Open Access

Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

  • Zoi Lanara1, 2,
  • Efstathia Giannopoulou3,
  • Marta Fullen4,
  • Evangelos Kostantinopoulos2,
  • Jean-Christophe Nebel4,
  • Haralabos P Kalofonos3,
  • George P Patrinos2 and
  • Cristiana Pavlidis2Email author
Contributed equally
Human Genomics20137:14

DOI: 10.1186/1479-7364-7-14

Received: 25 March 2013

Accepted: 1 May 2013

Published: 5 June 2013

Abstract

A large number of common disorders, including cancer, have complex genetic traits, with multiple genetic and environmental components contributing to susceptibility. A literature search revealed that even among several meta-analyses, there were ambiguous results and conclusions. In the current study, we conducted a thorough meta-analysis gathering the published meta-analysis studies previously reported to correlate any random effect or predictive value of genome variations in certain genes for various types of cancer. The overall analysis was initially aimed to result in associations (1) among genes which when mutated lead to different types of cancer (e.g. common metabolic pathways) and (2) between groups of genes and types of cancer. We have meta-analysed 150 meta-analysis articles which included 4,474 studies, 2,452,510 cases and 3,091,626 controls (5,544,136 individuals in total) including various racial groups and other population groups (native Americans, Latinos, Aborigines, etc.). Our results were not only consistent with previously published literature but also depicted novel correlations of genes with new cancer types. Our analysis revealed a total of 17 gene-disease pairs that are affected and generated gene/disease clusters, many of which proved to be independent of the criteria used, which suggests that these clusters are biologically meaningful.

Keywords

Cancer Meta-analysis Gene Association Interaction Single-nucleotide polymorphism Alleles Clustering

Introduction

Cancer is the result of a complicated process that involves the accumulation of both genetic and epigenetic alterations in various genes [1]. The somatic genetic alterations in cancer include point mutations, small insertion/deletion events, translocations, copy number changes and loss of heterozygosity [2]. These changes either augment the action and/or expression of an oncoprotein or silence tumour suppressor genes. Single-nucleotide polymorphism (SNP) is the most common form of genetic variation in the human genome. Although common SNPs for disease prediction are not ready for widespread use [3], recent genome-wide association studies (GWASs) using high-throughput techniques have identified regions of the genome that contain SNPs with alleles that are associated with increased risk for cancer such as FGFR2 in breast cancer [47].

The knowledge on gene mutations that predispose tumour initiation or tumour development and progress will give an advantage in cancer patients' treatment. Despite the complexity and variability of cancer genome, numerous studies have examined the correlation of genome variation with cancer development and progression [8]. However, ambiguous results have been generated from the attempt to link genome variants with cancer prediction or detection. A literature search revealed that even among several meta-analyses, there were unclear results and conclusions.

We have, therefore, conducted a thorough meta-analysis of meta-analysis studies previously reported to correlate the random effect or predictive value of genome variations in certain genes for various types of cancer. The aim of the overall analysis was the detection of correlations (1) among genes whose mutation might lead to different types of cancer (e.g. common metabolic pathways) and (2) between groups of genes and types of cancer.

Methods

We performed a thorough field synopsis by studying published meta-analysis studies involving the association of various types of cancer with SNPs located in certain genomic regions. For each published meta-analysis included in our study, we also investigated the number of patients (cases) and controls, date, type of study, study group details (e.g. gender, race, age, etc.), measures included, allele and genotype frequency and also the outcome of each study, i.e. if there was an association or not, the interactions noticed in each of these studies, etc.

We have meta-analysed 150 meta-analysis articles (Additional file 1), which included 4,474 studies, 2,452,510 cases and 3,091,626 controls (5,544,136 individuals in total). The meta-analyses that have been meta-analysed included various racial groups, e.g. Caucasians, Far Eastern populations (Asian, Chinese, Japanese, Korean, etc.), African-American and other population groups (native Americans, Latinos, Aborigines, etc.). Three types of studies were included: (1) pooled analysis, (2) GWAS and (2) other studies, e.g. search in published reports. Collected data consisted of a list of genes, genomic variants and diseases with a known genotype-phenotype association (whether or not a given variation has an impact on susceptibility to a given disease). The principle of our study was to use data mining techniques to find groups (referred to as clusters hereafter) of genes or diseases that behave similarly according to related data. Such groupings will make it possible to find different cancer types susceptible to similar genotypes as well as different genes associated to similar cancer types. Furthermore, our approach would facilitate predicting whether susceptibility to one type of cancer may be indicative of predisposition to another cancer type. Moreover, the association between a group of genes and a given phenotype may suggest that these genes interact or belong to the same biochemical pathway. In order to allow data mining analysis, genotype-phenotype associations had to be classified within a fixed set of categories, i.e. yes/small yes/may/no. Moreover, genes or diseases with fewer than two entries were not considered in our analysis since their clustering would not be meaningful.

Then, data were processed using a state-of-the-art general purpose clustering tool, CLUTO [9]. Data analysis consisted in finding the tightest and most reliable groupings. Since CLUTO offers a wide range of methods, and many different scoring schemes can be used to estimate similarity between genotypes or phenotypes, cluster reliability was assessed by their robustness to clustering criteria (details are provided in Additional file 1). As a consequence, each putative association has been qualified as either ‘highly consistent’ or ‘moderately consistent’. The biological significance of those clusters was, first, evaluated using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) [10, 11], a biological database and web resource of known and predicted protein-protein interactions. The STRING database contains information from numerous sources, including experimental data, computational prediction methods and public text collections. It is widely accessible, and it is regularly updated. Second, literature research was performed to complete this initial evaluation.

Results and discussion

In this study, we performed a meta-analysis of published meta-analysis studies to investigate possible correlations among genes and SNPs and various types of cancer, as well as among gene-gene and/or gene-environmental interactions. Furthermore, an advanced literature research was applied in order to evaluate our results obtained from our meta-analysis. Our data were not only consistent with previously published literature but we have also depicted novel correlations of genes with new types of cancer. Our analysis showed a total of ten cancer-related genes that are affected (Table 1).
Table 1

Summary of genes and SNPs identified by meta-analysis to be positively correlated with various cancers

Gene

Cancer type

SNPs

References

Supporting references

  

rs number

Other name

  

ERCC2

BC

rs13181

p.K715Q

[12, 13]

[1417]

ERCC2

BC

rs1799793

p.D312N

[12, 18]

[1416]

ERCC2

LC

rs13181

p.K751Q

[19, 20]

[17, 21, 22]

ERCC2

LC

rs1799793

p.D312N

[23]

[17, 21, 22]

CCND1

BC

rs603965

c.870G>A

[24]

[2531]

CYP2E1

CRC

rs3813867

NA

[32]

[3241]a

CYP2E1

HNC

rs3813867

NA

[42, 43]

[44]

CYP2E1

HNC

rs6413432

NA

[42]

[44]

GSTP1

CRC

rs1695

p.I105V

[45]

[39, 4655]

IL6

BC

rs1800795

c.-174G>C

[56, 57]

 

MTHFR

GC

rs1801131

c.1298A>C

[58]

[59, 60]b

MTHFR

BC

rs1801131

c.677C>T, c.1298A>C

[61, 62]

[63, 64]

SOD2

BC

rs4880

p.V16A, p.A9V

[62, 65, 66]

 

TGFB1

BC

rs1800469

NA

[6769]

 

TGFB1

BC

rs1800470

NA

[67, 7073]

 

TGFB1

BC

rs1982073

NA

[74]

[64, 7577]

TP53

BC

rs1042522

p.R72P

[78, 79]

[8094]

TP53

UBC

rs1042522

p.R72P

[95]

[96100]

TP53

CRC

rs1042522

p.R72P

[78, 101103]

[104108]

TP53

CRC

rs17878362

NA

[78]

[104108]

TP53

EC

rs1042522

p.R72P

[109, 110]

[111]

TP53

LC

rs1042522

p.R72P

[78]

[112117]

TP53

LC

rs17878362

NA

[78]

[112117]

VEGFA

BC

rs3025039, rs699947

c.936C>T, c.-2578C>A

[20, 45, 118, 119]

[120]

These findings are supported by the published literature. aFor a different SNP (rs1329149); bfor c.677C>T and c.1298A>C. NA not available.

Correlation of SNPs' genes with various types of cancer

The association highlighted by our meta-analysis between the CYP2E1 gene and colorectal cancer (CRC), head and neck cancer (HNC) and liver cell carcinoma (LLC) is supported by published data [3339, 44, 121]. An additional literature search to evaluate our initial results revealed novel correlations of the gene combination CYP2E1 and GSTM1 with prostate cancer (PC) susceptibility, lung cancer (LC) and bladder cancer (UBC) as shown in Table 2[126128]. A similar correlation was found in CRC using a knockdown model [32, 40, 41]. Studies not only confirm the possibility of association between the CCND1 gene and breast cancer (BC) [25] but also suggest involvement with squamous cell carcinoma (SCC), oesophageal cancer (EC), oral cancer (OC) and malignant glioma (MG), as arisen from the interaction between the CCND1 and CCND3 genes [26, 122124]. This is further corroborated in mouse model studies that show association of CCND1 with BC [25, 2731, 153] and PC [125].
Table 2

Summary of genes and SNPs identified by further literature search as positively correlated with various cancers

Gene

Cancer type

SNPs

References

  

rs number

Other name

 

CCND1

OC

rs603965

c.870G>A

[26, 122124]

CCND1

PC

rs603965

c.870G>A

[125]

CYP2E1

PC

NA

NA

[126]

CYP2E1

LC

NA

NA

[127]

CYP2E1

UBC

NA

NA

[128]

CYP2E1

OC

NA

NA

[40]

ERCC2

OC

rs1799793, rs13181

p.D312N, p.K751Q

[23]

ERCC2

HNC

rs1799793, rs13181

p.D312N, p.K751Q

[129131]

GSTP1

PC

rs1695

p.I105V

[126, 128, 132, 133]

MTHFR

BCC

rs1801131

c.677C>T, c.1298A>C

[134]

MTHFR

ALL

rs1801131

c.677C>T, c.1298A>C

[59, 135, 136]

MTHFR

LC

rs1801131

c.677C>T, c.1298A>C

[137]

MTHFR

UBC

rs1801131

c.677C>T, c.1298A>C

[138]

MTHFR

CC

rs1801131

c.677C>T, c.1298A>C

[139]

MTHFR

NHL

rs1801131

c.677C>T, c.1298A>C

[140, 141]

MTHFR

HNC

rs1801131

c.677C>T, c.1298A>C

[142]

TGFB1

GC

rs1982073

c.+29C>T

[143]

TGFB1

LC

rs1982073

c.+29C>T

[144]

TGFB1

PC

rs1982073

c.+29C>T

[145]

TGFB1

PC

rs1982073

c.+29C>T

[146]

TGFB1

CRC

rs1982073

c.+29C>T

[147]

TP53

EmCa

rs1042522/rs17878362

p.R72P

[148]

TP53

PC

rs1042522/rs17878362

p.R72P

[114, 149]

TP53

OVCa

rs1042522/rs17878362

p.R72P

[150]

TP53

GC

rs1042522/rs17878362

p.R72P

[151]

TP53

OC

rs1042522/rs17878362

p.R72P

[152]

NA not available.

Moreover, as far as the ERCC2 is concerned along with the association of ERCC1 gene with BC and LC which is already confirmed [1417, 21, 22], we have also identified from our further literature search on humans the existence of an association with OC [26] and with HNC [129131]. There were no similar mouse studies that could confirm or overrule our findings.

Our findings regarding the GSTP1 gene are confirmed by the published literature [39, 4655]. Furthermore, we have noticed an association with PC derived from the combination of GSTM1 and CYP1A1[126, 128, 132, 133]. Likewise, previous experimental evidence supports the association we found between the MTHFR gene and BC, basal cell carcinoma (BCC) [63, 134] and gastric cancer (GC) [59, 60]. An association was also found between MTHFR gene with other types of cancer, such as acute lymphoblastic leukaemia (ALL) [135, 136, 154], LC [137], UBC coming from interaction between CTH and GSTM1[138], CRC [139], non-Hodgkin's lymphoma (NHL) [140, 141], BC [64] and HNC [142]. Specifically, in the case of NHL, the gene combination of MTHFR and TYMS might influence the susceptibility to NHL[140, 141].

Concerning TGFB1, apart from the BC [64] that was confirmed from the results of our further literature search on humans and on mouse model [75, 76], we have noticed also the following associations with gastric dysplasia, LC, pancreatic cancer (PanC) and BC [77, 143146]. Also, an association of TGFB1 with CRC was found using a mouse model [147].

In addition for TP53 gene, we have observed in the results of our meta-analysis that it is associated with BC, UBC, CRC, EC and LC [8087, 96100, 104108, 111113, 149]. We have observed also that TP53 gene might be associated with OC [88, 148], too. Concerning the literature research on knockout mice, we have confirmed the associations with BC [8994] and LC [114117], and we have found also associations with ovarian cancer (OVCa) [150], GC [151] and OC [152]. Moreover for the VEGFA gene, based on further literature TGFB1 research, we have confirmed the association with BC [120], but we had not found any other evidence supporting the association with other types of cancer.

Correlations between groups of genes and various types of cancer

We have examined and confirmed the highly consistent gene clustering results over further literature search via STRING. Our search revealed additional types of cancer, except from the types that we have studied in our meta-analysis that seems to be related with pair of genes. STRING database reports binding interaction between GSTP1 and GSTM1 genes, activating interaction between MMP2 and EGF genes, between VEGFA and IL1B genes and between MMP-9 and IL8 genes (Table 3). The application of our machine learning method has highlighted that those pair of genes have similar association profiles and, therefore, might be involved in the same pathways. The genes that do not appear in the associations do not probably correlate with the presence of a certain type of cancer.
Table 3

Putative gene-gene associations with various cancer types

Gene associations

Considered phenotypes

Comments

STRING confirmation

Literature confirmation

Gene 1

Gene 2

    

GSTP1

GSTM1

4

 

Binding interaction

[Reference]: study type

TGFB1

IL6

5

4 of 5 based on ‘yes’

  

MMP2

EGF

3

Based on ‘yes’

Activating interaction

 

VEGFA

IL1B

2

 

Activating interaction

 

MMP9

IL8

4

Based on ‘may’

Activating interaction

KEGG: same process

MMP1

MMP3

5

Based on ‘may’

  
First, in our meta-analyses, we observed that the interaction between IL6 and TGFB1 genes was associated to the following types of cancer: BC, CRC, GC, LC and PC as shown in Table 4. Although further literature search on humans could not validate our highly consistent results, we discovered that these interactions are associated to additional types of cancer, such as HNC [187], CRC [158], renal cancer (RC), small cell lung cancer [188], malignant melanoma (MM) [189192] and OVCa [193]. Additionally, regarding our further research on the interaction between IL6 and TGFB1 genes on mouse models, we have confirmed our initial results principally for BC [155157] and PC [159] and have noticed associations with epithelial cancer [194], skin tumour [195], LC [196], OVCa and cervical cancer (CC) [197, 198] and HNSCC [199]. Second, we found that the interaction between MMP-2 and EGF was associated with LC, BC and GC (Table 4). Subsequently with a further literature search, we confirmed the association with BC osteolysis [163, 164] and also found new associations with EC [200], LC, RC and PC [162]. Furthermore, in some cases, we have observed the association of the aforementioned genes with OSCC [201]. In this study, EGF induced MMP-1 expression that is required for type I collagen degradation. In addition, MMP-1 is also associated with human papillomavirus [202] and BC [165].
Table 4

Summary of gene-gene interactions and the corresponding SNPs in these genes

Gene 1

Gene 2

Cancer type

SNP's gene 1

SNP's gene 2

References (gene 1)

References (gene 2)

Supporting references

   

rs number

Other name

rs number

Other name

   

IL6

TGFB1

BC

rs1800795

c.-174G>C

rs1800469, rs1800470

c.-509C>T, p.T29C

[56]

[6770, 7274]

[155157]

IL6

TGFB1

CRC

rs1800795

c.-174G>C

rs1800470

p.T29C

[57]

[71]

[158]

IL6

TGFB1

GC

rs1800795

c.-174G>C

rs1800470

p.T29C

[57]

[71]

 

IL6

TGFB1

LC

rs1800795

c.-174G>C

rs1800470

p.T29C

[57]

[71]

 

IL6

TGFB1

PC

rs1800795

c.-174G>C

rs1800470

p.T29C

[57]

[71]

[159]

MMP2

EGF

LC

rs2438650

c.-1306C>T

rs4444903

c.61A>G

[160]

[161]

[162]

MMP2

EGF

BC

rs2438650

c.-1306C>T

rs4444903

c.61A>G

[160]

[161]

[163165]

MMP2

EGF

GC

rs2438650

c.-1306C>T

rs4444903

c.61A>G

[160]

[161]

 

VEGFA

IL1B

BC

rs3025039

c.936C>T

rs114327

NA

[166169]

[170]

[171]

VEGFA

IL1B

BC

rs699947

c.-2578C>A

rs1143634

NA

[172]

[170]

[171]

VEGFA

IL1B

BC

NA

NA

rs16944

NA

NA

[170]

[171]

VEGFA

IL1B

GC

rs3025039

c.936C>T

rs3087258

NA

[45]

[173]

 

VEGFA

IL1B

GC

rs699947

c.-2578C>A

NA

IL1B-31-ami

[95]

[173]

 

MMP9

IL8

BC

rs3918242

c.-1562C>T

rs4073

c.-251A>T

[160]

[174]

[171]

MMP9

IL8

CRC

rs3918242

c.-1562C>T

rs4073

c.-251A>T

[160]

[174]

 

MMP9

IL8

GC

rs3918242

c.-1562C>T

rs4073

c.-251A>T

[160]

[175]

 

MMP9

IL8

LC

rs3918242

c.-1562C>T

rs4073

c.-251A>T

[160]

[174]

 

MMP1

MMP3

BC

rs1799750

c.-1607 1G>2G

rs3025058

c.-1171 5A>6A

[176]

[176]

 

MMP1

MMP3

CRC

rs1799750

c.-1607 1G>2G

rs3025058

c.-1171 5A>6A

[176]

[176]

 

MMP1

MMP3

HNC

rs1799750

c.-1607 1G>2G

rs3025058

c.-1171 5A>6A

[176]

[176]

 

MMP1

MMP3

LC

rs1799750

c.-1607 1G>2G

rs3025058

c.-1171 5A>6A

[176]

[176]

[177, 178]

MMP1

MMP3

OVCa

rs1799750

c.-1607 1G>2G

rs3025058

c.-1171 5A>6A

[176]

[176]

 

GSTP1

GSTM1

CRC

rs1695

p.I105V

rs1065411

GSTM1 present/null

[45]

[179]

 

GSTP1

GSTM1

BC

rs1695

p.I105V

rs1065412

GSTM1 present/null

[180]

[181]

[182, 183]

GSTP1

GSTM1

OVCa

rs1695

p.I105V

rs1065413

GSTM1 present/null

[184]

[184]

 

GSTP1

GSTM1

UBC

rs1695

p.I105V

rs1065414

GSTM1 present/null

[185]

[186]

 

These were identified in our meta-analysis. Their correlation with various cancer types is also shown. NA not available.

Another interesting interaction that was revealed from our analysis was between the VEGFA and IL1B genes that were associated with BC and GC (Table 4). After proceeding with a further literature search, we have not found similar results - except from one report [171] - but we have identified additional associations with HNC, ALL, laryngeal carcinoma and MM [203206]. For MMP-9 and IL8 interaction, there was no study confirming our initial results for BC, CRC and GC on neither humans nor mouse models. We have observed though that there was evidence for an association with nasopharyngeal carcinoma [171], LC [177, 178] and UBC [207]. Similarly, we could not find any study that could support the interactions between MMP-1 and MMP-3 and GSTP1 with GSTM1, although two studies confirmed that GSTP1 and GSTM1 interactions could be associated with BC [182, 183] (Table 4).

Indications from further literature search on human models revealed associations for MMP-1 and MMP-3 with types of cancer such as BCC, metatypical cancer of the skin [208], colorectal adenoma and RC [209, 210], and for GSTP1 and GSTM1, endometrial cancer (EmCa) [211], LC [212], multiple myeloma (observed no significant association to prostatic adenoma and adenocarcinoma) [213], PC [133, 214], ALL [215], chronic myeloid leukaemia [216] and PanC [217].

We have then attempted to depict the various types of cancers according to the number of SNPs and genes and/or gene clusters found from our meta-analysis to be meaningfully associated with certain cancer types. Our data indicate that BC is correlated more often than the other types of cancer both with the number of SNPs (Figure 1A) as well as with the number of genes or gene clusters (Figure 1B). This observation underlies the heterogeneity of BC, indicating that it is, most likely, not a single disease but a spectrum of related disease states.
Figure 1

The distribution of various cancer types. According to (A) the number of SNPs per cancer type and (B) the number of genes or gene correlations per cancer type. By extrapolating the data in Tables 1, 2, 3 and 4, it seems that the number of genome variations and genes is profoundly bigger in BC, probably indicating that this type of cancer is not a single disease but, most likely, a spectrum of related disease states.

Conclusions

In essence, our meta-analysis study generated clusters of genes and diseases, many of which proved to be independent of the criteria used, which suggests that these clusters are most likely biologically meaningful. Preliminary study of some clusters and of our results shows that indeed these genes interact. As regards the associations, with a further literature analysis on human and mouse models, we have also found meaningful gene associations related to other cancer types not previously reported in the literature, an observation that warrants further investigation.

Notes

Declarations

Acknowledgements

This study was conducted to fulfil the requirements of an undergraduate thesis, jointly with the Universities of Trieste, Italy and Patras, Greece. This work was partly funded by the University of Patras research budget and a European Commission grant (GEN2PHEN; FP7-200754) to GPP.

Authors’ Affiliations

(1)
Faculty of Mathematical, Physical and Natural Sciences, Department of Biological Sciences, University of Trieste
(2)
School of Health Sciences, Department of Pharmacy, University of Patras, University Campus
(3)
Clinical Oncology Laboratory, Division of Oncology, Department of Medicine, University of Patras
(4)
School of Computing and Information Systems, Faculty of Science, Engineering and Computing, Kingston University

References

  1. Lea IA, Jackson MA, Li X, Bailey S, Peddada SD, Dunnick JK: Genetic pathways and mutation profiles of human cancers: site- and exposure-specific patterns. Carcinogenesis. 2007, 28 (9): 1851-1858.PubMed CentralPubMedView ArticleGoogle Scholar
  2. Dutt A, Beroukhim R: Single nucleotide polymorphism array analysis of cancer. Curr Opin Oncol. 2007, 19 (1): 43-49.PubMedView ArticleGoogle Scholar
  3. Chung CC, Chanock SJ: Current status of genome-wide association studies in cancer. Hum Genet. 2011, 130 (1): 59-78.PubMedView ArticleGoogle Scholar
  4. Rae JM, Skaar TC, Hilsenbeck SG, Oesterreich S: The role of single nucleotide polymorphisms in breast cancer metastasis. Breast Cancer Res. 2008, 10 (1): 301-PubMed CentralPubMedView ArticleGoogle Scholar
  5. Teraoka SN, Bernstein JL, Reiner AS, Haile RW, Bernstein L, Lynch CF, Malone KE, Stovall M, Capanu M, Liang X, Smith SA, Mychaleckyj J, Hou X, Mellemkjaer L, Boice JD, Siniard A, Duggan D, Thomas DC, WECARE Study Collaborative Group, Concannon P: Single nucleotide polymorphisms associated with risk for contralateral breast cancer in the women's environment, cancer, and radiation epidemiology (WECARE) study. Breast Cancer Res. 2011, 13 (6): R114-PubMed CentralPubMedView ArticleGoogle Scholar
  6. Inaki K, Liu ET: Structural mutations in cancer: mechanistic and functional insights. Trends Genet. 2012, 28 (11): 550-559.PubMedView ArticleGoogle Scholar
  7. You JS, Jones PA: Cancer genetics and epigenetics: two sides of the same coin?. Cancer Cell. 2012, 22 (1): 9-20.PubMed CentralPubMedView ArticleGoogle Scholar
  8. Lopez-Lazaro M: A new view of carcinogenesis and an alternative approach to cancer therapy. Mol Med. 2010, 16 (3–4): 144-153.PubMed CentralPubMedGoogle Scholar
  9. Rasmussen MD, Deshpande MS, Karypis G, Johnson J, Crow JA, Retzel EF: wCLUTO: a Web-enabled clustering toolkit. Plant Physiol. 2003, 133 (2): 510-516.PubMed CentralPubMedView ArticleGoogle Scholar
  10. Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, Jensen LJ, von Mering C: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39 (Database issue): D561-D568.PubMed CentralPubMedView ArticleGoogle Scholar
  11. Search Tool for the Retrieval of Interacting Genes/Proteins. http://www.string-db.org,
  12. Jiang Z, Li C, Xu Y, Cai S, Wang X: Associations between XPD polymorphisms and risk of breast cancer: a meta-analysis. Breast Cancer Res Treat. 2010, 123 (1): 203-212.PubMedView ArticleGoogle Scholar
  13. Qiu LX, Yao L, Zhang J, Zhu XD, Zhao XM, Xue K, Mao C, Chen B, Zhan P, Yuan H, Hu X-C: XPD Lys751Gln polymorphism and breast cancer susceptibility: a meta-analysis involving 28,709 subjects. Breast Cancer Res Treat. 2010, 124 (1): 229-235.PubMedView ArticleGoogle Scholar
  14. Wang HC, Liu CS, Wang CH, Tsai RY, Tsai CW, Wang RF, Chang CH, Chen YS, Chiu CF, Bau DT, Huang CY: Significant association of XPD Asp312Asn polymorphism with breast cancer in Taiwanese patients. Chin J Physiol. 2010, 53 (2): 130-135.PubMedView ArticleGoogle Scholar
  15. Han W, Kim KY, Yang SJ, Noh DY, Kang D, Kwack K: SNP-SNP interactions between DNA repair genes were associated with breast cancer risk in a Korean population. Cancer. 2012, 118 (3): 594-602.PubMedView ArticleGoogle Scholar
  16. Hussien YM, Gharib AF, Awad HA, Karam RA, Elsawy WH: Impact of DNA repair genes polymorphism (XPD and XRCC1) on the risk of breast cancer in Egyptian female patients. Mol Biol Rep. 2012, 39 (2): 1895-1901.PubMedView ArticleGoogle Scholar
  17. Yin J, Vogel U, Wang C, Liang D, Ma Y, Wang H, Yue L, Liu D, Ma J, Sun X: Hapmap-based evaluation of ERCC2, PPP1R13L, and ERCC1 and lung cancer risk in a Chinese population. Environ Mol Mutagen. 2012, 53 (3): 239-245.PubMedView ArticleGoogle Scholar
  18. Yao L, Qiu LX, Yu L, Yang Z, Yu XJ, Zhong Y, Hu XC: The association between ERCC2 Asp312Asn polymorphism and breast cancer risk: a meta-analysis involving 22,766 subjects. Breast Cancer Res Treat. 2010, 123 (1): 227-231.PubMedView ArticleGoogle Scholar
  19. Zhang J, Gu SY, Zhang P, Jia Z, Chang JH: ERCC2 Lys751Gln polymorphism is associated with lung cancer among Caucasians. Eur J Cancer. 2010, 46 (13): 2479-2484.PubMedView ArticleGoogle Scholar
  20. Zhan P, Wang Q, Wei SZ, Wang J, Qian Q, Yu LK, Song Y: ERCC2/XPD Lys751Gln and Asp312Asn gene polymorphism and lung cancer risk: a meta-analysis involving 22 case–control studies. J Thorac Oncol. 2010, 5 (9): 1337-1345.PubMedView ArticleGoogle Scholar
  21. Yin J, Vogel U, Ma Y, Qi R, Wang H, Yue L, Liang D, Wang C, Li X, Song T: HapMap-based study of a region encompassing ERCC1 and ERCC2 related to lung cancer susceptibility in a Chinese population. Mutat Res. 2011, 713 (1–2): 1-7.PubMedView ArticleGoogle Scholar
  22. Christiani DC: ERCC2/XPD polymorphisms and lung cancer risk. J Thorac Oncol. 2011, 6 (1): 233-author reply 233–235PubMedView ArticleGoogle Scholar
  23. Zhang J, Qiu LX, Leaw SJ, Hu XC, Chang JH: The association between XPD Asp312Asn polymorphism and lung cancer risk: a meta-analysis including 16,949 subjects. Med Oncol. 2011, 28 (3): 655-660.PubMedView ArticleGoogle Scholar
  24. Sergentanis TN, Economopoulos KP: Cyclin D1 G870A polymorphism and breast cancer risk: a meta-analysis comprising 9,911 cases and 11,171 controls. Mol Biol Rep. 2011, 38 (8): 4955-4963.PubMedView ArticleGoogle Scholar
  25. Millar EK, Dean JL, McNeil CM, O'Toole SA, Henshall SM, Tran T, Lin J, Quong A, Comstock CE, Witkiewicz A, Musgrove EA, Rui H, Lemarchand L, Setiawan VW, Haiman CA, Knudsen KE, Sutherland RL, Knudsen ES: Cyclin D1b protein expression in breast cancer is independent of cyclin D1a and associated with poor disease outcome. Oncogene. 2009, 28 (15): 1812-1820.PubMed CentralPubMedView ArticleGoogle Scholar
  26. Miyashita H, Mori S, Tanda N, Nakayama K, Kanzaki A, Sato A, Morikawa H, Motegi K, Takebayashi Y, Fukumoto M: Loss of heterozygosity of nucleotide excision repair factors in sporadic oral squamous cell carcinoma using microdissected tissue. Oncol Rep. 2001, 8 (5): 1133-1138.PubMedGoogle Scholar
  27. Ghosh-Choudhury N, Ghosh-Choudhury G, Celeste A, Ghosh PM, Moyer M, Abboud SL, Kreisberg J: Bone morphogenetic protein-2 induces cyclin kinase inhibitor p21 and hypophosphorylation of retinoblastoma protein in estradiol-treated MCF-7 human breast cancer cells. Biochim Biophys Acta. 2000, 1497 (2): 186-196.PubMedView ArticleGoogle Scholar
  28. Musgrove EA, Hui R, Sweeney KJ, Watts CK, Sutherland RL: Cyclins and breast cancer. J Mammary Gland Biol Neoplasia. 1996, 1 (2): 153-162.PubMedView ArticleGoogle Scholar
  29. Sutherland RL, Hamilton JA, Sweeney KJ, Watts CK, Musgrove EA: Expression and regulation of cyclin genes in breast cancer. Acta Oncol. 1995, 34 (5): 651-656.PubMedView ArticleGoogle Scholar
  30. Taneja P, Frazier DP, Kendig RD, Maglic D, Sugiyama T, Kai F, Taneja NK, Inoue K: MMTV mouse models and the diagnostic values of MMTV-like sequences in human breast cancer. Expert Rev Mol Diagn. 2009, 9 (5): 423-440.PubMed CentralPubMedView ArticleGoogle Scholar
  31. Yang C, Ionescu-Tiba V, Burns K, Gadd M, Zukerberg L, Louis DN, Sgroi D, Schmidt EV: The role of the cyclin D1-dependent kinases in ErbB2-mediated breast cancer. Am J Pathol. 2004, 164 (3): 1031-1038.PubMed CentralPubMedView ArticleGoogle Scholar
  32. Zhou GW, Hu J, Li Q: CYP2E1 PstI/RsaI polymorphism and colorectal cancer risk: a meta-analysis. World J Gastroenterol. 2010, 16 (23): 2949-2953.PubMed CentralPubMedView ArticleGoogle Scholar
  33. Silva TD, Felipe AV, Pimenta CA, Barao K, Forones NM: CYP2E1 RsaI and 96-bp insertion genetic polymorphisms associated with risk for colorectal cancer. Genet Mol Res. 2012, 11 (3): 3138-3145.PubMedView ArticleGoogle Scholar
  34. Sameer AS, Nissar S, Qadri Q, Alam S, Baba SM, Siddiqi MA: Role of CYP2E1 genotypes in susceptibility to colorectal cancer in the Kashmiri population. Hum Genomics. 2011, 5 (6): 530-537.PubMed CentralPubMedGoogle Scholar
  35. Yang H, Zhou Y, Zhou Z, Liu J, Yuan X, Matsuo K, Takezaki T, Tajima K, Cao J: A novel polymorphism rs1329149 of CYP2E1 and a known polymorphism rs671 of ALDH2 of alcohol metabolizing enzymes are associated with colorectal cancer in a southwestern Chinese population. Cancer Epidemiol Biomarkers Prev. 2009, 18 (9): 2522-2527.PubMedView ArticleGoogle Scholar
  36. Kury S, Buecher B, Robiou-du-Pont S, Scoul C, Sebille V, Colman H, Le Houerou C, Le Neel T, Bourdon J, Faroux R, Ollivry J, Lafraise B, Chupin LD, Bézieau S: Combinations of cytochrome P450 gene polymorphisms enhancing the risk for sporadic colorectal cancer related to red meat consumption. Cancer Epidemiol Biomarkers Prev. 2007, 16 (7): 1460-1467.PubMedView ArticleGoogle Scholar
  37. Chen K, Jin MJ, Fan CH, Song L, Jiang QT, Yu WP, Ma XY, Yao KY: A case–control study on the association between genetic polymorphisms of metabolic enzymes and the risk of colorectal cancer. Zhonghua Liu Xing Bing Xue Za Zhi. 2005, 26 (9): 659-664.PubMedGoogle Scholar
  38. van der Logt EM, Bergevoet SM, Roelofs HM, Te Morsche RH, Dijk Y, Wobbes T, Nagengast FM, Peters WH: Role of epoxide hydrolase, NAD(P)H:quinone oxidoreductase, cytochrome P450 2E1 or alcohol dehydrogenase genotypes in susceptibility to colorectal cancer. Mutat Res. 2006, 593 (1–2): 39-49.PubMedView ArticleGoogle Scholar
  39. Landi S, Gemignani F, Moreno V, Gioia-Patricola L, Chabrier A, Guino E, Navarro M, de Oca J, Capella G, Canzian F: A comprehensive analysis of phase I and phase II metabolism gene polymorphisms and risk of colorectal cancer. Pharmacogenet Genomics. 2005, 15 (8): 535-546.PubMedView ArticleGoogle Scholar
  40. Cotterchio M, Boucher BA, Manno M, Gallinger S, Okey AB, Harper PA: Red meat intake, doneness, polymorphisms in genes that encode carcinogen-metabolizing enzymes, and colorectal cancer risk. Cancer Epidemiol Biomarkers Prev. 2008, 17 (11): 3098-3107.PubMed CentralPubMedView ArticleGoogle Scholar
  41. Chen J, Huang XF: The signal pathways in azoxymethane-induced colon cancer and preventive implications. Cancer Biol Ther. 2009, 8 (14): 1313-1317.PubMedView ArticleGoogle Scholar
  42. Tang K, Li Y, Zhang Z, Gu Y, Xiong Y, Feng G, He L, Qin S: The PstI/RsaI and DraI polymorphisms of CYP2E1 and head and neck cancer risk: a meta-analysis based on 21 case–control studies. BMC Cancer. 2010, 10: 575-PubMed CentralPubMedView ArticleGoogle Scholar
  43. Lu D, Yu X, Du Y: Meta-analyses of the effect of cytochrome P450 2E1 gene polymorphism on the risk of head and neck cancer. Mol Biol Rep. 2011, 38 (4): 2409-2416.PubMedView ArticleGoogle Scholar
  44. Garcia SM, Curioni OA, de Carvalho MB, Gattas GJ: Polymorphisms in alcohol metabolizing genes and the risk of head and neck cancer in a Brazilian population. Alcohol Alcohol. 2010, 45 (1): 6-12.PubMedView ArticleGoogle Scholar
  45. Economopoulos KP, Sergentanis TN: GSTM1, GSTT1, GSTP1, GSTA1 and colorectal cancer risk: a comprehensive meta-analysis. Eur J Cancer. 2010, 46 (9): 1617-1631.PubMedView ArticleGoogle Scholar
  46. Ebrahimkhani S, Asgharian AM, Nourinaier B, Ebrahimkhani K, Vali N, Abbasi F, Zali MR: Association of GSTM1, GSTT1, GSTP1 and CYP2E1 single nucleotide polymorphisms with colorectal cancer in Iran. Pathol Oncol Res. 2012, 18 (3): 651-656.PubMedView ArticleGoogle Scholar
  47. Sameer AS, Qadri Q, Siddiqi MA: GSTP1 I105V polymorphism and susceptibility to colorectal cancer in Kashmiri population. DNA Cell Biol. 2012, 31 (1): 74-79.PubMedView ArticleGoogle Scholar
  48. Wang J, Joshi AD, Corral R, Siegmund KD, Marchand LL, Martinez ME, Haile RW, Ahnen DJ, Sandler RS, Lance P, Stern MC: Carcinogen metabolism genes, red meat and poultry intake, and colorectal cancer risk. Int J Cancer. 2012, 130 (8): 1898-1907.PubMedView ArticleGoogle Scholar
  49. Sainz J, Rudolph A, Hein R, Hoffmeister M, Buch S, von Schonfels W, Hampe J, Schafmayer C, Volzke H, Frank B, Brenner H, Försti A, Hemminki K, Chang-Claude J: Association of genetic polymorphisms in ESR2, HSD17B1, ABCB1, and SHBG genes with colorectal cancer risk. Endocr Relat Cancer. 2011, 18 (2): 265-276.PubMedView ArticleGoogle Scholar
  50. Jones BA, Christensen AR, Wise JP, Yu H: Glutathione S-transferase polymorphisms and survival in African-American and white colorectal cancer patients. Cancer Epidemiol. 2009, 33 (3–4): 249-256.PubMedView ArticleGoogle Scholar
  51. Skjelbred CF, Saebo M, Hjartaker A, Grotmol T, Hansteen IL, Tveit KM, Hoff G, Kure EH: Meat, vegetables and genetic polymorphisms and the risk of colorectal carcinomas and adenomas. BMC Cancer. 2007, 7: 228-PubMed CentralPubMedView ArticleGoogle Scholar
  52. Talseth BA, Meldrum C, Suchy J, Kurzawski G, Lubinski J, Scott RJ: Genetic polymorphisms in xenobiotic clearance genes and their influence on disease expression in hereditary nonpolyposis colorectal cancer patients. Cancer Epidemiol Biomarkers Prev. 2006, 15 (11): 2307-2310.PubMedView ArticleGoogle Scholar
  53. Romero RZ, Morales R, Garcia F, Huarriz M, Bandres E, De la Haba J, Gomez A, Aranda E, Garcia-Foncillas J: Potential application of GSTT1-null genotype in predicting toxicity associated to 5-fluouracil irinotecan and leucovorin regimen in advanced stage colorectal cancer patients. Oncol Rep. 2006, 16 (3): 497-503.PubMedGoogle Scholar
  54. Probst-Hensch NM, Sun CL, Van Den Berg D, Ceschi M, Koh WP, Yu MC: The effect of the cyclin D1 (CCND1) A870G polymorphism on colorectal cancer risk is modified by glutathione-S-transferase polymorphisms and isothiocyanate intake in the Singapore Chinese health study. Carcinogenesis. 2006, 27 (12): 2475-2482.PubMedView ArticleGoogle Scholar
  55. Gaustadnes M, Orntoft TF, Jensen JL, Torring N: Validation of the use of DNA pools and primer extension in association studies of sporadic colorectal cancer for selection of candidate SNPs. Hum Mutat. 2006, 27 (2): 187-194.PubMedView ArticleGoogle Scholar
  56. Yu KD, Di GH, Fan L, Chen AX, Yang C, Shao ZM: Lack of an association between a functional polymorphism in the interleukin-6 gene promoter and breast cancer risk: a meta-analysis involving 25,703 subjects. Breast Cancer Res Treat. 2010, 122 (2): 483-488.PubMedView ArticleGoogle Scholar
  57. Xu B, Niu XB, Wang ZD, Cheng W, Tong N, Mi YY, Min ZC, Tao J, Li PC, Zhang W, Wu HF, Zhang ZD, Wang ZJ, Hua LX, Feng NH, Wang XR: IL-6–174G>C polymorphism and cancer risk: a meta-analysis involving 29,377 cases and 37,739 controls. Mol Biol Rep. 2011, 38 (4): 2589-2596.PubMedView ArticleGoogle Scholar
  58. Dong X, Wu J, Liang P, Li J, Yuan L, Liu X: Methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and gastric cancer: a meta-analysis. Arch Med Res. 2010, 41 (2): 125-133.PubMedView ArticleGoogle Scholar
  59. Wang Z, Chen JQ, Liu JL, Qin XG, Huang Y: Polymorphisms in ERCC1, GSTs, TS and MTHFR predict clinical outcomes of gastric cancer patients treated with platinum/5-Fu-based chemotherapy: a systematic review. BMC Gastroenterol. 2012, 12: 137-PubMed CentralPubMedView ArticleGoogle Scholar
  60. Balassiano K, Lima S, Jenab M, Overvad K, Tjonneland A, Boutron-Ruault MC, Clavel-Chapelon F, Canzian F, Kaaks R, Boeing H, Meidtner K, Trichopoulou A, Laglou P, Vineis P, Panico S, Palli D, Grioni S, Tumino R, Lund E, Bueno-de-Mesquita HB, Numans ME, Peeters PH, Ramon Quirós J, Sánchez MJ, Navarro C, Ardanaz E, Dorronsoro M, Hallmans G, Stenling R, et al: Aberrant DNA methylation of cancer-associated genes in gastric cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC-EURGAST). Cancer Lett. 2011, 311 (1): 85-95.PubMedView ArticleGoogle Scholar
  61. Qi X, Ma X, Yang X, Fan L, Zhang Y, Zhang F, Chen L, Zhou Y, Jiang J: Methylenetetrahydrofolate reductase polymorphisms and breast cancer risk: a meta-analysis from 41 studies with 16,480 cases and 22,388 controls. Breast Cancer Res Treat. 2010, 123 (2): 499-506.PubMedView ArticleGoogle Scholar
  62. Qiu LX, Zhang J, Li WH, Zhang QL, Yu H, Wang BY, Wang LP, Wang JL, Wang HJ, Liu XJ, Luo ZG, Wu XH: Lack of association between methylenetetrahydrofolate reductase gene A1298C polymorphism and breast cancer susceptibility. Mol Biol Rep. 2011, 38 (4): 2295-2299.PubMedView ArticleGoogle Scholar
  63. Perel'muter VM, Zav'ialova MV, Vtorushin SV, Slonimskaia EM, Kritskaia NG, Garbukov E, Litviakov NV, Stakheeva MN, Babyshkina NN, Malinovskaia EA, Denisov EV, Grigor'eva ES, Nazarenko MS, Sennikov SV, Goreva EP, Kozlov VA, Voevoda MI, Maksimov VN, Beliavskaia VA, Cherdyntseva NV: Genetic and clinical and pathological characteristics of breast cancer in premenopausal and postmenopausal women. Adv Gerontol. 2008, 21 (4): 643-653.PubMedGoogle Scholar
  64. Stevens VL, McCullough ML, Pavluck AL, Talbot JT, Feigelson HS, Thun MJ, Calle EE: Association of polymorphisms in one-carbon metabolism genes and postmenopausal breast cancer incidence. Cancer Epidemiol Biomarkers Prev. 2007, 16 (6): 1140-1147.PubMedView ArticleGoogle Scholar
  65. Qiu LX, Mao C, Yao L, Yu KD, Zhan P, Chen B, Liu HG, Yuan H, Zhang J, Xue K, Hu XC: XRCC3 5′-UTR and IVS5-14 polymorphisms and breast cancer susceptibility: a meta-analysis. Breast Cancer Res Treat. 2010, 122 (2): 489-493.PubMedView ArticleGoogle Scholar
  66. Chen Y, Pei J: Possible risk modifications in the association between MnSOD Ala-9Val polymorphism and breast cancer risk: subgroup analysis and evidence-based sample size calculation for a future trial. Breast Cancer Res Treat. 2011, 125 (2): 495-504.PubMedView ArticleGoogle Scholar
  67. Qi X, Zhang F, Yang X, Fan L, Zhang Y, Chen L, Zhou Y, Chen X, Zhong L, Jiang J: Transforming growth factor-beta1 polymorphisms and breast cancer risk: a meta-analysis based on 27 case–control studies. Breast Cancer Res Treat. 2010, 122 (1): 273-279.PubMedView ArticleGoogle Scholar
  68. Huang Y, Hao Y, Li B, Xie J, Qian J, Chao C, Yu L: Lack of significant association between TGF-beta1-590C/T polymorphism and breast cancer risk: a meta-analysis. Med Oncol. 2011, 28 (2): 424-428.PubMedView ArticleGoogle Scholar
  69. Woo SU, Park KH, Woo OH, Yang DS, Kim AR, Lee ES, Lee JB, Kim YH, Kim JS, Seo JH: Association of a TGF-beta1 gene -509 C/T polymorphism with breast cancer risk: a meta-analysis. Breast Cancer Res Treat. 2010, 124 (2): 481-485.PubMedView ArticleGoogle Scholar
  70. Gu D, Zhuang L, Huang H, Cao P, Wang D, Tang J, Chen J: TGFB1 T29C polymorphism and breast cancer risk: a meta-analysis based on 10,417 cases and 11,455 controls. Breast Cancer Res Treat. 2010, 123 (3): 857-861.PubMedView ArticleGoogle Scholar
  71. Wei BB, Xi B, Wang R, Bai JM, Chang JK, Zhang YY, Yoneda R, Su JT, Hua LX: TGFbeta1 T29C polymorphism and cancer risk: a meta-analysis based on 40 case–control studies. Cancer Genet Cytogenet. 2010, 196 (1): 68-75.PubMedView ArticleGoogle Scholar
  72. Ma X, Chen C, Xiong H, Li Y: Transforming growth factorbeta1 L10P variant plays an active role on the breast cancer susceptibility in Caucasian: evidence from 10,392 cases and 11,697 controls. Breast Cancer Res Treat. 2010, 124 (2): 453-457.PubMedView ArticleGoogle Scholar
  73. Huang Y, Li B, Qian J, Xie J, Yu L: TGF-beta1 29T/C polymorphism and breast cancer risk: a meta-analysis involving 25,996 subjects. Breast Cancer Res Treat. 2010, 123 (3): 863-868.PubMedView ArticleGoogle Scholar
  74. Qiu LX, Yao L, Mao C, Chen B, Zhan P, Xue K, Zhang J, Yuan H, Hu XC: TGFB1 L10P polymorphism is associated with breast cancer susceptibility: evidence from a meta-analysis involving 47,817 subjects. Breast Cancer Res Treat. 2010, 123 (2): 563-567.PubMedView ArticleGoogle Scholar
  75. Radisky DC, Hartmann LC: Mammary involution and breast cancer risk: transgenic models and clinical studies. J Mammary Gland Biol Neoplasia. 2009, 14 (2): 181-191.PubMed CentralPubMedView ArticleGoogle Scholar
  76. Stuelten CH, Busch JI, Tang B, Flanders KC, Oshima A, Sutton E, Karpova TS, Roberts AB, Wakefield LM, Niederhuber JE: Transient tumor-fibroblast interactions increase tumor cell malignancy by a TGF-Beta mediated mechanism in a mouse xenograft model of breast cancer. PLoS One. 2010, 5 (3): e9832-PubMed CentralPubMedView ArticleGoogle Scholar
  77. Zheng W: Genetic polymorphisms in the transforming growth factor-beta signaling pathways and breast cancer risk and survival. Methods Mol Biol. 2009, 472: 265-277.PubMedView ArticleGoogle Scholar
  78. Hu Z, Li X, Qu X, He Y, Ring BZ, Song E, Su L: Intron 3 16 bp duplication polymorphism of TP53 contributes to cancer susceptibility: a meta-analysis. Carcinogenesis. 2010, 31 (4): 643-647.PubMedView ArticleGoogle Scholar
  79. Ma Y, Yang J, Liu Z, Zhang P, Yang Z, Wang Y, Qin H: No significant association between the TP53 codon 72 polymorphism and breast cancer risk: a meta-analysis of 21 studies involving 24,063 subjects. Breast Cancer Res Treat. 2011, 125 (1): 201-205.PubMedView ArticleGoogle Scholar
  80. Chunder N, Mandal S, Roy A, Roychoudhury S, Panda CK: Differential association of BRCA1 and BRCA2 genes with some breast cancer-associated genes in early and late onset breast tumors. Ann Surg Oncol. 2004, 11 (12): 1045-1055.PubMedView ArticleGoogle Scholar
  81. Rebbeck TR: Inherited genetic predisposition in breast cancer. A population-based perspective. Cancer. 1999, 86 (11 Suppl): 2493-2501.PubMedView ArticleGoogle Scholar
  82. Rossner P, Gammon MD, Zhang YJ, Terry MB, Hibshoosh H, Memeo L, Mansukhani M, Long CM, Garbowski G, Agrawal M, Agrawal M, Kalra TS, Gaudet MM, Teitelbaum SL, Neugut AI, Santella RM: Mutations in p53, p53 protein overexpression and breast cancer survival. J Cell Mol Med. 2009, 13 (9B): 3847-3857.PubMed CentralPubMedView ArticleGoogle Scholar
  83. Nkhata KJ, Ray A, Schuster TF, Grossmann ME, Cleary MP: Effects of adiponectin and leptin co-treatment on human breast cancer cell growth. Oncol Rep. 2009, 21 (6): 1611-1619.PubMedGoogle Scholar
  84. Palanca Suela S, Esteban Cardenosa E, Barragan Gonzalez E, de Juan Jimenez I, Chirivella Gonzalez I, Segura Huerta A, Guillen Ponce C, Montalar Salcedo J, Martinez de Duenas E, Castel Sanchez V, Bolufer Gilabert P, Group for Assessment of Hereditary Cancer of Valencia Community: CASP8 D302H polymorphism delays the age of onset of breast cancer in BRCA1 and BRCA2 carriers. Breast Cancer Res Treat. 2010, 119 (1): 87-93.PubMedView ArticleGoogle Scholar
  85. Fanale D, Amodeo V, Corsini LR, Rizzo S, Bazan V, Russo A: Breast cancer genome-wide association studies: there is strength in numbers. Oncogene. 2012, 31 (17): 2121-2128.PubMedView ArticleGoogle Scholar
  86. Cherdyntseva NV, Denisov EV, Litviakov NV, Maksimov VN, Malinovskaya EA, Babyshkina NN, Slonimskaya EM, Voevoda MI, Choinzonov EL: Crosstalk between the FGFR2 and TP53 genes in breast cancer: data from an association study and epistatic interaction analysis. DNA Cell Biol. 2012, 31 (3): 306-316.PubMedView ArticleGoogle Scholar
  87. Lo Nigro C, Vivenza D, Monteverde M, Lattanzio L, Gojis O, Garrone O, Comino A, Merlano M, Quinlan PR, Syed N, Purdie CA, Thompson A, Palmieri C, Crook T: High frequency of complex TP53 mutations in CNS metastases from breast cancer. Br J Cancer. 2012, 106 (2): 397-404.PubMed CentralPubMedView ArticleGoogle Scholar
  88. Mathoulin-Portier MP, Viens P, Cowen D, Bertucci F, Houvenaeghel G, Geneix J, Puig B, Bardou VJ, Jacquemier J: Prognostic value of simultaneous expression of p21 and mdm2 in breast carcinomas treated by adjuvant chemotherapy with antracyclin. Oncol Rep. 2000, 7 (3): 675-680.PubMedGoogle Scholar
  89. Blackburn AC, Jerry DJ: Knockout and transgenic mice of Trp53: what have we learned about p53 in breast cancer?. Breast Cancer Res. 2002, 4 (3): 101-111.PubMed CentralPubMedView ArticleGoogle Scholar
  90. Bennett CN, Green JE: Genomic analyses as a guide to target identification and preclinical testing of mouse models of breast cancer. Toxicol Pathol. 2010, 38 (1): 88-95.PubMed CentralPubMedView ArticleGoogle Scholar
  91. Girardini JE, Napoli M, Piazza S, Rustighi A, Marotta C, Radaelli E, Capaci V, Jordan L, Quinlan P, Thompson A, Mano M, Rosato A, Crook T, Scanziani E, Means AR, Lozano G, Schneider C, Del Sal G: A Pin1/mutant p53 axis promotes aggressiveness in breast cancer. Cancer Cell. 2011, 20 (1): 79-91.PubMedView ArticleGoogle Scholar
  92. Alsner J, Jensen V, Kyndi M, Offersen BV, Vu P, Borresen-Dale AL, Overgaard J: A comparison between p53 accumulation determined by immunohistochemistry and TP53 mutations as prognostic variables in tumours from breast cancer patients. Acta Oncol. 2008, 47 (4): 600-607.PubMedView ArticleGoogle Scholar
  93. Bourdon JC, Khoury MP, Diot A, Baker L, Fernandes K, Aoubala M, Quinlan P, Purdie CA, Jordan LB, Prats AC, Lane DP, Thompson AM: p53 mutant breast cancer patients expressing p53gamma have as good a prognosis as wild-type p53 breast cancer patients. Breast Cancer Res. 2011, 13 (1): R7-PubMed CentralPubMedView ArticleGoogle Scholar
  94. Besaratinia A, Pfeifer GP: Applications of the human p53 knock-in (Hupki) mouse model for human carcinogen testing. FASEB J. 2010, 24 (8): 2612-2619.PubMed CentralPubMedView ArticleGoogle Scholar
  95. Jiang DK, Ren WH, Yao L, Wang WZ, Peng B, Yu L: Meta-analysis of association between TP53 Arg72Pro polymorphism and bladder cancer risk. Urology. 2010, 76 (3): 765-767. e761PubMedView ArticleGoogle Scholar
  96. Burger M, Burger SJ, Denzinger S, Wild PJ, Wieland WF, Blaszyk H, Obermann EC, Stoehr R, Hartmann A: Elevated microsatellite instability at selected tetranucleotide repeats does not correlate with clinicopathologic features of bladder cancer. Eur Urol. 2006, 50 (4): 770-775. discussion 776PubMedView ArticleGoogle Scholar
  97. Zuiverloon TC, Abas CS, van der Keur KA, Vermeij M, Tjin SS, van Tilborg AG, Busstra M, Zwarthoff EC: In-depth investigation of the molecular pathogenesis of bladder cancer in a unique 26-year old patient with extensive multifocal disease: a case report. BMC Urol. 2010, 10: 5-PubMed CentralPubMedView ArticleGoogle Scholar
  98. Kompier LC, van Tilborg AA, Zwarthoff EC: Bladder cancer: novel molecular characteristics, diagnostic, and therapeutic implications. Urol Oncol. 2010, 28 (1): 91-96.PubMedView ArticleGoogle Scholar
  99. Jarmalaite S, Andrekute R, Scesnaite A, Suziedelis K, Husgafvel-Pursiainen K, Jankevicius F: Promoter hypermethylation in tumour suppressor genes and response to interleukin-2 treatment in bladder cancer: a pilot study. J Cancer Res Clin Oncol. 2010, 136 (6): 847-854.PubMedView ArticleGoogle Scholar
  100. Lin HY, Huang CH, Yu TJ, Wu WJ, Yang MC, Lung FW: p53 codon 72 polymorphism as a progression index for bladder cancer. Oncol Rep. 2012, 27 (4): 1193-1199.PubMed CentralPubMedGoogle Scholar
  101. Dahabreh IJ, Linardou H, Bouzika P, Varvarigou V, Murray S: TP53 Arg72Pro polymorphism and colorectal cancer risk: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. 2010, 19 (7): 1840-1847.PubMedView ArticleGoogle Scholar
  102. Economopoulos KP, Sergentanis TN, Zagouri F, Zografos GC: Association between p53 Arg72Pro polymorphism and colorectal cancer risk: a meta-analysis. Onkologie. 2010, 33 (12): 666-674.PubMedView ArticleGoogle Scholar
  103. Wang JJ, Zheng Y, Sun L, Wang L, Yu PB, Dong JH, Zhang L, Xu J, Shi W, Ren YC: TP53 codon 72 polymorphism and colorectal cancer susceptibility: a meta-analysis. Mol Biol Rep. 2011, 38 (8): 4847-4853.PubMedView ArticleGoogle Scholar
  104. Goodman JE, Mechanic LE, Luke BT, Ambs S, Chanock S, Harris CC: Exploring SNP-SNP interactions and colon cancer risk using polymorphism interaction analysis. Int J Cancer. 2006, 118 (7): 1790-1797.PubMed CentralPubMedView ArticleGoogle Scholar
  105. Zhang Y, Liu L, Tang Y, Chen C, Wang Q, Xu J, Yang C, Miao X, Wei S, Chen J, Nie S: Polymorphisms in TP53 and MDM2 contribute to higher risk of colorectal cancer in Chinese population: a hospital-based, case–control study. Mol Biol Rep. 2012, 39 (10): 9661-9668.PubMedView ArticleGoogle Scholar
  106. Lopez I, PO L, Tucci P, Alvarez-Valin F, AC R, Marin M: Different mutation profiles associated to P53 accumulation in colorectal cancer. Gene. 2012, 499 (1): 81-87.PubMedView ArticleGoogle Scholar
  107. Kanaan Z, Rai SN, Eichenberger MR, Barnes C, Dworkin AM, Weller C, Cohen E, Roberts H, Keskey B, Petras RE, Crawford NP, Galandiuk S: Differential microRNA expression tracks neoplastic progression in inflammatory bowel disease-associated colorectal cancer. Hum Mutat. 2012, 33 (3): 551-560.PubMed CentralPubMedView ArticleGoogle Scholar
  108. Aizat AA, Shahpudin SN, Mustapha MA, Zakaria Z, Sidek AS, Abu Hassan MR, Ankathil R: Association of Arg72Pro of P53 polymorphism with colorectal cancer susceptibility risk in Malaysian population. Asian Pac J Cancer Prev. 2011, 12 (11): 2909-2913.PubMedGoogle Scholar
  109. Wang B, Wang D, Zhang D, Li A, Liu D, Liu H, Jin H: Pro variant of TP53 Arg72Pro contributes to esophageal squamous cell carcinoma risk: evidence from a meta-analysis. Eur J Cancer Prev. 2010, 19 (4): 299-307.PubMedView ArticleGoogle Scholar
  110. Zhao Y, Wang F, Shan S, Qiu X, Li X, Jiao F, Wang J, Du Y: Genetic polymorphism of p53, but not GSTP1, is association with susceptibility to esophageal cancer risk - a meta-analysis. Int J Med Sci. 2010, 7 (5): 300-308.PubMed CentralPubMedView ArticleGoogle Scholar
  111. Bashash M, Yavari P, Hislop TG, Shah A, Sadjadi A, Babaei M, Le N, Brooks-Wilson A, Malekzadeh R, Bajdik C: Comparison of two diverse populations, British Columbia, Canada, and Ardabil, Iran, indicates several variables associated with gastric and esophageal cancer survival. J Gastrointest Cancer. 2011, 42 (1): 40-45.PubMed CentralPubMedView ArticleGoogle Scholar
  112. Duenas M, Santos M, Aranda JF, Bielza C, Martinez-Cruz AB, Lorz C, Taron M, Ciruelos EM, Rodriguez-Peralto JL, Martin M, Larrañaga P, Dahabreh J, Stathopoulos GP, Rosell R, Paramio JM, García-Escudero R: Mouse p53-deficient cancer models as platforms for obtaining genomic predictors of human cancer clinical outcomes. PLoS One. 2012, 7 (8): e42494-PubMed CentralPubMedView ArticleGoogle Scholar
  113. Perez-Morales R, Mendez-Ramirez I, Castro-Hernandez C, Martinez-Ramirez OC, Gonsebatt ME, Rubio J: Polymorphisms associated with the risk of lung cancer in a healthy Mexican Mestizo population: application of the additive model for cancer. Genet Mol Biol. 2011, 34 (4): 546-552.PubMed CentralPubMedView ArticleGoogle Scholar
  114. Deeb KK, Michalowska AM, Yoon CY, Krummey SM, Hoenerhoff MJ, Kavanaugh C, Li MC, Demayo FJ, Linnoila I, Deng CX, Lee EY, Medina D, Shih JH, Green JE: Identification of an integrated SV40 T/t-antigen cancer signature in aggressive human breast, prostate, and lung carcinomas with poor prognosis. Cancer Res. 2007, 67 (17): 8065-8080.PubMedView ArticleGoogle Scholar
  115. Campling BG, el-Deiry WS: Clinical implications of p53 mutations in lung cancer. Methods Mol Med. 2003, 75: 53-77.PubMedGoogle Scholar
  116. Meylan E, Dooley AL, Feldser DM, Shen L, Turk E, Ouyang C, Jacks T: Requirement for NF-kappaB signalling in a mouse model of lung adenocarcinoma. Nature. 2009, 462 (7269): 104-107.PubMed CentralPubMedView ArticleGoogle Scholar
  117. Fujiwara T, Cai DW, Georges RN, Mukhopadhyay T, Grimm EA, Roth JA: Therapeutic effect of a retroviral wild-type p53 expression vector in an orthotopic lung cancer model. J Natl Cancer Inst. 1994, 86 (19): 1458-1462.PubMedView ArticleGoogle Scholar
  118. Jiang DK, Yao L, Ren WH, Wang WZ, Peng B, Yu L: TP53 Arg72Pro polymorphism and endometrial cancer risk: a meta-analysis. Med Oncol. 2011, 28 (4): 1129-1135.PubMedView ArticleGoogle Scholar
  119. Chen MB, Li C, Shen WX, Guo YJ, Shen W, Lu PH: Association of a LSP1 gene rs3817198T>C polymorphism with breast cancer risk: evidence from 33,920 cases and 35,671 controls. Mol Biol Rep. 2011, 38 (7): 4687-4695.PubMedView ArticleGoogle Scholar
  120. Bachelder RE, Crago A, Chung J, Wendt MA, Shaw LM, Robinson G, Mercurio AM: Vascular endothelial growth factor is an autocrine survival factor for neuropilin-expressing breast carcinoma cells. Cancer Res. 2001, 61 (15): 5736-5740.PubMedGoogle Scholar
  121. Eriksson L, Ahluwalia M, Spiewak J, Lee G, Sarma DS, Roomi MJ, Farber E: Distinctive biochemical pattern associated with resistance of hepatocytes in hepatocyte nodules during liver carcinogenesis. Environ Health Perspect. 1983, 49: 171-174.PubMed CentralPubMedView ArticleGoogle Scholar
  122. Volm M, Koomagi R, Rittgen W: Clinical implications of cyclins, cyclin-dependent kinases, RB and E2F1 in squamous-cell lung carcinoma. Int J Cancer. 1998, 79 (3): 294-299.PubMedView ArticleGoogle Scholar
  123. Anayama T, Furihata M, Takeuchi T, Sonobe H, Sasaguri S, Matsumoto M, Ohtsuki Y: Insufficient effect of p27(KIP1) to inhibit cyclin D1 in human esophageal cancer in vitro. Int J Oncol. 2001, 18 (1): 151-155.PubMedGoogle Scholar
  124. Buschges R, Weber RG, Actor B, Lichter P, Collins VP, Reifenberger G: Amplification and expression of cyclin D genes (CCND1, CCND2 and CCND3) in human malignant gliomas. Brain Pathol. 1999, 9 (3): 435-442. discussion 432–433PubMedView ArticleGoogle Scholar
  125. Comstock CE, Augello MA, Benito RP, Karch J, Tran TH, Utama FE, Tindall EA, Wang Y, Burd CJ, Groh EM, Hoang HN, Giles GG, Severi G, Hayes VM, Henderson BE, Le Marchand L, Kolonel LN, Haiman CA, Baffa R, Gomella LG, Knudsen ES, Rui H, Henshall SM, Sutherland RL, Knudsen KE: Cyclin D1 splice variants: polymorphism, risk, and isoform-specific regulation in prostate cancer. Clin Cancer Res. 2009, 15 (17): 5338-5349.PubMed CentralPubMedView ArticleGoogle Scholar
  126. Murata M, Watanabe M, Yamanaka M, Kubota Y, Ito H, Nagao M, Katoh T, Kamataki T, Kawamura J, Yatani R, Shiraishi T: Genetic polymorphisms in cytochrome P450 (CYP) 1A1, CYP1A2, CYP2E1, glutathione S-transferase (GST) M1 and GSTT1 and susceptibility to prostate cancer in the Japanese population. Cancer Lett. 2001, 165 (2): 171-177.PubMedView ArticleGoogle Scholar
  127. Uematsu F: Genetic polymorphisms of drug-metabolizing enzymes and susceptibility to lung cancer-relevance to smoking. Nihon Rinsho. 1996, 54 (2): 513-517.PubMedGoogle Scholar
  128. Murray GI, Taylor VE, McKay JA, Weaver RJ, Ewen SW, Melvin WT, Burke MD: Expression of xenobiotic metabolizing enzymes in tumours of the urinary bladder. Int J Exp Pathol. 1995, 76 (4): 271-276.PubMed CentralPubMedGoogle Scholar
  129. Hu YY, Yuan H, Jiang GB, Chen N, Wen L, Leng WD, Zeng XT, Niu YM: Associations between XPD Asp312Asn polymorphism and risk of head and neck cancer: a meta-analysis based on 7,122 subjects. PLoS One. 2012, 7 (4): e35220-PubMed CentralPubMedView ArticleGoogle Scholar
  130. Yuan H, Niu YM, Wang RX, Li HZ, Chen N: Association between XPD Lys751Gln polymorphism and risk of head and neck cancer: a meta-analysis. Genet Mol Res. 2011, 10 (4): 3356-3364.PubMedView ArticleGoogle Scholar
  131. Kumar A, Pant MC, Singh HS, Khandelwal S: Associated risk of XRCC1 and XPD cross talk and life style factors in progression of head and neck cancer in north Indian population. Mutat Res. 2012, 729 (1–2): 24-34.PubMedView ArticleGoogle Scholar
  132. Clapper ML: Genetic polymorphism and cancer risk. Curr Oncol Rep. 2000, 2 (3): 251-256.PubMedView ArticleGoogle Scholar
  133. Likhin FA, Bartnovskii AE, Vdovichenko KK, Abramov AA, Belokhvostov AS: Characteristics of methyl-specific PCR-test of glutathione-S-transferase P1 gene in plasm DNA and cellular urinary precipitate for differential diagnosis of prostatic adenoma and adenocarcinoma. Urologiia. 2005, 4: 12-15.PubMedGoogle Scholar
  134. Zhang Z, Liu W, Jia X, Gao Y, Hemminki K, Lindholm B: Use of pyrosequencing to detect clinically relevant polymorphisms of genes in basal cell carcinoma. Clin Chim Acta. 2004, 342 (1–2): 137-143.PubMedView ArticleGoogle Scholar
  135. Krajinovic M, Lemieux-Blanchard E, Chiasson S, Primeau M, Costea I, Moghrabi A: Role of polymorphisms in MTHFR and MTHFD1 genes in the outcome of childhood acute lymphoblastic leukemia. Pharmacogenomics J. 2004, 4 (1): 66-72.PubMedView ArticleGoogle Scholar
  136. Petra BG, Janez J, Vita D: Gene-gene interactions in the folate metabolic pathway influence the risk for acute lymphoblastic leukemia in children. Leuk Lymphoma. 2007, 48 (4): 786-792.PubMedView ArticleGoogle Scholar
  137. Matakidou A, El Galta R, Rudd MF, Webb EL, Bridle H, Eisen T, Houlston RS: Prognostic significance of folate metabolism polymorphisms for lung cancer. Br J Cancer. 2007, 97 (2): 247-252.PubMed CentralPubMedView ArticleGoogle Scholar
  138. Moore LE, Malats N, Rothman N, Real FX, Kogevinas M, Karami S, Garcia-Closas R, Silverman D, Chanock S, Welch R, Tardón A, Serra C, Carrato A, Dosemeci M, García-Closas M: Polymorphisms in one-carbon metabolism and trans-sulfuration pathway genes and susceptibility to bladder cancer. Int J Cancer. 2007, 120 (11): 2452-2458.PubMedView ArticleGoogle Scholar
  139. Curtin K, Slattery ML, Ulrich CM, Bigler J, Levin TR, Wolff RK, Albertsen H, Potter JD, Samowitz WS: Genetic polymorphisms in one-carbon metabolism: associations with CpG island methylator phenotype (CIMP) in colon cancer and the modifying effects of diet. Carcinogenesis. 2007, 28 (8): 1672-1679.PubMed CentralPubMedView ArticleGoogle Scholar
  140. Weiner AS, Beresina OV, Voronina EN, Voropaeva EN, Boyarskih UA, Pospelova TI, Filipenko ML: Polymorphisms in folate-metabolizing genes and risk of non-Hodgkin's lymphoma. Leuk Res. 2011, 35 (4): 508-515.PubMedView ArticleGoogle Scholar
  141. Lee KM, Lan Q, Kricker A, Purdue MP, Grulich AE, Vajdic CM, Turner J, Whitby D, Kang D, Chanock S, Rothman N, Armstrong BK: One-carbon metabolism gene polymorphisms and risk of non-Hodgkin lymphoma in Australia. Hum Genet. 2007, 122 (5): 525-533.PubMedView ArticleGoogle Scholar
  142. Suzuki T, Matsuo K, Hasegawa Y, Hiraki A, Wakai K, Hirose K, Saito T, Sato S, Ueda R, Tajima K: One-carbon metabolism-related gene polymorphisms and risk of head and neck squamous cell carcinoma: case–control study. Cancer Sci. 2007, 98 (9): 1439-1446.PubMedView ArticleGoogle Scholar
  143. Kim SH, Lee SH, Choi YL, Wang LH, Park CK, Shin YK: Extensive alteration in the expression profiles of TGFB pathway signaling components and TP53 is observed along the gastric dysplasia-carcinoma sequence. Histol Histopathol. 2008, 23 (12): 1439-1452.PubMedGoogle Scholar
  144. Gemma A, Uematsu K, Hagiwara K, Takenoshita S, Kudoh S: Mechanism of resistance to growth inhibition by transforming growth factor-beta 1 (TGF-beta 1) in primary lung cancer and new molecular targets in therapy. Gan To Kagaku Ryoho. 2000, 27 (8): 1253-1259.PubMedGoogle Scholar
  145. Jonson T, Albrechtsson E, Axelson J, Heidenblad M, Gorunova L, Johansson B, Hoglund M: Altered expression of TGFB receptors and mitogenic effects of TGFB in pancreatic carcinomas. Int J Oncol. 2001, 19 (1): 71-81.PubMedGoogle Scholar
  146. Franzen P, Ichijo H, Miyazono K: Different signals mediate transforming growth factor-beta 1-induced growth inhibition and extracellular matrix production in prostatic carcinoma cells. Exp Cell Res. 1993, 207 (1): 1-7.PubMedView ArticleGoogle Scholar
  147. Maggio-Price L, Treuting P, Bielefeldt-Ohmann H, Seamons A, Drivdahl R, Zeng W, Lai L, Huycke M, Phelps S, Brabb T, Iritani BM: Bacterial infection of Smad3/Rag2 double-null mice with transforming growth factor-beta dysregulation as a model for studying inflammation-associated colon cancer. Am J Pathol. 2009, 174 (1): 317-329.PubMed CentralPubMedView ArticleGoogle Scholar
  148. Hirshfield KM, Rebbeck TR, Levine AJ: Germline mutations and polymorphisms in the origins of cancers in women. J Oncol. 2010, 2010: 297671-PubMed CentralPubMedView ArticleGoogle Scholar
  149. Volate SR, Kawasaki BT, Hurt EM, Milner JA, Kim YS, White J, Farrar WL: Gossypol induces apoptosis by activating p53 in prostate cancer cells and prostate tumor-initiating cells. Mol Cancer Ther. 2010, 9 (2): 461-470.PubMed CentralPubMedView ArticleGoogle Scholar
  150. Bernardini MQ, Baba T, Lee PS, Barnett JC, Sfakianos GP, Secord AA, Murphy SK, Iversen E, Marks JR, Berchuck A: Expression signatures of TP53 mutations in serous ovarian cancers. BMC Cancer. 2010, 10: 237-PubMed CentralPubMedView ArticleGoogle Scholar
  151. Shimada S, Mimata A, Sekine M, Mogushi K, Akiyama Y, Fukamachi H, Jonkers J, Tanaka H, Eishi Y, Yuasa Y: Synergistic tumour suppressor activity of E-cadherin and p53 in a conditional mouse model for metastatic diffuse-type gastric cancer. Gut. 2012, 61 (3): 344-353.PubMedView ArticleGoogle Scholar
  152. Sano D, Xie TX, Ow TJ, Zhao M, Pickering CR, Zhou G, Sandulache VC, Wheeler DA, Gibbs RA, Caulin C, Myers JN: Disruptive TP53 mutation is associated with aggressive disease characteristics in an orthotopic murine model of oral tongue cancer. Clin Cancer Res. 2011, 17 (21): 6658-6670.PubMed CentralPubMedView ArticleGoogle Scholar
  153. Furth PA, Cabrera MC, Diaz-Cruz ES, Millman S, Nakles RE: Assessing estrogen signaling aberrations in breast cancer risk using genetically engineered mouse models. Ann N Y Acad Sci. 2011, 1229: 147-155.PubMed CentralPubMedView ArticleGoogle Scholar
  154. de Jonge R, Hooijberg JH, van Zelst BD, Jansen G, van Zantwijk CH, Kaspers GJ, Peters GJ, Ravindranath Y, Pieters R, Lindemans J: Effect of polymorphisms in folate-related genes on in vitro methotrexate sensitivity in pediatric acute lymphoblastic leukemia. Blood. 2005, 106 (2): 717-720.PubMedView ArticleGoogle Scholar
  155. Durfort T, Tkach M, Meschaninova MI, Rivas MA, Elizalde PV, Venyaminova AG, Schillaci R, Francois JC: Small interfering RNA targeted to IGF-IR delays tumor growth and induces proinflammatory cytokines in a mouse breast cancer model. PLoS One. 2012, 7 (1): e29213-PubMed CentralPubMedView ArticleGoogle Scholar
  156. Calogero RA, Cordero F, Forni G, Cavallo F: Inflammation and breast cancer. Inflammatory component of mammary carcinogenesis in ErbB2 transgenic mice. Breast Cancer Res. 2007, 9 (4): 211-PubMed CentralPubMedView ArticleGoogle Scholar
  157. Schiff R, Osborne CK: Endocrinology and hormone therapy in breast cancer: new insight into estrogen receptor-alpha function and its implication for endocrine therapy resistance in breast cancer. Breast Cancer Res. 2005, 7 (5): 205-211.PubMed CentralPubMedView ArticleGoogle Scholar
  158. Lahm H, Petral-Malec D, Yilmaz-Ceyhan A, Fischer JR, Lorenzoni M, Givel JC, Odartchenko N: Growth stimulation of a human colorectal carcinoma cell line by interleukin-1 and -6 and antagonistic effects of transforming growth factor beta 1. Eur J Cancer. 1992, 28A (11): 1894-1899.PubMedView ArticleGoogle Scholar
  159. Yu C, Yao Z, Jiang Y, Keller ET: Prostate cancer stem cell biology. Minerva Urol Nefrol. 2012, 64 (1): 19-33.PubMed CentralPubMedGoogle Scholar
  160. Peng B, Cao L, Ma X, Wang W, Wang D, Yu L: Meta-analysis of association between matrix metalloproteinases 2, 7 and 9 promoter polymorphisms and cancer risk. Mutagenesis. 2010, 25 (4): 371-379.PubMedView ArticleGoogle Scholar
  161. Zhang YM, Cao C, Liang K: Genetic polymorphism of epidermal growth factor 61A>G and cancer risk: a meta-analysis. Cancer Epidemiol. 2010, 34 (2): 150-156.PubMedView ArticleGoogle Scholar
  162. Willmarth NE, Ethier SP: Amphiregulin as a novel target for breast cancer therapy. J Mammary Gland Biol Neoplasia. 2008, 13 (2): 171-179.PubMedView ArticleGoogle Scholar
  163. Guise TA: Breaking down bone: new insight into site-specific mechanisms of breast cancer osteolysis mediated by metalloproteinases. Genes Dev. 2009, 23 (18): 2117-2123.PubMed CentralPubMedView ArticleGoogle Scholar
  164. Lacroix M, Body JJ: Regulation of c-fos and c-jun expression by calcitonin in human breast cancer cells. Calcif Tissue Int. 1997, 60 (6): 513-519.PubMedView ArticleGoogle Scholar
  165. Chen MB, Wu XY, Shen W, Wei MX, Li C, Cai B, Tao GQ, Lu PH: Association between polymorphisms of trinucleotide repeat containing 9 gene and breast cancer risk: evidence from 62,005 subjects. Breast Cancer Res Treat. 2011, 126 (1): 177-183.PubMedView ArticleGoogle Scholar
  166. Gu D, Wang M: VEGF 936C>T polymorphism and breast cancer risk: evidence from 5,729 cases and 5,868 controls. Breast Cancer Res Treat. 2011, 125 (2): 489-493.PubMedView ArticleGoogle Scholar
  167. Qiu LX, Wang K, Yang S, Mao C, Zhao L, Yao L, Zhang J, Zhang QL, Sun S, Xue K: Current evidences on vascular endothelial growth factor polymorphisms and breast cancer susceptibility. Mol Biol Rep. 2011, 38 (7): 4491-4494.PubMedView ArticleGoogle Scholar
  168. Yang DS, Park KH, Woo OH, Woo SU, Kim AR, Lee ES, Lee JB, Kim YH, Kim JS, Seo JH: Association of a vascular endothelial growth factor gene 936 C/T polymorphism with breast cancer risk: a meta-analysis. Breast Cancer Res Treat. 2011, 125 (3): 849-853.PubMedView ArticleGoogle Scholar
  169. Xu B, Li JM, Tong N, Tao J, Li PC, Song NH, Zhang W, Wu HF, Feng NH, Hua LX: VEGFA +936C>T polymorphism and cancer risk: a meta-analysis. Cancer Genet Cytogenet. 2010, 198 (1): 7-14.PubMedView ArticleGoogle Scholar
  170. Liu X, Wang Z, Yu J, Lei G, Wang S: Three polymorphisms in interleukin-1beta gene and risk for breast cancer: a meta-analysis. Breast Cancer Res Treat. 2010, 124 (3): 821-825.PubMedView ArticleGoogle Scholar
  171. Oh JS, Kucab JE, Bushel PR, Martin K, Bennett L, Collins J, DiAugustine RP, Barrett JC, Afshari CA, Dunn SE: Insulin-like growth factor-1 inscribes a gene expression profile for angiogenic factors and cancer progression in breast epithelial cells. Neoplasia. 2002, 4 (3): 204-217.PubMed CentralPubMedView ArticleGoogle Scholar
  172. Cao C, Ying T, Fang JJ, Sun SF, Lv D, Chen ZB, Ma HY, Yu YM, Ding QL, Shu LH, Deng Z-C: Polymorphism of vascular endothelial growth factor -2578C/A with cancer risk: evidence from 11263 subjects. Med Oncol. 2011, 28 (4): 1169-1175.PubMedView ArticleGoogle Scholar
  173. Xue H, Lin B, Ni P, Xu H, Huang G: Interleukin-1B and interleukin-1 RN polymorphisms and gastric carcinoma risk: a meta-analysis. J Gastroenterol Hepatol. 2010, 25 (10): 1604-1617.PubMedView ArticleGoogle Scholar
  174. Gao LB, Pan XM, Jia J, Liang WB, Rao L, Xue H, Zhu Y, Li SL, Lv ML, Deng W, Chen TY, Wei YG, Zhang L: IL-8–251A/T polymorphism is associated with decreased cancer risk among population-based studies: evidence from a meta-analysis. Eur J Cancer. 2010, 46 (8): 1333-1343.PubMedView ArticleGoogle Scholar
  175. Liu L, Zhuang W, Wang C, Chen Z, Wu XT, Zhou Y: Interleukin-8–251 A/T gene polymorphism and gastric cancer susceptibility: a meta-analysis of epidemiological studies. Cytokine. 2010, 50 (3): 328-334.PubMedView ArticleGoogle Scholar
  176. Peng B, Cao L, Wang W, Xian L, Jiang D, Zhao J, Zhang Z, Wang X, Yu L: Polymorphisms in the promoter regions of matrix metalloproteinases 1 and 3 and cancer risk: a meta-analysis of 50 case–control studies. Mutagenesis. 2010, 25 (1): 41-48.PubMedView ArticleGoogle Scholar
  177. Wysoczynski M, Ratajczak MZ: Lung cancer secreted microvesicles: underappreciated modulators of microenvironment in expanding tumors. Int J Cancer. 2009, 125 (7): 1595-1603.PubMed CentralPubMedView ArticleGoogle Scholar
  178. Xie Y, Zhang H, Sheng W, Xiang J, Ye Z, Yang J: Adenovirus-mediated ING4 expression suppresses lung carcinoma cell growth via induction of cell cycle alteration and apoptosis and inhibition of tumor invasion and angiogenesis. Cancer Lett. 2008, 271 (1): 105-116.PubMedView ArticleGoogle Scholar
  179. Gao Y, Cao Y, Tan A, Liao C, Mo Z, Gao F: Glutathione S-transferase M1 polymorphism and sporadic colorectal cancer risk: an updating meta-analysis and HuGE review of 36 case–control studies. Ann Epidemiol. 2010, 20 (2): 108-121.PubMedView ArticleGoogle Scholar
  180. Lu S, Wang Z, Cui D, Liu H, Hao X: Glutathione S-transferase P1 Ile105Val polymorphism and breast cancer risk: a meta-analysis involving 34,658 subjects. Breast Cancer Res Treat. 2011, 125 (1): 253-259.PubMedView ArticleGoogle Scholar
  181. Qiu LX, Yuan H, Yu KD, Mao C, Chen B, Zhan P, Xue K, Zhang J, Hu XC: Glutathione S-transferase M1 polymorphism and breast cancer susceptibility: a meta-analysis involving 46,281 subjects. Breast Cancer Res Treat. 2010, 121 (3): 703-708.PubMedView ArticleGoogle Scholar
  182. Singh V, Parmar D, Singh MP: Do single nucleotide polymorphisms in xenobiotic metabolizing genes determine breast cancer susceptibility and treatment outcomes?. Cancer Invest. 2008, 26 (8): 769-783.PubMedView ArticleGoogle Scholar
  183. Curran JE, Weinstein SR, Griffiths LR: Polymorphisms of glutathione S-transferase genes (GSTM1, GSTP1 and GSTT1) and breast cancer susceptibility. Cancer Lett. 2000, 153 (1–2): 113-120.PubMedView ArticleGoogle Scholar
  184. Economopoulos KP, Sergentanis TN, Vlahos NF: Glutathione S-transferase M1, T1, and P1 polymorphisms and ovarian cancer risk: a meta-analysis. Int J Gynecol Cancer. 2010, 20 (5): 732-737.PubMedView ArticleGoogle Scholar
  185. Zeng FF, Liu SY, Wei W, Yao SP, Zhu S, Li KS, Wan G, Zhang HT, Zhong M, Wang BY: Genetic polymorphisms of glutathione S-transferase T1 and bladder cancer risk: a meta-analysis. Clin Exp Med. 2010, 10 (1): 59-68.PubMedView ArticleGoogle Scholar
  186. Moore LE, Baris DR, Figueroa JD, Garcia-Closas M, Karagas MR, Schwenn MR, Johnson AT, Lubin JH, Hein DW, Dagnall CL, Colt JS, Kida M, Jones MA, Schned AR, Cherala SS, Chanock SJ, Cantor KP, Silverman DT, Rothman N: GSTM1 null and NAT2 slow acetylation genotypes, smoking intensity and bladder cancer risk: results from the New England bladder cancer study and NAT2 meta-analysis. Carcinogenesis. 2011, 32 (2): 182-189.PubMed CentralPubMedView ArticleGoogle Scholar
  187. Tsukuda M, Nagahara T, Yago T, Matsuda H, Yanoma S: Production of granulocyte colony-stimulating factor by head and neck carcinomas. Biotherapy. 1993, 6 (3): 183-187.PubMedView ArticleGoogle Scholar
  188. Lagadec PF, Saraya KA, Balkwill FR: Human small-cell lung-cancer cells are cytokine-resistant but NK/LAK-sensitive. Int J Cancer. 1991, 48 (2): 311-317.PubMedView ArticleGoogle Scholar
  189. Enzmann V, Faude F, Kohen L, Wiedemann P: Secretion of cytokines by human choroidal melanoma cells and skin melanoma cell lines in vitro. Ophthalmic Res. 1998, 30 (3): 189-194.PubMedView ArticleGoogle Scholar
  190. Kimura F, Nakamura Y, Sato K, Wakimoto N, Kato T, Tahara T, Yamada M, Nagata N, Motoyoshi K: Cyclic change of cytokines in a patient with cyclic thrombocytopenia. Br J Haematol. 1996, 94 (1): 171-174.PubMedView ArticleGoogle Scholar
  191. Reinhold D, Bank U, Buhling F, Lendeckel U, Ulmer AJ, Flad HD, Ansorge S: Transforming growth factor-beta 1 (TGF-beta 1) inhibits DNA synthesis of PWM-stimulated PBMC via suppression of IL-2 and IL-6 production. Cytokine. 1994, 6 (4): 382-388.PubMedView ArticleGoogle Scholar
  192. Nikolova PN, Pawelec GP, Mihailova SM, Ivanova MI, Myhailova AP, Baltadjieva DN, Marinova DI, Ivanova SS, Naumova EJ: Association of cytokine gene polymorphisms with malignant melanoma in Caucasian population. Cancer Immunol Immunother. 2007, 56 (3): 371-379.PubMedView ArticleGoogle Scholar
  193. Freedman RS, Deavers M, Liu J, Wang E: Peritoneal inflammation - a microenvironment for epithelial ovarian cancer (EOC). J Transl Med. 2004, 2 (1): 23-PubMed CentralPubMedView ArticleGoogle Scholar
  194. Jarnicki A, Putoczki T, Ernst M: Stat3: linking inflammation to epithelial cancer - more than a “gut” feeling?. Cell division. 2010, 5: 14-PubMed CentralPubMedView ArticleGoogle Scholar
  195. Rundhaug JE, Fischer SM: Molecular mechanisms of mouse skin tumor promotion. Cancers. 2010, 2 (2): 436-482.PubMed CentralPubMedView ArticleGoogle Scholar
  196. Mischek D, Steinborn R, Petznek H, Bichler C, Zatloukal K, Sturzl M, Gunzburg WH, Hohenadl C: Molecularly characterised xenograft tumour mouse models: valuable tools for evaluation of new therapeutic strategies for secondary liver cancers. J Biomed Biotechnol. 2009, 2009: 437284-PubMed CentralPubMedView ArticleGoogle Scholar
  197. Hiss D: Optimizing molecular-targeted therapies in ovarian cancer: the renewed surge of interest in ovarian cancer biomarkers and cell signaling pathways. Journal of Oncology. 2012, 2012: 737981-PubMed CentralPubMedView ArticleGoogle Scholar
  198. Luo J, Solimini NL, Elledge SJ: Principles of cancer therapy: oncogene and non-oncogene addiction. Cell. 2009, 136 (5): 823-837.PubMed CentralPubMedView ArticleGoogle Scholar
  199. Chen Z, Yan B, Van Waes C: The role of the NF-kappaB transcriptome and proteome as biomarkers in human head and neck squamous cell carcinomas. Biomark Med. 2008, 2 (4): 409-426.PubMed CentralPubMedView ArticleGoogle Scholar
  200. Shimada Y, Imamura M: Prognostic factors for esophageal cancer–from the viewpoint of molecular biology. Gan To Kagaku Ryoho. 1996, 23 (8): 972-981.PubMedGoogle Scholar
  201. Ziober BL, Turner MA, Palefsky JM, Banda MJ, Kramer RH: Type I collagen degradation by invasive oral squamous cell carcinoma. Oral Oncol. 2000, 36 (4): 365-372.PubMedView ArticleGoogle Scholar
  202. Nuovo GJ: In situ detection of PCR-amplified metalloproteinase cDNAs, their inhibitors and human papillomavirus transcripts in cervical carcinoma cell lines. Int J Cancer. 1997, 71 (6): 1056-1060.PubMedView ArticleGoogle Scholar
  203. Liss C, Fekete MJ, Hasina R, Lam CD, Lingen MW: Paracrine angiogenic loop between head-and-neck squamous-cell carcinomas and macrophages. Int J Cancer. 2001, 93 (6): 781-785.PubMedView ArticleGoogle Scholar
  204. Stachel D, Albert M, Meilbeck R, Kreutzer B, Haas RJ, Schmid I: Bone marrow Th2 cytokine expression as predictor for relapse in childhood acute lymphoblastic leukemia (ALL). Eur J Med Res. 2006, 11 (3): 102-113.PubMedGoogle Scholar
  205. Melinceanu L, Sarafoleanu C, Lerescu L, Tucureanu C, Caras I, Salageanu A: Impact of smoking on the immunological profile of patients with laryngeal carcinoma. J Med Life. 2009, 2 (2): 211-218.PubMed CentralPubMedGoogle Scholar
  206. Lazar-Molnar E, Hegyesi H, Toth S, Falus A: Autocrine and paracrine regulation by cytokines and growth factors in melanoma. Cytokine. 2000, 12 (6): 547-554.PubMedView ArticleGoogle Scholar
  207. Inoue K, Wood CG, Slaton JW, Karashima T, Sweeney P, Dinney CP: Adenoviral-mediated gene therapy of human bladder cancer with antisense interleukin-8. Oncol Rep. 2001, 8 (5): 955-964.PubMedGoogle Scholar
  208. Snarskaia ES, Molochkov VA, Frank GA, Zavalishina LA: Matrix metalloproteinases and their tissue inhibitors in basal cell and metatypical cancer of the skin. Arkhiv Patologii. 2005, 67 (3): 14-16.PubMedGoogle Scholar
  209. Lievre A, Milet J, Carayol J, Le Corre D, Milan C, Pariente A, Nalet B, Lafon J, Faivre J, Bonithon-Kopp C, Olschwang S, Bonaiti-Pellié C, Laurent-Puig P, Members of the ANGH group: Genetic polymorphisms of MMP1, MMP3 and MMP7 gene promoter and risk of colorectal adenoma. BMC Cancer. 2006, 6: 270-PubMed CentralPubMedView ArticleGoogle Scholar
  210. Ricketts C, Zeegers MP, Lubinski J, Maher ER: Analysis of germline variants in CDH1, IGFBP3, MMP1, MMP3, STK15 and VEGF in familial and sporadic renal cell carcinoma. PLoS One. 2009, 4 (6): e6037-PubMed CentralPubMedView ArticleGoogle Scholar
  211. Esteller M, Garcia A, Martinez-Palones JM, Xercavins J, Reventos J: Susceptibility to endometrial cancer: influence of allelism at p53, glutathione S-transferase (GSTM1 and GSTT1) and cytochrome P-450 (CYP1A1) loci. Br J Cancer. 1997, 75 (9): 1385-1388.PubMed CentralPubMedView ArticleGoogle Scholar
  212. Kozhemiakin LA, Bulavin DV, Morozov VI, Osipov EV, Zolotarev DV: Effectiveness of the glutathione s-transferase assay in diagnosing lung cancer. Vopr Onkol. 1995, 41 (1): 33-38.PubMedGoogle Scholar
  213. Schilthuizen C, Broyl A, van der Holt B, de Knegt Y, Lokhorst H, Sonneveld P: Influence of genetic polymorphisms in CYP3A4, CYP3A5, GSTP1, GSTM1, GSTT1 and MDR1 genes on survival and therapy-related toxicity in multiple myeloma. Haematologica. 2007, 92 (2): 277-278.PubMedView ArticleGoogle Scholar
  214. Lavender NA, Benford ML, VanCleave TT, Brock GN, Kittles RA, Moore JH, Hein DW, Kidd LC: Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among men of African descent: a case–control study. BMC Cancer. 2009, 9: 397-PubMed CentralPubMedView ArticleGoogle Scholar
  215. Saadat I, Saadat M: The glutathione S-transferase mu polymorphism and susceptibility to acute lymphocytic leukemia. Cancer Lett. 2000, 158 (1): 43-45.PubMedView ArticleGoogle Scholar
  216. Ovsepian VA, Vinogradova E, Sherstneva ES: Cytochrome P4501A1, glutathione S-transferase M1 and T1 gene polymorphisms in chronic myeloid leukemia. Genetika. 2010, 46 (10): 1360-1362.PubMedGoogle Scholar
  217. Vrana D, Pikhart H, Mohelnikova-Duchonova B, Holcatova I, Strnad R, Slamova A, Schejbalova M, Ryska M, Susova S, Soucek P: The association between glutathione S-transferase gene polymorphisms and pancreatic cancer in a central European Slavonic population. Mutat Res. 2009, 680 (1–2): 78-81.PubMedView ArticleGoogle Scholar

Copyright

© Lanara et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.