Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Polymorphisms of PRLHR and HSPA12A and risk of gastric and colorectal cancer in the Chinese Han population

  • Qinghua Su1,
  • Yuan Wang2,
  • Jun Zhao1,
  • Cangjian Ma1,
  • Tao Wu1,
  • Tianbo Jin3, 4 and
  • Jinkai Xu1Email author
BMC Gastroenterology201515:107

https://doi.org/10.1186/s12876-015-0336-9

Received: 15 May 2015

Accepted: 12 August 2015

Published: 25 August 2015

Abstract

Background

Gastric and colorectal cancers have a major impact on public health, and are the most common malignant tumors in China. The aim of this research was to study whether polymorphisms of CHCHD3P1-HSP90AB7P, GRID1, HSPA12A, PRLHR, SBF2, POLD3 and C11orf93-C11orf92 genes are associated with the risk of gastric and colorectal cancers in the Chinese Han population.

Methods

We genotyped seven single nucleotide polymorphisms (SNPs) from seven genes. We selected 588 patients with gastric cancer and 449 with colorectal cancer, along with 703 healthy controls. All these SNPs were evaluated using the χ2 test and genetic model analysis.

Results

The genotype “A/T” of rs12413624 in PRLHR gene was associated with a decreased risk of colorectal cancer in allele model analysis [odds ratio (OR) = 0.81; 95 % confidence interval (CI) = 0.68–0.97; p = 0.018] and log-additive model analysis (OR = 0.81; 95 % CI = 0.66–0.98; p = 0.032). The genotype “A/G” of rs1665650 in HSPA12A gene was associated with a decreased risk of gastric cancer in overdominant model analysis (OR = 0.77; 95 % CI = 0.60–0.99; p = 0.038).

Conclusions

Our results provide evidence that variants of PRLHR gene are a protective factor in colorectal cancer and variants of HSPA12A gene are a protective factor in gastric cancer in the Chinese Han population.

Background

Gastric and colorectal cancers are two of the most widespread cancers worldwide [1]. Both gastrointestinal malignancies are leading causes of cancer-related death in East Asia, Eastern Europe, parts of Central and South America. With improvements in the standard of living and changes in lifestyle, food and the environment, the incidences of gastric and colorectal cancers are constantly increasing in China, where they are now the third most frequent malignancies [2, 3].

In the present study, the low-risk susceptibility markers were previously reported in genome-wide association studies as being related to the risk of digestive system cancer: rs10795668 (10p14), rs10788473 (10q23.1), rs1665650 (10q25.3), rs12413624 (10q26.11), rs10500715 (11p15.4), rs3824999 (11q13.4) and rs3802842 (11q23.1) [47].

The prolactin releasing hormone receptor (PRLHR), also known as G-protein-coupled receptor 10, is the receptor for prolactin releasing peptide (PrRP). Numerous studies suggest digestive disease was associated with regulation of feeding and a pivotal role of PrRP in the homeostatic regulation of feeding and energy balance [8]. Evidence from our group has shown that central administration of PrRP decreases feeding and body weight gain in rats and mice, without causing adverse effects [9]. HSPA12A is a member of the heat shock protein (HSP) family and a common molecule within cells that act as a chaperone in conditions of stress, including carcinogenesis [10]. Overexpression of HSPA12A might be associated with poor survival in hepatocellular carcinoma. There is a good correlation between the expression of HSPs and the resistance of cancer cells to chemotherapy [11]. GRID1 gene encodes glutamate receptor δ1, a subunit of glutamate receptor channels that mediate most of the fast excitatory synaptic transmission in the central nervous system and play key roles in synaptic plasticity [12]. SBF2 gene appears to influence the sorting and degradation of cell surface receptors, such as epidermal growth factor receptor, with resultant alterations in downstream signaling [4].

The aim of this study was to investigate the relationship between CHCHD3P1-HSP90AB7P, GRID1, HSPA12A, PRLHR, SBF2, POLD3, and C11orf93-C11orf92 genes and susceptibility to gastric and colorectal cancers in the Chinese Han population.

Methods

Ethics statement

The protocol in this study conformed to the principles of the Declaration of Helsinki and was ratified by the Ethical Committee of the Second Affiliated Hospital, Xi’an Jiaotong University School of Medicine, China.

Study population

We recruited 588 patients with gastric cancer and 449 with colorectal cancer between December 2010 and November 2014 from the Department of General Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University School of Medicine. All of the study participants were from the Chinese Han population living in the area of Xi’an. Confirmed cases were patients who were newly diagnosed and histologically confirmed. According to the recruitment and exclusion standards, we surveyed the patients using a self-designed questionnaire including demographic factors such as age, gender, and education, and potential risk factors including smoking, dietary conditions, alcohol consumption, and family history of cancer [13]. The controls were 703 healthy individuals who were selected from June 2011 to October 2014 from the Medical Examination Center, Department of General Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University School of Medicine. The controls were all Chinese Han living in Xi’an city and surrounding area. We excluded patients with chronic diseases of the kidneys, heart, liver and brain. All participants gave signed informed consent prior to participation in the study.

Genotyping

We genotyped seven single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) > 5 % in seven genes in the HapMap Asian population. Genomic DNA was stored at −20 °C and was extracted from whole blood by the phenol–chloroform extraction method. Using an extraction kit (GoldMag, China), we isolated DNA from the samples. DNA concentration was measured by spectrometry (DU530 UV/VIS spectrophotometer; Beckman Instruments, Fullerton, CA, USA). We designed the Multiplexed SNP Mass EXTEND assay using Sequenom MassARRAY Assay Design version 4.0 software [14].

Statistical analysis

The genotype frequencies of each SNP in the control subjects were checked using the Hardy–Weinberg equilibrium (HWE). Power analysis was carried out using the online calculator at http://sampsize.sourceforge.net/iface/s3.html. Data analysis was performed using SPSS version 16.0 statistical package (SPSS, Chicago, IL, USA) and Microsoft Excel. P < 0.05 was considered to represent statistical significance. Differences in the distribution were analyzed using logistic regression. The genotype frequencies of cases and controls were calculated using a χ2 test [15, 16]. Odds ratios (ORs) and 95 % confidence intervals (CIs) were tested using unconditional logistic regression analysis with adjustment for age and gender [17]. The allele, overdominant and log-additive models were applied using PLINK software (http://pngu.mgh.harvard.edu/purcell/plink/) to assess the association of SNPs with the risk of gastric and colorectal cancers.

Results

The 588 gastric cancer cases comprised 392 men and 196 women with a mean age of 58.12 ± 11.66 years. The 449 colorectal cancer cases comprised 260 men and 189 women with a mean age of 59.09 ± 11.78 years. The 703 healthy controls comprised 396 men and 307 women with a mean age of 48.57 ± 9.43 years. We found no differences between gender and age distribution. The characteristics of the patients and controls are shown in Table 1. The primers of the seven selected SNPs are shown in Table 2, which were designed by Sequenom MassARRAY Assay Design 4.0 Software [14]. Seven SNPs in seven genes were analyzed in this study. SNP ID, gene, HWE test results, minor/major alleles, and MAF of cases and controls of all the SNPs are shown in Table 3. The minor allele of each SNP, a risk factor, was compared with the wild-type allele.
Table 1

Demographic characteristics of patients with gastric and colorectal cancers, and controls

Group (N)

Age (years)

Gender (male/female)

P valuea

P valueb

Healthy controls (N = 703)

48.57 ± 9.43

396/307

Gastric cancer cases (N = 588)

58.12 ± 11.66

392/196

0.21

0.54

Colorectal cancer cases (N = 449)

59.09 ± 11.78

260/189

0.32

0.25

aP value is based on the age versus healthy controls in the study

bP value is based on the gender versus healthy controls in the study

Table 2

Primers used for this study

SNP ID

1st – PCR primer sequences

2nd – PCR primer sequences

UEP sequences

rs10795668

ACGTTGGATGAATACTTGTACCTTGGTGGG

ACGTTGGATGTCATCTATGAGCAGCAGCAG

gcGAAAGAGAAAAAGTTAGATTCTTA

rs10788473

ACGTTGGATGCAGGAAGTGACAGCTATCTC

ACGTTGGATGGGCTTCATTGGGAGCTAGTG

ggggaTCCAAGCTACGGCTCACCTGG

rs1665650

ACGTTGGATGCCAACTGAGGATGATTTGAC

ACGTTGGATGGGTTGTTTGGCTACTCAAAG

ctccAAATGTCTATCGCCTTTAC

rs12413624

ACGTTGGATGGCTAGGTGTGGCACTGTTTG

ACGTTGGATGTTATGCAACTGGTCCTGGTC

tgggtTGGTCCTGGTCAGATGTTAT

rs10500715

ACGTTGGATGAGGCTTGAGATTTGGAAGGC

ACGTTGGATGCCATCTTTAGATCTTCTCTC

cttTTTAGATCTTCTCTCAGTCTA

rs3824999

ACGTTGGATGCTAAATCCCCTTTGCTGGAC

ACGTTGGATGGATCAGAGAACTACAAGCAC

TTCTCCATTGGTTCTCTAA

rs3802842

ACGTTGGATGCATCGTTTTGTTAGGAAGAC

ACGTTGGATGGGCCCCTAAAATGAGGTGAA

aagGAGGTGAATTTCTGGGA

PCR polymerase chain reaction, UEP unextended mini-sequencing primer

Table 3

Basic information of candidate SNPs in this study

SNP ID

Gene

HWE p value

Alleles A/B

MAF control

MAF case

Gastric cancer

Colorectal cancer

rs10795668

CHCHD3P1–HSP90AB7P

0.1739

A/G

0.384

0.369

0.348

rs10788473

GRID1

0.7497

T/C

0.383

0.391

0.378

rs1665650

HSPA12A

1

A/G

0.312

0.316

0.33

rs12413624

PRLHR

0.3968

A/T

0.431

0.405

0.381

rs10500715

SBF2

0.9056

G/T

0.198

0.212

0.205

rs3824999

POLD3

0.7421

C/A

0.361

0.346

0.391

rs3802842

C11orf92–C11orf93

0.3986

C/A

0.435

0.441

0.478

A/B stands for minor/major alleles on the control sample frequencies

SNPs are excluded at 5 % HWE P level

Further model association analyses used logistic tests including allele model, overdominant model and log-additive model (Table 4). The genotype “A/T” of rs12413624 is associated with a decreased risk of colorectal cancer by allele model analysis (OR = 0.81; 95 % CI = 0.68–0.97; p = 0.018) and log-additive model analysis (OR = 0.81; 95 % CI = 0.66–0.98; p = 0.032). The genotype “A/G” of rs1665650 was associated with a decreased risk of gastric cancer risk by overdominant model analysis (OR = 0.77; 95 % CI = 0.60–0.99; p = 0.038).
Table 4

Association of SNPs with risk of gastric and colorectal cancers based on logistic tests adjusted by gender and age

SNP ID

Model

Genotype

Gastric cancer

Colorectal cancer

OR (95 % CI)

P value

OR (95 % CI)

P value

rs10795668

Allele model

A/G

0.94

(0.80–1.10)

0.419

0.86

(0.70–1.02)

0.082

 

Overdominant model

A/G

0.97

(0.76–1.23)

0.78

0.89

(0.68–1.17)

0.41

 

Log - additive model

0.95

(0.80–1.13)

0.56

0.86

(0.71–1.04)

0.12

rs10788473

Allele model

T/C

1.04

(0.88–1.22)

0.655

0.98

(0.82–1.17)

0.817

 

Overdominant model

T/C

0.86

(0.68–1.10)

0.24

1.06

(0.81–1.39)

0.66

 

Log - additive model

1.04

(0.88–1.24)

0.63

0.91

(0.75–1.11)

0.34

rs1665650

Allele model

A/G

1.02

(0.86–1.21)

0.85

1.09

(0.91–1.30)

0.363

 

Overdominant model

A/G

0.77

(0.60–0.99)

0.038*

1.04

(0.80–1.36)

0.78

 

Log - additive model

1

(0.83–1.20)

0.99

1.16

(0.95–1.42)

0.15

rs12413624

Allele model

A/T

0.9

(0.77–1.05)

0.191

0.81

(0.68–0.96)

0.018*

 

Overdominant model

A/T

0.91

(0.72–1.17)

0.47

0.9

(0.69–1.18)

0.44

 

Log - additive model

0.93

(0.77–1.11)

0.39

0.81

(0.66–0.98)

0.032*

rs10500715

Allele model

G/T

1.09

(0.90–1.32)

0.387

1.04

(0.85–1.28)

0.699

 

Overdominant model

G/T

1.1

(0.85–1.42)

0.48

0.94

(0.71–1.26)

0.69

 

Log - additive model

1.07

(0.87–1.32)

0.53

1.05

(0.83–1.32)

0.7

rs3824999

Allele model

C/A

0.94

(0.80–1.10)

0.432

1.14

(0.95–1.35)

0.151

 

Overdominant model

C/A

0.81

(0.64–1.04)

0.1

0.98

(0.75–1.28)

0.9

 

Log - additive model

0.92

(0.77–1.10)

0.37

1.13

(0.93–1.37)

0.21

rs3802842

Allele model

C/A

1.02

(0.88–1.20)

0.774

1.19

(1.00–1.40)

0.541

 

Overdominant model

C/A

0.99

(0.77–1.26)

0.91

1.09

(0.84–1.42)

0.52

 

Log - additive model

0.96

(0.81–1.14)

0.68

1.14

(0.94–1.37)

0.18

*p < 0.05, statistical significance

Discussion

Gastric and colorectal cancers are the most frequent malignancies diagnosed worldwide and the most common cause of cancer mortality in China [18]. Environmental components are risk factors for the development of gastric and colorectal cancers, such as Helicobacter Pylori infection, salted food intake, changed lifestyle and smoking, and their mortality rates are continually increasing in China [19, 20].

In this study, we showed that the PRLHR gene, which is mapped to chromosome 10q26.11, contained an SNP (genotype “A/T” of rs12413624) associated with a increased risk of colorectal cancer. PRLHR is the receptor for PrRP (also known as G-protein-coupled receptor 10) and has pivotal functions in press hormone release and feeding behavior [21]. PrRP, a hormone, may be secreted from peripheral tissues (pancreas, placenta, adrenal) upon the anterior pituitary, or may be secreted from hypophysiotropic neurons by an indirect pivotal mechanism [22]. It was also reported that PRLHR, as well as involvement in the physiological responses to central dministration of PrRP, may play roles in other processes, such as feeding behavior, pathogenesis of uterine fibroids, energy expenditure, obesity and the pivotal control of blood pressure [8, 23, 24]. According to previous reports, rs12413624 is associated with pancreatic ductal adenocarcinoma risk in individuals of European descent but not in Japanese and Chinese populations [25]. We discovered the relationship between rs12413624 in the PRLHR gene and colorectal cancer in the allele and the log-additive models. However, we did not find any correlation between the PRLHR gene and gastric cancer. It is necessary to study the biological functions of the PRLHR gene in further research.

We genotyped “A/G” of rs1665650 in HSPA12A gene, which is mapped to chromosome 10q25.3, and associated with a decreased risk of gastric cancer. HSPA12A, heat shock 70-kDa protein 12A, is a novel and atypical member of the HSP70 family in animals. Its effects are diverse and include involvement in the development of atherosclerotic lesions in mice [26]. Cancer cells experience high levels of proteotoxic stress and rely upon stress response pathways for survival and proliferation, thereby becoming dependent on proteins such as stress-inducible HSPs. It is reported that overexpression of HSPA12A in hepatocellular carcinoma tissues is significantly related to poor survival [11]. During carcinogenesis, expression of HSPs is altered in many tumor types. Increased levels of HSPA are related to malignancy, metastasis, poor prognosis, and resistance to therapeutic strategies, including chemotherapy or radiation in glioblastoma, and breast, bladder, endometrial and cervical carcinomas [2729]. It is also reported that the associations between rs1665650, rs3824999 and colorectal cancer are not strongly modified by gender, alcohol, smoking, aspirin, and various dietary factors [30]. Our results showed that rs1665650 in HSPA12A gene is correlated with gastric cancer risk, and we did not find a significant association with the risk of colorectal cancer. Further research should use a larger number of samples and focus on understanding the mechanisms by which HSPA12A gene influences pathogenesis and progression.

The rs10795668 in CHCHD3P1-HSP90AB7P gene is associated with the risk of colorectal cancer in Poland, Estonia, Lithuania and Latvia [31]. Somatic exonuclease domain mutations in POLE gene have been identified in colorectal and endometrial cancer patients, and show an association with hypermutability and microsatellite stability [32]. In both population that included cases of European descent and in a combined analysis with cases from China, SNPs in the SBF2 gene were associated with survival time among patients with pancreatic adenocarcinoma [7]. However, we did not find that SNPs and genes were associated with gastric or colorectal cancers in our study.

There were several limitations to our study. First, all the samples were from the Chinese Han population living in Xi’an city or its surrounding area and from the same hospital. There were a substantial number of confounding factors that may have caused type I errors (false-positive results) in our association study. Second, we also performed Bonferroni correction of the 21 tests and found no significant results. However, the main weakness of Bonferroni correction is that the interpretation of a finding depends on the number of other tests performed. True important differences may be deemed nonsignificant since the likelihood of type II errors also increased [33]. Finally, our samples included 1037 cases (588 gastric cancer and 449 colorectal cancer) and 703 healthy controls and we performed a power analysis that showed that the power of seven SNPs was < 0.75. The sample size was not large enough for association studies and a larger sample size is required to confirm our findings.

Conclusions

This study shows that PRLHR gene is a protective factor in colorectal cancer and HSPA12A gene is a protective factor in gastric cancer. We demonstrated a relationship between polymorphisms of PRLHR and HSPA12A gene and the risk of gastric and colorectal cancers in the Chinese Han population.

Notes

Abbreviations

PRLHR: 

Receptor for prolactin releasing peptide

HSPA12A: 

Heat shock protein 12A

SNPs: 

Single nucleotide polymorphisms

MAF: 

Minor allele frequency

HWE: 

Hardy - Weinberg equilibrium

ORs: 

Odds ratios

CIs: 

Confidence intervals

PCR: 

Polymerase chain reaction

UEP: 

Unextended mini-sequencing primer

Declarations

Acknowledgements

This work was supported by the National 863 High-Technology Research and Development Program (No. 2012AA02A519). We are also grateful to the clinicians and other hospital staff who contributed to the blood sample and data collection for this study.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of General Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University School of Medicine
(2)
Inner Mongolia Medical University
(3)
National Engineering Research Center for Miniaturized Detection Systems
(4)
School of Life Sciences, Northwest University

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29.View ArticlePubMedGoogle Scholar
  2. Akanuma N, Hoshino I, Akutsu Y, Murakami K, Isozaki Y, Maruyama T, et al. MicroRNA-133a regulates the mRNAs of two invadopodia-related proteins, FSCN1 and MMP14, in esophageal cancer. Br J Cancer. 2014;110(1):189–98.View ArticlePubMedGoogle Scholar
  3. Siegel R, Desantis C, Jemal A. Colorectal cancer statistics, 2014. CA Cancer J Clin. 2014;64(2):104–17.View ArticlePubMedGoogle Scholar
  4. Wu C, Kraft P, Stolzenberg-Solomon R, Steplowski E, Brotzman M, Xu M, et al. Genome-wide association study of survival in patients with pancreatic adenocarcinoma. Gut. 2014;63(1):152–60.View ArticlePubMedGoogle Scholar
  5. Fernandez-Rozadilla C, Cazier JB, Tomlinson IP, Carvajal-Carmona LG, Palles C, Lamas MJ, et al. A colorectal cancer genome-wide association study in a Spanish cohort identifies two variants associated with colorectal cancer risk at 1p33 and 8p12. BMC Genomics. 2013;14:55.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Dunlop MG, Dobbins SE, Farrington SM, Jones AM, Palles C, Whiffin N, et al. Common variation near CDKN1A, POLD3 and SHROOM2 influences colorectal cancer risk. Nat Genet. 2012;44(7):770–6.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Panagiotou OA, Ioannidis JP, Genome-Wide Significance P. What should the genome-wide significance threshold be? Empirical replication of borderline genetic associations. Int J Epidemiol. 2012;41(1):273–86.View ArticlePubMedGoogle Scholar
  8. Varghese BV, Koohestani F, McWilliams M, Colvin A, Gunewardena S, Kinsey WH, et al. Loss of the repressor REST in uterine fibroids promotes aberrant G protein-coupled receptor 10 expression and activates mammalian target of rapamycin pathway. Proc Natl Acad Sci U S A. 2013;110(6):2187–92.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Dodd GT, Luckman SM. Physiological roles of GPR10 and PrRP signaling. Front Endocrinol. 2013;4:20.View ArticleGoogle Scholar
  10. Zhu Y, Ren C, Wan X, Zhu Y, Zhu J, Zhou H, et al. Gene expression of Hsp70, Hsp90 and Hsp110 families in normal palate and cleft palate during mouse embryogenesis. Toxicol Ind Health. 2013;29(10):915–30.View ArticlePubMedGoogle Scholar
  11. Yang Z, Zhuang L, Szatmary P, Wen L, Sun H, Lu Y, et al. Upregulation of heat shock proteins (HSPA12A, HSP90B1, HSPA4, HSPA5 and HSPA6) in tumour tissues is associated with poor outcomes from HBV-related early-stage hepatocellular carcinoma. Int J Med Sci. 2015;12(3):256–63.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Yamazaki M, Araki K, Shibata A, Mishina M. Molecular cloning of a cDNA encoding a novel member of the mouse glutamate receptor channel family. Biochem Biophys Res Commun. 1992;183(2):886–92.View ArticlePubMedGoogle Scholar
  13. Wen YY, Pan XF, Loh M, Tian Z, Yang SJ, Lv SH, et al. ADPRT Val762Ala and XRCC1 Arg194Trp polymorphisms and risk of gastric cancer in Sichuan of China. Asian Pac J Cancer Prev. 2012;13(5):2139–44.View ArticlePubMedGoogle Scholar
  14. Trembizki E, Smith H, Lahra MM, Chen M, Donovan B, Fairley CK, et al. High-throughput informative single nucleotide polymorphism-based typing of Neisseria gonorrhoeae using the Sequenom MassARRAY iPLEX platform. J Antimicrob Chemother. 2014;69(6):1526–32.View ArticlePubMedGoogle Scholar
  15. Kochl S, Niederstatter H, Parson W. DNA extraction and quantitation of forensic samples using the phenol-chloroform method and real-time PCR. Methods Mol Biol. 2005;297:13–30.PubMedGoogle Scholar
  16. Adamec C. [Example of the use of the nonparametric test. Test X2 for comparison of 2 independent examples]. Ceskoslovenske zdravotnictvi. 1964;12:613–9.PubMedGoogle Scholar
  17. Bland JM, Altman DG. Statistics notes. The odds ratio. BMJ. 2000;320(7247):1468.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Atoum MF, Tchoporyan MN. Association between circulating vitamin D, the Taq1 vitamin D receptor gene polymorphism and colorectal cancer risk among Jordanians. Asian Pac J Cancer Prev. 2014;15(17):7337–41.View ArticlePubMedGoogle Scholar
  19. Wan DS. [Epidemiologic trend of and strategies for colorectal cancer]. Ai zheng = Aizheng = Chinese journal of cancer. 2009;28(9):897–902.View ArticlePubMedGoogle Scholar
  20. D’Elia L, Galletti F, Strazzullo P. Dietary salt intake and risk of gastric cancer. Cancer Treat Res. 2014;159:83–95.View ArticlePubMedGoogle Scholar
  21. Samson WK, Taylor MM. Prolactin releasing peptide (PrRP): an endogenous regulator of cell growth. Peptides. 2006;27(5):1099–103.View ArticlePubMedGoogle Scholar
  22. Morales T, Sawchenko PE. Brainstem prolactin-releasing peptide neurons are sensitive to stress and lactation. Neuroscience. 2003;121(3):771–8.View ArticlePubMedGoogle Scholar
  23. Samson WK, Resch ZT, Murphy TC. A novel action of the newly described prolactin-releasing peptides: cardiovascular regulation. Brain Res. 2000;858(1):19–25.View ArticlePubMedGoogle Scholar
  24. Bjursell M, Lenneras M, Goransson M, Elmgren A, Bohlooly YM. GPR10 deficiency in mice results in altered energy expenditure and obesity. Biochem Biophys Res Commun. 2007;363(3):633–8.View ArticlePubMedGoogle Scholar
  25. Campa D, Rizzato C, Bauer AS, Werner J, Capurso G, Costello E, et al. Lack of replication of seven pancreatic cancer susceptibility loci identified in two Asian populations. Cancer Epidemiol Biomarkers Prev. 2013;22(2):320–3.View ArticlePubMedGoogle Scholar
  26. Han Z, Truong QA, Park S, Breslow JL. Two Hsp70 family members expressed in atherosclerotic lesions. Proc Natl Acad Sci U S A. 2003;100(3):1256–61.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Syrigos KN, Harrington KJ, Karayiannakis AJ, Sekara E, Chatziyianni E, Syrigou EI, et al. Clinical significance of heat shock protein-70 expression in bladder cancer. Urology. 2003;61(3):677–80.View ArticlePubMedGoogle Scholar
  28. Piura B, Rabinovich A, Yavelsky V, Wolfson M. [Heat shock proteins and malignancies of the female genital tract]. Harefuah. 2002;141(11):969–72. 1010, 1009.PubMedGoogle Scholar
  29. Thanner F, Sutterlin MW, Kapp M, Rieger L, Kristen P, Dietl J, et al. Heat-shock protein 70 as a prognostic marker in node-negative breast cancer. Anticancer Res. 2003;23(2A):1057–62.PubMedGoogle Scholar
  30. Kantor ED, Hutter CM, Minnier J, Berndt SI, Brenner H, Caan BJ, et al. Gene-environment interaction involving recently identified colorectal cancer susceptibility Loci. Cancer Epidemiol Biomarkers Prev. 2014;23(9):1824–33.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Serrano-Fernandez P, Dymerska D, Kurzawski G, Derkacz R, Sobieszczanska T, Banaszkiewicz Z, et al. Cumulative small effect genetic markers and the risk of colorectal cancer in Poland, Estonia, Lithuania, and Latvia. Gastroenterol Res Pract. 2015;2015:204089.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Briggs S, Tomlinson I. Germline and somatic polymerase epsilon and delta mutations define a new class of hypermutated colorectal and endometrial cancers. J Pathol. 2013;230(2):148–53.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Perneger TV. What’s wrong with Bonferroni adjustments. BMJ. 1998;316(7139):1236–8.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© Su et al. 2015

Advertisement