Abstract
Background
Thymidylate synthase (TYMS) polymorphisms are reported to be related to susceptibility to some cancers. However, no study exists on TYMS polymorphisms and glioma risk. This study aimed to evaluate the relationship between two common TYMS gene variants (rs1059394 C>T, rs2847153 G>A) and glioma susceptibility.
Methods
This case-control study included 605 patients and 1300 cancer-free individuals. Genotyping was performed using Sequenom Mass-ARRAY. We determined odds ratios (ORs) and their 95% confidence intervals (CIs) to estimate the correlations.
Results
The analysis revealed that rs1059394 TT and CT+TT genotype had significantly low glioma risk (TT to CC: OR = 0.71, 95% CI = 0.52–0.97, P = 0.03; CT+TT to CC: OR = 0.74, 95% CI = 0.55–0.99, P = 0.04). However, no significant difference was found between rs2847153 and glioma risk in any genetic model (P﹥0.05). In high-grade gliomas, the GA and GA+AA genotypes of rs2847153 made the majority of genotypes, compared with GG genotype (GA to GG: OR = 2.01, 95% CI = 1.39–2.91, P < 0.001; GA+AA to GG: OR = 1.78, 95% CI =1.25–2.54, P < 0.001). Moreover, online expression quantitative trait locus (eQTL) analysis indicated that these two polymorphisms may alter TYMS gene expression in transformed fibroblast cells.
Conclusion
Our study provides evidence of the effect of TYMS rs1059394 on the susceptibility of glioma. In high-grade gliomas, compared with GG genotype, the GA and GA+AA genotypes of rs2847153 comprise a larger proportion.
Keywords: TYMS, glioma, gene variant, susceptibility, case-control study
Introduction
Glioma was the most common type of brain cancer, accounting for almost 80% of brain malignancies.1 Gliomas were divided into grades I to IV, based on the World Health Organization (WHO) classification scheme.2 The 5-year survival rate for glioblastoma patients, accounting for 45% of all gliomas, was just 5–6%.3,4 Various risk factors were considered to be associated with gliomas, such as exposing to high doses of ionizing radiation, allergies or atopic disease, and hereditary genetic disorders (family history).5,6 Similar to other tumors, hereditary factors seem to be an important factor in the occurrence of glioma. It was reported that single-nucleotide polymorphisms (SNPs) were the most frequent single-nucleotide variations that occur in a specific position. Numerous SNPs, such as those in XRCC1/4, ERCC1/4, MGMT, PARP1, and MTHFR have been demonstrated to contribute to glioma susceptibility.1,7
The thymidylate synthase (TYMS) gene is located at human chromosome band 18p11.32. TYMS is essential for de novo biosynthesis of thymidylate (TMP), cell proliferation and survival.8 Inhibition of TYMS expression leads to thymidylate depletion and thymineless death, accompanied by DNA damage, apoptosis, and chromosome aberrations.9 Currently, several TYMS SNPs have been reported to be correlated with susceptibility to cancers including breast, lung, gastric, colorectal, and ovarian cancers.10–14
A previous study presented that TYMS expressed positively in 27.39% of lymph node of low-grade glioma patients.15 However, no studies illuminated the association between TYMS gene polymorphism and the glioma risk. Therefore, this case-control study aimed to clarify the correlation between two common TYMS gene variants (rs1059394 C>T, rs2847153 G>A) and glioma susceptibility.
Materials And Methods
Study Population
The protocol of this study was approved by the Ethics Committee of the Second Affiliated Hospital of Xi’an Jiaotong University Shaanxi Province (Xi’an, China). All patients gave written informed consent prior to participation in the study. This study was conducted in accordance with the Declaration of Helsinki.
This study consisted of 605 patients with gliomas (mean age: 40.71±18.28 years) who underwent surgical resection; they were consecutively recruited between September 2010 and May 2014 at Tangdu Hospital, which is affiliated with the Fourth Military Medical University in China. Eligible patients were diagnosed with glioma based on imaging and pathology, and were untreated with chemotherapy or radiotherapy before surgery. Healthy controls included 1,300 age- and sex-matched healthy individuals (mean age: 41.68±13.54 years) who underwent a checkup at the same hospital during the same period of time. Basic characteristics of patients and controls were collected, including ethnicity, age, sex, WHO grade, extent of resection, radiotherapy, and chemotherapy strategy.
Genotyping Assay
Peripheral blood was collected in ethylenediaminetetraacetic acid tubes and stored at −80°C after centrifugation. We then extracted genomic DNA from whole blood using the Universal Genomic DNA Extraction Kit (TaKaRa, Kyoto, Japan). DNA concentrations were assessed using spectrophotometry (DU530 UV/VIS spectrophotometer, Beckman Instruments, Fullerton, CA, USA). In total, two tag-SNPs (rs1059394 and rs2847153) were selected in our study. The Multiplexed SNP Mass EXTEND assay was designed by Sequenom Mass ARRAY Assay Design (version3.0, Agena Bioscience, San Diego, CA, USA),16 which was referred to in previous studies.17–19 SNP genotyping was carried out using Sequenom Mass-ARRAY RS1000. Sequenom Typer 4.0 software was used to analyze data.16,20 Primers of each SNP are presented in Table 1.
Table 1.
Primers Used For This Study
| SNP_ID | 1st-PCRP | 2nd-PCRP | UEP_SEQ |
|---|---|---|---|
| rs1059394 | ACGTTGGATGGTATCGACAGGATCATACTC | ACGTTGGATGCGACCTGTTGTAATTGCTCC | cATTGCTCCTCATGTCC |
| rs2847153 | ACGTTGGATGTCTTTAAGTAGGCTGGT CCC |
ACGTTGGATGAGAAAAGATCTGGGAGG GTG |
gCAAAGAAGGGATCAG ACT |
Notes: 1st-PCRP, reverse primer; 2nd-PCRP, forward primer.
Genotype-Phenotype Association
eQTL are regions of the genome containing DNA sequence variants that influence the expression level of one or more genes. We conducted the expression quantitative trait loci (eQTL) analysis using GTEx portal web site (http://www.gtexportal.org/home/) to predict potential associations between the two SNPs and TYMS gene expression levels.21 The GTEx Portal provides open access to data including gene expression, QTLs, and histology images.
Statistical Analysis
Statistical analyses were performed using the software R (version 3.5.1). The Chi-square test was used to examine Hardy- Weinberg equilibrium (HWE) based on gene frequencies in individuals. We used univariate logistic regression analysis to evaluate differences in the genotype distributions of the two SNPs between the cases and controls. The glioma risk associated with the TYMS rs1059394 and rs2847153 genotypes were estimated using odds ratios (ORs) and their 95% confidence intervals (CIs). For all tests, a two-tailed P-value < 0.05 was considered statistically significant.
Results
Characteristics Of The Study Population
All the participants were of Han Chinese Ethnicity. There were no significant differences between the two groups regarding age or sex (P = 0.195 and, P = 0.534, respectively). The patients included 335 (55.4%) men and 270 (44.6%) women, with 267 patients younger than 40 years of age, and 338 patients older than 40 years of age. A total of 382 (63.1%) patients were classified with low-grade glioma (WHO grades I–II) and 223 (36.9%) with high-grade glioma (WHO grades III–IV). There were 416 (68.8%) patients with glioma who underwent gross-total tumor surgical resection and 189 (31.2%) who underwent near-total or sub-total resection. In total, 545 (90.1%) patients received radiotherapy treatment, and 250 (41.3%) patients received chemotherapy. The basic characteristics of the participants are listed in Table 2.
Table 2.
The Characteristics Of Gliomas Cases And Cancer-Free Controls
| Characteristics | Cases | Control | P value* |
|---|---|---|---|
| Number | 605 | 1300 | |
| Age (mean ± SD) | 40.71±18.28 | 41.68±13.54 | 0.195 |
| <40 years | 267 | 561 | |
| ≥40 years | 338 | 739 | 0.688 |
| Sex | |||
| Male | 335 | 700 | |
| Female | 270 | 600 | 0.534 |
| WHO Grade | |||
| I-II | 382 | ||
| III-IV | 223 | ||
| Surgery | |||
| STR & NTR | 189 | ||
| GTR | 416 | ||
| Radiotherapy | |||
| No | 60 | ||
| Yes | 545 | ||
| Chemotherapy | |||
| No | 355 | ||
| Yes | 250 |
Note: *T-test or two-sided χ2-test.
Abbreviations: STR, subtotal resection; NTR, near total resection; GTR, gross total resection; SD, Standard Deviation.
TYMS Polymorphisms In The Patients With Glioma And Controls
The genotypic frequency for the TYMS rs1059394 and rs2847153 polymorphisms conformed to HWE (P = 0.53 and P = 0.47, respectively). The genotypic and allelic frequencies of TYMS rs1059394 and rs2847153 are presented in Table 3. Compared with the wildtype genotype of rs1059394, we found that TT and CT+TT genotype carriers had significantly decreased glioma risk (TT to CC: OR = 0.71, 95% CI = 0.52–0.97, P = 0.03; CT+TT to CC: OR = 0.74, 95% CI =0.55–0.99, P = 0.04). However, no statistically significant difference was found between rs2847153 and glioma risk in genetic models (P﹥0.05).
Table 3.
Genotype Frequencies Of TYMS Polymorphisms In Cases And Controls
| Model | Genotype | Control (n, %) | Case (n, %) | OR (95% CI) | P-value* |
|---|---|---|---|---|---|
| rs1059394 HWE: P=0.53 | |||||
| Co-dominant | CC | 131(10.1%) | 80 (13.2%) | 1.00 (reference) | |
| Heterozygote | CT | 548(42.1%) | 255 (42.2%) | 0.76(0.56–1.04) | 0.09 |
| Homozygote | TT | 621(47.8%) | 270 (44.6%) | 0.71(0.52–0.97) | 0.03 |
| Dominant | CC | 131(10.1%) | 80 (13.2%) | 1.00 (reference) | |
| CT+TT | 1169(89.9%) | 525(86.8%) | 0.74(0.55–0.99) | 0.04 | |
| Recessive | CC+CT | 679(52.2%) | 335(55.4%) | 1.00 (reference) | |
| TT | 621(47.8%) | 270(44.6%) | 0.88(0.73–1.07) | 0.20 | |
| Overdominant | CC+TT | 752(51.9%) | 350(57.8%) | 1.00 (reference) | |
| CT | 548(42.1%) | 255(42.2%) | 1.00(0.82–1.22) | 1.00 | |
| Allele | C | 810(31.2%) | 415(34.5%) | 1.00 (reference) | |
| T | 1790(68.8%) | 795(65.5%) | 0.87(0.75–1.00) | 0.05 | |
| rs2847153ª HWE: P=0.47 | |||||
| Co-dominant | GG | 534(41.1%) | 223(36.9%) | 1.00 (reference) | |
| Heterozygote | GA | 589(45.3%) | 295(48.9%) | 1.20(0.97–1.48) | 0.09 |
| Homozygote | AA | 177(13.6%) | 86(14.2%) | 1.16(0.86–1.57) | 0.32 |
| Dominant | GG | 534(41.1%) | 223(36.9%) | 1.00 (reference) | |
| GA+AA | 766(58.9%) | 381(63.1%) | 1.19(0.98–1.45) | 0.09 | |
| Recessive | GG+GA | 1123(86.4%) | 518(85.8%) | 1.00 (reference) | |
| AA | 177(13.6%) | 86(14.2%) | 1.05(0.80–1.39) | 0.71 | |
| Overdominant | GG+AA | 711(44.7%) | 309(51.2%) | 1.00 (reference) | |
| GA | 589(45.3%) | 295(48.8%) | 1.15(0.95–1.39) | 0.15 | |
| Allele | G | 1657(63.7%) | 741(61.3%) | 1.00 (reference) | |
| A | 943(36.3%) | 467(38.7%) | 1.11(0.96–1.28) | 0.16 | |
Notes: *Univariate logistic regression analysis for the distributions of genotype and allele frequencies. Adjusted for age and sex. ªGenotype deletion: cases n=1. The Co-dominant, Dominant, Recessive, Overdominant, Allele represented five models.
Abbreviations: HWE, Hardy–Weinberg Equilibrium; OR, Odd Ratio; CI, Confidence Interval.
Relationship Between TYMS SNPs And Clinical Characteristics Of Glioma
We evaluated the correlations between the rs1059394 and rs2847153 polymorphisms and clinical characteristics of patients with glioma, including age, sex, and WHO grade. As shown in Table 4, in high-grade gliomas, the GA and GA+AA genotypes of rs2847153 were significantly increased, with the GG genotype as the reference (GA to GG: OR = 2.01, 95% CI = 1.39–2.91, P < 0.001; GA+AA to GG: OR = 1.78, 95% CI =1.25–2.54, P < 0.001). There was a balanced genotype distribution in rs1059394 polymorphisms (Table 5).
Table 4.
The Associations Between The TYMS rs2847153 Polymorphisms And Clinical Characteristics Of Gliomas Patients
| Characteristics | Genotype Distributions | |||
|---|---|---|---|---|
| GG | GA | AA | GA+AA | |
| Age | ||||
| <40/≥40 | 106/117 | 123/172 | 38/48 | 161/220 |
| OR (95% CI) | 1.00 (Reference) | 1.27 (0.82–1.80) | 1.14 (0.69–1.89) | 1.23 (0.89–1.73) |
| P-value* | 0.185 | 0.597 | 0.208 | |
| Sex | ||||
| Male/Female | 116/107 | 169/126 | 50/36 | 219/162 |
| OR (95% CI) | 1.00 (Reference) | 0.81 (0.57–1.15) | 0.78 (0.47–1.29) | 0.80 (0.58–1.12) |
| P-value* | 0.233 | 0.334 | 0.193 | |
| WHO Grade | ||||
| I+II/III+IV | 159/64 | 163/132 | 59/27 | 222/159 |
| OR (95% CI) | 1.00 (Reference) | 2.01 (1.39–2.91) | 1.14 (0.66–1.95) | 1.78 (1.25–2.54) |
| P-value* | <0.001 | 0.641 | 0.001 | |
Notes: *Univariate logistic regression analysis for the distributions of genotype frequencies. Genotype distributions including all the genotype of TYMS rs2847153 polymorphisms.
Abbreviations: OR, Odd Ratio; CI, Confidence Interval.
Table 5.
The Associations Between The TYMS Rs1059394 Polymorphisms And Clinical Characteristics Of Gliomas Patients
| Characteristics | Genotype Distributions | |||
|---|---|---|---|---|
| CC | CT | TT | CT+TT | |
| Age | ||||
| <40/≥40 | 38/42 | 118/137 | 111/159 | 229/296 |
| OR (95% CI) | 1.00 (reference) | 1.05 (0.63–1.74) | 1.30 (0.78–2.14) | 1.17 (0.73–1.87) |
| P-value* | 0.848 | 0.311 | 0.515 | |
| Sex | ||||
| Male/Female | 13/37 | 145/110 | 147/123 | 292/233 |
| OR (95% CI) | 1.00 (reference) | 0.88 (0.53–1.46) | 0.97 (0.59–1.61) | 0.93 (0.58–1.49) |
| P-value* | 0.625 | 0.913 | 0.754 | |
| WHO Grade | ||||
| I+II/III+IV | 46/34 | 162/93 | 174/96 | 336/189 |
| OR (95% CI) | 1.00 (reference) | 0.78 (0.47–1.30) | 0.75 (0.45–1.25) | 0.76 (0.47–1.23) |
| P-value* | 0.333 | 0.26 | 0.263 | |
Note: *Univariate logistic regression analysis for the distributions of genotype frequencies.
Abbreviations: OR, Odd Ratio; CI, Confidence Interval.
Expression Quantitative Trait Loci
To investigate the potential biological effects of the two significant SNPs on the TYMS gene expression, we explored eQTL analysis by GTEx portal. The results indicated that genotypes of both SNPs were significantly associated with TYMS gene expression in transformed fibroblasts cells (Figure 1).
Figure 1.
Analysis of the rs1059394 and rs2847153 polymorphisms in the TYMS gene in transformed fibroblast cells. Shown is the eQTL analysis for the (A) rs1059394 and (B) rs2847153 polymorphisms in the TYMS gene in transformed fibroblast cells (GTEx portal).
Discussion
Gliomas are highly malignant with a poor prognosis, although early diagnosis and improved treatment are widely implemented. In addition, there were 296,851 new cases of brain and nervous system cancer, and glioma accounted for the majority of brain cancers.22,23 In China, 1,016,000 new cases of brain and central nervous system cancer were reported in 2015.24 It was suggested that genetic factors were primarily responsible for glioma genesis,25 and there was still a lack of prospective molecular biomarkers for glioma.
TYMS is reported to be associated with folate metabolism, and it catalyzes conversion of deoxyuridine-5́- monophosphate into deoxythymidine-5́-monophosphate. It is suggested that TYMS down regulation can influence DNA repair mechanisms, which is related to cell transformation and cancer development.26 TYMS is also an important target of 5-fluorouracil (5-FU), inhibition of TYMS by fluorodeoxyuridine monophosphate (an active metabolite of 5-FU) results in DNA damage and cell death.15,27 Therefore, functional genetic variants of TYMS may lead to cancer, and TYMS maybe a molecular biomarker. It is indicated that TYMS genetic polymorphisms are correlated with the susceptibility of different cancers.
The TYMS polymorphisms rs1059394 (C>T) and rs2847153 (G>A) have been investigated in a few cancers. Rs1059394 TT genotypes were found to be correlated with a significantly increased risk of gastric cancer.12 Further stratified analysis indicated that the rs1059394 T variant allele was associated with a significantly decreased risk of breast cancers in patients with a smoking history.10 In addition, as for patients with non-small cell lung cancer, rs2847153 in TYMS may be helpful for prognosis and personalized treatment.28 There have been no studies about TYMS polymorphisms and glioma risk previously.
Our study evaluated the relationship between TYMS polymorphisms (rs1059394 and rs2847153) and glioma risk. Compared with the wildtype genotype of rs1059394, we found that TT and CT+TT genotype carriers had a significantly decreased glioma risk, indicating that rs1059394 C>T was associated with the low susceptibility of glioma. In high-grade gliomas, the GA and GA+AA genotypes of rs2847153 were significantly increased, which means that GA or GA+AA genotypes may predict a worse prognosis. Therefore, the polymorphism of TYMS may be biomarkers of clinical outcomes and personalized treatment. A study evaluated the expression of TYMS gene in the metastatic lymph node and primary foci of low-grade glioma, with a significant positive TYMS expression.15 The specific mechanism of this is unclear, which is a potential subject on high-grade glioma for further evaluation.
Our study also had some limitations. Firstly, all samples originated from a hospital in the northwest region of China, which inevitably led to selection bias. Second, we did not stratify our analysis for tumor subtypes because the sample size was circumscribed. Third, due to the limits of data, we did not analyze the impact of other factors, such as dose radiation exposure, lifestyle, family history, tumor size and outcome. Hence, this deserves further investigation with a multi-center, case-control study in the future.
To summarize, our study indicated that TYMS polymorphisms were associated with glioma susceptibility. The rs1059394 C>T variant could decrease the risk of glioma. In addition, the rs2847153 G>A variant might predict worse survival in glioma patients. Further functional and multi-center case-control studies are needed to clarify the association between TYMS polymorphisms and the susceptibility of glioma.
Acknowledgments
We thank all members of our study team for their whole-hearted cooperation and the included participants for their wonderful cooperation.
Funding Statement
National Natural Science Foundation of China; Grant/Award Number: 81471670. Key Research and Development Plan, Shaanxi Province, China; Grant/Award Number: 2017ZDXM-SF-066.
Ethical Approval And Informed Consent
All procedures performed in studies involving human participants were in accordance with the Helsinki declaration. Informed consent was obtained from all individual participants included in the study.
Author Contributions
All authors contributed towards data analysis, drafting and critically revising the paper, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.
References
- 1.Liu K, Jiang Y. Polymorphisms in DNA repair gene and susceptibility to glioma: a systematic review and meta-analysis based on 33 studies with 15 SNPs in 9 genes. Cell Mol Neurobiol. 2017;37(2):263–274. doi: 10.1007/s10571-016-0367-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Louis DN, Ohgaki H, Wiestler OD, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007;114(2):97–109. doi: 10.1007/s00401-007-0243-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Ostrom QT, Bauchet L, Davis FG, et al. The epidemiology of glioma in adults: a “state of the science” review. Neuro-oncology. 2014;16(7):896–913. doi: 10.1093/neuonc/nou087 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Visser O, Ardanaz E, Botta L, et al. Survival of adults with primary malignant brain tumours in Europe; Results of the EUROCARE-5 study. Eur J Cancer. 2015;51(15):2231–2241. doi: 10.1016/j.ejca.2015.07.032 [DOI] [PubMed] [Google Scholar]
- 5.Bauchet L, Ostrom QT. Epidemiology and molecular epidemiology. Neurosurg Clin N Am. 2019;30(1):1–16. doi: 10.1016/j.nec.2018.08.010 [DOI] [PubMed] [Google Scholar]
- 6.Savage N. Searching for the roots of brain cancer. Nature. 2018;561(7724):S50–S51. doi: 10.1038/d41586-018-06709-2 [DOI] [PubMed] [Google Scholar]
- 7.Kumawat R, Gowda SH, Debnath E, et al. Association of Single Nucleotide Polymorphisms (SNPs) in genes encoding for folate metabolising enzymes with glioma and meningioma in Indian population. Asian Pacif J Cancer Prev. 2018;19(12):3415–3425. doi: 10.31557/APJCP.2018.19.12.3415 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hori T, Takahashi E, Ayusawa D, Takeishi K, Kaneda S, Seno T. Regional assignment of the human thymidylate synthase (TS) gene to chromosome band 18p11.32 by nonisotopic in situ hybridization. Hum Genet. 1990;85(6):576–580. doi: 10.1007/bf00193577 [DOI] [PubMed] [Google Scholar]
- 9.Chen D, Jansson A, Sim D, Larsson A, Nordlund P. Structural analyses of human thymidylate synthase reveal a site that may control conformational switching between active and inactive states. J Biol Chem. 2017;292(32):13449–13458. doi: 10.1074/jbc.M117.787267 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Guan X, Liu H, Ju J, et al. Genetic variant rs16430 6bp > 0bp at the microRNA-binding site in TYMS and risk of sporadic breast cancer risk in non-Hispanic white women aged</= 55 years. Mol Carcinog. 2015;54(4):281–290. doi: 10.1002/mc.22097 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Feng W, Guo X, Huang H, et al. Polymorphism rs3819102 in thymidylate synthase and environmental factors: effects on lung cancer in Chinese population. Curr Probl Cancer. 2019;43(1):66–74. doi: 10.1016/j.currproblcancer.2018.07.005 [DOI] [PubMed] [Google Scholar]
- 12.Shen R, Liu H, Wen J, et al. Genetic polymorphisms in the microRNA binding-sites of the thymidylate synthase gene predict risk and survival in gastric cancer. Mol Carcinog. 2015;54(9):880–888. doi: 10.1002/mc.22160 [DOI] [PubMed] [Google Scholar]
- 13.Amirfallah A, Kocal GC, Unal OU, Ellidokuz H, Oztop I, Basbinar Y. DPYD, TYMS and MTHFR genes polymorphism frequencies in a series of Turkish colorectal cancer patients. J Pers Med. 2018;8:4. doi: 10.3390/jpm8040045 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kelemen LE, Earp M, Fridley BL, et al. rs495139 in the TYMS-ENOSF1 region and risk of ovarian carcinoma of mucinous histology. Int J Mol Sci. 2018;19:9. doi: 10.3390/ijms19092473 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ding B, Gao M, Li Z, Xu C, Fan S, He W. Expression of TYMS in lymph node metastasis from low-grade glioma. Oncol Lett. 2015;10(3):1569–1574. doi: 10.3892/ol.2015.3419 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Gabriel S, Ziaugra L, Tabbaa D. SNP genotyping using the Sequenom MassARRAY iPLEX platform. Curr Protocols Human Genet. 2009. Chapter 2:Unit2.12. [DOI] [PubMed] [Google Scholar]
- 17.Lin S, Wang M, Liu X, et al. FEN1 gene variants confer reduced risk of breast cancer in chinese women: a case-control study. Oncotarget. 2016;7(47):78110–78118. doi: 10.18632/oncotarget.12948 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tian T, Wang M, Zheng Y, et al. Association of two FOXP3 polymorphisms with breast cancer susceptibility in Chinese Han women. Cancer Manag Res. 2018;10:867–872. doi: 10.2147/CMAR.S158433 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Dai Z, Tian T, Wang M, et al. Genetic polymorphisms of estrogen receptor genes are associated with breast cancer susceptibility in Chinese women. Cancer Cell Int. 2019;19:11. doi: 10.1186/s12935-019-0727-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Thomas RK, Baker AC, Debiasi RM, et al. High-throughput oncogene mutation profiling in human cancer. Nat Genet. 2007;39(3):347–351. doi: 10.1038/ng1975 [DOI] [PubMed] [Google Scholar]
- 21.GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–585. doi: 10.1038/ng.2653 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424. doi: 10.3322/caac.21492 [DOI] [PubMed] [Google Scholar]
- 23.Benson VS, Pirie K, Schuz J, Reeves GK, Beral V, Green J. Mobile phone use and risk of brain neoplasms and other cancers: prospective study. Int J Epidemiol. 2013;42(3):792–802. doi: 10.1093/ije/dyt072 [DOI] [PubMed] [Google Scholar]
- 24.Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–132. doi: 10.3322/caac.21338 [DOI] [PubMed] [Google Scholar]
- 25.Haque A, Banik NL, Ray SK. Molecular alterations in glioblastoma: potential targets for immunotherapy. Prog Mol Biol Transl Sci. 2011;98:187–234. doi: 10.1016/B978-0-12-385506-0.00005-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Mandola MV, Stoehlmacher J, Zhang W, et al. A 6 bp polymorphism in the thymidylate synthase gene causes message instability and is associated with decreased intratumoral TS mRNA levels. Pharmacogenetics. 2004;14(5):319–327. [DOI] [PubMed] [Google Scholar]
- 27.Mitchell LA, Lopez Espinoza F, Mendoza D, et al. Toca 511 gene transfer and treatment with the prodrug, 5-fluorocytosine, promotes durable antitumor immunity in a mouse glioma model. Neuro-oncology. 2017;19(7):930–939. doi: 10.1093/neuonc/nox037 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Dong H, Bao D, Guo X, et al. Effect of thymidylate synthase gene polymorphism on the response to chemotherapy and clinical outcome of non-small cell lung cancer patients. Tumour Biol. 2015;36(9):7151–7157. doi: 10.1007/s13277-015-3447-6 [DOI] [PubMed] [Google Scholar]