Genetic Variant XRCC1 rs1799782 (C194T) and Risk of Cancer Susceptibility in Indian Population: A Meta-analysis of Case–Control Studies

Abstract

X-ray repair cross-complementing group 1 (XRCC1) plays a key role in the base excision repair pathway, as a scaffold protein that brings together proteins of the DNA repair complex. Several studies have reported contradictory results for XRCC1 exon 6 C>T (rs1799782) gene polymorphism and cancer risk in Indian population has provided inconsistent results. Therefore, we have performed this meta-analysis to evaluate the relationship between XRCC1 exon 6 C>T gene polymorphism and risk of cancer by published studies. We searched PubMed and Google scholar web databases to cover all studies published on association between XRCC1 exon 6 C>T gene polymorphism and cancer risk. The meta-analysis was carried out and pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) were used to appraise the strength of association. In order to derive a more precise estimation of the association, A total of 3197 confirmed cancer cases and 3819 controls were included from eligible seventeen case-controls studies. Results from overall pooled analysis demonstrated suggested that that variant allele (T vs. C: OR 1.301, 95% CI 1.003–1.688, p = 0.047) was associated with the risk of overall cancer. Other genetic models; heterozygous (TC vs. CC: OR 1.108, 95% CI 0.827–1.485, p = 0.491), homozygous (TT vs. CC: OR 1.479, 95% CI 0.877–2.493, p = 0.142), dominant (TT+TC vs. CC: OR 1.228, 95% CI 0.899–1.677, p = 0.196) and recessive (TT vs. TC+CC: OR 1.436, 95% CI 0.970–2.125, p = 0.071) did not reveal statistical association. Publication bias observation was also considered and none was detected during the analysis. The present meta-analysis suggested that the variant allele T of XRCC1 exon 6 gene polymorphism was associated with the risk of cancer. It is therefore pertinent to confirm this finding in a large sample size to divulge the mechanism of this polymorphism and cancer risk in Indian population.

Introduction

Cancer is still one of the most common dreadful disease across the world. New cases of virtually all types of cancer are rising and the burden of cancer in worldwide was estimated to have 18.1 million. new cases and 9.6 million. deaths in 2018 [1]. In India, cancer incidence is predicted to reach 1,148,757 cases in the year 2020 that may lead to a huge socio-economic burden [2]. The exact mechanism of initiation of cancer is still complicated because it is documented as a highly heterogeneous disease. Epidemiological studies have suggested the significant correlation between genetic factors and the risk of cancer [3]. Identification of genetic markers of cancer is essentially required which could be used clinically to predict the individual risk of progression and outcome. Recently genome-wide association studies (GWAS) have clearly revealed that single-nucleotide polymorphisms (SNPs) is the most common form of human genetic variation that play an important role in defining individual susceptibility to cancer [4]. Therefore, exploring genetic markers through genetic studies may assist in understanding the pathogenesis of carcinogenesis and may serve as a powerful tool of prevention and progression in future.

Human genome is constantly damaged by with endogenous and exogenous sources. Multiple DNA repair system exists to provide cellular defense mechanism against DNA damage exposure [5]. Evidence endorses cancer to be initiated by DNA damage, which if not repaired, can cause errors during DNA synthesis. Studies have suggested that inherited impairment due to genetic variant at one or more loci may altered individual’s DNA repair capacity and elevated the risk of developing cancer [6].

DNA repair gene involved in the base excision repair (BER) pathway, one of the main defense mechanisms, is generally considered to constitute the fundamental defense against small lesions such as single strand breaks (SSBs), non-bulky adducts, oxidative damage, alkylation, and methylation [7]. It has been well accepted that the SNPs in the base excision repair (BER) gene can change the individual repair capacity in response to DNA damage [8].

X-ray repair cross complementing group 1 (XRCC1) gene maps on chromosome 19q13.2 and play a key role in DNA repair involved in BER pathway [9]. XRCC1 acts as a scaffolding protein and interacts with enzymatic components of each stage of DNA strand break repair and involved to bring together poly (ADP-ribose) polymerase (PARP), DNA ligase III, and DNA polymerase β via its various domain and polynucleotide kinase at the site of DNA damage [10, 11].

Studies have demonstrated that the ability of DNA-repair genes (such as XRCC1) is correlated with the pathogenesis of malignant tumors and drug resistance [12]. Several polymorphisms have been identified in XRCC1 gene, among them one common genetic variant contributing to amino acid substitutions in XRCC1 at codon 194 (exon 6, base C>T, amino acid Arg to Trp, rs1799782) occurs at a conserved residue in humans have been studied widely [13]. Functional significance of XRCC1 exon 6 C>T is mainly due to its location in an evolutionarily conserved linker region. keeping in mind the significant role of XRCC1 gene in DNA repair and mutation, several studies have investigated the association between XRCC1 exon 6 C>T polymorphism and various cancer risk in Indian populations. However, individual published studies reveal inconclusive results [14–30]. It is possible that discrepancies in findings result from individual studies may have been underpowered to detect overall effects. With an aim of addressing inconsistencies in the findings of these individual studies, we performed a meta-analysis based on published studies to obtain better conclusion of the postulated genetic association between the XRCC1 exon 6 C>T and susceptibility to cancer in Indian population.

Materials and Methods

Identification and Eligibility of Studies

A series of literature search was performed in PubMed (Medline) and Google scholar database to identify studies that had investigated the association of XRCC1 exon 6 C>T polymorphism and cancer risk in Indian population, last updated searched was performed on June 2019. The following medical subject headings were used as key word for our search: “XRCC1 OR X-ray repair cross complementing group 1” and “polymorphism OR mutation OR variant” and “Cancer OR malignancy OR tumor” and “Indian OR India”. The search was focused on studies that had been carried out in humans. In addition, references of retrieved articles were also screened for other additional eligible studies.

Inclusion and Exclusion Criteria

Inclusion criteria for this meta-analysis were the following: (1) must evaluated the association between XRCC1 exon 6 C>T polymorphism and cancer risk, (2) use a case–control design, (3) recruited histologically confirmed cancer patients and healthy controls, (4) Provided complete genotype distribution of case and control. In addition, the following exclusion criteria were used: (1) repeated or overlapping studies, (2) case only studies, (3) review articles, (4) animal studies, (5) no usable data reported.

Data abstraction and Quality Assessment

The data were abstracted using a standardized protocol of inclusion and exclusion criteria. The following data were extracted from each study: last name of the first author, publication year, study region, types of cancer, sample size of case and controls, genotype frequencies in both cases and controls and source of genotyping.

Statistical Analysis

Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the associations between XRCC1 exon 6 C>T gene polymorphism and cancer risk under the allele, heterozygous, homozygous, dominant, and recessive models [31]. Statistical heterogeneity between studies was performed by the chi-square-based Q-test [32]. Heterogeneity was considered significant at p value < 0.05, to avoid underestimation of the presence of heterogeneity. If p value (> 0.05) it means heterogeneity exists across articles, and random effects model (Mantel–Haenszel method) was used to measure the pooled OR; otherwise the fixed effects model (Mantel–Haenszel method) was used [33, 34]. Moreover, I² statistics was also employed to efficiently test the heterogeneity. The degree of heterogeneity was expressed as I ² and was divided into low (I²< 25%), medium (I² ~ 50%), and high (I² > 75%) heterogeneity groups. I² > 50% was regarded as indicating substantial heterogeneity [35]. Potential publication bias was estimated by Begg’s tests and Egger’s test [36, 37]. p value < 0.05 was considered as representation of statistically significant publication bias. Hardy–Weinberg equilibrium (HWE) was also tested in the control group, which was measured via chi-square test. A sensitivity analysis was conducted to test the impact of removing each single study on the pooled result. The statistical analysis involved in this meta-analysis was performed by Comprehensive Meta-Analysis (CMA) Version 2 software program (Biostat Inc., USA). All p values were two sided and statistical significance level was considered as p value < 0.05 for this meta-analysis.

Results

Description of Literature Search

We identified a total of seventy eight studies by scrupulous literature search from the PubMed (Medline) and Google scholar. All retrieved articles were carefully examined according the preset selection (inclusion–exclusion) criteria. Finally, after reading the abstracts and the full texts, a total of seventeen research articles were included in the meta-analysis (Fig. 1). Furthermore, studies either showing XRCC1 polymorphism to predict survival OR as an indicator for response to therapy of patients were excluded straightaway. Similarly, research articles investigating the levels of XRCC1 mRNA or protein expression and relevant review articles were also excluded. Details of eligible studies, cancer types, publication year, distribution of genotypes and minor allele frequency (MAF) in the controls and cases have shown been shown in Tables 1 and 2.

Fig. 1.

Prisma flow diagram for inclusion and exclusion of studies in the meta-analysis on XRCC1 exon 6 C>T gene polymorphism

Table 1.

Main characteristics of all published studies included in the meta-analysis

References	Cancer	Association	Control	Cases	Source
Maqbool et al. [14]	Skin	Yes	60	68	Blood
Pavithra et al. [15]	Gastric	No	400	200	Blood
Konathala et al. [16]	Cervical	NM	150	125	Blood
Devi et al. [17]	Breast	No	534	464	Blood
Singh et al. [18]	Lung	No	325	330	Blood
Ghosh et al. [19]	Gastric	No	82	70	Blood
Bajpai et al. [20]	Cervical	Increased	68	65	Blood, Tissue
Annamaneni et al. [21]	CML	Increased	350	350	Blood
Kumar et al. [22]	SCCHN	Reduced	278	278	Blood
Mittal et al. [23]	Bladder	No	250	212	Blood
Mittal et al. [23]	Prostate	No	250	195	Blood
Srivastava et al. [24]	Gallbladder	No	204	173	Blood
Kiran et al. [25]	Hepatocellular carcinoma	Increased	171	63	Blood
Mitra et al. [26]	Breast	Reduced	225	151	Blood
Pachouri et al. [27]	Lung	Increased	122	103	Blood
Ramachandran et al. [28]	Oral	Increased	110	110	Blood
Joseph et al. [29]	Leukemia	Increased	117	117	Blood
Chacko et al. [30]	Breast	Increased	123	123	Blood

NM not mentioned, CML chronic myeloid leukemia

Table 2.

Genotypic distribution of XRCC1 exon 6 C>T gene polymorphism included in meta-analysis

References	Controls				Cancer cases
	Genotype			Minor allele	Genotype			Minor allele	HWE
	CC	CT	TT	MAF	CC	CT	TT	MAF	p value
Maqbool et al	44	11	5	0.17	36	20	12	0.32	0.004
Pavithra et al	303	90	7	0.13	158	40	2	0.11	0.915
Konathala et al	40	85	25	0.45	82	36	7	0.20	0.076
Devi et al	338	166	30	0.21	263	178	23	0.24	0.114
Singh et al	267	55	3	0.09	256	72	2	0.11	0.928
Ghosh et al	58	24	0	0.14	50	14	6	0.18	0.120
Bajpai et al	44	11	13	0.27	11	16	38	0.70	0.001
Annamaneni et al	7	113	230	0.81	12	81	257	0.85	0.103
Kumar et al	121	131	26	0.32	144	111	23	0.28	0.263
Mittal et al	207	41	2	0.09	172	37	3	0.10	0.984
Mittal et al	203	43	4	0.10	157	29	9	0.12	0.334
Srivastava et al	162	40	2	0.10	145	26	2	0.08	0.786
Kiran et al	27	64	52	0.58	8	43	12	0.53	0.359
Mitra et al	6	53	166	0.85	2	15	134	0.93	0.481
Pachouri et al	52	47	23	0.38	40	39	24	0.42	0.042
Ramachandran et al	90	19	1	0.09	66	37	7	0.23	0.997
Joseph et al	91	22	4	0.12	77	32	8	0.20	0.085
Chacko et al	96	23	4	0.12	79	35	9	0.21	0.093

MAF minor allele frequency, HWE Hardy Weinberg equilibrium

Publication Bias

Publication bias was detected based on the symmetry of funnel plot and Begg’s and Egger’s tests. The shape of funnel plot did not elucidate any obvious asymmetry in all of the comparison models. Egger’s regression test, a linear regression approach for measuring funnel plot on the natural logarithm scale of the OR was used to provide statistical evidence to the funnel plot symmetry that also showed no evidence of publication bias (Table 3).

Table 3.

Statistics to test publication bias and heterogeneity in meta-analysis XRCC1 exon 6 C>T polymorphism

Comparisons	Egger’s regression analysis			Heterogeneity analysis			Model used for meta-analysis
	Intercept	95% confidence interval	p value	Q value	Pheterogeneity	I2 (%)
T Vs C	3.43	− 0.91–7.77	0.11	133.47	0.01	87.26	Random
TT vs CC	1.48	− 0.94–3.92	0.21	66.80	0.01	74.55	Random
TC vs CC	0.50	− 2.44–3.46	0.72	83.68	0.01	79.68	Random
TT+TC vs CC	1.43	− 1.96–4.84	0.38	107.61	0.01	84.20	Random
TT vs TC+CC	0.49	− 1.35–2.34	0.57	57.76	0.01	70.57	Random

Evaluation of Heterogeneity

For each genetic contrast, we estimated the between study heterogeneity across all of the eligible comparisons using Q-test and I² statistics. Heterogeneity was observed in all genetic models, i.e., ***allele (T vs. C), homozygous (TT vs. CC), heterozygous (TC vs. CC), dominant (TT+TC vs. CC) and recessive (TT vs. TC+CC). Thus, random effect model was applied to synthesize the data for above these models (Table 3).

Association of XRCC1 exon 6 C>T Gene Polymorphism and Overall Cancer Susceptibility

We pooled all seventeen studies together and included 3197 confirmed cases and 3819 controls to evaluate the relationship between the XRCC1 exon 6 C>T gene polymorphism and the risk of overall cancer. In a random model, pooled analysis revealed that variant allele (T vs. C: OR 1.301, 95% CI 1.003–1.688, p = 0.047) was associated with the risk of overall cancer. However, heterozygous (TC vs. CC: OR 1.108, 95% CI 0.827–1.485, p = 0.491), homozygous (TT vs. CC: OR 1.479, 95% CI 0.877–2.493, p = 0.142), dominant (TT+TC vs. CC: OR 1.228, 95% CI 0.899–1.677, p = 0.196) and recessive (TT vs. TC+CC: OR 1.436, 95% CI 0.970–2.125, p = 0.071) genetic models did not indicate statistical association (Figs. 2, 3, 4, 5 and 6).

Fig. 2.

Forest plot of allele (T vs. C) model for overall cancer risk. The squares and horizontal lines correspond to the study specific OR and 95% CI

Fig. 3.

Forest plot of heterozygous (TC vs. CC) model for overall cancer risk. The squares and horizontal lines correspond to the study specific OR and 95% CI

Fig. 4.

Forest plot of homozygous (TT vs. CC) model for overall cancer risk. The squares and horizontal lines correspond to the study specific OR and 95% CI

Fig. 5.

Forest plot of dominant (TT+TC vs. CC) model for overall cancer risk. The squares and horizontal lines correspond to the study specific OR and 95% CI

Fig. 6.

Forest plot of recessive (TT vs. TC+CC) model for overall cancer risk. The squares and horizontal lines correspond to the study specific OR and 95% CI

Sensitivity Analysis

Sensitivity analyses were performed by removing one individual study sequentially from the analysis at a time to evaluate the stability of the present meta-anlaysis. The results showed that no single study can significantly affect pooled OR before and after exclusion of the study (Figure not shown). Hence the result of the present meta-analysis is relatively stable.

Discussion

Currently, new biomarkers in genetics and molecular biology have gradually become important monitoring indicators in cancer prevention and early diagnosis and treatment. DNA repair mechanisms that are important factor in maintaining genomic stability of human genome is gradually being recognized as clues to individual’s susceptibility for cancer. It is therefore important to understand the mechanisms underlying the effects of genetic variants in DNA repair genes resulting in cancer susceptibility, which could be crucial to understand the molecular pathogenesis involved in cancer. From a clinical and scientific perspective, SNPs are potential biomarker for diagnostic and therapeutic in cancer.

BER is a versatile DNA repair system that eradicates a wide range of DNA lesions including endogenous DNA lesions as well as damages produced during episodes of inflammation and exposures to ionizing radiation or a variety of chemical carcinogen including UV-induced lesions [38]. Studies have reported a XRCC1 deletion mutation in null homozygous mice is embryonic lethal [39]. XRCC1 has two BRCA1 carboxyl-terminal (BRCT) domains (BRCT1 and BRCT2), located centrally and at the C-terminal end, respectively. BRCT2 is responsible for binding and stabilizing DNA ligase III and is required for single-strand breaks and gaps repair (SSBR), specifically during the G0/G1 phases of the cell cycle [40]. The centre of BRCT1 domain binds to and down-regulates the single-strand breaks and gaps recognition protein PARP1 and is required for efficient SSBR during both G1 and S/G2 phases of the cell cycle.

Based on the consideration of biological functions of XRCC1 exon 6 C>T. we undertook the present study to clarify the role played by the XRCC1 exon 6 C>T gene polymorphism in cancer among Indian population. Meta-analysis is a useful tool to summarizing inconsistent results from different studies because it increases sample size and statistical power and resolve discrepancy in genetic association studies [41, 42]. To our knowledge, this study is the first study which providing combined data on XRCC1 exon 6 C>T genetic variant and overall cancer risk of Indian population. The associations for the allele contrast, homozygous, heterozygous, dominant and recessive model were examined. After rigorous statistical analysis, we detected that individuals harboring variant allele of XRCC1 exon 6 C>T polymorphism might have an increased risk of cancer. It may biologically plausible that the T allele result in amino acid substitutions and henceforth may modify the wild-type protein function and alter cellular ability to repair endogenous and exogenous DNA damage, supporting to disease susceptibility. Studies reported that the SNPs in BER gene can change the individual repair capacity in response to DNA damage and modulate cancer risk [43]. Although, cancer is a complicated multi–genetic disease, a single genetic variant is usually inadequate to predict the risk of this deadly disease [44].

We have taken considerable effort and resources into testing possible association between XRCC1 exon 6 C>T gene polymorphisms and overall cancer risk, there are still some limitations in this meta-analysis. Heterogeneity is an important issue while interpreting the results of meta-analysis, although, it can be minimized by applying random-effects model [45]. In the present study we detected inter-study heterogeneity. The source of heterogeneity may arise possibly due to regional lifestyle varied among populations from different parts of India [46], another point could be recruitment of control subjects. Gene environment interaction and adjusted OR have not been performed due to the limited number of data.

The current meta-analysis suggested some key advantages in several aspects. (1) Funnel plot and Egger linear regression in the present meta-analyses indicated that the whole pooled result is unbiased. (2) Substantial number of cases and controls being pooled from different studies, significantly increased statistical power of the analysis. (3) the quality of case–control studies included in current meta-analysis was good and met our inclusion criterion. And lastly sensitivity analysis also showed that the results were robust and were not influenced by any single study.

In summary, the research of the association of XRCC1 exon 6 C>T gene polymorphism is very prevalent but conflicting simultaneously presently for Indian population. Our meta-analysis suggested that XRCC1 exon 6 C>T variant play a role in the overall cancer risk in Indian population. Further, public-health implications of our finding should be carefully considered. However, replicative studies with larger sample size are desirable to authenticate the risk associated with this variant allele of DNA repair as potential contributors to cancer risk.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.