The Role of Polymorphisms in Genes of PI3K/Akt …The PI3K/Akt signaling pathway is implicated in...

The Role of Polymorphisms in Genes of PI3K/Akt Signaling Pathway on Prostate

Cancer Risk: A Meta-Analysis and Systematic Review

Meng Zhang1,2,3#; Zhihao Ni3#; Jinbo Chen4; Li Zhang1; Song Wu2*; Chaozhao Liang1*

1. Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China

2. Shenzhen Following Precision Medical Institute, Shenzhen Luohu Hospital Group, Shenzhen 518000, China

3. Graduate School of Anhui Medical University, Hefei, China

4. Department of Urology, Xiangya Hospital, Central South University, NO.87 Xiangya Road, Changsha,

Hunan 410008, China

5. Institute of Urology, Anhui Medical University, Hefei, China

6. Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, China

# These authors contributed equally to the work.

* Correspondence:

*Name: Chaozhao Liang;

Department of Urology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China

Tel.: +86 55162923440

Email address: [email protected]

*Name: Song Wu

Shenzhen Following Precision Medical Institute, Shenzhen Luohu Hospital Group, Shenzhen 518000, China

Tel.: +86 755 8220 3083


mailto:[email protected]


Abstract

Background and Objective: Increasing evidence suggested that polymorphisms in genes of

PI3K/Akt pathway were closely related to prostate cancer (PCa) risk. Nevertheless, these results are

controversial and inconclusive. Here, we conducted a comprehensive updated meta-analysis and

systematic review to precisely illustrate the association between polymorphisms in genes of

PI3K/Akt signaling pathway and PCa risk. Materials and Methods: The gene set of PI3K/Akt

pathway was referenced from the Kyoto Encyclopedia of Genes and Genomes (KEGG) website.

Relevant studies were identified by the systematically researching on PubMed, Web of Science and

Google Scholar databases up to October 1, 2017. The odds ratios (ORs) with a corresponding 95%

confidential intervals (95%CIs) were applied to test their associations. All the analyses were

conducted by using Stata 12.0 (Stata Corporation, USA). Results: Finally, 38 articles comprising

62 case-control studies were enrolled for 13 polymorphisms in genes of PI3K/Akt pathway.

However, overall results failed to present a positive association between polymorphisms in genes of

PI3K/Akt pathway and PCa risk. Nevertheless, in the subgroup analysis by ethnicity, we identified

that IL-6-rs1800795 polymorphism was associated with an increased risk of PCa for Caucasian

individuals in dominant model (MM + MW vs. WW: OR = 1.245, 95%CI = 1.176-1.318, P < 0.001).

Conclusion: Our work suggests that polymorphisms in genes of PI3K/Akt Signaling Pathway are

not risk factor for PCa. Further well-designed studies with larger samples and precise designs are

demanded to corroborate our findings.

Keywords: PI3K/Akt; polymorphism; prostate cancer

Introduction

For males worldwide, prostate cancer (PCa) has the second highest incidence of cancers. Each

year, approximately 238,590 new cases and 29,720 death are reported according to cancer statistics,

2013. In view of the anfractuous pathogenesis of PCa, interconnected cell signaling pathways and

transmissions which manipulates the survival, evolution and apoptosis of cells1-3, would provide us

new inspirations of the prevention and treatment of PCa patients.

Among diverse pathways, genes encompassed in phosphoinositide 3-kinase (PI3K)/Akt

signaling pathway, such as toll-like receptor-4 (TLR4), vascular endothelial growth factor (VEGF),

interleukin 6 (IL-6), insulin receptor substrate 1 (IRS1) and insulin-like growth factor 1 (IGF1),

appear with more common mutations or amplifications in PCa (Figure S1 and Figure S2). PI3K is

a phosphatidylinositol kinase which is encoded by the PIK3CA gene. It consists of a catalytic subunit

p110 and regulatory subunit p85. Akt is a cytoplasmic serine-threonine protein kinase which

promotes the progression of cell cycle and inhibits cell apoptosis. The PI3K/Akt signaling pathway

is implicated in different cellular functions, including survival, growth, proliferation, metabolism

and angiogenesis. Currently, the relationships between polymorphisms in genes of PI3K/Akt

signaling pathway and PCa risk have been an area of intense investigations but with mixed results4,5.

For instance, Balistreri et al.6 pointed out that there existed a significant association between

polymorphisms in TRL4 and an increased risk of PCa, a result consistent with both Wang et al.’s7

and Chen et al.’s8 work. However, Shui et al.9 has conducted a case-control study comprising 1,267

controls and 1,286 PCa cases and found that genetic variation across TLR4 alone is not strongly

associated with PCa risk. As for polymorphisms in IGF1, Schildkraut et al.10 revealed the significant

association between genetic polymorphisms in IGF1 and PCa risk among Black and White men. On

the contrary, Neuhausen et al.11 failed to find any positive connection between IGF1 polymorphisms

and PCa risk. In addition, for IL-6-rs1800795, both Kesarwani et al.12 and Mandal et al.’s13 studies

supported the role of IL-6-rs1800795 polymorphism in PCa, while the result was inconsistent with

Bao et al.’s14 work.

Hence, previous studies had presented inconsistent views between polymorphisms

encompassed in genes of PI3K/Akt signaling pathway and PCa risk. Considering that, we conducted

the current updated meta-analysis in order to precisely evaluate their associations on the foundation

of all available eligible studies, providing with convincible evidence for the prevention and/or

targeted therapy for PCa patients.

Material and Methods

Acquisition of the PI3K/Akt Pathway Gene Set

The gene set of PI3K/Akt pathway was referenced to the Kyoto Encyclopedia of Genes and

Genomes (KEGG) website (http://www.kegg.jp/kegg-bin/show_pathway?hsa04151). The gene set

was originally provided via the KEGG signaling database, and encompassed the following 101

genes: ANGPT2, ANGPT4, IL2RB, CD19, COL1A, IL3, COL2A, IL3RA, COL4A, COL6A, IL6,

COL9A, CSF1, CSF1R, CSF3, CSF3R, EFNA, EGF, EGFR, EPHA2, EPO, EPOR, FGF, FGF1,

FGF2, FGFR1, FGFR2, FGFR3, FGFR4, FLT1, FLT4, GH, GHR, IL6R, GRB2, HGF, HRAS, IFNA,

IFNAR1, IFNAR2, IFNB, IGF1, IGF1R, IGH, IL2, IL2RA, IL2RG, IL4, IL4R, IL7, IL7R, INS, INSR,

IRS1, JAK1, JAK2, JAK3, KDR, KIT, KITLG, KRAS, LAMA1_2, LAMA3_5, LAMA4, LAMB1,

LAMB2, LAMB3, LAMB4, LAMC1, LAMC2, MAP2K1, MAP2K2, MAPK1, MAPK2, MAPK3, MET,

NGFA, NGFB, NGFR, NRAS, OSM, OSMR, PDGFA, PDGFB, PDGFC_D, PDGFRA, PDGFRB,

PGF, PIK3AP1, PRL, PRLR, RAC1, RAF1, SOS, SYK, TEK, TLR2, TLR4, VEGFA, VEGFB and

VEGFC-D.

Study Description

To evaluate the connections between polymorphisms in genes of PI3K/Akt pathway and PCa

risk, we conducted the present study by combining all accessible studies together from diverse

databases, including Web of Science, PubMed, and China National Knowledge Infrastructure

(CNKI) databases. The integrated keywords were: (‘genes’ OR ‘abbreviations of genes’) AND

(‘cancer’ OR ‘tumor’ OR ‘carcinoma’ OR ‘neoplasms’) AND (‘polymorphism’ OR ‘mutation’ OR

‘variant’ OR ‘SNP’ OR ‘genotype’). At the same time, we used the integrated keywords (Gene_ID

& prostate cancer) to search on Google, and performed the hand screening from all highly connected

results. Besides, eExtra studies were collected via the reference lists of the identified studies. The

final date of retrieval was in October 1, 2017. The whole studies in the analysis were firstly

published in the primary literature with no reproduction in other studies.

Inclusion and Exclusion Criteria

The inclusion criteria in this analysis were: (1) the cases were PCa patients and the controls

were no history of cancers; 2) cohort studies or case-control studies concerning the relationships

between polymorphisms in genes of PI3K/Akt signaling pathway and PCa risk; (3) the raw data of

genotype frequency can be extracted. The exclusion criteria were as follows: (1) the raw data were

not accessible; (2) case-only studies that didn’t have control groups; (3) family-based association

studies; and (4) Review papers.

Data Extraction

All of the data extraction work should be completed independently by 2 of the authors

according to the prelisted inclusion criteria. And the arguments should be solved by another

expert(s). You didn’t mention the procedure in your article. In addition, we extracted data from each

case-control study, including genotype frequencies, name of first author; year of publication;

ethnicity and number of cases and controls. In addition, we used The Newcastle-Ottawa Scale (NOS)

to evaluate the quality of enrolled studies.

Statistical analysis

The meta-analysis was conducted to assess the associations between polymorphisms in genes

of PI3K/Akt pathway and PCa risk. Hardy-Weinberg equilibrium (HWE) in the control group was

tested15. To make a more comprehensive meta-analysis, five genetic models were adopted, including

allele contrast (M vs. W), codominant (MM vs. WW and MW vs. WW), dominant (MM + MW vs.

WW) and recessive models (MM vs. MW + WW). The impact of relationship was evaluated by

odds ratio (OR) with a corresponding 95% confidential intervals (95%CI). What’s more, when the

heterogeneity (P > 0.1 as the standard) 16 was assessed, the I2-based Q statistic was used (I2 = 0–

25%: no heterogeneity; I2 = 25%-50%: moderate heterogeneity; I2 = 50%-75%: large heterogeneity;

I2 = 75%-100%: extreme heterogeneity) 17, which represented the weighted sum of the squared

difference between the overall effect size and the effect size from every study. When I2 > 50% or PQ

≤ 0.1, substantial heterogeneity was existed, then, a random-effects model was used; otherwise, the

fixed-effects model was be applied With regard to the random effects and fixed effects, I2 was

considered as the criterion: the fixed effects were chosen when I2 > 50%, otherwise random effects

were chosen. It has been recognized that when results of the component studies differ among

themselves, random effects incorporate an estimate of the inter-study variance and provide wider

95%CIs18. The analyses were conducted using Stata 12.0 (Stata Corporation, USA), and all P values

were two-tailed.

Results

Main Characteristics of the Enrolled Studies

After initial screening, there were 1,166 results related to the search words enrolled. After

reading the important information such as the titles and abstracts, 51 potential eligible studies were

selected for next step full-text view. When a further screening was conducted, 13 of these studies

were excluded for not associated with PCa risk. Finally, 38 articles with 62 case-control studies were

left for data extraction (Table 1) 12,19-54.

Of them, there were 2,170 cases and 1,587 controls for TLR4-rs1927914 polymorphism (from

three studies), 3,842 cases and 3,143 controls for TLR4-rs10759932 polymorphism (from 4 studies),

3,508 cases and 2,960 controls for TLR4-rs2149356 polymorphism (from 4 studies), 1,467 cases

and 1,551 controls for TLR4-rs4986790 polymorphism (from 4 studies), 3,985 cases and 3,438

controls for TLR4-rs11536889 polymorphism (from 5 studies), 2,380 cases and 2,357 controls for

TLR4-rs7873784 polymorphism (from 3 studies), 632 cases and 685 controls for VEGF-rs833061

polymorphism (from three studies), 1,511 cases and 821 controls for VEGF-rs1570360

polymorphism (from three studies), 1,243 cases and 1,620 controls for IRS1-rs1801278

polymorphism (from four studies), 2,289 cases and 2,114 controls for FGFR4-rs351855

polymorphism (from three studies), 1,805 cases and 3,235 controls for IL-6-rs1800796

polymorphism (from three studies), 10,625 cases and 12,353 controls for IL-6-rs1800795

polymorphism (from eight studies), 2,217 cases and 2,471 controls for IGF1-(CA) 19 polymorphism

(from seven studies), respectively. In addition, the study selection processes for these

polymorphisms were showed in Figure S3-8.

Furthermore, of the 62 case-control studies, 41 sets were performed on Caucasian populations,

seven sets on Asian populations, six sets on African populations, and the other eight were based on

mixed ethnic groups (including at least one race). Controls of 42 studies were population-based (P-

B), while the other 20 studies were hospital-based (H-B). The quality of the enrolled studies was

assessed by NOS and presented in Table S1.

Quantitative synthesis

Results of the association between polymorphisms in genes of PI3K/Akt pathway and PCa risk

were showed in Table 2 and Table S2. However, the pooled results suggested negative associations

between all the 13 polymorphisms in six genes of PI3K/Akt signaling pathway and PCa risk.

However, in the subgroup analysis by ethnicity, we found that IL-6-rs1800795 polymorphism

was associated with an increased risk of PCa in dominant model for Caucasian population (MM +

MW vs. WW: OR=1.245, 95%CI = 1.176-1.318, P < 0.001, Figure 1A). Furthermore, in the

subgroup analysis by source of control, we also found an increased risk of PCa for P-B groups in

dominant model (MM + MW vs. WW; OR = 1.246, 95%CI = 1.177-1.319, P < 0.001, Figure 1B).

Although subgroup analyses were also conducted for other polymorphisms in genes of PI3K/Akt

signaling pathway, negative results were found.

Sensitivity analysis and Publication bias

Sensitivity analysis was conducted by excluding one single study each time, and no evidence

was observed suggesting pooled ORs shift (Table S3). In addition, we used Begg's funnel plot and

Egger's regression test to assess potential publication bias. As for TLR4-rs1927914, TLR4-

rs10759932, TLR4-rs2149356, TLR4-rs4986790, TLR4-rs11536889, TLR4-rs7873784, VEGF-

rs833061, VEGF-rs1570360, IRS1-rs1801278, FGFR4-rs351855, IL-6-rs1800796, IGF1-(CA)19

polymorphisms, no evidence of publication bias was identified by viewing the shape of Begg's

funnel plot, which was further validated by Egger’s regression test. However, for IL-6-rs1800795

polymorphism, potential publication bias was existed (P = 0.016) (Table S4). In that case, we further

conducted sensitivity analysis by using the trim and fill method55, and imputed studies provide a

symmetrical funnel plot (data not shown), indicating publication bias was not existed.

Discussion

Recently, enormous studies suggested that polymorphisms in genes of PI3K/Akt pathway may

play an important role in the prevention, diagnosis and treatment of PCa. For example, TLR4 is the

main component of TLRs and has been positively investigated in inflammation and cancer. Previous

studies had confirmed that two polymorphisms in TLR4 (rs4986790 and rs4986791) owned

susceptibility to various type of cancers, including PCa56. VEGF is the most significant regulator of

angiogenesis in human, and it plays a significant role in the occurrence and development of PCa49,50.

It had been identified that there were many genetic variants in the VEFG gene57, but the conclusions

were remained inconsistent23,52-57. The IRS1 gene Gly972Arg (rs1801278) polymorphisms had been

found a significant association with increased cancer risk58. In vitro studies have proved that the

IRS1 gene rs1801278 polymorphism impaired insulin-stimulated signaling pathway, especially

through the PI3-kinase pathway59. What’s more, as a docking protein for the insulin-like growth

factor receptor 1 (IGF1R) 60,61, IRS1 controls IGF-1 mediated cell growth and survival60. Thus the

polymorphisms of IGF-1 gene also related to the cancer risks including PCa61. Fibroblast growth

factor receptor 4 (FGFR4) is one member of the family of fibroblast growth factor receptors

(FGFR1-4), which displays complicated biological activities such as angiogenic and mitogenic

activity. Previous study had presented that its gene polymorphism was related to PCa risks62. The

human IL-6 gene encodes IL-6, a cytokine which adjusts the level of inflammation. Two

polymorphisms on the promoter region of IL-6, rs1800795 (-174G/C) and rs1800796 (-572C/G)

have been identified to be associated with IL-6 production63. And these association with risks of

cancer have been published in a previous meta-analysis64,65. Furthermore, although these studies

and meta-analysis provided some clues for separate polymorphisms in one or more genes of

PI3K/Akt pathway and PCa risk, these results were not fully consistent, or even contradictory at

sometimes. Therefore, we performed current meta-analysis in order to provide a comprehensive

accurate assessment of the associations of these polymorphisms in genes of PI3K/Akt pathway with

PCa risk. To the best of our knowledge, this is the first pooled study that analyzed the associations

between 13 polymorphisms in six pivotal genes of PI3K/Akt pathway and PCa risk. Meanwhile,

further analyses were conducted in different subgroups to explore the potential associations or

heterogeneity sources.

Nevertheless, overall results revealed that none of these polymorphisms was associated with

PCa risk. Then, we performed subgroup analysis based on ethnicity, source of control (population-

based or hospital-based) and HWE status (conform or not conform). For IL-6-rs1800795

polymorphism, when the stratification analysis was conducted by ethnicity, we found that a

statistically significant increased risk of PCa was identified in the dominant model for Caucasians.

However, in the meta-analysis conducted by Liu et al.66, they did not reveal a significant connection

between IL-6-rs1800795 polymorphism and PCa risk in Caucasian. For other polymorphisms, null

association was uncovered when the stratified analyses were conducted based on ethnicity, source

of control or HWE status.

Although we were surprised by these negative results, the high quality of these included studies

and the substantial amount of data strengthened the possibility that the lack of association was not

caused by chance. For those comparisons that did not exhibit a statistically significant association,

may be as a result of the characteristics of low-penetrance genes. Moreover, although these

polymorphisms assessed were appropriate candidates, they only account for some of the factors,

and ignored other factors such as obesity, diet and environment. We summarized the advantages of

current work. Firstly, although many meta-analyses provided some clues for separate

polymorphisms in one or more genes of PI3K/Akt pathway and PCa risk, the current one provide a

more comprehensive accurate assessment of the associations of all available polymorphisms in

genes of PI3K/Akt pathway with PCa risk. To the best of our knowledge, this is the first pooled

study that analyzed the associations between 13 polymorphisms in six pivotal genes of PI3K/Akt

pathway and PCa risk. Secondly, we applied classic formula to adjust the P-values, which removed

most of the marginal or false-positive P-values, making the final pool results more convincing.

Thirdly, we found IL-6-rs1800795 polymorphism could be served as a risk prediction marker for

Caucasian PCa patients. Our results provided some clues for the future clinical research that

polymorphisms in genes of this pathway may not suitable for high-risk prostate cancer patients’

screening. There are also several deficiencies that should be addressed. Firstly, other factors such as

the density of prostate-specific antigen (PSA), living conditions and histological types, the stage

and grades of PCa should be included to get more precise results. Secondly, for many

polymorphisms of these inclusive genes, relatively small samples were included for the assessment,

such as rs1927914 polymorphism. Finally, we ignored that there were many individual characters

such as age, obesity, alcohol, consumption and other lifestyle risk factors which could influence our

conclusions.

Overall, our meta-analysis provided no statistically significant association between the 13

polymorphisms in six genes of PI3K/Akt signaling pathway and PCa risk. However, a significantly

increased risk of PCa in Caucasian individuals was identified for IL-6-rs1800795 polymorphism in

the dominant model. Due to the limitations of these included studies, as well as the risk factors we

ignored, further well-designed studies with larger samples are warranted to verify our findings.

Acknowledgments

This study was supported by the Natural Science Foundation of Guangdong Province, China

(2017A030313800).

Authors’ contributions

M.Z., J.M. and Z.N. designed the studies and drafted the manuscript. M.Z. and Z.N. performed

the statistical analysis. L.Z., S.W. and C.L. managed the experimental design, reviewed the

manuscript. All authors read and approved the final manuscript.

Conflict of Interest Statement

The authors declared no potential conflicts of interest with respect to the research, authorship,

and/or publication of this article.

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Table 1. Characteristics of the enrolled studies.

Gene SNP First Author Year Genotyping

Method Ethnicity

Source of

Control

Case Control

WW WM MM WW WM MM Y(HWE)

TLR4 rs1927914 Chen et al. 2005 MassARRAY Caucasian P-B 297 301 60 290 288 91 Y

rs1927914 Zheng et al. 2004 MassARRAY Caucasian P-B 625 596 154 341 354 81 Y

rs1927914 Song et al. 2009 PCR-RFLP Asian H-B 69 54 14 48 87 7 N

rs10759932 Chen et al. 2005 MassARRAY Caucasian P-B 511 155 11 472 197 12 Y


rs10759932 Shui et al. 2012 MALDI-TOF Caucasian P-B 897 260 27 908 244 27 N

rs10759932 Cheng et al. 2007 Sequencing Caucasian H-B 370 117 119 358 143 4 N

rs2149356 Chen et al. 2005 MassARRAY Caucasian P-B 320 286 61 305 275 91 N

rs2149356 Zheng et al. 2004 MassARRAY Caucasian P-B 603 423 136 331 224 74 N

rs2149356 Shui et al. 2012 MALDI-TOF Caucasian P-B 579 489 106 576 460 119 Y



rs4986790 Cheng et al. 2007 TaqMan Caucasian H-B 439 66 1 456 48 2 Y

rs4986790 Wang et al. 2009 TaqMan Caucasian P-B 230 24 0 216 35 0 Y

rs4986790 Balistreri et al. 2010 PCR-RFLP Caucasian H-B 49 1 0 111 13 1 Y

rs11536889 Chen et al. 2005 MassARRAY Caucasian P-B 515 167 10 513 159 15 Y







rs7873784 Cheng et al. 2007 Sequencing Caucasian H-B 362 130 13 346 146 14 Y

IL-6 rs1800796 Wang et al. 2009 TaqMan Caucasian P-B 233 19 1 225 25 0 Y

rs1800796 Pierce et al. 2009 TaqMan Caucasian P-B 156 19 0 1740 192 2 Y

rs1800796 Pierce et al. 2009 TaqMan Mixed P-B 37 2 1 251 41 6 N

rs1800796 Sun et al. 2004 Microarray Caucasian P-B 1226 109 2 675 74 4 Y

rs1800795 Mandal et al. 2014 PCR Mixed H-B 108 44 12 74 44 22 N

rs1800795 Zhang et al. 2010 Sequenom Mixed P-B 80 86 27 100 75 22 Y

rs1800795 Zabaleta et al. 2009 TaqMan Caucasian H-B 19 34 21 126 163 112 N

rs1800795 Zabaleta et al. 2009 TaqMan Mixed H-B 10 2 3 41 10 6 N

rs1800795 Dossus et al. 2010 GoldenGate Caucasian P-B 3594 3218 1125 3832 3402 274 N


rs1800795 Moore et al. 2009 TaqMan Caucasian P-B 191 485 281 196 401 250 Y

rs1800795 Pierce et al. 2009 TaqMan Caucasian P-B 48 96 31 648 805 305 N

rs1800795 Pierce et al. 2009 TaqMan Mixed P-B 34 5 1 216 43 1 Y

rs1800795 Kesarwani et al. 2008 PCR Asia H-B 102 84 14 103 87 10 Y

rs1800795 Bao et al. 2008 TaqMan Asia P-B 136 0 0 120 0 0 N

rs1800795 Michaud et al. 2006 TaqMan Caucasian P-B 170 223 91 230 293 90 Y

IGF1 (CA)19 Chu et al. 2006 Sequenom Caucasian P-B 75 28 21 73 76 25 Y

(CA)19 Chu et al. 2006 Sequenom Mixed P-B 4 17 17 2 20 16 Y

(CA)19 Neuhausen et al. 2005 PCR Caucasian H-B 78 86 29 107 124 32 Y

(CA)19 Schildkraut et al. 2005 PCR Mixed P-B 20 39 35 28 33 20 Y

(CA)19 Norihiko et al. 2005 PCR Asian H-B 155 130 18 289 172 20 Y

(CA)19 Friedrichsen et al. 2005 PCR-RFLP Mixed P-B 73 289 219 64 237 219 Y

(CA)19 Nam et al. 2003 PCR-RFLP Mixed P-B 64 230 189 103 253 192 Y

(CA)19 Wenndy et al. 2007 PCR Mixed H-B 324 28 49 289 26 51 N

VEGF rs833061 Fukuda et al. 2007 PCR-RFLP Asian H-B 143 103 24 132 97 23 Y

rs833061 Onen et al. 2008 PCR-RFLP Mixed P-B 33 89 11 50 94 13 N

rs833061 Lin et al. 2003 PCR-RFLP Asian H-B 60 32 4 43 72 4 N

rs833061 Onen et al. 2008 PCR-RFLP Caucasian P-B 33 89 11 50 94 13 N

rs1570360 Sfar et al. 2006 RFLP-PCR Caucasian H-B 58 37 6 36 50 14 Y

rs1570360 Jacobs et al. 2008 TaqMan Caucasian P-B 557 489 126 210 194 54 Y

rs1570360 McCarron et al. 2013 TaqMan Caucasian P-B 114 109 15 120 109 34 Y

IRS1 rs1801278 Andrea et al. 2011 PCR Caucasian H-B 56 5 0 106 12 1 Y

rs1801278 Fall et al. 2008 PCR Mixed H-B 489 73 2 662 90 6 Y

rs1801278 Li et al. 2013 PCR Mixed P-B 386 50 2 422 65 1 Y

rs1801278 Neuhausen et al. 2005 PCR Caucasian P-B 118 50 12 160 81 14 Y

FGFR4 rs351855 FitzGerald et al. 2009 SNPlex™ Caucasian P-B 587 544 123 631 496 124 Y

rs351855 FitzGerald et al. 2009 SNPlex™ Mixed P-B 104 39 3 60 18 2 Y

rs351855 Lee et al. 2010 TaqMan Caucasian P-B 183 182 32 235 167 37 Y

rs351855 Zhiyong et al. 2010 PCR Asian H-B 133 196 163 67 152 125 Y

Note: Hardy-Weinberg equilibrium (HWE); population-based (P-B); hospital-based (H-B); Mixed: more than two descendants; W: wild allele; M: mutated allele;

PCR: Polymerase chain reaction; RFLP-PCR: restriction fragment length polymorphism-Polymerase chain reaction

Table 2. Details of the association between IL-6-rs1800795 polymorphism and prostate cancer risk.

Comparison Subgroup N PH PZ Random Fixed

M vs. W Overall 11 0.000 0.207 1.108 (0.945-1.300) 1.347 (1.292-1.403)

M vs. W Asia 1 1.000 0.692 1.065 (0.780-1.453) 1.065 (0.780-1.453)

M vs. W Caucasian 6 0.000 0.071 1.171 (0.986-1.391) 1.370 (1.313-1.429)

M vs. W H-B 4 0.020 0.772 0.946 (0.648-1.380) 0.926 (0.765-1.122)

M vs. W P-B 7 0.000 0.038 1.188 (1.010-1.397) 1.371 (1.315-1.431)

M vs. W N 5 0.000 0.528 1.106 (0.809-1.514) 1.430 (1.365-1.499)

M vs. W Y 6 0.710 0.053 1.090 (0.999-1.190) 1.090 (0.999-1.190)

WM vs. WW Overall 11 0.103 0.269 1.080 (0.951-1.227) 1.033 (0.975-1.096)

WM vs. WW Asia 1 1.000 0.903 0.975 (0.650-1.463) 0.975 (0.650-1.463)

WM vs. WW Caucasian 6 0.061 0.177 1.112 (0.953-1.296) 1.036 (0.975-1.100)

WM vs. WW H-B 4 0.380 0.678 0.941 (0.709-1.250) 0.943 (0.716-1.243)

WM vs. WW P-B 7 0.054 0.156 1.116 (0.959-1.299) 1.038 (0.978-1.102)

WM vs. WW N 5 0.048 0.512 1.101 (0.826-1.466) 1.022 (0.958-1.089)

WM vs. WW Y 6 0.353 0.217 1.088 (0.935-1.266) 1.092 (0.950-1.256)

MM vs. WW Overall 11 0.000 0.211 1.411 (0.823-2.421) 2.609 (2.359-2.885)

MM vs. WW Asia 1 1.000 0.428 1.414 (0.600-3.329) 1.414 (0.600-3.329)

MM vs. WW Caucasian 6 0.000 0.214 1.529 (0.783-2.986) 2.778 (2.502-3.085)

MM vs. WW H-B 4 0.043 0.960 0.982 (0.473-2.036) 0.912 (0.606-1.372)

MM vs. WW P-B 7 0.000 0.119 1.671 (0.877-3.184) 2.790 (2.513-3.097)

MM vs. WW N 5 0.000 0.464 1.431 (0.548-3.734) 3.601 (3.177-4.082)

MM vs. WW Y 6 0.682 0.025 1.233 (1.028-1.479) 1.231 (1.027-1.477)

WM + MM vs. WW Overall 11 0.047 0.040 1.147 (1.006-1.308) 1.228 (1.162-1.298)

WM + MM vs. WW Asia 1 1.000 0.920 1.020 (0.689-1.510) 1.020 (0.689-1.510)

WM + MM vs. WW Caucasian 6 0.292 <0.001 1.224 (1.113-1.346) 1.245 (1.176-1.318)

WM + MM vs. WW H-B 4 0.114 0.505 0.936 (0.631-1.390) 0.917 (0.710-1.184)

WM + MM vs. WW P-B 7 0.296 <0.001 1.227 (1.117-1.349) 1.246 (1.177-1.319)

WM + MM vs. WW N 5 0.017 0.359 1.150 (0.853-1.552) 1.252 (1.178-1.331)

WM + MM vs. WW Y 6 0.500 0.082 1.124 (0.985-1.283) 1.124 (0.985-1.282)

MM vs. WM + WW Overall 11 0.000 0.315 1.331 (0.762-2.323) 2.292 (2.093-2.509)

MM vs. WM + WW Asia 1 1.000 0.402 1.430 (0.620-3.300) 1.430 (0.620-3.300)

MM vs. WM + WW Caucasian 6 0.000 0.366 1.388 (0.682-2.826) 2.404 (2.189-2.640)

MM vs. WM + WW H-B 4 0.087 0.866 0.949 (0.516-1.746) 0.899 (0.618-1.308)

MM vs. WM + WW P-B 7 0.000 0.218 1.543 (0.774-3.074) 2.429 (2.211-2.669)

MM vs. WM + WW N 5 0.000 0.585 1.323 (0.485-3.606) 3.390 (3.015-3.812)

MM vs. WM + WW Y 6 0.449 0.171 1.114 (0.956-1.298) 1.112 (0.955-1.296)

Note: Hardy-Weinberg equilibrium (HWE); P-B: population-based; H-B: hospital-based; Y: Studies conformed to HWE; N: studies did not conform to HWE; Mixed:

more than two descendant; *P value less than [0.05/ (5*13)] means statistically significant.

Figure 1. Forest plots of the association between IL-6-rs1800795 polymorphism and prostate cancer risk. Subgroup analysis by ethnicity (A) and source of control

(B).

Supplementary Information

The Role of Polymorphisms in Genes of PI3K/Akt Signaling Pathway on Prostate Cancer Risk:

A Meta-Analysis and Systematic Review

Meng Zhang1,2,3#; Zhihao Ni3#; Jialin Meng1,3; Li Zhang1; Chaozhao Liang1*; Song Wu2*

1. Department of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China

2. Shenzhen Following Precision Medical Institute, Shenzhen Luohu Hospital Group, Shenzhen

518000, China

3. Graduate School of Anhui Medical University, Hefei, China

# These authors contributed equally to the work.

* Correspondence:

*Name: Chaozhao Liang;

Department of Urology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China

Tel.: +86 55162923440


*Name: Song Wu

Shenzhen Following Precision Medical Institute, Shenzhen Luohu Hospital Group, Shenzhen 518000,

China

Tel.: +86 755 8220 3083




Table S1. Methodological quality of the included studies according to the NOS.

Polymorphism Author Ethnicity YearSource ofcontrol

NOS scoreSummary Level

Selection Comparability Exposure

IL-6-rs1800795 Moore et al. Caucasian 2009 P-B 3 2 3 8 APierce et al. Caucasian 2009 P-B 3 2 3 8 APierce et al. Mixed 2009 P-B 3 2 3 8 AMichaud et al. Caucasian 2006 P-B 4 2 3 9 ABAO et al. Asian 2008 P-B 4 2 3 9 ADossus et al. Caucasian 2010 P-B 3 2 3 8 AKesarwani et al. Asian 2008 P-B 3 2 2 7 AZabaleta et al. Caucasian 2009 H-B 3 1 3 7 AZabaleta et al. Mixed 2009 H-B 3 1 3 7 AWang et al. Caucasian 2009 P-B 3 2 3 8 AMandal et al. Mixed 2014 H-B 3 2 3 8 A

IL-6-rs1800796 Wang et al. Caucasian 2009 P-B 3 2 3 8 APierce et al. Caucasian 2009 P-B 3 2 3 8 APierce et al. Mixed 2009 P-B 3 2 3 8 Asun et al. Caucasian 2004 P-B 3 2 3 8 A

TLR4-rs11536889 Chen et al. Caucasian 2005 P-B 3 2 3 8 AZheng et al. Caucasian 2004 P-B 3 2 3 8 AShui et al. Caucasian 2012 P-B 2 2 3 7 ACheng et al. Caucasian 2007 H-B 3 2 3 8 AWang et al. Caucasian 2009 P-B 3 2 3 8 AZheng et al. Caucasian 2004 P-B 3 2 3 8 A

TLR4-rs4986790 Chen et al. Caucasian 2005 P-B 3 2 3 8 ACheng et al. Caucasian 2007 H-B 3 2 3 8 AWang et al. Caucasian 2009 P-B 3 2 3 8 ABalistreri et al. Caucasian 2010 H-B 2 2 3 7 A

TLR4-rs2149356 Chen et al. Caucasian 2005 P-B 3 2 3 8 AZheng et al. Caucasian 2004 P-B 3 2 3 8 AShui et al. Caucasian 2012 P-B 2 2 3 7 ACheng et al. Caucasian 2007 H-B 3 2 3 8 A

TLR4-rs10759932 Chen et al. Caucasian 2005 P-B 3 2 3 8 AZheng et al. Caucasian 2004 P-B 3 2 3 8 AShui et al. Caucasian 2012 P-B 2 2 3 7 ACheng et al. Caucasian 2007 H-B 3 2 3 8 A

TLR4-rs1927914 Chen et al. Caucasian 2005 P-B 3 2 3 8 AZheng et al. Caucasian 2004 P-B 3 2 3 8 ASong et al. Asian 2009 H-B 3 2 3 8 A

TLR4-rs7873784 Chen et al. Caucasian 2005 P-B 3 2 3 8 AShui et al. Caucasian 2012 P-B 2 2 3 7 ACheng et al. Caucasian 2007 H-B 3 2 3 8 A

IGF1-(CA)19 Chen et al. Caucasian 2006 P-B 4 2 3 9 AChen et al. Mixed 2006 P-B 4 2 3 9 ASchildkraut et al. Mixed 2005 P-B 4 2 2 8 ANeuhausen et al. Caucasian 2005 P-B 4 2 3 9 ATsuchiya et al. Asian 2005 H-B 3 1 3 7 AFriedrichsen et al. Mixed 2005 P-B 4 2 3 9 ANam et al. Mixed 2003 P-B 3 1 3 7 AHernandez et al. Mixed 2007 H-B 4 2 3 9 A

IRS1-rs1801278 Saracevic et al. Caucasian 2011 H-B 3 2 3 8 AFall et al. Mixed 2008 H-B 4 2 2 8 ALi et al. Mixed 2005 P-B 3 2 3 8 ANeuhausen et al. Caucasian 2005 P-B 4 2 3 9 A

VEGF-rs833061 Fukuda et al. Asian 2007 H-B 4 1 3 8 AOnen et al. Mixed 2008 P-B 4 1 2 7 ALin et al. Asian 2003 P-B 4 2 3 9 A

VEGF-rs1570360 Sfar et al. Caucasian 2006 H-B 4 2 3 9 A

Jacobs et al. Caucasian 2008 P-B 4 2 2 8 AMcCarron et al. Caucasian 2013 P-B 4 2 2 8 A

FGFR4-rs351855 FitzGerald et al. Caucasian 2009 P-B 4 2 3 9 AHo et al. Caucasian 2010 P-B 4 2 3 9 AMa et al. Asian 2010 H-B 3 2 3 8 A

This table identifies ‘high’ quality choices with a ‘star’. A study can be awarded a maximum of one star for each numbered item within the Selection and Exposure

categories. A maximum of two stars can be given for Comparability; NOS: Newcastle-Ottawa Scale.

Table S2. Details of the association between polymorphisms in genes of PI3K/Akt signaling pathway and prostate cancer risk.

Gene SNP Comparison Subgroup N PH PZ Random FixedIL-6 rs1800796 M vs. W Overall 4 0.565 0.077 0.819 (0.654-1.026) 0.815 (0.650-1.022)

M vs. W Caucasian 3 0.500 0.133 0.836 (0.664-1.053) 0.836 (0.662-1.056)M vs. W Y 3 0.500 0.133 0.836 (0.664-1.053) 0.836 (0.662-1.056)WM vs. WW Overall 4 0.399 0.117 0.837 (0.659-1.064) 0.825 (0.649-1.049)WM vs. WW Caucasian 3 0.510 0.221 0.859 (0.674-1.095) 0.858 (0.672-1.096)WM vs. WW Y 3 0.510 0.221 0.859 (0.674-1.095) 0.858 (0.672-1.096)MM +WM vs. WW Overall 4 0.484 0.092 0.823 (0.651-1.041) 0.816 (0.644-1.033)MM +WM vs. WW Caucasian 3 0.513 0.168 0.844 (0.664-1.072) 0.844 (0.662-1.074)MM +WM vs. WW Y 3 0.513 0.168 0.844 (0.664-1.072) 0.844 (0.662-1.074)MM vs. WW Overall 4 0.449 0.573 0.742 (0.237-2.320) 0.727 (0.240-2.205)MM vs. WW Caucasian 3 0.295 0.473 0.727 (0.151-3.492) 0.627 (0.175-2.243)MM vs. WW Y 3 0.295 0.473 0.727 (0.151-3.492) 0.627 (0.175-2.243)MM vs. WM +WW Overall 4 0.441 0.613 0.771 (0.247-2.411) 0.751 (0.247-2.279)MM vs. WM +WW Caucasian 3 0.298 0.487 0.734 (0.154-3.491) 0.637 (0.178-2.276)MM vs. WM +WW Y 3 0.298 0.487 0.734 (0.154-3.491) 0.637 (0.178-2.276)

TLR4 rs10759932 M vs. W Overall 4 0.000 0.389 1.203 (0.790-1.832) 1.187 (1.081-1.304)M vs. W P-B 3 0.036 0.776 0.972 (0.799-1.182) 0.990 (0.890-1.100)M vs. W N 2 0.000 0.258 1.574 (0.717-3.455) 1.460 (1.281-1.664)M vs. W Y 2 0.017 0.642 0.924 (0.664-1.287) 0.949 (0.829-1.087)WM vs. WW Overall 4 0.038 0.337 0.910 (0.751-1.103) 0.934 (0.835-1.045)WM vs. WW P-B 3 0.032 0.620 0.944 (0.750-1.187) 0.964 (0.853-1.089)WM vs. WW N 2 0.080 0.690 0.940 (0.696-1.271) 0.975 (0.829-1.146)WM vs. WW Y 2 0.028 0.455 0.875 (0.616-1.242) 0.898 (0.769-1.049)MM +WM vs. WW Overall 4 0.000 0.633 1.067 (0.818-1.392) 1.062 (0.954-1.182)MM +WM vs. WW P-B 3 0.027 0.682 0.953 (0.759-1.198) 0.975 (0.866-1.097)MM +WM vs. WW N 2 0.021 0.185 1.278 (0.889-1.837) 1.227 (1.055-1.428)MM +WM vs. WW Y 2 0.017 0.540 0.891 (0.616-1.288) 0.917 (0.788-1.067)

MM vs. WW Overall 4 0.000 0.230 2.374 (0.579-9.741) 2.788 (2.061-3.769)MM vs. WW P-B 3 0.501 0.542 1.113 (0.768-1.613) 1.121 (0.777-1.618)MM vs. WW N 2 0.000 0.373 5.278 (0.136-205.281) 4.283 (2.891-6.346)MM vs. WW Y 2 0.282 0.435 1.199 (0.690-2.085) 1.222 (0.739-2.018)MM vs. WM +WW Overall 4 0.000 0.224 2.447 (0.578-10.36) 2.912 (2.158-3.929)MM vs. WM +WW P-B 3 0.560 0.521 1.120 (0.773-1.621) 1.127 (0.782-1.625)MM vs. WM +WW N 2 0.000 0.381 5.402 (0.124-234.883) 4.463 (3.023-6.589)MM vs. WM +WW Y 2 0.366 0.381 1.242 (0.747-2.065) 1.252 (0.758-2.067)

TLR4 rs4986790 M vs. W Overall 4 0.051 0.774 0.940 (0.618-1.432) 1.011 (0.810-1.263)M vs. W H-B 2 0.040 0.608 0.581 (0.073-4.610) 1.155 (0.811-1.646)M vs. W P-B 2 0.140 0.607 0.880 (0.556-1.392) 0.928 (0.698-1.234)M vs. W Y 3 0.021 0.530 0.781 (0.361-1.689) 0.972 (0.725-1.304)WM vs. WW Overall 4 0.037 0.894 0.969 (0.609-1.543) 1.063 (0.840-1.346)WM vs. WW H-B 2 0.046 0.665 0.638 (0.084-4.874) 1.241 (0.853-1.806)WM vs. WW P-B 2 0.086 0.697 0.894 (0.508-1.572) 0.960 (0.708-1.302)WM vs. WW Y 3 0.016 0.578 0.790 (0.345-1.812) 1.006 (0.740-1.368)MM +WM vs. WW Overall 4 0.040 0.824 0.950 (0.602-1.497) 1.038 (0.823-1.309)MM +WM vs. WW H-B 2 0.041 0.635 0.603 (0.075-4.870) 1.204 (0.832-1.742)MM +WM vs. WW P-B 2 0.105 0.695 0.882 (0.521-1.492) 0.942 (0.699-1.270)MM +WM vs. WW Y 3 0.016 0.546 0.776 (0.341-1.767) 0.989 (0.729-1.341)MM vs. WW Overall 3 0.983 0.398 0.608 (0.192-1.924) 0.608 (0.192-1.926)MM vs. WW H-B 2 0.857 0.596 0.593 (0.086-4.067) 0.593 (0.086-4.095)MM vs. WW P-B 1 1.000 0.510 0.617 (0.147-2.595) 0.617 (0.147-2.595)MM vs. WW Y 2 0.857 0.596 0.593 (0.086-4.067) 0.593 (0.086-4.095)MM vs. WM +WW Overall 3 0.971 0.392 0.605 (0.191-1.911) 0.604 (0.191-1.914)MM vs. WM +WW H-B 2 0.808 0.600 0.597 (0.087-4.092) 0.596 (0.086-4.122)MM vs. WM +WW P-B 1 1.000 0.499 0.609 (0.145-2.559) 0.609 (0.145-2.559)MM vs. WM +WW Y 2 0.808 0.600 0.597 (0.087-4.092) 0.596 (0.086-4.122)

TLR4 rs1927914 M vs. W Overall 3 0.306 0.104 0.917 (0.820-1.025) 0.922 (0.836-1.017)M vs. W Caucasian 2 0.245 0.200 0.933 (0.827-1.051) 0.935 (0.845-1.036)

M vs. W P-B 2 0.245 0.200 0.933 (0.827-1.051) 0.935 (0.845-1.036)M vs. W Y 2 0.245 0.200 0.933 (0.827-1.051) 0.935 (0.845-1.036)WM vs. WW Overall 3 0.009 0.211 0.804 (0.571-1.132) 0.900 (0.784-1.034)WM vs. WW Caucasian 2 0.484 0.560 0.958 (0.829-1.107) 0.958 (0.829-1.107)WM vs. WW P-B 2 0.484 0.560 0.958 (0.829-1.107) 0.958 (0.829-1.107)WM vs. WW Y 2 0.484 0.560 0.958 (0.829-1.107) 0.958 (0.829-1.107)MM +WM vs. WW Overall 3 0.053 0.166 0.834 (0.645-1.078) 0.893 (0.783-1.019)MM +WM vs. WW Caucasian 2 0.937 0.348 0.936 (0.816-1.074) 0.936 (0.816-1.074)MM +WM vs. WW P-B 2 0.937 0.348 0.936 (0.816-1.074) 0.936 (0.816-1.074)MM +WM vs. WW Y 2 0.937 0.348 0.936 (0.816-1.074) 0.936 (0.816-1.074)MM vs. WW Overall 3 0.089 0.565 0.888 (0.592-1.332) 0.877 (0.702-1.096)MM vs. WW Caucasian 2 0.047 0.425 0.827 (0.518-1.319) 0.854 (0.680-1.074)MM vs. WW P-B 2 0.047 0.425 0.827 (0.518-1.319) 0.854 (0.680-1.074)MM vs. WW Y 2 0.047 0.425 0.827 (0.518-1.319) 0.854 (0.680-1.074)MM vs. WM +WW Overall 3 0.012 0.986 1.005 (0.592-1.706) 0.918 (0.744-1.133)MM vs. WM +WW Caucasian 2 0.020 0.505 0.838 (0.499-1.408) 0.872 (0.703-1.084)MM vs. WM +WW P-B 2 0.020 0.505 0.838 (0.499-1.408) 0.872 (0.703-1.084)MM vs. WM +WW Y 2 0.020 0.505 0.838 (0.499-1.408) 0.872 (0.703-1.084)

TLR4 rs11536889 M vs. W Overall 5 0.013 0.742 1.031 (0.861-1.234) 1.003 (0.910-1.105)M vs. W P-B 4 0.014 0.980 0.997 (0.812-1.226) 0.975 (0.878-1.082)M vs. W Y 4 0.014 0.980 0.997 (0.812-1.226) 0.975 (0.878-1.082)WM vs. WW Overall 5 0.000 0.798 1.036 (0.789-1.362) 0.998 (0.892-1.116)WM vs. WW P-B 4 0.000 0.966 1.007 (0.725-1.400) 0.974 (0.864-1.098)WM vs. WW Y 4 0.000 0.966 1.007 (0.725-1.400) 0.974 (0.864-1.098)MM +WM vs. WW Overall 5 0.001 0.763 1.038 (0.817-1.318) 1.000 (0.898-1.115)MM +WM vs. WW P-B 4 0.001 0.977 1.004 (0.756-1.334) 0.973 (0.866-1.092)MM +WM vs. WW Y 4 0.001 0.977 1.004 (0.756-1.334) 0.973 (0.866-1.092)MM vs. WW Overall 5 0.616 0.931 1.014 (0.733-1.404) 1.014 (0.734-1.402)MM vs. WW P-B 4 0.603 0.759 0.945 (0.660-1.354) 0.945 (0.661-1.352)MM vs. WW Y 4 0.603 0.759 0.945 (0.660-1.354) 0.945 (0.661-1.352)

MM vs. WM +WW Overall 5 0.514 0.877 1.025 (0.741-1.417) 1.026 (0.743-1.416)MM vs. WM +WW P-B 4 0.446 0.847 0.965 (0.674-1.380) 0.965 (0.676-1.379)MM vs. WM +WW Y 4 0.446 0.847 0.965 (0.674-1.380) 0.965 (0.676-1.379)

TLR4 rs2149356 M vs. W Overall 4 0.270 0.523 0.976 (0.895-1.064) 0.976 (0.905-1.052)M vs. W P-B 3 0.258 0.297 0.955 (0.866-1.052) 0.957 (0.881-1.039)M vs. W N 3 0.142 0.550 0.973 (0.853-1.110) 0.972 (0.885-1.067)WM vs. WW Overall 4 0.928 0.402 1.046 (0.941-1.163) 1.046 (0.941-1.163)WM vs. WW P-B 3 0.906 0.567 1.034 (0.922-1.160) 1.034 (0.922-1.160)WM vs. WW N 3 0.805 0.572 1.039 (0.909-1.189) 1.039 (0.909-1.189)MM +WM vs. WW Overall 4 0.646 0.830 1.011 (0.916-1.116) 1.011 (0.916-1.116)MM +WM vs. WW P-B 3 0.607 0.902 0.993 (0.892-1.106) 0.993 (0.892-1.106)MM +WM vs. WW N 3 0.442 0.947 1.004 (0.886-1.138) 1.004 (0.886-1.138)MM vs. WW Overall 4 0.155 0.188 0.895 (0.721-1.112) 0.897 (0.763-1.055)MM vs. WW P-B 3 0.162 0.080 0.843 (0.658-1.080) 0.851 (0.710-1.020)MM vs. WW N 3 0.073 0.507 0.897 (0.650-1.237) 0.902 (0.741-1.098)MM vs. WM +WW Overall 4 0.178 0.099 0.877 (0.718-1.070) 0.878 (0.753-1.025)MM vs. WM +WW P-B 3 0.165 0.046 0.831 (0.656-1.053) 0.838 (0.704-0.997)MM vs. WM +WW N 3 0.087 0.388 0.879 (0.655-1.179) 0.885 (0.735-1.066)

TLR4 rs7873784 M vs. W Overall 3 0.493 0.143 0.920 (0.823-1.029) 0.920 (0.823-1.029)M vs. W P-B 2 0.266 0.274 0.929 (0.806-1.071) 0.932 (0.821-1.058)M vs. W Y 2 0.432 0.463 0.951 (0.832-1.087) 0.951 (0.832-1.087)WM vs. WW Overall 3 0.787 0.262 0.928 (0.814-1.057) 0.928 (0.814-1.057)WM vs. WW P-B 2 0.960 0.504 0.951 (0.820-1.102) 0.951 (0.820-1.102)WM vs. WW Y 2 0.527 0.273 0.917 (0.786-1.070) 0.917 (0.786-1.070)MM +WM vs. WW Overall 3 0.735 0.185 0.918 (0.809-1.042) 0.918 (0.809-1.042)MM +WM vs. WW P-B 2 0.607 0.373 0.937 (0.812-1.081) 0.937 (0.812-1.081)MM +WM vs. WW Y 2 0.463 0.335 0.929 (0.799-1.079) 0.929 (0.799-1.079)MM vs. WW Overall 3 0.144 0.282 0.822 (0.483-1.399) 0.815 (0.562-1.183)MM vs. WW P-B 2 0.051 0.600 0.797 (0.341-1.863) 0.795 (0.519-1.216)MM vs. WW Y 2 0.519 0.743 1.083 (0.672-1.745) 1.083 (0.673-1.743)

MM vs. WM +WW Overall 3 0.138 0.328 0.840 (0.492-1.435) 0.831 (0.574-1.204)MM vs. WM +WW P-B 2 0.049 0.621 0.807 (0.345-1.888) 0.804 (0.527-1.227)MM vs. WM +WW Y 2 0.559 0.664 1.110 (0.690-1.787) 1.111 (0.691-1.785)

IRS1 rs1801278 M vs. W Overall 4 0.879 0.579 0.949 (0.785-1.146) 0.948 (0.785-1.145)M vs. W Caucasian 2 0.554 0.612 0.923 (0.673-1.267) 0.922 (0.672-1.264)M vs. W H-B 2 0.476 0.904 0.984 (0.734-1.321) 0.982 (0.732-1.317)M vs. W P-B 2 0.796 0.533 0.924 (0.722-1.184) 0.924 (0.722-1.184)WM vs. WW Overall 4 0.672 0.541 0.937 (0.757-1.159) 0.936 (0.756-1.158)WM vs. WW Caucasian 2 0.921 0.358 0.830 (0.559-1.234) 0.830 (0.559-1.234)WM vs. WW H-B 2 0.570 0.687 1.068 (0.779-1.465) 1.067 (0.778-1.463)WM vs. WW P-B 2 0.987 0.234 0.839 (0.629-1.120) 0.839 (0.629-1.120)MM +WM vs. WW Overall 4 0.800 0.539 0.938 (0.762-1.154) 0.937 (0.762-1.153)MM +WM vs. WW Caucasian 2 0.740 0.442 0.864 (0.595-1.257) 0.864 (0.594-1.255)MM +WM vs. WW H-B 2 0.517 0.881 1.026 (0.752-1.399) 1.024 (0.751-1.395)MM +WM vs. WW P-B 2 0.924 0.338 0.873 (0.661-1.153) 0.873 (0.661-1.153)MM vs. WW Overall 4 0.667 0.964 1.006 (0.512-1.975) 0.985 (0.510-1.902)MM vs. WW Caucasian 2 0.716 0.782 1.121 (0.512-2.451) 1.116 (0.512-2.433)MM vs. WW Mixed 2 0.285 0.614 0.763 (0.178-3.274) 0.726 (0.209-2.517)MM vs. WW H-B 2 0.857 0.318 0.482 (0.115-2.026) 0.481 (0.114-2.022)MM vs. WW P-B 2 0.625 0.573 1.239 (0.577-2.663) 1.245 (0.582-2.663)MM vs. WM +WW Overall 4 0.633 0.940 1.050 (0.538-2.049) 1.026 (0.535-1.966)MM vs. WM +WW Caucasian 2 0.701 0.674 1.184 (0.547-2.564) 1.179 (0.546-2.545)MM vs. WM +WW H-B 2 0.842 0.314 0.480 (0.114-2.015) 0.479 (0.114-2.010)MM vs. WM +WW P-B 2 0.644 0.481 1.304 (0.613-2.776) 1.310 (0.618-2.777)

VEGF rs833061 M vs. W Overall 4 0.019 0.667 0.935 (0.688-1.270) 0.969 (0.823-1.141)M vs. W Asian 2 0.016 0.323 0.732 (0.394-1.359) 0.820 (0.653-1.031)M vs. W H-B 2 0.016 0.323 0.732 (0.394-1.359) 0.820 (0.653-1.031)M vs. W P-B 2 1.000 0.223 1.158 (0.915-1.466) 1.158 (0.915-1.466)M vs. W N 3 0.007 0.679 0.905 (0.566-1.448) 0.963 (0.783-1.183)WM vs. WW Overall 4 0.000 0.748 0.905 (0.490-1.668) 0.936 (0.742-1.182)

WM vs. WW Asian 2 0.001 0.319 0.571 (0.190-1.718) 0.704 (0.521-0.953)WM vs. WW H-B 2 0.001 0.319 0.571 (0.190-1.718) 0.704 (0.521-0.953)WM vs. WW P-B 2 1.000 0.057 1.435 (0.989-2.082) 1.435 (0.989-2.082)WM vs. WW N 3 0.000 0.782 0.874 (0.336-2.272) 0.908 (0.671-1.227)MM +WM vs. WW Overall 4 0.001 0.754 0.912 (0.512-1.624) 0.941 (0.752-1.178)MM +WM vs. WW Asian 2 0.002 0.317 0.590 (0.210-1.660) 0.727 (0.545-0.970)MM +WM vs. WW H-B 2 0.002 0.317 0.590 (0.210-1.660) 0.727 (0.545-0.970)MM +WM vs. WW P-B 2 1.000 0.062 1.416 (0.982-2.041) 1.416 (0.982-2.041)MM +WM vs. WW N 3 0.000 0.791 0.884 (0.356-2.196) 0.915 (0.680-1.231)MM vs. WW Overall 4 0.872 0.781 1.063 (0.693-1.629) 1.062 (0.693-1.629)MM vs. WW Asian 2 0.712 0.774 0.920 (0.521-1.624) 0.920 (0.521-1.625)MM vs. WW H-B 2 0.712 0.774 0.920 (0.521-1.624) 0.920 (0.521-1.625)MM vs. WW P-B 2 1.000 0.452 1.282 (0.671-2.449) 1.282 (0.671-2.449)MM vs. WW N 3 0.771 0.618 1.163 (0.644-2.098) 1.162 (0.644-2.097)MM vs. WM +WW Overall 4 0.991 0.983 1.004 (0.671-1.504) 1.004 (0.671-1.504)MM vs. WM +WW Asian 2 0.747 0.974 1.009 (0.581-1.753) 1.009 (0.581-1.753)MM vs. WM +WW H-B 2 0.747 0.974 1.009 (0.581-1.753) 1.009 (0.581-1.753)MM vs. WM +WW P-B 2 1.000 0.997 0.999 (0.552-1.807) 0.999 (0.552-1.807)MM vs. WM +WW N 3 0.960 0.908 1.033 (0.598-1.784) 1.033 (0.598-1.783)

VEGF rs1570360 M vs. W Overall 3 0.025 0.086 0.769 (0.569-1.038) 0.853 (0.748-0.974)M vs. W P-B 2 0.366 0.153 0.904 (0.786-1.039) 0.904 (0.786-1.038)WM vs. WW Overall 3 0.055 0.347 0.838 (0.579-1.211) 0.908 (0.754-1.092)WM vs. WW P-B 2 0.645 0.822 0.978 (0.805-1.189) 0.978 (0.805-1.189)MM +WM vs. WW Overall 3 0.031 0.175 0.767 (0.522-1.126) 0.859 (0.721-1.023)MM +WM vs. WW P-B 2 0.909 0.432 0.929 (0.772-1.117) 0.929 (0.772-1.117)MM vs. WW Overall 3 0.041 0.072 0.542 (0.278-1.056) 0.688 (0.514-0.923)MM vs. WW P-B 2 0.095 0.219 0.681 (0.368-1.258) 0.755 (0.554-1.027)MM vs. WM +WW Overall 3 0.074 0.085 0.604 (0.340-1.071) 0.721 (0.545-0.953)MM vs. WM +WW P-B 2 0.061 0.247 0.675 (0.347-1.313) 0.765 (0.571-1.025)

IGF1 (CA)19 M vs. W Overall 8 0.003 0.526 1.057 (0.891-1.253) 1.059 (0.969-1.157)

M vs. W Caucasian 2 0.056 0.534 0.874 (0.572-1.336) 0.908 (0.732-1.125)M vs. W H-B 3 0.071 0.502 1.087 (0.852-1.386) 1.100 (0.948-1.276)M vs. W P-B 5 0.003 0.782 1.038 (0.799-1.346) 1.036 (0.927-1.158)M vs. W Y 7 0.003 0.383 1.087 (0.901-1.312) 1.083 (0.986-1.190)WM vs. WW Overall 8 0.001 0.944 1.011 (0.734-1.393) 1.093 (0.936-1.276)WM vs. WW Caucasian 2 0.004 0.286 0.594 (0.229-1.546) 0.667 (0.486-0.915)WM vs. WW H-B 3 0.226 0.146 1.145 (0.864-1.518) 1.178 (0.945-1.468)WM vs. WW P-B 5 0.000 0.773 0.919 (0.515-1.638) 1.017 (0.819-1.263)WM vs. WW Y 7 0.000 0.938 1.014 (0.706-1.456) 1.105 (0.941-1.298)MM +WM vs. WW Overall 8 0.001 0.719 1.054 (0.792-1.403) 1.097 (0.954-1.261)MM +WM vs. WW Caucasian 2 0.013 0.349 0.700 (0.332-1.476) 0.748 (0.559-1.002)MM +WM vs. WW H-B 3 0.096 0.499 1.109 (0.822-1.496) 1.138 (0.938-1.379)MM +WM vs. WW P-B 5 0.001 0.984 1.005 (0.597-1.693) 1.054 (0.860-1.290)MM +WM vs. WW Y 7 0.001 0.639 1.082 (0.777-1.507) 1.140 (0.979-1.327)MM vs. WW Overall 8 0.069 0.250 1.175 (0.893-1.547) 1.155 (0.960-1.389)MM vs. WW Caucasian 2 0.352 0.875 1.036 (0.669-1.605) 1.036 (0.669-1.602)MM vs. WW H-B 3 0.217 0.585 1.135 (0.770-1.673) 1.088 (0.803-1.475)MM vs. WW P-B 5 0.043 0.420 1.192 (0.778-1.826) 1.195 (0.948-1.507)MM vs. WW Y 7 0.096 0.141 1.255 (0.927-1.700) 1.239 (1.009-1.521)MM vs. WM +WW Overall 8 0.204 0.499 1.086 (0.902-1.308) 1.050 (0.912-1.209)MM vs. WM +WW Caucasian 2 0.908 0.287 1.250 (0.829-1.886) 1.250 (0.829-1.886)MM vs. WM +WW H-B 3 0.317 0.627 1.090 (0.791-1.504) 1.076 (0.800-1.447)MM vs. WM +WW P-B 5 0.116 0.613 1.104 (0.850-1.433) 1.042 (0.888-1.223)MM vs. WM +WW Y 7 0.188 0.333 1.135 (0.922-1.397) 1.077 (0.927-1.250)

FGFR4 rs351855 M vs. W Overall 4 0.033 0.785 1.026 (0.854-1.233) 1.037 (0.947-1.135)M vs. W Caucasian 2 0.478 0.050 1.110 (1.000-1.232) 1.110 (1.000-1.232)M vs. W P-B 3 0.773 0.044 1.111 (1.003-1.231) 1.111 (1.003-1.231)WM vs. WW Overall 4 0.009 0.634 1.078 (0.791-1.469) 1.132 (0.994-1.289)WM vs. WW Caucasian 2 0.308 0.004 1.233 (1.064-1.429) 1.231 (1.067-1.421)WM vs. WW P-B 3 0.595 0.003 1.232 (1.071-1.417) 1.232 (1.071-1.417)

MM +WM vs. WW Overall 4 0.007 0.736 1.053 (0.780-1.420) 1.102 (0.975-1.246)MM +WM vs. WW Caucasian 2 0.341 0.008 1.201 (1.049-1.376) 1.201 (1.049-1.376)MM +WM vs. WW P-B 3 0.635 0.007 1.202 (1.052-1.372) 1.202 (1.052-1.372)MM vs. WW Overall 4 0.192 0.472 0.915 (0.686-1.222) 0.929 (0.760-1.136)MM vs. WW Caucasian 2 0.890 0.552 1.076 (0.845-1.370) 1.076 (0.845-1.370)MM vs. WW P-B 3 0.964 0.569 1.072 (0.844-1.361) 1.072 (0.844-1.362)MM vs. WM +WW Overall 4 0.929 0.455 0.934 (0.780-1.118) 0.934 (0.780-1.118)MM vs. WM +WW Caucasian 2 0.897 0.867 0.980 (0.777-1.236) 0.980 (0.777-1.236)MM vs. WM +WW P-B 3 0.973 0.847 0.978 (0.777-1.230) 0.978 (0.777-1.230)

Note: Hardy-Weinberg equilibrium (HWE); P-B: population-based; H-B: hospital-based; Y: Studies conformed to HWE; N: studies did not conform to HWE; Mixed:

more than two descendant; *P value less than [0.05/ (5*13)] means statistically significant.

Table S3. Details of the sensitivity analyses for the polymorphisms in genes of PI3K/Akt signaling pathway and prostate cancer risk.

Polymorphism Comparison Study Omitted Estimate 95%CI Effect Model

rs1800795 (IL-6)M vs. W Mandal et al. (2014) 1.752 1.214-2.530 Random

Zhang et al. (2010) 0.760 0.564-1.025Zabaleta et al. (2009) 0.883 0.622-1.255Zabaleta et al. (2009) 0.658 0.259-1.672Dossus et al. (2010) 0.679 0.647-0.713Wang et al. (2009) 1.037 0.806-1.333Moore et al. (2009) 0.941 0.825-1.073Pierce et al. (2009) 0.818 0.656-1.021Pierce et al. (2009) 0.988 0.429-2.274Kesarwani et al. (2008) 0.939 0.688-1.281Michaud et al. (2006) 0.873 0.735-1.037Bao et al. (2008) (Excluded)

MM vs. WW Mandal et al. (2014) 2.676 1.248-5.739 RandomZhang et al. (2010) 0.652 0.345-1.230Zabaleta et al. (2009) 0.804 0.411-1.573Zabaleta et al. (2009) 0.488 0.104-2.295Dossus et al. (2010) 0.228 0.199-0.263Wang et al. (2009) 1.008 0.597-1.700Moore et al. (2009) 0.867 0.667-1.127Pierce et al. (2009) 0.729 0.455-1.168Pierce et al. (2009) 0.157 0.010-2.576Kesarwani et al. (2008) 0.707 0.300-1.666Michaud et al. (2006) 0.731 0.514-1.040Bao et al. (2008) (Excluded)

MW vs. WW Mandal et al. (2014) 0.685 0.411-1.143 FixedZhang et al. (2010) 1.433 0.935-2.197Zabaleta et al. (2009) 1.383 0.753-2.540

Zabaleta et al. (2009) 0.820 0.155-4.348Dossus et al. (2010) 1.009 0.944-1.078Wang et al. (2009) 0.837 0.567-1.234Moore et al. (2009) 1.241 0.977-1.577Pierce et al. (2009) 1.610 1.121-2.311Pierce et al. (2009) 0.739 0.273-1.996Kesarwani et al. (2008) 0.975 0.650-1.463Michaud et al. (2006) 1.030 0.791-1.341Bao et al. (2008) (Excluded)

MM vs. MW +WW Mandal et al. (2014) 1.720 1.083-2.733 RandomZhang et al. (2010) 0.687 0.460-1.025Zabaleta et al. (2009) 0.754 0.430-1.323Zabaleta et al. (2009) 0.780 0.231-2.641Dossus et al. (2010) 0.794 0.745-0.846Wang et al. (2009) 1.145 0.793-1.653Moore et al. (2009) 0.828 0.661-1.037Pierce et al. (2009) 0.647 0.458-0.915Pierce et al. (2009) 1.154 0.457-2.915Kesarwani et al. (2008) 0.980 0.662-1.451Michaud et al. (2006) 0.902 0.703-1.155Bao et al. (2008) (Excluded)

MM + MW vs. WW Mandal et al. (2014) 2.362 1.123-4.966 RandomZhang et al. (2010) 0.773 0.423-1.411Zabaleta et al. (2009) 0.978 0.564-1.696Zabaleta et al. (2009) 0.471 0.103-2.156Dossus et al. (2010) 0.229 0.200-0.263Wang et al. (2009) 0.908 0.567-1.455Moore et al. (2009) 1.007 0.822-1.234Pierce et al. (2009) 0.975 0.649-1.465

Pierce et al. (2009) 0.151 0.009-2.457Kesarwani et al. (2008) 0.699 0.303-1.614Michaud et al. (2006) 0.743 0.540-1.023Bao et al. (2008) (Excluded)

rs1800796(IL-6)

M vs. W Wang et al. (2009) 1.216 0.671-2.201 FixedPierce et al. (2009) 0.930 0.573-1.509Pierce et al. (2009) 1.855 0.653-5.269Sun et al. (2004) 1.305 0.975-1.747

MM vs. WW Wang et al. (2009) 1.305 0.975-1.747 FixedPierce et al. (2009) 0.345 0.014-8.517Pierce et al. (2009) 0.450 0.021-9.406Sun et al. (2004) 0.884 0.104-7.555

MW vs. WW Wang et al. (2009) 0.734 0.393-1.370 FixedPierce et al. (2009) 1.104 0.670-1.818Pierce et al. (2009) 0.331 0.077-1.426Sun et al. (2004) 0.811 0.595-1.105

MM vs. MW +WW Wang et al. (2009) 1.294 0.699-2.396 FixedPierce et al. (2009) 0.915 0.556-1.507Pierce et al. (2009) 2.309 0.684-7.800Sun et al. (2004) 1.276 0.941-1.731

MM + MW vs. WW Wang et al. (2009) 0.336 0.014-8.287 FixedPierce et al. (2009) 0.454 0.022-9.495Pierce et al. (2009) 0.801 0.094-6.833Sun et al. (2004) 3.565 0.651-19.508

(CA)19(IGF1)

M vs. W Chen et al. (2006) 1.443 1.015-2.053 RandomChen et al. (2006) 1.062 0.538-2.097Neuhausen et al. (2005) 0.935 0.712-1.228Schildkraut et al. (2005) 0.594 0.389-0.908Tsuchiya et al. (2005) 0.749 0.592-0.947

Friedrichsen et al. (2005) 1.107 0.930-1.317Nam et al. (2003) 0.817 0.684-0.976Hernandez et al. (2007) 1.137 0.869-1.488

MM vs. WW Chen et al. (2006) 1.223 0.630-2.375 RandomChen et al. (2006) 1.882 0.302-11.729Neuhausen et al. (2005) 0.804 0.450-1.438Schildkraut et al. (2005) 0.408 0.184-0.903Tsuchiya et al. (2005) 0.596 0.306-1.160Friedrichsen et al. (2005) 1.141 0.777-1.675Nam et al. (2003) 0.631 0.436-0.915Hernandez et al. (2007) 1.167 0.764-1.781

MW vs. WW Chen et al. (2006) 0.359 0.209-0.615 RandomChen et al. (2006) 0.425 0.069-2.613Neuhausen et al. (2005) 0.951 0.637-1.421Schildkraut et al. (2005) 1.655 0.791-3.460Tsuchiya et al. (2005) 1.409 1.044-1.902Friedrichsen et al. (2005) 1.069 0.733-1.559Nam et al. (2003) 1.463 1.021-2.096Hernandez et al. (2007) 0.961 0.550-1.676

MM vs. MW +WW Chen et al. (2006) 2.118 1.324-3.386 RandomChen et al. (2006) 2.118 0.364-12.320Neuhausen et al. (2005) 0.989 0.677-1.444Schildkraut et al. (2005) 0.512 0.261-1.003Tsuchiya et al. (2005) 0.696 0.521-0.930Friedrichsen et al. (2005) 1.024 0.715-1.466Nam et al. (2003) 0.660 0.470-0.926Hernandez et al. (2007) 1.121 0.787-1.596

MM + MW vs. WW Chen et al. (2006) 0.823 0.437-1.549 FixedChen et al. (2006) 0.898 0.363-2.226

Neuhausen et al. (2005) 0.783 0.456-1.346Schildkraut et al. (2005) 0.553 0.287-1.065Tsuchiya et al. (2005) 0.687 0.357-1.321Friedrichsen et al. (2005) 1.203 0.944-1.532Nam et al. (2003) 0.839 0.651-1.081Hernandez et al. (2007) 1.163 0.764-1.771

rs11536889(TLR4)

M vs. W Chen et al. (2005) 1.021 0.821-1.269 RandomZheng et al. (2004) 0.822 0.675-1.001Shui et al. (2012) 1.257 1.059-1.491Cheng et al. (2007) 0.835 0.641-1.087Wang et al. (2009) 0.922 0.666-1.276

MM vs. WW Chen et al. (2005) 1.506 0.670-3.383 FixedZheng et al. (2004) 1.340 0.623-2.882Shui et al. (2012) 0.855 0.508-1.439Cheng et al. (2007) 0.720 0.336-1.542Wang et al. (2009) 0.872 0.287-2.646

MW vs. WW Chen et al. (2005) 1.046 0.815-1.342 RandomZheng et al. (2004) 1.346 1.079-1.680Shui et al. (2012) 0.685 0.560-0.838Cheng et al. (2007) 1.176 0.861-1.607 FixedWang et al. (2009) 1.094 0.746-1.604

MM vs. MW +WW Chen et al. (2005) 1.004 0.788-1.278 RandomZheng et al. (2004) 1.675 1.397-2.008Shui et al. (2012) 1.013 0.827-1.242Cheng et al. (2007) 0.960 0.720-1.281Wang et al. (2009) 1.017 0.706-1.464

MM + MW vs. WW Chen et al. (2005) 1.522 0.679-3.413 FixedZheng et al. (2004) 1.426 0.664-3.061Shui et al. (2012) 0.789 0.470-1.325

Cheng et al. (2007) 0.744 0.348-1.589Wang et al. (2009) 0.895 0.297-2.702

rs4986790(TLR4)

M vs. W Chen et al. (2005) 0.938 0.668-1.317 RandomCheng et al. (2007) 0.752 0.518-1.091Wang et al. (2009) 1.511 0.885-2.580Balistreri et al. (2010) 6.319 0.823-48.492

MM vs. WW Chen et al. (2005) 1.620 0.385-6.809 FixedCheng et al. (2007) 1.925 0.174-21.310Wang et al. (2009) 1.332 0.053-33.269Balistreri et al. (2010) (Excluded)

MW vs. WW Chen et al. (2005) 1.151 0.796-1.665 RandomCheng et al. (2007) 1.428 0.963-2.118Wang et al. (2009) 0.644 0.371-1.118Balistreri et al. (2010) 0.174 0.022-1.369

MM vs. MW +WW Chen et al. (2005) 0.901 0.630-1.290 RandomCheng et al. (2007) 0.718 0.487-1.060Wang et al. (2009) 1.553 0.894-2.696Balistreri et al. (2010) 6.180 0.791-48.316

MM + MW vs. WW Chen et al. (2005) 1.642 0.391-6.897 FixedCheng et al. (2007) 2.004 0.181-22.170Wang et al. (2009) 1.217 0.049-30.373Balistreri et al. (2010) (Excluded)

rs2149356 (TLR4)M vs. W Chen et al. (2005) 1.172 0.997-1.378 Fixed

Zheng et al. (2004) 0.984 0.847-1.144Shui et al. (2012) 1.017 0.898-1.153Cheng et al. (2007) 0.935 0.781-1.119

MM vs. WW Chen et al. (2005) 1.565 1.092-2.244Zheng et al. (2004) 0.991 0.725-1.356 FixedShui et al. (2012) 1.128 0.848-1.502Cheng et al. (2007) 0.905 0.631-1.298

MW vs. WW Chen et al. (2005) 0.991 0.789-1.245Zheng et al. (2004) 1.037 0.840-1.279Shui et al. (2012) 1.058 0.891-1.256Cheng et al. (2007) 1.116 0.852-1.462

MM vs. MW +WW Chen et al. (2005) 1.107 0.893-1.372 FixedZheng et al. (2004) 0.971 0.800-1.179Shui et al. (2012) 0.978 0.831-1.151Cheng et al. (2007) 0.899 0.699-1.156

MM + MW vs. WW Chen et al. (2005) 1.559 1.105-2.198 FixedZheng et al. (2004) 1.006 0.744-1.360Shui et al. (2012) 1.157 0.879-1.524Cheng et al. (2007) 0.958 0.687-1.335

rs10759932(TLR4)

M vs. W Chen et al. (2005) 1.288 1.040-1.595 RandomZheng et al. (2004) 0.919 0.770-1.096Shui et al. (2012) 0.946 0.798-1.121Cheng et al. (2007) 0.424 0.343- 0.525

MM vs. WW Chen et al. (2005) 1.181 0.516- 2.702 RandomZheng et al. (2004) 0.664 0.347- 1.268Shui et al. (2012) 0.988 0.575-1.698Cheng et al. (2007) 0.035 0.013-0.095

MW vs. WW Chen et al. (2005) 0.727 0.569-0.928 RandomZheng et al. (2004) 1.040 0.848-1.274Shui et al. (2012) 1.079 0.885-1.314Cheng et al. (2007) 0.792 0.596-1.052

MM vs. MW +WW Chen et al. (2005) 1.363 1.073-1.731 RandomZheng et al. (2004) 0.936 0.768-1.140Shui et al. (2012) 0.933 0.771-1.128Cheng et al. (2007) 0.644 0.500-0.828

MM + MW vs. WW Chen et al. (2005) 1.086 0.476-2.479 RandomZheng et al. (2004) 0.670 0.352-1.278

Shui et al. (2012) 1.004 0.585-1.723Cheng et al. (2007) 0.033 0.012-0.089

rs1927914(TLR4)

M vs. W Chen et al. (2005) 1.151 0.980-1.353 FixedZheng et al. (2004) 1.017 0.891-1.161Song et al. (2009) 1.292 0.906-1.843

MM vs. WW Chen et al. (2005) 1.553 1.079-2.235 RandomZheng et al. (2004) 0.964 0.714-1.301Song et al. (2009) 0.719 0.270-1.914

MW vs. WW Chen et al. (2005) 1.021 0.812-1.283 RandomZheng et al. (2004) 0.919 0.762-1.107Song et al. (2009) 0.432 0.262-0.713

MM vs. MW +WW Chen et al. (2005) 3.426 2.601-4.514 RandomZheng et al. (2004) 4.475 3.451-5.803Song et al. (2009) 13.626 5.896-31.492

MM + MW vs. WW Chen et al. (2005) 1.035 0.711-1.506 RandomZheng et al. (2004) 1.757 1.379-2.238Song et al. (2009) 0.911 0.418-1.988

rs7873784(TLR4)

M vs. W Chen et al. (2005) 1.174 0.958-1.438 FixedShui et al. (2012) 1.013 0.862-1.192Cheng et al. (2007) 1.137 0.898-1.440

MM vs. WW Chen et al. (2005) 1.940 1.044-3.608 FixedShui et al. (2012) 0.816 0.444-1.500Cheng et al. (2007) 1.127 0.522-2.431

MW vs. WW Chen et al. (2005) 0.956 0.749-1.219 FixedShui et al. (2012) 0.948 0.787-1.142Cheng et al. (2007) 0.851 0.644-1.124

MM vs. MW +WW Chen et al. (2005) 1.120 0.887-1.415 FixedShui et al. (2012) 1.037 0.865-1.242Cheng et al. (2007) 1.171 0.894-1.533

MM + MW vs. WW Chen et al. (2005) 1.916 1.034-3.549 FixedShui et al. (2012) 0.805 0.438-1.477Cheng et al. (2007) 1.077 0.501-2.315

rs833061(VEGFA)

M vs. W Fukuda et al. (2007) 1.020 0.779-1.33 RandomOnen et al. (2008) 0.864 0.619-1.206Lin et al. (2003) 1.924 1.239-2.988

MM vs. WW Fukuda et al. (2007) 1.924 1.239-2.988 FixedOnen et al. (2008) 1.038 0.559-1.92Lin et al. (2003) 0.780 0.312-1.948

MW vs. WW Fukuda et al. (2007) 0.980 0.681-1.411 RandomOnen et al. (2008) 1.435 0.847-2.429Lin et al. (2003) 0.319 0.180-0.564

MM vs. MW +WW Fukuda et al. (2007) 1.024 0.726-1.444 RandomOnen et al. (2008) 0.706 0.421-1.185Lin et al. (2003) 2.946 1.687-5.143

MM + MW vs. WW Fukuda et al. (2007) 1.029 0.565-1.875 FixedOnen et al. (2008) 1.001 0.433-2.316Lin et al. (2003) 0.800 0.195-3.286

rs1570360(VEGFA)

M vs. W Sfar et al. (2006) 1.996 1.300-3.067 RandomJacobs et al. (2008) 1.064 0.904-1.252McCarron et al. (2013) 1.230 0.941-1.607

MM vs. WW Sfar et al. (2006) 3.759 1.325-10.666 RandomJacobs et al. (2008) 1.137 0.796-1.623McCarron et al. (2013) 2.153 1.114-4.164

MW vs. WW Sfar et al. (2006) 0.459 0.253-0.832 RandomJacobs et al. (2008) 0.950 0.755-1.196McCarron et al. (2013) 1.053 0.728-1.522

MM vs. MW +WW Sfar et al. (2006) 2.398 1.359-4.231 RandomJacobs et al. (2008) 1.070 0.861-1.328

McCarron et al. (2013) 1.096 0.771-1.557MM + MW vs. WW Sfar et al. (2006) 2.578 0.948-7.006 Random

Jacobs et al. (2008) 1.110 0.791-1.557McCarron et al. (2013) 2.207 1.170-4.165

rs1801278(IRS1)

M vs. W Saracevic et al. (2011) 1.462 0.514-4.160 FixedFall et al. (2008) 0.985 0.725-1.338Li et al. (2005) 1.122 0.775-1.625Neuhausen et al. (2005) 1.051 0.754-1.464

MM vs. WW Saracevic et al. (2011) 1.592 0.064-39.707 FixedFall et al. (2008) 2.216 0.445-11.026Li et al. (2005) 0.457 0.041-5.064Neuhausen et al. (2005) 0.860 0.384-1.928

MW vs. WW Saracevic et al. (2011) 0.789 0.265-2.351 FixedFall et al. (2008) 1.098 0.789-1.528Li et al. (2005) 0.841 0.567-1.247Neuhausen et al. (2005) 0.837 0.547-1.281

MM vs. MW +WW Saracevic et al. (2011) 1.374 0.466-4.049Fall et al. (2008) 0.945 0.684-1.307Li et al. (2005) 1.161 0.787-1.713Neuhausen et al. (2005) 1.130 0.758-1.684

MM + MW vs. WW Saracevic et al. (2011) 1.557 0.062-38.789 FixedFall et al. (2008) 2.242 0.451-11.150Li et al. (2005) 0.448 0.040-4.954Neuhausen et al. (2005) 0.813 0.367-1.802

rs351855(FGFR4)

M vs. W FitzGerald et al. (2009) 0.920 0.816-1.038 RandomFitzGerald et al. (2009) 0.875 0.504-1.518Ho et al. (2010) 0.843 0.682-1.041Ma et al. (2010) 1.244 1.022-1.514

MM vs. WW FitzGerald et al. (2009) 0.938 0.713-1.233 Fixed

FitzGerald et al. (2009) 1.156 0.188-7.112Ho et al. (2010) 0.900 0.540-1.501 FixedMa et al. (2010) 1.522 1.046-2.215

MW vs. WW FitzGerald et al. (2009) 1.179 0.999-1.391 RandomFitzGerald et al. (2009) 1.250 0.657-2.377Ho et al. (2010) 1.399 1.052-1.862Ma et al. (2010) 0.650 0.452-0.933

MM vs. MW +WW FitzGerald et al. (2009) 0.865 0.739-1.012 RandomFitzGerald et al. (2009) 0.825 0.444-1.534Ho et al. (2010) 0.742 0.565-0.975Ma et al. (2010) 1.532 1.098-2.137

MM + MW vs. WW FitzGerald et al. (2009) 1.012 0.778-1.316 FixedFitzGerald et al. (2009) 1.222 0.200-7.471Ho et al. (2010) 1.050 0.641-1.720Ma et al. (2010) 1.152 0.863-1.538

M: mutated allele; W: wild allele.

Table S4. P values of Egger’s test for the polymorphisms in genes of PI3K/Akt signaling pathway.

PolymorphismEgger’s test

P > |t|rs1927914 0.393rs10759932 0.704rs2149356 0.961rs4986790 0.169rs11536889 0.354rs7873784 0.370rs833061 0.463rs1570360 0.144rs1801278 0.129rs351855 0.929rs1800796 0.895rs1800795 0.016(CA)19 0.954

Figure S1. Oncoprint of genes in PI3K/Akt signaling pathway according to Cbioportal website.

Figure S2. Alteration frequencies for genes encompassed in PI3K/Akt signaling pathway according

to Cbioportal website.

Figure S3. Flow chart of studies selection process for polymorphisms in TLR4 gene.

Figure S4. Flow chart of studies selection process for polymorphisms in IL-6 gene.

Figure S5. Flow chart of studies selection process for polymorphisms in VEGF gene.

Figure S6. Flow chart of studies selection process for polymorphisms in IRS1 gene.

Figure S7. Flow chart of studies selection process for polymorphisms in FGFR4 gene.

Figure S8. Flow chart of studies selection process for polymorphisms in IGF1 gene.

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