https://orcid.org/0000-0002-0328-0023
Figures
Abstract
Background
The XRCC3 p.Thr241Met (rs861539) polymorphism has been extensively studied for its association with glioma risk, but results remain conflicting. Therefore, we performed a systematic review and meta-analysis to resolve this inconsistency.
Methods
Studies published up to June 10, 2022, were searched in PubMed, Web of Science, Scopus, VIP, Wanfang, and China National Knowledge Infrastructure databases and screened for eligibility. Then, the combined odds ratio (OR) of the included studies was estimated based on five genetic models, i.e., homozygous (Met/Met vs. Thr/Thr), heterozygous (Thr/Met vs. Thr/Thr), dominant (Thr/Met + Met/Met vs. Thr/Thr), recessive (Met/Met vs. Thr/Thr + Thr/Met) and allele (Met vs. Thr). The study protocol was preregistered at PROSPERO (registration number: CRD42021235704).
Results
Overall, our meta-analysis of 14 eligible studies involving 12,905 subjects showed that the p.Thr241Met polymorphism was significantly associated with increased glioma risk in both homozygous and recessive models (homozygous, OR = 1.381, 95% CI = 1.081–1.764, P = 0.010; recessive, OR = 1.305, 95% CI = 1.140–1.493, P<0.001). Subgroup analyses by ethnicity also revealed a statistically significant association under the two aforementioned genetic models, but only in the Asian population and not in Caucasians (P>0.05).
Citation: Tan SC, Low TY, Hussain HMJ, Sharzehan MAK, Sito H, Kord-Varkaneh H, et al. (2022) Association between XRCC3 p.Thr241Met polymorphism and risk of glioma: A systematic review and meta-analysis. PLoS ONE 17(10): e0276313. https://doi.org/10.1371/journal.pone.0276313
Editor: Yanhong Liu, Baylor College of Medicine, UNITED STATES
Received: October 16, 2021; Accepted: October 4, 2022; Published: October 20, 2022
Copyright: © 2022 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: Research in SCT’s laboratory is supported by the Research University Grant of Universiti Kebangsaan Malaysia (No. GUP-2020-076), the Fundamental Research Grant Scheme of the Ministry of Higher Education, Malaysia (No. FRGS/1/2019/SKK08/UKM/02/9), and the Higher Institution Center of Excellence (HICoE) grant of the Ministry of Higher Education, Malaysia (No. AKU49). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of this manuscript.
Competing interests: The authors declare no conflicts of interest.
Introduction
Gliomas refer to a type of tumor that originates from the glial cells located in the brain or spine. This type of cancer accounts for ~30% of tumors of the brain and central nervous system and ~80% of total cases of malignant brain tumors [1, 2]. The tumor has a five-year survival rate of approximately 60%. However, this rate can be improved to as high as 74% if adequate surveillance by biopsy is performed in conjunction with early resection for low-grade gliomas [3, 4]. Depending on their aggressiveness, gliomas can be classified into four different grades, namely grade I, II, III, and IV. While grade I and II gliomas (low-grade gliomas) are comparatively benign and grow slowly, grade III (malignant glioma) and IV (glioblastoma multiforme or glioblastoma) gliomas are very aggressive and grow rapidly. Notably, glioblastoma is the most common and deadly form of primary malignant glioma in adults. It accounts for ~70% of gliomas and has a median survival of only 12–14 months [2, 5].
Although gliomas are a heterogeneous disease with comparatively unclear etiology, risk factors for gliomas are thought to consist of genetic predisposition and environmental influences [2, 3]. Genetic disorders such as type 1 and type 2 neurofibromatosis and tuberous sclerosis complex [6], as well as obesity and body height [7], have been shown to predispose to gliomas. In addition, several genome-wide association studies (GWAS) have reported the involvement of multiple genetic polymorphisms in mediating glioma risk [8, 9]. On the other hand, environmental factors such as ionizing radiation, ultraviolet radiation, cytomegalovirus infection, environmental carcinogens, and diet have been associated with an increased risk of developing glioma, although only the first two risk factors have been clearly demonstrated [3, 10–13].
Among a variety of genetic factors, germline polymorphisms have been widely associated with cancer risk [14, 15]. In particular, polymorphisms of DNA repair genes such as ERCC1 [16], ERCC2 (XPD) [17], XRCC1 [18], XRCC3 [18], XRCC4 [19], XRCC5, XRCC6 [20], XRCC7 [21], MGMT [22], CHAF1 [23], LIG4 [19], and GLTSCR1 [17] have been associated with glioma risk [24, 25]. As genetic material, DNA is constantly exposed to endogenous and exogenous attacks that can manifest themselves in cellular metabolic processes and genotoxic or clastogenic stresses, including ionizing radiation and ultraviolet radiation mentioned earlier. These attacks can cause cross-links between DNA and proteins, oxidative damage to DNA, and single- and double-strand breaks in DNA chains [26]. However, this damage, which can affect the integrity of the genome, is continuously and effectively repaired by various inherent DNA repair pathways. These cellular DNA repair pathways can function in several ways and rely on the mechanisms of homologous recombination repair (HRR), nonhomologous end joining, base excision repair, and nucleotide excision repair. However, common polymorphisms in these DNA repair genes can impair DNA repair, leading to a higher risk of developing gliomas and other forms of malignancy [18–22].
This meta-analysis focuses on the XRCC3 (X-ray repair cross complementing 3) gene, which encodes a RecA/Rad51-related protein involved in HRR. Of the polymorphisms in XRCC3, only p.Thr241Met (rs861539) has been frequently studied with respect to its association with glioma risk. This polymorphism is located in exon 7 of the gene and involves a C-to-T transition at codon 241 [27]. Unlike other polymorphisms in XRCC3, the nonsynonymous p.Thr241Met polymorphism causes the replacement of a polar amino acid (threonine) with a nonpolar one (methionine), which can significantly affect the functionality of the protein product. For this reason, this polymorphism has been studied in numerous malignancies, including bladder cancer, head and neck cancer, ovarian cancer, and colorectal cancer, and significant associations have been demonstrated in some cases [28–32]. However, in gliomas, the association between this polymorphism and susceptibility to this cancer remains unclear [18, 21, 24, 33–44]. To clarify this, we performed a meta-analysis to further investigate the association between the XRCC3 p.Thr241Met polymorphism and glioma risk.
Materials and methods
Literature search
We comprehensively searched PubMed, Web of Science, Scopus, VIP, Wanfang, and China National Knowledge Infrastructure databases for relevant studies, including grey literature, published through June 10, 2022. The terms used in these searches were: (brain tumor OR glioma OR glioblastoma OR astrocytoma OR oligodendroglioma OR GBM OR glioblastoma multiforme) AND (XRCC3 OR X-Ray repair cross complementing 3 OR DNA repair gene) AND (polymorphism OR mutation OR variant OR variation). No language restriction was applied. We then included studies that met our prespecified inclusion criteria, i.e., (i) examined the association between the XRCC3 p.Thr241Met polymorphism and glioma risk, (ii) were observational studies, such as case-control studies, and (iii) reported genotype and/or allele frequencies or adequate information to infer these frequencies. We focused only on observational studies because other types of studies, such as randomized controlled trials, are not ethically acceptable in the context of genetic studies. In contrast, we excluded a study if (i) it was not an original study, (ii) the research was performed on cell lines, animal models, or other non-human subjects, and (iii) the study was a duplication of other publications. We also pre-registered our study protocol with PROSPERO (registration number: CRD42021235704).
Extraction of data and quality appraisal
Two investigators independently extracted the following information from the eligible studies: (i) first author’s name, (ii) publication year, (iii) ethnicity, (iv) source of controls (hospital-based vs. population-based), (v) country where the study was conducted, (vi) number of subjects per group, (vii) frequency of polymorphic genotypes and alleles, and (viii) Hardy-Weinberg equilibrium (HWE) of genotype distribution in controls. If data on HWE were missing in any of the studies, a goodness-of-fit test was performed. If data were missing, the corresponding author of the included studies was contacted by email. The quality of the studies was then assessed using a modified version of the Newcastle-Ottawa Scale [45], which assessed (i) whether the definition of cases was appropriate, (ii) whether cases were representative of populations, (iii) whether controls were recruited from the community and the genotype distribution conformed to HWE, (iv) whether controls were defined as having no history of disease, (v) whether cases and controls were comparable in terms of ethnic homogeneity, (vi) whether there was no evidence of population stratification, (vii) whether quality control procedures and blinding were used in genotyping, (viii) whether cases and controls were genotyped using the same method, and (ix) whether the genotyping call rate exceeded 99% (S1 File). If the answer to each of the above criteria was "yes", the study received one star for each criterion. A study had to receive at least 5 stars to be considered of high methodological quality.
Meta-analysis
STATA (ver. 16.0) was used to pool data from included studies. P < 0.05 was considered statistically significant, unless otherwise stated. The association between XRCC3 p.Thr241Met polymorphism and glioma risk was assessed using five genetic models: homozygous (Met/Met vs. Thr/Thr), heterozygous (Thr/Met vs. Thr/Thr), dominant (Thr/Met + Met/Met vs. Thr/Thr), recessive (Met/Met vs. Thr/Thr + Thr/Met), and allele (Met vs. Thr). Examination of multiple genetic models was required because genetic association studies do not adopt a specific model [46]. Subsequently, Cochran’s Q and I² tests were used to measure the heterogeneity of the studies, which was considered high when P<0.1 or I² >50% [47]. In this case, a random-effects method was used to derive the odds ratio (OR). On the other hand, when heterogeneity was low, a fixed-effects method was used. The Z test was then used to measure the significance of the pooled OR, and forest plots were created to visually display the results. Subgroup analyses were then conducted by several variables, including ethnicity, source of control, and HWE status, as these variables have long been known to affect genetic associations [48–50]. To examine whether the results were primarily influenced by one of the studies, we also performed a sensitivity analysis using the leave-one-out method. Publication bias was assessed using Begg’s test, and Egger’s test, and visual inspection of funnel plots.
Results
Study selection
We first identified 2,052 entries from the search databases (PubMed, N = 1,127; Scopus, N = 46; WoS, N = 838; CNKI, N = 9; Wanfang, N = 19; VIP, N = 13). We then removed 488 duplicate entries and subjected the remaining 1,564 articles to title and abstract screening. This process identified only 27 articles as potentially relevant. We then screened these 27 articles for eligibility by reviewing the full texts. This resulted in a further exclusion of 13 articles, so that finally only 14 articles describing 14 relevant studies were considered for quantitative data synthesis. The flow diagram of the study selection is shown in Fig 1.
Characteristics of the studies
The final 14 eligible studies included a total of 12,905 study participants, including 5,852 cases and 7,053 controls (Table 1). Eight (8) of these 14 studies were conducted in China, with subjects from the Asian population [28, 33, 37, 39, 40, 42–44]; the other seven studies were predominantly Caucasian–with three studies from the United States [21, 24, 36] and one each from Spain [41] and Brazil [34], and one study from multiple countries [18]. In addition, 11 studies used hospital-based controls [21, 24, 33, 36, 37, 39–44], whereas the remaining 3 studies opted for population-based controls [18, 34, 36]. In addition, all studies except Liu et al. [36] reported the frequencies of the three genotypes (Thr/Thr, Thr/Met, and Met/Met) separately. Instead, Liu et al. 2009 [36] combined the homozygous wild type (Thr/Thr) and heterozygous genotypes (Thr/Met). Moreover, of all 14 studies, the distribution of control genotypes did not deviate significantly from HWE in only five studies (P>0.05) [21, 33, 37, 39, 41]. Nevertheless, all 14 studies were of high quality (S1 Table).
Quantitative data synthesis
The combined results of the 14 studies are shown in Table 2, and the corresponding forest plots are shown in Fig 2. High statistical heterogeneity was detected under in all genetic models; therefore, a random-effects model was used in the data synthesis. We detected a statistically significant association between the XRCC3 p.Thr241Met polymorphism and increased glioma risk when the homozygous model and the recessive model were applied (homozygous, OR = 1.381, 95% CI = 1.081–1.764, P = 0.010; recessive, OR = 1.305, 95% CI = 1.140–1.493, P<0.001). However, no significant association was found in the heterozygous (OR = 1.040, 95% CI = 0.908–1.191, P = 0.574), dominant (OR = 1.082, 95% CI = 0.939–1.247, P = 0.277), and allele (OR = 1.097, 95% CI = 0.972–1.237, P = 0.133) models. Sensitivity analysis revealed that the pooled OR was not significantly affected by any of the studies (S1 Fig).
Subgroup analysis
In the ethnicity-based subgroup analysis, we again found a statistically significant association between XRCC3 p.Thr241Met and increased glioma risk in the homozygous (OR = 1.704, 95% CI = 1.358–2.317, P<0.001) and recessive models (OR = 1.305, 95% CI = 1.140–1.493, P<0.001) in the Asians (Table 2). In contrast, no significant association was observed in Caucasians in all genetic models (P>0.05). Subgroup analysis by source of controls revealed that the association in hospital-based controls was significant only in the homozygous model (OR = 1.364, 95% CI = 1.015–1.832, P = 0.040), whereas in population-based controls, the polymorphism was associated with increased glioma risk only in the recessive model (OR = 1.338, 95% CI = 1.071–1.670, P = 0.010). Interestingly, subgroup analysis by HWE status revealed no significant association for studies whose genotype distribution conformed to the equilibrium. For studies in which the genotype distribution deviated significantly from HWE, a significant association was found in the homozygous (OR = 1.588, 95% CI = 1.200–2.102, P = 0.001), dominant (OR = 1.124, 95% CI = 1.027–1.229, P = 0.011), recessive (OR = 1.518, 95% CI = 1.164–1.981, P = 0.002), and allele (OR = 1.185, 95% CI = 1.053–1.333, P = 0.005) models.
Publication bias
We did not detect any noticeable asymmetry in any of the funnel plots (Fig 3), indicating that there was no publication bias. This finding was also supported by the results of Begg’s and Egger’s tests (homozygous, Begg’s P = 1.000, Egger’s P = 0.811; heterozygous, Begg’s P = 0.222, Egger’s P = 0.185; dominant, Begg’s P = 0.542, Egger’s P = 0.340; recessive, Begg’s P = 0.784, Egger’s P = 0.857; allele, Begg’s P = 0.542, Egger’s P = 0.643).
Discussion
The XRCC3 protein belongs to the RecA/Rad51-related family of proteins involved in HRR. This protein is encoded by a ~18kb gene, XRCC3, located on chromosome 14q32.33 [51]. The gene product is a ~38kDa protein that belongs to the five paralogs of RAD51, i.e., XRCC2, XRCC3, RAD51B, RAD51C, and RAD51D, all of which share 20–30% structural similarity with RAD51 [52]. These paralogs typically function by forming complex assemblies that can facilitate RAD51-mediated activities to repair DNA double-strand breaks via HRR. Therefore, depletion of any of these paralogs often leads to a reduction in the DNA damage response [53]. Two important protein complexes formed by these paralogs are (i) the BCDX2 complex, which includes XRCC2, RAD51B, RAD51C, and RAD51D, and (ii) the CX3 complex, which includes XRCC3 as well as RAD51C [54, 55]. These two complexes have been reported to be involved in two distinct steps of HRR: the BCDX2 complex contributes to the stabilization of the nucleoprotein filament of RAD51, whereas the CX3 complex acts after the recruitment of RAD51 to the damage sites [53]. Although the exact biochemical mechanisms of the CX3 complex have remained unclear, the human CX3 complex has been shown to promote RAD51 nucleofilament remodeling and stability, as well as strand invasion, whereas its other proposed role is to help recruit specialized factors to catalyze fork restoration through branch migration or controlled fork processing [56].
Reduced levels of XRCC3 protein have been shown to lead to increased rates of chromosome segregation errors, aneuploidy, and other chromosome aberrations [57]. Notably, cells deficient in XRCC3 have been shown to have lower HRR and hypersensitivity to cross-linking agents, genotoxic alkylating agents, UV radiation, and ionizing radiation [58, 59]. Similarly, common polymorphisms in this gene have been shown to lead to a derailed DNA damage response that may increase the likelihood of tumor development [60]. It is also known that XRCC3 polymorphisms play an important role in the treatment of gliomas. In current practice, temozolomide is the chemotherapeutic agent of choice for high-grade gliomas [61]. Recently, the XRCC3 polymorphism was found to contribute to temozolomide resistance in glioblastoma cells by mediating the repair of DNA double-strand breaks [62]. However, this may not be the whole story about the contribution of XRCC3 to temozolomide resistance. XRCC3 has also been found to contribute to mitochondrial biogenesis by promoting mitochondrial DNA integrity [63]. Mitochondria are known to enhance temozolomide chemoresistance, suggesting a role for XRCC3 in the treatment of gliomas [64, 65].
Given the important role of XRCC3 in HRR, its abundance and 3-D conformation require well-tuned regulation. As both protein structure and expression can be affected by genetic polymorphisms, a number of GWAS have investigated the association between XRCC3 polymorphisms and risk for cancers, including gliomas [33, 37, 39, 66–68]. Among the polymorphisms in XRCC3, we focused on p.Thr241Met, an exonic polymorphism involving a C-to-T transition at codon 241 in exon 7 [27]. This transition replaces threonine (Thr) with methionine (Met) and may affect XRCC3 functions, activities, and interactions [69]. We chose to perform a meta-analysis of this polymorphism because p.Thr241Met has been extensively studied with respect to its association with gliomas, with often conflicting results. For example, whereas Custódio et al. [34] found that the Met allele of p.Thr241Met was significantly associated with a 3-fold increase in glioma risk, Huang et al. [39] found that the same allele was associated with a 1.6-fold decrease in glioma risk. On the other hand, Rodriguez-Hernandez et al. [41] showed that there was no significant association between the polymorphism and glioma risk. These inconsistencies could be due to numerous factors, such as the ethnicity of the study population, and the sample size (and thus statistical power) of the studies [70, 71]. A well-conducted meta-analysis is essential to address these inconsistencies. However, the most recent meta-analyses on this topic were conducted many years ago [72–77] and did not include several eligible studies. The lack of meticulous data extraction in meta-analysis can lead to misleading results [78]. Therefore, in this work, we meticulously performed a systematic review and meta-analysis to investigate the association between the XRCC3 p.Thr241Met polymorphism and glioma risk.
Our results showed that the p.Thr241Met polymorphism was associated with increased glioma risk only in the homozygous and recessive models, but not in the heterozygous, dominant, and allele models. Within each model, we additionally clustered our analysis by different subgroups, i.e., ethnicity, source of controls, and HWE status. Our analysis in the Asian subgroup again showed that only the homozygous and recessive models were associated with increased glioma risk, whereas in the Caucasian subgroup, no significance was found in any of the genetic models. In addition, studies using hospital- and population-based controls appeared to result in different risk profiles, and studies in which the genotypic distribution of controls deviated from HWE showed more significant associations. It should be noted that the number of participants in each subgroup was much smaller than in the overall analysis. Therefore, there is a possibility that the subgroup analyses were not sufficiently powered to detect possible associations [79].
The greatest strength of our meta-analysis lies in the extensive collection of genetic association studies on the XRCC3 p.Thr241Met polymorphism performed on a large population. Moreover, the selected parameter studied, i.e., the p.Thr241Met polymorphism, is a well-established genetic variation that can be accurately genotyped using contemporary genotyping technologies. Therefore, the genotyping results obtained in different studies are reliable and well comparable with each other. Nevertheless, there are some limitations to this study as well. First, the influence of gene-gene and gene-environment interactions was not investigated because information on this aspect was lacking in the included studies. In addition, the study population, especially that of Asian populations, was mainly from China and therefore not very diverse. Furthermore, our meta-analysis examined only one polymorphism, whereas analysis of multiple genetic polymorphisms in the same gene might provide a more complete picture of the role of XRCC3 in mediating glioma risk.
Conclusions
In conclusion, our results show that there is a significant association between the XRCC3 p.Thr241Met polymorphism and increased glioma risk, but only when the homozygous and recessive models were adopted. When divided into subgroups by ethnicity, similar results were observed only in the Asian population. In addition, no obvious trend was observed when data were stratified by source of controls and HWE status, presumably because of low study power. Therefore, further genetic association studies are needed to provide a more accurate assessment of the association between the polymorphism and glioma risk.
Supporting information
S1 Table. Quality assessment of the included studies.
https://doi.org/10.1371/journal.pone.0276313.s001
(DOCX)
S1 Fig. Sensitivity analysis of the meta-analysis.
https://doi.org/10.1371/journal.pone.0276313.s002
(ZIP)
S1 File. Modified Newcastle-Ottawa scale for meta-analysis of genetic association studies.
https://doi.org/10.1371/journal.pone.0276313.s003
(DOCX)
Acknowledgments
The authors thank Academic Proofreading (https://www.academicproofreading.uk) for English language editing.
References
- 1. Goodenberger ML, Jenkins RB. Genetics of adult glioma. Cancer Genet. 2012;205: 613–621. pmid:23238284
- 2. Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med. 2008;359: 492–507. pmid:18669428
- 3. Ohgaki H, Kleihues P. Epidemiology and etiology of gliomas. Acta Neuropathol. 2005;109: 93–108. pmid:15685439
- 4. Jakola AS, Myrmel KS, Kloster R, Torp SH, Lindal S, Unsgård G, et al. Comparison of a strategy favoring early surgical resection vs a strategy favoring watchful waiting in low-grade gliomas. JAMA. 2012;308: 1881–1888. pmid:23099483
- 5. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJ, Janzer RC, et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 2009;10: 459–466. pmid:19269895
- 6. Reuss D, von Deimling A. Hereditary tumor syndromes and gliomas. Recent Results Cancer Res. 2009;171: 83–102. pmid:19322539
- 7. Moore SC, Rajaraman P, Dubrow R, Darefsky AS, Koebnick C, Hollenbeck A, et al. Height, body mass index, and physical activity in relation to glioma risk. Cancer Res. 2009;69: 8349–8355. pmid:19808953
- 8. Shete S, Hosking FJ, Robertson LB, Dobbins SE, Sanson M, Malmer B, et al. Genome-wide association study identifies five susceptibility loci for glioma. Nat Genet. 2009;41: 899–904. pmid:19578367
- 9. Wrensch M, Jenkins RB, Chang JS, Yeh RF, Xiao Y, Decker PA, et al. Variants in the CDKN2B and RTEL1 regions are associated with high-grade glioma susceptibility. Nat Genet. 2009;41: 905–908. pmid:19578366
- 10. Dziurzynski K, Chang SM, Heimberger AB, Kalejta RF, McGregor Dallas SR, Smit M, et al. Consensus on the role of human cytomegalovirus in glioblastoma. Neuro Oncol. 2012;14: 246–255. pmid:22319219
- 11. Strong MJ, Blanchard E 4th, Lin Z, Morris CA, Baddoo M, Taylor CM, et al. A comprehensive next generation sequencing-based virome assessment in brain tissue suggests no major virus—tumor association. Acta Neuropathol Commun. 2016;4: 71. pmid:27402152
- 12. Sadetzki S, Chetrit A, Freedman L, Stovall M, Modan B, Novikov I. Long-term follow-up for brain tumor development after childhood exposure to ionizing radiation for tinea capitis. Radiat Res. 2005;163: 424–432. pmid:15799699
- 13. Davis F, Il’yasova D, Rankin K, McCarthy B, Bigner DD. Medical diagnostic radiation exposures and risk of gliomas. Radiat Res. 2011;175: 790–796. pmid:21466382
- 14. Tan SC, Low TY, Mohamad Hanif EA, Sharzehan MAK, Kord-Varkaneh H, Islam MA. The rs9340799 polymorphism of the estrogen receptor alpha (ESR1) gene and its association with breast cancer susceptibility. Sci Rep. 2021;11: 18619. pmid:34545128
- 15. Najafi S, Tan SC, Raee P, Rahmati Y, Asemani Y, Lee EHC, et al. Gene regulation by antisense transcription: A focus on neurological and cancer diseases. Biomed Pharmacother. 2022;145: 112265. pmid:34749054
- 16. Wrensch M, Kelsey KT, Liu M, Miike R, Moghadassi M, Sison JD, et al. ERCC1 and ERCC2 polymorphisms and adult glioma. Neuro Oncol. 2005;7: 495–507. pmid:16212814
- 17. Yang P, Kollmeyer TM, Buckner K, Bamlet W, Ballman KV, Jenkins RB. Polymorphisms in GLTSCR1 and ERCC2 are associated with the development of oligodendrogliomas. Cancer. 2005;103: 2363–2372. pmid:15834925
- 18. Kiuru A, Lindholm C, Heinavaara S, Ilus T, Jokinen P, Haapasalo H, et al. XRCC1 and XRCC3 variants and risk of glioma and meningioma. J Neurooncol. 2008;88: 135–142. pmid:18330515
- 19. Liu Y, Zhou K, Zhang H, Shugart YY, Chen L, Xu Z, et al. Polymorphisms of LIG4 and XRCC4 involved in the NHEJ pathway interact to modify risk of glioma. Hum Mutat. 2008;29: 381–389. pmid:18165945
- 20. Liu Y, Zhang H, Zhou K, Chen L, Xu Z, Zhong Y, et al. Tagging SNPs in non-homologous end-joining pathway genes and risk of glioma. Carcinogenesis. 2007;28: 1906–1913. pmid:17389609
- 21. Wang LE, Bondy ML, Shen H, El-Zein R, Aldape K, Cao Y, et al. Polymorphisms of DNA repair genes and risk of glioma. Cancer Res. 2004/08/18. 2004;64: 5560–5563. pmid:15313891
- 22. Felini MJ, Olshan AF, Schroeder JC, North KE, Carozza SE, Kelsey KT, et al. DNA repair polymorphisms XRCC1 and MGMT and risk of adult gliomas. Neuroepidemiology. 2007;29: 55–58. pmid:17898525
- 23. Bethke L, Webb E, Murray A, Schoemaker M, Johansen C, Christensen HC, et al. Comprehensive analysis of the role of DNA repair gene polymorphisms on risk of glioma. Hum Mol Genet. 2008;17: 800–805. pmid:18048407
- 24. Rajaraman P, Hutchinson A, Wichner S, Black PM, Fine HA, Loeffler JS, et al. DNA repair gene polymorphisms and risk of adult meningioma, glioma, and acoustic neuroma. Neuro Oncol. 2010;12: 37–48. pmid:20150366
- 25. Adel Fahmideh M, Schwartzbaum J, Frumento P, Feychting M. Association between DNA repair gene polymorphisms and risk of glioma: a systematic review and meta-analysis. Neuro Oncol. 2014;16: 807–814. pmid:24500421
- 26. Vogelstein B, Kinzler KW. Cancer genes and the pathways they control. Nat Med. 2004;10: 789–799. pmid:15286780
- 27. Shen MR, Jones IM, Mohrenweiser2 H. Nonconservative Amino Acid Substitution Variants Exist at Polymorphic Frequency in DNA Repair Genes in Healthy Humans1. 1998.
- 28. Yin QH, Liu C, Li L, Zu XY, Wang YJ. Association between the XRCC3 T241M polymorphism and head and neck cancer susceptibility: a meta-analysis of case-control studies. Asian Pac J Cancer Prev. 2012;13: 5201–5205. pmid:23244135
- 29. Zhao Y, Deng X, Wang Z, Wang Q, Liu Y. Genetic polymorphisms of DNA repair genes XRCC1 and XRCC3 and risk of colorectal cancer in Chinese population. Asian Pac J Cancer Prev. 2012;13: 665–669. pmid:22524842
- 30. Cheng CX, Xue M, Li K, Li WS. Predictive value of XRCC1 and XRCC3 gene polymorphisms for risk of ovarian cancer death after chemotherapy. Asian Pac J Cancer Prev. 2012;13: 2541–2545. pmid:22938418
- 31. Zhu X, Zhong Z, Zhang X, Zhao X, Xu R, Ren W, et al. DNA repair gene XRCC3 T241M polymorphism and bladder cancer risk in a Chinese population. Genet Test Mol Biomarkers. 2012;16: 640–643. pmid:22299591
- 32. Tan SC. Low penetrance genetic polymorphisms as potential biomarkers for colorectal cancer predisposition. J Gene Med. 2018;20: e3010. pmid:29424105
- 33. Zhao P, Zou P, Zhao L, Yan W, Kang C, Jiang T, et al. Genetic polymorphisms of DNA double-strand break repair pathway genes and glioma susceptibility. BMC Cancer. 2013/05/15. 2013;13: 234. pmid:23663450
- 34. Custódio AC, Almeida LO, Pinto GR, Santos MJ, Almeida JRW, Clara CA, et al. Variation in DNA repair gene XRCC3 affects susceptibility to astrocytomas and glioblastomas. Genet Mol Res. 2012;11: 332–339. pmid:22370935
- 35. Liu HB, Peng YP, Dou CW, Su XL, Gao NK, Tian FM, et al. Comprehensive study on associations between nine SNPs and glioma risk. Asian Pac J Cancer Prev. 2012/12/19. 2012;13: 4905–4908. pmid:23244079
- 36. Liu Y, Scheurer ME, El-Zein R, Cao Y, Do KA, Gilbert M, et al. Association and interactions between DNA repair gene polymorphisms and adult glioma. Cancer Epidemiol Biomarkers Prev. 2009/01/07. 2009;18: 204–214. pmid:19124499
- 37. Zhou K, Liu Y, Zhang H, Liu H, Fan W, Zhong Y, et al. XRCC3 haplotypes and risk of gliomas in a Chinese population: A hospital-based case-control study. Int J Cancer. 2009;124: 2948–2953. pmid:19330829
- 38. Yang P, Liu H, Lin J, Yang H. The Association of rs4753426 Polymorphism in the Melatonin Receptor 1B (MTNR1B) Gene and Susceptibility to Adolescent Idiopathic Scoliosis: A Systematic Review and Meta-analysis. Pain Physician. 2015/10/03. 2015;18: 419–431. pmid:26431121
- 39. Huang JY, Yang JF, Qu Q, Qu J, Liu F, Liu FE, et al. DNA repair gene XRCC3 variants are associated with susceptibility to glioma in a Chinese population. Genet Mol Res. 2015;14: 10569–10575. pmid:26400288
- 40. Gao K, Mu SQ, Wu ZX. Investigation of the effects of single-nucleotide polymorphisms in DNA repair genes on the risk of glioma. Genet Mol Res. 2014;13: 1203–1211. pmid:24634177
- 41. Rodriguez-Hernandez I, Perdomo S, Santos-Briz A, Garcia JL, Gomez-Moreta JA, Cruz JJ, et al. Analysis of DNA repair gene polymorphisms in glioblastoma. Gene. 2014;536: 79–83. pmid:24325908
- 42. Xu G, Wang M, Xie W, Bai X. DNA repair gene XRCC3 Thr241Met polymorphism and susceptibility to glioma: A case-control study. Oncol Lett. 2014/07/12. 2014;8: 864–868. pmid:25013509
- 43. Luo KQ, Mu SQ, Wu ZX, Shi YN, Peng JC. Polymorphisms in DNA repair genes and risk of glioma and meningioma. Asian Pac J Cancer Prev. 2013/03/29. 2013;14: 449–452. pmid:23534771
- 44. Pan WR, Li G, Guan JH. Polymorphisms in DNA repair genes and susceptibility to glioma in a chinese population. Int J Mol Sci. 2013/02/07. 2013;14: 3314–3324. pmid:23385236
- 45. Tan SC, Ismail MP, Duski DR, Othman NH, Ankathil R. FAS c.-671A>G polymorphism and cervical cancer risk: a case–control study and meta-analysis. Cancer Genet. 2017;211. pmid:28279307
- 46. Lee YH. Meta-analysis of genetic association studies. Ann Lab Med. 2015;35: 283–287. pmid:25932435
- 47.
Deeks JJ, Higgins JPT, Altman DG, on behalf of the Cochrane Statistical Methods Group. Analysing data and undertaking meta-analyses. Cochrane Handbook for Systematic Reviews of Interventions. John Wiley & Sons, Ltd; 2019. pp. 241–284. https://doi.org/10.1002/9781119536604.ch10
- 48. Hattersley AT, McCarthy MI. What makes a good genetic association study? Lancet (London, England). 2005;366: 1315–1323. pmid:16214603
- 49. Salanti G, Sanderson S, Higgins JPT. Obstacles and opportunities in meta-analysis of genetic association studies. Genet Med Off J Am Coll Med Genet. 2005;7: 13–20. pmid:15654223
- 50. Trikalinos TA, Salanti G, Khoury MJ, Ioannidis JPA. Impact of violations and deviations in Hardy-Weinberg equilibrium on postulated gene-disease associations. Am J Epidemiol. 2006;163: 300–309. pmid:16410351
- 51. Tebbs RS, Zhao Y, Tucker JD, Scheerer JB, Siciliano MJ, Hwang M, et al. Correction of chromosomal instability and sensitivity to diverse mutagens by a cloned cDNA of the XRCC3 DNA repair gene. Proc Natl Acad Sci U S A. 1995;92: 6354–6358. pmid:7603995
- 52. Sullivan MR, Bernstein KA. RAD-ical new insights into RAD51 regulation. Genes (Basel). 2018;9. pmid:30551670
- 53. Chun J, Buechelmaier ES, Powell SN. Rad51 paralog complexes BCDX2 and CX3 act at different stages in the BRCA1-BRCA2-dependent homologous recombination pathway. Mol Cell Biol. 2013;33: 387–395. pmid:23149936
- 54. Masson JY, Stasiak AZ, Stasiak A, Benson FE, West SC. Complex formation by the human RAD51C and XRCC3 recombination repair proteins. Proc Natl Acad Sci U S A. 2001;98: 8440–8446. pmid:11459987
- 55. Masson JY, Tarsounas MC, Stasiak AZ, Stasiak A, Shah R, McIlwraith MJ, et al. Identification and purification of two distinct complexes containing the five RAD51 paralogs. Genes Dev. 2001;15: 3296–3307. pmid:11751635
- 56. Berti M, Teloni F, Mijic S, Ursich S, Fuchs J, Palumbieri MD, et al. Sequential role of RAD51 paralog complexes in replication fork remodeling and restart. Nat Commun. 2020;11: 3531. pmid:32669601
- 57. Thacker J. The RAD51 gene family, genetic instability and cancer. Cancer Lett. 2005;219: 125–135. pmid:15723711
- 58. Caldecott K, Jeggo P. Cross-sensitivity of gamma-ray-sensitive hamster mutants to cross-linking agents. Mutat Res. 1991;255: 111–121. pmid:1922147
- 59. Pierce AJ, Johnson RD, Thompson LH, Jasin M. XRCC3 promotes homology-directed repair of DNA damage in mammalian cells. Genes Dev. 1999;13: 2633–2638. pmid:10541549
- 60. Jiao L, Hassan MM, Bondy ML, Wolff RA, Evans DB, Abbruzzese JL, et al. XRCC2 and XRCC3 gene polymorphism and risk of pancreatic cancer. Am J Gastroenterol. 2008;103: 360–367. pmid:17986315
- 61. Attarian F, Taghizadeh-Hesary F, Fanipakdel A, Javadinia SA, Porouhan P, PeyroShabany B, et al. A Systematic Review and Meta-Analysis on the Number of Adjuvant Temozolomide Cycles in Newly Diagnosed Glioblastoma. Frontiers in oncology. 2021. p. 779491. pmid:34900732
- 62. Roos WP, Frohnapfel L, Quiros S, Ringel F, Kaina B. XRCC3 contributes to temozolomide resistance of glioblastoma cells by promoting DNA double-strand break repair. Cancer Lett. 2018;424: 119–126. pmid:29574277
- 63. Mishra A, Saxena S, Kaushal A, Nagaraju G. RAD51C/XRCC3 Facilitates Mitochondrial DNA Replication and Maintains Integrity of the Mitochondrial Genome. Mol Cell Biol. 2018;38. pmid:29158291
- 64. Taghizadeh-Hesary F, Akbari H, Bahadori M. Anti-mitochondrial Therapy: A Potential Therapeutic Approach in Oncology. Preprints. 2022. p. 2022010171.
- 65. Oliva CR, Nozell SE, Diers A, McClugage SG 3rd, Sarkaria JN, Markert JM, et al. Acquisition of temozolomide chemoresistance in gliomas leads to remodeling of mitochondrial electron transport chain. J Biol Chem. 2010;285: 39759–39767. pmid:20870728
- 66. Han S, Zhang H-T, Wang Z, Xie Y, Tang R, Mao Y, et al. DNA repair gene XRCC3 polymorphisms and cancer risk: a meta-analysis of 48 case–control studies. Eur J Hum Genet 2006 1410. 2006;14: 1136–1144. pmid:16791138
- 67. Ali AM, AbdulKareem H, Anazi M Al, Parine NR, Shaik JP, Alamri A, et al. Polymorphisms in DNA Repair Gene XRCC3 and Susceptibility to Breast Cancer in Saudi Females. Biomed Res Int. 2016;2016. pmid:26881229
- 68. Tan SC, Ankathil R. Genetic susceptibility to cervical cancer: role of common polymorphisms in apoptosis-related genes. Tumor Biol. 2015;36. pmid:26242271
- 69. Williams K, Sobol RW. Mutation research/fundamental and molecular mechanisms of mutagenesis: special issue: DNA repair and genetic instability. Mutat Res. 2013;743–744: 1–3. pmid:23688353
- 70. Tan SC, Lim PY, Fang J, Mokhtar MFM, Hanif EAM, Jamal R. Association between MIR499A rs3746444 polymorphism and breast cancer susceptibility: a meta-analysis. Sci Rep. 2020;10: 1–10. pmid:32103099
- 71. Huang T, Shu Y, Cai Y-D. Genetic differences among ethnic groups. BMC Genomics. 2015;16: 1093. pmid:26690364
- 72. Liang H-J, Yan Y-L, Liu Z-M, Chen X, Peng Q-L, Wang J, et al. Association of XRCC3 Thr241Met polymorphisms and gliomas risk: evidence from a meta-analysis. Asian Pac J Cancer Prev. 2013;14: 4243–4247. pmid:23991984
- 73. Zhao B, Ye J, Li B, Ma Q, Su G, Han R. DNA repair gene XRCC3 Thr241Met polymorphism and glioma risk: a meta-analysis. Int J Clin Exp Med. 2013;6: 438–443. pmid:23844267
- 74. Lin J, Kou Y. Association between the Thr241Met polymorphism of X-ray repair cross-complementing group 3 gene and glioma risk: evidence from a meta-analysis based on 4,136 cases and 5,233 controls. Tumour Biol J Int Soc Oncodevelopmental Biol Med. 2014;35: 425–432. pmid:23918307
- 75. Jiang J, Quan X-F, Zhang L, Wang Y-C. The XRCC3 Thr241Met polymorphism influences glioma risk—a meta-analysis. Asian Pac J Cancer Prev. 2013;14: 3169–3173. pmid:23803098
- 76. Feng Y, Zeng M, Xu Q. Association between XRCC3 T241M polymorphism and glioma risk: A meta-analysis. Tumor Biol. 2014;35: 5589–5592. pmid:24633885
- 77. Qi L, Yu HH-Q, Zhang Y, Ding L, Zhao D-HD, Lv P, et al. A Comprehensive Meta-analysis of Genetic Associations Between Key Polymorphic Loci in DNA Repair Genes and Glioma Risk. Mol Neurobiol. 2017;54: 1314–1325. pmid:26843108
- 78. Low TY, Chin K-Y, Tan SC. On the critical importance of meticulous data extraction for meta-analysis of genetic association study. Arch Physiol Biochem. 2021; 1–2. pmid:33689514
- 79. Button KS, Ioannidis JPA, Mokrysz C, Nosek BA, Flint J, Robinson ESJ, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14: 365–376. pmid:23571845