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. 2024 Oct 23;10(22):e39740. doi: 10.1016/j.heliyon.2024.e39740

The 14-bp insertion/deletion as a promising gene polymorphism to understand cancer risk: Evidence from a systematic review and comprehensive meta-analysis

Kalthoum Tizaoui a,⁎⁎, Mohamed Ali Ayadi b, Ines Zemni b, Abdel Halim Harrath c, Roberta Rizzo d, Nadia Boujelbene e, Inès Zidi f,⁎
PMCID: PMC11599971  PMID: 39605806

Abstract

Background

HLA-G is associated with cancer cell escape. The 3′UTR polymorphism is involved in the regulation of membrane-bound HLA-G and soluble HLA-G proteins. The aim of our study was to assess the association of the HLA-G 14-bp insertion (I)/deletion (D) polymorphism with cancer susceptibility and its interaction with clinicopathological features and environmental factors.

Methods

A meta-analysis was performed to investigate the association between the HLA-G 14-bp I/D polymorphism and different types of cancers according to the Prisma guidelines.

Results

Thirty-nine publications that studied the 14-bp I/D polymorphism in cancers met our inclusion criteria. The findings of the meta-analysis showed a significant association between the 14-bp I/D polymorphism and cancer risk under the allelic contrast model D vs. I (OR = 1,112, 95 % CI = 1,009–1,227; P = 0,033) suggesting that the D allele was a risk factor for cancer susceptibility. Stratification by cancer type demonstrated a significant association of the 14-bp I/D polymorphism with breast cancer under the D vs. I contrast allele model (OR = 1,267, 95 % CI = 1,028–1,563; P = 0,027). No significant association was found for digestive, cervical, haematological and thyroid cancers. A comparison of groups stratified by ethnicity showed a significant association for Caucasians under the D vs. I model (OR = 1,147, 95 % CI = 1,002–1,313; P = 0,047); and for mixed ethnicities under the DD + DI vs. II (OR = 1,388, 95 % CI = 1,083–1,780; P = 0,010) and DI vs. II (OR = 1,402, 95 % CI = 1,077–1,824; P = 0,012) models. A comparison of cancer risks associated with the 14-bp I/D polymorphism according to geographic location revealed significant risks for the D allele and DD genotype in North Africa, the Middle East and South America. However, no significant susceptibility to cancer associated with the 14-bp I/D polymorphism was shown for Europe and North Asia. The findings of a meta-analysis of subgroups by disease stage showed a significant association in both early and advanced stages, with the 14-bp deletion variant being a risk factor. Similarly, a significant cancer risk was shown for the 14-bp deletion variant in both low- and high-grade cancers. Finally, the risk associated with the 14-bp I/D polymorphism was higher in cancers with concomitant viral infection with human papillomavirus (HPV), hepatitis B virus (HBV) or hepatitis C virus (HCV).

Conclusion

The findings of the overall meta-analysis showed a significant association between the HLA-G 14-bp I/D polymorphism and cancer susceptibility. The findings stratified analysis and subgroup comparisons showed that the 14-bp I/D deletion variant was associated with an increased risk of breast cancer. The HLA-G 14-bp I/D polymorphism may interact with individual and clinicopathological factors to alter cancer risk. These promising findings for cancer risk provide the basis for further studies that explore 14bp I/D polymorphism in cancer screening and immunotherapeutic approach.

Keywords: HLA-G gene, 14-bp I/D polymorphism, Cancer, Meta-analysis

1. Introduction

Human leukocyte antigen-G (HLA-G), is a non-classical major histocompatibility complex class I (MHC-I) antigen [1] encoded by a gene located in region 6p21.3 of chromosome 6 [2]. HLA-G is predominantly expressed at the maternal–fetal interface [3], and has primarily been associated with maternal-fetal tolerance [1]. HLA-G protects the fetus from trophoblast damage caused by maternal natural killer (NK) cells [4] and cytotoxic-T cells (CTLs) [5]. HLA-G is secreted under restrictive physiological conditions in fetal tissues, adult immune-privileged organs and cells of the hematopoietic lineage [1]. HLA-G is also found in pathological conditions such as cancer, viral infections, inflammatory diseases, autoimmune diseases, and transplantation [6]. In cancer, HLA-G expression is heterogeneous and strongly associated with an immunosuppressive microenvironment, advanced tumor stage, poor therapeutic response, and poor prognosis [7,8].

The first identified and most studied polymorphism of the HLA-G gene is the 14-base-pair insertion/deletion (14-bp I/D) located in the 3′UTR (rs66554220/rs371194629) [9]. The 14-bp presence or absence (insertion or deletion, respectively) polymorphism was found to be associated with HLA-G transcript levels and mRNA stability. The presence of a 14-base segment has been shown to be associated with decreased mRNA production, and the absence of this segment (deletion) appears to stabilize mRNA enhancing HLA-G expression [10,11]. HLA-G transcripts presenting the 14-base segment can be further processed by removing 92 bases from the primary mRNA transcript [10], giving rise to a shorter HLA-G transcript reported to be more stable than the full-length isoform [12]. Taken together, the published results provide evidence for a direct relationship between the 14-bp I/D polymorphism and HLA-G protein expression.

Evidence is accumulating for an important role of the HLA-G 14-bp I/D polymorphism in various cancers, but the results of some studies are contradictory or inconclusive. In the current meta-analysis, data from published individual studies were pooled to further explore the association between the HLA-G 14-bp I/D polymorphism and cancer and shed light on the most significant modulating factors investigated in the primary studies.

2. Methods

2.1. Identification of eligible studies and data extraction

We searched for published studies investigating the association between the HLA-G 14-bp I/D polymorphism and cancer in MEDLINE, EMBASE, and Cochrane databases (up to October 2024) using Medical Subject Heading (MeSH) and keyword combinations, such as “HLA-G, “14-bp I/D polymorphism” and “cancer”. Furthermore, additional studies not indexed by the MEDLINE, EMBASE and Cochrane databases were included, and the references cited in the collected papers were reviewed. Studies were considered eligible based on the following inclusion criteria: testing for the HLA-G 14-bp I/D polymorphism in cancers and in healthy controls. Studies were excluded if they: (1) included redundant or incomplete data or (2) were reviews, meta-analyses or case reports. From each study, the following information was extracted: primary author, publication year, country of the study, ethnicity, allele and genotype frequencies of the HLA-G 14-bp polymorphism, type of cancer, stage/grade of cancer, and viral infection status. Two independent reviewers KT and IZI extracted the data on the methods and results from the original studies and analyzed them. Discrepancies were resolved by consensus among the reviewers. The meta-analysis was conducted according to the recommendations in the PRISMA guidelines [13].

2.2. Statistical analyses

We performed a meta-analysis to test the allelic, recessive, homozygous, dominant and codominant models of the HLA-G 14-bp polymorphism. For dichotomous data, odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated. Heterogeneity was quantified using I2, varying from 0 to 100 % and reflecting the proportion of variation between the studies due to heterogeneity rather than chance [13]. I2 values of 25, 50, and 75 % were considered to indicate low, moderate, and high heterogeneity, respectively. The random-effects model assumes that there is significant variation in different studies and, therefore, tests both sampling errors within the study and variances between studies [14]. The tau squared (τ2) test reflects the variance of the true effect sizes, and is used to test the variance of the effect size parameters across the study population while tau (τ) is the estimated standard deviation of underlying true effects across studies [15]. Publication bias was assessed by using Egger's test [16]. A comprehensive meta-analysis program (Biostat, Englewood, NJ, USA) was used to perform statistical manipulations.

3. Results

3.1. Studies included in the meta-analysis

We identified 150 studies using electronic and manual search methods; of these, 72 were selected for full-text screening based on the title and abstract. Reviews or meta-analyses (15) studies for which the full texts were not available (2), and studies with missing or irrelevant data (16) were excluded. Therefore, in total, 39 articles met our inclusion criteria [[17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55]]: (Table 1, Fig. 1).

Table 1.

Characteristics of studies included in the meta-analysis.

Author Cancer Type Country Geographic location Ethnicity Chi2
Durmanova 2024 Head and Neck Squamous Cell Carcinoma Slovakia Europe Caucasian 0,680
Okumura 2024 Hepatocellular Cancer Japan North Asia Asian 0,955
Becerra-Loaiza 2023 Breast Cancer Mexico South America Mixed 0,803
Garrach2023 Colorectal Cancer Tunisia North Africa Caucasian 0,265
Al-Tamimi 2022 Leukemia Saudi Arabia Middle East Caucasian 3,022
Bucova 2022 Glioma Slovakia Europe Caucasian 0,609
Dhouioui 2022 Colorectal Cancer Tunisia North Africa Caucasian 0,951
Gan 2022 Cervical Cancer China North Asia Asian 0,863
Haghi 2021 Breast cancer Iran Middle East Caucasian 0,846
de Magalhaes 2021 Glioma Brazil South America Mixed 6,397
Vaquero-Yuste 2021 Gastric Cancer Spain Europe Caucasian 0,279
Kadiam 2020 Breast Cancer India South Asia Caucasian 0,043
Abu hassan 2019 Colorectal Cancer Saudi Arabia Middle East Caucasian 0,416
El Bassiouny 2019 Hepatocellular carcinoma Egypt Middle East Caucasian 0,800
Al Omar 2019 Breast Cancer Saudi Arabia Middle East Caucasian 1,037
Ouni 2019 Breast Cancer Tunisia North Africa Caucasian 1183
Tawfeek 2018 Non-Hodgkin Lymphoma Egypt Middle East Caucasian 0,694
Agnihotri 2017 Head and Neck Squamous Cell Carcinoma India South Asia Caucasian 2,331
de Figueiredo-Feitosa 2017 Thyroid carcinoma Brazil South America Mixed 2,735
Marques 2017 Colorectal cancer Brazil South America Mixed 0,425
Garziera 2016 Colorectal Cancer Italy Europe Caucasian 5,666
Zambra 2016 Prostate Cancer Brazil South America Mixed 0,843
Zidi 2016 Breast Cancer Tunisia North Africa Caucasian 0,049
Bielska 2015 Diffuse Large B-Cell Lymphoma Poland Europe Caucasian 5,301
Haghi 2015 Breast Cancer Iran Middle East Caucasian 11,033
Wisniewski 2015 Lung cancer Poland Europe Caucasian 0,265
Bortolotti 2014 Cervical cancer Italy Europe Caucasian 3,204
Jeong 2014 Breast Cancer South Korea North Asia Asian 0,356
Ramos 2014 Breast Cancer Brazil South America Mixed 0,106
Yang 2014 Cervical cancer Taiwan North Asia Asian 0,325
Eskandarani-Nasab 2013 Breast Cancer Iran Middle East Caucasian 1,996
Kim 2013 Hepatocellular Carcinoma South Korea North Asia Asian 0,346
Silva 2013 Cervical Cancer Brazil South America Mixed 2,527
Teixeira 2013 Hepatocellular carcinoma Brazil South America Mixed 3,167
Chen 2012 Esophageal cancer China North Asia Asian 0,320
Dardano 2012 Thyroid Carcinoma Italy Europe Caucasian 1,789
Ferguson 2012 Cervical cancer Canada North America Caucasian 0,521
Jiang 2011 Hepatocellular Carcinoma China North Asia Asian 0,392
Lau 2011 Neuroblastoma Newzeland Australia Caucasian 0,001
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Bold: Control population not in Hardy Weinberg Equilibrium (at ddl = 1, α = 5 %, Chi2 = 3,83).

Fig. 1.

Fig. 1

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Flow diagram of the systematic review and meta-analysis literature search results (HLA-G 14-bp I/D polymorphism and cancer).

Meta-analysis of the association between the HLA-G 14-bp I/D polymorphism and cancer susceptibility: Overall analysis.

The results of the meta-analysis showed a significant association between the 14-bp I/D polymorphism and cancer risk under the D vs. I contrast allele model (OR = 1,112, 95 % CI = 1,009–1,227; P = 0,033) (Table 2, Fig. 2). High heterogeneity (I2 >50 %) and Tau squared varying between 0.067 and 0,240 were observed. The P-value for heterogeneity was significant (P-het<0,05). The observed heterogeneity and interstudy variance were not surprising given the clinicopathological features and population differences among studies. Publication bias was not significant in any model (Fig. 3).

Table 2.

Association between HLA-G 14-bp I/D polymorphism and cancers under the random effects model: Overall analysis.

Genetic models
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P- het τ2 P-Begg (2-tailed) P-Egger (2-tailed)
D vs. I 41 1,112 1,009 1,227 0,033 71,301 0,000 0,067 NS NS
DD vs. DI + II 41 1,125 0,988 1,279 0,075 63,601 0,000 0,102 NS NS
DD + DI vs. II 41 1,169 0,986 1,386 0,072 67,020 0,000 0,182 NS NS
DD + II vs. DI 41 1,015 0,905 1,139 0,799 59,400 0,000 0,076 NS NS
DI vs. II 41 1,124 0,944 1,338 0,190 64,314 0,000 0,184 NS NS
DD vs. II 41 1,210 0,996 1,469 0,055 67,116 0,000 0,238 NS NS
DD vs. DI 41 1,093 0,961 1,243 0,175 58,435 0,000 0,093 NS NS
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs.

Fig. 2.

Fig. 2

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Forest plot of the association between 14-bp I/D polymorphism and cancers risk with the random effects model under the allele contrast model D vs. I. Forest plot shows the odds ratio and respective 95 % confidence intervals for the different studies included in the meta-analysis. For each study in the forest plot, the area of the black square is proportional to study weight and the horizontal bar represents the 95 % confidence interval. Z-score: the standardized expression of a value in terms of its relative position in the full distribution of values. CI, confidence interval.

Fig. 3.

Fig. 3

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Funnel plot of the association between 14-bp I/D polymorphism and cancers risk with the random effects model under the allele contrast model D vs. I.

3.2. Subgroup analysis according to different types of cancer

Stratification by cancer type demonstrated a significant association of the 14-bp I/D polymorphism with breast cancer (10 studies) under the D vs. I contrast allele model (OR = 1,267, 95 % CI = 1,028–1,563; P = 0,027) (Table 3, Fig. 4). No significant association was shown for digestive, cervical, hematological and thyroid cancers (Table 3). Neuroblastoma (1 study), glioma (2 studies), lung cancer (1 study), and head and neck cancer (2 studies) were underrepresented to be analyzed as subgroups. Heterogeneity and variance among studies were significantly reduced compared to the overall analysis (Table 3).

Table 3.

Association between 14-bp I/D polymorphism and cancers: Subgroup analysis according to cancer type.

Genetic models Subgroups
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P-het τ2 P-Begg P-Egger
D vs. I Breast Cancer 10 1,267 1,028 1,563 0,027 73,711 0,000 0,079 NS NS
Cervical Cancer 5 1,039 0,829 1,303 0,738 53,186 0,074 0,033 NS NS
Gastrointestinal cancers 8 1,167 0,922 1,475 0,198 74,651 0,000 0,082 NS NS
Hematological cancers 4 1,235 0,783 1,947 0,364 85,510 0,000 0,184 NS NS
Hepatocellular Cancer 5 1,036 0,738 1,453 0,839 72,277 0,006 0,100 NS NS
Thyroid Cancer 3 0,905 0,701 1,168 0,442 21,142 0,281 0,012 NS NS
DD vs. DI + II Breast Cancer 10 1,327 0,998 1,764 0,052 66,171 0,002 0,133 NS NS
Cervical Cancer 5 1,167 0,880 1,547 0,284 42,385 0,139 0,041 NS NS
Gastrointestinal cancers 8 1,268 0,934 1,722 0,129 67,068 0,003 0,125 NS NS
Hematological cancers 4 1,193 0,907 1,570 0,207 0,000 0,696 0,000 NS NS
Hepatocellular Cancer 5 1,049 0,693 1,588 0,821 67,398 0,015 0,136 NS NS
Thyroid Cancer 3 0,725 0,433 1,213 0,221 49,692 0,137 0,101 NS NS
DD + DI vs. II Breast Cancer 10 1,292 0,961 1,738 0,090 54,626 0,019 0,107 NS NS
Cervical Cancer 5 0,953 0,564 1,608 0,856 69,446 0,011 0,231 NS NS
Gastrointestinal cancers 8 1,126 0,796 1,593 0,503 62,146 0,010 0,147 NS NS
Hematological cancers 4 1,666 0,529 5,248 0,384 92,751 0,000 1,265 NS NS
Hepatocellular Cancer 5 1,207 0,745 1,957 0,444 31,853 0,209 0,094 NS 0,027
Thyroid Cancer 3 1,093 0,746 1,602 0,648 0,000 0,544 0,000 NS NS
DD + II vs. DI Breast Cancer 10 1,045 0,870 1,256 0,635 33,768 0,138 0,028 NS NS
Cervical Cancer 5 1,155 0,752 1,774 0,510 74,296 0,004 0,167 NS NS
Gastrointestinal cancers 8 1,131 0,944 1,355 0,181 18,635 0,282 0,013 NS NS
Hematological cancers 4 0,875 0,446 1,714 0,696 86,496 0,000 0,407 NS NS
Hepatocellular Cancer 5 1,025 0,793 1,325 0,850 24,524 0,258 0,021 NS NS
Thyroid Cancer 3 0,696 0,461 1,050 0,084 35,298 0,213 0,048 NS NS
DI vs. II Breast Cancer 10 1,215 0,929 1,589 0,155 39,910 0,092 0,066 NS NS
Cervical Cancer 5 0,907 0,475 1,734 0,769 76,439 0,002 0,395 NS NS
Gastrointestinal cancers 8 1,001 0,740 1,355 0,993 43,154 0,091 0,078 NS NS
Hematological cancers 4 1,627 0,475 5,569 0,439 92,852 0,000 1,458 NS 0,033
Hepatocellular Cancer 5 1,206 0,823 1,769 0,336 0,000 0,529 0,000 NS NS
Thyroid Cancer 3 1,276 0,851 1,914 0,239 0,000 0,551 0,000 NS NS
DD vs. II Breast Cancer 10 1,415 0,969 2,066 0,073 61,711 0,005 0,201 NS NS
Cervical Cancer 5 0,993 0,626 1,573 0,975 53,162 0,074 0,136 NS NS
Gastrointestinal cancers 8 1,313 0,834 2,068 0,239 71,795 0,001 0,293 NS NS
Hematological cancers 4 1,744 0,617 4,932 0,294 87,871 0,000 0,982 NS NS
Hepatocellular Cancer 5 1,191 0,632 2,245 0,589 52,374 0,078 0,250 NS NS
Thyroid Cancer 3 0,875 0,546 1,402 0,579 7,236 0,340 0,016 NS NS
DD vs. DI Breast Cancer 10 1,239 0,960 1,599 0,099 52,710 0,025 0,085 NS NS
Cervical Cancer 5 1,190 0,802 1,765 0,387 63,618 0,027 0,119 NS NS
Gastrointestinal cancers 8 1,247 0,947 1,643 0,116 53,477 0,035 0,081 NS NS
Hematological cancers 4 1,116 0,832 1,498 0,463 0,000 0,707 0,000 NS NS
Hepatocellular Cancer 5 1,042 0,728 1,492 0,823 52,922 0,075 0,082 NS NS
Thyroid Cancer 3 0,661 0,390 1,123 0,126 47,118 0,151 0,102 NS NS
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs.

Fig. 4.

Fig. 4

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Forest plot of the association between 14-bp I/D polymorphism and cancer risk: Subgroup analysis according to cancer type under the allele contrast model D vs. I.

3.3. Subgroup analysis according to ethnicity

Stratification by ethnicity showed significant association for Caucasians under the D vs. I model (OR = 1,147, 95 % CI = 1,002–1,313; P = 0,047) (Table 4). Mixed ethnicities showed significant associations (DD + DI vs. II; OR = 1,388, 95 % CI = 1,083–1,780; P = 0,010, and DI vs. II; OR = 1,402, 95 % CI = 1,077–1,824; P = 0,012) (Table 4). The allelic contrast model showed no significant association (Fig. 5). After stratification by ethnicity, Caucasian and mixed ethnic heterogeneity remained significant, while Asian heterogeneity was low to moderate (Table 4).

Table 4.

Association between 14-bp I/D polymorphism and cancers: Subgroup analysis according to ethnicity.

Genetic Model Subgroups
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P- het τ2 P-Begg P-Egger
D vs. I Asian 8 1,038 0,842 1,280 0,725 67,069 0,003 0,057 NS NS
Caucasian 25 1,147 1,002 1,313 0,047 77,366 0,000 0,085 NS NS
Mixed 8 1,119 0,937 1,336 0,215 40,153 0,111 0,025 NS NS
DD vs. DI + II Asian 8 1,072 0,832 1,382 0,589 61,501 0,011 0,077 NS NS
Caucasian 25 1,190 0,997 1,421 0,054 68,172 0,000 0,127 NS NS
Mixed 8 0,995 0,727 1,361 0,975 56,372 0,025 0,108 0,035 0,016
DD + DI vs. II Asian 8 1,014 0,679 1,516 0,945 51,926 0,042 0,159 NS NS
Caucasian 25 1,160 0,925 1,455 0,199 75,085 0,000 0,223 NS NS
Mixed 8 1,388 1,083 1,780 0,010 0,000 0,494 0,000 NS NS
DD + II vs. DI Asian 8 1,084 0,881 1,333 0,447 42,273 0,096 0,036 NS NS
Caucasian 25 1,056 0,905 1,232 0,492 64,727 0,000 0,092 NS NS
Mixed 8 0,831 0,630 1,095 0,188 50,701 0,048 0,077 NS 0,029
DI vs. II Asian 8 0,965 0,660 1,411 0,856 41,664 0,101 0,116 NS NS
Caucasian 25 1,096 0,868 1,384 0,441 72,929 0,000 0,230 NS NS
Mixed 8 1,402 1,077 1,824 0,012 0,000 0,507 0,000 NS NS
DD vs. II Asian 8 1,030 0,648 1,637 0,901 60,023 0,014 0,244 NS NS
Caucasian 25 1,253 0,967 1,624 0,089 74,234 0,000 0,290 NS NS
Mixed 8 1,332 0,949 1,868 0,097 27,200 0,211 0,063 NS NS
DD vs. DI Asian 8 1,091 0,858 1,389 0,477 52,875 0,038 0,060 NS NS
Caucasian 25 1,159 0,973 1,380 0,098 62,321 0,000 0,113 NS NS
Mixed 8 0,898 0,644 1,252 0,526 56,542 0,024 0,122 NS 0,010
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs.

Fig. 5.

Fig. 5

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Forest plot of the association between 14-bp I/D polymorphism and cancer risk with the random effects model: Subgroup analysis according to ethnicity under the allele contrast model D vs. I.

3.4. Subgroup analysis according to geographic locations

The 14-bp I/D polymorphism was linked to cancers in all geographic locations; however the most significant associations were detected in North Africa (D vs. I, OR = 1,377, 95 % CI = 1,193–1,590; P = 0,000; DD vs. DI + II, OR = 1,561, 95 % CI = 1,241–1,964; P = 0,000; DD + DI vs. II, OR = 1,442, 95 % CI = 1,145–1,816; P = 0,002; DD vs. II, OR = 1,800, 95 % CI = 1,362–2,378; P = 0,000; DD vs. DI, OR = 1,435, 95 % CI = 1,121–1,837; P = 0,004) (Table 5, Fig. 6). Similarly, the 14-bp I/D polymorphism was highly associated with cancers in the Middle East (D vs. I, OR = 1,453, 95 % CI = 1,135–1,860; P = 0,003; DD vs. DI + II, OR = 1,529, 95 % CI = 1,201–1,945; P = 0,001; DD + DI vs. II, OR = 1,718, 95 % CI = 1,015–2,908; P = 0,044; DD vs. II, OR = 2,024, 95 % CI = 1,205–3,398; P = 0,008); DD vs. DI, OR = 1,363, 95 % CI = 1,100–1,691; P = 0,005) (Table 5, Fig. 6). Eight studies from South America showed a high risk of 14-bp deletion variants under the DD + DI vs. II (OR = 1,388, 95 % CI = 1,083–1,780; P = 0,010) and DI vs. II (OR = 1,402, 95 % CI = 1,077–1,824; P = 0,012) models (Table 5).

Table 5.

Association between 14-bp I/D polymorphism and cancers: Subgroup analysis according to geographic locations.

Genetic models Subgroup
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P-het τ2 P-Begg P-Egger
D vs. I Europe 8 0,956 0,780 1,171 0,663 67,979 0,003 0,054 NS NS
Middle East 9 1,453 1,135 1,860 0,003 73,911 0,000 0,099 NS NS
North Africa 4 1,377 1,193 1,590 0,000 0,000 0,686 0,000 NS NS
North Asia 8 1,038 0,842 1,280 0,725 67,069 0,003 0,057 NS NS
South America 8 1,119 0,937 1,336 0,215 40,153 0,111 0,025 NS NS
DD vs. DI + II Europe 8 0,925 0,697 1,229 0,591 61,591 0,011 0,095 NS NS
Middle East 9 1,529 1,201 1,945 0,001 35,193 0,136 0,046 NS NS
North Africa 4 1,561 1,241 1,964 0,000 0,000 0,772 0,000 NS NS
North Asia 8 1,072 0,832 1,382 0,589 61,501 0,011 0,077 NS NS
South America 8 0,995 0,727 1,361 0,975 56,372 0,025 0,108 0,035 0,016
DD + DI vs. II Europe 8 0,961 0,692 1,332 0,809 59,872 0,015 0,122 NS NS
Middle East 9 1,718 1,015 2,908 0,044 80,203 0,000 0,460 NS NS
North Africa 4 1,442 1,145 1,816 0,002 0,000 0,552 0,000 NS NS
North Asia 8 1,014 0,679 1,516 0,945 51,926 0,042 0,159 NS NS
South America 8 1,388 1,083 1,780 0,010 0,000 0,494 0,000 NS NS
DD + II vs. DI Europe 8 0,980 0,774 1,240 0,867 52,634 0,039 0,056 NS NS
Middle East 9 1,051 0,779 1,420 0,743 65,155 0,003 0,130 NS NS
North Africa 4 1,066 0,863 1,316 0,555 4,432 0,371 0,002 NS NS
North Asia 8 1,084 0,881 1,333 0,447 42,273 0,096 0,036 NS NS
South America 8 0,831 0,630 1,095 0,188 50,701 0,048 0,077 NS 0,029
DI vs. II Europe 8 0,976 0,712 1,340 0,883 51,854 0,042 0,099 NS NS
Middle East 9 1,550 0,893 2,689 0,119 79,568 0,000 0,502 NS NS
North Africa 4 1,259 0,982 1,614 0,069 0,000 0,407 0,000 NS NS
North Asia 8 0,965 0,660 1,411 0,856 41,664 0,101 0,116 NS NS
South America 8 1,402 1,077 1,824 0,012 0,000 0,507 0,000 NS NS
DD vs. II Europe 8 0,923 0,624 1,364 0,686 62,879 0,009 0,183 NS NS
Middle East 9 2,024 1,205 3,398 0,008 72,140 0,000 0,403 NS NS
North Africa 4 1,800 1,362 2,378 0,000 0,000 0,753 0,000 NS NS
North Asia 8 1,030 0,648 1,637 0,901 60,023 0,014 0,244 NS NS
South America 8 1,332 0,949 1,868 0,097 27,200 0,211 0,063 NS NS
DD vs. DI Europe 8 0,923 0,701 1,214 0,566 53,823 0,034 0,078 NS NS
Middle East 9 1,363 1,100 1,691 0,005 11,404 0,340 0,012 NS NS
North Africa 4 1,435 1,121 1,837 0,004 0,000 0,640 0,000 NS NS
North Asia 8 1,091 0,858 1,389 0,477 52,875 0,038 0,060 NS NS
South America 8 0,898 0,644 1,252 0,526 56,542 0,024 0,122 NS 0,011
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs.

Fig. 6.

Fig. 6

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Forest plot of the association between 14-bp I/D polymorphism and cancer risk with the random effects model: Sub-group analysis according to geographic location under the codominant model DD vs. DI.

3.5. Subgroup analysis according to cancer stages, grades and concomitant viral infections

Subgroup analysis by disease stage (early stages (I + II) vs. advanced stages (III + IV)) showed a significant association under the allele contrast D vs. I model for both early (OR = 1,393, 95 % CI = 1,074–1,808; P = 0,013) and advanced (OR = 1,641, 95 % CI = 1,124–2,395; P = 0,010) stages (Table 6). We also found similar significant effect risks of the 14-bp deletion variant under the DD vs. DI + II and DD vs. II models for both early and advanced cancer stages (Table 6). However, the DD + DI vs. II model showed a significant association only in advanced stages (OR = 2,126, 95 % CI = 1,066–4,241; P = 0,032) (Table 6). We should note that caution should be taken when interpreting these results since only five studies were included for early stages, and only 4 studies were included in advanced stages.

Table 6.

Association between the 14-bp I/D polymorphism and cancers: Subgroup analysis according to cancer stages.

Genetic models Stages
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P-het τ2 P-Begg P-Egger
D vs. I Advanced stage 4 1,641 1,124 2,395 0,010 70,69 0,017 0,103 NS NS
Early stage 5 1,393 1,074 1,808 0,013 58,82 0,046 0,051 NS NS
DD vs. DI + II Advanced stage 4 1,501 1,064 2,118 0,021 16,90 0,307 0,021 NS NS
Early stage 5 1,482 1,139 1,929 0,003 0,00 0,555 0,000 0,027 NS
DD + DI vs. II Advanced stage 4 2,126 1,066 4,241 0,032 65,79 0,033 0,294 NS NS
Early stage 5 1,762 0,914 3,398 0,091 80,51 0,000 0,442 NS NS
DD + II vs. DI Advanced stage 4 0,764 0.,572 1,020 0,068 8,49 0,351 0,008 NS NS
Early stage 5 0,865 0,536 1,396 0,552 75,11 0,003 0,222 NS NS
DI vs II Advanced stage 4 2,009 0,982 4,111 0,056 64,07 0,039 0,310 NS NS
Early stage 5 1,642 0,801 3,366 0,176 81,60 0,000 0,537 NS NS
DD vs II Advanced stage 4 2,558 1,120 5,845 0,026 67,60 0,026 0,441 NS NS
Early stage 5 1,957 1,103 3,471 0,022 63,06 0,029 0,262 NS NS
DD vs DI Advanced stage 4 1,168 0,842 1,619 0,352 0,00 0,714 0,000 NS 0,006
Early stage 5 1,282 0,950 1,729 0,104 9,46 0,352 0,011 NS 0,041
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs. Early stage includes stages I + II. Advanced stage includes stages III + IV.

For stratification by cancer grade, the association was more significant in low grades than in high grades under the D vs. I, DD vs. DI + II, DD + DI vs. II, DI vs. II and DD vs. II genetic models (Table 7). However, caution should be taken when interpreting these results due to the limited number of included studies. Notably, after stratification by cancer grade, the heterogeneity was not significant for any genetic model (P-het >0,05).

Table 7.

Association between the14-bp I/D polymorphism and cancers: Subgroup analysis according to cancer grades.

Genetic models Grades
Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P-het τ2 P-Begg P-Egger
D vs. I High grade 4 1,288 0,992 1,672 0,057 19,13 0,295 0,014 NS NS
Low grade 5 1,354 1,148 1,597 0,000 0,00 0,604 0,000 NS NS
DD vs. DI + II High grade 4 1,529 1,079 2,166 0,017 0,00 0,484 0,000 NS NS
Low grade 5 1,451 1,111 1,896 0,006 3,52 0,387 0,004 NS NS
DD + DI vs. II High grade 4 1,246 0,724 2,144 0,428 38,02 0,184 0,117 NS NS
Low grade 5 1,496 1,142 1,960 0,003 0,00 0,992 0,000 NS NS
DD + II vs. DI High grade 4 1,121 0,746 1,685 0,581 31,65 0,222 0,055 NS NS
Low grade 5 0,978 0,774 1,235 0,850 0,00 0,623 0,000 NS NS
DI vs. II High grade 4 1,121 0,624 2,012 0,702 39,75 0,173 0,141 NS NS
Low grade 5 1,350 1,009 1,804 0,043 0,00 0,984 0,000 NS NS
DD vs. II High grade 4 1,573 0,908 2,724 0,106 22,46 0,276 0,074 NS NS
Low grade 5 1,768 1,281 2,440 0,001 0,00 0,781 0,000 NS NS
DD vs. DI High grade 4 1,449 0,999 2,102 0,051 0,00 0,440 0,000 NS NS
Low grade 5 1,296 0,973 1,726 0,076 3,14 0,389 0,004 NS NS
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs. Low grade includes Grades I + II. Low grade includes Grades III + IV.

Stratification by viral infection status (viral infection vs. not) revealed highly significant risks of the 14-bp deletion variant in cancer patients with concomitant viral infection with either human papillomavirus (HPV) or hepatitis B virus (HBV) or hepatitis C virus (HCV) under the D vs. I (OR = 1,555, 95 % CI = 1,031–2,346; P = 0,035), DD vs. DI + II (OR = 1,438, 95 % CI = 1,116–1,853; P = 0.005) and DD vs. DI (OR = 1,399, 95 % CI = 1,065–1,839; P = 0.016) models (Table 8, Fig. 7). No heterogeneity was revealed in the DD vs. DI + II and DD vs. DI genetic models.

Table 8.

Association between 14-bp I/D polymorphism and cancers: Subgroup analysis according to viral infection.

Genetic models Viral infection Effect size and 95 % interval
 Heterogeneity
N OR Lower limit Upper limit P-value I2 P-het τ2 P-Begg P-Egger
D vs. I Viral infections- 3 1,523 1,002 2,316 0,049 67,63 0,046 0,092 NS NS
Viral infections+ 4 1,555 1,031 2,346 0,035 76,35 0,005 0,132 NS 0,006
DD vs. DI + II Viral infections- 3 1,403 1,011 1,947 0,043 0,00 0,894 0,000 NS NS
Viral infections+ 4 1,438 1,116 1,853 0,005 0,00 0,500 0,000 NS NS
DD + DI vs. II Viral infections- 3 2,412 0,610 9,536 0,209 87,72 0,000 1,291 NS NS
Viral infections+ 4 2,163 0,704 6,646 0,178 85,47 0,000 1.077 NS 0,035
DD + II vs. DI Viral infections- 3 0,817 0,354 1,887 0,636 84,07 0,002 0,456 NS NS
Viral infections+ 4 1,040 0,632 1,711 0,878 69,54 0,020 0,175 NS NS
DI vs II Viral infections- 3 2,207 0,481 10,125 0,308 88,58 0,000 1,602 NS NS
Viral infections+ 4 1,830 0,571 5,866 0,309 84,70 0,000 1,155 NS 0,029
DD vs II Viral infections- 3 2,551 0,745 8,742 0,136 81,78 0,004 0,964 NS NS
Viral infections+ 4 2,421 0,804 7,288 0,116 83,13 0,000 1,003 NS 0,046
DD vs DI Viral infections- 3 1,218 0,855 1,733 0,275 0,00 0,684 0,000 NS NS
Viral infections+ 4 1,399 1,065 1,839 0,016 0,00 0,892 0,000 NS NS
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Bold: significant P-value (<0,05); N: number of studies; NS: Not Significant; OR: odds ratio; I2 : heterogeneity test; τ2 , tau-squared; I/D : insertion/deletion; P- het, p-heterogeneity ; bp: base pairs. The overall analysis gives estimations among subgroups; Viral infection+: presence infection; Viral infection-: absence infection.

Fig. 7.

Fig. 7

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Forest plot of the association between 14-bp I/D polymorphism and cancer risk with the random effects model: Subgroup analysis according to viral infection under DD vs. DI + II genetic model.

4. Discussion

In the current meta-analysis, independent results from studies related to the HLA-G 14-bp I/D polymorphism in various types of cancer (breast cancer, cervical cancer, gastrointestinal cancers, hepatocellular cancer, hematological cancers, and thyroid cancer) were pooled. With the meta-analysis, more accurate data were provided than with individual studies as the statistical power and analytical resolution were increased. We identified an association between the 14-bp I/D polymorphism and cancer. Our results are consistent with the results of the meta-analysis of Jiang et al., conducted in 2019, demonstrating that the HLA-G 14-bp I/D polymorphism may play an important role in reducing cancer susceptibility [56]. Interestingly, the results of a recent comprehensive meta-analysis by de Almeida et al., conducted in 2018, were inconclusive but suggested that other variation sites observed in the HLA-G 3′UTR have well-established roles in the posttranscriptional regulation of HLA-G expression, and the complete 3′UTR segment should be analyzed in terms of disease susceptibility rather than a single polymorphism [57].

In this meta-analysis, we showed that the HLA-G 14-bp I/D polymorphism may contribute to breast cancer susceptibility as found by Li et al. 2015 and Ge et al. (only for Asians) [58,59]. Elsewhere, the results of the meta-analysis of Zhang et al. suggested that the HLA-G 14-bp I/D polymorphism was not associated with total cancer risk but was associated with hepatocellular carcinoma risk [60]. In this meta-analysis, we did not find the same result. The discrepancies between study findings may be due to the differences in the number of included studies and the analytical methods used.

Interestingly, the results of our subgroup analysis showed that the 14-bp I/D polymorphism was associated with cancer in Caucasians, Asians, and mixed-race individuals. Moreover, the 14-bp polymorphism was associated with cancer in all geographic locations except Europe and North Asia. Particularly, in North Africa and in the Middle East, the DD genotype and D allele were significant risk factors for cancer.

The 14-bp deletion and insertion alleles have been extensively studied in relation to HLA-G molecule stability and expression. The presence of the 14 bases is associated with decreased mRNA production of most membrane-bound and soluble isoforms, and absence of this segment (deletion) results in mRNA stabilization and higher HLA-G expression [61,62]. The HLA-G transcript, which includes a 14-base segment, can be further processed by removing 92 bases from the complete mRNA [10], giving rise to a short HLA-G transcript reported to be more stable [12]. The results of published studies showed that the deletion variant increases HLA-G expression [7,63,64]. The deletion allele is usually associated with high levels of HLA-G allowing tumor progression. Furthermore, HLA-G expression was found to correlate with adverse clinicopathological parameters such as clinical stage, lymph node status, metastasis, and histologic grade, but not with tumor status [65].

The HLA-G 14-bp I/D polymorphism affects the levels of surface and soluble HLA-G expression, and the overexpression of HLA-G molecules contributes to creating tolerogenic conditions [66]. HLA-G protein expression can be driven by genetic variations in the 3′UTR and in the promoter region. Svendsen et al. showed that 14-bp insertion at the HLA-G 3′UTR significantly increased the inhibition of natural killer (NK) cell cytotoxicity in the K562 cell line compared to the deleted form, while the ratio of the soluble to the membrane form of HLA-G1 was higher in those with the deletion [11]. Several data indicate that the 14-bp I/D polymorphism is in strong linkage disequilibrium (LD) with other HLA-G polymorphisms in the 5′ upstream regulatory region (5′URR) and 3′UTR, suggesting that in a higher or lower HLA-G expression, and some HLA haplotypes are in LD in some populations [67]. These observations show the importance of investigating LD in polymorphic studies in different populations [67].

Interestingly, five single nucleotide polymorphisms (SNPs) in the 3′UTR of the HLA-G gene are predicted to affect the miRNA target sites, and HLA-G deregulation serves as a prognostic marker in some cancers [68]. Furthermore, cancer-associated microenvironmental variability can affect HLA-G protein expression. Genetic variation in the 3′ UTR, which involves multiple target sites of microRNAs (miRNAs), regulates HLA-G expression at the posttranscriptional level [69]. Indeed, six miRNAs (miR-148a, miR-148b, miR-152, miR-133a, miR628-5p, and miR-548q) have been reported to regulate HLAG expression [69].

Importantly, HLA-G expression has been linked to high viral infection [70,71]. Indeed, several viruses, including HBV and HCV have been shown to induce the expression of HLA-G [72,73]. A progressive increase in HLA-G protein expression in HPV-infected cervix and cervical cancer has been reported [74]. Indeed, gradual upregulation of HLA-G expression favors HPV persistence in the submissive host response microenvironment, further leading to cervical cancer [74]. Both tumor cells and viruses employ a major common strategy to evade the host's immune response particularly the expression of HLA-G molecules that can modulate immune responses [[75], [76], [77], [78]]. In line with this hypothesis, we demonstrated that viral concomitant infection could enhance susceptibility to cancer. However, the limited number of primary studies leads us to take these results with caution.

It has become increasingly evident that the HLA-G molecule is involved in modulating both innate and adaptive immune responses and in promoting immune escape in various types of cancers [[10], [11], [12], [13]] and infectious diseases [[14], [15], [16]].

Finally, the findings of our meta-analysis showed an overall significant association between the HLA-G 14-bp I/D polymorphism and cancer risk. A major limitation of this meta-analysis was related to the limitation of primary studies that lacked information on gene-gene interactions, family history, tobacco smoking, treatment, and other regulating factors. Additionally, heterogeneity was found between individual studies and subgroups. This could be due to the etiological and physiopathological differences in the studied cancers, and to ethnicity differences.

5. Conclusion

The current meta-analysis is the most comprehensive and extensive study into how the HLA-G 14 bp I/D polymorphism is involved in cancer susceptibility. The 14 bp deletion was found to be a significant risk factor for susceptibility to cancer. The clinicopathological and environmental factors investigated here altered the risk of cancer, but their mechanisms of action need further investigation. Based on our results and previous functional studies, the 14 bp I/D polymorphism seems to be a good target for both cancer diagnosis and prognosis.

CRediT authorship contribution statement

Kalthoum Tizaoui: Writing – original draft, Resources, Methodology, Formal analysis. Mohamed Ali Ayadi: Writing – review & editing, Data curation. Ines Zemni: Writing – review & editing, Validation, Data curation. Abdel Halim Harrath: Writing – review & editing, Validation, Data curation. Roberta Rizzo: Writing – review & editing, Validation, Data curation. Nadia Boujelbene: Writing – original draft, Validation, Data curation. Inès Zidi: Writing – review & editing, Visualization, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation.

Data availability

Data included in article material is referenced in the article.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Ines Zidi is currently serving as an Associate Editor for Heliyon Immunology. Although she was not involved in the review of this specific manuscript, she is disclosing this position to ensure transparency and uphold the integrity of the review process for this submission. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by Ministry of Higher Education and Scientific Research of Tunisia. The authors extend their appreciation to the Researchers Supporting Project number (RSP2024R17) at King Saud University, Riyadh, Saudi Arabia.

Contributor Information

Kalthoum Tizaoui, Email: kalttizaoui@gmail.com.

Inès Zidi, Email: ines.zidi@istmt.utm.tn.

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