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Abstract

Aim:

The lncRNA growth arrest-special 5 (GAS5) is a critical tumor suppressor lncRNA, and its expression level has been found to be decreased in many types of cancers. So GAS5 polymorphisms are also likely to influence predisposition to many types of malignant diseases. Nevertheless, the relationships between GAS5 polymorphisms and cancer are still controversial. Thus, the authors designed this meta-analysis to get a more statistically reliable conclusion.

Methods:

The authors searched PubMed, Embase, and Web of Science for eligible studies. A total of 12 eligible studies involving 8693 cancer cases and 10,805 controls were pooled and analyzed in this meta-analysis.

Results:

Among GAS5 polymorphisms, only GAS5 rs145204276 insertion/deletion polymorphism could be analyzed in a meta-analysis with regard to predisposition to cancer since no any other GAS5 polymorphisms were explored by at least two individual genetic association studies. All eligible studies were found to be of Asian origin. Although the overall pooled meta-analysis results did not show any significant associations between rs145204276 insertion/deletion polymorphism and a predisposition to cancer, rs145204276 insertion/deletion polymorphism was demonstrated to be significantly associated with a predisposition to gastric cancer (dominant comparison: P<0.0001; recessive comparison: P=0.005; over-dominant comparison: P=0.0003; over-dominant comparison: P<0.0001) in Asians in further subgroup analyses.

Conclusions:

This meta-analysis demonstrated that GAS5 rs145204276 insertion/deletion polymorphism was associated with a predisposition to gastric cancer in Asians. Nevertheless, considering that this positive finding was only based on three eligible studies from the same area, future studies with larger sample sizes in other populations are still warranted to test the robustness of our findings.

Keywords

Rs145204276 insertion/deletion polymorphism Cancer Meta-analysis Asians

Introduction

Cancer is the second most common cause of death worldwide.^1-2 Although its definite pathogenesis mechanisms are still unclear, accumulating evidence suggests that genetic architecture plays a vital role in its development. First, the incidences of many types of cancer have been found to be much higher in subjects with positive family history in first-degree relatives, and genetic background is probably one of reasons behind this phenomenon.^3-8 Second, previous genetic association studies have also detected numerous susceptible genetic loci of cancer in different populations.^9-12 However, the pathogenesis mechanisms of cancer are very complicated, and genetic factors that contribute to the development of cancer still need intensive explorations.

Long non-coding RNAs (lncRNAs) regulate the expression of protein-encoding genes, and serve as crucial regulators of many vital cellular processes, including proliferation, differentiation, and apoptosis.^13,14 Recently, lncRNAs have also been widely investigated regarding their associations with cancer, and it has been demonstrated that they play vital roles in carcinogenesis and cancer progression.^15,16 The lncRNA growth arrest-special 5 (GAS5) regulates the expression of protein-encoding genes through association with the eukaryotic translation initiation factor-4E (eIF4E) or acting as a competing endogenous RNA (ceRNA).^17,18 It is a critical tumor suppressor lncRNA growth arrest-special 5 (GAS5), and its expression level has also been found to be decreased in many types of cancers.^19-22 Therefore, if a polymorphism is of potential functional significance and can impact the expression of GAS5, it is likely that this polymorphism might also influence the predisposition to many types of malignant diseases.

In the last two decades, investigators across the world have extensively explored relationships between GAS5 polymorphisms and cancer risk, yet the relationships between GAS5 polymorphisms and cancer risk are still controversial. Thus, the authors designed this meta-analysis to obtain a more statistically reliable conclusion regarding relationships between GAS5 polymorphisms and cancer risk by pooling the results of related studies.

Materials and methods

The PRISMA guideline was followed by the authors when conducting this meta-analysis.²³

Literature search and inclusion criteria

Literature searching of PubMed, Web of Science, and Embase was performed by the authors using the following terms: “GAS5 or GAS-5 or growth arrest-specific 5” and “polymorphism or variant or variation or mutation or SNP or genome-wide association study or genetic association study or genotype or allele” and “cancer or tumor or carcinoma or neoplasm or malignancy.” The authors also checked the references of retrieved articles for additional related studies.

Eligible studies must meet all of three inclusion criteria: (a) formally published genetic association studies evaluating relationships between GAS5 polymorphisms and cancer risk; (b) provide genotypic distributions of GAS5 polymorphisms in patients with cancer and controls; and (c) the full manuscript is available in English. Articles were excluded when at least one of the following three conditions was fulfilled: (a) studies not concerning GAS5 polymorphisms and cancer risk; (b) reviews, expert comments, or conference abstracts; and (c) case series only involved patients with cancer. When duplicate reports were observed during the literature search, only the most complete one with the largest sample size was included for the pooled analyses.

Data extraction and quality assessment

We extracted the following items from eligible studies: (a) surname of the first author; (b) year of online publication; (c) country and ethnicity of involved subjects; (d) number of patients and controls in each study; and (e) genotypic distributions of GAS5 polymorphisms in patients and control subjects. We also calculated P values of Hardy-Weinberg equilibrium (HWE) based on genotypic distributions of GAS5 polymorphisms.

The authors used Newcastle-Ottawa scale (NOS) to assess the quality of included studies.²⁴ Its score range is from 0 to 9, and the methodology quality of a study is considered to be good if it can get a score of more than 7.

Data extraction and the quality assessment of included studies were performed by two authors separately. We wrote to the corresponding authors of eligible studies for additional data if we failed to extract necessary information from the included studies.

Statistical analyses

The authors used Review Manager to pool the meta-analysis results. The authors used odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) to evaluate relationships between GAS5 polymorphisms and any predisposition to cancer. We compared the genetic distributions of GAS5 polymorphisms among cases and controls in dominant, recessive, over-dominant, and allele models. The dominant genetic model is defined as M/M versus M/m + m/m; the recessive genetic model is defined as m/m versus M/M +M/m; the over-dominant genetic model is defined as M/m versus M/M + m/m; and the allele genetic model is defined as M versus m. The authors set the statistically significant threshold at 0.05 and used I² statistics to estimate the heterogeneity. The DerSimonian-Laird method was used to pool the results if I² was larger than 50%; if not, the Mantel-Haenszel method was used. Subgroup analyses were conducted by the type of cancer, and the stability of pooled meta-analyses results was examined by omitting one study each time and pooling the results of the other studies. Publication biases were examined by using funnel plots and Egger’s tests.

Results

Characteristics of included studies

Ninety-three articles were retrieved by the authors through the literature search strategy mentioned above. The authors assessed 20 articles for eligibility after omitting unrelated and repeated reports. A further 5 reviews and 3 case series were excluded by the authors. A total of 12 studies were finally pooled in our meta-analyses (Figure 1). Extracted data of eligible studies are summarized in Table 1.

Figure 1.

Flowchart of study selection for the present study.

Table 1.

The characteristics of included studies.

First author, year	Country	Ethnicity	Type of disease	Sample size	Genotype distribution		P-value for HWE	NOS score
First author, year	Country	Ethnicity	Type of disease	Sample size	Cases Controls		P-value for HWE	NOS score
Aminian 2019	Iran	Asian	Gastric cancer	130/230	88/36/6	126/84/20	0.271	8
Li 2017	China	Asian	Lung cancer	600/600	287/270/43	246/292/62	0.068	8
Li 2018a	China	Asian	Gastric cancer	853/954	461/334/58	454/415/85	0.476	8
Li 2018b	China	Asian	Gastric cancer	1253/1354	682/483/88	638/593/123	0.376	8
Lin 2019	Taiwan	Asian	Prostate cancer	579/579	263/252/64	237/270/72	0.717	8
Tang 2019	China	Asian	Breast Cancer	575/602	310/220/45	279/261/62	0.934	8
Tao et al. 2015²⁶	China	Asian	Hepatocellular carcinoma	1034/1052	414/480/140	504/468/82	0.062	8
Xu 2018	China	Asian	Osteosarcoma	132/1270	80/42/10	616/543/111	0.575	8
Yuan 2018	China	Asian	Glioma	404/820	154/198/52	419/346/55	0.144	8
Zheng et al. 2016²⁰	China	Asian	Colorectal cancer	1400/1400	738/550/112	639/610/151	0.763	8
Zhu 2016	China	Asian	Colorectal carcinoma	813/926	317/387/109	444/409/73	0.112	8
Zhu 2017	China	Asian	Cervical cancer	920/1018	364/433/123	486/459/73	0.011	8

HWE: Hardy-Weinberg equilibrium; NA: not available; NOS: Newcastle-Ottawa scale.

Meta-analysis results of GAS5 polymorphisms and cancer

Among the GAS5 polymorphisms, only rs145204276 insertion/deletion polymorphism could be analyzed in the meta-analysis with regard to predisposition to cancer, since no any other GAS5 polymorphisms were explored by at least two individual genetic association studies. Twelve studies, including 8693 cancer cases and 10,805 controls, were eligible for estimation of the relationship between the GAS5 rs145204276 polymorphism and cancer risk. All eligible studies were found to be of Asian origin. Although the overall pooled meta-analyses results did not show any significant associations between the rs145204276 insertion/deletion polymorphism and a predisposition to cancer, the rs145204276 insertion/deletion polymorphism was demonstrated to be significantly associated with a predisposition to gastric cancer (dominant comparison: P<0.0001; recessive comparison: P=0.005; over-dominant comparison: P=0.0003; over-dominant comparison: P<0.0001) in Asians in further subgroup analyses (see Table 2 and Supplementary Figure 1).

Table 2.

Results of pooled meta-analyses.

Population	Sample size	Dominant comparisonins/ins vs. ins/del + del/del			Recessive comparisondel/del vs. ins/ins + ins/del			Over-dominant comparisonins/del vs. ins/ins + del/del			Allele comparisonIns vs. del
Population	Sample size	P value	OR (95% CI)	I² statistic	P value	OR (95% CI)	I² statistic	P value	OR (95% CI)	I² statistic	P value	OR (95% CI)	I² statistic
Overall	8693/10805	0.45	1.08 (0.89, 1.31)	91%	0.80	1.04 (0.78, 1.38)	87%	0.12	0.92 (0.83, 1.02)	67%	0.60	1.05 (0.88, 1.24)	93%
Gastric cancer	2236/2538	<0.0001	1.35 (1.20, 1.51)	0%	0.005	0.73 (0.59, 0.91)	0%	0.0003	0.81 (0.72, 0.91)	0%	<0.0001	1.27 (1.16, 1.39)	0%

CI: confidence interval; del: deletion; ins: insertion; NA: not available; OR: odds ratio.

The values in bold represent statistically significant differences between cases and controls.

Sensitivity analyses

Obvious heterogeneities were observed in all comparisons of overall pooled analyses. Stabilities of pooled meta-analyses results were further examined by omitting one study each time and pooling the results of the other studies. We found that heterogeneities remained significant for all comparisons, and no alterations of results were detected in the sensitivity analyses (see Table 3).

Table 3.

Results of sensitivity analyses.

Population	Sample size	Dominant comparisonins/ins vs. ins/del + del/del			Recessive comparisondel/del vs. ins/ins + ins/del			Over-dominant comparisonins/del vs. ins/ins + del/del			Allele comparisonIns vs. del
Population	Sample size	P value	OR (95% CI)	I² statistic	P value	OR (95%CI)	I² statistic	P value	OR (95%CI)	I² statistic	P value	OR (95%CI)	I² statistic
All included	8693/10805	0.45	1.08 (0.89, 1.31)	91%	0.80	1.04 (0.78, 1.38)	87%	0.12	0.92 (0.83, 1.02)	67%	0.60	1.05 (0.88, 1.24)	93%
Aminian 2019 excluded	8563/10575	0.68	1.04 (0.85, 1.28)	91%	0.62	1.08 (0.80, 1.44)	88%	0.18	0.93 (0.84, 1.04)	68%	0.87	1.01 (0.85, 1.21)	93%
Li 2017 excluded	8093/10205	0.59	1.06 (0.86, 1.31)	91%	0.61	1.08 (0.80, 1.46)	87%	0.17	0.92 (0.82, 1.03)	69%	0.76	1.03 (0.86, 1.23)	93%
Li 2018a excluded	7840/9851	0.59	1.06 (0.86, 1.31)	91%	0.66	1.07 (0.79, 1.45)	87%	0.19	0.93 (0.83, 1.04)	68%	0.75	1.03 (0.86, 1.24)	93%
Li 2018 b excluded	7440/9451	0.61	1.06 (0.85, 1.31)	91%	0.66	1.07 (0.79, 1.46)	87%	0.22	0.93 (0.83, 1.04)	66%	0.77	1.03 (0.86, 1.23)	93%
Lin 2019 excluded	8114/10226	0.54	1.07 (0.86, 1.32)	91%	0.74	1.05 (0.77, 1.44)	88%	0.16	0.92 (0.82, 1.03)	70%	0.69	1.04 (0.86, 1.25)	93%
Tang 2019 excluded	8118/10203	0.61	1.06 (0.86, 1.30)	91%	0.66	1.07 (0.79, 1.45)	87%	0.20	0.93 (0.83, 1.04)	68%	0.77	1.03 (0.86, 1.23)	93%
Tao et al. 2015²⁶ excluded	7659/9753	0.27	1.12 (0.92, 1.37)	90%	0.90	0.98 (0.73, 1.31)	85%	0.07	0.90 (0.81, 1.01)	66%	0.37	1.08 (0.91, 1.29)	92%
Xu 2018 excluded	8561/9535	0.68	1.04 (0.85, 1.28)	91%	0.74	1.05 (0.78, 1.42)	88%	0.22	0.94 (0.84, 1.04)	65%	0.81	1.02 (0.86, 1.22)	93%
Yuan 2018 excluded	8289/9985	0.19	1.14 (0.94, 1.38)	90%	0.87	0.98 (0.73, 1.31)	86%	0.06	0.89 (0.81, 1.01)	59%	0.31	1.09 (0.92, 1.29)	92%
Zheng et al. 2016²⁰ excluded	7293/9405	0.61	1.06 (0.85, 1.31)	91%	0.63	1.08 (0.80, 1.46)	86%	0.20	0.93 (0.82, 1.04)	68%	0.77	1.03 (0.86, 1.23)	93%
Zhu 2016 excluded	7880/9879	0.25	1.12 (0.92, 1.37)	90%	0.91	0.98 (0.73, 1.32)	86%	0.05	0.90 (0.81, 1.00)	64%	0.37	1.08 (0.91, 1.29)	92%
Zhu 2017 excluded	7773/9787	0.27	1.12 (0.92, 1.37)	90%	0.86	0.97 (0.73, 1.30)	85%	0.07	0.90 (0.81, 1.01)	66%	0.37	1.08 (0.91, 1.29)	92%

CI: confidence interval; del: deletion; ins: insertion; NA: not available; OR: odds ratio.

Publication biases

Publication biases were examined by funnel plots and Egger’s tests. The funnel plots were symmetrical overall (see Supplementary Figure 2). We further calculated P values for Egger’s tests, and found that the P values were greater than 0.05 in all comparisons (dominant comparison, P =0.362; recessive comparison, P =0.409; over-dominant comparison, P =0.298; allele comparison, P =0.461), which suggested that our pooled meta-analyses results were not likely to be severely influenced by publication bias.

Discussion

This meta-analysis, for the first time, has summarized the association of GAS5 polymorphisms with a predisposition to cancer. The pooled meta-analyses results demonstrated that GAS5 rs145204276 insertion/deletion polymorphism was associated with a predisposition to gastric cancer. The trends of associations remained unchanged in sensitivity analyses, suggesting that our pooled meta-analyses results were statistically quite stable.

However, a few points need to be considered when interpreting our findings. First, previous experimental studies have demonstrated that the rs145204276 insertion/deletion polymorphism might result in the altered gene expression of GAS5.^19,25,26 Although the mechanisms of lncRNA GAS5 in cancer development are still not fully understood, several reports have already illustrated a down-regulation of GAS5 in various types of cancers.^20-22 Also, it has been demonstrated that the down-regulation of GAS5 is associated with a poor prognosis, whereas the up-regulation of GAS5 is associated with a more promising treatment outcome in many types of cancers.^27-30 Thus, it is likely that the rs145204276 insertion/deletion polymorphism might influence the normal functioning of GAS5 and influence the predisposition to many types of malignancies. Second, although we pooled the results of published studies, there was only one report for many types of malignancies, so future genetic association studies are still needed to estimate the relationship between the rs145204276 insertion/deletion polymorphism and the predisposition to different types of cancers. Third, we aimed to investigate all GAS5 polymorphisms at the beginning. However, we did not find a sufficient number of published articles to support pooled meta-analyses of other GAS5 polymorphisms, so we only examined the rs145204276 insertion/deletion polymorphism in this meta-analysis. Fourth, all eligible studies were found to be of Asian origin, so the relationship between this polymorphism and cancer risk should also be tested by researchers in other populations. Fifth, significant heterogeneities were observed in overall pooled analyses, which suggested that the effects of the GAS5 rs145204276 insertion/deletion polymorphism on the predisposition to different types of cancers may be different. Therefore, detailed mechanism studies are also warranted to reveal the exact roles of GAS5 in different types of cancers.

Like all meta-analyses, a few limitations of our pooled meta-analyses should be acknowledged. First, our pooled meta-analyses results were derived from pooling unadjusted findings since the authors did not have raw data of eligible studies.³¹ Second, environmental factors might also influence the relationships between GAS5 polymorphisms and cancer risk. However, most investigators only focused on genetic associations in their work, so genetic–environmental interactions were not explored in this meta-analysis.³² Third, we did not consider grey literature. Even though the funnel plots were symmetrical overall, and the P values of Egger’s tests were greater than 0.05, publication bias may still affect the robustness of our pooled results.³³

Conclusions

This meta-analysis has demonstrated that the GAS5 rs145204276 insertion/deletion polymorphism was associated with a predisposition to gastric cancer in Asians. Nevertheless, considering that this positive finding was only based on three eligible studies from the same area, future studies with larger sample sizes in other populations are still warranted to test the robustness of our findings.

Supplemental Material

Supplementary_material – Supplemental material for LncRNA growth arrest-special 5 polymorphisms and predisposition to cancer: A meta-analysis

Supplemental material, Supplementary_material for LncRNA growth arrest-special 5 polymorphisms and predisposition to cancer: A meta-analysis by Hairong Cai, Wenyan Xu and Xian Zhang in The International Journal of Biological Markers

Footnotes

Acknowledgements

None.

Authors’ contributions

Hairong Cai and Xian Zhang designed this study. Hairong Cai and Wenyan Xu searched the literature. Hairong Cai and Wenyan Xu analyzed the data. Hairong Cai and Xian Zhang wrote the manuscript. All authors have approved the final manuscript as submitted.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Statement of ethics

This article does not contain any studies with human participants or animals performed by any of the authors, thus ethical approval and informed consent are not required.

ORCID iD

Xian Zhang

Supplemental material

Supplemental material for this article is available online.

References

Siegel

Miller

Jemal

Cancer statistics, 2017. CA Cancer J Clin 2017; 67: 7–30.

Ferlay

Soerjomataram

Dikshit

, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer 2015; 136: E359–E386.

Wood

Daniels

, et al. American Society of Clinical Oncology Expert Statement: collection and use of a cancer family history for oncology providers. J Clin Oncol 2014; 32: 833–840.

Brenner

Kloor

Pox

CP.

Colorectal cancer. Lancet 2014; 383: 1490–1502.

Forner

Llovet

Bruix

Hepatocellular carcinoma. Lancet 2012; 379: 1245–1255.

Choi

Kim

Gastric cancer and family history. Korean J Intern Med 2016; 31: 1042–1053.

Randi

Pelucchi

Negri

, et al. Family history of urogenital cancers in patients with bladder, renal cell and prostate cancers. Int J Cancer 2007; 121: 2748–2752.

Lin

Huang

Yan

, et al. A family history of cancer and lung cancer risk in never-smokers: A clinic-based case-control study. Lung Cancer 2015; 89: 94–98.

Stenzinger

Weichert

Genetic profiling of cancers of the digestive system: biological insights and clinical implications. Pathobiology 2017; 84: 306–322.

10.

Sharma

Bhatia

Agarwal

, et al. Gastrointestinal cancers: molecular genetics and biomarkers. Can J Gastroenterol Hepatol 2018; 2018: 4513860.

11.

Mascaux

Tsao

Hirsch

FR.

Genomic testing in lung cancer: past, present, and future. J Natl Compr Canc Netw 2018; 16: 323–334.

12.

Lynch

Snyder

Lynch

Hereditary breast cancer: practical pursuit for clinical translation. Ann Surg Oncol 2012; 19: 1723–1731.

13.

Jarroux

Morillon

Pinskaya

History, discovery, and classification of lncRNAs. Adv Exp Med Biol 2017; 1008: 1–46.

14.

Khorkova

Hsiao

Wahlestedt

Basic biology and therapeutic implications of lncRNA. Adv Drug Deliv Rev 2015; 87: 15–24.

15.

Yang

Yuan

LncRNA: a link between RNA and cancer. Biochim Biophys Acta 2014; 1839: 1097–1109.

16.

Bhan

Soleimani

Mandal

SS.

Long noncoding RNA and cancer: a new paradigm. Cancer Res 2017; 77: 3965–3981.

17.

Lou

Gupta

The long non-coding RNA GAS5 cooperates with the eukaryotic translation initiation factor 4E to regulate c-Myc translation. PLoS One 2014; 9: e107016.

18.

Zhang

Zhao

SJ.

LncRNA Gas5 acts as a ceRNA to regulate PTEN expression by sponging miR-222-3p in papillary thyroid carcinoma. Oncotarget 2017; 9: 3519–3530.

19.

Huang

Wen

, et al. Genetic variation of lncRNA GAS5 contributes to the development of lung cancer. Oncotarget 2017; 8: 91025–91029.

20.

Zheng

Song

Xiao

, et al. LncRNA GAS5 contributes to lymphatic metastasis in colorectal cancer. Oncotarget 2016; 7: 83727–83734.

21.

Mei

, et al. Down-regulation of long non-coding RNA GAS5 is associated with the prognosis of hepatocellular carcinoma. Int J Clin Exp Pathol 2014; 7: 4303–4309.

22.

Sun

Jin

Xia

, et al. Decreased expression of long noncoding RNA GAS5 indicates a poor prognosis and promotes cell proliferation in gastric cancer. BMC Cancer 2014; 14: 319.

23.

Moher

Liberati

Tetzlaff

, et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; 151: 264–269.

24.

Stang

Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010; 25: 603–605.

25.

Sun

, et al. Polymorphism in the promoter region of lncRNA GAS5 is functionally associated with the risk of gastric cancer. Clin Res Hepatol Gastroenterol 2018; 42: 478–482.

26.

Tao

Wang

, et al. Association between indel polymorphism in the promoter region of lncRNAGAS5 and the risk of hepatocellular carcinoma. Carcinogenesis 2015; 36: 1136–1143.

27.

Yan

Zhang

, et al. Long non-coding RNA GAS5 polymorphism predicts a poor prognosis of acute myeloid leukemia in Chinese patients via affecting hematopoietic reconstitution. Leuk Lymphoma 2017; 58: 1948–1957.

28.

Cao

Liu

, et al. Decreased expression of lncRNA GAS5 predicts a poor prognosis in cervical cancer. Int J Clin Exp Pathol 2014; 7: 6776–6783.

29.

Xue

Jiang

, et al. Long noncoding RNA GAS5 inhibits tumorigenesis and enhances radiosensitivity by suppressing miR-135b expression in non-small cell lung cancer. Oncol Res 2017; 25: 1305–1316.

30.

Zhang

Wang

, et al. GAS5 is downregulated in gastric cancer cells by promoter hypermethylation and regulates adriamycin sensitivity. Eur Rev Med Pharmacol Sci 2016; 20: 3199–3205.

31.

Pan

Huang

, et al. The association between PON1 (Q192R and L55M) gene polymorphisms and risk of cancer: a meta-analysis based on 43 studies. Biomed Res Int 2019; 2019: 5897505.

32.

Moazeni-Roodi

Tabasi

Ghavami

, et al. Investigation of ATG16L1 rs2241880 polymorphism with cancer risk: a meta-analysis. Medicina (Kaunas) 2019; 55: E425.

33.

Shen

Xiao

Yin

, et al. Associations between UGT2B7 polymorphisms and cancer susceptibility: A meta-analysis. Gene 2019; 706: 115–123.

Supplementary Material

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