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Research Papers:

Gene polymorphisms in the PI3K/AKT/mTOR signaling pathway contribute to prostate cancer susceptibility in Chinese men

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Oncotarget. 2017; 8:61305-61317. https://doi.org/10.18632/oncotarget.18064

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Ting Liu, Abulajiang Gulinaer, Xiaoli Shi, Feng Wang, Hengqing An, Wenli Cui and Qiaoxin Li _

Abstract

Ting Liu1,*, Abulajiang Gulinaer1,*, Xiaoli Shi1, Feng Wang2, Hengqing An2, Wenli Cui1 and Qiaoxin Li1

1Department of Pathology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China

2Department of Urology, The First Affiliated Hospital, Xinjiang Medical University, Urumqi, China

*These authors have contributed equally to this work and should be considered co-first authors

Correspondence to:

Qiaoxin Li, email: liqiaoxin2141@aliyun.com

Keywords: case-control study, prostate cancer, genetic susceptibility, PI3K/AKT/mTOR pathway, polymorphism

Abbreviations: SNPs: single nucleotide polymorphisms; PCa: prostate cancer; OR: odds ratio; CI: confidence interval

Received: February 24, 2017    Accepted: April 15, 2017    Published: May 22, 2017

ABSTRACT

In this hospital-based case-control study of 413 prostate cancer (PCa) cases and 807 cancer-free controls, we investigated the role of functional single nucleotide polymorphisms (SNPs) of pivotal genes in the PI3K/AKT/mTOR pathway. We genotyped 17 SNPs in mTOR, Raptor, AKT1, AKT2, PTEN, and K-ras and found that 4 were associated with PCa susceptibility. Among the variants, the homozygote variant CC genotype of mTOR rs17036508 C>T were associated with higher PCa risk than the wild TT genotypes (adjusted OR = 3.73 (95% CI = 1.75-7.94), P = 0.001). The GT genotype of mTOR rs2295080 G>T was more protective than the TT genotypes (adjusted OR=0.54 (95% CI=0.32-0.91), P=0.020). The distributions of Raptor rs1468033 A>G genotypes differed between cases and controls, especially in subgroups defined by age, BMI, smoking status, and ethnicity. The CT/CC genotypes of AKT2 rs7250897 C>T were associated with an increased risk of PCa, particularly in subgroups of age >71 and BMI >24 kg/m2. These findings suggest that SNPs in the PI3K/AKT/mTOR pathway may contribute to the risk of PCa in Chinese men.


Gene polymorphisms in the PI3K/AKT/mTOR signaling pathway contribute to prostate cancer susceptibility in Chinese men | Liu | Oncotarget

INTRODUCTION

Prostate cancer (PCa) is the leading malignancy in developed nations and ranks second in cancer deaths worldwide [1, 2]. Its incidence has increased rapidly in China [3, 4], with an estimated incidence of 1 in 10000 in 2010 [5]. Although environmental and genetic factors are associated with PCa carcinogenesis, the actual causes are unknown. Published genome-wide association studies (GWAS) identified 40 single nucleotide polymorphisms (SNPs) that were associated with human PCa risk [6]. Lu et al. demonstrated that SNPs associated with PCa risk were enriched in the androgen receptor (AR)-binding sites [7, 8]. These findings led to further functional studies to better understand PCa susceptibility.

The PI3K/AKT/mTOR signaling pathway is involved in many human malignancies, including PCa [911]. Nearly 25-70% of PCa cases show altered PI3K/AKT/mTOR signaling with higher prevalence in metastatic tumors. AKT is a proto-oncogene that is phosphorylated by a variety of extracellular stimuli and regulates multiple cellular processes involved in cell survival, growth, differentiation and proliferation. Genomic mutations of PTEN are reported for nearly 50% of primary PCa, especially in advanced disease [12]. Although AR is not a known physiological substrate of AKT, two AKT phosphorylation consensus sequences were identified in AR [13, 14]. It was postulated that some AKT substrate, eukaryotic initiation factor 4E-binding protein-1 (4EBP1) and ribosomal protein S6 kinase (P70) were associated with AR synthesis and resulted in PCa. Given these findings, the role of PI3K/AKT/mTOR pathway in PCa carcinogenesis needs to be established.

Previous pre-GWAS studies demonstrated that genetic variants in PI3K/AKT/mTOR pathway were associated with cancer risk, including PCa [15, 16]. However, they needed to be experimentally verified [7, 8]. In the present study, we analyzed if 17 SNPs in six pivotal genes (K-ras, PTEN, AKT1, AKT2, mTOR, and Raptor) of the mTOR pathway were associated with PCa susceptibility.

RESULTS

Characteristics of the subjects

Overall, there were no differences in distributions of age, smoking status, and BMI index between the 413 cases and 807 cancer-free controls (Table 1). Among the case subjects, 321 (77.7%) Han and 92 (22.3%) Uygur case subjects were included. Of those, 221 (53.5%) cases had Gleason scores <8 and the remaining 192 (46.5%) had Gleason scores ≥ 8.

Table 1: Distribution of demographic and clinico-pathological characteristics of prostate cancer patients and cancer-free controls from Chinese men

Variables

Cases no. (%)

Controls no. (%)

Pa

All subjects

413 (100)

807 (100)

Age, yr (Mean ± SD)

72 ±7.59

72 ±7.65

0.713

≤71

171 (41.4)

343 (42.5)

>71

242 (58.6)

464 (57.5)

Ethnic group

0.179

 Han

321 (77.7)

599 (74.2)

 Uygur

92 (22.3)

208 (25.8)

BMI (kg/m2)

0.346

≤24

217 (52.5)

401 (49.7)

>24

196 (47.5)

406 (50.3)

Smoking status

0.452

 Never

190 (46.0)

353 (43.7)

 Ever

223 (54.0)

454 (56.3)

Gleason score

<8

221 (53.5)

>=8

192 (46.5)

SD: standard deviation; BMI: body mass index.

aTwo-sided chi-square tests were used to calculate differences in the frequency distribution of genotypes between cases and controls.

Genotype distributions and their association with PCa risk

Among the 17 SNPs analyzed, three variants of two genes were associated with PCa risk, and another variant was associated with PCa risk by stratification analysis (Table 2). The genotype distributions for mTOR rs17036508 C>T (P=0.001) and rs2295080 G>T (P=0.048) were different between cases and controls. The homozygote variant genotypes CC of mTOR rs17036508 C>T were associated with PCa risk compared with genotypes TT (adjusted OR=3.73 (95% CI=1.75-7.94), P=0.001). Also, heterozygote genotypes GT of mTOR rs2295080 G>T were protective compared to homozygote genotypes TT (adjusted OR=0.54 (95% CI= 0.32-0.91), P = 0.020). Furthermore, variants rs17036508 C>T [additive: adjusted OR=1.31 (95% CI =1.04-1.65), P=0.023; recessive: adjusted OR=3.69 (95% CI =1.74-7.83), P=0.001] and rs2295080 G>T [dominant: adjusted OR=0.59 (95% CI = 0.36-0.96), P=0.035] were also associated with PCa risk. For Raptor variants, heterozygote genotypes AG of polymorphism rs1468033 A>G were associated with PCa risk compared to genotypes GG (adjusted OR=1.61 (95% CI =1.25-2.06), P <0.001). We also found that rs1468033 A>G was associated with PCa risk [additive model: adjusted OR=1.42 (95% CI =1.17-1.73), P<0.001; dominant model: adjusted OR=1.61 (95% CI = 1.26-2.05), P<0.001]. Further analysis of the combined genotypes of these three SNPs and AKT2 rs7250897 C>T showed enhanced PCa susceptibility with increasing numbers of putative high-risk genotypes (Ptrend<0.001) (Table 3).

Table 2: Logistic regression analysis of associations between genotypes of PI3K/AKT/mTOR genes and prostate cancer risk in Chinese men

Variables (HWE)a

Cases
(N=1004)

Controls
(N=1051)

Pb

Crude OR
(95% CI)

P

Adjusted OR
(95% CI)b

Pc

mTOR rs1034528 HWE:0.131

GG

260(63.0)

507(62.8)

1.00

1.00

CG

132(32.0)

274(34.0)

0.94(0.73-1.21)

0.632

0.93(0.72-1.20)

0.563

CC

21(5.1)

26(3.2)

1.58(0.87-2.85)

0.134

1.59(0.87-2.90)

0.129

Additive model

0.248

1.06(0.86-1.30)

0.612

1.05(0.85-1.30)

0.656

Dominant model

0.965

1.00(0.0.78-1.27)

0.965

0.98(0.77-1.26)

0.895

Recessive model

0.110

1.61(0.89-2.90)

0.113

1.63(0.90-2.96)

0.105

mTOR rs17036508 HWE:0.451

TT

299(72.4)

610(75.6)

1.00

1.00

CT

94(22.8)

186(23.1)

1.03(0.78-1.37)

0.744

1.05(0.79-1.40)

0.744

CC

20(4.8)

11(1.4)

3.71(1.76-7.84)

0.001

3.73(1.75-7.94)

0.001

Additive model

0.001

1.29(1.03-1.63)

0.029

1.31(1.04-1.65)

0.023

Dominant model

0.226

1.18(0.90-1.55)

0.226

1.20(0.92-1.58)

0.188

Recessive model

<0.001

3.68(1.75-7.76)

0.001

3.69(1.74-7.83)

0.001

mTOR rs12122605 HWE:0.533

CC

249(60.3)

488(60.5)

1.00

1.00

CT

142(34.4)

283(35.1)

0.98(0.76-1.27)

0.897

0.98(0.76-1.26)

0.871

TT

22(5.3)

36(4.5)

1.20(0.69-2.08)

0.522

1.22(0.70-2.14)

0.479

Additive model

0.791

1.03(0.84-1.26)

0.767

1.03(0.84-1.27)

0.755

Dominant model

0.951

1.01(0.79-1.28)

0.951

1.01(0.79-1.29)

0.961

Recessive model

0.501

1.21(0.70-2.08)

0.502

1.23(0.71-2.14)

0.456

mTOR rs2295080 HWE:0.085

TT

236(57.1)

454(56.3)

1.00

1.00

GT

145(35.1)

316(39.2)

0.53(0.32-0.89)

0.015

0.54(0.32-0.91)

0.020

GG

32(7.8)

37(4.6)

0.60(0.37-1.00)

0.050

0.62(0.38-1.03)

0.064

Additive model

0.048

0.94(0.77-1.14)

0.532

0.95(0.78-1.16)

0.633

Dominant model

0.024

0.58(0.35-0.93)

0.025

0.59(0.36-0.96)

0.035

Recessive model

0.768

1.04(0.82-1.32)

0.768

1.05(0.83-1.34)

0.676

Raptor rs1468033 HWE:0.278

GG

165(34.0)

415(51.4)

1.00

1.00

AG

217(52.5)

336(41.6)

1.62(1.27-2.08)

<0.001

1.61(1.25-2.06)

<0.001

AA

31(7.5)

56(6.9)

1.39(0.87-2.24)

0.172

1.61(0.99-2.62)

0.053

Additive model

0.001

1.37(1.13-1.65)

0.001

1.42(1.17-1.73)

<0.001

Dominant model

<0.001

1.59(1.25-2.02)

<0.001

1.61(1.26-2.05)

<0.001

Recessive model

0.716

1.09(0.69-1.72)

0.716

1.27(0.80-2.02)

0.317

Raptor rs2271610 HWE:0.558

GG

243(58.8)

497(61.6)

1.00

1.00

CG

140(33.9)

269(33.3)

1.06(0.82-1.37)

0.632

1.07(0.82-1.38)

0.640

CC

30 (7.3)

41(5.1)

1.50(0.91-2.46)

0.111

1.64(0.99-2.70)

0.054

Additive model

0.272

1.14(0.94-1.39)

0.177

1.17(0.96-1.43)

0.117

Dominant model

0.352

1.12(0.88-1.43)

0.353

1.14(0.89-1.45)

0.299

Recessive model

0.123

1.46(0.90-2.38)

0.125

1.60(0.98-2.62)

0.062

Raptor rs2271612 HWE:0.138

CC

185(44.8)

346(42.9)

1.00

1.00

CT

182(44.1)

350(43.4)

0.97(0.76-1.25)

0.829

0.98(0.76-1.26)

0.861

TT

46(11.1)

111(13.8)

0.78(0.53-1.14)

0.197

0.76(0.52-1.13)

0.174

additive model

0.424

0.91(0.76-1.08)

0.274

0.90(0.76-1.08)

0.259

Dominant model

0.522

0.93(0.73-1.18)

0.522

0.93(0.73-1.18)

0.527

Recessive model

0.197

0.79(0.55-1.13)

0.197

0.77(0.53-1.12)

0.169

Raptor rs2292639 HWE:0.085

CC

129(31.2)

235(29.1)

1.00

1.00

AC

202(48.9)

394(48.8)

0.93(0.71-1.23)

0.625

0.94(0.71-1.24)

0.645

AA

82(19.9)

178(22.1)

0.84(0.60-1.18)

0.310

0.86(0.61-1.21)

0.376

additive model

0.597

0.92(0.78-1.09)

0.315

0.93(0.78-1.10)

0.378

Dominant model

0.445

0.90(0.70-1.17)

0.445

0.91(0.70-1.18)

0.490

Recessive model

0.374

0.88(0.65-1.17)

0.374

0.89(0.66-1.20)

0.450

Raptor rs3751932 HWE:0.541

CC

290(20.2)

562(69.6)

1.00

1.00

CT

112(27.1)

220(27.3)

0.99(0.76-1.29)

0.921

0.99(0.0.76-1.30)

0.966

TT

11(2.7)

25(3.1)

0.85(0.41-1.76)

0.666

0.90(0.43-1.86)

0.773

additive model

0.909

0.97(0.77-1.21)

0.753

0.98(0.78-1.23)

0.848

Dominant model

0.835

0.97(0.75-1.26)

0.836

0.99(0.76-1.28)

0.907

Recessive model

0.671

0.86(0.42-1.76)

0.672

0.90(0.44-1.86)

0.775

Raptor rs3751934 HWE:0.885

CC

136(32.9)

278(34.5)

1.00

1.00

AC

200(48.4)

393(48.7)

1.04(0.80-1.36)

0.772

1.02(0.78-1.33)

0.912

AA

77 (18.6)

136 (16.9)

1.16(0.82-1.64)

0.409

1.12(0.79-1.59)

0.523

additive model

0.708

1.07(0.90-1.27)

0.433

1.05(0.89-1.25)

0.563

Dominant model

0.596

1.07(0.83-1.38)

0.596

1.04(0.81-1.34)

0.748

Recessive model

0.435

1.13(0.83-1.54)

0.436

1.11(0.81-1.52)

0.509

AKT1 rs2494750 HWE:0.165

GG

128(31.0)

240(29.7)

1.00

1.00

CG

196(47.5)

382(47.3)

0.96(0.73-1.27)

0.783

0.97(0.74-1.28)

0.831

CC

89(21.6)

185(22.9)

0.90(0.65-1.26)

0.542

0.89(0.64-1.24)

0.484

additive model

0.830

0.95(0.81-1.12)

0.547

0.94(0.80-1.11)

0.497

Dominant model

0.652

0.94(0.73-1.22)

0.651

0.94(0.73-1.22)

0.657

Recessive model

0.586

0.92(0.69-1.23)

0.586

0.90(0.68-1.21)

0.493

AKT1 rs2494752 HWE:0.122

AA

161(39.0)

305(37.8)

1.00

1.00

AG

189(45.8)

365(45.2)

0.98(0.76-1.27)

0.884

1.01(0.78-1.32)

0.921

GG

63(15.3)

137(17.0)

0.87(0.61-1.24)

0.445

0.87(0.61-1.25)

0.459

additive model

0.736

0.94(0.80-1.12)

0.496

0.95(0.80-1.13)

0.558

Dominant model

0.686

0.95(0.75-1.21)

0.685

0.98(0.76-1.25)

0.837

Recessive model

0.442

0.88(0.64-1.22)

0.442

0.87(0.63-1.20)

0.396

AKT2 rs2304186 HWE:0.036

GG

120(29.1)

224(27.8)

1.00

1.00

GT

209(50.6)

430(53.3)

0.91(0.69-1.20)

0.491

0.91(0.69-1.20)

0.493

TT

84(20.3)

153(19.0)

1.03(0.73-1.45)

0.890

1.00(0.71-1.43)

0.981

additive model

0.669

1.00(0.84-1.19)

0.984

0.99(0.83-1.18)

0.932

Dominant model

0.633

0.94(0.72-1.22)

0.633

0.93(0.72-1.22)

0.605

Recessive model

0.564

1.09(0.81-1.47)

0.564

1.07(0.79-1.44)

0.660

AKT2 rs7250897 HWE:0.106

TT

181(43.8)

397(49.2)

1.00

1.00

CT

190(46.0)

324(40.2)

1.29(1.00-1.65)

0.049

1.29(1.00-1.67)

0.047

CC

42(10.2)

86(10.7)

1.07(0.71-1.61)

0.742

1.11(0.73-1.67)

0.631

additive model

0.139

1.12(0.94-1.33)

0.226

1.13(0.94-1.35)

0.183

Dominant model

0.633

1.24(0.98-1.58)

0.076

1.25(0.99-1.60)

0.066

Recessive model

0.564

0.95(0.64-1.40)

0.794

0.98(0.66-1.45)

0.909

AKT2 rs7254617 HWE:0.120

GG

298(72.2)

581(72.0)

1.00

1.00

AG

102(24.7)

214(26.5)

0.93(0.71-1.22)

0.600

0.93(0.71-1.23)

0.619

AA

13(3.2)

12(1.5)

2.11(0.95-4.69)

0.066

2.00(0.90-4.47)

0.091

additive model

0.134

1.06(0.84-1.34)

0.621

1.06(0.83-1.34)

0.654

Dominant model

0.953

0.99(0.76-1.29)

0.953

0.99(0.76-1.29)

0.949

Recessive model

0.053

2.15(0.97-4.76)

0.058

2.04(0.91-4.54)

0.082

PTEN rs701848 HWE:0.212

TT

134(32.5)

245(30.4)

1.00

1.00

CT

210(50.9)

415(51.4)

0.93(0.71-1.21)

0.570

0.93(0.71-1.22)

0.588

CC

69(16.7)

147(18.2)

0.86(0.60-1.22)

0.399

0.87(0.61-1.25)

0.460

additive model

0.687

0.93(0.78-1.10)

0.386

0.93(0.78-1.11)

0.442

Dominant model

0.456

0.91(0.70-1.17)

0.456

0.91(0.71-1.18)

0.492

Recessive model

0.514

0.90(0.66-1.23)

0.514

0.92(0.67-1.26)

0.584

K-ras rs7312175 HWE:0.127

GG

294(71.2)

592(73.4)

1.00

1.00

AG

210(25.9)

192(23.8)

1.12(0.85-1.48)

0.411

1.18(0.90-1.56)

0.238

AA

12(2.9)

23(2.9)

1.05(0.52-2.14)

0.892

1.07(0.52-2.19)

0.860

additive model

0.712

1.09(0.87-1.36)

0.478

1.13(0.90-1.42)

0.314

Dominant model

0.421

1.12(0.86-1.45)

0.421

1.17(0.90-1.53)

0.250

Recessive model

0.956

1.02(0.50-2.07)

0.956

1.02(0.50-2.09)

0.952

OR: odds ratio; CI: confidence interval.

aHard-Wenberg equilibrium test for controls.

bTwo-sided Chi-square tests were used to calculate differences in the frequency distribution of genotypes between cases and controls.

cAdjusted for age, smoking, and BMI status in logistic regress models.

The results were in bold, if the 95% CI excluded 1 or P<0.05.

Table 3: Combined effects of risk genotypes of of PI3K/AKT/mTOR genes by dominant genetic models

Variables genotypes

Cases
(N=413)

Controls
(N=807)

Pa

Crude OR
(95% CI)

P

Adjusted OR
(95% CI)a

Pb

0

33(8.0)

142(17.6)

<0.001

1.00

1.00

1

114(27.6)

251(31.1)

1.95(1.26-3.03)

0.003

2.04(1.31-3.18)

0.002

2

178 (43.1)

226 (28.0)

3.39(2.21-5.19)

<0.001

3.50(2.27-5.38)

<0.001

3

86 (20.8)

188(23.3)

1.97(1.25-3.11)

0.004

2.07(1.30-3.28)

0.002

4

2(0.5)

0(0)

——

——

Ptrend<0.001

0

33(8.0)

142(17.6)

<0.001

1.00

1.00

≥1

380(92.0)

665(82.4)

2.46(1.65-3.67)

<0.001

2.56(1.71-3.84)

<0.001

aChi-square test was used to calculate the genotype frequency distributions.

bObtained under dominant models in logistic regression analyses with adjustment for age, smoking status and BMI.

The results were in bold, if the 95% CI excluded 1 or P<0.05.

Stratification analysis of PCa risk associated with significant variants

The mTOR rs17036508 CT/CC genotypes correlated with increased PCa risk particularly in subgroup of age ≤71 (dominant model: adjusted OR =1.81 (95% CI =1.19-2.77), P = 0.006). Further, the mTOR rs17036508 CC genotypes were associated with increased PCa risk by recessive genetic model for subgroups of age ≤71(adjusted OR=4.75 (95%CI =1.78-12.71), P = 0.002), age >71 (adjusted OR=4.88 (95%CI =1.65-14.38), P = 0.004), BMI ≤24 kg/m2 (adjusted OR=3.20 (95%CI =1.2-8.53), P = 0.02), BMI >24 kg/m2 (adjusted OR=8.11 (95%CI =2.62-25.11), P< 0.001), ever smokers (adjusted OR=6.39 (95%CI =2.46-16.6), P< 0.001), and Uygur population (adjusted OR=5.09 (95%CI =2.2-11.78), P< 0.001). On the contrary, the mTOR rs2295080 GT/GG genotypes were associated with decreased PCa risk by a dominant genetic model in BMI >24 kg/m2 and ever smokers subgroups. However, the mTOR rs2295080 GG genotypes were associated with PCa risk among age subgroups according to the recessive genetic model. For Raptor rs1468033 A>G, AG/AA genotypes were associated with increased PCa risk by the dominant genetic model, particularly in subgroups of age >71(adjusted OR=1.81 (95%CI =1.31-2.48), P = 0.003), BMI >24kg/m2 (adjusted OR=2.02 (95%CI =1.42-2.87), P< 0.001), never smokers (adjusted OR=1.61 (95%CI =1.21-2.30), P = 0.009), ever smokers (adjusted OR=1.58 (95%CI =1.13-2.19), P = 0.068), and Uygur population (adjusted OR=1.66 (95%CI =1.26-2.20), P< 0.001). Furthermore, increased PCa risk was observed by recessive genetic model for the BMI >24 kg/m2 subgroup (adjusted OR=5.13 (95% CI=1.94-13.59), P = 0.001). Increased PCa risk was observed for AKT2 rs7250897 C>T by the dominant genetic model, particularly in subgroups of age >71(adjusted OR=1.83 (95%CI =1.33-2.52), P = 0.002) and BMI >24kg/m2 (adjusted OR=1.58 (95%CI =1.12-2.24), P = 0.010). However, further homogeneity tests showed no difference in risk estimates between subgroups for most strata except age group by mTOR rs17036508 CT/CC genotypes (P=0.032), mTOR rs2295080 GG genotypes (P=0.002), AKT2 rs7250897 CT/CC genotypes (P<0.001) and BMI group by Raptor rs1468033 AA genotypes (P<0.001). The details are shown in Table 4.

Table 4: Stratification analysis for associations between PI3K/AKT/mTOR variants and prostate cancer risk in Chinese men

Variables

mTOR
rs17036508

Adjusted

Pa

Phom

mTOR
rs2295080

Adjusted

Pa

Phom

Raptor
rs1468033

Adjusted

Pa

Phom

AKT2
rs7250897

Adjusted

Pa

Phom

(Cases/controls)

OR (95%CI)a

(Cases/controls)

OR (95%CI)a

(Cases/controls)

OR (95%CI)a

(Cases/controls)

OR (95%CI)a

By DOM

CT+CC

TT

GT+GG

TT

AG+AA

GG

CT+CC

TT

Age, yr (median)

≤71

53/69

118/274

1.81(1.19-2.77)

0.006

0.032

158/329

13/14

0.54(0.25-1.19)

0.127

0.741

102/180

69/163

1.38(0.94-2.01)

0.097

0.227

82/191

89/152

0.76(0.52-1.10)

0.145

<0.001

>71

66/128

176/336

1.02(0.72-1.45)

0.926

223/441

19/23

0.60(0.32-1.14)

0.120

146/212

96/252

1.81(1.31-2.48)

<0.001

150/219

92/245

1.83(1.33-2.52)

<0.001

BMI, kg/m2

≤24

63/105

154/296

1.23(0.85-1.79)

0.280

0.536

203/382

14/19

0.70(0.34-1.45)

0.339

0.366

123/203

94/198

1.25(0.89-1.75)

0.196

0.062

114/212

103/189

0.99(0.71-1.39)

0.959

0.051

>24

56/92

140/314

1.36(0.93-1.01)

0.117

178/388

18/18

0.43(0.22-0.86)

0.016

125/189

71/217

2.02(1.42-2.87)

<0.001

118/198

78/208

1.58(1.12-2.24)

0.010

Smoking status

Never

50/80

140/273

1.22(0.81-1.84)

0.341

0.835

179/335

11/18

0.87(0.40-1.89)

0.728

0.152

113/169

77/184

1.61(1.12-2.30)

0.009

0.982

102/171

88/182

1.24(0.87-1.77)

0.231

0.938

Ever

69/117

154/337

1.34(0.94-1.92)

0.109

202/435

21/19

0.42(0.22-0.80)

0.008

135/223

88/231

1.58(1.13-2.19)

0.007

130/239

93/215

1.24(0.89-1.72)

0.206

Ethnic group

Han

24/52

68/156

1.12(0.63-1.97)

0.703

0.504

82/194

10/14

0.61(0.26-1.45)

0.266

0.871

51/96

41/112

1.44(0.87-2.36)

0.155

0.693

51/94

41/114

1.52(0.92-2.51)

0.099

0.358

Uygur

95/145

226/454

1.33(0.98-1.81)

0.068

299/576

22/23

0.56(0.30-1.02)

0.057

197/296

124/303

1.66(1.26-2.20)

<0.001

181/316

140/283

1.18(0.90-1.56)

0.237

Variables

mTOR
rs17036508

Adjusted

Pa

Phom

mTOR
rs2295080

Adjusted

Pa

Phom

Raptor
rs1468033

Adjusted

Pa

Phom

AKT2
rs7250897

Adjusted

Pa

Phom

(cases/controls)

OR (95%CI)a

(cases/controls)

OR (95%CI)a

(cases/controls)

OR (95%CI)a

(cases/controls)

OR (95%CI)a

by REM

CC

TT+CT

GG

TT+GT

AA

GG+AG

CC

TT+CT

Age, yr (median)

≤71

157/337

14/6

4.75(1.78-12.71)

0.002

0.854

81/128

90/215

0.67(0.46-0.97)

0.035

0.002

158/318

13/25

1.19(0.54-2.42)

0.635

0.882

154/303

17/40

0.89(0.49-1.63)

0.710

0.577

>71

231/459

11/5

4.88(1.65-14.38)

0.004

96/225

146/239

1.46(1.06-2.00)

0.021

224/433

18/31

1.34(0.72-2.49)

0.351

217/418

25/46

1.05(0.62-1.76)

0.858

BMI, kg/m2

≤24

206/394

11/7

3.20(1.20-8.53)

0.020

0.210

90/176

127/225

1.09(0.78-1.53)

0.622

0.592

200/351

17/50

0.66(0.37-1.18)

0.163

<0.001

193/352

24/49

0.89(0.53-1.51)

0.674

0.943

>24

182/402

14/4

8.11(2.62-25.11)

<0.001

87/177

109/229

0.97(0.69-1.37)

0.859

182/400

14/6

5.13(1.94-13.6)

0.001

178/369

18/37

0.99(0.55-1.80)

0.985

Smoking status

Never

182/348

8/5

3.01(0.97-9.34)

0.057

0.352

81/157

109/196

1.08(0.76-1.55)

0.663

0.775

180/333

10/20

0.94(0.43-2.07)

0.884

0.588

170/319

20/34

1.11(0.62-1.99)

0.729

0.507

Ever

206/448

17/6

6.39(2.46-16.60)

<0.001

96/196

127/258

1.00(0.72-1.39)

0.987

202/418

21/36

1.45(0.82-2.59)

0.206

201/402

22/52

0.86(0.51-1.47)

0.589

Ethnic group

Han

87/205

5/3

3.90(0.90-16.98)

0.069

0.794

37/89

55/119

1.08(0.65-1.78)

0.778

0.764

85/191

7/17

1.10(0.43-2.80)

0.848

0.675

84/188

8/20

0.95(0.40-2.26)

0.902

0.893

Uygur

301/591

20/8

5.09(2.20-11.78)

<0.001

140/264

181/335

1.05(0.79-1.38)

0.747

297/560

24/39

1.34(0.78-2.30)

0.284

287/533

34/66

0.99(0.63-1.53)

0.946

BMI, body mass index. a Obtained under dominant models in logistic regression analyses with adjustment for age, smoking status and BMI. b,c According to the current WHO recommendations.

PhomP value for homogeneiy test. DOM: dominant genetic model; REM: recessive genetic model.

The results were in bold, if P<0.05.

Association of high-order interactions with PCa risk

We performed the multifactor dimensionality reduction (MDR) analysis by including the genotypes of four significant genetic factors (mTOR rs17036508 CC and rs2295080 TT, Raptor rs1468033 AG/AA, and AKT2 rs7250897 TT/CT) and four environmental risk factors (age at diagnosis, smoking status, race, and BMI). Among all 8 factors, the Raptor rs1468033 A>G variant was the best one-factor model. Likewise, interactions between mTOR rs17036508 CC, rs2295080 TT and Raptor rs1468033 AG/AA represented the best three-factor model. Age was the most promising environmental risk factor associated with the four genetic factors. The details are presented in Table 5. Subsequent hierarchical cluster analysis placed BMI and smoking status, race and rs17036508 C>T, rs2295080 G>T and rs1468033 A>G, age and rs7250897 C>T on the same branch. This suggested that interactions in this eight-locus model may modulate PCa risk (data not shown).

Table 5: MDR analysis for the risk of prostate cancer prediction in an Chinese population

Best interaction models

Cross-validation

Average
prediction error

P-valuea

rs1468033

100/100

0.4566

0.0001

rs2295080 rs1468033

100/100

0.3451

p < 0.0001

rs17036508 rs2295080 rs1468033

100/100

0.3434

p < 0.0001

age rs17036508 rs2295080 rs1468033

99/100

0.4066

p < 0.0001

age rs17036508 rs2295080 rs1468033 rs7250897

78/100

0.4254

p < 0.0001

BMI smoking_status race rs17036508 rs2295080 rs1468033

45/100

0.4467

p < 0.0001

smoking_status age race rs17036508 rs2295080 rs1468033 rs7250897

61/100

0.4022

p < 0.0001

BMI smoking_status age race rs17036508 rs2295080 rs1468033 rs7250897

100/100

0.5066

p < 0.0001

MDR: multifactor dimensionality reduction.

The best model with maximum cross-validation consistency and minimum prediction error rate was in bold.

aP-value for 1000-fold permutation test.

Finally, we analyzed the false-positive report probability (FPRP) values at variant prior probability levels for all positive findings (Table 6). Some higher statistical power (81.7%-89.6%) was observed under the assumption of prior probability of 0.25. However, some significant findings were still noteworthy at prior probability of 0.01 in spite of the lower statistical power (range from 5% to 45%). Some findings with greater FPRP values need further validation in larger studies.

Table 6: False-positive report probability values for associations between the PCa risk and the frequency of genotypes of PI3K/AKT/mTOR variants

Genotype

Crude OR (95%CI)

Pa

Statistical powerb

Prior probability

0.25

0.1

0.01

0.001

0.0001

All patients

mTOR rs17036508 CC vs TT

3.71(1.76-7.84)

0.0006

0.008

0.18

0.397

0.879

0.986

0.999

mTOR rs17036508 CC vs CT/TT

3.68(1.74-7.76)

0.0003

0.005

0.15

0.345

0.853

0.983

0.998

mTOR rs2295080 GT vs TT

0.53(0.32-0.89)

0.0484

0.862

0.144

0.336

0.848

0.982

0.998

mTOR rs2295080 GG/GT vs TT

0.57(0.35-0.93)

0.0236

0.829

0.079

0.204

0.738

0.966

0.997

raptor rs1468033 AG vs GG

1.62(1.27-2.08)

0.0006

0.415

0.004

0.013

0.125

0.591

0.935

raptor rs1468033 AA/AG vs GG

1.59(1.25-2.02)

0.0001

0.284

0.001

0.003

0.034

0.26

0.779

mTOR rs17036508

 Age≤71 yrs, CT/CC vs TT

1.69(1.11-2.57)

0.0143

0.363

0.106

0.262

0.796

0.975

0.997

 Age≤71 yrs, CC vs CT/CC

4.24(1.56-11.50)

0.0022

0.61

0.011

0.031

0.263

0.783

0.973

 Age>71 yrs,CC vs CT/CC

3.14(1.02-9.69)

0.0366

0.876

0.111

0.273

0.805

0.977

0.998

 BMI<=24,CC vs CT/CC

2.72(1.02-7.24)

0.0378

0.896

0.112

0.275

0.807

0.977

0.998

 BMI>24,CC vs CT/CC

5.40(1.67-17.45)

0.0017

0.571

0.009

0.026

0.228

0.748

0.968

 Ever smoking, CC vs CT/TT

4.62(1.73-12.33)

0.0008

0.457

0.005

0.016

0.148

0.636

0.946

 uygur, CC vs CT/TT

4.39(1.89-10.21)

0.0002

0.160

0.004

0.011

0.110

0.555

0.926

mTOR rs2295080

 BMI>24, GG/GT vs TT

0.46(0.23-0.90)

0.0213

0.833

0.071

0.187

0.717

0.962

0.996

 Ever smoking, GG/GT vs TT

0.42(0.22-0.80)

0.0067

0.722

0.027

0.077

0.479

0.903

0.989

 Age≤71 yrs, GG vs GT/TT

0.66(0.46-0.96)

0.0288

0.504

0.146

0.34

0.85

0.983

0.998

 Age>71 yrs, GG vs GT/TT

1.43(1.04-1.96)

0.0255

0.817

0.086

0.219

0.755

0.969

0.997

Raptor rs1468033

 Age>71 yrs, AA/AG vs GG

1.81(1.32-2.48)

0.0002

0.276

0.002

0.006

0.067

0.42

0.879

 BMI>24, AA/AG vs GG

2.02(1.42-2.87)

<0.0001

0.198

0.002

0.005

0.048

0.335

0.835

 Ever smoking, AA/AG vs GG

1.59(1.15-2.20)

0.0051

0.547

0.027

0.077

0.48

0.903

0.989

 Never smoking, AA/AG vs GG

1.60(1.12-2.28)

0.0099

0.712

0.040

0.111

0.579

0.933

0.993

 Uygur group, AA/AG vs GG

1.63(1.23-2.14)

0.0005

0.433

0.003

0.010

0.103

0.536

0.920

 BMI>24, AA vs AG/GG

5.13(1.94-13.56)

0.0003

0.382

0.002

0.007

0.072

0.440

0.887

AKT2 rs7250897

 Age>71 yrs, CT/CC vs TT

1.82(1.33-2.51)

0.0002

0.284

0.002

0.006

0.065

0.413

0.876

 BMI>24, CT/CC vs TT

1.60(1.12-2.25)

0.0085

0.662

0.037

0.104

0.56

0.928

0.992

Combined effect (risk genotype)

0 vs >=1

2.46(1.65-3.67)

<0.0001

0.050

0.006

0.018

0.165

0.666

0.952

OR: odds ratio; CI: confidence interval; BMI: body mass index.

aChi-square test was used to calculate the genotype frequency distributions.

bStatistical power was calculated using the number of observations in the subgroup and the OR and P values in this table.

The results in false-positive report probability analysis were in bold, if the prior probability < 0.2.

DISCUSSION

In the current single institution based case-control study, functional SNPs of six pivotal genes of PI3K/AKT/mTOR pathway were associated with PCa risk. Briefly, the mTOR rs17036508 CC, mTOR rs2295080 GG/GT, Raptor rs1468033 AG/AA, and AKT rs7250897 TT/CT genotypes were associated with PCa risk, especially in age, BMI, smoker-status, and ethnic subgroups. To the best of our knowledge, this is the first post-GWAS study analyzing associations of these six pivotal genes of PI3K/AKT/mTOR pathway with PCa risk.

The mTOR gene is a critical cellular protein with more than 2651 SNPs reported across the whole region [17]. However, only few SNPs have been associated with PCa susceptibility. Two studies showed that mTOR rs2295080 GT/GG genotypes protected against PCa risk in Han Chinese populations [18, 19]. These findings were consistent with mixed Chinese populations in the present study, but not in separate ethnic subgroups (Han or Uygur). The effects of the mTOR rs2295080 GT/GG genotypes were also observed in esophageal squamous cell carcinoma, gastric carcinoma, and renal cell carcinoma [2022]. In vitro and in vivo studies suggested that mTOR rs2295080 T allele probably increased the affinity of special transcription factors to this promoter region and contributed to enhanced mTOR activity [20]. Variant mTOR rs2295080 was linked to eight potential functional variants of mTOR with higher linkage disequilibrium (LD) coefficient >0.8, including rs1064261, rs1074078, rs1135172, rs1883965, rs4845860, rs6540965, and rs6671083. Among these, the variant rs1064261 probably interrupted the exonic splicing enhancer or silencer motif and correlated with neuroendocrine tumors [23], gastric cancer [24], and esophageal squamous cell carcinoma [25]. Similarly, as a probable transcription factor binding site of mTOR, variant rs1883965 was associated with esophageal carcinoma, gastric cancer, and hepatocellular carcinoma [26]. Additionally, it was predicted that variant rs17036508 of mTOR was located within a miRNA binding site and an exonic splicing enhancer or silencer motif, thereby affecting pre-RNA splicing. The association between rs17036508 and PCa was also observed previously [19]. In the present study, we found that homozygote variant carriers of rs17036508 were more likely to develop cancer compared to homozygote wild carriers. In theory, polymorphism rs17036508 located in 3’-UTR of angiopoietin-like 7 gene (ANGPTL7), resulting in upregulation of ANGPTL7 by hypoxia in cancer cells, which exerts a pro-angiogenesis effect [27]. Taken together, it is biologically plausible that these two polymorphisms might mediate tumor formation by regulating the expression of mTOR and ANGPTL7 simultaneously.

The Raptor gene, located in 17q25.3 with 34 exons, regulates responses to nutrient and insulin levels [28]. The Raptor protein forms a stoichiometric complex with the mTOR kinase, and is associated with 4EBP1and P70. Previous in vitro and in vivo studies have shown that Raptor acts as a scaffold protein that regulates mTOR-dependent signaling [29]. One of the mechanisms involves changes in phosphorylation status of Raptor. However, the mechanism through which Raptor regulates these processes is only beginning to be established. In a recent study, Raptor gene polymorphisms were associated with increased risk for bladder cancer; physical activity, energy balance and genetic variants in the mTOR pathway co-coordinately influenced bladder cancer risk [30]. In this study, variant Raptor rs1468033 was associated with PCa risk, particularly in subgroups of age, BMI, and smoking status, similar to previous findings. Silico analysis indicated that rs1468033 was found with several potential functional polymorphisms of Raptor gene, including rs2292639, rs499609, rs6565500, and rs9899178. Among these, rs2292639, rs4999609, and rs6565500 were predicted as transfactor binding sites, whereas rs9899178 was predicted as splicing site. It is plausible that these variants modulate the expression of Raptor gene, and affect mTOR activity.

Although mTOR activity was previously implicated in promoting PCa cell invasion, the role of AKT2 was not known. The different AKT isoforms (AKT1, AKT2, and AKT3) play contradictory regulatory roles; for example, AKT1 activation inhibits cell migration whereas AKT2 promotes it [31, 32]. Many studies have indicated that AKT2 is a more promising therapeutic target than PI3K due to its involvement in normal cellular processes. AKT2 downregulated GSK3b, which modulates cell migration [33]. Additionally, AKT negatively regulates Mdm2 expression during abnormal stress and promotes tumorigenesis [34]. Recently, Chen et al. reported that AKT2 rs7254617 increased prostate cancer risk [18]. Also, a Caucasian study showed that AKT2 variant rs3730050 was associated with poor prognosis of bladder cancer patients. Therefore, AKT isoforms are cancer susceptibility genes. In this study, AKT2 rs7250897 was associated with increased PCa risk by a dominant genetic model in subgroups of age >71and BMI >24kg/m2, indicating that rs7250897 altered AKT2 expression, which subsequently affected synthesis of adipose-related proteins. Since the three variants were far from each other, we postulated that they were associated with carcinogenesis by different mechanisms. These findings need to be further validated.

There were few limitations in the present study that need to be addressed. First, the limited sample size in our study may have decreased detection of weaker genetic effects in carcinogenesis. Second, information regarding predisposition to PCa was not collected for the analysis and might confuse the stratified positive associations. Third, the case-control study may have inherent selection and information biases. Because of insufficient medical records, we could not perform correlative analyses between stages of PCa and variants of AKT pathway that may have provided more information in PCa carcinogenesis. Moreover, further in vitro and in vivo experiments are necessary to unravel molecular mechanisms for the genetic associations that we have postulated in this study.

In summary, we showed that variants of PI3K/AKT/mTOR signal pathway genes may associate with PCa risk. The combined genotypes of these variants enhanced PCa risk with increasing numbers of putative high-risk genotypes. Moreover, the three-factor model (rs17036508, rs2295080, rs1468033) was the best model to predict PCa risk. In conclusion, our study postulated that the genetic variants may alter the expression and activity of mTOR leading to PCa susceptibility.

MATERIALS AND METHODS

Patients and controls

We recruited 413 newly diagnosed PCa cases and 807 frequency-matched cancer-free controls from genetically unrelated Chinese Han and Uygur participants in Xinjiang province between January 2003 and January 2015. The cases were histopathologically confirmed as primary prostate adenocarcinoma at the First Affiliated Hospital of Xinjiang medical University (XJMU). Pathological grades of the PCa were determined by Gleason scores from the radical prostatectomy specimens according to the latest WHO criteria [35]. The recruited healthy controls were extracted from males who had health check-up in the First Affiliated Hospital of XJMU during the same period. Individuals with serum PSA >4ng/ml were excluded from the control group.

All subjects were interviewed with a written informed consent. Response rate was 92% and 90% for cases and controls, respectively. The experimental and research protocols were approved by the Institutional Review Board of XJMU.

Selection of single nucleotide polymorphisms

For the six pivotal genes in PI3K/AKT/mTOR pathway, selection strategy for the potentially functional SNPs was based on the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP) and SNPinfo web server (http://snpinfo.niehs.nih.gov/snpfunc.htm). The criteria were: 1) minor allele frequency (MAF) was at least 5% in Chinese populations; 2) SNPs were potentially functional according to SNPinfo prediction platform. Ultimately, 17 potentially functional variants were selected involving mTOR (rs1034528 C>G, rs1703658 C>T, rs12122605 T>C, and rs2295080 G>T), Raptor (rs2271610 C>G, rs2271612 T>C, rs2292639 A>C, rs3751932 T>C, rs3751934 A>C), AKT1 (rs2494750 C>G, rs2494752 G>A), AKT2 (rs2304186 T>G, rs7250897 C>T, rs7254617 A>G), PTEN (rs701848 C>T), and K-ras (rs7312175 A>G) based on the bioinformatics analysis performed with HaploView software 4.2. All these SNPs were genotyped by the TaqMan real-time PCR method as described previously [36]. In this study, the quality control strategy was established as follows: (1) the discrepancy rate in all positive controls was less than 0.1%; (2) the results with >95% call rates and 100% concordance for duplicated specimens were favorable for further analysis.

Statistical analysis

We performed the Pearson’s χ2-test for the differences in selected variables between cases and controls. Crude and adjusted ORs and their 95% CIs were computed from both univariate and multivariate unconditional logistic regression models. We further evaluated the stratified associations based on the significant genetic models accompanied by the homogeneity Q-tests for the strata. For all the significant findings, we calculated FPRP with the assumption of different prior probabilities to detect any possible false positive associations. Only FPRP values<0.2 were considered noteworthy [37]. All statistical analyses were performed with SAS 9.1 statistical software (SAS, Cary, NC, USA) unless stated otherwise. All P values were two-sided with a significance level of P<0.05.

The multifactor dimensionality reduction (MDR) analysis was conducted by the MDR V2.0 beta 8.2 software (http://www.multifactordimensionalityreduction.org/) to identify the best n-factor interaction model [38]. We further performed the interaction dendrograms and graphs for risk loci of this study, and the color of branches and lines referred to the type of interaction, with green-to-yellow-to-red indicating weak-to-strong interactions.

Author contributions

QL conceived and designed the experiments; TL, AG and FW performed the experiments; HA and XS analyzed the data; WC contributed reagents, materials and analysis tools; TL and QL wrote the paper.

CONFLICTS OF INTEREST

No potential conflicts of interest were disclosed.

FUNDING

This study was supported by the funds from the National Natural Science Foundation of China (no. 81460513). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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