DOI: https://doi.org/10.21203/rs.3.rs-1394340/v1
The study aims to provide a comprehensive account of the association of the EGF 61A/G polymorphism (rs4444903) and susceptibility to hepatocellular carcinoma (HCC).
Electronic searches of the Chinese National Knowledge Infrastructure (CNKI), Wanfang, Chinese Scientific Journal Database (VIP), PubMed, Web of Science, and Embase were systematically conducted to select studies by two independent researchers. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated to assess the strength of the association.
In general, a total of 18 articles were enrolled with 2692 cases and 5835 controls for rs4444903. The meta-analysis results of the pooling all studies showed that the EGF + 61A/G polymorphism was significantly associated with an increased risk of virus-related hepatocellular carcinoma in all genetic models. In subgroup analyzes based upon ethnicity, a significantly elevated association was observed between EGF + 61A/G polymorphism and the risk of virus-related hepatocellular carcinoma in Asian populations (G vs. A: OR = 1.15, P < 0.001, 95% CI: 1.02–1.29, I2 = 40.1%), European populations (G vs. A: OR = 1.59, P < 0.001, 95% CI: 1.05–2.41, I2 = 0.0%) and African populations (G vs. A: OR = 4.46, P < 0.001, 95% CI 1.53–13.02, I2 = 93%)
EGF gene + 61A/G polymorphism is significantly associated with the increased risk of HCC, particularly in Asian populations.
Hepatocellular carcinoma is one of the most common and lethal cancer worldwide.[1] The estimated annual number of cancer patients has increased by more than 500,000, and the five-year survival rate in developing countries is only 5%, which means that although the diagnosis and treatment have made significant advances, the prognosis is still very poor.[2, 3] HCC is a kind of cancer with multi-factors, such as chronic infection with hepatitis B or hepatitis C virus, excessive alcohol consumption, high cigarette smoking and many etiological factors.[4] At present, HCC has been proven to be induced by infammation, virus-associated hepatocellular carcinoma is the most common type of liver cancer and more than 80% of HCC patients in China are associated with chronic hepatitis B virus (HBV) infection.[5, 6]
Today, most diagnoses of virus-related hepatocellular carcinoma are made after the disease has progressed substantially. And there is no effective therapy for most virus-related hepatocellular carcinoma patients. Therefore, Effective screening of high-risk groups for chemoprevention is of great significance to the treatment of virus-related hepatocellular carcinoma.[7] Serum alpha-fetoprotein measurement and liver imaging are currently the main methods for screening high-risk groups. However, due to the low sensitivity and specificity, their effectiveness is questionable and limited.[8, 9] In order to improve prevention and treatment strategies, identification of molecular markers associated with increased risk of virus-related hepatocellular carcinoma is necessary.
In recent years, several important signaling pathways have been systematically studied in virus-related hepatocellular carcinoma. These pathways regulate physiological processes such as the growth and differentiation of tumor cells, the regeneration of blood vessels, and the migration of tumor cells.[10] Epidermal growth factor (EGF) plays a significant role in cell proliferation, differentiation and tumorigenesis of epithelial tissues.[11] The EGF 61A > G polymorphism (rs4444903) is a functional SNP in the 5’ untranslated region of the EGF gene.[12, 13] It results in higher EGF levels in individuals with EGF genotype G/G in comparison to the A/A genotype.[14] Studies have shown that the signal pathway of the epidermal growth factor (EGF) plays an important role in the occurrence of HCC. EGF can stimulate the proliferation of epidermal and epithelial cells, which have a strong relationship with embryo growth, tissue repair, regeneration, and tumorigenesis.[15, 16] The transient profile of EGF RNA accumulation suggests that an increase in EGF levels may catalyze a cascade of events preceding the first wave of liver DNA replication in hepatocytes isolated by collagenase perfusion.[17] The EGF activates the epidermal growth factor receptor (EGFR) as a ligand with biological effect through signal transduction.[18] Ibrahim et al have proved EGF had a significant risk of hepatitis C viral cirrhotic development in cirrhotic patients by increased expression in liver tumor tissue from hepatitis C viral cirrhotic patients.[19]
In this study, we performed a meta-analysis of all eligible studies to clarify the relationship between EGF polymorphism and the risk of virus-related hepatocellular carcinoma.
The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria were used for this meta-analysis (Supplement S1).[20]
Literature-searching strategy
We performed a computerized literature search by two independent researchers with following 5 electronic databases: Chinese National Knowledge Infrastructure (CNKI), Wanfang, Chinese Scientific Journals Database (VIP), Pubmed, Web of Science, and Embase from their start date to April 2021. We used the following keywords and medical subject heading terms: (“Epidermal growth factor” or “EGF”) and (“polymorphism” or “variant” or “SNP” OR “mutation”) and (“hepatocellular carcinoma” or “liver cell carcinoma” or “liver cancer”)
Inclusion criteria
Studies included in the meta-analysis had to meet all the following criteria: (1) evaluating the association between EGF polymorphism and virus-related hepatocellular carcinoma risk, (2) using unrelated individuals, (3) providing sufficient data for estimating an odds ratio (OR) with its 95% confidence interval (CI), (4) using case–control, cohort or crosssectional design, (5) published in English or Chinese. The corresponding authors were contacted to obtain missing information, and some studies were excluded if critical missing information was not obtained. Reviews, case reports, family-based studies, case-only studies, and studies without sufficient data were excluded. When a study reported results on different sub-populations based on ethnicity or geographical region, we treated each sub-population as a separate comparison. If more than one article was published using the same subjects, only the study with the largest sample size was selected.
Data extraction
All data were extracted independently by two investigators. Disagreement was resolved by discussion. The following data were extracted: authors, name of the journal, year of publication, ethnicity, and country of study population, inclusion and exclusion criteria, characteristics of cases and controls, numbers of HCC cases and controls, matching criteria, source of controls, HCC confirmation, study design, genotyping methods, genotype frequencies of cases and controls, and interactions between environmental factors or genes.
Quality score assessment
The quality of the studies was independently assessed by the same two investigators. Any disagreement was resolved by discussion between the two investigators. The total scores ranged from 0 (worst) to 24 (best). Studies scoring <16 were classified as “low quality”, and those scoring ≥16 as “high quality”.
Statistical analysis
The unadjusted OR with 95% CI was used to assess the strength of the association between the EGF polymorphism and the risk of virus-related hepatocellular carcinoma. The pooled ORs were performed under the allelic contrast (G versus A), codominant model (homozygote comparison: GG versus AA, heterozygote comparison: GA versus AA), dominant model (GG + GA versus AA), and recessive model (GG versus GA + AA), respectively. Heterogeneity between studies was measured using a Q statistic test and an I-square statistic. P less than 0.10 (P<0.10) was considered representative of significant statistical heterogeneity due to the low power of the statistic. I2 ranges between 0 and 100%, and represents the proportion of between-study variability that can be attributed to heterogeneity rather than chance. I2 values of 25%, 50% and 75% were defined as low, moderate and high estimates. If the significant Q-statistic indicated heterogeneity across studies, the random-effects model was used, otherwise the fixedeffects model was adopted. The Z test was used to assess the significance of the pooled OR and a P value less than 0.05 (P<0.05) was considered significant.
Subgroup analyses were stratified by racial descent, study quality, source of controls, type of controls, and number of cases, respectively. Furthermore, metaregression analysis[21] was performed to investigate five potential sources of heterogeneity including ethnicity (Asian populations versus non-Asian populations), study quality (high quality studies versus low quality studies), source of controls (hospital-based versus Population-based), type of controls (healthy controls versus controls with chronic liver diseases) and number of cases (<100 versus ≥100). Statistical significance was defined as a P-value less than 0.10 (P<0.10) because of the relatively weak statistical power.
To evaluate the stability of the results, sensitivity analyzes were performed by sequential omission of individual studies under various comparisons in the overall and Asian populations, respectively. Publication bias was investigated by funnel plot. Funnel plot asymmetry was assessed by the method of linear regression test. The Hardy-Weinberg equilibrium (HWE) was tested using the χ2 test. All P-values were two-sided. Data analyzes were performed using the software Stata version 11.0 software.
As shown in Fig. 1, a total of 584 articles were initially obtained by searching the databases. After duplicate check by Endnote 20, 435 articles remained. Excluded 37articles based on browsing the titles and abstracts. According to the inclusion criteria, 5 of the remaining 23 records were further excluded based on full-text review. In total, 21 studies (18 articles) were eligible including 2692 virus-related hepatocellular carcinoma cases and 5835 controls were identified and included in this meta-analysis.
The characteristics of the 21 included studies are shown in Table 1. Of all eligible studies, 11 were conducted in Asian populations, 2 in European populations, 5 in African populations, and 3 in mixed populations. In all studies, the cases were histologically confirmed (17 studies) or diagnosed by elevated α-fetoprotein and different iconography changes (abdominal ultrasound and triphasic computed tomography). All controls were free of cancer. 4 studies used healthy populations, 5 studies used patients with chronic liver diseases (HBV infection, HCV infection, cirrhosis), and 12 studies included healthy subjects and patients with chronic liver diseases as controls. The sample size of the total participants ranged from 75 to 1774, with a mean of 406. Quality scores for individual studies ranged from 11.5 to 21, with 10 of the 21 studies classified as high quality. 20 studies used peripheral blood, and one study used FFPE to extract genome DNA. 14 studies used the polymerase chain reaction-restriction fragment length polymorphism assay (PCR-RFLP), 6 studies used the Taqman method, and 1 study used Matrix-Assisted Laser Desorption/ Ionization Time of flight mass spectrometry (MALDI-TOF-MS) to genotype the EGF + 61 A/G polymorphism. The genotype distribution in the controls of all studies was consistent with HWE.
first author | year | country(Ethnicity) | source of controls | type of controls | sample origin | genotyping methods | sample size (case/control) | genotype frequency (case/control) | G allele frequency | HWE | Quality score | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GG | GA | AA | |||||||||||
Tanabe-FRA | 2008 | France(European) | HB | Cirrhosis | Peripheral bolld | PCR-RFLP | 44/77 | 15/12 | 17/37 | 12/28 | 39.60% | Y | 13.5 |
Tanabe-USA | 2008 | USA(mixed) | HB | HBV/HCV/Cirrhosis | Peripheral bolld | PCR-RFLP | 59/148 | 23/32 | 27/65 | 9/51 | 43.60% | y | 14.5 |
Qi | 2009 | China(Asian) | HB and PB | Healthy/HBV | Peripheral bolld | PCR-RFLP | 215/380 | 102/182 | 98/160 | 15/38 | 68.90% | Y | 21 |
Wang-GX | 2009 | China(Asian) | HB | Healthy/HBV | Peripheral bolld | PCR-RFLP | 376/477 | 190/208 | 154/221 | 32/48 | 66.80% | Y | 17.5 |
Wang-JS | 2009 | China(Asian) | HB | Healthy/HBV | Peripheral bolld | PCR-RFLP | 186/198 | 107/93 | 65/88 | 14/17 | 69.20% | Y | 18 |
Li | 2010 | China(Asian) | HB and PB | Healthy/Cirrhosis | Peripheral bolld | PCR-RFLP | 186/338 | 96/161 | 82/145 | 8/32 | 69.10% | Y | 19.5 |
Abu Dayyeh | 2011 | USA(mixed) | HB | HCV | Peripheral bolld | PCR-RFLP | 66/750 | 26/178 | 25/350 | 15/222 | 47.10% | Y | 16.5 |
Chen | 2011 | China(Asian) | HB | Healthy/HBV/Cirrhosis | Peripheral bolld | PCR-RFLP | 120/240 | 62/106 | 51/110 | 7/24 | 67.10% | Y | 19 |
Abbas | 2012 | Egypt(African) | HB | Healthy/HCV/Cirrhosis | Peripheral bolld | PCR-RFLP | 20/60 | 7/9 | 9/28 | 4/23 | 38.30% | Y | 12 |
Cmet | 2012 | Italy(European) | HB and PB | Healthy/HBV | Peripheral bolld | PCR-RFLP | 18/361 | 4/66 | 10/172 | 4/123 | 42.10% | Y | 16 |
Shi | 2012 | China(Asian) | HB | Healthy | Peripheral bolld | PCR-RFLP | 73/117 | 18/13 | 31/52 | 24/52 | 33.30% | Y | 13.5 |
El-Bendary | 2013 | Egypt(African) | HB | HCV/Cirrhosis | Peripheral bolld | PCR-RFLP | 133/105 | 57/9 | 43/36 | 33/60 | 25.70% | Y | 12 |
Suenaga | 2013 | Japan(Asian) | HB | Healthy/HBV/HCV | Peripheral bolld | PCR-RFLP | 208/290 | 108/161 | 89/104 | 11/25 | 73.40% | Y | 11.5 |
Wu | 2013 | China(Asian) | HB and PB | Healthy/HBV | Peripheral bolld | TaqMan | 404/1370 | 206/647 | 153/576 | 45/147 | 68.20% | Y | 17.5 |
Yuan-USA | 2013 | USA(mixed) | HB | Healthy | Peripheral bolld | TaqMan | 117/225 | 28/63 | 61/102 | 28/60 | 50.70% | Y | 19 |
Yuan-CHN | 2013 | China(Asian) | HB | Healthy/HBV/HCV | Peripheral bolld | TaqMan | 250/245 | 25/20 | 99/107 | 126/118 | 30.00% | Y | 15 |
Wei | 2016 | China(Asian) | HB | HCV | Peripheral bolld | MALDI-TOF-MS | 47/213 | 30/101 | 15/98 | 2/14 | 72.1% | Y | 12.5 |
El Sergany | 2017 | Egypt(African) | HB | Healthy | Peripheral bolld | TaqMan | 50/50 | 42/2 | 5/6 | 3/42 | 49.50% | Y | 15.5 |
Gholizadeh | 2017 | Iranian(Asian) | HB | Healthy | FFPE/Healthy | PCR-RFLP | 40/106 | 4/34 | 25/48 | 11/24 | 51% | Y | 13 |
Asar | 2020 | Egypt(African) | HB | Healthy/HCV | Peripheral bolld | TaqMan | 30/60 | 11/11 | 10/34 | 9/15 | 48.89% | Y | 13 |
Baghdadi | 2020 | Egypt(African) | HB | Healthy/Cirrhosis | Peripheral bolld | TaqMan | 50/25 | 20/4 | 23/13 | 7/8 | 56% | Y | 21 |
Abbreviations: HB, Hospital-based; PB, Population-based; HBV, control subjects were hepatitis B virus carriers; HCV, control subjects were hepatitis C virus carriers; PCR-RFLP, polymerase chain reaction-restriction fragment length polymorphism; MALDI-TOF-MS, Matrix-Assisted Laser Desorption/ Ionization Time of Flight Mass Spectrometry; HWE: Hardy-Weinberg equilibrium in control population; Y, yes; N, no. |
The results of pooling all studies showed that the EGF + 61A/G polymorphism was significantly associated with an increased virus-related hepatocellular carcinoma risk under all genetic models (G vs. A:OR = 1.56, P < 0.001, 95% CI: 1.26–1.94, I2 = 86.8%; GG vs. GA + AA: OR = 1.67, P < 0.001, 95% CI 1.29–2.15, I2 = 79%; GG + GA vs. AA: OR = 1.67, P < 0.001, 95% CI: 1.26–2.20, I2 = 70.2%; GG vs. AA: OR = 2.18, P < 0.001, 95% CI 1.50–3.16, I2 = 76.6%; GA vs. AA: OR = 1.20, P < 0.001, 95% CI 1.03–1.39, I2 = 23.7%)(Table 2).
Subgroup | No. comparisons | sample size ( case/control) | GG vs. GA + AA | GG + GA vs. AA | GG vs. AA | GA vs. AA | G vs. A | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
OR (95% CI) | I2(%) | OR (95% CI) | I2(%) | OR (95% CI) | I2(%) | OR (95% CI) | I2(%) | OR (95% CI) | I2(%) | |||
overall | 21 | 2680/5835 | 1.67(1.29–2.15)* | 79# | 1.67(1.26–2.20)* | 70.2# | 2.18(1.50–3.16)* | 76.6# | 1.20(1.03–1.39)* | 23.7 | 1.56(1.26–1.94)* | 86.8# |
Racial descent | ||||||||||||
Asian | 11 | 2105/3974 | 1.21(1.01–1.45)* | 52.5# | 1.18(0.99–1.42) | 6.5 | 1.39(1.07–1.82)* | 34 | 1.11(0.92–1.32) | 4.5 | 1.15(1.02–1.29)* | 40.1 |
European | 2 | 62/438 | 2.07(0.98–4.38) | 12.6 | 1.61(0.84–3.12) | 0 | 2.51(1.10–5.72)* | 0 | 1.30(0.64–2.63) | 0 | 1.59(1.05–2.41)* | 0 |
African | 5 | 271/300 | 6.97(2.34–20.71)* | 79.8# | 4.12(1.23–13.81)* | 86.5# | 9.74(2.43–39.12)* | 82.9# | 1.55(0.99–2.41) | 60.8# | 4.46(1.53–13.02)* | 93# |
Mixed | 3 | 242/1123 | 1.55(0.79–3.06) | 77.3# | 1.57(0.96–2.58) | 46.9 | 1.94(0.87–4.33) | 73.1# | 1.37(0.94-2.00) | 11 | 1.45(0.93–2.26) | 77.1# |
Study quality | ||||||||||||
High quality | 9 | 1720/4003 | 1.26(1.07–1.48)* | 38.4 | 1.26(1.04–1.52)* | 0 | 1.46(1.13–1.89)* | 29.2 | 1.16(0.95–1.42) | 0 | 1.20(1.08–1.34)* | 24.2 |
Low quality | 12 | 960/1832 | 2.34(1.30–4.20)* | 85.6# | 2.03(1.22–3.38)* | 80.7# | 3.00(1.48–6.08)* | 82.3# | 1.24(1.00-1.54)* | 40 | 1.98(1.25–3.14)* | 91.7# |
Source of controls | ||||||||||||
Population-based | 4 | 823/2449 | 1.12(0.95–1.32) | 0 | 1.37(0.90–2.10) | 41.9 | 1.35(0.93–1.95) | 21.6 | 1.18(0.88–1.58) | 50.9# | 1.12(0.99–1.27) | 0 |
Hospital-based | 17 | 1857/3386 | 1.94(1.38–2.73)* | 81.6# | 1.75(1.24–2.45)* | 73.6# | 2.44(1.53–3.89)* | 78.8# | 1.20(1.01–1.43)* | 20.3 | 1.72(1.29–2.29)* | 88.8# |
Type of controls | ||||||||||||
Healthy controls | 15 | 2124/4919 | 1.47(1.10–1.95)* | 78.5# | 1.40(1.03–1.90)* | 69.1# | 1.68(1.14–2.47)* | 72.9# | 1.08(0.92–1.27) | 20.4 | 1.40(1.10–1.79)* | 86.4# |
Patients with chronic liver diseases | 6 | 556/916 | 2.39(1.33–4.28)* | 79.4# | 2.77(1.99–3.86)* | 0 | 4.13(2.29–7.45)* | 52# | 1.89(1.32–2.70)* | 0 | 2.02(1.32–3.09)* | 82.7# |
Number of cases | ||||||||||||
> 100 | 17 | 2530/5640 | 1.42(1.14–1.77)* | 72.1# | 1.44(1.17–1.78)* | 45.9 | 1.78(1.29–2.45)* | 67.7# | 1.18(1.01–1.37)* | 0 | 1.33(1.12–1.56)* | 76.7# |
≤ 100 | 4 | 150/195 | 6.99(1.51–32.40)* | 84.3# | 4.44(0.70-28.16) | 89.9# | 9.73(1.31–71.99)* | 86.7# | 1.53(0.83–2.82) | 70.6# | 4.58(0.95–22.09) | 94.7# |
genotyping methods | ||||||||||||
PCR-RFLP | 14 | 1732/3647 | 1.56(1.17–2.07)* | 75.6# | 1.68(1.35–2.09)* | 22.6 | 2.14(1.46–3.13)* | 65.5# | 1.39(1.14–1.70)* | 0 | 1.44(1.17–1.77)* | 78.1# |
TaqMan | 6 | 901/1975 | 2.41(1.14–5.11)* | 87.7# | 1.87(0.91–3.84) | 88.3# | 2.74(1.08–6.95)* | 88# | 0.98(0.78–1.23) | 61.9# | 2.15(1.15–4.01)* | 94.4# |
MALDI-TOF-MS | 1 | 47/213 | 1.96(1.02–3.76)* | / | 1.58(0.35–7.21) | / | 2.08(0.45–9.67 | / | 1.07(0.22–5.19) | / | 1.66(0.96–2.86) | / |
OR, odds ratio; 95% CI, 95% confidence interval. | ||||||||||||
*Significant results, P-value < 0.05. | ||||||||||||
#Random effect estimate. |
In subgroup analyzes based upon ethnicity, a significantly elevated association was observed between EGF + 61A/G polymorphism and the risk of virus-related hepatocellular carcinoma in Asian populations (G vs. A: OR = 1.15, P < 0.001, 95% CI: 1.02–1.29, I2 = 40.1%), European populations (G vs. A: OR = 1.59, P < 0.001, 95% CI: 1.05–2.41, I2 = 0.0%) and African populations (G vs. A: OR = 4.46, P < 0.001, 95% CI 1.53–13.02, I2 = 93%), respectively (Fig. 2). When stratifying by study quality, the results showed that EGF + 61A/G polymorphism was associated with an increased virus-related hepatocellular carcinoma risk both in high-quality studies (G vs. A: OR = 1.20, P < 0.001, 95% CI: 1.08–1.34, I2 = 24.2%) and in low-quality studies (G vs. A: OR = 1.98, P < 0.001, 95% CI: 1.25–3.14, I2 = 91.7%). In subgroup analyzes by source of controls, the results showed that the EGF + 61A/G polymorphism was significantly associated with the risk of virus-related hepatocellular carcinoma in hospital-based studies (G vs. A: OR = 1.72, P < 0.001, 95% CI 1.29–2.29, I2 = 88.8%), but not in population-based studies (G vs. A: OR = 1.12, P = 0.202, 95% CI 0.99–1.27, I2 = 0.0%). Furthermore, according to the chronic liver disease status in Asian controls, a significant association was obtained between EGF + 61A/G polymorphism and virus-related hepatocellular carcinoma in patients with chronic liver diseases (G vs. A: OR = 2.02, P < 0.001, 95% CI 1.32–3.09, I2 = 82.7%), and in healthy controls (G vs. A: OR = 1.40, P < 0.001, 95% CI 1.10–1.79, I2 = 86.4%) (Table 2).
The Q-statistic indicated statistically significant heterogeneity between all studies under all genetic models except for the comparison of heterozygote comparison (Table 2). However, in the subgroup analyses by ethnicity, the between-study heterogeneity was not observed in Asian populations, European populations or African populations. Moreover, meta-regression indicated that both ethnicity and study quality significantly contributed to the heterogeneity for EGF + 61A/G polymorphism (Table 3).
Factor | GG vs. GA + AA | GG + GA vs. AA | GG vs. AA | GA vs. AA | G vs. A | |||||
---|---|---|---|---|---|---|---|---|---|---|
t | p | t | p | t | p | t | p | t | p | |
Racial descent | -2.43 | 0.025 | -1.76 | 0.095 | -2.29 | 0.034 | -1.29 | 0.212 | -2.17 | 0.043 |
Source of controls | 1.07 | 0.298 | 0.36 | 0.722 | 0.73 | 0.473 | -0.08 | 0.939 | 0.84 | 0.41 |
Type of controls | 0.91 | 0.373 | 2.12 | 0.048 | 1.7 | 0.106 | 2.77 | 0.012 | 0.71 | 0.486 |
Genotyping methods | 0.68 | 0.507 | -0.03 | 0.975 | 0.25 | 0.803 | -2.05 | 0.054 | 0.73 | 0.477 |
Sample size | -1.22 | 0.236 | -0.89 | 0.386 | -1.15 | 0.265 | -0.50 | 0.626 | -1.11 | 0.28 |
Quality score | 2.6 | 0.018 | 1.9 | 0.073 | 2.34 | 0.031 | 0.75 | 0.461 | 2.54 | 0.02 |
Sensitivity analysis was performed by sequential omission of individual studies. Pooled ORs were consistently significant in general populations or Asian populations by omitting one study at a time under the allelic contrast, recessive model, and homozygote comparison, suggesting robustness of our results. And Begg’s funnel plots were prepared and Egger’s test was performed on the final set of 21 studies to assess publication bias for reported comparisons of EGF + 61A/G polymorphism and virus-related hepatocellular carcinoma risk. The results showed that risk of publication bias may exist in overall population, but a low risk of publication bias in Asian populations(Fig. 3, 4, Table 4).
Population | genetic model | Begg (z|p) | Egger (t|p) |
---|---|---|---|
All | G vs. A | 3.11|0.002 | 2.87|0.01 |
Asian | G vs. A | 1.09|0.276 | 0.43|0.68 |
All | GG vs. GA + AA | 2.39|0.017 | 2.78|0.012 |
Asian | GG vs. GA + AA | 0.93|0.35 | 0.04|0.968 |
All | GG + GA vs. AA | 2.08|0.037 | 2.90|0.009 |
Asian | GG + GA vs. AA | 1.09|0.276 | 2.48|0.035 |
All | GG vs. AA | 2.87|0.004 | 2.72|0.014 |
Asian | GG vs. AA | 1.56|0.119 | 0.73|0.482 |
All | GA vs. AA | 1.72|0.085 | 3.18|0.005 |
Asian | GA vs. AA | 1.40|0.161 | 2.59|0.029 |
In recent years, research on the relationship between the EGF61 gene polymorphism and malignant tumor susceptibility has gradually increased,[22] including HCC.HCC is a complex disease in which the environment and the host interact with multiple genes.[23] Currently recognized risk factors for HCC include liver virus, aflatoxins, alcoholic liver cirrhosis, etc.[4] However, only a small number of people exposed to the above risk factors eventually develop HCC, which suggests that host genetic factors may play an important role in the pathogenesis of HCC. This plays an important role in the occurrence and development of tumors.[24, 25] EGF activates the EGF pathway by combining with transmembrane endothelial growth factor receptors to promote cell proliferation and differentiation, thereby enhancing the carcinogenic rate of various carcinogens. But the results reported by the research are not the same. In this study, 21 cohorts (18 articles) were eligible, including 2692 virus-related hepatocellular carcinoma and 5835 controls were identified and included in this meta-analysis. Overall, the EGF + 61A/G polymorphism was significantly associated with an increased HCC risk under all genetic models. However, the cross-research found considerable heterogeneity. Meta-regression shows that race and research quality have a significant impact on the heterogeneity of the EGF + 61A/G polymorphism. However, in the subgroup analysis, race and learning quality, this important association still exists in each subgroup, and inter-study heterogeneity becomes insignificant in Asia, Europe, or African populations. In addition, the sensitivity analysis further strengthened the validity of the positive correlation in the overall population and the Asian population, indicating the credibility of our results.
It is possible that the effects of genetic factors related to cancer are different across various ethnic populations. A large number of studies have shown that the relationship between EGF + 61A/G polymorphism and HCC susceptibility differs between ethnicity. Tanabe et al.[26] included two independent research populations, that one of the research populations was Caucasian, and the other research population was composed of whites, blacks, Asians, and Hispanics. Abu et al.[27] compared white people with black people. Jiang et al.[22] studied the Asian population, European population and the African population. The same result has been proved that the EGF + 61A/G polymorphism was significantly associated with an increased risk of HCC risk under all genetic models, and the relationship between EGF + 61A/G polymorphism and HCC susceptibility differs between races. In this study, ethnicity was also identified as a potential source of heterogeneity by meta-regression and subgroup analyses. The results showed that the frequency of EGF + 61G allele was highest in Asian populations, intermediate in European populations, and lowest in African populations. And the higher prevalence of the EGF + 61G allele might lead to a higher HCC prevalence among Asian populations. In subgroup analyzes based upon ethnicity, a significantly elevated association was observed between EGF + 61A/G polymorphism and the risk of virus-related hepatocellular carcinoma in Asian populations (G vs. A: OR = 1.15, P < 0.001, 95% CI: 1.02–1.29, I2 = 40.1%), European populations (G vs. A: OR = 1.59, P < 0.001, 95% CI: 1.05–2.41, I2 = 0.0%) and African populations (G vs. A: OR = 4.46, P < 0.001, 95% CI 1.53–13.02, I2 = 93%).
This article has certain limitations. Our results showed that the EGF + 61A/G polymorphism was significantly associated with the risk of HCC in a hospital-based study, but not in population-based study, but not in a population-based study. Therefore, the results should be treated with caution, as the controls from hospital-based studies may not be representative of the general population. Larger population-based studies are needed to further confirm the association between EGF + 61A/G polymorphism and HCC susceptibility. In this meta-analysis, 11 of the 21 studies were classified as low quality. Research on low-quality designs usually does not exclude the true effects of factors that may bias estimates, and may lead to incorrect conclusions. However, high-quality and low-quality studies of the associated polymorphism between EGF + 61A/G and HCC risk indicate that this bias will not affect the final results. In addition, aside from genetic factors, there are other factors related to the development of HCC, such as exposure to aflatoxin B1, smoking, and habitual alcoholism were not considered. Finally, the number of studies included in the meta-analysis for European populations and African populations was relatively small, which may lead to low statistical power and generate fluctuation in estimation.
However, this study combines currently published research on the relationship between EGF61A/G gene polymorphism and HCC susceptibility, merges and analyzes the meta-analysis method, and the conclusions reached are credible. However, due to the limitations mentioned above, more studies with a rigorous design, larger sample size, and wider perspectives will be needed in the future in terms of environmental factors and related gene polymorphisms, in order to obtain more reliable gene effects and more precision. The inherent relationship between EGF gene polymorphism and HCC susceptibility provides better preventive measures and treatment options for HCC.
In summary, this meta-analysis suggests that EGF gene + 61A/G polymorphism is significantly associated with the increased risk of HCC, particularly in Asian populations. More studies with rigorous design, larger sample size, and wider perspectives will be needed in the future in terms of environmental factors and related gene polymorphisms, to obtain more reliable gene effects and more precision.
Acknowledgments
We are grateful to all researchers in the enrolled studies.
Author contributions
Min Zhang had full access to all the data in the study and takes responsibility for the integrity of the data and the precision of the data analysis; the concept and design were carried out by Lingling Xu and Min Zhang; acquisition of data was carried out by Lingling Xu and Qianbo Wu; analysis and interpretation of the data was performed by Lingling Xu and Qianbo Wu; drafting of the manuscript was carried out by Lingling Xu; critical review of the manuscript for important intellectual content was carried out by Min Zhang; statistical analysis was carried out by Lingling Xu and Qianbo Wu; administrative, technical or material support: none; supervision, None;
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The authors declare there is no competing interests.