Abstract
Single-nucleotide polymorphisms located in the microRNA biogenesis pathway could alter the risk for developing prostate cancer. The present study was intended to identify common genetic variants responsible for prostate cancer susceptibility in the GEMIN4 gene. The high-resolution melting method was used to genotype seven polymorphisms (rs7813, rs4968104, rs3744741, rs2740348, rs1062923, rs910925, and rs910924) in the GEMIN4 gene in 300 prostate cancer patients and 244 matched controls. The encouraging discovery in this study was in the rs2740348. Patients carrying the variant heterozygote GC genotype in the rs2740348 were at a 36% decreased risk of prostate cancer (odds ratio [OR]=0.64; 95% confidence interval [CI]=0.42, 0.99). Similarly, this variant allele carrier showed significant risk for prostate cancer (OR=0.64). In addition, subjects carrying the homozygote TT genotype in the rs7813 had a significantly increased risk of prostate cancer (OR=2.53, 95% CI=1.07, 6.28). Two common haplotypes were found to be associated with decreased risk of prostate cancer. In the subgroup analysis, higher risk of more severity of prostate cancer (clinical stage III and IV) was observed in individuals with the rs7813 TT genotype (OR=2.64, 95% CI=1.02, 7.64), while lower risk of more severity of prostate cancer was observed in individuals with the rs3744741 T allele (OR=0.69, 95% CI=0.50, 0.96). Overall, our study provides substantial support for the association between the GEMIN4 gene and the risk of prostate cancer.
Introduction
Prostate cancer accounts for ∼14% of the total new cancer cases and ranks as the 6th cancer death in men (Jemal et al., 2011). Prostate cancer is recognized as a complex and heterogeneous disease. Evidence obtained from a family-based linkage analysis and a pathway-based association study has identified a large scale of genetic variants contributing to the risk of prostate cancer. However, it will still take time to elucidate the highly specific mechanisms of the gene interaction and network in the development and progression of prostate cancer.
MicroRNAs (miRNAs) are a kind of noncoding RNA molecules about 20 nucleotides in length. It is known that miRNAs induce gene silence in the posttranscriptional regulation through Watson–Crick-based bonding to the 3′ untranslated regions (3′ UTR) of target messenger RNAs (Esquela-Kerscher and Slack, 2006; Wu et al., 2006). MiRNAs regulate about 30% of human genes, and alternations in their processing might lead to cancer occurrence and progress (Chiosea et al., 2006; Thomson et al., 2006; Kumar et al., 2007). It has been proved that pre-miRNA gene polymorphisms might predict the risk of prostate cancer, indicating the potential association between variations in the miRNA biogenesis pathway and the development of prostate cancer (George et al., 2011).
GEMIN4 protein is accepted as a key member of the GEMIN protein family that is involved in multiple pathological processes. It was reported that this protein was a shared part of the survival of motor neuron complex and a 15S ribonucleoprotein complex (miRNPs). MiRNPs, containing a protein in the AGO protein, Eif2C, are essential in the miRNA splicing and mature (Charroux et al., 1999, 2000; Hutvagner and Zamore, 2002). The GEMIN4 protein was also referred to as an important molecule in the RNA-induced silencing complex (RISC) that participated in the mature process of miRNAs, the target RNA recognition and repression (Hannon, 2002; Nelson et al., 2004). Other independent studies provided similar evidence that the involvement of the GEMIN4 protein in the RISC played a critical role in the processing of miRNAs (Dostie et al., 2003; Nelson, et al., 2004). Thus, abnormity in the GEMIN4 protein might result in the differential expression of some specific miRNAs that are related to the malignant tumors.
The human GEMIN4 gene is located at 17q13 with a large number of the identified polymorphisms. Wan et al. found that single-nucleotide polymorphisms (SNPs) in the GEMIN 4 gene had a potential effect on the DNA repair in the heptacellular carcinoma cells and contributed to the development of heptacellular cancer (Wan et al., 2004). Recent studies indicated that polymorphisms in the GEMIN4 gene were associated with the etiology and clinical outcome of cancers, such as bladder cancer (Yang et al., 2008), renal cancer (Horikawa et al., 2008; Lin et al., 2010), and ovarian cancer (Liang et al., 2010). Although it is obvious that variations in the GEMIN 4 gene may take part in the carcinogenesis, the concrete mechanism of their effects on prostate cancer remains largely unknown. Therefore, we conducted a case-control study that evaluates the association between common polymorphisms in the GEMIN 4 gene and the risk of prostate cancer to provide genetic evidence of miRNA machinery genes for prostate cancer. This is, to our knowledge, the first genetic association study for miRNA machinery genes for prostate cancer.
Materials and Methods
Study subjects
Three hundred prostate cancer patients (mean age: 71.43±9.38) were recruited from the inpatient unit of the West China Hospital and Sichuan Provincial People's Hospital in Chengdu, China. There were no age or cancer stage strictures on recruitment. All participants were individuals in Chinese Han ethnicity with pathologically confirmed diagnosis of prostate cancer from Jan 2003 to May 2010. Consensus diagnosis of each patient and evaluation of the clinical stage were made by two independent urologists. Evaluation of the clinical stage was based on the combination of Gleason scores and tumor-node-metastasis (TNM) stage (drafted by AJCC and UICC in 1997). All the patients were followed till November 2011. Two hundred and forty-four age-matched healthy male subjects (mean age:71.15±9.64) in the Chinese Han population who had received a health examination in the same period were chosen as the control group. They were interviewed in an attempt to exclude cancer history. Their total PSA level was lower than 2 ng/mL. Informed consent was obtained from all the participants.
SNP selection
Seven functional SNP loci in the GEMIN4 gene, consisting of one in the 3′ UTR region and six in the exons, were identified in the International HapMap Project (www.hapmap.org) and dbSNP (www.ncbinlmnihgov/projects/SNP) databases (Table 1).
Table 1.
The Characteristics of the Seven Polymorphisms
| SNP ID | Position | Major/minor allele | MAF (%) | PCR primers |
|---|---|---|---|---|
| rs7813 | Arg1033Cys | C>T | 30.0 | 5′GACTTTGAGCAAGACCAACCCTTCTGTCA3′(forward) 5′CCGATGCCCTCAGCAATGGACTTTA3′(reverse) |
| rs4968104 | Glu593Val | T>A | 16.2 | 5′TGAAAGTGGCAGATGAATTGGGACC3′(forward) 5′AAATGTGCAGCCTGGCTGTGGT 3′(reverse) |
| rs3744741 | Arg684Gln | C>T | 23.4 | 5′ACGGGGAGCAGGTCTGGAGC3′(forward) 5′CCTGCCTTTCTTGAGGTTAGATGTTG3′(reverse) |
| rs2740348 | Gln450Glu | G>C | 11.0 | 5′GAAGAAGTGGGCCTTCTCGGACG3′(forward) 5′TCTATCACTGTTTCCAGCAGCCTCAA3′(reverse) |
| rs1062923 | Ile739Thr | T>C | 8.8 | 5′TCCCAAGGAGAAGCGGTGCC3′(forward) 5′CCGGGGAGAAGGTCTCAGCATT3′(reverse) |
| rs910925 | Ala579Gly | C>G | 30.2 | 5′TGGTCAATCTCGGCACCCACAAG3′(forward) 5′ATGACACCATGAAAGTGGCAGATGAATT3′(reverse) |
| rs910924 | - | C>T | 17.1 | 5′GCAAGCTCGGGGTCCAGCGTAAA3′(forward) 5′CCAGACAGCAGCGTTCCGGATCCTAG3′(reverse) |
SNP, single-nucleotide polymorphism; MAF, minor allele frequency; PCR, polymerase chain reaction.
Genomic DNA extracting and genotyping
DNA was extracted from peripheral blood samples (EDTA anticoagulant) by using the QIAamp® DNA Blood mini kit (Qiagen) according to the manufacturer's instructions. Genotyping of the samples was performed in the LightCycler® 480 Real-Time polymerase chain reaction (PCR) System (Roche Diagnostics), based on the high-resolution melting (HRM) method. The PCR primer design was done through Primer Priemer 5.0, Oligo 11.0, and Blast in the (pubmed) Pubmed database (Table 1). The PCR mixture for the seven polymorphisms contained the ingredient, namely 2.0 μL purified genomic DNA (10 ng/μL), 0.4 μL forward primer (10 pmol/μL), 0.4 μL reverse primer (10 pmol/μL), 1.0 μL 20×EVA-GREEN, 1.5 μL dNTP (2.5 mM), 0.1 μL Hot Star Taq® Plus DNA Polymerase, 2.0 μL 10× buffer (with Mg2+), and 12.6 μL water. The amplification condition of 50 cycles after denaturation was set as follows: 95°C for 10 s, 60°Cfor 15 s, and 72°C for 25 s. Then, the products underwent a process of denaturation for 1 min at 95°C and cooling for 1 min at 40°C. HRM analysis was performed by slowly heating from 65°C to 95°C at a rate of 0.01°C /s, followed by cooling down to 40°C. Standard DNA samples representing different genotypes of each SNP were identified by DNA sequencing using BigDye Terminator v3.1 Cycle Sequencing Kit and ABI 3130 genetic analyzer (Applied Biosystems) with the same primers used in the previous PCR system.
Statistical analysis
Statistical analysis was performed by SPSS 13.0. The exact probability method was used to test the Hardy–Weinberg equilibrium in controls. p-value≤0.01 was considered the statistically significant disequilibrium. The statistical significance for the differences in genotypes was determined by Pearson χ2 analysis. Haplotype analysis was done by the SHEsis software (Shi and He, 2005; Li et al., 2009). The calculation of odds ratio (OR) and 95% confidence interval (95% CI) was conducted with the risk option of crosstabs to estimate the prostate cancer risk. All p-values were two sided. The threshold of statistical significance for the prostate cancer risk tests was p≤0.05.
Result
Demographic characteristics
The characteristics of 300 prostate cancer patients and 244 healthy controls recruited in our study are presented in Table 2. No significant difference was detected with regard to age distribution between cases and controls (t=0.35, p=0.73). The adenocarcinoma was the pathological character for all the patients. Overall, 48.33% of the patients were in moderate differentiation degree, the rest of whom were in high (35.00%) and low differentiation degree (16.67%), respectively. There were 81 (27.00%) patients in clinical stages I and II, whereas 219 (73.00%) patients were in stages III and IV. Genotype distributions for all the polymorphisms in the control group were in Hardy–Weinberg equilibrium.
Table 2.
Demographic Characteristics in Cases and Controls
| Variables | Subgroups | Cases (N=300) (%) | Controls (N=244) | p |
|---|---|---|---|---|
| Age (years) | 71.43±9.38 | 71.15±9.64 | 0.73 | |
| I | 34 (11.33) | |||
| II | 47 (15.67) | |||
| III | 75 (25.00) | |||
| Clinical stage | IV | 144 (48.00) | ||
| Low | 50 (16.67) | |||
| Differentiation degree | Moderate | 145 (48.33) | ||
| High | 105 (35.00) |
The main effect of individual SNP on the risk of prostate cancer
Table 3 presents the data obtained from the genotype of individual polymorphisms in all subjects. Considerable differences between cases and controls were observed in two SNPs (rs2740348 and rs7813). The rs2740348 polymorphism was found to be associated with a decreased risk of prostate cancer, in which the subjects carrying the variant heterozygote GC genotype and C allele were at a 36% lower risk for prostate cancer than the wild genotype GG and G allele (OR=0.64; 95% CI=0.42, 0.99). In addition, the rs7813 polymorphism was associated with an increased risk of prostate cancer, among which the subjects carrying the TT genotype were at a 2.53-fold elevated risk for prostate cancer when compared with the CC genotype (OR=2.53; 95% CI=1.07, 6.28). The power calculation values were 0.65 and 0.61 for rs7813 and rs2740348, respectively. However, no significant associations with the prostate cancer risk were found among the remaining five polymorphisms.
Table 3.
The Polymorphisms of the GEMIN4 Gene and the Risk of Prostate Cancer
| |
|
Patients (N=300) |
Controls (N=244) |
|
||
|---|---|---|---|---|---|---|
| Polymorphism | N | % | n | % | OR (95% CI) | |
| CC | 10 | 3.33 | 19 | 7.79 | 1 | |
| CT | 98 | 32.67 | 81 | 33.20 | 2.29 (0.95, 5.84) | |
| TT | 192 | 64.00 | 144 | 59.02 | 2.53 (1.07, 6.28) | |
| C | 118 | 19.67 | 119 | 24.39 | 1 | |
| rs7813 | T | 482 | 80.33 | 369 | 75.61 | 1.32 (0.98, 1.78) |
| TT | 259 | 86.33 | 201 | 82.37 | 1 | |
| TA/AA | 41 | 13.67 | 43 | 17.62 | 0.74 (0.45, 1.21) | |
| T | 559 | 93.17 | 445 | 91.19 | 1 | |
| rs4968104 | A | 41 | 6.83 | 43 | 8.81 | 0.76 (0.47, 1.22) |
| CC | 188 | 62.67 | 142 | 58.20 | 1 | |
| CT | 92 | 30.67 | 82 | 33.61 | 0.85 (0.58, 1.25) | |
| TT | 20 | 6.67 | 20 | 8.20 | 0.76 (0.37, 1.54) | |
| C | 468 | 78.00 | 366 | 75.00 | 1 | |
| rs3744741 | T | 132 | 22.00 | 122 | 25.00 | 0.85 (0.63, 1.13) |
| GG | 246 | 82.00 | 182 | 74.60 | 1 | |
| GC/CC | 54 | 18.00 | 62 | 25.40 | 0.64 (0.42, 0.99) | |
| G | 546 | 91.00 | 426 | 87.30 | 1 | |
| rs2740348 | C | 54 | 9.00 | 62 | 12.70 | 0.64 (0.42, 0.99) |
| TT | 298 | 99.33 | 240 | 98.36 | 1 | |
| TC/CC | 2 | 0.67 | 4 | 1.64 | 0.40 (0.04, 2.84) | |
| T | 598 | 99.67 | 484 | 99.18 | 1 | |
| rs1062923 | C | 2 | 0.33 | 4 | 0.82 | 0.40 (0.04, 2.84) |
| CC | 23 | 7.67 | 18 | 7.37 | 1 | |
| CG | 135 | 45.00 | 103 | 42.21 | 1.03 (0.49, 2.10) | |
| GG | 142 | 47.33 | 123 | 50.40 | 0.90 (0.44, 1.84) | |
| C | 181 | 30.17 | 139 | 28.48 | 1 | |
| rs910925 | G | 419 | 69.83 | 349 | 71.52 | 0.92 (0.70, 1.21) |
| CC | 240 | 80.00 | 190 | 77.87 | 1 | |
| CT/TT | 60 | 20.00 | 54 | 22.13 | 0.88 (0.57, 1.36) | |
| C | 540 | 90.00 | 434 | 88.93 | 1 | |
| rs910924 | T | 60 | 10.00 | 54 | 11.07 | 0.89 (0.59, 1.34) |
The bold numbers mean the p-value is <0.05.
OR, odds ratio; CI, confidence interval.
Haplotypes analysis
Frequencies of the seven common haplotypes are listed in Table 4. Taking the most common type in the study population, MWWWWMW (in the order of rs7813-rs4968104-rs3744741-rs2740348-rs1062923-rs910925-rs910924) as the reference, subjects carrying the MWWMWMW haplotype had a 50% lower risk for prostate cancer (OR=0.50, 95% CI=0.25, 0.99). The other haplotype (MWMMWMW) was also associated with the development of prostate cancer, conferring a much lower risk (OR=0.44, 95% CI=0.19, 1.00).
Table 4.
Haplotypes and the Risk of Prostate Cancer
| |
Cases (N=300) |
Controls (N=244) |
|
|
||
|---|---|---|---|---|---|---|
| Haplotype | n | % | n | % | OR (95% CI) | P |
| H1 (MWWWWMW) | 107 | 35.67 | 60 | 24.59 | 1 | |
| H2 (MWMWWMW) | 65 | 21.67 | 50 | 20.49 | 0.73(0.44, 1.22) | 0.20 |
| H3 (MWWMWMW) | 23 | 7.67 | 26 | 10.66 | 0.50(0.25, 0.99) | 0.03 |
| H4 (MMWWWMM) | 19 | 6.33 | 19 | 7.79 | 0.56(0.26, 1.22) | 0.11 |
| H5 (MWMMWMW) | 14 | 4.67 | 18 | 7.38 | 0.44(0.19, 1.00) | 0.03 |
| H6(MMMWWMM) | 13 | 4.33 | 12 | 4.92 | 0.61(0.24, 1.56) | 0.25 |
| H7 (MWWWWMM) | 8 | 2.67 | 4 | 1.64 | 1.12(0.29, 5.30) | 0.86 |
| Others | 51 | 17.00 | 55 | 22.54 | - | - |
The haplotype analysis was in the order of rs7813, rs4968104, rs3744741, rs2740348, rs1062923, rs910925, and rs910924.
The bold numbers mean the p-value is <0.05.
W, wild genotypes; M, heterozygous and mutant homozygous genotypes.
Association with the risk of prostate cancer severity
Table 5 lists the genetic frequencies of individual SNP in the subgroups according to different clinical stages. Patients were divided into two subgroups according to the clinical stages: group1 (patients in clinical stages I and II) and group2 (patients in clinical stages III and IV). A higher risk for prostate cancer progression (clinical stages III and IV) was observed in subjects with the rs7813 TT genotype when compared with those with the CC genotype (OR=2.64, 95% CI=1.02, 7.64, Power=0.59). Meanwhile, a lower risk for severity was found in the rs3744741 T-allele carriers (OR=0.69, 95% CI=0.50, 0.96, Power=0.64). Moreover, in the case of the rs2740348 polymorphism, the lower risk was also found for the genotype GC (OR=0.46, 95% CI=0.21, 0.95, Power=0.61).
Table 5.
The Polymorphisms of the GEMIN4 Gene and the Risk of Prostate Cancer in Different clinical Stages
| |
|
Controls (N=244) |
Clinical stage I and II (N=81) |
Clinical stage III and IV (N=219) |
Clinical stage III and IV vs. Clinical stage I and II |
||
|---|---|---|---|---|---|---|---|
| Polymorphism | Genotype and allele | n | n | OR | n | OR | OR |
| CC | 19 | 3 | 1 | 7 | 1 | 1 | |
| CT | 81 | 26 | 2.03 (0.53, 11.52) | 72 | 2.41 (0.90, 7.17) | 1.19 (0.18, 5.68) | |
| TT | 144 | 52 | 2.29 (0.63, 12.52) | 140 | 2.64 (1.02, 7.64) | 1.15 (0.19, 5.28) | |
| C | 119 | 32 | 1 | 86 | 1 | 1 | |
| rs7813 | T | 369 | 130 | 1.31 (0.83, 2.10) | 352 | 1.32 (0.95, 1.83) | 1.01 (0.62, 1.61) |
| TT | 201 | 73 | 1 | 186 | 1 | 1 | |
| TA/AA | 43 | 8 | 0.51 (0.20, 1.17) | 33 | 0.83 (0.49, 1.40) | 1.62 (0.69, 4.25) | |
| T | 445 | 154 | 1 | 405 | 1 | 1 | |
| rs4968104 | A | 43 | 8 | 0.54 (0.21, 1.19) | 33 | 0.84 (0.51, 1.39) | 1.57 (0.69, 4.02) |
| CC | 142 | 41 | 1 | 147 | 1 | 1 | |
| CT | 82 | 30 | 1.27 (0.71, 2.26) | 62 | 0.73 (0.48, 1.11) | 0.58 (0.32, 1.05) | |
| TT | 20 | 10 | 1.73 (0.67, 4.24) | 10 | 0.48 (0.20, 1.13) | 0.28 (0.10, 0.81) | |
| C | 366 | 112 | 1 | 356 | 1 | 1 | |
| rs3744741 | T | 122 | 50 | 1.34 (0.88, 2.01) | 82 | 0.69 (0.50, 0.96) | 0.52 (0.34, 0.80) |
| GG | 182 | 70 | 1 | 176 | 1 | 1 | |
| GC/CC | 62 | 11 | 0.46 (0.21, 0.95) | 43 | 0.72 (0.50, 1.14) | 1.55 (0.73, 3.54) | |
| G | 426 | 151 | 1 | 395 | 1 | 1 | |
| rs2740348 | C | 62 | 11 | 0.50 (0.23, 0.99) | 43 | 0.75 (0.48, 1.15) | 1.55 (0.73, 3.54) |
| TT | 240 | 81 | 1 | 217 | 1 | 1 | |
| TC/CC | 4 | 0 | - | 2 | 0.55 (0.05, 3.91) | - | |
| T | 484 | 162 | - | 436 | 1 | 1 | |
| rs1062923 | C | 4 | 0 | - | 2 | 0.56 (0.05, 3.90) | - |
| CC | 18 | 9 | 1 | 14 | 1 | 1 | |
| CG | 103 | 32 | 0.62 (0.24, 1.74) | 103 | 1.29 (0.57, 2.95) | 2.07 (0.72, 5.69) | |
| GG | 123 | 40 | 0.65 (0.25, 1.78) | 102 | 1.07 (0.47, 2.44) | 1.64 (0.58, 4.44) | |
| C | 139 | 50 | 1 | 131 | 1 | 1 | |
| rs910925 | G | 349 | 112 | 0.89 (0.60, 1.34) | 307 | 0.93 (0.70, 1.25) | 1.05 (0.69, 1.57) |
| CC | 190 | 65 | 1 | 175 | 1 | 1 | |
| CT/TT | 54 | 16 | 0.87 (0.43, 1.67) | 44 | 0.88 (0.55, 1.42) | 1.02 (0.52, 2.08) | |
| C | 434 | 146 | 1 | 394 | 1 | 1 | |
| rs910924 | T | 54 | 16 | 0.88 (0.46, 1.62) | 44 | 0.90 (0.57, 1.40) | 1.02 (0.54, 2.00) |
The bold numbers mean the p-value is <0.05.
Discussion
The link between the miRNAs and prostate cancer has been highlighted by many previous studies (Linsley et al., 2007; Raveche et al., 2007; Sooryanarayana et al., 2008; Ruping et al., 2009). A number of polymorphisms including two SNPs (hsa-mir196a2 and hsa-mir499) in pre-miRNA genes have been identified as potential risk factors for prostate cancer in different races (Amundadottir et al., 2006; Eeles et al., 2008; Thomas et al., 2008; George et al., 2011). However, until recently, there are gaps in our knowledge of the genetic resources in the differential expression of the miRNAs, resulting in the development of prostate cancer. In this study, significant associations were found between polymorphisms in the GEMIN4 gene and the risk of prostate cancer. This result would lead to the further elucidation of the precise mechanisms in the distinguished expression of miRNAs and the relationship between the genetic variants in the miRNA biogenesis pathway genes and the susceptibility of prostate cancer.
This study investigated seven polymorphisms in an important miRNA machinery gene GEMIN4 gene in a total of 544 subjects. As main observations of our study, the rs2740348 and rs7813 could cause changes in the prostate cancer risk. The possible explanation is that the two SNP loci are located in the exons, the functional region of the GEMIN4 gene, which may play a potential role in the regulation of protein expression. The expression of the GEMIN4 protein is tightly associated with the biogenesis of related miRNAs, which may alter the risk of prostate cancer. Further functional studies are required to investigate the underlying mechanisms of these two polymorphisms in the synthesis of the GEMIN4 protein in the Chinese Han population. In addition, our findings are consistent with the results that these two polymorphisms, rs2740348 and rs7813, have potential roles in carcinogenesis, such as renal cell carcinoma (Horikawa et al., 2008), heptacellular carcinoma (Wan et al., 2004), bladder cancer (Yang et al., 2008), and ovarian cancer (Liang et al., 2010) in other studies. No significant associations between the other five polymorphisms and prostate cancer risk were observed in our study. Thus, they may play less important roles in the pathogenesis of prostate cancer. Nevertheless, the lack of association may be due to the limited sample size. Two polymorphisms exhibited such a low minor allele frequency that the testing efficiency could not be high enough to avoid type II error completely. So, case-control studies with large samples should be performed to testify the associations with these five SNPs of negative results in our studies, and meta-analysis may also be necessary in further studies.
In the haplotype analysis, we found that H3 (MWWMWMW) and H5 (MWMMWMW) might contribute to a decreased risk of prostate cancer in the Chinese Han population (rs7813, rs4968104, rs3744741, rs2740348, rs1062923, rs910925, and rs910924). Previous studies evaluated the seven SNPs (rs7813, rs4968104, rs3744741, rs2740348, rs1062923, rs910925, and rs910924) and found that the haplotype MWWMWMW was associated with a lower risk of bladder cancer in the Caucasian population (Yang et al., 2008). In addition, the MWWMWW haplotype consisting of six SNPs in GEMIN4 (rs7813, rs4968104, rs3744741, rs2740348, rs1062923, and rs910924) conferred the decreased risk of renal cell carcinoma in Caucasians, African Americans, and Mexican Americans (Horikawa et al., 2008).
MiRNA profiling has identified some miRNAs with distinguished expression in the invasiveness and metastasis of prostate cancer, such as miR-224, miR-21, miR-let7, miR-100, and miR-218 (Zhang et al., 2007; Prueitt et al., 2008; Leite et al., 2011). The distinguished expression may be caused by variations in the miRNA biogenesis. Alterations of the miRNA processing pathways could potentially impose on the miRNA transcription, splicing, and transcriptional regulation of genes that affect cell proliferation and apoptosis. Thus, polymorphisms in the miRNA biogenesis pathways may also contribute to disease progresses. A study has pointed out that the rs3744741 polymorphism might be an independent factor for the decreased risk of death in renal cell carcinoma in Caucasians (hazard ratio =0.39; 95% CI=0.19, 0.77) (Lin et al., 2010). In consistence with the previous study, significant associations with prostate cancer progression were also identified for the rs3744741 in our study. Besides, the rs7813 and rs2740348 might also potentially affect the severity of prostate cancer.
In summary, this study demonstrates that the GEMIN4 gene, one of the miRNA machinery genes, contributes to the alternative risk of prostate cancer. Additional studies with a larger sample size should be performed to validate our results, especially in the Caucasian population with a much higher prevalence of prostate cancer.
Acknowledgment
This study was supported by grants from the National Natural Science Foundation of China (No. 81101939).
Authors' Contributions
Liu JM and Liu JN finished genomic DNA extraction, HRM analysis, statistical analysis, and prepared the article. Wei MT, He YZ, Liao BH, and Liao G participated in collecting blood samples. Li H and Huang J conceived of the study, participated in its design and coordination, and helped prepare the article. All authors read and approved the final manuscript.
Disclosure Statement
The authors declare that they have no competing interests.
References
- Amundadottir L.T. Sulem P. Gudmundsson J. Helgason A. Baker A. Agnarsson B.A. Sigurdsson A. Benediktsdottir K.R. Cazier J.B. Sainz J. Jakobsdottir M. Kostic J. Magnusdottir D.N. Ghosh S. Agnarsson K. Birgisdottir B. Le Roux L. Olafsdottir A. Blondal T. Andresdottir M. Gretarsdottir O.S. Bergthorsson J.T. Gudbjartsson D. Gylfason A. Thorleifsson G. Manolescu A. Kristjansson K. Geirsson G. Isaksson H. Douglas J. Johansson J.E. Bälter K. Wiklund F. Montie J.E. Yu X. Suarez B.K. Ober C. Cooney K.A. Gronberg H. Catalona W.J. Einarsson G.V. Barkardottir R.B. Gulcher J.R. Kong A. Thorsteinsdottir U. Stefansson K. A common variant associated with prostate cancer in European and African populations. Nat Genet. 2006;38:652–658. doi: 10.1038/ng1808. [DOI] [PubMed] [Google Scholar]
- Charroux B. Pellizzoni L. Perkinson R.A. Shevchenko A. Mann M. Dreyfuss G. GEMIN3: a novel DEAD box protein that interacts with SMN, the spinal muscular atrophy gene product, and is a component of gems. J Cell Biol. 1999;147:1181–1194. doi: 10.1083/jcb.147.6.1181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Charroux B. Pellizzoni L. Perkinson R.A. Yong J. Shevchenko A. Mann M. Dreyfuss G. GEMIN4: a novel component of the SMN complex that is found in both gems and nucleoli. J Cell Biol. 2000;148:1177–1186. doi: 10.1083/jcb.148.6.1177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Chiosea S. Jelezcova E. Chandran U. Acquafondata M. McHale T. Sobol R.W. Dhir R. Up-regulation of dicer, a component of the microRNA machinery, in prostate adenocarcinoma. Am J Pathol. 2006;169:1812–1820. doi: 10.2353/ajpath.2006.060480. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Dostie J. Mourelatos Z. Yang M. Sharma A. Dreyfuss G. Numerous microRNPs in neuronal cells containing novel microRNAs. RNA. 2003;9:180–186. doi: 10.1261/rna.2141503. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Eeles R.A. Kote-Jarai Z. Giles G.G. Olama A.A. Guy M. Jugurnauth S.K. Mulholland S. Leongamornlert D.A. Edwards S.M. Morrison J. Field H.I. Southey M.C. Severi G. Donovan J.L. Hamdy F.C. Dearnaley D.P. Muir K.R. Smith C. Bagnato M. Ardern-Jones A.T. Hall A.L. O'Brien L.T. Gehr-Swain B.N. Wilkinson R.A. Cox A. Lewis S. Brown P.M. Jhavar S.G. Tymrakiewicz M. Lophatananon A. Bryant S.L. Multiple newly identified loci associated with prostate cancer susceptibility. Nat Genet. 2008;40:316–321. doi: 10.1038/ng.90. [DOI] [PubMed] [Google Scholar]
- Esquela-Kerscher A. Slack F.J. Oncomirs-microRNAs with a role in cancer. Nat Rev Cancer. 2006;6:259–269. doi: 10.1038/nrc1840. [DOI] [PubMed] [Google Scholar]
- George G.P. Gangwar R. Raju K. Mandal Sankhwar S.N. Mittal R.D. Genetic variation in microRNA genes and prostate cancer risk in North Indian population. Mol Biol Rep. 2011;38:1609–1615. doi: 10.1007/s11033-010-0270-4. [DOI] [PubMed] [Google Scholar]
- Hannon G.J. RNA interference. Nature. 2002;418:244–251. doi: 10.1038/418244a. [DOI] [PubMed] [Google Scholar]
- Horikawa Y. Wood C.G. Yang H. Zhao H. Ye Y. Gu J. Lin J. Habuchi T. Wu X. Single nucleotide polymorphisms of microRNA machinery genes modify the risk of renal cell carcinoma. Clin Cancer Res. 2008;14:7956–7962. doi: 10.1158/1078-0432.CCR-08-1199. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hutvagner G. Zamore P.D. A microRNA in a multiple turnover RNAi enzyme complex. Science. 2002;297:2056–2060. doi: 10.1126/science.1073827. [DOI] [PubMed] [Google Scholar]
- Jemal A. Bray F. Ferlay J. Melissa M. Ward E. Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90. doi: 10.3322/caac.20107. [DOI] [PubMed] [Google Scholar]
- Kumar M.S. Lu J. Mercer K.L. Golub T.R. Jacks T. Imprired microRNA processing rnhances cellular transformation and tumorigenesis. Nat Genet. 2007;39:673–677. doi: 10.1038/ng2003. [DOI] [PubMed] [Google Scholar]
- Leite K.R. Sousa-Canavez J.M. Reis S.T. Tomiyama A.H. Camara-Lopes L.H. Sañudo A. Antunes A.A. Srougi M. Change in expression of miR-let7c, miR-100, and miR-218 from high grade localized prostate cancer to metastasis. Urol Oncol. 2011;29:265–269. doi: 10.1016/j.urolonc.2009.02.002. [DOI] [PubMed] [Google Scholar]
- Li Z. Zhang Z. He Z. Tang W. Li T. Zeng Z. He L. Shi Y. A partition-ligation-combination-subdivision EM algorithm for haplotype inference with multiallelic markers: update of the SHEsis. Cell Res. 2009. http://analysis.bio-x.cn. pp. 519–523.http://analysis.bio-x.cn [DOI] [PubMed]
- Liang D. Meyer L. Chang D.W. Lin J. Pu X. Ye Y. Gu J. Wu X. Lu K. Genetic variants in MicroRNA biosynthesis pathways and binding sites modify ovarian cancer risk, survival, and treatment response. Cancer Res. 2010;70:9765–9776. doi: 10.1158/0008-5472.CAN-10-0130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lin J. Horikawa Y. Tamboli P. Clague J. Wood C.G. Wu X. Genetic variations in microRNA-related genes are associated with survival and recurrence in patients with renal cell carcinoma. Carcinogenesis. 2010;31:1805–1812. doi: 10.1093/carcin/bgq168. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Linsley P.S. Schelter J. Burchard J. Kibukawa M. Martin M.M. Bartz S.R. Johnson J.M. Cummins J.M. Raymond C.K. Dai H. Chau N. Cleary M. Jackson A.L. Carleton M. Lim L. Transcripts targeted by the microRNA-16 family cooperatively regulate cell cycle progression. Mol Cell Biol. 2007;27:2240–2252. doi: 10.1128/MCB.02005-06. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Nelson P.T. Hatzigeorgiou A.G. Mourelatos Z. miRNP: mRNA association in polyribosomes in a human neuronal cell line. RNA. 2004;10:387–394. doi: 10.1261/rna.5181104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Prueitt R.L. Yi M. Hudson R.S. Wallace T.A. Howe T.M. Yfantis H.G. Lee D.H. Stephens R.M. Liu C.G. Calin G.A. Croce C.M. Ambs S. Expression of MicroRNAs and protein-coding genes associated with perineural invasion in prostate cancer. Prostate. 2008;68:1152–1164. doi: 10.1002/pros.20786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Raveche E.S. Salerno E. Scaglione B.J. Manohar V. Abbasi F. Lin Y.-C. Fredrickson T. Landgraf P. Ramachandra S. Huppi K. Toro J.R. Zenger V.E. Metcalf R.A. Marti G.E. Abnormal microRNA-16 locus with synteny to human 13q14 linked to CLL in NZB mice. Blood. 2007;109:5079–5086. doi: 10.1182/blood-2007-02-071225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Ruping S. Xuping F. Yao L. Yi X. Yumin M. Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumors. BMC Genomics. 2009;10:93. doi: 10.1186/1471-2164-10-93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Shi Y.Y. He L. SHEsis, a powerful software platform for analyses of linkage disequilibrium, haplotype construction, and genetic association at polymorphism loci. Cell Res. 2005;15:97–98. doi: 10.1038/sj.cr.7290272. [DOI] [PubMed] [Google Scholar]
- Sooryanarayana V. Qi C. Ram-Shankar M. Sunita S. Xiaosong W. Bushra A. Bharathi L. Xuhong C. Xiaojun J. Kalpana R. Chad B. Jindan Y. Jung H. K. Bo H. Patrick T. Chandan K.S. Robert J.L. Nallasivam P. Christopher A.M. Arul M.C. Genomic loss of microRNA-101 leads to overexpression of histone methyltransferase EZH2 in cancer. Science. 2008;322:1695–1699. doi: 10.1126/science.1165395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Thomas G. Jacobs K.B. Yeager M. Kraft P. Wacholder S. Orr N. Yu K. Chatterjee N. Welch R. Hutchinson A. Crenshaw A. Cancel-Tassin G. Staats B.J. Wang Z. Gonzalez-Bosquet J. Fang J. Deng X. Berndt S.I. Calle E.E. Feigelson H.S. Thun M.J. Rodriguez C. Albanes D. Virtamo J. Weinstein S. Schumacher F.R. Giovannucci E. Willett W.C. Cussenot O. Valeri A. Andriole G.L. Crawford E.D. Tucker M. Gerhard D.S. Fraumeni J.F., Jr. Hoover R. Hayes R.B. Hunter D.J. Chanock S.J. Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet. 2008;40:310–315. doi: 10.1038/ng.91. [DOI] [PubMed] [Google Scholar]
- Thomson J.M. Newman M. Parker J.S. Morin-Kensicki E.M. Wright T. Hammond S.M. Extensive posttranscriptional regulation of microRNAs and its implications for cancer. Genes Dev. 2006;20:2202–2207. doi: 10.1101/gad.1444406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Wan D. He M. Wang J. Qiu X. Zhou W. Luo Z. Chen J. Gu J. Two variants of the human hepatocellular carcinoma-associated HCAP1 gene and their effect on the growth of the human liver cancer cell line Hep3B. Genes Chromosomes Cancer. 2004;39:48–58. doi: 10.1002/gcc.10293. [DOI] [PubMed] [Google Scholar]
- Wu L. Fan J. Belasco J.G. MicroRNAs direct rapid deadenylation of mRNA. Proc Natl Acad Sci U S A. 2006;103:4034–4039. doi: 10.1073/pnas.0510928103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Yang H. Dinney C.P. Ye Y. Zhu Y. Grossman H.B. Wu X. Evaluation of genetic variants in microRNA-related genes and risk of bladder cancer. Cancer Res. 2008;68:2530–2537. doi: 10.1158/0008-5472.CAN-07-5991. [DOI] [PubMed] [Google Scholar]
- Zhang B. Pan X. Cobb G.P. Anderson T.A. MicroRNAs as oncogenes and tumor suppressors. Dev Biol. 2007;302:1–12. doi: 10.1016/j.ydbio.2006.08.028. [DOI] [PubMed] [Google Scholar]