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. 2017 Mar 17;8(24):38693–38705. doi: 10.18632/oncotarget.16309

The TERT rs2736100 polymorphism increases cancer risk: A meta-analysis

Hui Li 1,*, Yanyan Xu 2,*, Hua Mei 3, Liang Peng 4, Xiaojie Li 5, Jianzhou Tang 4,5
PMCID: PMC5503564  PMID: 28418878

Abstract

Abnormal telomerase activity is implicated in cancer initiation and development. The rs2736100 T > G polymorphism in the telomerase reverse transcriptase (TERT) gene, which encodes the telomerase catalytic subunit, has been associated with increased cancer risk. We conducted a meta-analysis to more precisely assess this association. After a comprehensive literature search of the PubMed and EMBASE databases up to November 1, 2016, 61 articles with 72 studies comprising 108,248 cases and 161,472 controls were included in our meta-analysis. Studies were conducted on various cancer types. The TERT rs2736100 polymorphism was associated with increased overall cancer risk in five genetic models [homozygous model (GG vs. TT): odds ratio (OR) = 1.39, 95% confidence interval (95% CI) = 1.26-1.54, P < 0.001; heterozygous model (TG vs. TT): OR = 1.16, 95% CI = 1.11-1.23, P < 0.001; dominant model (TG + GG vs. TT): OR = 1.23, 95% CI = 1.15-1.31, P < 0.001; recessive model (GG vs. TG + TT): OR = 1.25, 95% CI = 1.16-1.35, P < 0.001; and allele contrast model (G vs. T): OR = 1.17, 95% CI = 1.12-1.23, P < 0.001]. A stratified analysis based on cancer type associated the polymorphism with elevated risk of thyroid cancer, bladder cancer, lung cancer, glioma, myeloproliferative neoplasms, and acute myeloid leukemia. Our results confirm that the TERT rs2736100 polymorphism confers increased overall cancer risk.

Keywords: TERT, cancer, risk, meta-analysis, telomerase

INTRODUCTION

Cancer is a major public health problem worldwide, with an estimated 14.1 million new cancer cases and 8.2 million deaths in 2012 [1]. Carcinogenesis is a complex process, influenced by various genetic and environmental factors, such as smoking, poor diet, physical inactivity, reproductive changes and the growth and aging of the population [1, 2]. Telomeres, composed of the TTAGGG repeat sequence, are special chromatin structures located at each end of a chromosome. Telomeres maintain chromosomal integrity by protecting chromosome ends from DNA damage and end-to-end fusions [3]. Abnormally short telomeres may cause chromosomal instability, and consequentially contribute to cancer development. Telomerase (also known as terminal transferase), a reverse transcriptase enzyme, extends the 3′ end of chromosomal DNA by catalyzing the telomere synthesis reaction. Defects in telomerase activity have been observed in many human tumor cells, and telomere length was inversely associated with cancer incidence and mortality [4]. Telomerase reverse transcriptase (TERT), the telomerase catalytic subunit, maintains telomere stability [5]. In a previous genome-wide association study (GWAS), Shete, et al. discovered that certain TERT gene variants increase glioma susceptibility [6]. Since then, TERT variants have been associated with various cancers, including breast, lung, colorectal, ovarian, prostate, and gastric cancers [7, 8].

The TERT gene is located in 5p15.33. The rs2736100 T > G polymorphism in the second intron of the TERT gene has been associated with shortened telomere length in gastric cancer [9]. The association of this SNP with cancer susceptibility has been extensively explored, although the findings are as yet inconclusive. Several meta-analyses published in 2014 associated the TERT rs2736100 polymorphism with increased glioma and lung cancer susceptibility [10–14]. In 2012, Zou, et al. observed an association between this polymorphism and overall cancer risk [15], although their meta-analysis involved only 11 articles. However, between 2015 and 2016, more than 27 studies were published with large sample sizes [9, 16–37]. Thus, we performed an updated meta-analysis to more precisely assess the TERT rs2736100 polymorphism-cancer association, including 72 studies derived from 61 articles with 269,720 total subjects [6, 9, 16–74].

RESULTS

Study characteristics

We initially identified 432 records from the PubMed and EMBASE databases (Figure 1). After screening titles and abstracts, 268 articles were excluded and the full texts of the remaining 164 articles were further assessed. Articles were excluded for the following reasons: irrelevant association (87 articles), meta-analysis (7), and lacking sufficient raw data for further evaluation (12). Three additional articles were identified by manually screening the references of relevant articles. Finally, 72 studies extracted from 61 articles met our study inclusion criteria and were included in the current meta-analysis [6, 9, 16–74].

Figure 1. Flowchart of articles included in our meta-analysis.

Figure 1

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In most of the included studies, the TERT rs2736100 polymorphism genotypic distribution followed Hardy-Weinberg equilibrium (HWE) in controls, except for seven studies [6, 28, 43, 51, 63, 66, 72]. Since the genotype distributions of other polymorphisms were in compliance with HWE in these seven studies, we included these studies in the meta-analysis. In total, 72 studies with 108,248 cases and 161,472 controls were included in our pooled analysis. Studies were conducted on various cancer types, including lung (28 studies), glioma (5), colorectal (4), bladder (4), myeloproliferative neoplasms (MPN) (4), gastric (3), acute myeloid leukemia (AML) (2), breast (2), melanoma (2), and thyroid (2). The remaining 16 studies focused on different types of cancer, with one study for each type of cancer, and were grouped together as “other cancer” in our analyses. There were 37 studies conducted in Asians and 35 in Caucasians. Twenty-three studies included fewer than 500 controls, and 49 had 500 or more controls. Sixteen studies were categorized as low quality and 56 were high quality. The main characteristics of all the studies are summarized in Table 1.

Table 1. The main characteristics of all the studies included in the meta-analysis.

Surname Year Country Ethnicity Cancer type Cases Controls HWE Score
All TT TG GG All TT TG GG
Zhou 2016 China Asian ESCC 588 165 275 148 600 215 287 108 0.472 11
Zhang 2016 China Asian NC 855 265 428 162 1036 365 516 155 0.211 13
Yuan 2016 China Asian UTUC 212 83 81 48 289 86 144 59 0.928 10
Xing 2016 China Asian Lung cancer 418 216 164 38 410 268 124 18 0.452 10
Wang 2016 China Asian Lung cancer 500 131 257 112 500 178 242 80 0.881 11
Trifa 2016 Romania Caucasian MPN 529 76 255 198 433 124 213 96 0.802 13
Krahling 1 2016 Hungary Caucasian PMN 584 77 282 225 400 111 188 101 0.235 8
Krahling 2 2016 Hungary Caucasian CML 86 25 43 18 400 111 188 101 0.235 8
Krahling 3 2016 Hungary Caucasian AML 308 71 153 84 400 111 188 101 0.235 7
Gong 2016 China Asian Thyroid cancer 452 142 214 96 452 156 222 74 0.738 11
Ge 2016 China Asian Thyroid cancer 2300 644 1093 563 2300 875 1056 369 0.093 12
Dahlstrom 1 2016 Sweden Caucasian MPN 126 15 64 47 756 167 377 212 0.980 9
Dahlstrom 2 2016 China Asian MPN 101 17 52 32 101 33 50 18 0.722 8
Bayram 2016 Turkey Caucasian Gastric cancer 104 16 44 44 209 61 82 66 0.002 9
Li 2016 China Asian Lung cancer 391 109 201 81 337 117 159 61 0.587 9
Shiraishi 2016 Japan Asian Lung cancer 6830 2057 3386 1387 15155 5723 7133 2299 0.323 13
Wei 2015 China Asian Lung cancer 702 190 353 159 2520 814 1269 437 0.130 12
Shadrina 1 2015 Russia Caucasian Prostate cancer 360 102 183 75 358 105 165 88 0.150 11
Shadrina 2 2015 Russia Caucasian Breast cancer 642 192 310 140 523 132 280 111 0.097 12
Mosrati 2015 Sweden Caucasian AML 226 48 113 65 788 201 406 181 0.382 10
Liu 2015 China Asian Lung cancer 288 72 139 77 317 92 173 52 0.052 9
Du 2015 China Asian Gastric cancer 1105 360 557 188 994 346 464 184 0.197 11
de Martino 2015 Austria Caucasian RCC 241 61 120 60 375 97 181 97 0.502 10
Choi 2015 South Korea Asian Gastric cancer 243 34 107 102 246 38 122 86 0.625 8
Campa 2015 Germany Caucasian Pancreatic cancer 1724 445 861 418 3512 817 1763 932 0.764 13
Campa 2015 Germany Caucasian Multiple myeloma 2052 535 958 559 2633 634 1285 714 0.237 13
Adel Fahmideh 2015 Sweden Caucasian Brain tumor 240 61 103 76 478 109 256 113 0.120 12
Yin 2014 China Asian Lung cancer 524 139 273 112 524 186 255 83 0.777 11
Wang 2014 China Asian Lung cancer 1552 455 764 333 1605 549 780 276 0.971 12
Liorca-Cardenosa 2014 Spain Caucasian Melanoma 629 146 297 186 371 94 177 100 0.380 9
Zhao 2013 China Asian Lung cancer 1759 596 1163a 1163a 1804 674 1130a 1130a / 9
Sheng 2013 China Asian ALL 569 178 270 121 656 233 323 100 0.490 13
Pellatt 2013 USA Caucasian Breast cancer 3698 1450 1934 314 3534 1179 1674 681 0.047 11
Pellatt 1 2013 USA Caucasian Colon cancer 1555 410 798 347 1956 493 956 507 0.321 12
Pellatt 2 2013 USA Caucasian Rectal cancer 754 214 356 184 959 270 465 224 0.386 12
Myneni 2013 China Asian Lung cancer 352 122 141 89 447 157 212 78 0.659 8
Ma 2013 China Asian Bladder Cancer 177 55 87 35 961 340 455 166 0.516 10
Lan 2013 China Asian Lung cancer 193 43 109 41 197 70 103 24 0.137 9
Wang 2012 China Asian Cervical Cancer 1010 322 462 226 1006 352 480 174 0.637 11
Rajaraman b 2012 USA Caucasian Glioma 1854 / / / 4949 / / / / 12
Kinnersley 2012 UK Caucasian Colorectal cancer 16039 4191 8105 3743 16430 4090 8082 4258 0.039 12
Ito 2012 Japan Asian Lung cancer 716 248 340 128 716 279 329 108 0.496 12
Hofer 2012 Austria Caucasian Colorectal cancer 137 38 68 31 1705 458 859 388 0.700 11
Chen 2012 China Asian Lung cancer 196 45 101 50 229 69 112 48 0.838 10
Shiraishi 2012 Japan Asian Lung cancer 4648 1386 2265 997 12364 4650 5856 1858 0.838 13
Bae 2012 Korea Asian Lung cancer 1094 402 501 191 1100 422 522 156 0.790 10
Pande b 2011 USA Caucasian Lung cancer 1681 / / / 1235 / / / / 10
Nan 1 2011 USA Caucasian Melanoma 210 55 91 64 831 215 399 217 0.252 11
Nan 2 2011 USA Caucasian SCC 277 57 125 95 831 215 399 217 0.252 11
Nan 3 2011 USA Caucasian BCC 274 68 116 90 831 215 399 217 0.252 11
Kohno 2011 Japan Asian Lung cancer 377 142 175 53 325 116 165 39 0.090 9
Hu 2011 China Asian Lung cancer 8559 2393 4294 1872 9378 3231 4533 1614 0.724 13
Ding 2011 China Asian HC 1269 428 633 208 1322 449 651 222 0.591 12
Chen 2011 China Asian Glioma 953 244 515 194 1036 334 542 160 0.014 10
Jaworowsk 1 2011 Poland Caucasian Lung cancer 855 247 403 205 844 263 425 156 0.494 11
Jaworowsk 2 2011 Poland Caucasian Bladder Cancer 431 134 216 81 439 134 226 79 0.335 10
Jaworowsk 3 2011 Poland Caucasian Laryngeal cancer 413 124 211 78 406 130 199 77 0.956 10
Gago-Dominguez 1 2011 USA Caucasian Bladder Cancer 471 86 239 146 547 127 262 158 0.361 11
Gago-Dominguez 2 2011 USA Asian Bladder Cancer 499 141 260 98 525 174 274 77 0.064 10
Wang 2010 UK Caucasian Lung cancer 239 42 115 82 553 136 259 158 0.146 8
Turnbull 2010 UK Caucasian TGCT 1588 520 767 301 7683 1904 3718 2061 0.005 10
Miki 2010 Japan Asian Lung cancer 2086 622 1048 416 1103 4093 5246 1695 0.835 13
Kohno 2010 Japan Asian Lung cancer 1656 488 796 372 968 373 460 135 0.719 13
Hsiung 2010 China Asian Lung cancer 2308 599 1187 522 2321 852 1132 337 0.211 12
Yoon 2010 Korea Asian Lung cancer 1425 467 696 262 3011 1187 1405 419 0.921 11
Truong 1 2010 France Caucasian Lung cancer 9126 1878 4526 2722 11812 2853 5817 3142 0.116 13
Truong 2 2010 France Asian Lung cancer 1686 538 836 312 2101 775 1014 312 0.506 12
Schoemaker 2010 UK Caucasian Glioma 216 30 114 72 241 54 127 60 0.397 9
Shete 2009 USA Caucasian Glioma 4344 781 2213 1350 6457 1623 3122 1712 0.008 11
Landi b 2009 USA Caucasian Lung cancer 5739 / / / 5848 / / / / 11
Jin 2009 China Asian Lung cancer 1212 353 627 232 1339 450 658 231 0.719 13
Wrensch 2009 USA Caucasian Glioma 691 95 354 242 3981 1021 1904 1056 0.006 12
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Abbreviations: ESCC: esophageal squamous cell carcinoma; NC: nasopharyngeal carcinoma; UTUC: upper tract urothelial carcinomas; MPN: myeloproliferative neoplasms; CML: chronic myeloid leukemia; AML: acute myeloid leukemia; RCC: renal cell carcinoma; ALL: acute lymphoblastic leukemia; SCC: squamous cell carcinoma; BCC: basal cell carcinoma; HC: hepatocellular carcinoma; TGCT: testicular germ cell tumor; HWE: Hardy-Weinberg equilibrium

a: Number of cases and controls for TG and GG genotypers. b: The allele frequence in the three studies was provided to estimate the association under allele contrast model (G vs. T).

Meta-analysis results

Heterogeneity among studies was observed for all five genetic models. Consequently, the random effect model was applied to calculate odds ratios (ORs). Risk estimates indicated that the TERT rs2736100 polymorphism was associated with overall cancer risk via all five genetic models [homozygous model (GG vs. TT): OR=1.39, 95% confidence interval (CI)=1.26–1.54, P<0.001; heterozygous model (TG vs. TT): OR=1.16, 95% CI=1.11–1.23, P<0.001; dominant model (TG + GG vs. TT): OR=1.23, 95% CI=1.15–1.31, P<0.001; recessive model (GG vs. TG + TT): OR=1.25, 95% CI=1.16–1.35, P<0.001; and allele contrast model (G vs. T): OR=1.17, 95% CI=1.12–1.23, P<0.001 (Figure 2, Table 2)]. The stratified analysis by cancer type associated the TERT rs2736100 polymorphism with lung cancer risk (homozygous model: OR=1.60, 95% CI=1.49–1.71, P<0.001; heterozygous model: OR=1.25, 95% CI=1.20–1.31, P=0.008; dominant model: OR=1.33, 95% CI 1.26–1.39, P<0.001; recessive model: OR=1.40, 95% CI=1.32–1.48, P<0.001; and allele contrast model: OR=1.24, 95% CI=1.17–1.31, P<0.001). This polymorphism was also associated with increased risk for thyroid cancer, bladder cancer, glioma, MPN and AML. Inversely, the TERT rs2736100 polymorphism was associated with decreased colorectal cancer risk (homozygous model: OR=0.86, 95% CI=0.82–0.91, P=0.512; dominant model: OR=0.94, 95% CI=0.90–0.98, P=0.970; recessive model: OR=0.88, 95% CI=0.82–0.96, P=0.279; and allele contrast model: OR=0.93, 95% CI=0.90–0.96, P=0.548). Stratified analysis was also performed by patient ethnicity, sample size of controls, and quality score. Elevated cancer risk was found among Asians in all five genetic models and among Caucasians under all five genetic models except for the recessive model. Our results also associated the TERT rs2736100 polymorphism with elevated overall cancer risk in all subgroups divided by sample size of controls and quality score in all the five genetic models.

Figure 2. Forest plot of the association between the TERT rs2736100 polymorphism and overall cancer susceptibility in the allele contrast model.

Figure 2

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Table 2. Meta-analysis of TERT rs2736100 T>G polymorphism on cancer risk.

Variables Homozygous Heterozygous Recessive Dominant Allele
GG vs. TT TG vs. TT GG vs. (TG + TT) (TG +GG) vs. TT G vs. T
OR (95% CI) Phet I2 (%) OR (95% CI) Phet I2 (%) OR (95% CI) Phet I2 (%) OR (95% CI) Phet I2 (%) OR (95% CI) Phet I2 (%)
All 1.39 (1.26-1.54) <0.001 93.3 1.16 (1.11-1.23) <0.001 80.0 1.25 (1.16-1.35) <0.001 91.1 1.23 (1.15-1.31) <0.001 88.9 1.17 (1.12-1.23) <0.001 93.4
Cancer type
 Lung 1.60 (1.49-1.71) <0.001 65.7 1.25 (1.20-1.31) 0.008 45.5 1.40 (1.32-1.48) <0.001 61.2 1.33 (1.26-1.39) <0.001 58.6 1.24 (1.17-1.31) <0.001 89.4
 MPN 3.17 (2.51-4.00) 0.854 0.0 2.03 (1.64-2.51) 0.972 0.0 1.89 (1.59-2.24) 0.616 0.0 2.40 (1.97-2.94) 0.957 0.0 1.74 (1.56-1.95) 0.679 0.0
 AML 1.40 (1.04-1.88) 0.631 0.0 1.22 (0.94-1.59) 0.744 0.0 1.23 (0.97-1.56) 0.411 0.0 1.28 (1.00-1.64) 0.970 0.0 1.18 (1.02-1.37) 0.658 0.0
 Thyroid 1.79 (1.25-2.56) 0.076 68.3 1.26 (0.96-1.65) 0.085 66.2 1.62 (1.37-1.92) 0.266 19.3 1.38 (1.02-1.88) 0.041 76.0 1.33 (1.08-1.64) 0.040 76.4
 Gastric 1.39 (0.82-2.33) 0.028 72.1 1.22 (0.90-1.66) 0.204 37.2 1.19 (0.83-1.70) 0.044 68.1 1.31 (0.90-1.90) 0.085 59.4 1.22 (0.94-1.58) 0.023 73.5
 Breast 0.56 (0.25-1.28) <0.001 95.0 0.88 (0.73-1.07) 0.158 49.8 0.63 (0.24-1.64) <0.001 97.3 0.78 (0.71-0.85) 0.892 0.0 0.80 (0.61-1.04) 0.003 88.8
 Melanoma 1.18 (0.90-1.54) 0.890 0.0 1.00 (0.78-1.27) 0.444 0.0 1.18 (0.95-1.47) 0.700 0.0 1.06 (0.85-1.33) 0.570 0.0 1.09 (0.95-1.26) 0.922 0.0
 Colorectal 0.86 (0.82-0.91) 0.512 0.0 0.98 (0.93-1.03) 0.989 0.0 0.88 (0.82-0.96) 0.279 21.9 0.94 (0.90-0.98) 0.970 0.0 0.93 (0.90-0.96) 0.548 0.0
 Bladder 1.31 (1.08-1.59) 0.481 0.0 1.15 (0.98-1.34) 0.498 0.0 1.18 (1.00-1.39) 0.598 0.0 1.19 (1.02-1.38) 0.436 0.0 1.13 (1.03-1.25) 0.507 0.0
 Glioma 1.89 (1.52-2.35) 0.028 67.0 1.55 (1.30-1.84) 0.055 60.0 1.35 (1.21-1.49) 0.241 28.5 1.65 (1.37-1.99) 0.020 69.4 1.33 (1.25-1.42) 0.089 50.4
 Others 1.09 (0.89-1.32) <0.001 86.7 0.97 (0.88-1.07) 0.002 58.4 1.11 (0.95-1.29) <0.001 84.3 1.01 (0.89-1.13) <0.001 78.2 1.04 (0.94-1.15) <0.001 87.0
Ethnicity
 Asian 1.56 (1.46-1.67) <0.001 65.0 1.22 (1.17-1.28) 0.001 49.5 1.39 (1.32-1.46) <0.001 50.4 1.30 (1.28-1.36) <0.001 62.1 1.25 (1.20-1.29) <0.001 67.7
 Caucasian 1.22 (1.04-1.44) <0.001 94.4 1.12 (1.02-1.22) <0.001 83.6 1.11 (0.99-1.25) <0.001 92.5 1.16 (1.04-1.29) <0.001 90.7 1.11 (1.03-1.19) <0.001 94.2
Sample Size
 ≥ 500 1.34 (1.19-1.51) <0.001 95.1 1.16 (1.09-1.23) <0.001 83.7 1.22 (1.11-1.33) <0.001 93.6 1.21 (1.13-1.30) <0.001 91.5 1.15 (1.09-1.22) <0.001 95.1
 <500 1.52 (1.26-1.82) <0.001 72.5 1.19 (1.04-1.37) <0.001 66.2 1.34 (1.19-1.51) 0.001 55.1 1.29 (1.11-1.49) <0.001 73.3 1.23 (1.12-1.35) <0.001 74.1
Score
 High 1.33 (1.18-1.48) <0.001 94.5 1.15 (1.09-1.21) <0.001 82.7 1.22 (1.12-1.33) <0.001 92.8 1.20 (1.12-1.28) <0.001 90.8 1.15 (1.09-1.21) <0.001 94.5
 Low 1.72 (1.40-2.10) 0.001 60.5 1.30 (1.10-1.54) 0.003 57.0 1.41 (1.26-1.59) 0.154 27.4 1.40 (1.20-1.63) <0.001 65.7 1.30 (1.18-1.43) <0.001 60.5
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Abbreviations: MPN, Myeloproliferative neoplasms; AML, Acute myeloid leukemia

Heterogeneity and sensitivity analyses

Heterogeneity was detected amongst studies with respect to the association between the TERT rs2736100 polymorphism and overall cancer risk (homozygous model: P<0.001; heterozygous model: P<0.001; dominant model: P<0.001; recessive model: P<0.001; and allele contrast model: P<0.001). Therefore, we used the random effects model to generate pooled ORs and 95% CIs. Sensitivity analyses indicated that no single study could change the between-study heterogeneity and the results of meta-analysis.

Publication bias

The Begg's funnel plot and Egger's linear regression analysis did not reveal any evidence of publication bias in the meta-analysis (homozygous model: P=0.183; heterozygous model: P=0.805; dominant model: P=0.406; recessive model: P=0.085; and allele model: P=0.122; Figure 3).

Figure 3. Funnel plot analysis to evaluate publication bias.

Figure 3

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False positive report probability (FPRP) analyses

We calculated FPRP values for associations between the TERT rs2736100 T>G polymorphism and overall cancer risk using the five genetic models. FPRP values were all <0.20, suggesting that these associations were noteworthy (Table 3).

Table 3. False-positive report probability values for associations between the TERT rs2736100 T>G polymorphism and overall cancer risk.

Genetic models OR (95% CI) P Power Prior Probability
0.25 0.1 0.01 0.001 0.0001 0.00001
Homozygous (GG vs. TT) 1.39 (1.26-1.54) <0.001 0.555 0.000 0.000 0.000 0.000 0.000 0.000
Heterozygous (TG vs. TT) 1.16 (1.11-1.23) <0.001 0.872 0.000 0.000 0.000 0.001 0.008 0.073
Recessive (GG vs. TG + TT) 1.25 (1.16-1.35) <0.001 0.841 0.000 0.000 0.000 0.000 0.000 0.002
Dominant (TG +GG vs. TT) 1.23 (1.15-1.31) <0.001 0.957 0.000 0.000 0.000 0.000 0.000 0.000
Allele (G vs. T) 1.17 (1.12-1.23) <0.001 0.839 0.000 0.000 0.000 0.000 0.000 0.000
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DISCUSSION

Telomeres are special structures at the ends of eukaryotic chromosomes, and are responsible for protecting chromosomes from degradation, end-to-end fusion, and rearrangement [10]. Telomerase maintains proper telomere length by adding repetitive telomeric sequences to the 3′ ends of telomeres. Abnormal telomerase activity is implicated in the initiation and development of cancer and other age-associated diseases [75]. The TERT subunit of telomerase consists of three highly conserved domains: the RNA-binding domain (TRBD), the reverse transcriptase domain, and a carboxy-putative extension (CTE) proposed to constitute the putative thumb domain [75]. TERT is overexpressed in many human cancers [76]. The TERT rs2736100 polymorphism, localized in the second intron of the TERT gene, has been wildly studied with respect to cancer risk [7, 8]. However, the functional significance of the TERT rs2736100 polymorphism was not clear. Preliminary studies in gastric cancer suggested that this SNP is associated with decreased telomere length [9].

The present meta-analysis, comprising 108,248 cases and 161,472 controls, found that the TERT rs2736100 polymorphism increased overall cancer risk by 16–39%, suggesting that this SNP may contribute to carcinogenesis. A previous meta-analysis conducted by Zou, et al. in 2012 [15] also concluded that this polymorphism was associated with increased cancer risk. However, this analysis included only 11 case-control articles with 23,032 cases and 38,274 controls, which studied only lung cancer, glioma, and bladder cancer. Our stratified analysis by cancer type showed that the TERT rs2736100 polymorphism correlated with increased risk of lung cancer and glioma. Such associations were also observed in lung cancer- and glioma-specific meta-analyses published in 2014 [10–15, 77]. Between 2015 and 2016, at least 27 studies (6 studies on lung cancer) were published investigating the association between the TERT rs2736100 polymorphism and overall cancer susceptibility. To the best of our knowledge, ours is the largest meta-analysis of this association, with the strongest statistical power. Apart from lung cancer, glioma, and bladder cancer, our meta-analysis also investigated the association between the TERT rs2736100 polymorphism and risk of colorectal cancer (4 studies), MPN (4), gastric cancer (3), AML (2), breast cancer (2), melanoma (2), and thyroid cancer (2) as well as “other cancers” (16). We observed that this polymorphism was associated with decreased colorectal cancer risk. Since only four colorectal cancer studies were included in our meta-analysis, such an association might be a false positive, and validation will require further study.

The current meta-analysis had several limitations. First, there were substantial heterogeneities in the pooled study investigating the association between the TERT rs2736100 polymorphism and overall cancer risk. We reduced the degree of heterogeneity through stratified analyses by cancer type, patient ethnicity, sample size, and study quality score. Some cross-study heterogeneity might be attributed to differences among ethnic groups [78]. However, other sources of heterogeneity were not identified, such as control sources and genotyping methods. Second, the studies in this meta-analysis focused on Asian and Caucasian populations only, so we may not have had sufficient statistical power to evaluate associations based on ethnicity. Third, our results were based on unadjusted ORs due to the unavailability of confounding factor information for cases and controls (e.g., age, sex, smoking status, drinking status, and environmental exposure). Finally, lacking the original data from eligible studies limited our ability to explore gene-environment interactions.

In conclusion, our meta-analysis indicated that the TERT rs2736100 polymorphism was associated with increased overall cancer risk, especially lung cancer risk. Larger studies involving patients of different ethnicities are needed to confirm our findings.

MATERIALS AND METHODS

Identification of eligible studies

A comprehensive literature search of the PubMed and EMBASE databases was performed up to November 1, 2016. To find all eligible case-control studies that assessed the association between the TERT rs2736100 polymorphism and cancer risk, we used the following keywords: “TERT or telomerase reverse transcriptase”, “polymorphism or variant”, and “cancer or tumor or neoplasm or carcinoma”. We also evaluated additional studies by manually screening the references of both primary articles and reviews.

Inclusion and exclusion criteria

Eligible studies included in our analysis met the following criteria: (i) the TERT rs2736100 polymorphism-cancer risk association was assessed; (ii) case-control studies or cohort studies; (iii) sufficient data to calculate an OR with 95% CI; (iv) studies in English. Exclusion criteria were as follows: (i) case only studies; (ii) overlapping publications; (iii) abstract, case report, editorial comment, and review. Studies that deviated from HWE in controls were excluded, unless further evidence showed that another polymorphism was in HWE.

Data extraction

Two investigators independently extracted available data from each eligible study. The following information was collected: first author's surname, year of publication, country of origin, patient ethnicity, cancer type, numbers of cases and controls, genotype counts of cases and controls, results of the HWE test, and quality scores (low quality studies with score ≤9, high quality studies with score >9) [79]. Any disagreements were solved by discussion until a consensus was reached between the two investigators. If no consensus was reached, another investigator joined the discussion, and a final decision was made by a majority.

FPRP analysis

FPRP values were applied to assess the statistical power of our significant findings [80, 81]. An FPRP value of 0.20 was set as the criterion for noteworthiness. A prior probability of 0.1 was assigned to detect an OR of 0.67/1.50 (protective/risk effects) for an association with genotypes under investigation.

Statistical analysis

HWE in control subjects was assessed by chi-squared test. The strength of association between the TERT rs2736100 polymorphism and cancer risk was estimated by calculating crude ORs and their 95% CIs using all five genetic models: homozygous (GG vs. TT), heterozygous (TG vs. TT), dominant (GG vs. TG + TT), and recessive (TG + GG vs. TT), as well as the allele contrast model (G vs. T). Q-test was used to quantify heterogeneity among all eligible studies, and P>0.10 suggested a lack of heterogeneity among studies. Generally, the fixed effects model (Mantel–Haenszel method) or the random effects model (DerSimonian–Laird method) was employed in the absence (P≥0.10) or presence (P<0.10) of heterogeneity, respectively [82–84]. Heterogeneity was also estimated using the I2 test [85]. Subgroup analyses were conducted by patient ethnicity, cancer type, and study sample size. The Begg's funnel plot and the Egger's linear regression test were used to evaluate publication bias [86]. All statistical analyses were performed using STATA version 12.0 software (STATA Corporation, College Station, TX). All statistical analyses were two-sided. P<0.05 was considered statistically significant.

Footnotes

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

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