Purpose: Recent evidence indicates that small noncoding RNA molecules, known as microRNAs (miRNAs), are involved in cancer initiation and progression. We hypothesized that genetic variations in miRNAs and miRNA target sites could be associated with the efficacy of androgen-deprivation therapy (ADT) in men with prostate cancer.

Experimental Design: We systematically evaluated 61 common single nucleotide polymorphisms (SNPs) inside miRNAs and miRNA target sites in a cohort of 601 men with advanced prostate cancer treated with ADT. The prognostic significance of these SNPs on disease progression, prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM) after ADT were assessed by Kaplan–Meier analysis and Cox regression model.

Results: Four, seven, and four SNPs were significantly associated with disease progression, PCSM, and ACM, respectively, after ADT in univariate analysis. KIF3C rs6728684, CDON rs3737336, and IFI30 rs1045747 genotypes remained as significant predictors for disease progression; KIF3C rs6728684, PALLD rs1071738, GABRA1 rs998754, and SYT9 rs4351800 remained as significant predictors for PCSM; and SYT9 rs4351800 remained as a significant predictor for ACM in multivariate models that included clinicopathologic predictors. Moreover, strong combined genotype effects on disease progression and PCSM were also observed. Patients with a greater number of unfavorable genotypes had a shorter time to progression and worse prostate cancer-specific survival during ADT (P for trend < 0.001).

Conclusion: SNPs inside miRNAs and miRNA target sites have a potential value to improve outcome prediction in prostate cancer patients receiving ADT. Clin Cancer Res; 17(4); 1–9. ©2010 AACR.

Translational Relevance

Androgen-deprivation therapy (ADT) is the most common and effective systemic therapy for advanced prostate cancer, but outcome predictors for the efficacy of ADT are still scarce. Recent studies suggest that microRNAs might participate in cancer progression. Therefore, we hypothesized that single-nucleotide polymorphisms (SNPs) in microRNAs and microRNA target sites might have tremendous implications for prognosis after ADT. In the present study, we conducted a genome-wide search for SNPs located in pre-microRNAs and putative microRNA target sites, and investigated their prognostic significance on 3 outcomes of ADT: disease progression, prostate cancer-specific mortality, and all-cause mortality. Multivariate Cox proportional hazards analysis revealed that several SNPs located in microRNA genes and putative microRNA target sites were significantly associated with the 3 outcomes of ADT. Our results suggest that a simple and pretreatment analysis for microRNA SNPs might add significant prognostic value to the currently used indicators for outcome prediction in patients receiving ADT.

With the advent of prostate-specific antigen (PSA) screening, prostate cancer is being detected and treated earlier. However, approximately 10% to 20% of newly diagnosed prostate cancer patients present advanced disease, and many others will eventually relapse despite local treatments. Androgen deprivation therapy (ADT) is the most commonly used first-line treatment for advanced prostate cancer (1). Despite frequent responses, many patients on ADT progress to castration-resistant disease within 2–3 years (2). Once castration-resistant prostate cancer develops, the life expectancy of the patient is approximately 16–18 months (3). A variety of prediction parameters, such as tumor stage, Gleason score, and PSA kinetics, have been used in clinical practice to define the presentation of prostate cancer and adapt the treatment strategy (4–6). However, their prognostic capabilities are still limited and might be improved by the incorporation of other factors including genetic markers. Germ line genetic variants have been demonstrated to have the potential in identifying predisposition to aggressive prostate cancer and providing insight into biological pathways of initiation and progression of this complex disease (7).

MicroRNAs (miRNAs) are endogenous, small (about 22 nucleotides), nonprotein-coding, single-stranded RNA molecules involved in regulating the expression of other genes. MiRNAs are first transcribed as primary miRNAs (pri-miRNAs) with several hundred nucleotides, processed to the 70- to 100-nucleotides RNA hairpin intermediates, defined as pre-miRNAs, and then exported to cytoplasm and processed to mature miRNAs as part of the RNA-induced silencing complex (8). MiRNAs regulate gene expression by base pairing with sequences within the 3′-untranslated regions of target mRNAs, leading to mRNA cleavage or translation repression (9). Numerous studies have shown that aberrant expression of miRNAs contributes to the etiology of many common human diseases including cancer (10).

Genetic variants within miRNA genes might alter miRNA processing and ultimately change the expression level of the miRNA. Alternatively, genetic variants located in the miRNA binding sites of target mRNAs might disrupt miRNA-target interaction, resulting in the deregulation of target gene expression. In this regard, the most common genetic variation, single nucleotide polymorphisms (SNPs), in miRNA genes and their target sites might be ideal candidate biomarkers for cancer prognosis. To our knowledge, this is the first study conducting a genome-wide search for SNPs located in pre-miRNAs and putative miRNA target sites, and investigating their prognostic significance on disease progression, prostate cancer-specific mortality (PCSM), and all-cause mortality (ACM) in a cohort of prostate cancer patients receiving ADT.

Patient recruitment and data collection

The study population was extended from our hospital-based prostate cancer case-control study that has been described previously (11–16). In brief, patients with diagnosed and pathologically confirmed prostate cancer were actively recruited from 3 medical centers in Taiwan: Kaohsiung Medical University Hospital, Kaohsiung Veterans General Hospital, and National Taiwan University Hospital. The prostate cancer patients who had been treated with ADT (orchiectomy or LHRH agonist with or without antiandrogen), including those with disease recurrence after local treatments (radical prostatectomy or radiotherapy), were identified and followed up prospectively to evaluate genetic variants as prognostic predictors of clinical outcomes during ADT. Patients were excluded if the clinicopathologic information or follow-up period were insufficient, leaving 601 patients in this cohort. This study was approved by the Institutional Review Board of the 3 hospitals, and informed consent was obtained from each participant.

Data were collected on patients with disease baseline and clinicopathologic characteristics, as well as 3 treatment outcomes: time to progression, PCSM, and ACM. The PSA nadir was defined as the lowest PSA value achieved during ADT treatment (6, 17). Time to PSA nadir was defined as the duration of time it took for the PSA value to reach nadir after ADT initiation (4). Disease progression was defined as a serial rise in PSA, at least 2 rises in PSA (> 1 week apart), greater than the PSA nadir (18). Initiation of secondary hormone treatment for rising PSA was also considered as a progression event. Time to progression was defined as the duration of time it took to have a progression event once ADT was started. In general, patients are followed every month with PSA tests at 3-monthly intervals. The cause of death was obtained by matching patient personal identification numbers with the official cause of death registry provided by the Department of Health, Executive Yuan, Taiwan. Overall, 145 deaths were identified and 101 of these were from prostate cancer.

SNP selection and genotyping

We identified SNPs within miRNAs by intersection HapMap SNPs CHB (Han Chinese) table with sno/miRNA table (19), and identified SNPs within miRNA target sites by intersection HapMap SNPs CHB table with TS (TargetScan) miRNA sites table (20) from the UCSC table browser (NCBI36/hg18) (21). SNPs with a minor allele frequency less than 5% in HapMap CHB population or inside snoRNAs were excluded. Fourteen SNPs in miRNAs and 59 SNPs in miRNA target sites were initially selected for analysis.

Genomic DNA was extracted from peripheral blood using the QIAamp DNA Blood Mini Kit (Qiagen) and stored at –80°C until the time of study. Genotyping was performed as described previously (14) using Sequenom iPLEX matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry technology at the National Genotyping Center, Academia Sinica, Taiwan. The average genotype call rate for these SNPs was 97.1% and the average concordance rate was 99.8% among 55 blind duplicated quality control samples. Any SNP that did not conform to Hardy–Weinberg equilibrium (P < 0.001), below a genotyping call rate of 80%, or with a minor allele frequency less than 3%, was removed (n = 12). Thus, a total of 61 SNPs were included for further statistical analyses.

Statistical analysis

Patient clinicopathologic characteristics were summarized as number and percentage of patients or median and interquartile range of values. The continuous factors were dichotomized at the median value within the cohort, with the exception of PSA nadir, which was dichotomized at 0.2 ng/mL because of its correlation with disease progression and PCSM (5–6). The heterozygous and rare homozygous genotypes were collapsed in the analysis if the frequency of the rare homozygote was low (<2%) or if the homozygous and heterozygous genotypes had the same direction of effect. The associations of 61 individual SNPs and clinicopathologic characteristics with time to progression, PCSM, and ACM were assessed using the Kaplan-Meier analysis with log-rank test. Multivariate analyses to determine the interdependency of genotypes and other known prognostic factors, such as age at diagnosis, clinical stage, Gleason score, PSA at ADT initiation, PSA nadir, and time to PSA nadir, were carried out using Cox proportional hazards regression model. As we were testing 61 SNPs, false-discovery rates (q values) were calculated to determine the degree to which the tests for association were prone to false-positives (22). q values were estimated using R q value package (http://genomics.princeton.edu/storeylab/qvalue/) on the observed distribution of P values from the log-rank test for 61 SNPs. Statistical Package for the Social Sciences software version 16.0.1 (SPSS Inc.) was used for other statistical analyses. A 2-sided P value of < 0.05 was considered statistically significant.

The patients’ demographic and clinicopathologic characteristics are summarized in Table 1. Four hundred and fifteen (69%) patients had disease progression after ADT initiation, and the median time to progression was 22 months with a mean follow-up of 30.3 months (range, 3–120 months). One hundred and forty-five (24%) patients died, and 101 (17%) died of prostate cancer after a mean follow-up of 39 months (range, 3–125 months). The mean times to PCSM and ACM were 138 and 123 months, respectively. Metastatic stage of the disease, Gleason scores 8–10, higher PSA nadir, and shorter time to PSA nadir were significantly associated (P ≤ 0.006) with shorter time to progression, PCSM, and ACM. Age at diagnosis was only associated with ACM, and PSA level at ADT initiation was associated with shorter time to PCSM and ACM, but not time to progression.

Table 1.

Clinicopathologic characteristics of the study population and analyses of factors that predicted disease progression, PCSM, and ACM during ADT

VariableNo.* (%)Disease progressionPCSMACM
No. of events*Median (months)PNo. of events*Mean (months)PNo. of events*Mean (months)P
All patients 601 415 22  101 138  145 123  
Age at diagnosis (years) 
 Median (IQR) 73 (67–79)          
 ≤73 320 (53.2) 228 21 0.219 51 141 0.280 61 135 0.001 
 >74 281 (46.8) 186 25  50 127  84 105  
Clinical stage at diagnosis 
 T1/T2 189 (31.7) 117 25 0.005 13 145 <0.001 26 130 <0.001 
 T3/T4/N1 184 (30.8) 123 25  21 149  32 138  
 M1 224 (37.5) 172 17  67 110  87 94  
Gleason score at diagnosis 
 2–6 194 (33.0) 128 26 0.006 20 154 <0.001 34 141 <0.001 
 7 180 (30.6) 124 25  19 134  32 116  
 8–10 214 (36.4) 153 17  61 108  77 97  
PSA at ADT initiation (ng/mL) 
 Median (IQR) 35.0 (11.4–129)          
 <35 287 (49.6) 184 25 0.083 24 146 <0.001 44 132 <0.001 
 ≥35 292 (50.4) 211 19  76 117  98 103  
PSA nadir (ng/mL) 
 Median (IQR) 0.18 (0.01–1.33)          
 <0.2 301 (50.8) 186 31 <0.001 20 159 <0.001 37 145 <0.001 
 ≥0.2 292 (49.2) 228 14  80 110  106 95  
Time to PSA nadir (months) 
 Median (IQR) 10 (5–18)          
 <10 293 (49.4) 220 10 <0.001 65 121 <0.001 89 105 <0.001 
 ≥10 300 (50.6) 194 33  35 150  54 136  
VariableNo.* (%)Disease progressionPCSMACM
No. of events*Median (months)PNo. of events*Mean (months)PNo. of events*Mean (months)P
All patients 601 415 22  101 138  145 123  
Age at diagnosis (years) 
 Median (IQR) 73 (67–79)          
 ≤73 320 (53.2) 228 21 0.219 51 141 0.280 61 135 0.001 
 >74 281 (46.8) 186 25  50 127  84 105  
Clinical stage at diagnosis 
 T1/T2 189 (31.7) 117 25 0.005 13 145 <0.001 26 130 <0.001 
 T3/T4/N1 184 (30.8) 123 25  21 149  32 138  
 M1 224 (37.5) 172 17  67 110  87 94  
Gleason score at diagnosis 
 2–6 194 (33.0) 128 26 0.006 20 154 <0.001 34 141 <0.001 
 7 180 (30.6) 124 25  19 134  32 116  
 8–10 214 (36.4) 153 17  61 108  77 97  
PSA at ADT initiation (ng/mL) 
 Median (IQR) 35.0 (11.4–129)          
 <35 287 (49.6) 184 25 0.083 24 146 <0.001 44 132 <0.001 
 ≥35 292 (50.4) 211 19  76 117  98 103  
PSA nadir (ng/mL) 
 Median (IQR) 0.18 (0.01–1.33)          
 <0.2 301 (50.8) 186 31 <0.001 20 159 <0.001 37 145 <0.001 
 ≥0.2 292 (49.2) 228 14  80 110  106 95  
Time to PSA nadir (months) 
 Median (IQR) 10 (5–18)          
 <10 293 (49.4) 220 10 <0.001 65 121 <0.001 89 105 <0.001 
 ≥10 300 (50.6) 194 33  35 150  54 136  

NOTE. P ≤ 0.05 are in boldface.

Abbreviations: ADT, androgen-deprivation therapy; PCSM, prostate cancer-specific mortality; ACM, all-cause mortality; PSA, prostate-specific antigen; IQR, interquartile range.

*Column subtotals do not sum to 601 for no. of patients, 415 for no. of disease progression, 101 for PCSM, and 145 for ACM due to missing data.

P values were calculated using the log-rank test.

Of the 61 SNPs evaluated, 4, 7, and 4 polymorphisms showed a statistically significantly correlation with time to progression, PCSM, and ACM respectively, according to the log-rank test (Supplementary Table 1). KIF3C rs6728684, CDON rs3737336, ETS1 rs1128334, and IFI30 rs1045747 were associated with disease progression during ADT (nominal P ≤ 0.031), and all had a false-discovery rate (q value) less than 0.218 (Table 2). To assess the predictive effects of these SNPs beyond the clinical features to influence disease progression, we performed a multivariate analysis, adjusting for age at diagnosis, clinical stage, Gleason score, PSA level at ADT initiation, PSA nadir, and time to PSA nadir. After adjusting for these predictors, KIF3C rs6728684, CDON rs3737336, and IFI30 rs1045747 remained significant (P ≤ 0.035). A strong gene-dosage effect on disease progression during ADT was observed when these 3 SNPs were analyzed in combination (log-rank P < 0.001, Table 2 and Fig. 1A left panel). The time to progression decreased as the number of unfavorable genotypes increased, and the combined genotype remained as a significant predictor after adjusting for clinical factors (P for trend < 0.001, Table 2).

Figure 1.

Kaplan–Meier curves of: (A) time to progression during ADT for patients with 0, 1, or >1 unfavorable genotypes at the 3 genetic loci of interest; (B) time to PCSM during ADT for patients with 0, 1, or >1 unfavorable genotypes at the 4 genetic loci of interest; (C) time to ACM during ADT stratified by genotypes at SYT9 rs4351800; in all patients (left panel), in patients without distant metastasis (middle panel), or in patients with distant metastasis (right panel). Numbers in parentheses indicate the number of patients.

Figure 1.

Kaplan–Meier curves of: (A) time to progression during ADT for patients with 0, 1, or >1 unfavorable genotypes at the 3 genetic loci of interest; (B) time to PCSM during ADT for patients with 0, 1, or >1 unfavorable genotypes at the 4 genetic loci of interest; (C) time to ACM during ADT stratified by genotypes at SYT9 rs4351800; in all patients (left panel), in patients without distant metastasis (middle panel), or in patients with distant metastasis (right panel). Numbers in parentheses indicate the number of patients.

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Table 2.

Genotyping frequencies and the association of genotype with disease progression during ADT

Gene SNPGenotypeNo. of patientsNo. of eventsMedian (months)P*qHR (95% CI)P
KIF3C TT/TG 577 399 22 0.002 0.056 1.00  
rs6728684 GG 16 12 17   2.41 (1.31–4.43) 0.005 
CDON TT/TC 543 385 21 0.004 0.056 1.00  
rs3737336 CC 47 24 37   0.59 (0.38–0.91) 0.018 
ETS1 GG/GA 512 343 24 0.011 0.103 1.00  
 rs1128334 AA 58 48 16   1.16 (0.84–1.59) 0.368 
IFI30 TT/TC 524 361 22 0.031 0.218 1.00  
rs1045747 CC 16 12 14   1.89 (1.05–3.39) 0.035 
No. of unfavorable genotypes present‡ 
 47 24 37 <0.001  1.00  
1  519 367 22   1.63 (1.06–2.52) 0.027 
>1  31 23 14   3.17 (1.74–5.78) <0.001 
       P-trend <0.001 
Gene SNPGenotypeNo. of patientsNo. of eventsMedian (months)P*qHR (95% CI)P
KIF3C TT/TG 577 399 22 0.002 0.056 1.00  
rs6728684 GG 16 12 17   2.41 (1.31–4.43) 0.005 
CDON TT/TC 543 385 21 0.004 0.056 1.00  
rs3737336 CC 47 24 37   0.59 (0.38–0.91) 0.018 
ETS1 GG/GA 512 343 24 0.011 0.103 1.00  
 rs1128334 AA 58 48 16   1.16 (0.84–1.59) 0.368 
IFI30 TT/TC 524 361 22 0.031 0.218 1.00  
rs1045747 CC 16 12 14   1.89 (1.05–3.39) 0.035 
No. of unfavorable genotypes present‡ 
 47 24 37 <0.001  1.00  
1  519 367 22   1.63 (1.06–2.52) 0.027 
>1  31 23 14   3.17 (1.74–5.78) <0.001 
       P-trend <0.001 

NOTE. P ≤ 0.05 are in boldface.

Abbreviations: ADT, androgen-deprivation therapy; HR, hazard ratio; 95% CI, 95% confidence interval; PSA, prostate-specific antigen.

*P values were calculated using the log-rank test.

†HRs were adjusted for age, clinical stage, Gleason score, PSA at ADT initiation, PSA nadir, and time to PSA nadir.

‡Unfavorable genotypes refer to GG in KIF3C rs6728684, TT/TC in CDON rs3737336, and CC in IFI30 rs1045747.

hsa-mir-423 rs6505162, KIF3C rs6728684, PALLD rs1071738, ACSL1 rs2292899, GABRA1 rs998754, SYT9 rs4351800, and ZDHHC7 rs3210967 had statistically significant effects on PCSM (P ≤ 0.037), and all had a q value less than 0.187 (Table 3). Four SNPs, KIF3C rs6728684, PALLD rs1071738, GABRA1 rs998754, and SYT9 rs4351800, and their combined genotype remained as significant predictors for time to PCSM after adjusting for clinical factors (P ≤ 0.039). A significant combined genotype effect on PCSM was also observed, and the hazard ratios (HRs) for PCSM during ADT increased as the number of unfavorable genotypes increased (P for trend < 0.001, Table 3 and Fig. 1B left panel).

Table 3.

Genotyping frequencies and the association of genotype with PCSM during ADT

Pre-miRNA/Gene SNPGenotypeNo. of patientsNo. of eventsMean (months)P*qHR (95% CI)P
hsa-mir-423 CC 388 74 135 0.037 0.187 1.00  
 rs6505162 CA/AA 207 26 141   0.64 (0.40–1.01) 0.054 
KIF3C TT/TG 580 95 139 0.027 0.165 1.00  
rs6728684 GG 16 55   2.65 (1.05–6.70) 0.039 
PALLD GG 464 69 143 0.018 0.165 1.00  
rs1071738 GC/CC 130 31 119   2.12 (1.36–3.29) 0.001 
ACSL1 GG/GA 519 78 141 0.024 0.165 1.00  
 rs2292899 AA 73 19 121   1.31 (0.77–2.21) 0.316 
GABRA1 TT 172 38 129 0.028 0.165 1.00  
rs998754 TG/GG 400 59 137   0.59 (0.39–0.90) 0.015 
SYT9 AA/AC 526 83 141 0.006 0.165 1.00  
rs4351800 CC 66 18 82   2.89 (1.70–4.91) <0.001 
ZDHHC7 GG/GA 463 87 134 0.010 0.165 1.00  
 rs3210967 AA 133 13 144   0.77 (0.42–1.40) 0.389 
No. of unfavorable genotypes present‡ 
 271 35 137 <0.001  1.00  
1  250 39 141   1.57 (0.97–2.54) 0.064 
>1  79 27 85   4.20 (2.49–7.09) <0.001 
       P-trend <0.001 
Pre-miRNA/Gene SNPGenotypeNo. of patientsNo. of eventsMean (months)P*qHR (95% CI)P
hsa-mir-423 CC 388 74 135 0.037 0.187 1.00  
 rs6505162 CA/AA 207 26 141   0.64 (0.40–1.01) 0.054 
KIF3C TT/TG 580 95 139 0.027 0.165 1.00  
rs6728684 GG 16 55   2.65 (1.05–6.70) 0.039 
PALLD GG 464 69 143 0.018 0.165 1.00  
rs1071738 GC/CC 130 31 119   2.12 (1.36–3.29) 0.001 
ACSL1 GG/GA 519 78 141 0.024 0.165 1.00  
 rs2292899 AA 73 19 121   1.31 (0.77–2.21) 0.316 
GABRA1 TT 172 38 129 0.028 0.165 1.00  
rs998754 TG/GG 400 59 137   0.59 (0.39–0.90) 0.015 
SYT9 AA/AC 526 83 141 0.006 0.165 1.00  
rs4351800 CC 66 18 82   2.89 (1.70–4.91) <0.001 
ZDHHC7 GG/GA 463 87 134 0.010 0.165 1.00  
 rs3210967 AA 133 13 144   0.77 (0.42–1.40) 0.389 
No. of unfavorable genotypes present‡ 
 271 35 137 <0.001  1.00  
1  250 39 141   1.57 (0.97–2.54) 0.064 
>1  79 27 85   4.20 (2.49–7.09) <0.001 
       P-trend <0.001 

NOTE. P ≤ 0.05 are in boldface.

Abbreviations: ADT, androgen-deprivation therapy; HR, hazard ratio; 95% CI, 95% confidence interval; PSA, prostate-specific antigen.

*P values were calculated using the log-rank test.

†HRs were adjusted for age, clinical stage, Gleason score, PSA at ADT initiation, PSA nadir, and time to PSA nadir.

‡Unfavorable genotypes refer to GG in KIF3C rs6728684, GC/CC in PALLD rs1071738, TT in GABRA1 rs998754, and CC in SYT9 rs4351800.

Four SNPs, ACSL1 rs2292899, MTRR rs9332, GABRA1 rs998754, and SYT9 rs4351800, were significantly associated with time to ACM in the univariate analysis (P ≤ 0.046), and all had a q value less than 0.222 (Table 4). However, after adjusting for clinical predictors, only SYT9 rs4351800 remained a significant predictor for time to ACM in patients receiving ADT (HR 2.55, 95% CI 1.65–3.95, P < 0.001). Kaplan–Meier survival curves and log-rank test showed that the SYT9 rs4351800 CC genotype was significantly associated with poorer overall survival compared with the AA/AC genotypes (P = 0.001, Fig. 1C left panel).

Table 4.

Genotyping frequencies and the association of genotype with ACM during ADT

Gene SNPGenotypeNo. of patientsNo. of eventsMean (months)P*qHR (95% CI)P
ACSL1 GG/GA 519 116 125 0.046 0.222 1.00  
 rs2292899 AA 73 25 109   1.25 (0.80–1.96) 0.326 
MTRR CC/CT 571 132 125 0.033 0.222 1.00  
 rs9332 TT 17 76   1.27 (0.59–2.75) 0.545 
GABRA1 TT 172 53 113 0.020 0.222 1.00  
 rs998754 TG/GG 400 86 123   0.70 (0.49–1.00) 0.051 
SYT9 AA/AC 526 119 127 0.001 0.032 1.00  
rs4351800 CC 66 26 69   2.55 (1.65–3.95) <0.001 
Gene SNPGenotypeNo. of patientsNo. of eventsMean (months)P*qHR (95% CI)P
ACSL1 GG/GA 519 116 125 0.046 0.222 1.00  
 rs2292899 AA 73 25 109   1.25 (0.80–1.96) 0.326 
MTRR CC/CT 571 132 125 0.033 0.222 1.00  
 rs9332 TT 17 76   1.27 (0.59–2.75) 0.545 
GABRA1 TT 172 53 113 0.020 0.222 1.00  
 rs998754 TG/GG 400 86 123   0.70 (0.49–1.00) 0.051 
SYT9 AA/AC 526 119 127 0.001 0.032 1.00  
rs4351800 CC 66 26 69   2.55 (1.65–3.95) <0.001 

NOTE. P ≤ 0.05 are in boldface.

Abbreviations: ADT, androgen-deprivation therapy; HR, hazard ratio; 95% CI, 95% confidence interval; PSA, prostate-specific antigen.

*P values were calculated using the log-rank test.

†HRs were adjusted for age, clinical stage, Gleason score, PSA at ADT initiation, PSA nadir, and time to PSA nadir.

To further evaluate the clinical relevance of these miRNA SNPs, a substratification of high-risk patients based on clinical staging is performed. The combined genotypes and SYT9 rs4351800 still had significant effects on disease progression, PCSM, and ACM in patients with or without distant metastasis, respectively (P ≤ 0.040, Fig. 1 middle and right panels). This additional information leads to better risk prediction, and also supports that SNPs inside miRNAs and miRNA target sites might be independent predictors of clinical outcomes following ADT along with current clinicopathologic prognostic markers.

In this study, we found that 6 of 61 SNPs inside miRNAs and miRNA target sites were significantly associated with the disease progression, PCSM, or ACM in prostate cancer patients receiving ADT, thus validating our hypothesis. Notably, in multivariate analysis, these SNPs retained their association with the efficacy of ADT while controlling for the known clinicopathologic risk factors (age at diagnosis, clinical stage, Gleason score, PSA level at ADT initiation, PSA nadir, and time to PSA nadir), suggesting that these host genetic factors add information above and beyond currently used predictors. Moreover, strong combined genotype effects on disease progression and PCSM were also observed. To our knowledge, this is the first study to demonstrate a potential value of variants in miRNAs and miRNA target sites as predictors for the outcomes of ADT.

Of the 61 SNPs evaluated, KIF3C rs6728684, CDON rs3737336, and IFI30 rs1045747 showed significant associations with disease progression after adjusting for all clinical predictors. KIF3C, kinesin family member 3C, belongs to the family of kinesin motor proteins. Kinesins are microtubule-dependent molecular motors involved in intracellular transport and mitosis (23–24). KIF3C expression is highly enriched in nervous systems. Although the precise functions of KIF3C remain unknown, biochemical studies suggest that KIF3C is an anterograde motor which might be involved in synaptic vesicle trafficking, specialized functions that are associated with the normal development of nervous system and the formation of neuroendocrine tumors (25). ADT works through inhibition of androgen receptor in the prostate epithelium, or suppression of the secretion of factors from prostate stromal cells that are critical for the survival of prostate epithelial cells. Because neuroendocrine cells lack androgen receptor and are likely androgen-independent, it is conceivable that ADT will not eliminate these cells. Instead, neuroendocrine cells might be enriched after ADT to stimulate androgen-independent proliferation of prostate cancer, leading to the disease progression. A number of studies have also demonstrated that the presence of neuroendocrine phenotype in tumors is associated with worse prognosis and facilitation of prostate cancer progression during ADT (26). On the other hand, overexpression of KIF3C has been found to mediate docetaxel resistance in breast cancer cells by increasing the pools of free tubulin and promoting the dissociation of tubulin from microtubules to antagonize the effect of docetaxel (27). CDON, cell adhesion molecule-related/downregulated by oncogenes (cdon) homolog, is initially identified as a component of cell surface receptor complex that mediates cell–cell interactions during myogenic differentiation. Recent studies showed that CDON interacts with all Hedgehog (Hh) proteins and positively regulates Hh signaling (28). CDON maps to chromosome 11q23-q24, a region with frequent loss of heterozygosity in lung, breast, and ovarian cancers, suggesting that CDON could play a role in oncogenesis (29–31). Notably, androgen deprivation highly upregulated the expression of Hh ligands and Hh target genes in prostate cancer cells (32). The clinical relevance of this observation is also supported by the increase of Hh ligand production in prostate tumors after neoadjuvant hormone treatment (33). IFI30, interferon gamma-inducible protein 30, encodes a lysosomal thiol reductase that cleaves protein disulfide bonds, and is thought to have an important role in MHC class II-restricted antigen processing in antigen-presenting cells. Establishment of long-term immunity to block tumor recurrence depends on the recruitment and activation of T cells (34). Tumors can constitutively express both MHC class I and II molecules, necessary for tumor antigen presentation to T cells. Yet, malignant cells might evade or avoid T-cell surveillance through modulation of the IFI30 expression to disrupt the pathways for MHC-restricted tumor antigen presentation (35–36). Therefore, it is possible that the effect of these miRNA SNPs on ADT efficacy might be a result of their influence on the gene expressions of KIF3C, CDON, and IFI30, altering tumor neuroendocrine differentiation, Hh signaling, and host immunity.

Four SNPs, KIF3C rs6728684, PALLD rs1071738, GABRA1 rs998754, and SYT9 rs4351800, were significantly associated with PCSM after controlling for known clinical prognostic factors. Of these, SYT9 rs4351800 also showed significant association with ACM during ADT. PALLD, paladin, encodes a cytoskeletal protein that plays an essential role in the assembly and maintenance of several types of actin-dependent structures to control cell morphology, motility, cell adhesion and cell-extracellular matrix interactions. PALLD knockout mouse displayed defects in actin organization, cell adhesion, and cell motility (37), whereas PALLD is overexpressed in the most invasive population of cancer cells (38–40). These correlations suggested that deregulated PALLD might contribute to the aggressive/invasive pathologic cancer cell behavior. GABRA1, gamma-aminobutyric acid (GABA) A receptor alpha 1, is a member of the cys-loop family of ligand-gated ion channels, responsible for mediating the major inhibitory neurotransmitter, GABA, in the brain. GABRA1 maps to chromosome 5q34-q35, the region that has been implicated in the development and progression of bladder cancer (41–42). SYT9 encodes for the vesicular transport protein synaptotagmin IX, which regulates exocytosis of synaptic vesicles and appears to serve as a calcium sensor to trigger neurotransmitter release in neuroendocrine cells. A genetic alteration in SYT9, D445N, was found in some colorectal cancers (43). In addition, another closely related synaptotagmin member, SYT7, has also been identified as a prostate cancer-associated gene (44). Interestingly, both GABAergic pathway and synaptotagmins seem to be involved in the neuroendocrine differentiation of prostate cancer (45). Taken together, systematically evaluating common variants in miRNAs and miRNA target sites, our research lights up the pathways to influence the survival after ADT, such as KIF3C, GABRA1, and SYT9 in neuroendocrine differentiation, as well as PALLD in cell motility. However, the current findings are hypothesis-generating and further investigation is needed to determine the role of these SNPs/genes during prostate cancer progression.

In summary, we present the first epidemiologic evidence supporting the involvement of genetic variants within miRNAs and miRNA target sites in prostate cancer progression during ADT, and the use of individual as well as combined genotypes of miRNA-related variants to predict clinical outcomes after ADT. The results reported here are limited by analyzing the small number of patients in genetic subset and multiple comparisons. In addition, our homogeneous Chinese Han population might make our findings less generalizable to other ethnic groups. Although this study on miRNA SNPs and efficacy of ADT is at an early stage and the results need replication and laboratory-based functional validation, our findings are nevertheless encouraging in further investigating the genetic candidates implicated by reported SNPs, understanding the pathways of prostate cancer progression during ADT, and ultimately tailoring individual therapeutic interventions.

No potential conflicts of interest were disclosed.

We thank National Genotyping Center of National Research Program for Genomic Medicine, National Science Council, Taiwan, for their technical support.

National Science Council, Taiwan (grant number: NSC-98–2320-B-039–019-MY3), and China Medical University (grant number: CMU98-N1–21 and CMU98-C-12).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Pronzato
P
,
Rondini
M
. 
Hormonotherapy of advanced prostate cancer
Ann Oncol
2005
;
16
(
Suppl 4
):
iv80
4
.
2.
Walczak
JR
,
Carducci
MA
. 
Prostate cancer: a practical approach to current management of recurrent disease
Mayo Clin Proc
2007
;
82
:
243
9
.
3.
Pienta
KJ
,
Bradley
D
. 
Mechanisms underlying the development of androgen-independent prostate cancer
Clin Cancer Res
2006
;
12
:
1665
71
.
4.
Choueiri
TK
,
Xie
W
,
D'Amico
AV
, et al
Time to prostate-specific antigen nadir independently predicts overall survival in patients who have metastatic hormone-sensitive prostate cancer treated with androgen-deprivation therapy
Cancer
2009
;
115
:
981
7
.
5.
Hussain
M
,
Tangen
CM
,
Higano
C
, et al
Absolute prostate-specific antigen value after androgen deprivation is a strong independent predictor of survival in new metastatic prostate cancer: data from Southwest Oncology Group Trial 9346 (INT-0162)
J Clin Oncol
2006
;
24
:
3984
90
.
6.
Stewart
AJ
,
Scher
HI
,
Chen
MH
, et al
Prostate-specific antigen nadir and cancer-specific mortality following hormonal therapy for prostate-specific antigen failure
J Clin Oncol
2005
;
23
:
6556
60
.
7.
Zheng
SL
,
Sun
J
,
Wiklund
F
, et al
Cumulative association of five genetic variants with prostate cancer
N Engl J Med
2008
;
358
:
910
9
.
8.
Kim
VN
. 
MicroRNA biogenesis: coordinated cropping and dicing
Nat Rev Mol Cell Biol
2005
;
6
:
376
85
.
9.
Fabbri
M
,
Croce
CM
,
Calin
GA
. 
MicroRNAs
Cancer J
2008
;
14
:
1
6
.
10.
Esquela-Kerscher
A
,
Slack
FJ
. 
Oncomirs—microRNAs with a role in cancer
Nat Rev Cancer
2006
;
6
:
259
69
.
11.
Huang
SP
,
Chou
YH
,
Wayne Chang
WS
, et al
Association between vitamin D receptor polymorphisms and prostate cancer risk in a Taiwanese population
Cancer Lett
2004
;
207
:
69
77
.
12.
Huang
SP
,
Huang
CY
,
Wang
JS
, et al
Prognostic significance of p53 and X-ray repair cross-complementing group 1 polymorphisms on prostate-specific antigen recurrence in prostate cancer post radical prostatectomy
Clin Cancer Res
2007
;
13
:
6632
8
.
13.
Huang
SP
,
Huang
CY
,
Wu
WJ
, et al
Association of vitamin D receptor FokI polymorphism with prostate cancer risk, clinicopathological features and recurrence of prostate specific antigen after radical prostatectomy
Int J Cancer
2006
;
119
:
1902
7
.
14.
Huang
SP
,
Huang
LC
,
Ting
WC
, et al
Prognostic significance of prostate cancer susceptibility variants on prostate-specific antigen recurrence after radical prostatectomy
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
3068
74
.
15.
Huang
SP
,
Ting
WC
,
Chen
LM
, et al
Association analysis of Wnt pathway genes on prostate-specific antigen recurrence after radical prostatectomy
Ann Surg Oncol
2010
;
17
:
312
22
.
16.
Huang
SP
,
Wu
WJ
,
Chang
WS
, et al
p53 Codon 72 and p21 codon 31 polymorphisms in prostate cancer
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
2217
24
.
17.
Kwak
C
,
Jeong
SJ
,
Park
MS
,
Lee
E
,
Lee
SE
. 
Prognostic significance of the nadir prostate specific antigen level after hormone therapy for prostate cancer
J Urol
2002
;
168
:
995
1000
.
18.
Ross
RW
,
Oh
WK
,
Xie
W
, et al
Inherited variation in the androgen pathway is associated with the efficacy of androgen-deprivation therapy in men with prostate cancer
J Clin Oncol
2008
;
26
:
842
7
.
19.
Griffiths-Jones
S
,
Saini
HK
,
van Dongen
S
,
Enright
AJ
. 
miRBase: tools for microRNA genomics
Nucleic Acids Res
2008
;
36
:
D154
8
.
20.
Grimson
A
,
Farh
KK
,
Johnston
WK
,
Garrett-Engele
P
,
Lim
LP
,
Bartel
DP
. 
MicroRNA targeting specificity in mammals: determinants beyond seed pairing
Mol Cell
2007
;
27
:
91
105
.
21.
Karolchik
D
,
Hinrichs
AS
,
Furey
TS
, et al
The UCSC Table Browser data retrieval tool
Nucleic Acids Res
2004
;
32
:
D493
6
.
22.
Storey
JD
,
Tibshirani
R
. 
Statistical significance for genomewide studies
Proc Natl Acad Sci U S A
2003
;
100
:
9440
5
.
23.
Hirokawa
N
,
Noda
Y
,
Okada
Y
. 
Kinesin and dynein superfamily proteins in organelle transport and cell division
Curr Opin Cell Biol
1998
;
10
:
60
73
.
24.
Sharp
DJ
,
Rogers
GC
,
Scholey
JM
. 
Microtubule motors in mitosis
Nature
2000
;
407
:
41
7
.
25.
Muresan
V
,
Abramson
T
,
Lyass
A
, et al
KIF3C and KIF3A form a novel neuronal heteromeric kinesin that associates with membrane vesicles
Mol Biol Cell
1998
;
9
:
637
52
.
26.
Vashchenko
N
,
Abrahamsson
PA
. 
Neuroendocrine differentiation in prostate cancer: implications for new treatment modalities
Eur Urol
2005
;
47
:
147
55
.
27.
De
S
,
Cipriano
R
,
Jackson
MW
,
Stark
GR
. 
Overexpression of kinesins mediates docetaxel resistance in breast cancer cells
Cancer Res
2009
;
69
:
8035
42
.
28.
Kavran
JM
,
Ward
MD
,
Oladosu
OO
,
Mulepati
S
,
Leahy
DJ
. 
All mammalian hedgehog proteins interact with CDO and BOC in a conserved manner
J Biol Chem
2010
.
29.
Gabra
H
,
Watson
JE
,
Taylor
KJ
, et al
Definition and refinement of a region of loss of heterozygosity at 11q23.3-q24.3 in epithelial ovarian cancer associated with poor prognosis
Cancer Res
1996
;
56
:
950
4
.
30.
Negrini
M
,
Rasio
D
,
Hampton
GM
, et al
Definition and refinement of chromosome 11 regions of loss of heterozygosity in breast cancer: identification of a new region at 11q23.3
Cancer Res
1995
;
55
:
3003
7
.
31.
Rasio
D
,
Negrini
M
,
Manenti
G
,
Dragani
TA
,
Croce
CM
. 
Loss of heterozygosity at chromosome 11q in lung adenocarcinoma: identification of three independent regions
Cancer Res
1995
;
55
:
3988
91
.
32.
Chen
M
,
Tanner
M
,
Levine
AC
,
Levina
E
,
Ohouo
P
,
Buttyan
R
. 
Androgenic regulation of hedgehog signaling pathway components in prostate cancer cells
Cell Cycle
2009
;
8
:
149
57
.
33.
Azoulay
S
,
Terry
S
,
Chimingqi
M
, et al
Comparative expression of Hedgehog ligands at different stages of prostate carcinoma progression
J Pathol
2008
;
216
:
460
70
.
34.
Smyth
MJ
,
Godfrey
DI
,
Trapani
JA
. 
A fresh look at tumor immunosurveillance and immunotherapy
Nat Immunol
2001
;
2
:
293
9
.
35.
Haque
MA
,
Li
P
,
Jackson
SK
, et al
Absence of gamma-interferon-inducible lysosomal thiol reductase in melanomas disrupts T cell recognition of select immunodominant epitopes
J Exp Med
2002
;
195
:
1267
77
.
36.
Seliger
B
,
Maeurer
MJ
,
Ferrone
S
. 
Antigen-processing machinery breakdown and tumor growth
Immunol Today
2000
;
21
:
455
64
.
37.
Luo
H
,
Liu
X
,
Wang
F
, et al
Disruption of palladin results in neural tube closure defects in mice
Mol Cell Neurosci
2005
;
29
:
507
15
.
38.
Goicoechea
SM
,
Bednarski
B
,
Garcia-Mata
R
,
Prentice-Dunn
H
,
Kim
HJ
,
Otey
CA
. 
Palladin contributes to invasive motility in human breast cancer cells
Oncogene
2009
;
28
:
587
98
.
39.
Ryu
B
,
Jones
J
,
Hollingsworth
MA
,
Hruban
RH
,
Kern
SE
. 
Invasion-specific genes in malignancy: serial analysis of gene expression comparisons of primary and passaged cancers
Cancer Res
2001
;
61
:
1833
8
.
40.
Wang
W
,
Goswami
S
,
Lapidus
K
, et al
Identification and testing of a gene expression signature of invasive carcinoma cells within primary mammary tumors
Cancer Res
2004
;
64
:
8585
94
.
41.
Kram
A
,
Li
L
,
Zhang
RD
, et al
Mapping and genome sequence analysis of chromosome 5 regions involved in bladder cancer progression
Lab Invest
2001
;
81
:
1039
48
.
42.
von Knobloch
R
,
Bugert
P
,
Jauch
A
,
Kalble
T
,
Kovacs
G
. 
Allelic changes at multiple regions of chromosome 5 are associated with progression of urinary bladder cancer
J Pathol
2000
;
190
:
163
8
.
43.
Sjoblom
T
,
Jones
S
,
Wood
LD
, et al
The consensus coding sequences of human breast and colorectal cancers
Science
2006
;
314
:
268
74
.
44.
Walker
MG
,
Volkmuth
W
,
Sprinzak
E
,
Hodgson
D
,
Klingler
T
. 
Prediction of gene function by genome-scale expression analysis: prostate cancer-associated genes
Genome Res
1999
;
9
:
1198
203
.
45.
Hu
Y
,
Ippolito
JE
,
Garabedian
EM
,
Humphrey
PA
,
Gordon
JI
. 
Molecular characterization of a metastatic neuroendocrine cell cancer arising in the prostates of transgenic mice
J Biol Chem
2002
;
277
:
44462
74
.

Supplementary data