Abstract
The programmed cell death 6 (PDCD6) gene, originally identified as a pro-apoptotic gene, has recently been reported to have contradictory roles in different diseases and may promote cell proliferation. Here, we examined whether single nucleotide polymorphisms (SNPs) in PDCD6 were associated with endometrial cancer (EC). The genotypes of these two SNPs (rs3756712 and rs4957014) in PDCD6 were distinguished by polymerase chain reaction-restriction fragment length polymorphism in 238 patients with EC and 518 controls. Briefly, the T allele of rs3756712 was found to increase EC risk (P = 0.028, odds ratio [OR] = 0.747). Moreover, EC risk was associated with these two SNPs in different genetic models (P = 0.031, OR = 1.42 for rs3756712 in the dominant model; P = 0.019, OR = 0.63 for rs4957014 in the codominant model; P = 0.0073, OR = 0.65 for rs4957014 in the dominant model; P = 0.0076, OR = 0.66 for rs4957014 in the overdominant model). Results of stratified analyses revealed that rs4957012 was linked to body mass index (BMI) and parametrial invasion and that rs4957014 was associated with BMI, although this associated was not statistically significant (P = 0.065, OR = 4.42, 95% confidence interval = 1.06-18.51). Our results indicated that these two tag SNPs in PDCD6 were associated with EC, suggesting that PDCD6 may play a crucial role in the tumorigenesis of EC.
Keywords: Endometrial cancer, programmed cell death 6, single nucleotide polymorphisms, risk
Introduction
In diseases of the female reproductive system, endometrial cancer (EC) is the most prevalent malignant tumor in the United States of America, with an estimated 61380 new cases in 2017. The annual number of deaths has increased from 6000 in 1997 to an estimated 10920 in 2017 [1,2]. Moreover, in the last several years, obvious increases in morbidity related to EC have been observed in Asia [3-5].
According to previous research, certain external environmental factors, including obesity, nulliparity, unopposed estrogen exposure, early menarche, late menopause, and anovulation, have an impact on EC [6,7]. Nevertheless, the great majority of patients diagnosed with EC do not have any of these risk factors. Consequently, environmental factors cannot explain all cases of EC. Indeed, other studies have demonstrated that both genetic and environmental factors promote the pathogenesis and progression of EC [8,9].
Apoptosis, also called programmed cell death, is involved in physiological cell death. Many factors participate in the apoptotic pathway, including caspases, pro- and anti-apoptotic Bcl2 family members, and mitochondrial pro-apoptotic proteins. Previous studies have demonstrated that imbalances in apoptosis contribute to tumorigenesis in different tissues [10-16]. For example, programmed cell death 6 (PDCD6) is associated with apoptosis in the initial stage, and studies have demonstrated that PDCD6 can promote the development and progression of cancer.
In this study, we investigated whether the PDCD6 gene contributed to the development and progression of EC by genotyping of two tag single nucleotide polymorphisms (SNPs; rs3756712 and rs4957014) in the PDCD6 gene in 238 patients with EC and 518 controls and analyzed the associations between these two SNPs and EC risk.
Materials and methods
Study patients
In this case-control study, we enrolled Han women living in Sichuan province of southwest China. Patients with lethal diseases that could influence the final results were excluded, and 238 unrelated female patients with EC plus 518 female age-matched controls met the inclusion criteria and were recruited into the study. The age of patients with EC ranged from 25 to 81 years (mean ± standard deviation [SD], 51.98±9.86 years). The diagnosis of EC was made based on the pathologists’ histological examination of tissue from a biopsy at the Second University Hospital of Sichuan University from July 2013 to July 2014. We reviewed medical records of patients’ basic information, including age at diagnosis, menstrual status, pathological type, clinical stage, tumor differentiation, lymph node status, and parametrial invasion. The characteristics of the cases are shown in Table 1. The control group was composed of 518 healthy individuals, selected randomly from a physical examination at the same hospital. All controls had no abnormal clinical symptoms of EC, and all test results were normal (including gynecologic examination, B-mode ultrasound, and cervical cytology). Ethical approval for patient and control recruitment was obtained from the Medical Ethical Review Committee of West China Second University Hospital of Sichuan University, and all participants provided written or oral informed consent.
Table 1.
Descriptive characteristics of participants
| Characteristics | Number of cases (%) |
|---|---|
| Sample size | 238 |
| Mean age ± SD (range) | 51.98±9.862 (25-81) |
| Family history of cancer | |
| Yes | 21 (8.8%) |
| No | 217 (91.2%) |
| Menopausal status | |
| Premenopausal | 112 (47.1%) |
| Postmenopausal | 126 (52.9%) |
| Uterine bleeding | |
| Yes | 218 (91.6%) |
| No | 20 (8.4%) |
| FIGO stage | |
| 0 | 2 (0.84%) |
| I | 176 (73.95%) |
| II | 22 (9.24%) |
| III | 26 (10.92%) |
| IIII | 12 (5.04%) |
| FIGO grade | |
| G1 | 68 (28.6%) |
| G2 | 87 (36.6%) |
| G3 | 81 (34.0%) |
| Histology | |
| Endometrioid adenocarcinoma | 198 (83.2%) |
| Non-endometrioid adenocarcinoma | 40 (16.8%) |
| Myometrial invasion | |
| NO | 42 (17.6%) |
| <1/2 | 135 (56.7%) |
| ≥1/2 | 53 (22.3%) |
| Parametrial invasion | |
| NO | 198 (83.2%) |
| YES | 28 (11.8%) |
| Lymph node status | |
| NO | 186 (78.2%) |
| YES | 22 (9.2%) |
| IHC | |
| ER+ | 173/191 |
| PR+ | 168/190 |
| P53+ | 117/183 |
| KI67+ | 178/178 |
| BMI | |
| <32 | 215 (95.6%) |
| >32 | 10 (4.4%) |
DNA extraction and genotyping
DNA was extracted from 200 μL ethylenediaminetetraacetic acid-anticoagulated peripheral blood samples using a DNA isolation kit from Bioteke (Peking, China) and stored at -20°C. All procedures were implemented according to the manufacturer’s instructions. The polymorphic sites were selected from the public databases (dbSNP; http://www.ncbi.nlm.nih.gov/SNP/ and http://www.hapmap.org/). Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to genotype SNPs (rs3756712 and rs4957014) in the PDCD6 gene. Primers were created in the PIRA PCR designer (http://cedar.genetics.soton.ac.uk/public_html/ primer2.html) [17]. The primer sequences were as follows: 5’-TACAGTGGCAAAGGACCACA-3’ (forward), 5’-CACATTCCAGCACTCACCAC-3’ (reverse) for rs3756712 and 5’-TGGTGTTTCATACCATTGACACTTGC3’ (forward), 5’-CTCAGAACCAAGCAGGTTCCTTCA-3’ (reverse) for rs4957014. PCR was performed using a total volume of 25 μL, including 2.5 μL 10 × PCR buffer, 1.5 mM MgCl2, 0.15 mMdNTPs, 0.5 μM each primer, 100 ng genomic DNA, and 1 U Taq DNA polymerase. The PCR cycling conditions were as follows: an initial denaturing step for 5 min at 95°C; 33 cycles of 30 s at 95°C, 30 s at 60°C, and 30 s at 72°C; and a final extension step for 10 min at 72°C. PCR products were then digested overnight with restriction enzyme, and the digested PCR products were distinguished using 6% polyacrylamide gels stained with 1.5 g/L argent nitrates to detect the amplified products. The restriction enzyme for rs3756712 was RsaI, and allele G was cuttable, yielding two fragments of 66 and 99 bp; allele T was uncuttable, and the fragment was 165 bp. The restriction enzyme for rs4957014 was HphI, and allele G was cuttable, yielding two fragments of 13 and 100 bp; allele T was uncuttable, and the fragment was 113 bp. Ten percent of the samples were selected randomly to repeat the procedures, and the results were 100% concordant.
Statistical analysis
SPSS for Windows software package version 13.0 (SPSS Inc., Chicago, IL, USA) was used to analyze the data. Pearson’s chi-square tests were used to test Hardy-Weinberg equilibrium among the cases and controls. χ2 tests were used to analyze the allele and genotype frequency differences between cases and controls. Genotypic association tests in a case-control pattern, assuming codominant, dominant, recessive, and overdominant genetic models, were performed using SNPstats [18]. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the differences between alleles and genotypes. Probability values of 0.05 or less were considered statistically significant.
Results
SNPs in PDCD6 were successfully genotyped in participants and controls (Figure 1), and genotype distributions in control group were in line with the Hardy-Weinberg equilibrium.
Figure 1.
Photograph of the PDCD6 digested PCR product. A. Determination of SNP rs3756712 by PCR-polyacrylamide gel electrophoresis. M: 100-bp DNA marker; lanes 1, 2, 5 the TT homozygotes; lanes 3, 4 the GT heterozygotes; lanes 6, 7 the GG homozygote. B. Determination of SNP rs4957014 by PCR-polyacrylamide gel electrophoresis. M: 100-bp DNA marker; lanes 2, 8, 9, 10 the TT homozygotes; lanes 1, 3, 4, 5, 7 the GT heterozygotes; lane 6 the GG homozygote.
Allele frequencies of these two SNPs in patients with EC and controls are shown in Table 2. Significantly increased EC risk was found to be associated with the T allele of the SNP rs3756712 (P = 0.028, OR = 0.747, 95% CI = 0.576-0.969). As showed in Table 3, significant associations were observed with genotypes of rs3756712 and rs4957014 SNPs in different genetic models. For rs3756712, individuals with allele G (GT/GG genotype) in rs3756712 had a significantly decreased risk of EC compared with that of patients without allele G (TT genotypes) in the dominant model (P = 0.031, OR = 1.42, 95% CI = 1.03-1.94). For rs4957014, a significantly increased EC risk was associated with GT genotype carriers compared with TT homozygous carriers in the codominant model (P = 0.019, OR = 0.63, 95% CI = 0.45-0.87). In the dominant model, a significantly increased EC risk was associated with the G/T or G/G genotype compared with the T/T genotype (P = 0.0073, OR = 0.45, 95% CI = 0.47-0.89). Additionally, in the overdominant model, the G/T genotype was associated with increased EC risk compared with the T/T or G/G homozygous genotype (P = 0.0076, OR = 0.66, 95% CI = 0.48-0.90).
Table 2.
Allele frequencies of SNPs in PDCD6 among patients and controls and their association with EC risk
| SNP | Allele | Case | Control | P | OR (95% CI) |
|---|---|---|---|---|---|
| rs3756712 | T | 376 (79%) | 764 (74%) | 0.028 | 0.747 (0.576-0.969) |
| G | 100 (21%) | 272 (26%) | |||
| rs4957014 | T | 297 (62%) | 699 (67%) | 0.053 | 0.8 (0.638-1.003) |
| G | 179 (38%) | 337 (33%) |
Table 3.
Genotype frequencies of SNPs in PDCD6 among patients and controls in different genetic model
| SNP | Model | Genotype | Group = Ca | Group = Co | OR (95% CI) | P-value |
|---|---|---|---|---|---|---|
| rs3756712 | Codominant | T/T | 153 (64.3%) | 290 (56%) | 1 | 0.091 |
| G/T | 70 (29.4%) | 184 (35.5%) | 1.39 (0.99-1.94) | |||
| G/G | 15 (6.3%) | 44 (8.5%) | 1.55 (0.83-2.87) | |||
| Dominant | T/T | 153 (64.3%) | 290 (56%) | 1 | 0.031 | |
| G/T-G/G | 85 (35.7%) | 228 (44%) | 1.42 (1.03-1.94) | |||
| Recessive | T/T-G/T | 223 (93.7%) | 474 (91.5%) | 1 | 0.29 | |
| G/G | 15 (6.3%) | 44 (8.5%) | 1.38 (0.75-2.53) | |||
| Overdominant | T/T-G/G | 168 (70.6%) | 334 (64.5%) | 1 | 0.096 | |
| G/T | 70 (29.4%) | 184 (35.5%) | 1.32 (0.95-1.84) | |||
| rs4957014 | Codominant | T/T | 83 (34.9%) | 234 (45.2%) | 1 | 0.019 |
| G/T | 131 (55%) | 231 (44.6%) | 0.63 (0.45-0.87) | |||
| G/G | 24 (10.1%) | 53 (10.2%) | 0.78 (0.45-1.35) | |||
| Dominant | T/T | 83 (34.9%) | 234 (45.2%) | 1 | 0.0073 | |
| G/T-G/G | 155 (65.1%) | 284 (54.8%) | 0.65 (0.47-0.89) | |||
| Recessive | T/T-G/T | 214 (89.9%) | 465 (89.8%) | 1 | 0.95 | |
| G/G | 24 (10.1%) | 53 (10.2%) | 1.02 (0.61-1.69) | |||
| Overdominant | T/T-G/G | 107 (45%) | 287 (55.4%) | 1 | 0.0076 | |
| G/T | 131 (55%) | 231 (44.6%) | 0.66 (0.48-0.90) |
The results of stratified analyses by clinical characteristics are presented in Tables 4 and 5. Notably, rs3756712 was associated with body mass index (BMI) and parametrial invasion (Table 4), and rs4957014 was associated with BMI (Table 5), although this association was not statistically significant (P = 0.065, OR = 4.42, 95% CI = 1.06-18.51). No statistically significant differences in genotype or allele distribution were observed between the remaining clinical characteristics and these two SNPs.
Table 4.
Association between rs3756712 and patient’s characteristics
| Characteristics | Genotype | Genetic model | |||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| T/T | G/T | G/G | Dominant (TT VS. GT/GG) | Recessive (GG VS. TT/GT) | Overdominant (TT/GG VS. GT) | ||||
|
| |||||||||
| OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | ||||
| Age | |||||||||
| <52 year | 73 (66.4%) | 29 (26.4%) | 8 (7.3%) | 1.18 (0.69-2.02) | 0.53 | 0.74 (0.26-2.10) | 0.57 | 1.32 (0.75-2.31) | 0.34 |
| ≥52 year | 80 (62.5%) | 41 (32%) | 7 (5.5%) | ||||||
| Figo stage | |||||||||
| I | 117 (65.7%) | 49 (27.5%) | 12 (6.7%) | 1.28 (0.70-2.33) | 0.43 | 0.73 (0.20-2.67) | 0.62 | 1.42 (0.76-2.65) | 0.28 |
| II-IV | 36 (60%) | 21 (35%) | 3 (5%) | ||||||
| Grade | |||||||||
| G1 | 39 (57.4%) | 22 (32.4%) | 7 (10.3%) | 0.65 (0.37-1.17) | 0.15 | 0.44 (0.15-1.25) | 0.13 | 0.81 (0.44-1.49) | 0.51 |
| G2-G3 | 113 (67.3%) | 47 (28%) | 8 (4.8%) | ||||||
| BMI | |||||||||
| <32 | 139 (64.7%) | 66 (30.7%) | 10 (4.7%) | 1.22 (0.33-4.45) | 0.77 | 8.79 (1.97-39.14) | 0.012 | 0.25 (0.03-2.02) | 0.12 |
| >32 | 6 (60%) | 1 (10%) | 3 (30%) | ||||||
| Pathological type | |||||||||
| Endometrioid adenocarcinoma | 126 (63.6%) | 59 (29.8%) | 13 (6.6%) | 0.84 (0.41-1.73) | 0.64 | 0.75 (0.16-3.46) | 0.7 | 0.89 (0.42-1.91) | 0.77 |
| Non-endometrioid adenocarcinoma | 27 (67.5%) | 11 (27.5%) | 2 (5%) | ||||||
| Parametrial invasion | |||||||||
| NO | 126 (63.6%) | 57 (28.8%) | 15 (7.6%) | 0.97 (0.43-2.22) | 0.95 | 0.00 (0.00-NA) | 0.042 | 1.37 (0.60-3.16) | 0.46 |
| YES | 18 (64.3%) | 10 (35.7%) | 0 (0%) | ||||||
| Menopausal status | |||||||||
| Premenopausal | 72 (64.3%) | 31 (27.7%) | 9 (8%) | 1.00 (0.59-1.70) | 1 | 0.57 (0.20-1.66) | 0.3 | 1.17 (0.67-2.05) | 0.58 |
| Postmenopausal | 81 (64.3%) | 39 (30.9%) | 6 (4.8%) | ||||||
| Uterine bleeding | |||||||||
| NO | 14 (70%) | 3 (15%) | 3 (15%) | 1.33 (0.49-3.59) | 0.57 | 0.33 (0.08-1.28) | 0.14 | 2.51 (0.71-8.87) | 0.12 |
| YES | 139 (63.8%) | 67 (30.7%) | 12 (5.5%) | ||||||
Table 5.
Association between rs4957014 and patient’s characteristics
| Characteristics | Genotype | Genetic model | |||||||
|---|---|---|---|---|---|---|---|---|---|
|
| |||||||||
| T/T | G/T | G/G | Dominant (TT VS. GT/GG) | Recessive (GG VS. TT/GT) | Overdominant (TT/GG VS. GT) | ||||
|
| |||||||||
| OR (95% CI) | P-value | OR (95% CI) | P-value | OR (95% CI) | P-value | ||||
| Age | |||||||||
| <52 year | 37 (33.6%) | 60 (54.5%) | 13 (11.8%) | 0.90 (0.53-1.54) | 0.71 | 0.70 (0.30-1.64) | 0.41 | 1.04 (0.62-1.73) | 0.89 |
| ≥52 year | 46 (35.9%) | 71 (55.5%) | 11 (8.6%) | ||||||
| Figo stage | |||||||||
| I | 63 (35.4%) | 97 (54.5%) | 18 (10.1%) | 1.10 (0.59-2.03) | 0.77 | 0.99 (0.37-2.62) | 0.98 | 1.09 (0.61-1.97) | 0.77 |
| II-IV | 20 (33.3%) | 34 (56.7%) | 6 (10%) | ||||||
| Grade | |||||||||
| G1 | 24 (35.3%) | 35 (51.5%) | 9 (13.2%) | 1.01 (0.56-1.82) | 0.98 | 0.60 (0.24-1.45) | 0.26 | 1.23 (0.70-2.16) | 0.48 |
| G2-G3 | 59 (35.1%) | 95 (56.5%) | 14 (8.3%) | ||||||
| BMI | |||||||||
| <32 | 78 (36.3%) | 118 (54.9%) | 19 (8.8%) | 2.28 (0.47-10.99) | 0.27 | 4.42 (1.06-18.51) | 0.065 | 0.82 (0.23-2.92) | 0.76 |
| >32 | 2 (20%) | 5 (50%) | 3 (30%) | ||||||
| Pathological type | |||||||||
| Endometrioid adenocarcinoma | 69 (34.9%) | 107 (54%) | 22 (11.1%) | 0.99 (0.49-2.03) | 0.99 | 0.42 (0.10-1.87) | 0.21 | 1.28 (0.64-2.55) | 0.49 |
| Non-endometrioid adenocarcinoma | 14 (35%) | 24 (60%) | 2 (5%) | ||||||
| Parametrial invasion | |||||||||
| NO | 70 (35.4%) | 106 (53.5%) | 22 (11.1%) | 1.15 (0.50-2.69) | 0.74 | 0.30 (0.04-2.29) | 0.17 | 1.56 (0.69-3.55) | 0.28 |
| YES | 9 (32.1%) | 18 (64.3%) | 1 (3.6%) | ||||||
| Menopausal status | |||||||||
| Premenopausal | 37 (33%) | 61 (54.5%) | 14 (12.5%) | 0.86 (0.50-1.47) | 0.57 | 0.60 (0.26-1.42) | 0.24 | 1.05 (0.63-1.74) | 0.87 |
| Postmenopausal | 46 (36.5%) | 70 (55.6%) | 10 (7.9%) | ||||||
| Uterine bleeding | |||||||||
| NO | 7 (35%) | 11 (55%) | 2 (10%) | 1.01 (0.39-2.63) | 0.99 | 1.01 (0.22-4.65) | 0.99 | 1.00 (0.40-2.52) | 1 |
| YES | 76 (34.9%) | 120 (55%) | 22 (10.1%) | ||||||
Discussion
PDCD6, located on chromosome 5p15.33, is also referred to as apoptosis-linked gene-2 (ALG-2). The structural characteristics of this 22-kDa calcium-binding protein include a penta-EF-hand domain composed of five serially similar EF-hand structures [19,20]. Previous studies have demonstrated that PDCD6 can shuttle back and forth between the cytoplasm and nucleus, and the distribution is dependent on its interactions with distinct proteins [21]. Initially, PDCD6 was characterized as a pro-apoptotic protein and acted in T-cell receptor-, Fas-, and glucocorticoid-induced apoptosis [22,23], endoplasmic reticulum stress-induced cell death, neuronal apoptosis during organ formation, and some other physiological processes, such as signal transduction, membrane trafficking, and post-transcriptional control of gene expression. However, when the PDCD6 gene is knocked out in mice, apoptosis is not suppressed, indicating that the function of PDCD6 is redundant [19].
Many studies have analyzed PDCD6 expression in tumor tissues or cell lines and shown that this protein has contradictory effects in various tumors. For example, PDCD6 has been shown to act as an oncogene or tumor-suppressor gene in different types of cancers. PDCD6 is upregulated in lung cancer, breast cancer [24], colon cancer [25], hepatoma, and metastatic ovarian cancer cells. Moreover, in metastatic ovarian cancer cells, PDCD6 promotes the migration and invasion of cells [26,27], and patients with epithelial ovarian cancer having intermediate or high PDCD6 mRNA expression are at risk of contracting other diseases [26]. However, the relation between PDCD6 expression and survival is still not clear. Qin et al. observed a marked elevation in PDCD6 expression in breast cancer tissues and evaluated the relationships between PDCD6 expression and clinicpathological characteristics [24]. Moreover, PDCD6 has been shown to contribute to cancer cell viability [25,27], and decreased PDCD6 expression has been identified in gastric cancer [25,26], pediatric acute myelogenous leukemia [28], HeLa cells [29,30], and Mel290 cells. In glioblastoma, Zhang et al. identified PDCD6 as a novel tumor-suppressor gene. High levels of PDCD6 expression predicted a good prognosis, and downregulation of PDCD6 promoted the proliferation and migration of glioblastoma cells [31]. In additional studies, researchers have shown that there is a connection between high miR-183 expression plus low PDCD6 expression and aggressive clinicpathological features [28], and Gu et al. showed that miR-183 had a suppression effect on apoptosis and contributed to the proliferation and invasion of cancer cells by targeting PDCD4 [32]. Recent reports have also found decreased copy numbers and mRNA expression of PDCD6 in gastric cancer samples, suggesting that PDCD6 may be a significant prognostic biomarker in patients with advanced gastric cancer [33]. Additionally, compared with normal melanocytes, Mel290 cells express lower levels of PDCD6 mRNA and protein [29].
As an essential step in the development of tumors, angiogenesis plays an important role in tissue repair, tumor growth, and tumor metastasis. PDCD6 can block tumor development by suppressing angiogenesis under physiological conditions through targeting of the p70 ribosomal protein S6 kinase or phosphatidylinositol 3-kinase/mammalian target of rapamycin pathway [34]. According to a study by Rho et al., purified recombinant human PDCD6 blocked the invasion and proliferation of cells in a concentration- and time-dependent manner, and vascular endothelial growth factor-induced proliferation, invasion, and capillary-like structure tube formation in vitro were suppressed by PDCD6 overexpression. These findings demonstrated that PDCD6 is important for the modulation of cellular angiogenesis [34]. PDCD6 is also considered a novel p53-responsive gene and has been shown to accumulate in the nucleus, supporting its role in DNA damage-induced apoptosis. p53-dependent apoptosis is suppressed by PDCD6 knockdown [35], and knockdown of PDCD6 in HeLa cells increases the number of dead cells, enhances early apoptosis, and induces the accumulation of cells in the G2/M phase of the cell cycle. PDCD6 also promotes passage through cell cycle checkpoints, giving it an anti-apoptotic role in HeLa cells [30]. Because PDCD6 expression promotes apoptosis through activation of the mitochondrial pathway and is closely linked to the prognosis of patients with advanced gastric cancer, PDCD6 may be a molecular biomarker for the prognosis of gastric cancer [33]. Many PDCD6-interacting proteins have been shown to function in microtubule-related activities, and HEBP2 and PDCD6 coordinately regulate spindle orientation and positioning. Additionally, depletion of PDCD6 increases cytoplasmic calcium levels in breast cancer cells, suggesting that this protein may be involved in calcium signaling under physiological conditions [36-38].According to a study by Li et al., PDCD6 acts as a lysosome calcium sensor, physically associating with the minus-end-directed dynes-dynasty motor for regulation of lysosome positioning and motility [39].
Centrosome aberrations, including functional and structural defects, have been observed in tumorigenesis [40]. According to Qin et al., the localization of γ-tubulin and patricentric is disrupted by PDCD6 overexpression in breast cancer cells, leading to the formation of centrosome protein aggregates. Additionally, PDCD6 regulates the rearrangement of the cytoskeleton to drive migration and polarization in breast cancer cells [24]. Montaville et al. found a link between alternative splicing regulation and PDCD6, showing effects on basic cellular processes during cancer development [21]. Moreover, Maki reported that PDCD6 serves as a bridge and functions as an adaptor molecule [19,20].
To the best of our knowledge, this is the first report discussing the relationship between the polymorphisms in PDCD6 and EC in China. Our results demonstrated that the T allele of rs3756712 may increase EC risk and provided evidence that PDCD6 may serve as a biomarker for EC susceptibility.
However, our study had some limitations. First, we only enrolled 238 patients with EC and 518 controls, which may limit the statistical power of the analysis. Further studies genotyping more polymorphisms in the PDCD6 gene using a large, diverse population could contribute to identification of the true association between these SNPs and EC. Furthermore, the expression of PDCD6 in tumor tissues and cell lines, the influence of SNPs on EC tumorigenesis, and the molecular mechanisms through which PDCD6 is involved in the development of EC should be studied to further improve our understanding of the relationship between PDCD6 and EC risk.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 81572573) and the Applied Basic Research Programs of Science and Technology Commission Foundation of Sichuan Province (No. 2016SZ0013).
Disclosure of conflict of interest
None.
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