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. 2022 Jul 19;8(7):e09986. doi: 10.1016/j.heliyon.2022.e09986

Meta-analysis of HLA-G 14bp insertion/deletion polymorphism and soluble HLA-G revealed an association with digestive cancers initiation and prognosis

Sabrine Dhouioui a,1, Nadia Boujelbene a,b, Hadda-imene Ouzari a, Kalthoum Tizaoui a,1, Inès Zidi a,∗,1
PMCID: PMC9305369  PMID: 35874075

Abstract

Background/Objective

Conflicting results on the association between HLA-G and digestive cancers were reported. We conducted a meta-analysis to further investigate the true relationship between HLA-G and digestive cancers (DC).

Methods

Following PRISMA guidelines, we performed a meta-analysis including 7 case-control studies on HLA-G 14-bp Insertion/deletion (I/D) polymorphism, and 15 studies on soluble HLA-G (sHLA-G). Odds ratios (OR) and their corresponding 95% confidence intervals (CI) for genetic polymorphisms were calculated. The pooled OR was calculated under three genetic models: allelic, recessive, and dominant models. Concerning sHLA-G meta-analysis, standardized mean differences (SMDs) were calculated.

Results

The HLA-G 14-bp I/D was not associated with the risk of DC. However, in the subset of HBV/HCV positive hepato-cellular cancer (HCC) patients, we reported a significant association of HLA-G 14-bp I/D with the disease initiation under allelic (D vs. I; OR = 1.698, 95% CI = 1.263–2.282, p = 0.000), dominant (DD + ID vs. II; OR = 2.321, 95% CI = 1.277–4.218, p = 0.006)and recessive (DD vs. DI + II; OR = 1.739, 95% CI = 1.173–2.577, p = 0.006) genetic models. Interestingly, HLA-G 14-bp I/D was not associated with the disease initiation in HBV/HCV negative HCC patients. However, the infection by HBV/HCV seems to be implicated in the HCC development when we compared HBV/HCV positive patients to HBV/HCV negative patients under allelic (D vs. I; OR = 1.429, 95% CI = 1.029–1.983, p = 0.033, and dominant (DD + ID vs.II; OR = 1.981, 95% CI = 1.002–3.916, p = 0.049) genetic models.

Overall analysis of DC showed significant increased sHLA-G in patients compared to healthy controls (SMD = 3.341, 95% CI = 2.415–4.267, p = 0.000). In Asian patients with gastric cancer, sHLA-G was significantly increased in grade 3 compared to low grades (SMD = 0.448, 95% CI = 0.109–0.787, p = 0.000). Further analysis showed that sHLA-G was significantly increased in positive DC vascular invasion (SMD = 0.743, 95% CI = 0.385–1.100, p = 0.000). Accordingly, sHLA-G was associated with a poor prognosis for DC.

Conclusion

The current meta-analysis supports the significant role of HLA-G in DC. The HLA-G 14-bp I/D polymorphism was associated with HCC patients with concomitant HBV/HCV viral infections. Increased sHLA-G indicated a poor prognosis for DC cancer patients.

Keywords: Digestive cancer, HLA-G, Polymorphism, sHLA-G, Meta-analysis

Digestive cancer; HLA-G; Polymorphism; sHLA-G; Meta-analysis.

1. Introduction

Digestive cancers (DC), composed of oesophageal, colorectal, pancreatic, stomach, and liver cancers are common malignancies and account for one-quarter of the global cancer incidence [1]. Fortunately, recent years showed considerable improvements in DC diagnosis and treatment, including relief of symptoms and prolonged survival [2]. However, the efficacy of surgery and chemotherapy remains unsatisfactory, particularly if tumours aren’t detected and removed at an early stage. Therefore, novel biomarkers to improve cancer diagnosis and prognosis are crucial for reducing cancer burden and mortality. The expression of HLA-G, an immune tolerant and tumour promoting factor, has been extensively investigated, and its role as a novel immune checkpoint has been established [3, 4]. HLA-G has been described as a potent immune suppressive mediator observed in various malignancies and is strongly associated with tumour immune escape and metastasis [5]. Yie et al. reported that HLA-G protein was expressed in a majority of the primary site of gastric carcinomas and significantly correlated with tumour location, histological grade, depth of invasion, histological grade, host immune response, lymph nodal metastasis, and clinical stages of the disease [6]. A high frequency of tumour cell HLA-G expression and/or increased sHLA-G has been found in various body fluids in a variety of cancers [7]. Similarly to membrane HLA-G, peripheral sHLA-G was associated with advanced disease stage, tumour metastasis and/or poor prognosis [8, 9]. Both membrane-bound and sHLA-G have immune suppressive function [10]. The abnormal expression of HLA-G might be caused by genetic polymorphisms in HLA-G gene, as previously announced. Particularly, the HLA-G 14-bp insertion/deletion has been widely investigated in the context of cancer susceptibility and progression [11, 12, 13].

We aimed through the current meta-analysis to assess the significance of HLA-G in DC by studying the association of HLA-G 14-bp and sHLA-G with DC susceptibility and progression.

2. Methods

2.1. Literature search and inclusion/exclusion criteria

MEDLINE, EMBASE, Web of Science, and Cochrane databases (up to May, 2021) were searched using the terms “HLA-G” “polymorphism”, “sHLA-G,” and “digestive cancer”. The review process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [14]. Eligible studies included: (1) published cases/controls cohort studies evaluating the association of the HLA-G polymorphism with DC; (2) Availability of mean or median and standard deviation (SD) or range data of sHLA-G levels in patients and controls. Studies were excluded if they were reviews or case reports or if they present duplicate or incomplete data. The literature searches were performed in triplicate by three independent reviewers (SD, KT and IZ). Reviewers extracted the data on the methods and results from the original studies. Discrepancies were resolved by consensus among the reviewers. Flow diagrams of research strategy, exclusion and exclusion criteria are presented in Figures 1 and 2.

Figure 1.

Figure 1

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Flow diagram representing the selection process concerning HLA-G 14bp I/D polymorphism studies in digestive cancer.

Figure 2.

Figure 2

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Flow diagram representing the selection process concerning sHLA-G dosage studies in digestive cancer.

2.2. Statistical analyses

We evaluated the implication of HLA-G 14-bp Insertion (I)/Deletion (D) polymorphism and levels of soluble HLA-G (sHLA-G) in the initiation and prognosis of DC. Concerning HLA-G gene polymorphism, we meta-analyzed studies through the calculation of odds ratios (OR) and its corresponding 95% confidence interval (CI). The pooled OR was calculated under three genetic models: allelic, recessive, and dominant models.

Concerning sHLA-G meta-analysis, standardized mean differences (SMDs) were calculated. When median and range were reported, we calculated the mean ± SD (standard deviation) according to Hozo et al. [15]. Heterogeneity between studies was assessed by I2 and Tau2 value. I2 values were interpreted according to the Cochrane guidelines [16]. Tau2 test reflected the variance of the true effect sizes [17].

The Funnel plot measured the study size [18]. Egger’s test of the intercept estimated the sample size effect [19]. Publications bias was evaluated through funnel plot and Begg and Mazumdar rank correlation test [20]. Two-tailed PEgger, and PBegg values without continuity correction, were reported.

The random-effects model assuming significant variation in different studies and testing sampling errors and variances between studies [21, 22]. When homogeneity among studies was assumed, we used fixed effects model. Comprehensive meta-analysis software (Biostat, Englewood, NJ, USA) was used to perform statistical analysis. P ≤ 0.05 was considered statistically significant.

3. Results

3.1. Studies included in the meta-analysis

For HLA-G polymorphisms, we identified 67 studies. Of these articles, duplicates, non relevant papers based on titles, reviews and meta-analysis were excluded leaving 14 articles for full screening. Additional 5 articles were omitted due to lack or irrelevant data. Therefore, in total, 9 articles met our inclusion criteria [23, 24, 25, 26, 27, 28, 29, 30, 31] (Table 1, Figure 1).

Table 1.

Characteristics of individual studies included in the meta-analysis of HLA-G 14bp I/D polymorphism in digestive cancer.

Study Genotyping method Country Ethnicity Cases
 Controls
Sample Number Number HWE p-value
Dhouioui 2022 PCR Tunisia Caucasian CRC 233 241 0.186
Vaquero-Yuste 2021 PCR Spain Caucasian GC 107 55 0.597
Abu Hassan 2019 PCR Saudi-Arabia Caucasian CRC 105 119 0.519
El Bassiouny 2019 PCR Egypt Egyptian HCC 40 20 0.371
Garziera 2016 PCR Italy Caucasian CRC 308 294 0.017
Kim 2013 PCR South Korea Asian HCC 270 91 0.556
Teixeira 2013 PCR Brazil Mix HCC 109 202 0.075
Teixeira 2013a PCR Brazil Mix HBV or HCV positive HCC 75 202 0.075
Teixeira 2013b PCR Brazil Mix HBV or HCV negative HCC 34 202 0.075
Chen 2012 PCR China-Kazakan Asian EC 132 251 0.571
Chen 2012a PCR China-Han Asian EC 107 211 0.125
Jiang 2011 PCR China Asian HCC 318 599 0.531
Jiang 2011a PCR China Asian HBV positive HCC 222 60 0.237
Jiang 2011b PCR China Asian HBV negative HCC 96 539 0.009
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CRC: Colorectal cancer; GC: Gastric cancer; EC: Esophageal cancer; HCC: Hepatocellular cancer; HBV: Hepatitis B virus; HCV: Hepatitis C Virus; HWE: Hardy Weinberg Equilibrium.

I/D: Insertion/Deletion; Bold: significant P-value (≤0.05).

For sHLA-G, we identified 212 studies. Of these, duplicate and irrelevant papers were excluded based on titles. After excluding reviews and meta-analyses, 69 were selected for full-text screening based on the title and abstract. Of which, 30 articles were excluded based on abstract, and 24 were omitted due to lack or irrelevant data. Therefore, in total, 15 articles met our inclusion criteria for sHLA-G [25, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45], (Table 2, Figure 2). When there were at least two comparisons, the meta-analysis of HLA-G-related polymorphisms or sHLA-G was performed.

Table 2.

Characteristics of individual studies included in the meta-analysis of sHLA-G dosage in digestive cancer.

Study Method Manufacturer Country Ethnicity Sample type Cases
 Controls
 Units
Sample Number Mean ± SD Number Mean ± SD
Lázaro-Sánchez 2020 ELISA kit BioVendor Spain Caucasian Saliva CRC 15 25.23 ± 16.56 10 6.60 ± 2.03 U/mL
Abu Hassan 2019 ELISA kit MyBioSource Saudi-Arabia Caucasians Serum CRC 33 1.42 ± 0.70 30 1.04 ± 0.81 ng/mL
Farjadian 2018 ELISA kit Exbio Iran Caucasian Plasma GC 82 85.37 ± 60.83 45 56.99 ± 48.45 U/mL
Kirana 2017 ELISA kit Exbio Australia Oceania Plasma CRC 44 NI∗ NA NA U/mL
Li 2017 ELISA kit Exbio China Asian Plasma CRC 178 151.58 ± 88.25 113 37.88 ± 15.58 U/mL
Sun 2017 ELISA kit Ameko China Asian Ascite CC 10 17.59 ± 4.69 30 12.47 ± 3.68 μg/L
Sun 2017a ELISA kit Ameko China Asian Ascite GC 8 18.37 ± 4.63 30 12.47 ± 3.68 μg/L
Sun 2017b ELISA kit Ameko China Asian Ascite PC 6 21.42 ± 1.69 30 12.47 ± 3.68 μg/L
Khorrami 2016 ELISA kit Glory Science Iran Caucasian Serum GC 50 36.29 ± 1.66 50 11.23 ± 1.47 U/mL
Pan 2016 ELISA kit Exbio China Asian Plasma GC 81 55.90 ± 9.23 77 30.53 ± 2.55 U/mL
Xu 2016 ELISA kit Exbio China Asian Plasma GC 124 127.93 ± 52.98 130 75.78 ± 22.12 U/mL
Zheng 2014 ELISA kit BioVendor China Asian Plasma EC 60 71.10 ± 61.42 28 10.72 ± 7.32 U/mL
Park 2012 ELISA kits Exbio/BioVendor South Korea Asian Serum HCC 80 188.58 ± 24.65 50 16.18 ± 12.03 U/mL
Lin 2011 ELISA kit Exbio China Asian Plasma EC 41 143.28 ± 52.68 153 21.48 ± 9.82 U/mL
Zhu 2011 ELISA kit Exbio China Asian Serum CRC 144 124.30 ± 19.17 60 25.83 ± 6.43 U/mL
Wang 2011 ELISA kits Exbio/BioVendor China Asian Serum HCC 36 132.60 ± 31.40 25 47 ± 15.5 U/mL
Lin 2010 ELISA kits Exbio China Asian Plasma HCC 19 175.26 ± 126.67 86 18.44 ± 7.74 U/mL
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CC: Colon cancer; CRC: Colorectal cancer; EC: Esophageal cancer; GC: Gastric cancer; HCC: Hepatocellular cancer; NA: Not applicable; NI: Not indicated; PC: Pancreatic cancer; SD, Standard deviation, sHLA-G, soluble HLA-G. ∗No data for the total CRC cohort. Data are given only in CRC patients after stratifications.

3.2. Meta-analysis of HLA-G 14-bp I/D polymorphism and digestive cancers susceptibility

The HLA-G 14-bp I/D was not associated with the risk of DC neither under allelic, dominant or recessive genetic models (Table 3, Figure 3). After stratifications according ethnicity or type of cancer, we did not reach significant association between HLA-G 14-bp I/D and DC under allelic (D vs. I; OR = 1.127, 95% CI = 0.918–1.383, p = 0.254; Figure 3A), dominant (DD + ID vs. II; OR = 1.115, 95% CI = 0.813–1.530, p = 0.500; Figure 3B), and recessive (DD vs DI + II; OR = 1.214, 95% CI = 0.939–1.572, p = 0.138; Figure 3C) genetic models (Table 3).

Table 3.

Main results of the meta-analysis of HLA-G 14bp I/D polymorphism with digestive cancers.

Cancer type Ethnicity Genetic model N Odds ratio
 Heterogeneity
 PEgger PBegg
OR 95% CI POR I2(%) Tau2 PH
DC Overall D vs. I 10 1.127 0.918–1.383 0.254 73.86 0.075 0.000 0.998 0.858
DD + ID vs. II 1,115 0.813–1.530 0.500 58.18 0.136 0.011 0.983 0.721
DD vs DI + II 1,215 0.939–1.,572 0,138 66.62 0.105 0.001 0.764 0.858
Asian D vs. I 4 1.040 0.724–1.495 0.832 81.3 0.109 0.001 0.255 0.497
DD + ID vs. II 1.083 0.524–2.239 0.830 73.3 0.379 0.010 0.404 1
DD vs DI + II 1.162 0.956–1.411 0.131 71.6 0.112 0.014 0.293 0.042
CRC Overall D vs. I 3 1,015 0.698–1.477 0.937 81.58 0.089 0.004 0.934 1
DD + ID vs. II 0,891 0.607–1.308 0.555 53.56 0.061 0.116 0.635 1
DD vs DI + II 1,179 0.641–2.167 0.597 83.52 0.238 0.002 0.693 1
EC Asian D vs. I 2 1.107 0.8820–1.389 0.382 88 0.198 0.004 NA NA
DD + ID vs. II 1.179 0.735–1.890 0.495 85.5 0.682 0.009 NA NA
DD vs DI + II 1.121 0.806–1.558 0.497 80.4 0.236 0.024 NA NA
HCC Overall D vs. I 4 1.152 0.827–1.605 0.404 66.2 0.071 0.031 0.571 0.174
DD + ID vs. II 4 1.291 0.753–2.211 0.353 37.2 0.109 0.189 0.109 0.042
DD vs DI + II 4 1.213 0.836–1.760 0.310 54.3 0.072 0.087 0.860 0.174
Asian D vs. I 2 1.164 0.956–1.418 0.131 86.9 0.202 0.006 NA NA
DD + ID vs. II 1.349 0.805–2.263 0.256 76.4 1.050 0.040 NA NA
DD vs DI + II 1.184 0.931–1.506 0.169 81.5 0.186 0.020 NA NA
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bp: base pairs, CI: Confidence interval, CRC: Colorectal cancer, DC: Digestive cancers, EC: Esophageal cancer, HCC: Hepatocellular cancer, I/D: insertion/deletion, N: number of studies, NA: Not applicable, OR: odds ratio, PBegg: P-value associated to Begg and Mazumdar rank correlation test (Two-tailed) without continuity correction, PEgger: P-value associated to Egger’s test (Two-tailed), PH: P-value associated to heterogeneity, POR: P-value associated to OR, Bold: significant P-value (≤0.05).

Figure 3.

Figure 3

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Forest plot of the association between HLA-G 14-bp I/D polymorphism and digestive cancer risk with the random effects model. (A) Allelic model (D vs. I) alleles, (B) Dominant genotype (DD + ID vs. II) and (C) Recessive model (DD vs DI + II) in the overall population.

In the subset of HBV or HCV (HBV/HCV) positive HCC patients, we demonstrated a clear association of HLA-G 14-bp I/D with the disease initiation under allelic (D vs. I; OR = 1.698, 95% CI = 1.263–1.650, p = 0.000, Table 4), dominant (DD + ID vs, II; OR = 2.321, 95% CI = 1.277–4.218, p = 0.006, Table 4), and recessive (DD vs. DI + II; OR = 1.739, 95% CI = 1.173–2.577, p = 0.006, Table 4) genetic models. As expected, HLA-G 14-bp I/D was not associated to the disease initiation in the subset of HBV or HCV negative HCC patients (Table 4). Interestingly, the infection by HBV/HCV seems to be implicated in the HCC development when we compared HBV/HCV positive patients to HBV/HCV negative patients under allelic (D vs. I; OR = 1.429, 95% CI = 1.029–1.983, p = 0.033, Table 4) and dominant (DD + ID vs.II; OR = 1.981, 95% CI = 1.002–3.916, p = 0.049, Table 4) genetic models.

Table 4.

Main results of the meta-analysis on two studies of HLA-G 14bp I/D polymorphism with Hepatocellular cancer.

HLA-G 14bp I/D polymorphism
D vs. I DD + ID vs. II DD vs DI + II
Subset comparison N cases (N controls)
OR (95% CI) POR 		N cases (N controls)
OR (95% CI) POR 		N cases (N controls) OR (95% CI) POR
HBV/HCV positive vs. control 594 (524)
1.698 (1.263–2.282)
POR =0.000		 297 (262)
2.321 (1.277–4.218)
POR =0.006		 297 (262)
1.739 (1.173–2.577) POR =0.006
HBV/HCV negative vs. control 260 (1482)
1.238 (0.929–1.650)
POR = 0.145		 130 (741)
1.188 (0.684–2.064)
POR = 0.541		 130 (741)
1.342 (0.921–1.955)
POR = 0.126
HBV/HCV positive vs. HBV/HCV negative 594 (260)
1.429 (1.029–1.983)
POR =0.033		 297 (130)
1.981 (1.002–3.916)
POR =0.049		 297 (130)
1.385 (0.910–2.107) POR = 0.128
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bp: base pairs, CI: Confidence interval, HBV: Hepatitis B virus, HCV: Hepatitis C virus, I/D: insertion/deletion, N: number of studies, NA: Not applicable, OR: odds ratio, POR: P-value associated to OR, Bold: significant P-value (≤0.05).

3.3. Meta-analysis of soluble HLA-G levels in digestive cancer patients and controls

Overall analysis of DC cancers including CRC and GC showed significant increased sHLA-G in patients compared to healthy controls (SMD = 3.341, 95% CI = 2.415–4.267, p = 0.000; Table 5, Figure 4A). sHLA-G were significantly increased in both CRC and GC compared to healthy controls with sHLA- G mean was 2 points higher in GC patients (SMD = 4.043, 95% CI = 2.28–5.858, p = 0.000; Table 5) than in CRC group (SMD = 2.165, 95% CI = 0.506–3.824, p = 0.011; Table 5) (Figure 4B and C). sHLA-G levels were also increased in the serum/plasma in DC patients (SMD = 3.994, 95% CI = 2.793–5.195, p = 0.000; Table 5) and in the CRC patients subgroup (SMD = 2.686, 95% CI = 00.159–5.214, p = 0.037; Table 5).

Table 5.

Meta-analysis results of sHLA-G significance in digestive cancers initiation∗.

Ethnicity Fluidics Cancer type Effects Models N Standardized mean differences
 Heterogeneity
 PEgger PBegg
SMD SEM 95% CI PSMD I2(%) Tau2 PH
Overall All DC R 16 3.341 0.472 2.415–4.267 0.000 98 3.392 0.000 0.011 0.015
CRC R 5 2.165 0.847 0.506–3.824 0.011 97.9 3.473 0.000 0.686 0.327
GC R 5 4.043 0.926 2.28–5.858 0.000 98.46 4.011 0.000 0.142 0.142
Serum/Plasma DC R 11 3.994 0.613 2.793–5.195 0.000 98.6 3.952 0.000 0.004 0.016
CRC R 3 2.686 1.289 0.159–5.214 0.037 98.9 4.923 0.000 0.636 0.602
Caucasian All DC F 4 0.831 0.143 0.551–1.111 0.000 98.3 6.448 0.000 0.113 0.042
Serum/Plasma GC F 2 0.905 0.186 0.541–1.269 0.000 99.4 119.190 0.000 NA NA
Asian All DC F 12 2.292 0.072 2.150–4.434 0.000 97.6 2.849 0.000 0.034 0.11
Ascite DC F 3 1.666 0.257 1.163–2.169 0.000 48.7 0.193 0.143 0.083 0.117
Serum/Plasma CRC F 2 2.266 0.127 2.016–2.516 0.000 99.3 9.349 0.000 NA NA
EC F 2 2.625 0.190 2.253–2.997 0.000 98.8 6.328 0.000 NA NA
GC F 2 1.818 0.122 1.579–2.058 0.000 98.5 2.861 0.000 NA NA
HCC F 3 4.058 0.230 3.608–4.508 0.000 97.3 6.115 0.000 0.217 0.217
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CI: Confidence interval, CRC: Colorectal cancer; DC: digestive cancer; EC: Esophageal cancer; F:Fixed effects model, GC: Gastric cancer; HCC: Hepatocellular cancer; N: number of studies, NA: Not applicable, PBegg: P-value associated to Begg and Mazumdar rank correlation test (Two-tailed) without continuity correction, PEgger: P-value associated to Egger’s test (Two-tailed), PH: P-value associated to heterogeneity, PSMD: P-value associated to SMD, R: Random effects model, SEM: Standard errors of the mean, SMD: standardized mean differences, Bold: significant P-value (≤0.05). ∗ Cases vs. healthy controls.

Figure 4.

Figure 4

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Forest plot of the association between sHLA-G dosage and digestive cancers susceptibility in overall population (A) All digestive cancers, (B) Colorectal cancer, (C) Gastric cancer.

sHLA-G were increased in both Caucasian and Asian patients with sHLA-G found to be 2 fold higher in Asians (SMD = 2.292, 95% CI = 2.150–2.434, p = 0.000; Table 5) than in Caucasians (SMD = 0.831, 95% CI = 0.551–1.111, p = 0.000; Table 5). Of note, in Asians, sHLA-G was 2 fold higher in serum/plasma (SMD = 2.266, 95% CI = 2.016–2.516, p = 0.000; Table 5) than in ascites (SMD = 1.666, 95% CI = 1.163–2.169, p = 0.000; Table 5).

In Asians, more types of DC were investigated and all had significant increased sHLA-G compared to healthy controls (Table 5). HCC patients presented the highest level of sHLA-G (SMD = 4.058, 95% CI = 3.608–4.508, p = 0.000; Table 5) compared to CRC, EC and GC. Due to few meta-analysed studies, subgroups results should be taken with caution.

We further investigated differences between grades in relation to sHLA-G levels. We found that in GC Asian patients, sHLA-G was significantly increased in high grade (Grade 3) compared to low grades (Grade 1 or Grade 2) (SMD = 0.448, 95% CI = 0.109–0.787, p = 0.000; Table 6). Further analysis showed that sHLA-G was significantly increased in grade 3 of DC when compared to grade 1 (SMD = 0.464, 95% CI = 0.150–0.778, p = 0.004; Table 6). Positive vascular invasion presented significant increase in sHLA-G compared to negative vascular invasion in overall analysis (SMD = 0.743, 95% CI = 0.385–1.100, p = 0.000; Table 6) and in Asians (SMD = 0.721, 95% CI = 0.336–1.107, p = 0.000; Table 6). Accordingly, sHLA-G was associated to a poor prognosis in DC.

Table 6.

Meta-analysis results of sHLA-G significance in digestive cancers according to histoprognostic parameters.

Subset comparison Ethnicity Cancer type Effect models N Standardized mean differences
 Heterogeneity
 PEgger PBegg
SMD SEM 95% CI PSMD I2(%) Tau2 PH
Grade 3 vs (Grade 1 or Grade 2) Asian DC F 4 0.133 0.113 -0.089–0.356 0.239 88.7 0.406 0.000 0.565 1
CRC F 2 -0.104 0.150 -0.399–0.190 0.487 95 0.868 0.000 NA NA
GC F 2 0.448 0.173 0.109–0.787 0.000 0 0 0.488 NA NA
Grade 3 vs. Grade 1 Asian DC F 2 0.464 0.160 0.150–0.778 0.004 0 0 0.463 NA NA
Grade 3 vs. Grade 2 Asian DC F 2 -0.201 0.161 -0.516–0.115 0.212 94.2 0.864 0.000 NA NA
N+ ∗ vs. N0 Overall DC R 3 -0.078 0.228 -0.525–0.369 0.732 53.7 0.085 0.116 0.524 0.117
CRC R 2 0.105 0.138 -0.165–0.376 0.444 0 0 0.876 NA NA
Asian DC F 2 -0.023 0.143 -0.303–0.257 0.873 75.4 0.255 0.044 NA NA
PVI vs. NVI Overall DC R 3 0.743 0.182 0.385–1.100 0.000 0 0 0.866 0.208 0.602
PVI vs. NVI Asian HCC F 2 0.721 0.197 0.336–1.107 0.000 0 0 0.650 NA NA
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CI: Confidence interval, CRC: Colorectal cancer; DC: digestive cancer; F:Fixed effects model, GC: Gastric cancer; HCC: Hepatocellular cancer; N: number of studies, NA: Not applicable, NVI: Negative vascular invasion, PVI: Positive vascular invasion, PBegg: P-value associated to Begg and Mazumdar rank correlation test (Two-tailed) without continuity correction, PEgger: P-value associated to Egger’s test (Two-tailed), PH: P-value associated to heterogeneity, PSMD: P-value associated to SMD, R: Random effects model, SEM: Standard errors of the mean, SMD: standardized mean differences, Bold: significant P-value (≤0.05), ∗N+= (N1 or N2 or N1+N2).

3.4. Heterogeneity and publication bias

We detected substantial heterogeneity in meta-analyses investigating the genetic risk of 14-bp I/D for overall analysis and different DC subtypes (p < 0.05, Table 3). In meta-analyses investigating sHLA-G, heterogeneity was also detected. Clinical features could be major source of heterogeneity. Meta-analysis of genetic risk did not present a publication bias by means of Begg’ test (p Begg = 0.457) and symmetric funnel plot (Figure 5A). However, meta-analysis of sHLA-G presented a bias of publication (p Begg = 0.015) and asymmetric funnel plot (Figure 5B).

Figure 5.

Figure 5

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Funnel plot assessing presence/absence of publication bias. (A) In the allelic models of HLA-G 14bp Ins/Del polymorphism, (B)sHLA-G meta-analysis in digestive cancers.

4. Discussion

In recent years, more and more studies are investigating the implication of HLA-G in DC, but in some cases results are conflicting. Our meta-analysis pooled published studies and examined the relationships between both HLA-G 14-bp genetic polymorphism and sHLA-G with the risk of DC. We further investigated the significance of sHLA-G according to clinicopathological features of DC.

Our results did not support a significant implication of HLA-G 14-bp I/D in DC susceptibility. A previous meta-analysis restricted to only HCC did not show a significant implication of 14-bp I/D [46], which confirm our finding. Further analysis revealed a clear association of HLA-G 14bp I/D in HCC patients infected with either HBV or HCV. Expression of HLA-G has been associated with HBV/HCV infection, via increased viral load [47, 48]. In early stages of HCV associated liver infection, both soluble and membrane bound HLA-G protein production are increased [7]. It is also possible that the presence of HLA-G expression in the context of HBV/HCV infections could favour the escape of cancerous cells from immune-surveillance. Because only few studies were meta-analysed, further investigations are still needed to clearly establish the role of HCV/HBV infection and the influence of HLA-G 14-bp I/D in HCC. In the context of sHLA-G, overall analysis showed significant increased sHLA-G in patients with DC cancers. In fact, sHLA-G has been suggested as a good diagnostic factor to distinguish benign colorectal related disease from CRC [43]. Because the invasive nature of the disease and the tumour microenvironment are different across multiple DC, the expression of HLA-G varies among different cancer types. Therefore, we conducted a stratified analysis to investigate the relationship between sHLA-G and DC cancers by cancer type. sHLA-G was significantly increased in either CRC or GC, with sHLA- G mean was 2 points higher in GC patients than in CRC patients. A previous study indicated that plasma sHLA-G level was a potential biomarker for GC diagnosis [38]. A large-scale genomewide association (GWAS) study of East Asians (22,775 CRC patients and 47,731 controls) revealed that HLA-G is one of the leading loci associated with the risk of CRC [49]. Group analysis by ethnicity revealed that sHLA-G was increased in either Caucasians or Asians, with sHLA-G mean 2 fold higher in Asians. This result may be explained by differences in genetic backgrounds between ethnicities.

Analysis by disease grades showed that sHLA-G was significantly increased in high grade compared to low grades. In addition, positive vascular invasion presented significantly elevated sHLA-G compared to negative vascular invasion. Accordingly, our results suggest that sHLA-G is associated with a poor prognosis in DC. These findings support the conclusion drawn from previous studies that tumour HLA-G expression is closely related to tumour progression and poor clinical outcomes in patients with cancer. A recent meta-analysis by Peng et al, performed on immunohistochemical and ELISA dosage [50], showed significant association of HLA-G with poor prognosis in gastric cancer. In addition, this meta-analysis showed that there was a significant correlation between HLA-G expression and TNM stage, lymph node status, and histological grade [50]. Interestingly, subgroup metaanalysis showed that HLA-G expression was only associated with clinic-pathological features in ESCC [50]. Interestingly, enhanced HLA-G expression has been found to be greatly correlated with weak anti-tumour immune response, disease progression, and poor survival in CRC [51]. Thus, HLA-G has been suggested as an independent prognostic predictor of CRC [51]. Du et al. indicated that HLA-G expression was strongly associated with tumour progression and involved in tumour evasion by raising the frequency of infiltrating Tregs locally. Therefore, HLA-G expression is a factor that should be taken into account when considering immunotherapy as treatment option, and is also a promising predictor for worse prognosis in DC patients [52]. HLA-G expression can be affected by ILT4 that regulates the cell proliferation, invasion and migration of CRC. HLA-G fusion protein treatment also increased ILT4 expression in a dose-dependent manner, thereby activating protein kinase B (AKT) and extracellular signal-regulated kinase (ERK) signaling, and facilitating the proliferation, migration and invasion of CRC cells. HLA-G interacts with ILT4 to promote CRC progression through AKT and ERK signal activation [2]. Accordingly, ILT4 and HLA-G could be prognostic factors to predict poor clinical response and survival time in patients with CRC providing a novel strategy of blocking ILT4/HLA-G for the treatment of CRC [2]. Therefore, HLA-G has the potential to serve as a biomarker for DC cancers prognosis, and screening for this marker could allow for the early diagnosis and treatment.

Our meta-analysis presents substantial limitations related to heterogeneity and size data. We also detected a marginal publication bias in meta-analysis on sHLA-G, which is due to unpublished studies and language restriction. We particularly acknowledge that the number of included studies was relatively small in subgroups; which might weaken the statistical power of the results. All the studies included were observational studies, so substantial heterogeneity was inevitable in this meta-analysis due to the various regimens, populations and sample sizes. Therefore, more clinical studies are needed to give definitive conclusions.

5. Conclusion

The current meta-analysis suggests that HLA-G 14-pb I/D polymorphism is associated with HCC in the case of HBV/HCV infections. Increased sHLA-G indicates a poor prognosis for DC patients. sHLA-G is likely associated with prognostic clinical features of DC. Further larger studies are warranted to consolidate current findings.

Declarations

Author contribution statement

Sabrine DHOUIOUI, Kalthoum TIZAOUI: Data collection, Data analysis and interpretation, Drafting of manuscript.

Nadia BOUJELBENE: Medical validation, Critical data interpretation, Critical revision, Hadda-imene OUZARI: Critical data interpretation, Critical revision. Inès ZIDI: Study conception and design, supervision, Data analysis, Drafting of manuscript, Critical revision.

Funding statement

This work was supported by Ministry of ​ Higher Education and Scientific Research of Tunisia.

Data availability statement

Data will be made available on request.

Declaration of interests statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Acknowledgements

This research was supported by Ministry of Higher Education and Scientific Research of Tunisia.

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