Abstract
Background
Common genetic risk variants for prostate cancer (PCa) have been identified at approximately 170 loci using genome-wide association studies (GWAS), most of which were identified in European populations. Recently, GWAS were applied to a large Japanese cohort and identified 12 novel susceptibility loci associated with PCa risk. In this study, we aim to investigate PCa susceptibility loci in the Chinese population. The study data will be used to promote PCa risk control in China.
Methods
A total of 235 PCa patients and 252 control subjects (all unrelated) were enrolled in this case-control PCa study. Nine single nucleotide polymorphisms (SNPs) were genotyped in GATA2 (rs73862213, rs2335052, and rs10934857), ZMIZ1 (rs704017, rs77911174, and rs3740259), and SUN2 (rs78397383, rs5750680, and rs138705) genes. The associations between the candidate SNPs and PCa were analyzed using multiple-factor logistic regression and haplotype analysis.
Results
The allele frequency distributions of rs73862213 and rs2335052 in the GATA2 gene and rs704017 and rs77911174 in the ZMIZ1 gene were found to be significantly different between PCa cases and controls. Haplotype analysis revealed that the G-C-A haplotype of the GATA2 gene (order of SNPs: rs73862213-rs2335052-rs10934857) and the G-G-G haplotype of the ZMIZ1 gene (order of SNPs: rs704017-rs77911174-rs3740259) were associated with increased PCa risk. None of the SUN2 haplotypes were associated with PCa.
Conclusions
Our study data indicates that the minor alleles of rs73862213 and rs2335052 in the GATA2 gene and rs704017 and rs77911174 in the ZMIZ1 gene were associated with increased PCa risk. These findings greatly extended our knowledge of the etiology of PCa.
1. Introduction
Prostate cancer (PCa) is one of the most frequently diagnosed malignant tumors among males worldwide. Accordingly, PCa is the leading cause of cancer-related deaths in men according to Global Cancer Statistics 2018 [1]. Although PCa incidence and mortality rates vary substantially by race, ethnicity, and geography, PCa incidence remains highest in countries defined as “developed” (e.g., North America, Western and Northern Europe, and Australia). Men from East, Southeast, and South Central Asia have the lowest rates of prostate cancer death [2]. However, recent studies showed that PCa incidence and morbidity have been rapidly increasing in China, especially in urban areas [3].
The large differences in the incidence of PCa among different ethnic groups suggest involvement of specific genetic risk factors in these cancer disparities. Numerous marker genes, including HPC1, PCAP, HPCX, CAPB, HPC20, and HOXB13, were identified in hereditary PCa. These genes were shown to contain about 170 PCa-related risk loci identified in different cohorts using genome-wide association study (GWAS) methods [4–6]. However, most of these studies were conducted using data collected in populations of European ancestry or cohorts with mixed ethnicity. Large-scale GWAS was recently performed using an independent Japanese cohort [7]. The study confirmed the association between several previously reported loci and PCa risk in the Japanese population. Additionally, 12 novel PCa-linked susceptibility loci were identified, including rs1125927 (TMEM17), rs73862213 (GATA2), rs77911174 (ZMIZ1), and rs138708 (SUN2). Seven of the reported loci had particularly low minor allele frequency in European population [7].
Although the GATA2, ZMIZ1, and SUN2 gene-located loci were reported as risk markers for PCa in the Japanese cohort, the association between these genes and PCa was not tested in a Chinese population. In this replication study, the expression of nine single nucleotide polymorphisms (SNPs) of GATA2, ZMIZ1, and SUN2 genes, including previously reported rs73862213 (GATA2) and rs77911174 (ZMIZ1), was investigated as a potential PCa risk factor in the Chinese population.
2. Material and Methods
2.1. Study Subjects
In this study, a total of 235 PCa patients and 252 normal controls were recruited from the Shengjing Hospital of China Medical University from June 2014 to July 2019. The study protocol was approved by the human ethics committee of the Shengjing Hospital, China Medical University. All study participants signed the informed consent. The PCa diagnosis was confirmed by two experienced pathologists according to the prostate cancer guidelines of the Chinese Society of Clinical Oncology (CSCO). A comprehensive examination was performed in all participants. All recruited patients completed the approved study questionnaire. The control subjects with a family history of cancer were excluded. The clinical characteristics, including patient's age, level of prostate specific antigen (PSA), body mass index (BMI), Gleason score, and prognostic stage (according to the AJCC 8th Edition Cancer Staging Form), were recorded for all enrolled participants. Peripheral blood sample (5 mL) anticoagulated with EDTA was collected from all patients (both control and PCa groups).
2.2. Selection of SNPs and Genotype
Three PCa risk-related candidate genes (GATA2, ZMIZ1, and SUN2) were analyzed in this study. The genes were genotyped in a Chinese Han population in Beijing (CHB). The genotype data was downloaded and analyzed using Ensembl Project public data site (http://asia.ensembl.org/Homo_sapiens/Tools/VcftoPed?db=core). Loci with more than two alleles were excluded. SNP selection was conducted according to the following criteria: (a) SNPs with a potential role in regulation of translational functions (variants located in the 5′ or 3′ untranslated regions (UTRs) that may affect the splice of mRNA or missense variants that lead to a protein modification); (b) SNPs with minor allele frequency (MAF) > 0.05; and (c) SNPs that were reported as PCa risk-associated in the Japanese GWAS study 7. Thus, altogether, 9 SNPs from GATA2, ZMIZ1, and SUN2 candidate genes were included and analyzed in this study (Table 1).
Table 1.
SNPs in the GATA2, ZMIZ1, and SUN2 gene.
| Gene | Band location | SNPs | Functional location | Allele | MAF | HWE p in the control group |
|---|---|---|---|---|---|---|
| GATA2 | 3q21.3 | rs73862213 | Upstream | A/g | 0.078 | 0.208 |
| rs2335052 | Exon4 | C/t | 0.398 | 0.134 | ||
| rs10934857 | 3′-UTR | G/a | 0.136 | 0.089 | ||
| ZMIZ1 | 10q22.3 | rs704017 | Upstream | A/g | 0.291 | 0.756 |
| rs77911174 | Upstream | A/g | 0.335 | 0.103 | ||
| rs3740259 | 3′-UTR | G/a | 0.248 | 0.496 | ||
| SUN2 | 22q13.1 | rs78397383 | Upstream | A/c | 0.252 | 0.448 |
| rs5750680 | Upstream | C/t | 0.432 | 0.685 | ||
| rs138705 | Intron 17 | C/t | 0.481 | 0.107 |
Genomic DNA was isolated from all patients using a TIANamp Genomic DNA Kit (Tiangen Biotech, Beijing, China). Validation genotyping was performed using the TaqMan SNP genotyping assay and 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's instructions. The PCR reactions were conducted in a final volume of 10 μL amplification mix, including 5 μL of master mix (Applied Biosystems), 0.5 μL predesigned PCR primers and probes (Applied Biosystems), 50 ng gDNA, and ddH2O. Amplification was performed under the following conditions: an initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 15 min and annealing at 60°C for 1 min.
2.3. Statistical Analysis
Statistical analyses were performed using SPSS software (IBM-SPSS, version 22.0, Chicago, IL, USA). The demographic characteristics of the study subjects, including age, PSA, and BMI, were compared using independent samples t-test between case and control groups. The Hardy-Weinberg equilibrium test for each SNP in the control group was calculated. Genotypic association between each SNPs and PCa was analyzed using multiple-factor logistic regression test. Odds ratios (OR) and 95% confidence intervals (95% CIs) were calculated using the major genotype as the reference genotype after data adjustments for age and BMI. SHEsis statistical analysis (http://analysis.bio-x.cn/myanalysis.php) was used to estimate the association between haplotypes and PCa.
3. Result
3.1. Analysis of Demographic Characteristics of Study Participants
The demographic characteristics of 235 PCa patients and 252 controls are presented in Table 2. The mean ages of case and control groups were 69.43 ± 9.33 and 68.37 ± 8.47, respectively. No statistically significant differences in age between the two groups were found (p = 0.238). Furthermore, no statistically significant differences in reported BMI were detected between case and control groups (p = 0.352). The serum PSA levels, a diagnostic factor of PCa, were significantly higher in PCa patients than controls (35.53 ± 53.74 vs. 8.76 ± 9.25, p < 0.001).
Table 2.
Demographic characteristics of study subjects.
| Cases (n = 235) | Controls (n = 252) | p values | |
|---|---|---|---|
| Age | 69.43 ± 9.33 | 68.37 ± 8.47 | 0.238 |
| PSA | 35.53 ± 53.74 | 8.76 ± 9.25 | <0.001 |
| BMI | 23.76 ± 8.13 | 22.32 ± 8.56 | 0.352 |
| Gleason score | |||
| <6 (3 + 3) | 86 (36.6%) | — | |
| 7 (3 + 4, 4 + 3) | 71 (30.2%) | — | |
| 8 (4 + 4) | 42 (17.9%) | — | |
| 9-10 (4 + 5, 5 + 4, 5 + 5) | 36 (16.3%) | — | |
| Prognosis stage | |||
| Stage I | 78 (33.2%) | — | |
| Stage II | 85 (36.2%) | — | |
| Stage III | 46 (19.6%) | — | |
| Stage IV | 26 (11.0%) | — |
3.2. Genotype Distribution of SNPs
The Hardy-Weinberg equilibrium was estimated for each SNP in the control group. No deviations were observed (Table 1). Genotype distributions of candidate SNPs are shown in Table 3.
Table 3.
Genotype distribution of SNPs in case and control groups.
| Gene | rs No. | Genotype | Cases | Controls | OR (95% CI) | p value∗ |
|---|---|---|---|---|---|---|
| GATA2 | rs73862213 | AA | 181 | 215 | Ref | Ref |
| AG | 54 | 37 | 1.734 (1.091-2.753) | 0.018 | ||
| GG | 0 | 0 | — | — | ||
| AG + GG | 54 | 37 | 1.734 (1.091-2.753) | 0.018 | ||
| rs2335052 | CC | 72 | 104 | Ref | Ref | |
| CT | 113 | 107 | 1.525 (1.022-2.276) | 0.038 | ||
| TT | 50 | 41 | 1.762 (1.057-2.935) | 0.029 | ||
| CT + TT | 163 | 148 | 1.591 (1.095-2.312) | 0.015 | ||
| rs10934857 | GG | 166 | 186 | Ref | Ref | |
| GA | 57 | 57 | 1.120 (0.734-1.710) | 0.598 | ||
| AA | 12 | 9 | 1.494 (0.614-3.635) | 0.374 | ||
| GA + AA | 69 | 66 | 1.174 (0.788-1.742) | 0.435 | ||
| ZMIZ1 | rs704017 | AA | 91 | 124 | Ref | Ref |
| AG | 112 | 107 | 1.426 (0.977-2.083) | 0.066 | ||
| GG | 32 | 21 | 2.076 (1.124-3.834) | 0.018 | ||
| AG + GG | 144 | 128 | 1.533 (1.069-2.198) | 0.020 | ||
| rs77911174 | AA | 92 | 123 | Ref | Ref | |
| AG | 99 | 98 | 1.351 (0.916-1.992) | 0.129 | ||
| GG | 44 | 31 | 1.898 (1.113-.3.234) | 0.018 | ||
| AG + GG | 143 | 129 | 1.482 (1.034-2.124) | 0.032 | ||
| rs3740259 | GG | 123 | 146 | Ref | Ref | |
| AG | 90 | 89 | 1.200 (0.822-1.753) | 0.344 | ||
| AA | 22 | 17 | 1.536 (0.781-3.023) | 0.212 | ||
| AG + AA | 112 | 106 | 1.254 (0.877-1.794) | 0.215 | ||
| SUN2 | rs78397383 | AA | 124 | 136 | Ref | Ref |
| AC | 92 | 95 | 1.062 (0.729-1.547) | 0.753 | ||
| CC | 19 | 21 | 0.992 (0.510-1.933) | 0.982 | ||
| AC + CC | 111 | 116 | 1.049 (0.735-1.499) | 0.790 | ||
| rs5750680 | CC | 67 | 79 | Ref | Ref | |
| CT | 119 | 127 | 1.105 (0.733-1.666) | 0.634 | ||
| TT | 49 | 46 | 1.256 (0.749-2.107) | 0.388 | ||
| CT + TT | 168 | 173 | 1.145 (0.776-1.689) | 0.494 | ||
| rs138705 | CC | 72 | 75 | Ref | Ref | |
| CT | 115 | 113 | 1.060 (0.700-1.605) | 0.783 | ||
| TT | 48 | 64 | 0.781 (0.476-1.281) | 0.328 | ||
| CT + TT | 163 | 177 | 0.959 (0.651-1.413) | 0.833 |
∗: adjusted for age and BMI.
Significant association between GATA2 gene SNPs (rs2335052 and rs73862213) and PCa was detected. The individuals with CT (p = 0.038), TT (p = 0.029), and CT + TT (p = 0.015) genotypes of rs2335052 had a higher risk of PCa compared with the CC genotype. There was no GG genotype of rs73862213 detected in both case and control groups. However, the frequency of the AG genotype is significantly higher in cases than controls, indicating that the rs73862213 was associated with PCa risk.
For the ZMIZ1 gene, homozygotes of both rs77911174 and rs704017 were significantly associated with the risk of PCa, whereas no significant association was detected between the heterozygote genotype of these SNPs and the occurrence of PCa (p = 0.129 and 0.066, respectively). The dominant allele model for both rs77911174 and rs704017 demonstrated significant associations with PCa (AG + GG vs. AA, p = 0.032 and 0.020, respectively). No association with PCa was observed for rs10934857 of GATA2, rs3740259 of ZMIZ1, and the three SNPs of SUN2.
3.3. Haplotype Analysis of GATA2, ZMIZ1, and SUN2 Gene
The haplotypes of GATA2, ZMIZ1, and SUN2 genes were analyzed, and data is shown in Table 4. The G-C-A haplotype (order of SNPs: rs73862213-rs2335052-rs10934857) of the GATA2 gene, which was constructed with the minor allele of rs73862213, was significantly associated with the higher risk of PCa (p = 0.001). The individuals with the G-G-G haplotype of the ZMIZ1 gene (order of SNPs: rs704017-rs77911174-rs3740259) demonstrated an increased PCa risk; whereas the A-A-G haplotype was associated with a decreased PCa risk (both associations were considered to be significantly associated with PCa; p < 0.01). There was no association between all SUN2 haplotypes and PCa (p > 0.05).
Table 4.
Association between haplotypes of three candidate genes and PCa.
| Gene | Haplotype | Cases | Controls | OR (95% CI) | p |
|---|---|---|---|---|---|
| GATA2 | A-C-G | 107 (45.5%) | 121 (48.0%) | 0.853 (0.685-1.573) | 0.247 |
| A-T-G | 82 (34.9%) | 94 (37.3%) | 1.158 (0.835-1.867) | 0.083 | |
| G-C-A | 31 (13.2%) | 18 (7.14%) | 1.642 (1.236-2.768) | 0.001 | |
| A-C-A | 6 (2.55%) | 10 (3.97%) | 0.854 (0.638-1.769) | 0.125 | |
| ZMIZ1 | A-A-G | 54 (22.9%) | 71 (28.2%) | 0.569 (0.427-0.874) | <0.01 |
| A-G-G | 53 (22.5%) | 62 (24.6%) | 0.876 (0.644-1.527) | 0.254 | |
| G-A-G | 44 (18.7%) | 46 (18.2%) | 1.023 (0.856-1.735) | 0.538 | |
| A-A-A | 21 (8.94%) | 27 (10.7%) | 0.956 (0.628-1.837) | 0.633 | |
| G-A-A | 19 (8.08%) | 18 (7.14%) | 1.201 (0.953-1.674) | 0.573 | |
| G-G-G | 21 (8.94%) | 8 (3.17%) | 1.572 (1.248-2.639) | <0.01 | |
| SUN2 | A-T-C | 65 (27.7%) | 74 (29.4%) | 0.875 (0.673-1.864) | 0.363 |
| C-C-T | 42 (17.9%) | 48 (19.1%) | 0.924 (0.735-1.739) | 0.246 | |
| A-C-C | 41 (17.4%) | 42 (16.7%) | 1.183 (0.679-1.536) | 0.537 | |
| A-C-T | 33 (14.0%) | 34 (13.5%) | 1.045 (0.842-1.731) | 0.438 | |
| A-T-T | 31 (13.2%) | 32 (12.7%) | 1.039 (0.742-1.825) | 0.383 |
GATA2, order of SNPs: rs73862213-rs2335052-rs10934857. ZMIZ1, order of SNPs: rs704017-rs77911174-rs3740259. SUN2, order of SNPs: rs78397383-rs5750680-rs138705.
4. Discussion
PCa has a considerable underlying genetic basis. It has been reported using candidate-gene analysis (or GWAS) that a number of genetic loci are associated with PCa. A recent GWAS-based analysis of the Japanese cohort reported 12 novel PCa-linked susceptibility loci, which are different from the PCa-related loci identified in European populations. In this study, we discussed the associations between the candidate SNPs in GATA2, ZMIZ1, and SUN2 genes and PCa in the Chinese population.
GATA2 is one of the six members of the GATA transcription factor family that regulates cell differentiation. The gene is located on chromosome 3q21.3. The GATA2 gene has been associated with a variety of diseases, including coronary heart disease [8, 9], Parkinson's disease [10], and lung [11], colon [12], and prostate cancers [13]. As a transcription factor, GATA2 plays an important role in the regulation of androgen receptor (AR) signaling [14]. Previously, Liu et al. [12] concluded that the carriers of the SNP rs2335052 minor allele are exposed to a significantly increased risk of recurrent colorectal cancer and indicated reduced disease-free survival rate. In this study, 3 loci (rs73862213, rs2335052, and rs10934857) of the GATA2 gene were analyzed to test the association between these SNPs and PCa in the Chinese population. The results showed that heterozygous genotypes of rs73862213 were higher in the case than in the control group, indicating that the AG genotype is associated with PCa risk. Besides, the rs2335052 minor alleles correlated with the higher risk of PCa. Additionally, the haplotype analysis revealed a higher frequency of the G-C-A haplotype, which contained the G allele in rs73862213 of GATA2, in the case group compared to controls. This finding confirms that GATA2 may be a potential PCa-causing candidate gene. Neither the G-T-G nor the G-T-A, constructed with the minor allele of rs73862213 and rs2335052, were detected in this study because of the lower allele frequency and limited sample number.
The ZMIZ1 (zinc finger MIZ-type containing 1) gene is located on 10q22.3 chromosome and encodes the PIAS-like protein containing 1067 amino acid residues. ZMIZ1 belongs to the protein inhibitor of activated STAT (PIAS) family. The protein contains a ring finger region termed Miz, a nuclear localization sequence (NLS), and two proline-rich regions [13]. ZMIZ1 is an established transcriptional coactivator which can increase transcriptional activity of other DNA-binding factors, including AR, SMAD3, STAT, and p53. It has been shown that ZMIZ1 colocalizes with AR and enhances sumoylation of AR in vivo. Modified ZMIZ1 signaling was associated with various diseases, including vitiligo [13], multiple sclerosis [15], leukemia [16], prostate cancer [17], and inflammatory bowel disease [18]. Multiple studies reported that rs704017, located in the intron of the ZMIZ1 gene, is the genetic risk factor associated with colorectal cancers (CRC).
The rs704017 SNP is located in the intron 3 of ZMIZ1 antisense RNA 1 (ZMIZ1-AS1, a miscellaneous RNA (miscRNA)), residing in a strong enhancer region. A recent large-scale genetic study in an East Asian population defined this SNP as a risk factor for CRC [17]. Several GWAS conducted in Spanish [19], Japanese [20], and Han Chinese populations [21] supported this finding. Tan et al. [21] found that rs704017 is significantly associated with stage III or IV CRCs. Song et al. reported [22] that rs704017 was also associated with disease-free survival (DFS) in CRC patients. Consistently, the risk allele rs704017 was less frequently detected among the younger CRC patients (age < 50 years) compared to the older CRC patients (age ≥ 50 years), suggesting that rs704017 has a hereditary effect on CRC onset age [23].
In this study, analysis of three SNPs located in the ZMIZ1 gene showed that ZMIZ1 is a candidate PCa risk factor gene. The patients with GG homozygotes of rs77911174 and rs704017 had a higher risk of PCa than those with AA homozygotes. The same risk associations were observed for the dominant allele model (both AG + GG vs. AA, p < 0.05). However, for the heterozygote genotypes of the two SNPs, no significant differences in the occurrence of PCa between the case and control groups were detected (p = 0.129 and 0.066, respectively). Interestingly, the haplotype analysis demonstrated that the individuals with the G-G-G haplotype of ZMIZ1 (order of SNPs: rs704017-rs77911174-rs3740259), constructed with both minor allele of rs77911174 and rs704017, had an increased PCa risk (p < 0.01). Alternatively, individuals with the A-A-G haplotype had a decreased risk of PCa (p < 0.01). The results of genotype distributions and haplotype analysis suggested that the ZMIZ1 gene may be related with the occurrence of PCa.
SUN2 (SAD1/UNC84 domain protein-2) is the key component of the linker of nucleoskeleton and cytoskeleton (LINC) complex which plays an important role in nuclear-cytoplasmic connections [24]. The role of SUN2 as cancer suppressor was demonstrated in various cancers, including embryonal tumors [25], breast cancer [26], lung cancer [27], and PCa [28]. Moreover, SUN2 was found to enhance the chemotherapy sensitivity in lung cancer cells exposed to cisplatin. Higher SUN2 level predicts a better overall survival in lung cancers [27]. Recently, rs138708 in SUN2 (nonsynonymous SNP) was reported as PCa risk susceptibility loci in a Japanese population [7]. However, in this study, we did not observe the association between the three SNPs in SUN2 and PCa in the Chinese population. These findings are inconsistent with those data reported in Takata et al.'s study. Different populations and limited samples may contribute these discrepancies.
There are several limitations in our study. Firstly, the sample size is relatively small. A multicenter study with a large sample size may be more convincing. The small sample size is potentially the reason that we failed to replicate the association between the SUN2 gene and PCa. Secondly, we did not investigate the other susceptibility genes reported in the Japanese GWAS. Thirdly, we did not perform function-related studies using candidate genes in this study. The functional or biological significance related to PCa development is still unclear. Additionally, gene-to-gene and gene-to-environment interactions were not analyzed.
In summary, nine loci in GATA2, ZMIZ1, and SUN2 genes were studied in the Chinese population. Our data reported four important PCa susceptibility loci that have not been previously concerned in China. The results of our study indicate that rs73862213 and rs2335052 in the GATA2 gene and rs77911174 and rs704017 in the ZMIZ1 gene may be independent indicators for PCa risk in the Chinese population. Further large-scale studies are required to validate these findings and clarify the tested SNPs underlying mechanism of signaling.
Acknowledgments
We thank all the individuals that participated in this study. The authors would like to express their gratitude to EditSprings (https://www.editsprings.com/) for the expert linguistic services provided. This research was supported by the following funding: the basic project of scientific research funding of the Education Department of Liaoning Province No. JCZR2020014.
Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors declare that they have no competing interests.
References
- 1.Bray F., Ferlay J., Soerjomataram I., Siegel R. L., Torre L. A., Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians . 2018;68(6):394–424. doi: 10.3322/caac.21492. [DOI] [PubMed] [Google Scholar]
- 2.Rebbeck T. R. Prostate cancer genetics: variation by race, ethnicity, and geography. Seminars in Radiation Oncology . 2017;27(1):3–10. doi: 10.1016/j.semradonc.2016.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Zhu Y., Wang H. K., Qu Y. Y., Ye D. W. Prostate cancer in East Asia: evolving trend over the last decade. Asian Journal of Andrology . 2015;17(1):48–57. doi: 10.4103/1008-682X.132780. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Al Olama A. A., Kote-Jarai Z., Berndt S. I., et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nature Genetics . 2014;46(10):1103–1109. doi: 10.1038/ng.3094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Eeles R. A., Olama A. A., Benlloch S., et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nature Genetics . 2013;45(4):385–391. doi: 10.1038/ng.2560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Schumacher F. R., Al Olama A. A., Berndt S. I., et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nature Genetics . 2018;50(7):928–936. doi: 10.1038/s41588-018-0142-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Takata R., Takahashi A., Fujita M., et al. 12 new susceptibility loci for prostate cancer identified by genome-wide association study in Japanese population. Nature Communications . 2019;10(1):p. 4422. doi: 10.1038/s41467-019-12267-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Horne B. D., Hauser E. R., Wang L., et al. Validation study of genetic associations with coronary artery disease on chromosome 3q13-21 and potential effect modification by smoking. Annals of Human Genetics . 2009;73(6):551–558. doi: 10.1111/j.1469-1809.2009.00540.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Muiya N. P., Wakil S., Al-Najai M., et al. A study of the role of GATA2 gene polymorphism in coronary artery disease risk traits. Gene . 2014;544(2):152–158. doi: 10.1016/j.gene.2014.04.064. [DOI] [PubMed] [Google Scholar]
- 10.Kurzawski M., Bialecka M., Slawek J., Klodowska-Duda G., Drozdzik M. Association study of GATA-2 transcription factor gene (GATA2) polymorphism and Parkinson's disease. Parkinsonism & Related Disorders . 2010;16(4):284–287. doi: 10.1016/j.parkreldis.2009.10.006. [DOI] [PubMed] [Google Scholar]
- 11.Zhang L., Gao L., Shao M., Sun G. Y. A MYC target long non-coding RNA GATA2-AS1 regulates non-small cell lung cancer growth. Neoplasma . 2019;66(6):954–962. doi: 10.4149/neo_2019_190210N112. [DOI] [PubMed] [Google Scholar]
- 12.Liu X., Jiang B., Wang A., et al. GATA2 rs2335052 polymorphism predicts the survival of patients with colorectal cancer. PLoS One . 2015;10(8, article e0136020) doi: 10.1371/journal.pone.0136020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Li M., Fan Y., Wang Y., Xu J., Xu H. ZMIZ1 promotes the proliferation and migration of melanocytes in vitiligo. Experimental and Therapeutic Medicine . 2020;20(2):1371–1378. doi: 10.3892/etm.2020.8849. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Rodriguez-Bravo V., Carceles-Cordon M., Hoshida Y., Cordon-Cardo C., Galsky M. D., Domingo-Domenech J. The role of GATA2 in lethal prostate cancer aggressiveness. Nature Reviews Urology . 2017;14(1):38–48. doi: 10.1038/nrurol.2016.225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Fewings N. L., Gatt P. N., McKay F. C., et al. The autoimmune risk gene ZMIZ1 is a vitamin D responsive marker of a molecular phenotype of multiple sclerosis. Journal of Autoimmunity . 2017;78:57–69. doi: 10.1016/j.jaut.2016.12.006. [DOI] [PubMed] [Google Scholar]
- 16.Pinnell N., Yan R., Cho H. J., et al. The PIAS-like coactivator Zmiz1 is a direct and selective cofactor of Notch1 in T cell development and leukemia. Immunity . 2015;43(5):870–883. doi: 10.1016/j.immuni.2015.10.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhang B., Jia W. H., Matsuda K., et al. Large-scale genetic study in East Asians identifies six new loci associated with colorectal cancer risk. Nature Genetics . 2014;46(6):533–542. doi: 10.1038/ng.2985. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Jakobsen C., Cleynen I., Andersen P. S., et al. Genetic susceptibility and genotype-phenotype association in 588 Danish children with inflammatory bowel disease. Journal of Crohn's & Colitis . 2014;8(7):678–685. doi: 10.1016/j.crohns.2013.12.010. [DOI] [PubMed] [Google Scholar]
- 19.Arnau-Collell C., de Lima Y. S., Díaz-Gay M., et al. Colorectal cancer genetic variants are also associated with serrated polyposis syndrome susceptibility. Journal of Medical Genetics . 2020;57(10):677–682. doi: 10.1136/jmedgenet-2019-106374. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Abe M., Ito H., Oze I., Nomura M., Ogawa Y., Matsuo K. The more from East-Asian, the better: risk prediction of colorectal cancer risk by GWAS-identified SNPs among Japanese. Journal of Cancer Research and Clinical Oncology . 2017;143(12):2481–2492. doi: 10.1007/s00432-017-2505-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Tan C., Hu W., Huang Y., Zhou J., Zheng S. Risk of eighteen genome-wide association study-identified genetic variants for colorectal cancer and colorectal adenoma in Han Chinese. Oncotarget . 2016;7(47):77651–77663. doi: 10.18632/oncotarget.12750. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Song N., Kim K., Shin A., et al. Colorectal cancer susceptibility loci and influence on survival. Genes, Chromosomes & Cancer . 2018;57(12):630–637. doi: 10.1002/gcc.22674. [DOI] [PubMed] [Google Scholar]
- 23.Song N., Shin A., Park J. W., Kim J., Oh J. H. Common risk variants for colorectal cancer: an evaluation of associations with age at cancer onset. Scientific Reports . 2017;7(1):p. 40644. doi: 10.1038/srep40644. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chen X., Chen Y., Huang H. M., et al. SUN2: a potential therapeutic target in cancer. Oncology Letters . 2019;17(2):1401–1408. doi: 10.3892/ol.2018.9764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hsieh T. H., Chien C. L., Lee Y. H., et al. Downregulation of SUN2, a novel tumor suppressor, mediates miR-221/222-induced malignancy in central nervous system embryonal tumors. Carcinogenesis . 2014;35(10):2164–2174. doi: 10.1093/carcin/bgu105. [DOI] [PubMed] [Google Scholar]
- 26.Yi X., Wright L. E., Pagnotti G. M., et al. Mechanical suppression of breast cancer cell invasion and paracrine signaling to osteoclasts requires nucleo-cytoskeletal connectivity. Bone Research . 2020;8(1):p. 40. doi: 10.1038/s41413-020-00111-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Lv X. B., Liu L., Cheng C., et al. SUN2 exerts tumor suppressor functions by suppressing the Warburg effect in lung cancer. Scientific Reports . 2015;5 doi: 10.1038/srep17940. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Yajun C., Chen Y., Xiaosa L., et al. Loss of Sun2 promotes the progression of prostate cancer by regulating fatty acid oxidation. Oncotarget . 2017;8(52):89620–89630. doi: 10.18632/oncotarget.19210. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.