0 2246 3860 Random 0.84 [0.72, 0.99] 0.03 0.1 62.0
SCLC Overall Smoking (Asians) 1 —— —— 236 1206 Random 0.98 [0.67, 1.45] 0.94 —— ——
Non-smoking (Caucasians) 1 —— —— 78 1106 Random 0.81 [0.51, 1.30] 0.39 —— ——
Total 2 0.54 0 314 2312 Random 0.91 [0.68, 1.23] 0.54 0.54 0
LUAD
c
Overall Smoking (Asians) 1 —— —— 418 1206 Random 0.71 [0.50, 0.99] 0.04 —— ——
Non-smoking 3 0.006 80 1244 2554 Random 0.78 [0.50, 1.20] 0.26 —— ——
Total 4 0.02 71 1662 3760 Random 0.76 [0.55, 1.04] 0.08 0.73 0
Caucasians Non-smoking 1 —— —— 224 1106 Random 0.52 [0.38, 0.71] <0.0001 —— ——
Asians Smoking 1 —— —— 418 1206 Random 0.71 [0.50, 0.99] 0.04 —— ——
Non-smoking 2 0.73 0 1020 1448 Random 0.98 [0.77, 1.23] 0.84 —— ——
Total 3 0.29 19 1438 2654 Random 0.88 [0.70, 1.09] 0.23 0.12 57.8
LUSC
d
Overall Smoking (Asians) 1 —— —— 698 1206 Random 0.78 [0.59, 1.02] 0.07 —— ——
Non-smoking 2 0.89 0 206 1506 Random 0.83 [0.58, 1.18] 0.3 —— ——
Total 3 0.95 0 904 2712 Random 0.80 [0.64, 0.99] 0.04 0.77 0
Caucasians Non-smoking 1 —— —— 96 1106 Random 0.81 [0.53, 1.25] 0.35 —— ——
Asians Smoking 1 —— —— 698 1206 Random 0.78 [0.59, 1.02] 0.07 —— ——
Non-smoking 1 —— —— 110 400 Random 0.86 [0.46, 1.61] 0.64 —— ——
Total 2 0.77 0 808 1606 Random 0.79 [0.61, 1.01] 0.06 0.77 0
LC: Lung cancer; NSCLC: Non-small-cell lung carcinoma; SCLC: Small-cell lung carcinoma; LUAD: Lung adenocarcinoma; LUSC: Lung squamous cell carcinoma.
a: LC(Smoking Caucasians) vs. LC(Smoking Asians): Test for subgroup differences: Chi
2
= 0.65, df = 1 (P = 0.42), I
2
= 0%; LC(Non-smoking Caucasians) vs. LC(Non-
smoking Asians): Test for subgroup differences: Chi
2
= 4.05, df = 1 (P = 0.04), I
2
= 75.3%.
b: NSCLC(Non-smoking Caucasians) vs. NSCLC(Non-smoking Asians): Test for subgroup differences: Chi
2
= 5.45, df = 1 (P = 0.02), I
2
= 81.7%; NSCLC(Smoking
Asians) vs. SCLC(Smoking Asians): Test for subgroup differences: Chi
2
= 1.50, df = 1 (P = 0.22), I
2
= 33.5%; NSCLC(Non-smoking Caucasians) vs. SCLC(Non-smoking
Caucasians): Test for subgroup differences: Chi
2
= 0.69, df = 1 (P = 0.40), I
2
= 0%
c: LUAD(Non-smoking Caucasians) vs. LUAD(Non-smoking Asians): Test for subgroup differences: Chi
2
= 10.04, df = 1 (P = 0.002), I
2
= 90.0%; LUAD(Smoking
Asians) vs. LUSC(Smoking Asians): Test for subgroup differences: Chi
2
= 0.18, df = 1 (P = 0.67), I
2
= 0%; LUAD(Non-smoking overall) vs. LUSC(Non-smoking
overall): Test for subgroup differences: Chi
2
= 0.05, df = 1 (P = 0.82), I
2
= 0%; LUAD(Non-smoking Caucasians) vs. LUSC(Non-smoking Caucasians): Test for subgroup
differences: Chi
2
= 2.75, df = 1 (P = 0.10), I
2
= 63.6%; LUAD(Non-smoking Asians) vs. LUSC(Non-smoking Asians): Test for subgroup differences: Chi
2
= 0.14, df = 1
(P = 0.71), I
2
= 0%.
d: LUSC(Non-smoking Caucasians) vs. LUSC(Non-smoking Asians): Test for subgroup differences: Chi
2
= 0.02, df = 1 (P = 0.89), I
2
= 0%.
https://doi.org/10.1371/journal.pone.0309747.t002
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[0.77, 0.96]), and the strength of this negative association was equal to that of the Asian popula-
tion for the Caucasian population (Caucasians: [OR] = 0.84 /Asians: [OR] = 0.86, subgroup
differences: P = 0.75, I
2
= 0%) (Table 1,S8 Fig). Comparison of the strength of association
between LUAD and LUSC showed that the G allele variant of rs4975616 was as negatively
associated with the risk of developing LUAD in the overall population as it was with LUSC
(LUAD: [OR] = 0.86 /LUSC: [OR] = 0.84, subgroup differences: P = 0.61, I
2
= 0%), and also in
Caucasians (LUAD: [OR] = 0.83 /LUSC: [OR] = 0.84, subgroup differences: P = 0.59, I
2
= 0%)
and Asians (LUAD: [OR] = 0.92 /LUSC: [OR] = 0.86, subgroup differences: P = 0.31, I
2
=
2.9%) (Table 1).
3.4 Analysis of smoking status
Subgroup analyses using smoking status of LC patients showed that in the overall population,
the G allele variant of rs4975616 was negatively associated with the risk of developing LC in
both smokers ([OR] = 0.83, 95%CI [0.75, 0.92]) and non-smokers ([OR] = 0.78, 95%CI [0.71,
0.86]). And the strength of this negative association did not differ between smokers and non-
smokers (Smoking: [OR] = 0.83/Non-smoking: [OR] = 0.78, Subgroup differences: P = 0.41, I
2
= 0%) (Table 2,Fig 3). In the Caucasian population, the G allele variant was negatively associ-
ated with the risk of developing LC in both smokers ([OR] = 0.85, 95% CI [0.74, 0.96]) and
non-smokers ([OR] = 0.77, 95% CI [0.72, 0.81]), and the strength of this negative association
did not differ between smokers and non-smokers (Smoking: [OR] = 0.85/Non-smoking:[OR]
= 0.77, Subgroup differences: P = 0.18, I
2
= 45.0%) (Table 2,S9 Fig). In the Asian population,
the G allele variant was mainly negatively associated with the risk of developing LC in smokers
([OR] = 0.77, 95%CI [0.62, 0.94]) but not in non-smokers ([OR] = 0.97, 95%CI [0.78, 1.20]),
and the strength of this negative association was higher in smokers than in non-smokers
(Smoking: [OR] = 0.77/Non-smoking:[OR] = 0.97, Subgroup differences: P = 0.13, I
2
= 56.9%)
Fig 3. Forest plot of rs4975616 (G vs. A) for the smoking status of LC patients in the overall populations.
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(Table 2,S10 Fig). Comparisons between the two populations showed that the strength of this
negative association was higher in Caucasian non-smokers than in Asian non-smokers (Non-
smoking Caucasians: [OR] = 0.77/Non-smoking Asians:[OR] = 0.97, Subgroup differences:
P = 0.04, I
2
= 75.3%), whereas the strength of this negative association was the same for Cauca-
sian smokers as for Asian smokers (Smoking Caucasians: [OR] = 0.85/Smoking Asians: [OR]
= 0.77, Subgroup differences: P = 0.42, I
2
= 0%) (Table 2).
Among the different LC subtypes, the G allele variant of rs4975616 was negatively associ-
ated with the risk of NSCLC incidence in both Asian smokers ([OR] = 0.75, 95%CI[0.60,
0.93]) and Caucasian non-smokers ([OR] = 0.65, 95%CI[0.51, 0.83]), whereas it was not asso-
ciated with the risk of NSCLC development in Asian non-smokers ([OR] = 0.97, 95%CI[0.77,
1.22]) (Table 2,S11 and S12 Figs). Comparisons between the two populations showed that the
strength of this negative association was higher in Caucasian non-smokers than in Asian non-
smokers (Non-smoking Caucasians: [OR] = 0.65/Non-smoking Asians: [OR] = 0.97, Subgroup
differences: p = 0.02, I
2
= 81.7%) (Table 2). The G allele variant of rs4975616 was also nega-
tively associated with the risk of developing LUAD in both Asian smokers ([OR] = 0.71, 95%
CI[0.50, 0.99]) and Caucasian non-smokers ([OR] = 0.52, 95%CI[0.38, 0.71]), but not with the
risk of developing LUAD in Asian non-smokers ([OR] = 0.98, 95% CI [0.77, 1.23]) (Table 2,
S14 and S15 Figs). Comparisons between the two populations showed that the strength of this
negative association was higher in Caucasian non-smokers than in Asian non-smokers (Non-
smoking Caucasians: [OR] = 0.52/Non-smoking Asians: [OR] = 0.98, Subgroup differences:
p = 0.002, I
2
= 90.0%) (Table 2). In addition, the G allele variant was not negatively associated
with the risk of developing SCLC in Asian smokers ([OR] = 0.98, 95%CI [0.67, 1.45]) and Cau-
casian non-smokers ([OR] = 0.81, 95%CI [0.51, 1.30]) (Table 2,S13 Fig). The G allele variant
was not negatively associated with the risk of developing LUSC in Asian smokers ([OR] = 0.78,
95%CI [0.59, 1.02]), Asian non-smokers ([OR] = 0.86, 95%CI [0.46, 1.61]), and Caucasian
non-smokers ([OR] = 0.81, 95%CI [0.53, 1.25]) (Table 2,S16 and S17 Figs).
3.5 Heterogeneity analysis
In analyses of LC (including subtypes), heterogeneity was predominantly present in the Cauca-
sian population (Table 1,S5 Table) and was concentrated in the LUAD results of the Cauca-
sian population (P = 0.005, I
2
= 77%) (Table 1). The reasons for this may be related to factors
such as different sample sizes, different countries, different study methods, different genetic
testing methods, and different smoking status of the studies in these Caucasian populations.
The 95% PI for each comparison was calculated using CMA v4 software, and the results of the
additive, heterozygous, dominant and recessive genetic models and the results of the LUAD
for the Caucasian population were found to be changed (95% PI included 1) (Table 1,S5 Table
and S18 and S19 Figs), which suggests that these results can be somewhat unstable due to the
presence of heterogeneity. In addition, the LUAD result of Asian populations also showed
such change (95% PI included 1) (Table 1), the reason of which may be related to the small
sample size.
In the subgroup analyses of the patients’ smoking status, heterogeneity was still predomi-
nantly present in the Caucasian population (I
2
>50%) (Table 2). The calculation of 95% PI
showed changes in the LC results for smokers and non-smokers (95% PI included 1) (S6 Table
and S19 Fig), and the reasons for these changes may be related to the small number of studies,
small sample sizes, and a certain degree of heterogeneity. In addition, the remaining subgroups
could not have their 95% PI calculated accurately due to the small number of included studies.
Meta-regression using age and sex ratio (male%) of LC patients and minor allele frequency
(MAF) of controls as independent variables revealed that none of these factors was the main
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source of heterogeneity (P>0.05) (S20 Fig). Therefore, the generation of heterogeneity may
still be related to those factors mentioned before.
3.6 Publication bias
Most of the funnel plots appear to be symmetrical (S21 and S22 Figs). In LC, although the
Egger’s test results of G vs. A(Overall), GA vs. AA(Overall and Caucasians) and GG+GA vs.
AA(Overall) were all less than 0.05 (P
Egger
<0.05), their Begg’s test results were all more than
0.05 (P
Begg
>0.05). The Begg’s test result of GG vs. GA+AA(Asians) was less than 0.05
(P
Begg
<0.05), but its Egger’s test result was more than 0.05 (P
Egger
>0.05) (Table 1,S5 Table).
In LUAD, although the Egger’s test results for G vs. A (Overall and Caucasians) were all less
than 0.05 (P
Egger
<0.05), their Begg’s test results were all more than 0.05 (P
Begg
>0.05)
(Table 1). From these results, it cannot be suggested that they are biased. In addition, the
results of all other models were not significantly biased (P
Egger
<0.05 and P
Egger
<0.05)
(Table 1,S5 and S6 Tables).
3.7 Sensitivity analysis
The results of sensitivity analysis of all genetic models showed no significant sensitivity in any
of the studies, indicating that there was no significant difference in the result of the meta-anal-
ysis after removing any study (S7 Table and S23 and S24 Figs).
3.8 Trial sequential analysis (TSA)
The TSA analysis showed that the Z-curve (blue line) crossed both the traditional boundary
(green dashed line) and the TSA boundary (red line), proving that the results of LC, NSCLC,
LUAD, LUSC and LC smoking subgroups were stable and credible (S25 and S26 Figs).
4. Discussion
In a previous large GWAS, rs4975616 (A >G) located in the TERT-CLPTM1L region had
been found to be associated with the risk of LC [25]. However, several other studies had shown
inconsistent results [30–34]. Therefore, the association of the variant of rs4975616 with the
risk of LC currently lacked a unifying conclusion. The aim of this meta-analysis was to clarify
its association with LC and the differences in this association between patients of different eth-
nicities (Caucasian populations and Asian populations), different subtypes of LC, and different
smoking status. The results showed the negative association between the G allele variant of
rs4975616 and the risk of developing LC, and this negative association was present in both
Caucasian and Asian populations. Previous studies in Caucasian [11,25–27,37] and Asian
populations [28,29,40] reported similar results. These results confirm that the minor G allele
variant of rs4975616 reduces the risk of LC in both populations, while its major A allele (risk
allele) variant increases the risk of LC in both populations. Comparing the two populations,
we found that the G allele variant of rs4975616 was more strongly negatively associated with
the risk of LC development in the Caucasian population than in the Asian population, suggest-
ing that the variant of rs4975616 was differentially associated with the strength of the risk of
LC development in the two populations, and that Caucasians were more likely to have a higher
risk of LC development due to the variant of rs4975616. In addition, variants in rs4975616 did
not seem to be negatively associated with the risk of LC development in Asian populations in
any of the additive, heterozygous, dominant, and recessive models, implying that variants in
rs4975616 may not be associated with the risk of LC development in Asian populations. How-
ever, it is worth noting that some studies did not have complete genotypes reported, resulting
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in a smaller number of Asian populations included in these models (n = 4) and a smaller sam-
ple size of patients. Therefore, this result is inconclusive and needs to be verified by further
inclusion of more studies.
Across different LC subtypes, our results demonstrated negative associations of the G allele
variant of rs4975616 with the risk of developing NSCLC, LUAD, and LUSC, and these negative
associations were all present in both Caucasian and Asian populations. These results were the
same as those of previous studies in Caucasian populations [11,38,39,40] and Asian popula-
tions [40]. This evidence confirms that both populations are at risk for developing NSCLC,
LUAD, and LUSC due to variants in rs4975616. Comparing the two populations, we found
that the strength of the negative association between the G allele variant of rs4975616 and the
risk of developing NSCLC and LUAD was higher in Caucasian than in Asian populations,
indicating that the strength of the association between the variant of rs4975616 and the risk of
developing NSCLC and LUAD differed between the two populations, and that Caucasian pop-
ulations had a higher risk of developing NSCLC and LUAD due to the variant of rs4975616. In
addition, the strength of the negative association of LUSC incidence risk in the Caucasian pop-
ulation was the same as that in the Asian population, suggesting that the risk of LUSC inci-
dence is high in both populations. Comparisons between LUAD and LUSC revealed the same
strength of negative association between the G allele variant of rs4975616 and the risk of devel-
oping LUAD as that of LUSC, and in both populations. These results were broadly consistent
with those of previous studies [11,39,40]. It suggests that both subtypes of NSCLC are at high
risk of developing in both Caucasian and Asian populations, and that both Caucasian and
Asian populations are likely to be at risk of developing LUAD or LUSC due to the rs4975616
variant. In addition, our results found that the G allele variant of rs4975616 did not reduce the
risk of SCLC, suggesting that the rs4975616 variant may not be associated with the risk of
SCLC.
The results of epidemiologic investigations had shown that although smoking was identi-
fied as a major environmental risk factor for LC worldwide, only a small percentage of smokers
developed LC during their lifetime. In contrast, a large percentage of LC cases had no history
of smoking [41,42]. LC among never-smokers differed from LC among smokers in that a
large proportion of LC patients among never-smokers carried genetic variants of oncogenes
[43]. Previous studies had shown that genetic susceptibility to LC in never-smokers was associ-
ated with genetic variants with pan-cancer risk implications, and that gene-environment inter-
actions were important in LC etiology [37]. In order to clarify whether smoking or non-
smoking can cause the CLPTM1L rs4975616 variant and thus the risk of developing LC, we
conducted a subgroup analysis of patients’ smoking status. We found that the G allele variant
of rs4975616 was negatively associated with the risk of developing LC in both smokers and
non-smokers, and the strength of this negative association did not differ between smokers and
non-smokers. This confirms that smoking is indeed an important risk factor for the rs4975616
variant leading to LC, but it is not the only factor, because other factors like non-smoking can
also cause the rs4975616 variant leading to LC. Previous evidence suggested that patient’s edu-
cation, BMI, previous diagnosis of COPD, occupational exposure to pesticides, duration of
smoking, exposure to high levels of cooking emissions, and dietary factors (including less fish
and shrimp, vegetables, soy products, and nuts), as well as a high consumption of meat, were
all associated with the development of LC [44]. Thus, LC is a multi-causal disease triggered by
a combination of smoking, genetic, environmental and lifestyle factors.
In each ethnic group, we found this negative association in both Caucasian smokers and
Caucasian non-smokers, and there was no difference in the strength of this negative associa-
tion between them. This was the same as previous findings in Caucasian smokers [26] and
Caucasian non-smokers [37,38]. It implies that regardless of whether the Caucasian
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population smokes or not, they are likely to be at risk for LC due to the variant in rs4975616.
Therefore, smoking remains an important risk factor for causing the rs4975616 variant and
lead to LC in the Caucasian population, yet other factors like non-smoking can also induce the
rs4975616 variant and lead to LC in Caucasians. However, in Asian populations, we found that
this negative association appeared to be present only in smokers and not in non-smokers, and
the strength of this negative association was higher in smokers than in non-smokers. Previous
studies in Asian smokers [29] and Asian non-smokers [34] reported similar results. These
results suggest that smoking is also a major risk factor for causing variants in rs4975616 and
lead to LC in Asian populations, and other factors like non-smoking do not seem to contribute
to the development of LC. However, it is worth noting that the number of Asian non-smokers
included in this study was small (n = 2), so this conclusion needs to be verified by including
more studies. In addition, our results also showed that the strength of the negative association
for the risk of LC development was higher in Caucasian non-smokers than in Asian non-
smokers, suggesting that the Caucasian population is more at risk of LC development due to
other factors like non-smoking that cause variants in rs4975616 and lead to LC, and also pro-
vides further evidence that other factors like non-smoking that cause variants in rs4975616
and lead to LC development in Asian populations are at a lower risk.
Among different LC subtypes, we found that the G allele variant of rs4975616 was nega-
tively associated with the risk of developing NSCLC (LUAD) mainly in Caucasian non-smok-
ers and Asian smokers, suggesting that this negative risk association caused by the G allele
variant in Caucasian non-smokers and Asian smokers is mainly concentrated in the LC sub-
type of NSCLC (LUAD). Previous studies have reported similar results [29,38]. Therefore, in
the Caucasian population, other factors like non-smoking can cause the rs4975616 variant and
lead to NSCLC (LUAD), whereas smoking is the major risk factor for causing rs4975616 varia-
tion and thus NSCLC(LUAD) in Asian populations. In addition, we did not find the negative
association between the G allele variant of rs4975616 and the risk of developing NSCLC
(LUAD) in Asian non-smokers, the result that was the same as that of some previous studies
[32,34]. However, the evidence [45] had already confirmed that the common genetic variation
of TERT-CLPTM1L was associated with the risk of LUAD in non-smoking Asian women, and
the number of Asian non-smokers included in our study was small (n = 2). Therefore, this
result is inconclusive and more studies need to be included for verification. There is also the
fact that we did not look for data results on Caucasian smokers, and therefore could not deter-
mine the association between the rs4975616 variant and the risk of NSCLC (LUAD) develop-
ment in Caucasian smokers.
In addition, we found no risk-negative association between the G allele variant of
rs4975616 and the development of SCLC and LUSC in both smokers and non-smokers. How-
ever, because of the small number of included studies for both subtypes of LC, the association
between the G allele variant and SCLC and LUSC needs to be verified by further studies.
Limitations: ①The meta-analysis was based on the results of studies of different ethnicities,
different LC subtypes and different smoking status, so some heterogeneity and publication
bias will inevitably exist; ②The genetic testing and genotyping methods used in all the studies
were different, and some heterogeneity and publication bias will also exist; ③In terms of the
sample size, overall the present study was still sufficient. However, after subgroup analysis
based on different LC subtypes and different ethnicities, the sample size was still on the low
side, especially for LUAD and SCLC. This will inevitably produce some false-negative results
for the results of LUAD and SCLC; ④Although the current study discussed in detail about the
effects of smoking, genes, environment, lifestyle and other factors on LC, the sample size
included in the subgroup analysis of smoking status was still small. Therefore, the reliability of
the results of the association between smoking or non-smoking and the risk of LC (including
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each LC subtype) may be affected to a certain extent; ⑤Because fewer results have been
reported for other ethnicities (African population, mixed population), the results of the data
from these populations were not analyzed in the current study, and therefore these results do
not represent the association of the risk of LC among all ethnicities; ⑥All the literature
included in this study was in English and no literature in other languages was included.
5. Conclusion
The G allele variant of rs4975616 is negatively associated with the risk of LC and NSCLC
(LUAD, LUSC), and it is more strongly associated with the risk of LC and NSCLC (LUAD) in
Caucasians than in Asians. Therefore, compared with Asian populations, Caucasians are more
likely to have a higher risk of developing LC and NSCLC (LUAD) due to the rs4975616 vari-
ant. In Caucasian populations, smoking and other factors like non-smoking contribute to
rs4975616 variations leading to LC, and other factors like non-smoking also induce rs4975616
variations leading to NSCLC (LUAD). In Asian populations, smoking is the major risk factor
for the induction of rs4975616 variations leading to LC and NSCLC(LUAD). Therefore, the
risk factors for the development of LC are different between these two populations.
Supporting information
S1 Fig. Forest plot of rs4975616 (GG vs. AA) for LC.
(DOCX)
S2 Fig. Forest plot of rs4975616 (GA vs. AA) for LC.
(DOCX)
S3 Fig. Forest plot of rs4975616 (GG+GA vs. AA) for LC.
(DOCX)
S4 Fig. Forest plot of rs4975616 (GG vs. GA+AA) for LC.
(DOCX)
S5 Fig. Forest plot of rs4975616 (G vs. A) for NSCLC.
(DOCX)
S6 Fig. Forest plot of rs4975616 (G vs. A) for SCLC.
(DOCX)
S7 Fig. Forest plot of rs4975616 (G vs. A) for LUAD.
(DOCX)
S8 Fig. Forest plot of rs4975616 (G vs. A) for LUSC.
(DOCX)
S9 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LC patients in Cauca-
sians.
(DOCX)
S10 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LC patients in Asians.
(DOCX)
S11 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of NSCLC patients in the
overall populations.
(DOCX)
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S12 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of NSCLC patients in
Caucasians or Asians.
(DOCX)
S13 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of SCLC patients in over-
all populations.
(DOCX)
S14 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LUAD patients in the
overall populations.
(DOCX)
S15 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LUAD patients in
Caucasians or Asians.
(DOCX)
S16 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LUSC patients in the
overall populations.
(DOCX)
S17 Fig. Forest plot of rs4975616 (G vs. A) for the smoking status of LUSC patients in Cau-
casians or Asians.
(DOCX)
S18 Fig. The 95% prediction interval for the association of rs4975616 with LC. A: G vs. A;
B: GG vs. AA; C: GA vs. AA; D: GG+GA vs. AA; E: GG vs. GA+AA.
(DOCX)
S19 Fig. The 95% prediction interval for the association of rs4975616(G vs. A) with LC of
different ethnicity/pathological subtypes/smoking status. A: NSCLC; B: SCLC; C: LUAD;
D: LUSC; E: LC Smokers; F: LC Non-smokers.
(DOCX)
S20 Fig. The results of meta-regression. A: age of LC patients, set: age �60 or <60; B: sex
ratio (male%) of LC patients, set: male% �60% or <60%; C: minor allele frequency (MAF) of
controls, set: MAF �0.4 or <0.4.
(DOCX)
S21 Fig. Publication bias for the association of rs4975616 with LC. A: G vs. A; B: GG vs.
AA; C: GA vs. AA; D: GG+GA vs. AA; E: GG vs. GA+AA.
(DOCX)
S22 Fig. Publication bias for the association of rs4975616(G vs. A) with LC of different eth-
nicity/pathological subtypes/smoking status. A: NSCLC; B: LUAD; C: LUSC; D: LC Smoking
status.
(DOCX)
S23 Fig. Results of sensitivity analysis of rs4975616 in association with LC. A: G vs. A; B:
GG vs. AA; C: GA vs. AA; D: GG+GA vs. AA; E: GG vs. GA+AA.
(DOCX)
S24 Fig. Results of sensitivity analysis of rs4975616(G vs. A) in association with LC of dif-
ferent ethnicity/pathological subtypes/smoking status. A: NSCLC; B: LUAD; C: LUSC; D:
LC Smoking status.
(DOCX)
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S25 Fig. TSA results for the association of rs4975616 with LC. A: G vs. A; B: GG vs. AA; C:
GA vs. AA; D: GG+GA vs. AA; E: GG vs. GA+AA.
(DOCX)
S26 Fig. TSA results for the association of rs4975616(G vs. A) with LC in different patho-
logical subtypes/smoking status. A: NSCLC; B: LUAD; C: LUSC; D: LC Smokers; E: LC Non-
smokers.
(DOCX)
S1 Table. Literature search strategy and search results. A: PubMed search strategy. B: A
numbered table of all studies identified in the literature search.
(DOCX)
S2 Table. Basic features of the included study (1).
(DOCX)
S3 Table. Basic features of the included study (2).
(DOCX)
S4 Table. Newcastle Ottawa scale (NOS).
(DOCX)
S5 Table. The LC results of additive, heterozygous, dominant and recessive genetic models
in rs4975616.
(DOCX)
S6 Table. The publication bias and 95% prediction interval for the association of
rs4975616(G vs. A) with LC of different smoking status.
(DOCX)
S7 Table. The results of sensitivity analysis.
(DOCX)
Author Contributions
Conceptualization: Xiaozheng Wu.
Data curation: Xiaozheng Wu.
Formal analysis: Xiaozheng Wu.
Funding acquisition: Xiaozheng Wu.
Investigation: Xiaozheng Wu.
Methodology: Xiaozheng Wu, Wen Li.
Project administration: Xiaozheng Wu.
Resources: Xiaozheng Wu.
Software: Xiaozheng Wu.
Supervision: Yunzhi Chen.
Validation: Xiaozheng Wu.
Visualization: Xiaozheng Wu.
Writing – original draft: Xiaozheng Wu.
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The risk association of TERT-CLPTM1L rs4975616 (A>G) with lung cancer
PLOS ONE | https://doi.org/10.1371/journal.pone.0309747 September 10, 2024 16 / 19
Writing – review & editing: Wen Li, Yunzhi Chen.
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PLOS ONE
The risk association of TERT-CLPTM1L rs4975616 (A>G) with lung cancer
PLOS ONE | https://doi.org/10.1371/journal.pone.0309747 September 10, 2024 19 / 19
