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. 2022 May 4;12:878507. doi: 10.3389/fonc.2022.878507

Comprehensive Analysis of 29,464 Cancer Cases and 35,858 Controls to Investigate the Effect of the Cytotoxic T-Lymphocyte Antigen 4 Gene rs231775 A/G Polymorphism on Cancer Risk

Hongyuan Wan 1,2,†, Hangsheng Zhou 1,2,†, Yanyan Feng 1,2,†, Yongquan Chen 3,4, Lijie Zhu 2,*, Yuanyuan Mi 2,*
PMCID: PMC9114750  PMID: 35600409

Abstract

In our previous studies, we found that the rs231775 polymorphism of cytotoxic T-lymphocyte antigen 4 (CTLA-4) is associated with risks of different cancer types; however, the association remains controversial and ambiguous, so we conducted an in-depth meta-analysis to verify the association. A complete search of the PubMed, Google Scholar, Embase, Chinese databases, and Web of Science was conducted without regard to language limitations, covering all publications since November 20, 2021. The search criteria for cancer susceptibility associated with the polymorphism in the CTLA-4 gene rs231775 resulted in 87 case-control studies with 29,464 cases and 35,858 controls. The association strength was analyzed using odds ratios and 95% confidence intervals. Overall, we found that the CTLA-4 rs231775 polymorphism may reduce cancer risk. A stratified cancer type analysis showed that CTLA-4 rs231775 polymorphism was a risk factor for colorectal cancer and thyroid cancer; on the other hand, it was a protective factor for breast cancer, liver cancer, cervical cancer, bone cancer, head and neck, and pancreatic cancer. We also classified cancer into five systems and observed an increased association with digestive tract cancer, decreased associations with orthopedic tumors, tumors of the urinary system, and gynecological tumors. In the subgroup based on race, decreased relationships were observed in both Asians and Caucasians. The same decreased association was also shown in the analysis of the source of control analysis. Our present study indicates that the CTLA-4 rs231775 polymorphism contributes to cancer development and aggression.

Keywords: cancer, cytotoxic T-lymphocyte antigen 4, polymorphism, tumor marker, meta-analysis

Introduction

A major obstacle to increasing life expectancy is cancer, which is the primary cause of death worldwide. Cancer, in 112 of 183 countries, is also estimated to be the first or second leading cause of death before the age of 70 and third or fourth in 23 other countries (1), according to the World Health Organization analyses in 2019 (2). Across the globe, the incidence and mortality of cancer are rising rapidly; this is a result of both increasing longevity and population growth as well as changing patterns in the prevalence and distribution of cancer-causing factors, some of which are associated with social and economic development (1). The development of cancer involves multiple factors, including environmental and genetic factors (3).

One of the most common types of germline variants, the SNPs (single nucleotide polymorphisms), play a key role in human diseases, including cancer (2). Many SNPs associated with human cancer were identified through GWAS (genome-wide association studies) in the past decade (4, 5). Recent studies have noted that the expression levels of nearby genes may be influenced by these cancer risk-associated SNPs (4). Cancer treatment includes traditional surgery, chemotherapy, radiotherapy, and so on. In recent years, immunotherapy has gained more attention (6). The CTLA-4 (cytotoxic T-lymphocyte antigen 4) gene is located on chromosome 2q33 and has four exons (7). Cancer cells can acquire immune regulatory surface proteins like CTLA-4, which suppress the activation of immune cells, such as T cells (3, 8). In the early stages of tumorigenesis, it is possible that CTLA-4 may elevate the threshold of activation of T-cells as it inhibits T cell activation and proliferation. Furthermore, the CTLA-4 competitive binding to B7.1 inhibits IL-2 production and proliferation, both of which are essential in down-regulating T cell activity; in turn, this reduces anti-tumor responses and increases cancer susceptibility (5). Several SNPs in the CTLA-4 gene have been widely reported in tumors and non-tumors, such as rs4553808A/G, rs3087243G/A, rs5742909C/T, rs231726A/a, rs17268364, and rs231775A/G (9–13). The Rs231775 (+49) A/G polymorphism is one of the common SNPs in the CTLA-4 gene (4) and has been extensively reported in many types of cancers. Pavkovic et al. first reported a functional SNP in the CTLA-4 gene (rs231775), indicating that the G-allele frequency was highest among chronic lymphocytic leukemia patients who had developed autoimmune hemolytic anemia (14). Since then, the associations among rs231775 polymorphism and other types of cancer have been reported. In addition, Gouda et al. reported that the genotype (GG) was associated with relatively lower CTLA-4 expression levels than the other genotypes (like GC or CC) (11). To evaluate the effects of the functional SNP and cancer susceptibility, we carried out genotyping analyses among rs231775 A/G in 29,464 cases and 35,858 controls. Here, it would be helpful to explain the role of CTLA-4 in immune response control subsequent to completing its function. This is followed by how the polymorphism affects the function as to whether it increases or decreases the affinity of CTLA-4 to its ligand. The variability in the effect of the polymorphism on susceptibility to cancer warrants more in-depth discussions. Finally, we try to find a few potential explanations, which would add value in this regard.

Materials and Methods

Identifying and Evaluating Appropriate Studies

Searches were performed on the Embase, PubMed, Chinese database, Google Scholar, and Web of Science last updated November 20, 2021, using a keyword search that included ‘polymorphism’ or ‘carcinoma’ or ‘CTLA-4’ or ‘cytotoxic T-lymphocyte antigen 4’, or ‘variant’ and ‘cancer’ or ‘tumor’, regardless of language or publication year. These terms led to the retrieval of 592 articles, of which 87 matched the criteria for inclusion. Additionally, we manually searched references of the retrieved or review articles.

Criteria for Inclusion and Exclusion

The following criteria were required to be included in the review: (a) measured cancer risk in relation to CTLA-4 rs231775 polymorphism; (b) case-control studies; and (c) cases and controls have sufficient genotype numbers. Therefore, we also used the following exclusion criteria: (a) no population was used as control, (b) genotype frequency was not available, and (c) previous publications were duplicated.

Extraction of Data

Using the selection criteria, the data were extracted independently by two authors. The following data were collected: last name of the first author, publication year, ethnicity, country of origin, cancer type, the total number of cases and controls, source of controls, Hardy-Weinberg equilibrium (HWE) of controls, and genotyping methods.

Statistical Analysis

The first step was to stratify the subgroups based on cancer type. When a cancer type was reported in only one study, it is classified under the ‘others’ subgroup. In addition, we classified cancer into five systems: digestive tract cancer, orthopedic tumor, tumor of the urinary system, gynecological tumor, and hematological tumor. The ethnicity of the participants was categorized as Asian, Caucasian, and African using two different modes of classification, wherein the source of the control subgroup was analyzed: hospital-based (HB) and population-based (PB). On the basis of genotype frequencies in cases and controls, we calculated OR (odds ratios) with 95% CI (confidence intervals) of the association between CTLA-4 rs231775 polymorphism and the risk for cancer. The overall OR was analyzed using the Z-test (15). Heterogeneity was assessed using chi-square-based Q-tests. The Q-test showed no evidence of heterogeneity among the studies with a P-value greater than 0.05. We used the random-effects model when significant heterogeneity was detected (16); otherwise, the fixed-effects model was applied (16, 17). Using allelic contrast (G-allele vs. A-allele), homozygote comparison (GG vs. AA), dominant genetic model (GG+GA vs. AA), heterozygote comparison (GA vs. AA), and recessive genetic model (GG vs. GA+AA), we investigated the relationship between CTLA-4 rs231775 genetic variants and cancer risk. The Pearson chi-square test was used to calculate HWE in controls at P< 0.05. To estimate the likelihood of publication bias, Egger’s regression test and Begg’s funnel plots were used (18). All statistical assessments for this meta-analysis were conducted using Stata software V 11.0 (StataCorp LP, College Station, TX). We calculated the power and sample size of our meta-analysis using PS: Power and Sample Size Calculation (http://www.powerandsamplesize.com/) (19).

Meta-Regression

The source of publication bias was defined based on a random-effect meta-regression analysis using the publication bias, with publication year as subgroups, ethnicity, source of control, and methods of genotype set as independent variables and the log values regarded as dependent variables (20).

Bioinformatics Analysis

The expression of CTLA-4 between most types of tumors and para-cancerous tissue is shown from the GEPIA website (http://gepia.cancer-pku.cn/). On the same above-mentioned website, you can also find data about CTLA-4 expression levels in each tumor, which includes overall survival and disease-free survival.

Results

Meta-Analysis Study Selection and Characteristics

Throughout different databases, 592 articles were identified, and after a meticulous review, we included 87 varying case-control studies for this study (Figure 1). All essential information about included studies is shown in Table 1. Table 1 provides information on the first author, ethnicity, year of publication, cancer type, the numbers of controls and cases, genotyping methods and HWE, and control sources. According to the whole cancer susceptibility search criteria associated with the CTLA-4 rs231775 polymorphism, 87 case-control studies with 35,858 controls and 29,464 cases were retrieved. The controls mainly consisted of healthy populations. Therefore, we have compiled 25 Caucasian, 60 Asian, and 2 African case-control studies for our analyses. The controls in 53 studies came from the source of HB and 34 of PB. We examined the MAF (minor allele frequency) reported for the six major populations globally in the 1000 Genomes Browser (https://www.ncbi.nlm.nih.gov/snp/rs231775) (Figure 2A). Moreover, Asians exhibited significantly higher G-allele frequencies than Caucasian individuals both in cases (59.63% vs. 38.19%, P < 0.001) and controls (62.18% vs. 40.36%, P < 0.001) (Figure 2B). Third, we used the TCGA (The Cancer Genome Atlas) database to search for trends in the frequency of rs231775 polymorphism; our results indicated that the frequency of AA was relatively high compared to other genotypes, as shown in Figure 2C. The polymorphism is associated with prostate, artery, adipose-visceral, heart, nerve, pituitary, testis, and esophagus cancer (https://www.gtexportal.org/home/) (Figure 2D). All the controls except for eight studies were genotyped according to HWE. There is significantly more expression of CTLA-4 in tumor tissues than in normal tissue from four kinds of tumors (melanoma of the skin, head and neck squamous cell carcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, pancreatic adenocarcinoma, P< 0.05, Figures 3A, B). Furthermore, CTLA-4 high expression contributes to a poor overall survival rate in patients with head and neck squamous cell carcinoma (P< 0.01) (Figure 3C).

Figure 1.

Figure 1

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Flowchart illustrating the search strategy used to identify association studies for CTLA-4 rs231775 polymorphism and the total cancer risk.

Table 1.

Characteristics of studies of the CTLA-4 gene rs231775 A/G polymorphism and cancer risk included in our meta-analysis.

First author Year Origin Cancer type (1) Cancer type (2) Ethnicity Source Case Control HWE Method
Ge et al. (21) 2015 China Colorectal Digestive tract cancer Asian HB 572 626 0.095 PCR-RFLP
Fan et al. (22) 2012 China Colorectal Digestive tract cancer Asian HB 291 352 0.059 PCR-RFLP
Qi et al. (23) 2010 China Colorectal Digestive tract cancer Asian HB 124 407 0.902 PCR-LDR
Hadinia et al. (24) 2007 Iran Colorectal Digestive tract cancer Asian HB 105 190 0.097 PCR-RFLP
Liu et al. (25) 2015 China Liver Digestive tract cancer Asian HB 80 78 0.966 PCR-RFLP
Gu et al. (26) 2010 China Liver Digestive tract cancer Asian HB 367 407 0.902 PCR-LDR
Wang et al. (27) 2015 China Colorectal Digestive tract cancer Asian HB 311 289 0.001 TaqMan
Dilmec et al. (28) 2008 Turkey Colorectal Digestive tract cancer Caucasian HB 56 162 0.058 PCR-RFLP
Solerio et al. (29) 2005 Italy Colorectal Digestive tract cancer Caucasian HB 132 238 0.618 PCR-RFLP
Zou et al. (30) 2019 China Colorectal Digestive tract cancer Asian PB 979 1299 0.430 SNPscan Kit
Li et al. (31) 2015 China Colorectal Digestive tract cancer Asian PB 231 325 0.057 PCR-RFLP
Liu et al. (32) 2015 China Esophageal Digestive tract cancer Asian PB 604 664 0.283 PCR-LDR
Liu et al. (33) 2019 China Gastric Digestive tract cancer Asian PB 487 1470 0.926 SNPscan Kit
Tang et al. (34) 2015 China Gastric Digestive tract cancer Asian PB 330 590 0.179 PCR-LDR
Sun et al. (35) 2008 China Gastric Digestive tract cancer Asian PB 530 530 0.974 PCR-RFLP
Yang et al. (36) 2019 China Liver Digestive tract cancer Asian PB 575 920 0.893 SNPscan Kit
Hu et al. (37) 2010 China Liver Digestive tract cancer Asian PB 853 854 0.476 TaqMan
Lang et al. (38) 2012 China Pancreatic Digestive tract cancer Asian PB 602 651 0.056 PCR-RFLP
Yang et al. (39) 2012 China Pancreatic Digestive tract cancer Asian PB 368 926 0.828 PCR-RFLP
Cui et al. (40) 2013 China Colorectal Digestive tract cancer Asian PB 128 205 <0.001 PCR-RFLP
Hou et al. (41) 2010 China Gastric Digestive tract cancer Asian PB 205 262 0.001 PCR-RFLP
Kucukhuseyin et al. (42) 2015 Turkey Colorectal Digestive tract cancer Caucasian PB 80 115 0.467 PCR-RFLP
Mahajan et al. (43) 2008 Poland Gastric Digestive tract cancer Caucasian PB 301 411 0.393 TaqMan
Wagh et al. (44) 2018 Indian Cervical Gynecological tumor Asian HB 92 57 0.405 PCR-RFLP
Xiong et al. (45) 2014 China Cervical Gynecological tumor Asian HB 365 421 0.056 TaqMan
Gokhale et al. (46) 2013 Indian Cervical Gynecological tumor Asian HB 104 162 0.239 PCR-RFLP
Jiang et al. (47) 2011 China Cervical Gynecological tumor Asian HB 100 110 0.473 PCR-RFLP
Rahimifar et al. (48) 2010 Iran Cervical Gynecological tumor Asian HB 55 110 0.658 PCR-RFLP
Su et al. (49) 2007 China Cervical Gynecological tumor Asian HB 139 375 0.351 PCR-RFLP
Pawlak et al. (50) 2010 Poland Cervical Gynecological tumor Caucasian HB 141 217 0.610 PCR-RFLP
Li et al. (51) 2011 China Cervical Gynecological tumor Asian PB 314 320 0.339 PCR-RFLP
Hu et al. (37) 2010 China Cervical Gynecological tumor Asian PB 696 709 0.483 TaqMan
Castro et al. (52) 2009 Sweden Cervical Gynecological tumor Caucasian PB 953 1715 0.118 Multiplex PCR
Khorshied et al. (53) 2013 Egypt Lymphoma Hematological tumors African HB 181 200 0.416 PCR-RFLP
Hui et al. (54) 2014 China Leukemia Hematological tumors Asian HB 86 112 0.137 PCR-RFLP
Cheng et al. (55) 2006 China Lymphoma Hematological tumors Asian HB 62 250 0.323 PCR-RFLP
Suwalska et al. (56) 2008 Poland Leukemia Hematological tumors Caucasian HB 170 224 0.524 SNaPshot
Piras et al. (57) 2005 Italy Lymphoma Hematological tumors Caucasian HB 100 128 0.199 PCR-RFLP
Monne et al. (58) 2004 Italy Lymphoma Hematological tumors Caucasian HB 44 76 0.837 PCR-RFLP
Pavkovic et al. (59) 2003 Macedonia Lymphoma Hematological tumors Caucasian HB 130 100 0.533 PCR-RFLP
Liu et al. (60) 2013 China Lymphoma Hematological tumors Asian PB 291 300 0.163 PCR–LDR
Liu et al. (61) 2011 China Bone Orthopedic tumor Asian HB 267 282 0.053 PCR-RFLP
Kasamatsu et al. (62) 2020 Japan Myeloma Orthopedic tumor Asian HB 124 211 0.556 PCR-RFLP
Qin et al. (63) 2017 China Myeloma Orthopedic tumor Asian HB 86 154 0.201 TaqMan
Aldaiturriaga et al. (64) 2017 Spain Bone Orthopedic tumor Caucasian HB 66 125 0.101 PCR-RFLP
Feng et al. (65) 2013 China Bone Orthopedic tumor Asian PB 308 362 0.055 PCR-RFLP
Yang et al. (66) 2012 China Bone Orthopedic tumor Asian PB 223 302 0.054 PCR-RFLP
Wang et al. (67) 2011 China Bone Orthopedic tumor Asian PB 205 216 0.130 PCR-RFLP
Karabon et al. (68) 2012 Poland Bone Orthopedic tumor Caucasian PB 199 368 0.213 PCR-RFLP
Mao et al. (69) 2020 China Bladder Tumor of urinary tract Asian HB 354 434 0.812 PCR-RFLP
Jaiswal et al. (70) 2014 Indian Bladder Tumor of urinary tract Asian HB 212 200 0.981 PCR-RFLP
Wang et al. (71) 2013 China Bladder Tumor of urinary tract Asian HB 300 300 0.005 PCR-RFLP
Lopez et al. (72) 2009 Spain Renal Tumor of urinary tract Caucasian HB 125 176 0.766 TaqMan
Cozar et al. (73) 2007 Spain Renal Tumor of urinary tract Caucasian HB 96 176 0.766 PCR-RFLP
Karabon et al. (74) 2017 Poland Prostate Tumor of urinary tract Caucasian PB 301 301 0.503 PCR-RFLP
Tupikowski et al. (75) 2015 Poland Renal Tumor of urinary tract Caucasian PB 236 505 0.607 TaqMan
Babteen et al. (76) 2020 Egypt Breast African HB 93 179 0.164 TaqMan
Minhas et al. (77) 2014 Indian Breast Asian HB 250 250 0.197 PCR-RFLP
Wang et al. (78) 2007 China Breast Asian HB 117 148 0.926 PCR-RFLP
Ghaderi et al. (79) 2004 Iran Breast Asian HB 197 151 0.716 PCR-RFLP
Wu et al. (80) 2011 China Glioma Asian HB 653 665 0.841 PCR-LDR
Bharti et al. (81) 2013 Indian Head and neck Asian HB 130 180 0.622 PCR-RFLP
Erfani et al. (82) 2012 Iran Head and neck Asian HB 80 85 0.531 PCR-RFLP
Cheng et al. (83) 2011 China Head and neck Asian HB 205 205 0.054 PCR-RFLP
Xiong et al. 45) 2010 China Head and neck Asian HB 365 421 0.056 PCR-RFLP
Xiao et al. (84) 2009 China Head and neck Asian HB 457 485 0.730 PCR-RFLP
Wong et al. (85) 2006 China Head and neck Asian HB 118 147 0.314 PCR-RFLP
Liu et al. (86) 2015 China Lung Asian HB 231 250 0.059 PCR-RFLP
Khaghanzadeh et al. (87) 2010 Iran Lung Asian HB 123 122 0.763 PCR-RFLP
Abtahi et al. (88) 2018 Iran Thyroid Asian HB 164 100 0.965 PCR-RFLP
Chang et al. (89) 2017 China Thyroid Asian HB 324 350 0.062 PCR-RFLP
Ma et al. (90) 2015 China Lung Asian HB 528 600 0.031 PCR-RFLP
Isitmangil et al. (91) 2016 Turkey Breast Caucasian HB 79 76 0.402 PCR-RFLP
Kammerer et al. (92) 2010 Germany Head and neck Caucasian HB 83 40 0.287 RT-PCR
Queirolo et al. (93) 2013 Italy Melanoma Caucasian HB 14 45 0.802 PCR-RFLP
Antczak et al. (94) 2013 Poland Lung Caucasian HB 71 104 0.001 TaqMan
Chuang et al. (95) 2005 Germany Thymoma Caucasian HB 125 173 0.015 PCR-RFLP
Yu et al. (96) 2015 China Breast Asian PB 376 366 0.962 PCR-RFLP
Li et al. (97) 2012 China Breast Asian PB 576 553 0.739 PCR-RFLP
Sun et al. (35) 2008 China Breast Asian PB 2097 2140 0.053 PCR-RFLP
Sun et al. (35) 2008 China Head and neck Asian PB 1010 1008 0.684 PCR-RFLP
Chen et al. (98) 2017 China Lung Asian PB 520 1028 0.950 SNPscan Kit
Sun et al. (35) 2008 China Lung Asian PB 2205 2153 0.103 PCR-RFLP
Karabon et al. (99) 2011 Poland Lung Caucasian PB 208 324 0.089 PCR-RFLP
Gogas et al. (100) 2010 Greece Melanoma Caucasian PB 286 288 0.465 Multiplex PCR
Bouwhuis et al. (101) 2010 Germany Melanoma Caucasian PB 762 734 0.956 TaqMan
Welsh et al. (102) 2009 USA Skin Caucasian PB 1581 819 0.004 TaqMan
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HB, hospital-based; PB, population-based; SOC; source of control; PCR-RFLP, polymerase chain reaction followed by restriction fragment length polymorphism; PCR-LDR, polymerase chain reaction by ligase detection reaction; HWE, Hardy-Weinberg equilibrium of the control group.

Figure 2.

Figure 2

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(A) The MAF of minor-allele (mutant-allele) for CTLA-4 rs231775 polymorphism from the 1000 Genomes online database. (B) The frequency about G-allele or A-allele both in case and control groups. (C) The distribution of each genotype from online GTEx Portal (https://www.gtexportal.org/home/). (D) The risk frequency of rs231775 polymorphism in several diseases from the TCGA database.

Figure 3.

Figure 3

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Bioinformatics analysis about the CTLA-4 gene. (A) The CTLA-4 gene expression profile across all tumor samples and paired normal tissues. (B) CTLA-4 gene expression both in HNSC and PAAD. *P < 0.05. (C) Overall survival analysis for HNSC. HR, hazard ratio; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.

Meta-Analysis

Using 29,464 cases and 35,858 controls, the overall risk of CTLA-4 rs231775 is summarized in Table 2. CTLA-4 rs231775 polymorphism appears to decrease cancer risk in overall genetic models (G-allele vs. A-allele, OR = 0.94, 95%CI = 0.90-1.00, Pheterogeneity< 0.001, P = 0.037; GG vs. AA, OR = 0.86, 95%CI = 0.76-0.96, Pheterogeneity< 0.001, P = 0.010; GG vs. GA+AA, OR = 0.88, 95%CI = 0.82-0.94, Pheterogeneity< 0.001, P < 0.001). There were significant associations between CTLA-4 polymorphisms and two types of cancers (colorectal cancer: GA vs. AA, OR = 1.72, 95%CI = 1.13-2.60, Pheterogeneity< 0.001, P = 0.011; GG+GA vs. AA, OR = 1.52, 95%CI = 1.08-2.15, Pheterogeneity< 0.001, P = 0.017, Figure 4; thyroid cancer: G-allele vs. A-allele, OR = 1.50, 95%CI = 1.22-1.85, Pheterogeneity= 0.134, P< 0.001). On the other hand, significantly decreased associations were detected in six kinds of cancer (breast cancer: G-allele vs. A-allele, OR = 0.84, 95%CI = 0.78-0.90, Pheterogeneity= 0.221, P< 0.001, Figure 5; liver cancer: G-allele vs. A-allele, OR = 0.89, 95%CI = 0.82-0.98, Pheterogeneity= 0.151, P = 0.018; cervical cancer: G-allele vs. A-allele, OR = 0.88, 95%CI = 0.78-0.99, Pheterogeneity= 0.023, P = 0.028, Figure 6; bone cancer: GG+GA vs. AA, OR = 0.61, 95%CI = 0.38-0.99, Pheterogeneity< 0.001, P = 0.044, Figure 7; head and neck: G-allele vs. A-allele, OR = 0.79, 95%CI = 0.69-0.91, Pheterogeneity= 0.031, P =0.001, Figure 8; pancreatic cancer: G-allele vs. A-allele, OR = 0.72, 95%CI = 0.57-0.91, Pheterogeneity= 0.049, P = 0.006).

Table 2.

Stratified analysis of CTLA-4rs231775 A/G variation on cancer susceptibility.

Variables N Case/ G-allele vs. A-allele GA vs. AA GG vs. AA GG+GA vs. AA GG vs. GA+AA
rs231775 A/G Control OR (95%CI) Ph P OR (95%CI) Ph P OR (95%CI) Ph P OR (95%CI) Ph P OR (95%CI) Ph P
Total 87 29464/35858 0.94 (0.90-1.00) <0.001 0.037 1.01 (0.92-1.12) <0.001 0.773 0.86 (0.76-0.96) <0.001 0.010 0.96 (0.87-1.05) <0.001 0.353 0.88 (0.82-0.94) <0.001 <0.001
HWE 79 26215/33106 0.93 (0.89-0.98) ≤0.001 0.011 0.97 (0.88-1.06) ≤0.001 0.480 0.83 (0.74-0.93) ≤0.001 0.001 0.92 (0.84-1.01) ≤0.001 0.091 0.88 (0.82-0.94) ≤0.001 ≤0.001
Cancer Type (1)
Myeloma 2 210/365 1.17 (0.91-1.51) 0.896 0.209 0.91 (0.53-1.56) 0.138 0.737 1.22 (0.71-2.11) 0.420 0.478 1.05 (0.63-1.75) 0.232 0.858 1.33 (0.94-1.89) 0.578 0.104
Bladder cancer 3 866/934 1.19 (0.73-1.95) <0.001 0.481 1.24 (1.01-1.51) 0.086 0.040 1.38 (0.41-4.64) <0.001 0.603 1.24 (0.79-1.97) 0.004 0.353 1.27 (0.42-3.820 0.002 0.668
Breast cancer 8 3785/3863 0.84 (0.78-0.90) 0.221 <0.001 0.86 (0.69-1.07) 0.021 0.169 0.67 (0.57-0.80) 0.134 <0.001 0.81 (0.58-1.37) 0.022 0.042 0.79 (0.71-0.87) 0.370 <0.001
Colorectal cancer 11 3009/4208 1.15 (0.98-1.35) <0.001 0.094 1.72 (1.13-2.61) <0.001 0.011 1.24 (0.81-1.90) <0.001 0.319 1.52 (1.08-2.15) <0.001 0.017 0.91 (0.71-1.16) <0.001 0.440
Liver cancer 4 1875/2259 0.89 (0.82-0.98) 0.151 0.018 0.76 (0.62-0.94) 0.870 0.010 0.74 (0.60-0.90) 0.360 0.003 0.75 (0.61-0.91) 0.618 0.004 0.92 (0.81-1.04) 0.164 0.187
Gastric cancer 5 1853/3263 1.07 (0.85-1.35) <0.001 0.552 1.33 (0.87-2.01) 0.001 0.186 1.15 (0.75-1.80) 0.001 0.513 1.23 (0.81-1.87) <0.001 0.094 0.94 (0.83-1.06) 0.052 0.325
Cervical cancer 10 2959/4196 0.88 (0.78-0.99) 0.023 0.028 0.88 (0.70-1.10) 0.013 0.257 0.70 (0.52-0.94) 0.006 0.017 0.83 (0.66-1.03) 0.008 0.094 0.83 (0.70-0.99) 0.039 0.043
Thyroid cancer 2 488/450 1.50 (1.22-1.85) 0.134 <0.001 1.96 (1.34-2.87) 0.812 0.001 2.42 (1.48-3.95) 0.400 <0.001 2.13 (1.48-3.07) 0.805 <0.001 1.40 (1.05-1.88) 0.217 0.024
Other cancers 5 3264/2622 0.94 (0.87-1.01) 0.065 0.094 1.00 (0.78-1.29) 0.030 0.991 0.79 (0.7-0.93) 0.109 0.005 0.92 (0.81-1.04) 0.063 0.179 0.88 (0.69-1.11) 0.011 0.279
Lung cancer 7 3886/4581 0.95 (0.73-1.24) <0.001 0.724 0.98 (0.69-1.40) <0.001 0.927 0.97 (0.57-1.65) <0.001 0.901 0.94 (0.62-1.43) <0.001 0.774 1.01 (0.75-1.35) <0.001 0.968
Bone cancer 6 1268/1655 0.82 (0.63-1.05) 0.004 0.051 0.63 (0.40-1.00) 0.001 0.051 0.64 (0.38-1.09) 0.001 0.102 0.61 (0.38-0.99) <0.001 0.044 0.81 (0.69-0.95) 0.125 0.011
Renal cancer 3 457/857 0.85 (0.72-1.00) 0.143 0.056 0.92 (0.71-1.17) 0.125 0.485 0.71 (0.49-1.03) 0.272 0.069 0.85 (0.67-1.08) 0.109 0.185 0.73 (0.52-1.02) 0.485 0.062
Leukemia 2 256/336 0.91 (0.72-1.15) 0.987 0.432 1.10 (0.74-1.66) 0.362 0.634 0.88 (0.54-1.43) 0.592 0.607 1.01 (0.69-1.48) 0.499 0.966 0.78 (0.53-1.14) 0.84 0.197
Head and neck 8 2448/2571 0.79 (0.69-0.91) 0.031 0.001 0.92 (0.68-1.24) 0.004 0.577 0.60 (0.43-0.84) 0.034 0.003 0.80 (0.60-1.06) 0.004 0.123 0.69 (0.53-0.88) 0.017 0.003
Lymphoma 6 808/1054 0.91 (0.63-1.33) <0.001 0.625 0.99 (0.55-1.77) <0.001 0.974 1.12 (0.60-2.08) 0.040 0.726 0.96 (0.53-1.76) <0.001 0.899 1.00 (0.79-1.27) 0.264 0.985
Melanoma 3 1062/1067 1.04 (0.92-1.19) 0.486 0.504 1.14 (0.95-1.37) 0.306 0.165 1.00 (0.76-1.33) 0.767 0.983 1.11 (0.93-1.32) 0.349 0.233 0.95 (0.73-1.23) 0.814 0.706
Pancreatic cancer 2 970/1577 0.72 (0.57-0.91) 0.049 0.006 0.70 (0.53-0.92) 0.766 0.009 0.51 (0.38-0.67) 0.173 <0.001 0.60 (0.46-1.00) 0.347 <0.001 0.67 (0.57-0.79) 0.063 <0.001
Cancer Type (2)
Orthopedic tumor 8 1478/2020 0.88 (0.73-1.06) 0.001 0.192 0.68 (0.46-0.99) 0.001 0.048 0.74 (0.47-1.16) ≤0.001 0.192 0.87 (0.62-1.21) 0.006 0.408 0.94 (0.75-1.17) 0.032 0.562
Tumor of urinary tract 7 1624/2002 0.96 (0.76-1.22) ≤0.001 0.755 1.06 (0.86-1.32) ≤0.001 0.553 0.86 (0.53-1.39) 0.002 0.540 0.55 (0.42-0.71) ≤0.001 ≤0.001 0.84 (0.56-1.26) 0.009 0.398
Digestive tract cancer 23 8311//11971 1.02 (0.92-1.13) ≤0.001 0.692 1.25 (0.99-1.59) ≤0.001 0.061 0.99 (0.79-1.25) ≤0.001 0.952 1.32 (1.04-1.67) ≤0.001 0.022 0.91 (0.80-1.02) ≤0.001 0.098
Gynecological tumor 10 2959/4196 0.87 (0.78-0.99) 0.023 0.028 0.87 (0.69-1.10) 0.013 0.257 0.70 (0.52-0.94) 0.006 0.017 0.92 (0.74-1.14) 0.014 0.427 0.83 (0.69-0.99) 0.039 0.043
Hematological tumors 8 1064/1390 0.93 (0.71-1.21)≤ 0.001 0.577 1.04 (0.68-1.59) 0.001 0.839 1.07 (0.69-1.65) 0.069 0.755 0.82 (0.43-1.57) ≤0.001 0.556 0.93 (0.76-1.14) 0.349 0.480
Ethnicity
Asian 60 22851/27839 0.96 (0.90-1.02) <0.001 0.187 1.06 (0.93-1.20) <0.001 0.368 0.86 (0.75-1.00) <0.001 0.053 0.99 (0.88-1.12) <0.001 0.903 0.87 (0.81-0.95) <0.001 0.001
African 2 274/379 0.95 (0.41-2.19) 0.001 0.900 0.93 (0.28-3.10) <0.001 0.910 0.92 (0.25-3.36) 0.027 0.904 0.93 (0.27-3.13) <0.001 0.902 1.02 (0.60-1.72) 0.208 0.949
Caucasian 25 6339/7640 0.90 (0.81-0.99) <0.001 0.037 0.95 (0.83-1.09) <0.001 0.447 0.88 (0.74-1.04) 0.013 0.128 0.90 (0.78-1.04) <0.001 0.143 0.89 (0.81-0.97) 0.051 0.010
Source of control
HB 53 9844/12125 0.94 (0.86-1.03) <0.001 0.196 1.03 (0.89-1.19) <0.001 0.684 0.88 (0.73-1.06) <0.001 0.185 0.97 (0.84-1.12) <0.001 0.705 0.88 (0.77-1.00) <0.001 0.046
PB 34 19620/23733 0.93 (0.87-1.00) <0.001 0.036 0.98 (0.87-1.11) <0.001 0.761 0.82 (0.71-0.95) <0.001 0.007 0.93 (0.82-1.05) <0.001 0.241 0.86 (0.81-0.93) <0.001 <0.001
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Ph: the value of Q-test for the heterogeneity test; P: Z-test for the statistical significance of the OR.

Figure 4.

Figure 4

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Forest plot of the association between the CTLA-4 gene rs231775 polymorphism and colorectal cancer risk (G-allele vs. A-allele).

Figure 5.

Figure 5

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Forest plot of the association between the CTLA-4 gene rs231775 polymorphism and breast cancer risk (G-allele vs. A-allele).

Figure 6.

Figure 6

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Forest plot of the association between the CTLA-4 gene rs231775 polymorphism and cervical cancer risk (G-allele vs. A-allele).

Figure 7.

Figure 7

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Forest plot of the association between the CTLA-4 gene rs231775 polymorphism and bone cancer risk (G-allele vs. A-allele).

Figure 8.

Figure 8

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Forest plot of the association between the CTLA-4 gene rs231775 polymorphism and head and neck cancer risk (G-allele vs. A-allele).

We also classified tumors into five systems and observed a significant association between the polymorphism and digestive tract cancer (GG+GA vs. AA, OR = 1.32, 95%CI = 1.04-1.67, Pheterogeneity< 0.001, P = 0.022), however, decreased associations were observed in three kinds of systems (orthopedic tumor: GG vs. AA: OR = 0.68, 95%CI = 0.46-0.99, Pheterogeneity= 0.001, P = 0.048; urinary tract tumor: GG+GA vs. AA, OR = 0.55, 95%CI = 0.42-0.71, Pheterogeneity< 0.001, P < 0.001; gynecological tumor: G-allele vs. A-allele, OR = 0.87, 95%CI = 0.78-0.99, Pheterogeneity= 0.023, P = 0.028). In spite of variations in the frequency of occurrence of this sequence variant among ethnic groups, decreased cancer risk in both Asian (GG vs. GA+ AA, OR = 0.87, 95%CI = 0.81-0.95, Pheterogeneity< 0.001, P=0.001, Figure 9) and Caucasian (GG vs. GA+ AA, OR = 0.89, 95%CI = 0.81-0.97, Pheterogeneity= 0.051, P=0.010, Figure 10) populations was observed. On the basis of stratification by source of control, we evaluated an OR for the rs231775 polymorphism of CTLA-4, and found a decreased association in a recessive genetic model (HB: OR = 0.88, 95%CI = 0.77-1.00, Pheterogeneity< 0.001, P = 0.046; PB: OR = 0.86, 95%CI = 0.81-0.93, Pheterogeneity< 0.001, P <0.001) (Table 2).

Figure 9.

Figure 9

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Forest plot of cancer risk associated with the CTLA-4 gene rs231775 polymorphism in Asians (G-allele vs. A-allele model).

Figure 10.

Figure 10

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Forest plot of cancer risk associated with the CTLA-4 gene rs231775 polymorphism in Caucasians (G-allele vs. A-allele model).

Meta-Regression

Based on the year of publication, ethnicity, genotype methods, and source of control, a meta-regression analysis indicated that there was a significant association for the allele model (A-allele vs. G-allele) with a regression coefficient of 0.131, 0.464, 0.635, and 0.420, respectively, this suggests that the heterogeneity from the rs231775 polymorphism in cancer could not result from the year of publication, ethnicity, source of control, or genotype methods subgroups (Figures 11A–D) if the heterogeneity was found in the current study.

Figure 11.

Figure 11

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Random-effect meta-regression of log odds ratio vs. publication year (A), regular ethnicity (B), source of control (C), and genotype methods (D), respectively.

Discussion

Nearly 9 million people die of cancer each year worldwide (103). In the challenge of cancer treatment, immunotherapy has attracted remarkable interest among scientists because of its ability to kill tumor cells directly (14, 104). The Treg cell population expresses a number of immune-modulatory receptors, including CTLA-4, programmed cell death protein 1, and the vascular endothelial growth factor receptor (105). Activated T and Treg cells (106) express CTLA-4. Atkins et al. demonstrated improvement in the rate of survival of non-small cell lung cancer, renal cell carcinoma, melanoma, and head and neck squamous cell cancer by blocking the CTLA-4 immune checkpoint, which showed that the CTLA-4 gene is a promising target gene in the future treatment for cancer (107).

Previously, several meta-analyses were focused on the CTLA-4 polymorphisms, which showed the vital role of CTLA-4 in the susceptibility to many diseases, such as cancer. It was documented that the immune related gene CTLA-4 rs5742909 polymorphism had a significantly increased association with cervical carcinogenesis. Dai et al. found the CTLA-4 rs3087243 polymorphism may reduce breast cancer risk, however, rs4553808 may increase breast cancer risk in different ethnicity or genetic models (108, 109). Another polymorphism rs231775 is the most common SNP that has been reported in many tumors, however, a clear conclusion has not been gained yet despite few meta-analyses (110, 111).

Based on 87 case-control studies, we carried out a meta-analysis, which showed CTLA-4 rs231775 polymorphism plays an important role in cancer risks. According to the results, CTLA-4 rs231775 is strongly associated with the maximum cancer risk. Second, both Asian and Caucasian populations were significantly less likely to develop cancer when individuals carry the rs231775 G-allele. Last, individuals with the rs231775G allele may be at a lower risk for cancer in both HB and PB studies. The results of these studies recommend that the rs231775 polymorphism may contribute to cancer development. Next, based on the stratified cancer type analysis, CTLA-4 rs231775 polymorphism was found to be a risk factor for thyroid cancer and colorectal cancer; that is, in individuals carrying the G-allele, the risk of being diagnosed with cancer is increased; on the other hand, it proved to be a protective factor for liver cancer, breast cancer, cervical cancer, head and neck cancer, bone cancer, and pancreatic cancer, in other words, individuals carrying G-allele may have a lower risk of being diagnosed with cancer. However, no association was detected between this SNP and myeloma, bladder cancer, gastric cancer, lung cancer, renal cancer, leukemia, lymphoma, or melanoma. Some of the reasons why the same gene polymorphism plays different roles in different cancer types may be the difference in the pathogenesis of each kind of cancer, and the same gene and its polymorphism may have different functions and susceptibility.

Gene polymorphisms have the important property of their incidence varying widely across different ethnic populations or races. Based on the subgroup analysis by ethnicity, CTLA-4 rs231775 polymorphism was observed to be significantly associated with lower cancer risks in Asians and Caucasians, but not Africans, suggesting genetic diversity across ethnic groups. This difference can be explained by two factors: genetic and environmental differences among different ethnic groups, and linkage disequilibrium patterns between different populations. Polymorphisms may be related to the presence of closer causal variants in varying populations.

The meta-analysis we performed has certain limitations. To begin with, interactions between gene-environment, gene-gene, or different polymorphic loci of the same gene can modulate the risk for cancer, so researchers should investigate these factors in the future. Moreover, other covariates such as age, sex, family history, environmental factors, cancer stage, and lifestyle should be considered. Furthermore, the control group did not comprise strictly healthy controls. Even so, the meta-analysis we conducted has two advantages. First, data from numerous studies were pooled, significantly increasing the power of the analysis. Second, our selection criteria led to a satisfactory quality of case-control studies that are included in the current meta-analysis. Finally, the strength of the current study as per the software is ‘1’, which indicates the conclusions from our study are convincing and clear.

Conclusion

The meta-analysis in the current study suggests a significant association between CTLA-4 rs231775 polymorphism and some types of cancer and overall risk for cancer. Consequently, more large-scale studies, which are well-designed, are needed, with a focus on gene-environment and gene-gene interactions. Future research should provide a more comprehensive clarity of the association between the CTLA-4 rs231775 polymorphism and the risk of developing cancer.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Ethics Statement

Ethical review and approval was not required for this animal study, in accordance with the local legislation and institutional requirements.

Author Contributions

HW, YF, and HZ were major contributors in writing the manuscript. HW and YF created all the figures. HZ performed the literature search. LZ, YC and YM made substantial contributions to the design of the manuscript and revised it critically for important intellectual content. All authors have read and approved the final version of this manuscript.

Funding

This work was supported by National Natural Science Foundation (No. 81802576), Wuxi Commission of Health and Family Planning (No. T202024, J202012, Z202011), the Science and Technology Development Fund of Wuxi (No. WX18IIAN024, N20202021), and Jiangnan University Wuxi School of Medicine (No. 1286010242190070) and Wuxi “Taihu Talent Program”-High-end Talent in Medical and Healthentalent plan of Taihu Lake in Wuxi (Double Hundred Medical Youth Professionals Program) from Health Committee of Wuxi (No. BJ2020061).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors are thankful for the guidance of Professor YC.

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Data Availability Statement

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