Genetic variations in multiple myeloma I: effect on risk of multiple myeloma

Genetic variations in multiple myeloma I: effect on risk ofmultiple myelomaAnnette Vangsted1, Tobias W. Klausen2, Ulla Vogel3

1Department of Haematology, Roskilde Hospital, Copenhagen University, Roskilde; 2Department of Haematology, University Hospital of

Copenhagen at Herlev, Herlev, Copenhagen; 3National Research Centre for the Working Environment, Copenhagen, Denmark

The sequencing of the human genome sequence and the

analysis of the sequence in many humans in different

populations have identified millions of genetic variations

between individuals and ethnic groups. These variations

in the human genome may explain part of the observed

interindividual variations in the risk of disease. A varia-

tion in the DNA sequence that occurs at a frequency of

<1% in the population is defined as a mutation, and one

that occurs at higher frequency is termed a polymor-

phism. A somatic mutation is one that occurs only in

somatic cells, whereas an inherited mutation has occurred

in a germ cell. The stability of an inherited mutation in a

population depends on whether it favors the survival of

the individual to reproductive age and favors reproduc-

tion. If it does not, there will be evolutionary selection

against the mutation, and the fraction of carriers in the

population will be determined by a balance between new

mutations and the selective pressure.

Single nucleotide polymorphisms (SNPs) are the most

common DNA variations in the human genome and

occur on average once every 300–1000 bp. Frequent

SNPs are suspected to underlie factors for many com-

mon disorders attributed to polygenic influences. SNPs

with functional importance for gene function are often

expected to be located in the coding region of the gene

and are presumed to cause changes in the amino acid

sequence of the protein encoded by the gene. However,

most SNPs are not located in the coding region of the

gene, and SNPs with functional importance of gene func-

tion may be found in promoter regions where they influ-

ence the binding of transcriptions factors and therefore

influence the regulation of expression of the gene. A gene

locus is located at a chromosome and consists of a pair

of alleles. Humans can carry an identical set of alleles

(homozygotes) or two different alleles (heterozygotes) of

a given genetic variation. An individual¢s genotype refers

Abstract

Few risk factors have been established for the plasma cell disorder multiple myeloma, but some of these

like African American ethnicity and a family history of B-cell lymphoproliferative diseases suggest a genetic

component for the disease. Genetic variation represents the genetic basis of variability in a population.

The complex interplay between environment and genes for the development of cancer may therefore be

influenced by genetic variations. A genetic variation may change the function of the gene, and if the

genetic variation is associated with the risk of disease, that particular gene may be involved in the patho-

genesis of disease. Genes of interest are genes involved in the normal development and function of the

plasma cell and genes that protect us against exposures from the environment, for example, genes

involved in the metabolism of xenobiotics, metabolism of folate and methionine, as well as genes involved

in inflammation and DNA repair. Identification of genes with potential influence on cancer risk may help us

to establish relevant laboratory studies on exposure and dose–response assessment and may help us to

test the hypothesis in epidemiological studies. Knowledge of individual at high risk of cancer may offer

promising insight for the prevention of cancer.

Key words multiple myeloma; SNP; risk

Correspondence Annette Vangsted, MD, Clinical Associate Professor, Department of Haematology, Roskilde Hospital, Copenhagen

University, Køgevej 9-13, 4000 Roskilde, Denmark. Tel: +45 46333200; Fax: +45 46326994; e-mail: [email protected]

Accepted for publication 24 August 2011 doi:10.1111/j.1600-0609.2011.01700.x

REVIEW ARTICLE

European Journal of Haematology 88 (8–30)

8 ª 2011 John Wiley & Sons A/S

to the set of alleles of the gene they carry. Several genes

are highly polymorphic, and characterization of the spec-

trum of genetic variation may give additional informa-

tion. Many SNPs are inherited together in chunks, and

the shorter physical distance between the SNPs, the more

likely that they are inherited together. This is called link-

age disequilibrium and is defined at the non-random

association between alleles at different loci on the same

chromosome. Groups of SNPs, inherited together, in the

same chromosomal region are called haplotypes. Deter-

mination of haplotypes may be important for finding the

locus that causes disease and may illuminate combina-

tion effects of several SNPs on the same chromosome.

Determination of haplotype-blocks (blocks of SNPs with

high linkage disequilibrium) helps us in associating DNA

variation with phenotype.

Multiple myeloma (MM) is an end-stage neoplasia of

the B cell located mainly in the bone marrow. The estab-

lishment of the myeloma cell is dependent on the bone

marrow microenvironment. In the recent years, it has

become clear that the normal development of B cells is

far more complex than previously thought. This insight

may help us to understand the biology of the myeloma

disease. B cells undergo development in the bone marrow

and in the secondary peripheral lymphoid tissue such as

lymph nodes, the spleen, Waldeyer¢s ring, and the

Payer’s patches. In the bone marrow, the maturation of

B cells is independent of antigen stimulation. However,

when the B cell leaves the bone marrow, antigen expo-

sure is mandatory for activation but does not necessary

require help from T cells. In the bone marrow, the B cell

develops from a hematopoietic stem cell biased for lym-

phoid development (1) and normal development occurs

by interaction with bone marrow stromal cells by cell

adhesion molecules, interleukins and cytokines. The dif-

ferent stages of B-cell development in the bone marrow

are defined according to the rearrangement of the vari-

able (V), diversity (D), and joining (J) segments of the

immunoglobulin heavy chain gene (IgH) followed by the

rearrangement of the immunoglobulin light chain gene

segments (IgL) kappa and lambda (2).

The exact initiation step of the V(D)J rearrangement

process is still unclear but an early step is controlled by

the recombination activating genes RAG1 and RAG2 (2,

3). Only one of the two alleles of the recombined IgH

and Igk or Igl genes is functional as a result of a mecha-

nism called allelic exclusion (4). Immunoglobulins are

transcribed from both alleles, but under normal condi-

tions, only one allele is expressed as a functional immu-

noglobulin. When the B cell leaves the bone marrow, a

functional Ig heavy and light protein complex is pre-

sented on the cell surface as the B-cell receptor. At this

stage, the B-cell receptor is a monomeric IgM molecule.

An exact synchronized phenotypic presentation of CD

molecules on the B cell that parallel the changes in Ig

gene rearrangement is not established. The pro-B cells

express TDT, CD19+, CD10+, and CD34+ on the sur-

face, and at the time of IgH rearrangement, the pre-B

phenotype is CD79A, CD19+, CD10+, and CD34).

When the IgL gene is rearranged, the immature B cell

expresses CD20 and IgM on the surface, and when the

cell finally leaves the bone marrow as a mature, but

naive, B cell, it also expresses IgD. A B-cell response can

occur extrafollicularily and in the germinal center of the

secondary lymphoid tissue. The germinal center is usually

formed after an infection when the B cell encounters the

corresponding antigen and is activated by interaction

with T-follicular helper cells and follicular dendritic cells.

The activation of B cells and the selection mechanism of

high-affinity antibodies are complex. It involves the tran-

scriptions factor Pax5 and the expression of Bcl-6, CD40

ligand (CD154), interleukins, integrins, and adhesion

molecules. In the germinal center, the B cell undergoes

clonal expansion and two different changes in the immu-

noglobulin gene occur. One of them is somatic hypermu-

tations in the variable genes that generate

immunoglobulin with high affinity for the encountered

antigen, and the other is class switch recombination that

changes the isotype to IgA, IgG, and IgE with different

effector functions. The class switch recombination does

not interrupt the idiotype specificity of the immunoglob-

ulin. The somatic events in the immunoglobulin gene

cause DNA lesions that are repaired by the DNA repair

enzymes and in particular the DNA repair mechanism

non-homologous end joining (NHEJ) (3). Proliferating

and antibody-producing B cells are called plasmablasts.

They have migratory capacity and can become short- or

long-lived plasma cells. Genetic regulation of the plasma

cell program includes down-regulation of the B-cell gene

Pax5 and up-regulation of the genes IRF4 and PRDM1

encoding transcription factor B-lymphocyte-induced mat-

uration protein 1 (Blimp1) (5). New insight indicates that

plasma cells have different life spans and can originate

from extrafollicular regions and germinal centers. In con-

trast to plasma cells from follicular centers that produce

high-affinity antibodies and are long-lived plasma cells,

plasma cells from extrafollicular regions are short-lived

and produce low-affinity antibodies. The long-lived

plasma cells from the follicular centers reside in the bone

marrow as non-dividing memory plasma cells with the

capacity to produce Ig (6).

The occurrence of chromosomal abnormalities in the

myeloma cell is well established (7). In a large study of

1064 patients with MM, Avet-Loiseau et al. found that

cytogenetic abnormalities were present in up to 90% of

the patients by FISH analysis (8). The chromosomal

changes can be divided into two groups: hyperdiploid

changes, likely to occur by failures in mitosis, and non-

Vangsted et al. SNP and risk of multiple myeloma patients

ª 2011 John Wiley & Sons A/S 9

hyperdiploid changes generated by double-strand breaks

(DSB). The del(13) was the most frequent abnormality

and was found in 48% of the cases. Hyperdiploidy was

found in 39% followed by translocation in IgH locus at

14q32 with different partner genes. More than 20 partner

genes have been documented to be translocated to the

strong active enhancer of the IgH locus. The most fre-

quent IgH translocation is t(11,14) found in 21% of the

cases and t(4,14) found in 14% of the cases. Because

myeloma cells secrete a functional IgG and IgA immuno-

globulins, the illegitimate translocations must occur in

the non-functional allele. Furthermore, Avet-Loiseau

et al. found Myc-translocations in 13% of the cases.

Translocations of c-myc are considered a late event that

does not involve B-cell-specific recombination mecha-

nisms. Finally, del(17p) was found in 11% of the cases

(7, 8). The most frequent dysregulations of gene expres-

sion are caused by the chromosomal abnormalities in

genes that take part in the regulation of growth, prolifer-

ation, and differentiation of the myeloma cell. For exam-

ple, overexpression of genes in myeloma cells is a result

of the following translocations: t(11,14): the proto-onco-

gene cyclin D1, t(6, 14): cyclin D3, t(4, 14): the fibroblast

growth factor (FGF) receptor 3 and MMSET ⁄WHSC1,

and t(14, 16): c-maf. The numerous chromosomal abnor-

malities found in MM may be a result of genetic instabil-

ity which in turn might be caused by a defect DNA

repair system. Illegitimate translocations may provide the

plasma cells with one of the first DNA hits that send

them on the neoplastic track with the overexpression of

genes involved in the regulation of growth and prolifera-

tion.

The cellular part of the bone marrow consists of

hematopoietic stems cells and their successors, stromal

cells (endothelial cells, reticular cells, fibroblasts, and

adipocytes), osteoblasts, and osteoclast (9). Numerous

studies indicate that the myeloma cell depends on the

bone marrow microenvironment for survival. The com-

plex interplay between the myeloma cell and the bone

marrow microenvironment is in part mediated by cell–

cell interaction, cytokines, growth factors, and adhesion

molecules and is central for the growth and survival of

myeloma cells as well as for migration, angiogenesis,

bone disease (9–11), and impairment of the immune sys-

tem (12). Growth factors produced by the myeloma cell

include interleukin-6 (IL-6), insulin-like growth factor 1

(IGF-1), vascular endothelial growth factor (VEGF), and

stromal cell–derived factor 1a (SDF-1a), and growth fac-

tors produced by the bone marrow stromal cells include

the FGF, the tumor necrosis factor-a, and tumor necro-

sis factor-b (TNF-a and TNF-b) (13). They stimulate the

growth of myeloma cell by direct binding to receptors on

the myeloma cell or indirectly by stimulation of IL-6

production from the bone marrow stromal cells (11).

Another possible mechanism of the complex cytokine

network may be found in the myeloma cells themselves.

Studies on myeloma samples have shown genomic abnor-

malities in the nuclear factor-jB (NF-jB) pathway lead-

ing to constitutive activation of NF-jB (14, 15). NF-jBis a transcription factor regulating the transcription of

many genes. Examples of genes induced by NF-jBinclude genes that regulate the expression of pro-inflam-

matory cytokines, adhesion molecules, chemokines,

COX-2, and anti-apoptotic genes, genes involved in the

regulation of proliferation, and genes involved in the

innate and adaptive immune system. The importance of

the NF-jB pathway in the pathogenesis of MM is fur-

ther supported by the clinical studies. Treatments that

interfere with the NF-jB pathway, such as proteasome

inhibitors, thalidomide, and IMiDs, significantly influ-

ence outcome (11, 16–18).

The pathogenesis of the disease is unknown but the

frequent aberrant IgH translocation found in the mye-

loma cell strongly suggests that these rearrangements are

important. As myeloma cells have undergone immuno-

globulin V(D)J recombination, isotype switch recombina-

tion, and somatic hypermutations, they are considered

postgerminal center B cells established after antigen

selection (19). The bone marrow microenvironment fur-

ther supports the establishment of the myeloma cell.

Chronic inflammation and a dysfunctional DNA repair

system are now generally accepted as components of

many neoplasias (20). Microbial and viral infections as

well as several other agents cause chronic inflammation.

Environmental exposure of toxicants may persistently

stimulate the immune system as seen for tobacco smok-

ers, exposure to asbestos, and radiation. For the DNA

repair system, this persistent stimulation may induce the

overexpression of, for example, DNA recombination

pathways and result in DNA rearrangements and onco-

gene activation (21, 22).

A number of environmental exposures have consis-

tently been considered important in the etiology of mye-

loma. These include exposure to ionizing radiation,

benzene, solvents, chronic immune stimulation, and

occupations such as farming (23). However, results form

case–control studies are inconsistent and provide little

evidence to support a causal relationship between expo-

sure to toxicants and risk of disease probably due to

difficulties such as MM being an uncommon disease, the

lack of accurate measurement of underlying exposures,

incomplete medical records, and difficulties in the quanti-

fication of recurrent and chronic infections.

Only few risk factors are yet well-established risk fac-

tors for MM, for example, increasing age, male gender,

African American ethnicity, monoclonal gammopathy of

undetermined significance (MGUS), and a family history

of B-cell lymphoproliferative diseases (23, 24). These risk

SNP and risk of multiple myeloma patients Vangsted et al.


factors suggest a genetic component in the risk of the

disease. Epidemiologists have addressed lifestyle, occupa-

tion, toxicants, and immune function for risk of MM,

but no such strong risk factor has been identified (23).

Geneticists search for inborn variation in genes that, for

example, influence the control of detoxification processes,

the integrity of the DNA molecule, cell cycle regulation,

and immune function. They do not address changes in

the cancer cell but look for changes in the individual’s

genetic constitution that may have effect on the processes

that protect the normal cells from developing into a

cancer cell.

The role of exposure to toxic chemical such as ben-

zene, pesticides, oil, and organic solvents for risk of MM

has been explored, but no convincing links have been

found (23). As shown for chimney sweepers, cigarette

smokers, and exposure to asbestos, long-term exposure

to carcinogens is often required for demonstrating influ-

ence on cancer risk. Many chemicals that have been

identified as carcinogens need to be metabolized in the

body to reactive metabolites to exert their effect. The

reactive metabolites bind to DNA forming covalent

bonds. We have several DNA repair systems that have

the ability to recognize and remove damaged DNA bases

from DNA. The numerous chromosomal alterations that

have been found in MM cells reflect errors that occur

during the immunoglobulin rearrangement, hypermuta-

tion, and switch recombination during the maturation of

the B cell, but they may also reflect DNA damage caused

by both exogenous toxicants and endogenous metabo-

lites, such as chemicals, ischemia, UV light, viral infec-

tion, chronic inflammation, replication errors, and

chemotherapy. If DNA damage occurs, this may lead to

cell cycle arrest, activation of the DNA repair mecha-

nism, and repair of the damage. Unsuccessful DNA

repair may lead to base change that is transmitted during

cell division as a mutation. The cell may proceed its life

cycle with damaged DNA that eventually may lead to

cancer.

Enzymes that activate and detoxify mutagens, pharma-

ceuticals, and other xenobiotics are traditionally divided

into phase 1 and phase 2 enzymes (25). The phase 1

enzymes usually oxidize the xenobiotic, but sometimes

they reduce or isomerize the xenobiotic. The most com-

mon reactions are carried out by enzymes belonging to

the cytochrome P450 (CYP) superfamily. Whereas a few

CYPs are essential for the synthesis of hormones and

essential cofactors, most of the 57 enzymes identified in

humans are believed to have evolved to protect us

against food and environmental toxicants. The phase 2

enzymes couple cofactors to the xenobiotic (very often

the product of phase 1 metabolism) and make the prod-

uct more water soluble. The most important enzyme

groups are glucuronyl-, sulfo-, and glutathione transfer-

ases. Another important system that protects us against

xenobiotics is the membrane transporter systems. The

membrane transporter systems are transmembrane pro-

teins with drug efflux function. They were identified in

tumor cells that were resistant to cytostatic drugs.

Genes involved in the metabolism ofxenobiotics

Polymorphisms in phase 1 and 2 genes that change the

enzymatic function of the encoded proteins are widely

accepted as risk factors for the development of certain

types of cancer. For MM, analyses of genetic variation

have focused on enzymes involved in the metabolism and

detoxification of toxicants included in the human lifestyle

(obesity, diet, tobacco and alcohol abuse, and hormonal

factors), organic solvents, and pesticides. These enzymes

are, for example, CYP enzymes, glutathione s-transfer-

ases (GST), paraoxonase (PON1), N-acetyltransferases

(NAT 1 and NAT2), and P-glycoprotein (P-gp). The

P-gp is one of the best characterized transmembrane

transporter proteins found in MM and is encoded by the

MDR1 gene (26, 27).

Few studies address the association between DNA

variations in the CYP and risk of MM (Table 1). In a

study of 90 Caucasian myeloma cases by Lincz et al., no

association was found with MM for DNA variations in

CYP1A1 and CYP2E1 in Caucasians (28, 29). Kang

et al. reported a lowered risk for variant allele carriers of

CYP1A in 116 Koreans with MM (30). In a larger popu-

lation-based case–control study of 279 Caucasians and

African Americans, Gold et al. studied DNA variations

in CYP1B1 and CYP2C9 and concluded that variant

allele carriers of CYP1B1 (rs1056836), which may result

in higher enzyme activity and more toxic intermediate

compounds, may be at increased risk of MM in both

populations (31). In our recent study on DNA variations

in CYP2C19 and CYP2D6, the genotype frequencies in

our patient material were in accordance with data from

other Northern and Western European populations and

thus not associated with risk of MM (32).

Determination of DNA variations in the phase 2 genes

in relation to risk of MM yielded different results. No

influence of genetic variations in GSTM1, GSTP1,

NQO1, and MPO and risk of MM have been reported

(28–30, 33, 34). Lincz et al. found a higher incidence for

both NAT2 slow acetylator carriers, GST T1 null carri-

ers, and variant carriers of PON1, in 90 Caucasian

patients with MM (29). These results could not be con-

firmed by others (30, 31). In membrane transport sys-

tems, two small studies did not find any associations

with the selected genetic variations in the ABCB1 gene

with risk of MM (Table 1) (35, 36). We conclude that at

present, no validated associations were found for the



Tab

le1

Genetic

variation

and

associa

tion

with

risk

of

multip

lem

yelo

ma

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

Zheng

et

al.

(85)

Caucasia

ncase

⁄contr

ol

(73

1a

⁄129

2b)

IL6

1800795

)174G

>C

Pro

mote

rD

ecre

ase

RFLP

–P

CR

NS

4ab

TN

FA

)308G

>A

Pro

mote

rIn

cre

ase

NS

IL1B

TaqI

Tyr-

Lys

Exon

5In

cre

ase

NS

IL1R

aV

NTR

Intr

on

2In

cre

ase

NS

Ord

onez

et

al.

(40)

Caucasia

ncase

⁄contr

ol

(26

1ad

⁄200

2a3ad)

MTH

RF

677C

>T

Ala

222V

al

Decre

ase

Taqm

an

PC

R0.2

8

(0.1

0–0.7

7)

0.0

14ad*

Davie

set

al.

(81)

Caucasia

ncase

⁄contr

ol

(198

⁄250

2d3ab)

TN

FA

)308G

>A

Pro

mote

rIn

cre

ase

Taqm

an

PC

RN

S

LTA

+252A

>G

Intr

on

1In

cre

ase

NS

Haplo

type

308

GA+

252

AG

2.0

5

(1.2

6–3.3

5)

0.0

03

4c

Zheng

et

al.

(96)

Caucasia

ncase

⁄contr

ol

(73

1a

⁄109

2a)

IL10

Mic

rosate

llite

:

)1082G

>A

Pro

mote

rD

ecre

ase

Taqm

an

PC

RN

S

IL-1

0.G

:136

⁄136

Pro

mote

rIn

cre

ase

6.9

3

(2.6

4–18.3

)

<0.0

03

4ab

IL-1

0.R

:112

⁄114

3.1

1

(1.5

6–6.2

8)

<0.0

5

114

⁄116

Pro

mote

r0.0

2

(0.0

8–0.4

9)

<0.0

01

Zheng

et

al.

(105)

Caucasia

ncase

⁄contr

ol

(73

⁄100

3d)

CTLA

4(A

T)n

Mic

rosate

llite

:

86

⁄86

Taqm

anP

CR

86

⁄104

104

⁄104

0.4

8<

0.0

54ab

NS

NS

Gonza

lez-

Fra

ile

et

al.

(41)

Caucasia

ncase

⁄contr

ol

(107

1a

⁄86

2b3abd)

MTH

RF

677C

>T

Ala

222V

al

Glu

429A

la

Decre

ase

Taqm

an

PC

RN

S4d

1298A

>C

Decre

ase

NS

Roddam

et

al.

(56)

Caucasia

ncase

⁄contr

ol

(270

f⁄2

20

d)

LIG

48C

>T

Ala

3V

al

Taqm

an

PC

R0.4

9

(0.2

7–0.8

9)

<0.0

54c

**

26C

>T

Thr9

Ile

0.2

2

(0.0

7–0.7

)

NS

8C26

T<

0.0

5

Haplo

type

8T26

T0.0

5

<0.0

5

Ort

ega

et

al.

(109)

Bra

zilia

ncase

⁄contr

ol

(58

1e

⁄300

2a)

CO

L18A

14396G

>A

Asp

104A

sn

RFLP

–P

CR

NS

Lin

cz

et

al.

(42)

Caucasia

ncase

⁄contr

ol

(90

1a

⁄299

2b)

MTH

FR

677C

>T

Ala

222V

al

Decre

ase

RFLP

–P

CR

NS

4d

MR

HFR

1298A

>C

Glu

429A

laD

ecre

ase

NS

MS

2756A

>G

NS



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

Lin

cz

et

al.

(29)

Caucasia

ncase

⁄contr

ol

(90

1e

⁄205

2b)

GS

TT1

Pos>

null

LO

F1.9

6

(1.0

6–3.6

5)

0.0

44a

GS

TM

1P

os>

null

NS

PO

N1

Gln

192A

rg2.2

7

(1.0

6–4.7

6)

0.0

5

NA

T1

Slo

w>

rapid

NS

NA

T2

Slo

w>

rapid

1.8

9

(1.1

4–3.2

6)

0.0

1

CY

P1A

1N

S

Mazu

ret

al.

(97)

Caucasia

ncase

⁄contr

ol

(54

a⁄5

02)

IL6

1800795

)174G

>C

Pro

mote

rTaqm

an

PC

RN

S4ab

IL10

)1082G

>A

Pro

mote

rN

S

)819C

>T

Pro

mote

rN

S

)592A

>C

Pro

mote

rN

S

IL10

haplo

type

AC

CN

S

Morg

an

et

al.

(82)

Caucasia

ncase

⁄contr

ol

TN

FA

)1031T>

CP

rom

ote

rIH

Ganaly

sis

NS

(181

1b

⁄233

2d)

)863C

>A

Pro

mote

rN

S

)857C

>T

Pro

mote

rN

S

)308G

>A

Pro

mote

rIn

cre

ase

0.5

8

(0.3

9–0.8

7)

0.0

14c

)238G

>A

Incre

ase

NS

+489G

>A

Intr

on

1N

S

LTA

+252A

>G

0.7

3

(0.5

4–0.9

9)

0.0

5

Mic

rosate

llite

:N

S

TN

Fa

aN

S

TN

Fa

bN

S

TN

Fa

cN

S

TN

Fa

dIn

tron

IVN

S

TN

Fa

eN

S

Au

et

al.

(110)

Asia

ncase

⁄contr

ol

(82

1a

⁄1813

2d)

TN

FA

)380G

>A

Pro

mote

rR

FLP

–P

CR

NS

4d

Paneesha

et

al.

(111)

Caucasia

ncase

⁄contr

ol

(136

1d

⁄95

2e)

P2X

71513A

>C

LO

FR

FLP

–P

CR

NS

4d

Pem

bert

on

et

al.

(112)

Caucasia

ncase

⁄contr

ol

(126

1e

⁄108

2b)

SD

F1

(CX

CL12)

801G

>A

3¢-u

ntr

ansla

ted

regio

n

Incre

ase

RFLP

–P

CR

NS

4d

Chiu

solo

et

al.

(43)

Caucasia

ncase

⁄contr

ol

(100

1b

⁄100

2b3ab)

MTH

FR

677C

>T

Ala

222V

al

Decre

ase

SS

CP

-PC

RN

S

1298A

>C

Glu

429A

laD

ecre

ase

NS

Coze

net

al.

(75)

Am

erican

case

⁄contr

ol

(150

1f⁄1

26

2d3abd)

IL6

1800796

)572G

>C

Pro

mote

rTaqm

an

PC

R

or

RFLP

–P

CR

NS



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

)373A

nT

nN

S

1800795

)174G

>C

Pro

mote

rN

S

)597G

>A

Pro

mote

rN

S

IL6R

A8192284

358

Ala

)358

Asp

Exon

9N

S

Vangste

det

al.

(58)

Caucasia

ncase

⁄contr

ol

(348

1cf⁄7

53

2d3ab)

ER

CC

21052559

(13181)

2551A

>C

Lys

751G

lnE

xon

23

Taqm

an

PC

RN

S4a

1799793

Asp

312A

sn

Exon

10

NS

PP

P1R

13L

197064

IVS

14364A

>G

Intr

on

1N

S

CD

3E

AP

967591

)21G

>A

Exon

1N

S

ER

CC

1317770

Asn

118A

sn

Exon

4N

S

XR

CC

3861535

Thr2

41M

et

NS

Ostr

ovsky

et

al.

(113)

Isra

eli

case

⁄contr

ol

(44

⁄103

2b)

HP

SE

4696608;6

535455;

11099592

RFLP

–P

CR

NS

4d

4364254;6

856901

NS

4693602

36702C

>T

3¢-U

TR

30.0

26

Moon

et

al.

(44)

East

Asia

ncase

⁄contr

ol

(196

1e

⁄434

2b)

MTH

FR

677C

>T

Ala

222V

al

Decre

ase

Taqm

an

PC

RN

S

1298A

>C

Glu

429A

laD

ecre

ase

NS

Haplo

type

677

CC

⁄1298

CC

3.8

3

(1.2

–12.1

)

0.0

23

4c

Kim

et

al.

(45)

Kore

an

case

⁄contr

ol

(173

⁄1700

2d)

MTH

FR

677C

>A

,

1298

A>

C

Decre

ase

PC

RN

S

MS

2756A

>G

AG

:D

ecre

ase

NS

TS

2R

>3R

Decre

ase

0.6

1(0

.4–0.9

)0.0

24c

Ins

⁄del

NS

MTR

R66A

>G

Decre

ase

NS

Decre

ase

NS

Spin

ket

al.

(68)

Caucasia

ncase

⁄contr

ol

(157

1b

⁄196

2d)

NFK

B1A

3138953

)881A

>G

PC

R-b

ased

IHG

NS

2233409

)297C

>T

NS

1957106

212C

>T

NS

10782383

1059C

>T

NS

3138054

1678A

>G

0.6

1

(0.3

8–0.6

8)

0.0

42

4ad

2233419

2025C

>T

0.6

3

(0.3

9–1.0

0)

0.0

48

8904

2787C

>T

NS

2921A

>G

NS

Haplo

type

GC

CTA

TC

A2.2

9

(2.1

0–2.4

9)

0.0

006

Ort

ega

et

al.

(33)

Bra

zilia

ncase

⁄contr

ol

106

1e

⁄230

2b

GS

TM

1P

os>

null

Decre

ase

RFLP

–P

CR

NS

4ac

GS

TT1

Pos>

null

Decre

ase

NS



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

P53

arg

72pro

Exon

4N

S

Bro

wn

et

al.

(83)

Euro

pean

Am

erican

wom

en

case

⁄contr

ol

(127

1f⁄5

45

2d)

IL1A

17561;1

800587

TaqM

an

PC

R

NS

IL1B

1143634;1

143627,1

6944

NS

IL1R

N454078

NS

IL6

1800797;1

800795

NS

IL12A

582054;5

68408

NS

IL12B

3212227;

NS

LTA

7468868:9

09253

NS

TN

F1799724;1

800629;3

615525

NS

IL2

2069762

NS

IL4

2243248;2

243250;2

070874;

NS

2243368;2

243290

NS

IL4R

2107356

)28120T>

CP

rom

ote

r1.9

1

(1.0

8–3.3

8)

<0.0

54c

IL5

2069818;2

069822;2

069812

NS

2069807

NS

IL7R

1494555

NS

IL10

3024496;3

024509;3

024491

NS

1800872;1

800871;1

800896

NS

1800890

NS

IL10R

A9610

NS

IL13

1800925;1

295686;2

0541

NS

IL115

10833

NS

IL15R

2296135

NS

INFG

1861496;2

069705

NS

INFG

R1

3799488

NS

INFG

R2

1059293;2

070385

NS

IL8

4073;2

227307;2

227306;

NS

2227538

NS

IL8R

A2234671

NS

IL8R

B1126579;1

126580

NS

IL16

859;1

1325

NS

CC

R2

1799864;

NS

CC

R5

2734648;1

469149;3

33

NS

CX

CL12

1801157

NS

CX

3C

R1

3732379

NS

MIF

7556622

NS

TG

FB

11982073;1

800472

NS

TG

FB

R1

868

NS



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

VC

AM

11041163;3

176879

NS

ICA

M1

5491

NS

SE

LE

5361

NS

FC

GR

2A

1801274

)120A

>G

Pro

mote

r1.9

5

(1.0

6–3.6

0)

0.0

5a

TL4R

4986790

NS

CA

RD

15

2066844;2

066847

NS

RA

G1

2227973

NS

CTLA

4231775

NS

MP

O2333227

NS

LE

PR

7602;1

805096

NS

JA

K3

3008

NS

STA

T1

2066804

NS

CS

F2

1469149;2

5882

NS

Haplo

type:

LTA

xTN

F

IVS

1-8

2CIV

S1-9

0G

-1036

C-4

87

A-2

36

G

1.6

3

(1.0

2–2.6

1)

0.0

5

Abazi

s-s

tam

-boule

ih

et

al.

(93)

Caucasia

ncase

⁄contr

ol(7

41e

⁄160

2b)

IL1A

)889C

>T

PC

R1.7

8

(1.3

3–2.3

9)

<0.0

14ab

IL1B

)511C

>T

1.4

6

(1.1

6–1.8

5)

<0.0

1

IL1B

+3954C

>T

1.7

5

(1.3

4–2.2

9)

<0.0

1

IL1R

N+

11100

T>

C0.6

3

(0.4

8–0.3

8)

<0.0

1

Hayden

et

al.

(57)

Caucasia

ncase

⁄contr

ol(3

06

1f⁄2

63

2c3abd)

XR

CC

3861528;1

799794;8

61530;

861531;1

799796

Taqm

an

PC

R

NS

NS

XR

CC

41478486;1

382376;1

011980;

1011981;1

47843;

1193693;1

3178127;

2891980;

963248

NS

NS

NS

A>

GIn

tron

61.5

1

(1.1

0–2.0

8)

0.0

14d

*

NS

XR

CC

5828704;2

303400;2

07906;

207908;2

07916;2

07922;

675302;3

770500;3

770493;

1051677;2

07940;2

440;

1051685

NS

NS

NS

A>

G3

¢-UTR

8.3 (1

.05–65.4

)

0.0

2



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

Lin

cz

et

al.

(28)

Caucasia

ncase

⁄contr

ol(1

02

1e

⁄205

2b)

mE

H113T>

CE

xon

4R

FLP

–P

CR

NS

4a

mE

H139A

>G

Exon

3N

S

NQ

O1

187C

>T

NS

MP

O)

363G

>A

NS

CY

P2E

1)

1259R

saI

NS

)1019P

stI

NS

Lim

aet

al.

(48)

Bra

zilia

ncase

⁄contr

ol

(123

1e

⁄188

2b)

MTH

RF

677C

>T

Ala

222V

al

RFLP

–P

CR

NS

4a

1298A

>C

Glu

429A

laN

S

MS

2756A

>G

Decre

ase

2.3

1

(1.3

8–3.8

7)

0.0

01

MTR

R66A

>G

NS

TY

MS

2R

-3R

NS

Kadar

et

al.

(84)

Caucasia

ncase

⁄contr

ol(9

41e

⁄141

2d3ab)

TN

FA

)308G

>A

Pro

mote

rR

FLP

–P

CR

0.4

3

(0.1

9–0.9

7)

P=

0.0

41

4a

LTA

+252A

>G

NS

HS

P70-2

1267A

>G

NS

RA

GE

)429T>

CN

S

Hosgood

et

al.

(59)

Euro

pean

Am

erican

wom

en

case

⁄contr

ol

(128

1f⁄5

16

2d)

CA

SP

36948

)289C

>A

Exon

8N

S

CA

SP

31049216

+567T>

CE

xon

8N

S

CA

SP

813113

)271A

>T

Exon

14

NS

CA

SP

91052576

+32G

>A

Exon

50.5 (0

.3–0.9

)

P=

0.0

24c

CA

SP

10

39001150

)171

A>

GE

xon

3N

S

Kang

et

al.

(30)

Kore

ans

case

⁄contr

ol

(116

1a

⁄176

2a)

NQ

01

609

C>

Tpro

-ser

Decre

ase

Taqm

an

PC

R

NS

CY

P1A

1*1

⁄*2A

3¢n

on-c

odin

g0.4

3

(0.1

9–0.9

8)

0.0

45

4c

CY

P1A

12455A

>G

Ile

462val

Hig

hm

et

0.5

1

(0.2

6–0.9

8)

0.0

45

GS

TM

1P

os-n

ull

NS

GS

TT1

Pos-n

ull

NS

Maggin

iet

al.

(34)

Caucasia

ncase

⁄contr

ol(1

28

1c

⁄245

2b)

NQ

O1

1800566

C>

TP

ro187S

er

Exon

6D

ecre

ase

NS

4d

GS

TP

11695

A>

GIle

105V

al

Decre

ase

NS



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

Birm

ann

et

al.

(77)

US

case

⁄contr

ol

(82

1f⁄1

64

2d3ab)

IGF1

7965399;2

195239;

2373722;

1996656;3

5767;

12821878;

1019731;

219523910735380

1549593;1

520220;

5742665;

2946834;4

764876;

4764695

TaqM

an

PC

R

NS

4c

NS

NS

NS

NS

IGFB

P1

+3

1022865;1

553009;3

5539615;

22011638;4

988515;4

619;

2270628;3

110697;2

854746;

2854744,

2132570;6

670;

2453839;9

341105;

NS

NS

NS

NS

NS

IGFB

P2

3770473;9

341105;9

341130;

9341134;9

341145;9

341156;

9341193;9

341197;

NS

NS

NS

IGF1R

2229765

NS

IRS

11801278;1

7208470;1

801123

NS

IRS

2913949;8

74016;1

2584136;

9559646;2

241745;7

999797;

4771646;1

2585507;1

1618950

NS

NS

NS

IL6S

T1900173;1

1574780;1

0940495;

11744523

NS

NS

IL6

2069832;2

069837;2

069840

1800795

()174

G>

C)

1800796

()572

G>

C)

NS

NS

NS

IL6R

4845617;1

2083537;4

075015;

4845374;7

529229;1

0752641;

8192284;2

229238;4

845623

6684439

8193284

NS

NS

NS

C>

T2.9

(1,2

–7.0

)0.0

48

A>

C2.5

(1.1

–6.0

)0.0

38

Vangste

d

et

al.

(76)

Caucasia

ncase

⁄contr

ol(3

48

1cf⁄

753

2d3ab)

IL6

1800795

)174G

>C

Pro

mote

rTaqM

an

PC

R

NS

4a

IL10

1800872

)592C

>A

NS

PP

AR

c21801282

NS

CO

X2

5275;6

89466

NS

NFK

B1

28362491

NS

IL1B

1143627

)31T>

CIn

cre

ase

1.3

7

(1.0

5–1.8

0)

P<

0.0

1



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

Ala

dzs

ity

et

al.

(86)

Caucasia

ncase

⁄contr

ol

(100

1e

⁄99

2g3ab)

IL6

1800795

)174G

>C

Pro

mote

rTaqm

an

PC

RN

S4d

IL6R

8192284

448865A

>C

Asp

358A

laE

xon

6N

S

Gold

et

al.

(31)

Caucasia

nand

Afr

ican

Am

erican

case

⁄contr

ol

(279

1f⁄7

82

2d)

AH

R2066853

G>

CA

rg554Lys

Taqm

an

PC

RN

S

CY

P1B

11056836

G>

CV

al4

32Leu

Decre

ase

1.4

(1.0

–2.0

)N

A4c

CY

P2C

91799853

C>

TA

rg144C

ys

NS

EP

HX

12234922

A>

GH

is139A

rgN

S

NQ

O1

1800566

C>

TP

ro187S

er

NS

PO

N1

662

A>

GG

ln192A

rgN

S

Hosgood

et

al.

(60)

Caucasia

nW

om

en

case

⁄contr

ol(1

08

1f⁄

482

2d)

BC

L2L10

A)

SN

P

mic

roarr

ay

NS

4a

CA

SP

1⁄4

⁄5A

)N

S

BC

L2A

1A

)N

S

TP

53I3

A)

NS

CC

ND

A)

NS

FA

SA

)N

S

BC

L2L1

A)

NS

BC

L2L11

A)

NS

MD

M2

A)

NS

CA

SP

2A

)N

S

BC

L10

A)

NS

CA

SP

3A

)N

S

CA

SP

14

A)

NS

CA

SP

6A

)N

S

CA

SP

8A

P2

A)

NS

RIP

K2

A)

NS

BC

L2I2

A)

NS

FA

SLG

A)

NS

CA

SP

7A

)N

S

MY

CA

)N

S

TP

53

A)

NS

PIM

1A

)N

S

CA

SP

8⁄1

0A

)N

S

BA

X1805419;1

1667200;1

1667229;

2270939;3

765148;4

802527,

2270938;1

1667351

1042265

NS

NS

NS

G>

A3

¢UTR

Exon

0.4

0

(0.2

1–0.7

8)

0.0

07

CA

SP

92020902;4

646092;4

661636;

12130370;3

766160;4

646047;

7516435

NS

NS

A>

GIn

tron

1.6

5

(1.0

8–2.5

3)

0.0

05



Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n(n

o.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

PA

min

oacid

change

Gene

location

Functional

import

ance

of

variant

alle

leG

enoty

pin

gO

R⁄(C

I)P

valu

e

RIP

K1

10498658;2

326173;6

596945;

6920337;7

765221;7

775816

9391981

NS

NS

G>

C0.3

2(0

.12–0.8

1)

P=

0.0

16

Dra

inet

al.

(35)

Caucasia

ncase

⁄contr

ol

(71

1a

⁄92

2a3ab)

AB

CB

11045642

3435G

>T

Decre

ase

PC

R–R

FLP

NS

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Jam

rozi

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(36)

Caucasia

ncase

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ol

(111

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AB

CB

11236C

>T

Exon

12

PC

R–R

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4c

2677G

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la893S

er

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Haplo

type

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2h3ab)

TN

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PC

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)308G

>A

0.5

5

(0.3

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1)

0.0

24a

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n

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case

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>G

SN

P

mic

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ay

2.5

3

(1.5

9–4.0

2)

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g

7296859

215140C

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0.6

7

(0.4

8–0.9

4)

0.0

2

2707212

IVS

112306C

>T

0.6

8

(0.4

9–0.9

6)

0.0

3

1075838

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4

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7)

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LA

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2365094

IVS

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>C

1.4

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(1.0

8–2.0

4)

0.0

1

3782735

IVS

51362G

>A

0.6

7

(0.4

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3)

0.0

2

Purd

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al.

(107)

Non-H

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Caucasia

n

wom

en

case

⁄contr

ol

(103

1f⁄4

75

2d)

SE

RP

INE

12227667

A>

GS

NP

mic

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ay

0.4

3

(0.2

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6)

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CC

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01

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1



selected polymorphisms in genes involved in phase 1 and

phase 2 metabolisms.

Genes involved in folate and methioninemetabolism

Folate and methionine metabolism is important for

DNA synthesis and methylation. Aberrant methylation

of DNA cytosine residues of gene promoter regions is a

very frequent abnormality in human malignancies,

including MM, and it is associated with silencing of gene

function (37). Several enzymes have been identified in the

folate and methionine pathways, and genetic variations

in these genes change enzymatic activity and susceptibil-

ity to cancer. Figure 1 illustrates the folate and

methionine pathways. The enzyme 5,10-methylenetetra-

hydrofolate (5,10-methyleneTHF) is essential for DNA

replication because it is required for the synthesis of pur-

ine and thymidine (Fig. 1). Central for DNA methylation

is the enzyme 5,10-methylenetetrahydrofolate reductase

(MTHFR), which catalyzes the irreversible conversion of

5,10-methyleneTHF to 5-methyltetrahydrofolate (5-meth-

ylTHF), which is the main methyl donor for re-methyla-

tion of homocysteine to methionine.

The hypothesis is that low enzymatic activity of

MTHFR changes the susceptibility to cancer in two ways

as a result of increased amounts of 5,10-methyleneTHF

(Fig. 1). The first hypothesis suggests that low enzymatic

activity of MTHFR increases the amount of 5,10-methy-

leneTHF and thereby increases the synthesis of both

purines and thymidine available for DNA synthesis. This

leads to lower incidence of uracil misincorporation (38).

The second hypothesis suggests that low enzymatic

activity of MTHRF reduces the amount of methionine

Folic acid DHF THF

dUMPdMTP

DNA synthesis

Purines 10-formylTHF

5,10-methyleneTHF

5-metylTHF MTHFR

Homocysteine

Methionine

SAM

SAHDNA methylation

Thymidylate Synthetase

B12

MS

Figure 1 Schematic presentation of folic acid metabolism. DHF,

dihydrofolate; THF, tetrahydrofolate; SAM, S-adenosylmethionine;

SAH, S-adenosylhomocysteine; MS, methionine synthetase; MTHFR,

methylenetetrahydrofolate reductase.Tab

le1

Continued

Refe

rence

Eth

nic

ity

stu

dy

desig

n

(no.

of

patient

⁄contr

ols

)G

ene

Rs

num

ber

SN

P

Am

ino

acid

change

Gene

location

Functional

import

ance

of

variant

alle

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pin

gO

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valu

e

CD

I80

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<0.0

1

AC

AD

A)

B)

1M

Mcases,

no

description

of

sele

ction

of

cases

a;

patients

younger

than

65

years

b;

patients

treate

dw

ith

HD

Tc;

retr

ospective

analy

sis

d;

sin

gle

cente

rstu

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popula

tion

based

cases

f .2C

ontr

ols

,no

description

of

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of

contr

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a;

blo

od

donors

or

norm

alhealthy

indiv

idualas

contr

ols

b;

hospitalor

Univ

ers

ity

pers

onalc

;popula

tion

based

contr

ols

d;

patients

without

chro

nic

dis

eases

as

contr

ols

e;

fam

ilycontr

ols

f ;patients

with

oth

er

dis

eases

g;

indiv

iduals

without

cancerh

.3C

onfo

unders

,adju

ste

dfo

rconfo

unders

such

as

age

a,

sex

b,

stu

dy

site

cand

race

d.

4S

tatistics,

Fis

hers

exact

testa

;chi-square

dte

st

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Yate

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ection

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Pvalu

eg.

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ild-t

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genoty

pe.

LO

F,

loss

of

function.

A) F

or

rsnum

bers

see

supple

menta

rydata

pro

vid

ed

by

the

auth

or

inth

ere

fere

nce.

B) T

ota

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ses

ispro

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supple

menta

rydata

.



available for DNA methylation. Two genetic variations

in the MTHFR gene may lower the enzymatic activity of

MTHFR (Table 1) (39) and influence the risk of MM

(40–45). A meta-analysis by Zintzaras et al. (46) sug-

gested that in Caucasians, but not in East Asians, variant

allele carriers of the MTHFR 677C>T polymorphism

were at increased risk of MM. This result is not in accor-

dance with the first suggested hypothesis, where patients

with a lower enzymatic activity of MTHFR should have

a lower number of uracil misincorporations and there-

fore a lower risk of MM. The second hypothesis suggests

that decreased DNA methylation is associated with low

MTHFR enzymatic activity. If the second hypothesis

holds true, the results indicate that hypomethylation,

caused by partial 5-methylTHF depletion in variant

T-allele carriers, is associated with increased risk of MM.

The result does not support the general notion that

hypermethylation of genes is associated with silencing of

tumor suppressor genes but may support the hypothesis

that hypomethylation of cancer cells causes genomic

instability (37).

Methionine synthase (MS) is encoded by the gene

MTR. It catalyzes the transfer of the methyl group from

5-methylTHF to homocysteine. A polymorphism

2756A>G in the MTR gene may reduce enzymatic activ-

ity (47). Three studies explored the association of this

polymorphism with the risk of MM with diverging

results. In 90 Caucasians patients, Lincz et al. did not

find any association with risk of MM (42). In contrast,

Kim et al. (45) found that heterozygous carriers had

decreased risk of MM in a 173 East Asians, but no asso-

ciation was found for homozygous variant carriers. In

123 Brazilians, Lima et al. found that heterozygous carri-

ers had increased risk of MM (48).

In summary, the results from the meta-analysis indi-

cate that the variant T-allele of the polymorphism

MTHFR 677C>T is associated with increased MM

susceptibility.

DNA repair genes and genes regulating cellcycle and apoptosis

A network of nearly 150 DNA repair genes is involved

in the maintenance of genomic stability in humans (49).

At different checkpoints in the cell cycle, the DNA repair

processes identify and replace the damaged DNA. DNA

damage induces both pro- and anti-apoptotic responses

in the cells. Several genes are involved in the regulation

of apoptosis. Well-established pro-apoptotic proteins are

the tumor suppressor family p53, p63, and p73 and the

apoptosis-stimulating proteins of p53 (ASPP), ASPP1,

and ASPP2 that cause p53 to up-regulate pro-apoptotic

genes such as BAX (50). Anti-apoptotic genes include

BCL2 and PPP1R13L (RelA inhibitor or iASPP), which

was initially identified as gene encoding for a protein

that binds to the NF-jB p65 ⁄RelA dimer and inhibits its

transcriptional activity (51). Later studies revealed that

the PPP1R13L gene product also inhibits apoptosis by

binding to p53 and that it may function as an oncogene

(52, 53).

DNA repair can be divided into three main processes:

the recognition of damaged DNA by DNA polymerases

or other proteins, removal of the damaged nucleobases

by endonucleases, and replacement with the correct

DNA sequence by polymerases and ligases. The enzymes

involved in DNA repair are organized into four path-

ways according to the type of DNA damage. Base exci-

sion repair is responsible for the repair of small lesions;

nucleotide excision repair (NER) is mainly responsible

for the repair of a bulky adduct and intra-strand cross-

links, and double-strand base repair (DSR) involves two

mechanisms: NHEJ and homologous recombination.

Finally, mismatch repair operate to remove base

mismatches (54, 55).

Few studies have explored the effect of SNPs in DNA

repair genes and risk of MM (Table 1). In a larger popu-

lation-based study of 270 Caucasian cases, Roddam et al.

studied genetic variation in genes involved in DNA dou-

ble-strand repair involved in Ig class switch and V(D)J

recombination (56). They discovered that genetic varia-

tions in LIG4 were associated with lowered risk of MM

in Caucasians. Further studies on double-stranded breaks

were published by Hayden et al. (57), who found an

increased risk in 306 Caucasians cases carrying genetic

variations in the genes XRCC4 (rs963248) and XRCC5

(rs1051685). These data support the hypothesis that

changes in DNA repair genes influence the risk of MM.

In our study, no association was found between risk of

MM for polymorphisms and genetic variation in

ERCC1, PPP1R13L, ERCC2, and XRCC3 (58).

The results regarding polymorphisms in the DNA

repair genes have not been replicated in independent

studies, and final conclusions on their importance cannot

be drawn.

At present, few studies have explored genetic variation

in the caspase enzyme cascade that participates in the

regulation of myeloma cell growth. Hosgood et al. evalu-

ated genetic variations in genes related to the regulation

of cell cycle and apoptosis in 108 Caucasian women with

MM and compared the results to those of 482 Cauca-

sians controls (Table 1) (59, 60). The variant alleles of

the pro-apoptotic gene BAX gene (rs1042265) and a

polymorphism (rs9391981) in the cellular stress response

gene RIPK1 were associated with reduced risk of MM.

In contrast, the variant allele of a polymorphism in the

CASP9 gene (rs7516435) was associated with the

increased risk of MM. Genetic variations in the P53 gene

were studied in a single study of 106 Brazilian MM



patients with no statistically significant association with

the risk of disease (33). Further studies are needed to

confirm these results.

Genes involved in the regulation ofinflammation

The NF-jB family of transcription factors has a key

role in the regulation of apoptosis, cell growth, and

angiogenesis and in the modulation of the immune sys-

tem. A normal function of NF-jB is essential for the

innate and adaptive immune system (61). The NF-jBfamily consists of five family members: the p50 and its

precursor p105, the p52 and its precursor p100, RelA

(p65), RelB, and cRel. Two activation pathways are

described for NF-jB, the classical or canonical path-

way and the alternative pathway. In the cell, the NF-

jB proteins form hetero- or homodimers that are

retained in the cytoplasm by inhibitors of NF-jB(IkB). The classical pathway is stimulated by cytokines

and pathogens through cytokine- and toll-like recep-

tors. This leads to the activation of the IkB kinase

and subsequent degradation of IkB in the proteasome.

Upon degradation of IkB, the NF-jB proteins are

translocated into the nucleus where they regulate pro-

and anti-inflammatory gene expression in different

manners (61).The alternative pathway is activated by

CD40 ligand, lymphotoxin (LTa or b), and receptor

activator of NF-jB ligand (RANKL). Here, activation

is implemented by the NF-jB binding kinase NIK.

The mechanisms behind immunologic response to

exposure of environmental challenges are complex and

may vary depending on inherited DNA variations, the

exposed tissue, and cumulative antigenic exposure. In

this process, NF-jB is a key regulator of the inflamma-

tory response and NF-jB has been shown to link inflam-

mation with cancer (62). Genes activated by the NF-jBpathway include IL1B, TNFA, and IL6. Pro-inflamma-

tory cytokines influence the immune response by cell dif-

ferentiation and activation, proliferation, and apoptosis.

Besides their influence in the regulation of the immune

system, they are important growth factors for the mye-

loma cells. Several studies have shown an association

between polymorphisms in the genes IL6, IL1B, TNFA,

IL10, COX2, NFjB1, and PPARG and cancer risk

(63–67). It is therefore likely that genetic variation in key

regulators of the immune response may be important for

the risk and prognosis of MM.

Genes in the nuclear factor-jB pathway

IjBa is a family member of IjB, which binds and retains

NF-jB to the cytoplasm. The expression of IjBa can be

induced and it is part of the classical NF-jB pathway.

Its degradation is required for the translocation of

NF-jB to the nucleus. Spink et al. studied polymor-

phisms in the IjBa gene NFjB1A in 157 MM Caucasian

cases and compared the genotype distribution to that of

196 controls. They identified a high-risk haplotype in the

NFjB1A gene in nine patients with MM, which was not

present in the controls (68). This result has, however, not

been confirmed by others.

The IL6 gene

IL-6 is probably the most important growth factor for

myeloma cells. It is secreted from both myeloma cells

and bone marrow stromal cells and may function as a

paracrine and autocrine growth factor (69). IL-6 is

involved in three important signaling pathways in MM,

the Ras ⁄Raf ⁄MEK ⁄ERK cascade, the JAK2 ⁄STAT3,

and the PI3K ⁄Akt cascade, and thereby promotes sur-

vival, proliferation, and drug resistance of the myeloma

cells (70–72). Newly diagnosed MM patients with ele-

vated blood levels of IL-6 have a poor prognosis (73).

The IL6 polymorphism (rs1800795) is located in the pro-

moter region of the gene (Table 1). The wild-type allele

has a higher transcriptional level than the variant allele,

and the presence of the variant allele in healthy individu-

als results in low IL-6 blood levels (74). Several polymor-

phisms in the IL6 gene have been analyzed (rs2069832,

rs2069837, and rs2069840). Cozen et al. reported associa-

tions between genetic variations in the IL6 gene and risk

of MM but the number of patients was low (75). In our

population-based study on 348 Caucasian patients with

MM, we found no association between a polymorphism

in IL6 gene (rs1800795) and risk of MM (76). Further

support for the importance of IL-6 cytokine signaling

pathway in risk of MM is found in a small Caucasian

study of 24 men and 58 women by Birmann et al. who

found that several SNPs in the IL6 receptor gene were

associated with risk of MM (77).

The genes TNFA and LTA

The TNFA and LTA gene region 6p21 is embedded

within the human leukocyte antigen (HLA), class III

region on chromosome 6. The two genes are in linkage

disequilibrium as they are part of the same haplotype

block. The 6p21 region is highly polymorphic and con-

tains a number of immune response genes. The major

sources of the cytokines TNF-a and LT-a are macro-

phages, monocytes, and T cells, but also B lympho-

cytes and natural killer cells express LT-a. TNF-a is a

potent inducers of the IL-6 production in bone mar-

row stromal cells, and it induces the expression of sev-

eral adhesion molecules on myeloma cell lines (78).

The variant allele of TNFA-308G>A is a stronger



transcriptional activator than the wild-type allele (79).

The association between the polymorphism TNFA-

308G>A and risk of MM has been intensively studied

with different results (80–85) (Table 1). A decreased

risk of MM for carriers of the variant allele was found

in a study of 181 Caucasians by Morgan et al. and in

a small study of 94 Caucasian cases by Kadar et al.

Similar results were found in 210 Asian cases by Du

et al. In a larger study by Davies et al. on 198 Cauca-

sian cases matched with 250 population-based controls,

no association between polymorphisms in TNFA-

208G>A and LTA 252A>G and risk of MM was

found. Haplotype analysis revealed that double hetero-

zygous carriers of TNFA-208G>A and LTA 252A>G

had increased risk of MM. Morgan et al. also found a

decreased risk of MM in carriers of the variant allele

of LTA 252A>G, but this was not confirmed by oth-

ers (81, 83, 84). In a case–control study of 127 Ameri-

can women, the association of several SNPs including

TNFA-208G>A and LTA 252A>G was examined but

no association with risk of disease was found (83).

Meta-analysis of IL6-174C>G and TNFA-308A>G

All studies with data available on Caucasians of IL6-

174 C>G and TNFA-308A>G were included an aggre-

gated meta-analysis (76, 77, 81, 82, 84–87), except for

the study by Brown et al. (83) because this study only

included women and none of the other studies have

analyzed risk in relation to gender. Our meta-analysis

of IL6-174C>G (rs1800795) assessed the association of

homozygous wild-type versus variant allele carriers with

risk of MM, and our meta-analysis of TNFA-308A>G

examined the association of the homozygous wild-type

carriers versus all variant allele carriers with the risk of

MM. Associations were indicated as OR with corre-

sponding 95% CI. A pooled OR was estimated based

on the individual ORs. Statistical analysis included the

analysis of heterogeneity that was found for TNFA-

308A>G. The heterogeneity of TNFA-308A>G may

be explained by different selection of patients among

the studies. No associations were found with risk of

MM for polymorphisms IL6 (rs1800795) and TNFA-

308A> of homozygous wild-type allele carriers versus

all variant allele carriers but not for TNFA-308A>G as

<2% of cases and controls were homozygous carriers

of the variant allele for TNFA-308A>G. No associa-

tion between IL6 (rs1800795) and risk of MM was

found.

The IL1B gene

IL-1b is a potent stimulator of IL-6 production in both

bone marrow stromal cells and myeloma cells. It can

also induce the expression of numerous other cytokines

involved in the growth of myeloma cells, for example,

IL-8, VEGF, and TNF-a (88–90). The promoter poly-

morphism IL1B (rs1143627) affects the transcriptional

level of IL-1b in vitro, and carriers of haplotypes

encompassing the variant allele have the highest tran-

scription level of IL-1b (91). The polymorphisms IL1B

(rs1143627) and IL1B (rs16944) are in complete linkage

disequilibrium, and the two variant alleles cosegregate

completely. The functional importance of genetic varia-

tions in the IL1B promoter regions was supported by

Japanese researchers who showed that among Helicob-

acter pylori-infected patients, homozygous carriers of

the variant allele of the polymorphism IL1B (rs16944)

had higher level IL-1b mucosal levels than did carriers

of the wild-type allele (92). We and others have studied

several IL1B polymorphisms and associations with risk

of MM in Caucasians (76, 83, 85, 93). In our study on

348 Caucasian patients with MM, carriers of the vari-

ant C-allele of IL1B (rs1143627) had a 1.4-fold

increased risk of MM. This result was not confirmed by

Brown et al. in a study on 127 American women. Fur-

ther studies are needed to explore the importance of the

IL1B gene. In a small study of 74 Caucasian cases,

Abazis-Stamboulieh et al. found a protective effect of a

polymorphism in the IL-1 receptor antagonist gene

IL1RA1 as compared to 160 controls. This result was

not confirmed by Brown et al. in a study of 127 Ameri-

can women (83).

The IL10 gene

The anti-inflammatory cytokine IL-10 is produced by

macrophages, dendritic cells, and lymphocytes. It is a

fundamental factor in the lymphoid development of B

cells into plasma cells, and it is important for stimulating

the proliferation of B cells and growth of myeloma cells

(94). The IL10 polymorphism (rs1800872) is located in

the promoter region of the gene, and the variant allele of

IL10 (rs1800872) has been associated with a lower IL-10

expression (95). No association with risk of MM has

been found for the closely linked polymorphisms

)1082 G>A, )819 G>A, )592 and A>C in the IL10

promoter region (76, 96, 97). Chen et al. found that

microsatellite loci in the IL10 promoter was associated

with risk of MM and to influence the production of IL-

10 by lipopolysaccharide (LPS)-stimulated peripheral

blood mononuclear cell (PBMC) (96).

The COX2 and the PPARG gene

The isoenzyme COX-2 is induced by inflammatory stim-

uli, growth factors, carcinogens, ionizing radiation,

and hypoxia (99, 101). COX-2 is a key enzyme in the



production of prostaglandin (PG) from arachidonic acid.

In bone marrow biopsies, COX-2 immunoreactivity and

overexpression of COX-2 mRNA from myeloma cells is

a poor prognostic factor of the disease (102, 103).

Genetic variation has been identified in the COX2 gene

which changes the transcriptional levels of the gene (67,

104). PPARc is an important transcription factor and

member of the nuclear hormone receptor superfamily.

PPARc2 is primarily expressed in adipose tissue but

expression is also found in B-cell lymphomas and MM

cells (98). PPARc regulates the expression of COX2, and

a polymorphism in the PPARG2 gene has been found to

give less transcriptional activation of target genes (99).

PG 15d-PGJ2 is a natural ligand for the PPARc, and it

induces apoptosis in myeloma cell lines (100). We found

no association with risk of MM for selected functional

polymorphisms in COX2 and PPARG2 genes (rs1801282,

rs5275, and rs689466) in a study of 348 patients with

myeloma compared to subcohort of 753 randomly

selected controls from the Diet, Cancer and Health

cohort in Denmark (76).

Other studies, with significant findings on association

with polymorphisms in genes involved in the immune

response and risk of MM, include the genes CTLA4,

CD4, and CCR7 (Table 1). CTLA4 is important for

down-regulation of the T-cell function, and it was

hypothesized that lowered function of the gene product

may be important for the cytotoxic T-cell function

against tumor antigens. In a small study on 73 Cauca-

sians cases by Zheng et al., an association with microsat-

ellite polymorphisms in the gene and decreased risk of

MM was found (105). One polymorphism in the CTLA4

gene (rs 231775) was also investigated by Brown et al. in

127 women, with no significant associations found (83).

The CD4 gene is involved in antigen response and antitu-

mor response. In a study of non-Hispanic Caucasian

women, a higher risk of MM was found for variant allele

carriers in the CD4 gene (rs1064392); (106). The authors

suggested the hypothesis that a dysfunctional T-cell

response results in the loss of control of B-cell prolifera-

tion and thereby higher risk of MM.

Several studies have approached genetic variations in

genes involved in the immune system. For most of the

genes studied, the results have not been confirmed by

others. The most intensively studied genes are IL6 and

TNFA. Our meta-analysis on the functional polymor-

phisms IL6-174 C>G and TNFA-308A>G indicate that

they are not associated with risk of MM.

Genes involved in the regulation ofangiogenesis and growth factor–related genes

In a population-based study of 103 non-Hispanic Cauca-

sian women, Purdue et al. analyzed more than 200 genes

related to the immune function and found significant

associations with risk of MM for polymorphisms in three

genes (107). The genes SERPINE1, HGF, and the

chemokine receptor gene CCR7 are involved in growth

regulation and angiogenesis. Variant allele carriers of

SERPINE1 (rs2227667) and HGF (rs17501108) were at

significantly lowered risk of MM. The CCR7 gene is

involved in the trafficking of lymphocytes and dendritic

cells to lymph nodes. A significantly higher risk of MM

was found for variant carriers of CCR7 G>A

(rs3136685).

Conclusion on DNA variation and associationwith the risk of multiple myeloma

Genetic variations with functional importance for genes

controlling biologic defense mechanisms such as detoxi-

fying enzymes, immune response, and DNA repair may

be important for susceptibility to disease. In relation to

risk of MM, most studies, especially earlier studies,

have focused on polymorphisms in genes related to the

immune function and drug metabolism. In the later

papers, focus has been on polymorphisms in genes

important for growth regulation, apoptosis, and func-

tion of the innate immune system. MM is a polygenetic

disease and several genes may therefore be important,

and interplay with each other, in the pathogenesis of

the disease. Numerous genetic aberrations are found in

MM. Among those are the IgH translocations that are

found in 60–70% of MM cases (7). Rearrangement of

the immunoglobulin IgH locus and class switch recom-

bination are normal processes in the maturation of the

B cell and involve DNA DSB and DNA repair. One

hypothesis is that chronic exposure to toxicants or anti-

gens, resulting in an inflammatory response of B cells,

may lead to errors in this process and thereby several

chromosomal changes that eventually cause cancer

development. Two studies support the importance of

genes involved in IgH translocations and class switch

recombination for the risk of disease. One larger study

by Roddam et al. found that SNPs in the double-strand

DNA repair gene LIG4 were associated with decreased

risk of disease, and another study by Heyden et al.

found that SNPs in the genes XRCC4 and XRCC3

increased susceptibility to MM. However, the results

have not been confirmed by others.

Several studies have focused on SNPs in genes regu-

lating pro-inflammatory cytokines such as IL6, TNFA,

and IL1B. The results from these studies were inconsis-

tent. Our meta-analysis of the polymorphisms IL6-

174C>G and TNFA-308A>G did not confirm the

association of these SNPs with risk of disease. Several

significant findings in genes regulating the growth fac-

tors, apoptosis, and immune function must be explored



further and include the genes NFKB1A, IL1B, IL4R,

IL6R, CD4, HGF, SERPINE1, CCR7, RIPK1, LAG,

CASP9 and BAX. At present, these significant findings

have not been confirmed in independent studies.

In this review, only one statistically significant and

confirmed association between genetic variations and

risk of MM was found. The methylenetetrahydrofolate

reductase is an important enzyme in the folate

metabolism and thereby also for DNA synthesis and

methylation. A meta-analysis on the polymorphisms

677C>T and 1298A>C of the MTHRF (46) suggests

an increased risk of MM for carriers of variant

T-allele of MTHRF 677C>T. The findings are, how-

ever, based on a relatively small number of studies

and participants and do not support the suggested

hypothesis of how decreased enzymatic activity influ-

ences biology.

Results on SNP analysis are important but require

confirmation in independent studies. The literature

regarding the risk of MM is characterized by small case–

control studies increasing the risk of chance findings.

Large study groups of patients with MM and well-

matched healthy, randomly chosen controls are required

to ensure sufficient statistical power to detect small

effects of genetic variation and to ensure that findings

are not confounded by selection bias of the control

group. SNPs data must be reproducible and a high call

rate in the genotyping is required. Moreover, it is impor-

tant that the DNA originates from non-cancer cells to

avoid the loss-of-heterozygosity and mutations in clonal

expanded cancer cells. The study population, cases as

well as controls, should preferably be population based

and well described concerning age, ethnicity, and gender

(108).

Ten years of studies on SNP in relation to risk of

MM have passed. Comparison of studies from later

years from those performed in the beginning of the

millennium has been compromised by the lack of

presentation of relevant data including unique identi-

fication of the studied polymorphisms. The database:

http://www.ncbi.nlm.nih.gov/SNP/now provides relevant

information on results from SNP analyses, and most

scientific papers present unequivocal identification of

studied SNPs. The future challenge may be the collec-

tion of larger study populations and collaboration

between different scientific groups. Genomewide associ-

ation studies provide a useful tool to identify genetic

variations that are associated with disease. However,

multiple, large, and independent study groups are

required for such studies. This would require col-

laboration between many scientists but would

probably bring much new insight into the understand-

ing of important biologic pathways for the risk of

disease.

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Abbreviations

BAX BLC2-associated X protein

BCL B-cell CLL ⁄ lymphoma

BCL2L BCL2-like

CASP Caspase

CCR Chemokine receptor

COX Cytochrome c oxidase subunit II (cyclooxygenase)

CTLA-4 Cytotoxic T-lymphocyte antigen-4

CYP Cytochrome P-450

DSR Double-strand base repair

ERCC Excision-repair cross-complementing

GST Glutathione S-transferase

HGF Hepatocyte growth factor

HLA Human leukocyte antigen

IgH Immunoglobulin heavy chain

IgL Immunoglobulin light chain

IL Interleukin

IjB Inhibitors of NF-jB

LIG4 NHEJ DNA ligase IV

LPS Lipopolysaccharide

LT-a Lymphotoxin-alpha

MethyleneTHF 5,10-Methylenetetrahydrofolate

MethylTHF 5-Methytetrahydrofolate

MGUS Monoclonal gammopathy of undetermined significance

MM Multiple myeloma

MTHFR Methylenetetrahydrofolate reductase

MYC V-myc myelocytomatosis viral oncogene homolog

NAT N-acetyltransferases

NER Nucleotide excision repair

NFKB1 Nuclear factor of kappa light polypeptide gene

enhancer in B cell 1

NF-jB Nuclear factor-jB

NHEJ Non-homologous end joining

NQO1 NAD(P)H:quinone oxidoreductase 1

Pax Paired box 5

PBMC Peripheral blood mononuclear cells

P-gp P-glycoprotein

PON1 Paraoxonase 1

PPARc Nuclear receptor peroxisome proliferator–activated

receptor-c

PPP1R13L Protein phosphatase 1, regulatory (inhibitor)

subunit 13 like

RAI RelA-associated inhibitor, protein phosphatase 1

RFLP Restriction fragment length polymorphism

RIPK Receptor-interacting serine ⁄ threonine kinase

SDF -1 Stromal cell–derived factor-1

Serpine1 Serpin peptidase inhibitor

SNP Single nucleotide polymorphism

TNF-a Tumor necrosis factor alfa

XRCC X-ray repair cross-complementing group



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Genetic variations in multiple myeloma I: effect on risk of multiple myeloma

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