Abstract
Fibroblast growth factors (FGFs) and their receptors (FGFRs) have been implicated in prostate growth and are overexpressed in benign prostatic hyperplasia (BPH). In this study, we investigated whether single nucleotide polymorphisms (SNPs) of the FGFR genes (FGFR1 and FGFR2) were associated with BPH and its clinical phenotypes in a population of Korean men. We genotyped four SNPs in the exons of FGFR1 and FGFR2 (rs13317 in FGFR1; rs755793, rs1047100, and rs3135831 in FGFR2) using direct sequencing in 218 BPH patients and 213 control subjects. No SNPs of FGFR1 or FGFR2 genes were associated with BPH. However, analysis according to clinical phenotypes showed that rs1047100 of FGFR2 was associated with prostate volume in BPH in the dominant model (GA/AA versus GG, P = 0.010). In addition, a significant association was observed between rs13317 of FGFR1 and international prostate symptom score (IPSS) in the additive (TC versus CC versus TT, P = 0.0022) and dominant models (TC/CC versus TT, P = 0.005). Allele frequency analysis also showed significant association between rs13317 and IPSS (P = 0.005). These results suggested that FGFR genes could be related to progression of BPH.
1. Introduction
Benign prostatic hyperplasia (BPH) is the most common urological problem associated with aging in men. One-quarter of men in their 50s, one-third in their 60s, and half of men older than 80 have BPH [1]. BPH is characterized by hyperplasia of prostatic stromal and epithelial cells, and it manifests as a severe obstruction in urinary flow with discomfort and pain. The pathogenesis of BPH is not completely understood; however, the most significant risk factors for the development of BPH are androgen level and aging [2]. Growth factors and their receptors, including members of the fibroblast growth factor (FGF) family, insulin-like growth factor (IGF) family, epithelial growth factor (EGF) family, and transforming growth factor β (TGFB), which regulate the growth of prostatic stromal and epithelial cells, are also involved in the pathogenesis of BPH [3–5].
Genetic strategies have been used over the past few decades to investigate BPH. In particular, studies have shown that polymorphisms of growth factors and their receptor genes are associated with BPH. Indeed, previous studies have reported that a codon 10 polymorphism in TGFB1 was associated with the development of BPH in Japanese [6] and Iranian populations [7], suggesting the importance of the TGF pathway in the development of prostatic diseases. Mullan et al. [8] reported a significant association of a codon 10 polymorphism in TGFB1 with treatment for BPH, and an association of a CA-repeat polymorphismin EGFR with international prostate symptom score (IPSS) in BPH. Moreover, a CA-repeat polymorphism of 19-allele in IGF1 appears to increase the risk of BPH with a gene dosage effect in the Japanese population [9]. However, despite a widespread consensus on the involvement of growth factors in prostatic growth, attempts to address growth factor gene polymorphisms in patients with BPH have been limited.
FGFs are involved in multiple biological processes such as differentiation, motility, and proliferation and mediate their cellular responses by activating a family of four receptor tyrosine kinases, FGFR1 through FGFR4 [10]. FGFRs are present in various organs. In the human prostate, FGFR1-3 are abundantly expressed in the prostate stroma and/or epithelium [11–14], and FGFR4 has relatively low expression in the epithelium [15]. In the prostate, FGFRs play an important role in prostate organogenesis, and perturbations in FGFR expression have been potently implicated in prostatic disease [11]. Previous studies have reported that overexpression of FGFRs is important in the development of BPH [16–18]. Indeed, FGFR1 was observed to be increased in the stroma of BPH patients compared to that from normal prostates [16, 17], and increased expression of FGFR2 has been detected in BPH [18]. Furthermore, although no studies have reported changes in expression of FGFR4 in BPH, a genetic study showed a significant association between FGFR4 polymorphisms and BPH in a Japanese population [19]. In light of these findings, we postulated that FGFRs may be involved in the pathogenesis of BPH. In this study, we investigated the genetic associations between BPH and the FGFR genes (FGFR1 and FGFR2) in Korean BPH patients by analyzing single nucleotide polymorphisms (SNPs) of the FGFR genes.
2. Methods
2.1. Subjects
A total of 233 male patients with BPH (mean age ± standard deviation, 65.77 ± 9.46 years) and 213 male control subjects (mean age, 61.89 ± 8.26 years) were enrolled. All patients with BPH were from Kyung Hee University Hospital between January 2002 and December 2008, and in all patients, lower urinary tract symptoms were quantified using IPSS. Uroflowmetry was performed to measure peak urinary flow rate (Qmax) for all patients. Serum prostate-specific antigen (PSA) level was measured in all BPH patients. Patients with serum PSA level more than 4 ng/mL underwent transrectal ultrasound-guided prostate biopsy to rule out prostate cancer. Prostate size was assessed using transrectal ultrasound. Patients with prostate cancer, neurogenic bladder, urinary tract infection, uncontrolled diabetes mellitus, or cardiovascular disease were excluded. The clinical characteristics of BPH patients are summarized in Table 1. To determine the relationship between polymorphisms of FGFR genes and clinical phenotypes, BPH patients were divided into subgroups according to prostate volume (<30 mL or ≥30 mL), PSA level (<1.5 or ≥1.5 ng/mL), IPSS (<20 or ≥20), and (Qmax) (<10 or ≥10 mL/sec) [20, 21].
Table 1.
Clinical characteristics of benign prostatic hyperplasia (BPH) patients and control subjects.
| BPH | Control | |
|---|---|---|
| No. of subjects | 233 | 213 |
| Age (mean ± S.D.) | 65.77 ± 9.46 | 61.89 ± 8.26 |
| Prostate volume (mL) | ||
| Total | 38.34 ± 21.38 | |
| Inner | 17.99 ± 18.60 | |
| PSA (ng/mL) | ||
| Total | 4.38 ± 5.24 | |
| Free | 0.99 ± 1.22 | |
| IPSS | 17.05 ± 7.66 | |
| QoL | 3.61 ± 1.34 | |
| Uroflowmetry (mL/s) | ||
| Qmax | 11.33 ± 5.77 | |
| Qavg | 6.46 ± 3.67 | |
| VV (mL) | 109.00 ± 148.33 | |
| PVR (mL) | 57.59 ± 98.68 |
PSA: prostate-specific antigen; IPSS: international prostate symptom score; QoL: quality of life; Qmax: peak urinary flow rate; Qavg: average urinary flow rate; VV: voided volume; PVR: postvoid residual urine.
Normal healthy controls were recruited from men visiting the hospital for routine health checkups. All healthy control subjects underwent screening and had a normal PSA level (<4.0 ng/mL). They showed no clinical evidence of BPH, neurogenic bladder, urinary tract infection, diabetes mellitus, cardiovascular disease, or any other severe diseases.
Written informed consent was obtained from all patients and control subjects for the use of clinical data and samples, including DNA extracted from peripheral blood. The Institutional Review Board at Kyung Hee University Medical Center approved the protocol for this study.
2.2. SNP Selection and Genotyping
SNPs located in the exons [5′-untranslated region (UTR), coding region, and 3′UTR] of the FGFR1 and FGFR2 genes were selected from the National Center for Biotechnology Information SNP database (http://www.ncbi.nlm.nih.gov/projects/SNP/, BUILD 137). We excluded SNPs without data on genotype frequency, and those with a minor allele frequency (MAF) < 0.05 in Chinese and Japanese populations. Finally, we selected four SNPs [rs13317 (T/C, 3′UTR) for FGFR1; rs755793 (Met186Thr), rs1047100 (Val232Val) and rs3135831 (C/T, 3′UTR) for FGFR2]. DNA was isolated from peripheral blood samples using a DNA Isolation Kit for blood (Roche, IN, USA). SNP genotyping was conducted by direct sequencing using specific primers for rs13317 (sense: 5′-CCAACTTAGTGAAACCCCATCT-3′; antisense: 5′-CCCAACAAATACAGTCTGGTCA-3′), rs755793 (sense: 5′-TACTCATGGAGGGGAAGCTG-3′; antisense: 5′-CTGACATGGGCAATTGTGAC-3′), rs1047100 (sense: 5′-CATACCTTTCTTGCCTCCTTCA-3′; antisense: 5′-CAGAAGCAGCCTTGTAAAATGA-3′) and rs3135831 (sense: 5′-TGTATTTCCCAAACCTCTGTCC-3′; antisense: 5′-CACTGTCAAGGCTATAAACTGC-3′). PCR products were sequenced using an ABI PRISM 3730XL analyzer (PE Applied Biosystems, Foster City, CA, USA). Sequence data were analyzed using SeqManII software (DNASTAR Inc., Madison, WI, USA).
2.3. Statistical Analysis
SNPStats (http://bioinfo.iconcologia.net/index.php) and SPSS 18.0 software (SPSS Inc., Chicago, IL, USA) were used to analyze genetic data and determine Hardy-Weinberg equilibrium (HWE). Associations between SNPs and BPH, as well as any associations between the SNPs and BPH subgroups, were estimated by computing odds ratios (ORs) and 95% confidence intervals (CIs) with logistic regression analysis, controlling for age as a covariable. In the logistic regression analysis for each SNP, the models assuming additive inheritance (the risk increased r-fold for subjects with one minor allele and 2r-fold for subjects with two minor alleles), dominant inheritance (subjects with one or two minor alleles had the same relative risk for the disease), or recessive inheritance (subjects with two minor alleles were at increased risk of the disease) were used. To avoid chance findings due to multiple comparison, the Bonferroni correction was applied by lowering significance levels to P = 0.05/4 for the 4 SNPs.
3. Results
All SNPs analyzed in this study were polymorphic, and the genotype distributions of the SNPs were in HWE (P > 0.05). Differences in genotype distributions and allele frequencies for the four SNPs between BPH and control were analyzed. As shown in Table 2, rs1047100 of FGFR2 was associated with BPH in the additive (GA versus AA versus GG; P = 0.024, OR = 1.92, 95% CI = 1.07–3.43) and dominant models (GA/AA versus GG; P = 0.034, OR = 1.89, 95% CI = 1.04–3.45). Allele frequency analysis also showed an association between rs1047100 and BPH (P = 0.029, OR = 1.85, 95% CI = 1.06–3.23). However, the statistical significance did not remain after Bonferroni correction.
Table 2.
Frequencies of the genotypes and alleles of polymorphisms of FGFR genes in BPH patients and control subjects.
| Gene | SNP | Genotype/allele | Control | BPH | Models | OR (95% CI) | P |
|---|---|---|---|---|---|---|---|
| n = 213 (%) | n = 233 (%) | ||||||
| FGFR1 | rs13317 | T/T | 85 (39.9) | 83 (35.6) | Additive | 1.11 (0.83–1.47) | 0.48 |
| T/C | 100 (47.0) | 116 (49.8) | Dominant | 1.16 (0.79–1.72) | 0.45 | ||
| C/C | 28 (13.2) | 34 (14.6) | Recessive | 1.09 (0.63–1.90) | 0.75 | ||
| T | 270 (63.4) | 282 (60.5) | 1 | ||||
| C | 156 (36.6) | 184 (39.5) | 1.13 (0.86–1.48) | 0.38 | |||
|
| |||||||
| FGFR2 | rs755793 | T/T | 193 (90.6) | 208 (89.3) | Additive | 1.08 (0.59–2.00) | 0.80 |
| T/C | 20 (9.4) | 24 (10.3) | Dominant | 1.04 (0.55–1.96) | 0.90 | ||
| C/C | 0 (0.0) | 1 (0.4) | Recessive | NA (0.00-NA) | NA | ||
| T | 406 (95.3) | 440 (94.4) | 1 | ||||
| C | 20 (4.7) | 26 (5.6) | 1.20 (0.66–2.18) | 0.55 | |||
| rs1047100 | G/G | 193 (90.6) | 196 (84.1) | Additive | 1.92 (1.07–3.43) | 0.024 | |
| G/A | 20 (9.4) | 35 (15.0) | Dominant | 1.89 (1.04–3.45) | 0.034 | ||
| A/A | 0 (0.0) | 2 (0.9) | Recessive | NA (0.00-NA) | NA | ||
| G | 406 (95.3) | 427 (91.6) | 1 | ||||
| A | 20 (4.7) | 39 (8.4) | 1.85 (1.06–3.23) | 0.029 | |||
| rs3135831 | C/C | 92 (43.2) | 116 (49.8) | Additive | 0.83 (0.63–1.09) | 0.17 | |
| C/T | 88 (41.3) | 90 (38.6) | Dominant | 0.80 (0.55–1.18) | 0.26 | ||
| T/T | 33 (15.5) | 27 (11.6) | Recessive | 0.72 (0.41–1.25) | 0.24 | ||
| C | 272 (63.8) | 322 (69.1) | 1 | ||||
| T | 154 (36.2) | 144 (30.9) | 0.79 (0.60–1.04) | 0.10 | |||
We further analyzed the associations between SNPs and the clinical phenotypes of BPH (prostate volume, PSA level, IPSS, and Qmax). In analysis according to small or large prostate volume (<30 or ≥30 mL), we found that rs1047100 of FGFR2 was significantly associated with prostate volume in the additive (GA versus AA versus GG; P = 0.016, OR = 0.43, 95% CI = 0.21–0.87) and dominant models (GA/AA versus GG; P = 0.010, OR = 0.38, 95% CI = 0.18–0.81) (Table 3). However, statistical significance was only maintained in the dominant model after Bonferroni correction. The frequency of genotypes containing the A allele was lower in BPH patients with large prostate volume (GA = 9.6%, AA = 0.8%), compared to those with small prostate volume (GA = 21.5%, AA = 0.9%). Although allele frequency analysis also revealed that rs1047100 was associated with prostate volume (P = 0.021, OR = 0.45, 95% CI = 0.23–0.89), this result was not significant after Bonferroni correction.
Table 3.
Frequencies of the genotypes and alleles of polymorphisms of FGFR genes, based on small and large prostate volume, in subjects with BPH.
| Gene | SNP | Genotype/allele | Prostate volume (mL) | Models | OR (95% CI) | P | |
|---|---|---|---|---|---|---|---|
| <30 n = 107 (%) |
≥30 n = 125 (%) |
||||||
| FGFR1 | rs13317 | T/T | 36 (33.6) | 46 (36.8) | Additive | 1.03 (0.69–1.52) | 0.89 |
| T/C | 58 (54.2) | 58 (46.4) | Dominant | 0.88 (0.50–1.53) | 0.64 | ||
| C/C | 13 (12.2) | 21 (16.8) | Recessive | 1.41 (0.66–3.01) | 0.38 | ||
| T | 130 (60.7) | 150 (60.0) | 1 | ||||
| C | 84 (39.3) | 100 (40.0) | 1.03 (0.71–1.50) | 0.87 | |||
|
| |||||||
| FGFR2 | rs755793 | T/T | 93 (86.9) | 114 (91.2) | Additive | 0.54 (0.24–1.21) | 0.13 |
| T/C | 13 (12.2) | 11 (8.8) | Dominant | 0.55 (0.23–1.30) | 0.17 | ||
| C/C | 1 (0.9) | 0 (0.0) | Recessive | 0.00 (0.00-NA) | NA | ||
| T | 199 (93.0) | 239 (95.6) | 1 | ||||
| C | 15 (7.0) | 11 (4.4) | 0.61 (0.27–1.36) | 0.23 | |||
| rs1047100 | G/G | 83 (77.6) | 112 (89.6) | Additive | 0.43 (0.21–0.87) | 0.016 | |
| G/A | 23 (21.5) | 12 (9.6) | Dominant | 0.38 (0.18–0.81) | 0.010 | ||
| A/A | 1 (0.9) | 1 (0.8) | Recessive | 0.70 (0.04–11.33) | 0.80 | ||
| G | 189 (88.3) | 236 (94.4) | 1 | ||||
| A | 25 (11.7) | 14 (5.6) | 0.45 (0.23–0.89) | 0.021 | |||
| rs3135831 | C/C | 50 (46.7) | 65 (52) | Additive | 0.92 (0.63–1.36) | 0.68 | |
| C/T | 44 (41.1) | 46 (36.8) | Dominant | 0.88 (0.52–1.49) | 0.62 | ||
| T/T | 13 (12.2) | 14 (11.2) | Recessive | 0.96 (0.42–2.17) | 0.91 | ||
| C | 144 (67.3) | 176 (70.4) | 1 | ||||
| T | 70 (32.7) | 74 (29.6) | 0.87 (0.58–1.28) | 0.47 | |||
Bold characters represent statistically significant values (P < 0.05/4).
We also found a significant association between rs13317 of FGFR1 and IPSS in an analysis according to low or high IPSS (<20 or ≥20). As shown in Table 4, rs13317 of FGFR1 was associated with IPSS in the additive (TC versus CC versus TT, P = 0.0022, OR = 0.50, 95% CI = 0.32–0.79) and dominant models (TC/CC versus TT, P = 0.005, OR = 0.43, 95% CI = 0.23–0.78). Allele frequency analysis also revealed that rs13317 of FGFR1 was associated with IPSS (P = 0.005, OR = 0.55, 95% CI = 0.36–0.84). In particular, the frequency of the C allele was decreased in BPH patients with high IPSS (30.3%) compared to those with low IPSS (44.2%). These results remained significant after Bonferroni correction.
Table 4.
Frequencies of the genotypes and alleles of polymorphisms of FGFR genes, based on low and high international prostate symptom score (IPSS), in subjects with BPH.
| Gene | SNP | Genotype/allele | IPSS | Models | OR (95% CI) | P | |
|---|---|---|---|---|---|---|---|
| <20 n = 130 (%) |
≥20 n = 76 (%) |
||||||
| FGFR1 | rs13317 | T/T | 37 (28.5) | 36 (47.4) | Additive | 0.50 (0.32–0.79) | 0.0022 |
| T/C | 71 (54.6) | 34 (44.7) | Dominant | 0.43 (0.23–0.78) | 0.005 | ||
| C/C | 22 (16.9) | 6 (7.9) | Recessive | 0.40 (0.15–1.03) | 0.044 | ||
| T | 145 (55.8) | 106 (69.7) | 1 | ||||
| C | 115 (44.2) | 46 (30.3) | 0.55 (0.36–0.84) | 0.005 | |||
|
| |||||||
| FGFR2 | rs755793 | T/T | 113 (86.9) | 70 (92.1) | Additive | 0.53 (0.20–1.37) | 0.17 |
| T/C | 16 (12.3) | 6 (7.9) | Dominant | 0.54 (0.20–1.44) | 0.20 | ||
| C/C | 1 (0.8) | 0 (0) | Recessive | 0.00 (0.00-NA) | NA | ||
| T | 242 (93.1) | 146 (96.1) | 1 | ||||
| C | 18 (6.9) | 6 (3.9) | 0.55 (0.21–1.42) | 0.22 | |||
| rs1047100 | G/G | 107 (82.3) | 67 (88.2) | Additive | 0.60 (0.27–1.32) | 0.19 | |
| G/A | 21 (16.1) | 9 (11.8) | Dominant | 0.63 (0.27–1.45) | 0.27 | ||
| A/A | 2 (1.5) | 0 (0) | Recessive | 0.00 (0.00-NA) | NA | ||
| G | 235 (90.4) | 143 (94.1) | 1 | ||||
| A | 25 (9.6) | 9 (5.9) | 0.59 (0.27–1.30) | 0.19 | |||
| rs3135831 | C/C | 65 (50) | 36 (47.4) | Additive | 0.91 (0.60–1.39) | 0.67 | |
| C/T | 46 (35.4) | 35 (46) | Dominant | 1.17 (0.66–2.07) | 0.60 | ||
| T/T | 19 (14.6) | 5 (6.6) | Recessive | 0.41 (0.15–1.15) | 0.07 | ||
| C | 176 (67.7) | 107 (70.4) | 1 | ||||
| T | 84 (32.3) | 45 (29.6) | 0.88 (0.57–1.36) | 0.57 | |||
Bold characters represent statistically significant values (P < 0.05/4).
In analysis according to other clinical phenotypes (PSA level and Qmax), we were not able to find any association of polymorphisms in the FGFR genes (data not shown).
4. Discussion
We examined the association of polymorphisms of FGFR1 and FGFR2 with BPH and its clinical phenotypes in a Korean population. No significant association was detected between polymorphisms of FGFR1 and FGFR2, and BPH. However, in analysis according to clinical phenotypes, we found associations between rs1047100 of FGFR2 and prostate volume and between rs13317 of FGFR1 and IPSS.
BPH is a progressive disease found in many men, and numerous factors, including androgen level and aging, have been linked with the risk of BPH progression [22–24]. Prostate volume is the most extensively studied risk factor for BPH progression [25, 26]. Indeed, it was reported that men with prostate volume ≥ 30 mL were more likely to suffer moderate-to-severe symptoms (3.5-fold increase), decreased flow rates (2.5-fold increase), and acute urinary retention (3- to 4-fold increase), compared to men with prostate volume < 30 mL [27]. Reduced urinary flow, increased IPSS, and increased PSA have been also suggested as predictors of BPH progression [28]. Clinical study demonstrated that men with prostate volume ≥ 31 mL, PSA ≥ 1.6 ng/mL, or Qmax < 10.6 mL/sec at baseline had a significantly increased risk of overall clinical progression of BPH [29]. Although IPSS and PSA are simple BPH diagnostic factors used in the primary care setting, a previous study showed a high correlation between BPH diagnosed by simple tests (medical history, IPSS, digital rectal examination (DRE), and PSA) and that diagnosed by a full battery of tests including ultrasonographic assessment of residual and prostatic volume, and uroflowmetry [30]. Thus, these factors may also be useful as predictors of BPH progression. In our study, although polymorphisms of FGFR1 and FGFR2 were not associated with PSA level or Qmax in BPH patients, rs1047100 of FGFR2 and rs13317 of FGFR1 were associated with prostate volume and IPSS, respectively. These results indicated that polymorphisms of FGFR1 and FGFR2 may be related to the severity of BPH and implicated in the progression rather than the incidence of BPH. In particular, we found that the frequency of genotype containing the minor allele, A, of rs1047100 in FGFR2 was lower in BPH patients with large prostate volume than in those with small prostate volume. In addition, the frequency of the minor allele, C, of rs13317 in FGFR1 was significantly decreased in BPH patients with high IPSS. These finding indicated that patients with genotypes containing the A allele of rs1047100 or the C allele of rs13317 may be protected from severe progression of BPH.
Previous studies reported that FGFRs were abundantly expressed in the normal prostate [11–14], and that the expression of FGFR1 and FGFR2 was elevated in prostates of BPH patients [16–18]. To our knowledge, there are no reports indicating that the expression of FGFR1 or FGFR2 is upregulated or downregulated according to the alleles of rs1047100 and rs13317. However, rs13317 could be involved in regulating expression considering that it is located in the 3′UTR which modifies stability and transport of mRNA as well as translation efficiency [31], and whose SNPs are well known to increase the efficiency of 3′ end processing [32]. In addition, although rs1047100 is synonymous SNP, recent studies reported that synonymous SNPs play an important role in protein activities and specificities without influencing amino acid sequences [33, 34]. Thus, we postulated that FGFR1 and FGFR2 might be overexpressed in BPH but that the expression of FGFR1 and FGFR2 might be relatively low in BPH patients with genotypes containing the A allele of rs1047100 or the C allele of rs13317 compared to BPH patients without those. Furthermore, a previous study showed that rs13317 of FGFR1, which plays a role in wound healing and is a positive regulator of skeletal formation [35], was associated with delayed bone healing after bone fracture and, in particular, that the frequency of the C allele of rs13317 in FGFR1 was increased in individuals with delayed bone healing compared to those with uneventful healing [36]. However, in that study, no significant associations were observed between FGF polymorphisms and delayed bone healing [36]. Thus, they suggested the possibility that specific alterations in the receptor, despite FGFs functions, may be involved in triggering the pathologic process during fracture healing [36]. Given this report, we also postulated that rs13317 may affect the activity of FGFR1. Thus, FGFR1 activity may be relatively decreased in BPH patients with the C allele of rs13317, resulting in lower and slower progression of BPH in those individuals. Further studies are needed to determine how FGFR1 and FGFR2 polymorphisms affect their expression and/or activity in BPH progression.
The major limitation of our study was the small sample size used for comparison within BPH subgroups. However, this is the first genetic study on the relationship between SNPs of FGFR1 and FGFR2, and BPH. Our results revealed associations between FGFR1 and FGFR2, and the clinical phenotypes of BPH. Further studies with a larger sample sizes are needed to validate our results.
5. Conclusions
In conclusion, we found that polymorphisms of FGFR1 and FGFR2 were not associated with BPH. However, clinical analysis revealed that a FGFR2 polymorphism was associated with prostate volume and a FGFR1 polymorphism was associated with IPSS in BPH patients. These results suggest that FGFR1 and FGFR2 may be related to BPH severity and progression.
Conflict of Interests
The authors declare that they have no conflict of interests.
Acknowledgment
This work was supported by a Grant from Kyung Hee University in 2013 (KHU-20130169).
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