Asian Pacific Journal of Cancer Prevention, Vol 15, 2014837 [601582]

Asian Pacific Journal of Cancer Prevention, Vol 15, 2014837
DOI:http://dx.doi.org/10.7314/APJCP .2014.15.2.837
Effect of NUCKS-1 Overexpression on Cytokines Profiling in Obese Women with Breast CancerAsian Pac J Cancer Prev, 15 (2) , 837-845
Introduction
Obesity is a growing health problem increasing
worldwide, excess body weight has been linked to an
increased risk of postmenopausal breast cancer, and
growing evidence also suggests that obesity is associated
with poor prognosis in women diagnosed with early-
stage breast cancer (Ligibel, 2011). Hyperinsulinemia
has been correlated with body mass index (BMI), waist
circumference (WC), risk of recurrence and mortality in
breast cancer (Goodwin et al., 2002). The role of lipids in
cancer in maintenance of cell integrity is well documented,
any alteration in the plasma lipid profile in breast cancer
cases can increase its risk status and its measurement
may be helpful in evaluation of prognostic and diagnostic
importance of the disease (Cejas et al., 2004).
Lipid peroxidation is one of the most important factors
that involves cell membrane integrity and causation of
cancer (Kedzierska et al., 2010). Breast cancer is the most
common lethal malignancy accounting for nearly 23% of
1Departments of Medical Biochemistry, 2Departments of General Surgery, Faculty of Medicine, Tanta University, Tanta, Egypt *For
correspondence: nemaali2006@yahoo mail.comAbstract
Background : Overweight and obesity are recognized as major drivers of cancers including breast cancer.
Several cytokines, including interleukin-6 (IL-6), IL-10 and lipocalin 2 (LCN2), as well as dysregulated cell
cycle proteins are implicated in breast carcinogenesis. The nuclear, casein kinase and cyclin-dependent kinase
substrate-1 (NUCKS-1), is a nuclear DNA-binding protein that has been implicated in several human cancers,
including breast cancer. Objectives : The present study was conducted to evaluate NUCKS-1 mRNA expression
in breast tissue from obese patients with and without breast cancer and lean controls. NUCKS-1 expression was
correlated to cytokine profiles as prognostic and monitoring tools for breast cancer, providing a molecular basis
for a causal link between obesity and risk. Materials and Methods : This study included 39 females with breast
cancer (G III) that was furtherly subdivided into two subgroups according to cancer grading (G IIIa and G
IIIb) and 10 control obese females (G II) in addition to 10 age-matched healthy lean controls (G I). NUCKS-1
expression was studied in breast tissue biopsies by means of real-time PCR (RT-PCR). Serum cytokine profiles
were determined by immunoassay. Lipid profiles and glycemic status as well as anthropometric measures were
also recorded for all participants. Results : IL-6, IL-12 and LCN2 were significantly higher in control obese
and breast cancer group than their relevant lean controls (p<0.05), while NUCKS-1 mRNA expression was
significantly higher in the breast cancer group compared to the other groups (p<0.05). Significant higher levels
of IL-6, IL-12, and LCN2 as well as NUCKS-1 mRNA levels were reported in G IIIb than G IIIa, and positively
correlated with obesity markers in all obese patients. Conclusions : Evaluation of cytokine levels as well as related
gene expression may provide a new tool for understanding interactions for three axes of carcinogenesis, innate
immunity, inflammation and cell cycling, and hope for new strategies of management.
Keywords: Obesity – breast cancer – IL-6 – IL-12 – LCN2 – NUCKS-1RESEARCH ARTICLE
Effect of NUCKS-1 Overexpression on Cytokine Profiling in
Obese Women with Breast Cancer
Nema Ali Soliman1*, Doaa Hussein Zineldeen1, Osama Helmy El-Khadrawy2
all female cancers worldwide, with more than a million
new cases each year (Parkin, 2006). The biology of breast
cancer is complex, involving oncogenesis, evasion of host
immune defense mechanisms, angiogenesis, invasion and
metastasis (Xu et al., 2010). Recently, the contribution of
interleukins, pleiotropic cytokines, to cancer progression
has been demonstrated (Fernandez et al., 2013).
Lipocalin 2 (LCN2), interleukin-6 (IL-6) and IL-12
exert a variety of effects on the immune system, acute-
phase responses and hematopoiesis. Macrophages,
monocytes, adipocytes and lymphocytes as well as cancer
cells have been documented to produce and secrete LCN2,
IL-6, IL-12 and IL-10 (El-Kadre and Tinoco, 2013). These
cytokines act in an autocrine or paracrine manner, inducing
in vitro growth of ovarian cancer, cervical cancer, prostate
cancer, lung cancer, kidney cancer and melanoma cells
(Giri et al., 2001).
Furthermore, their contributions to the tumor
angiogenesis have been reported (Salgado et al., 2002;
Yang et al., 2013). Moreover, it has been shown that

Nema Ali Soliman et al
Asian Pacific Journal of Cancer Prevention, Vol 15, 2014838treatment effectiveness and prognosis of cancer are
determined by disease stage and activity of the immune
system modulated by different interleukins, e.g. LCN2,
IL-6, IL-12, IL-8 and IL-10 (Bauer et al., 2008). Nuclear,
Casein Kinase and Cyclin-dependent kinase Substrate-
one (NUCKS-1) is a nuclear, DNA-binding and highly
phosphorylated protein (Grundt et al., 2004). It belongs to
a module of co-expressed genes located on chromosomal
region 1q32.1, which is found to be amplified in breast
cancer (Naylor et al., 2005), as well as in other cancers
(Sargent et al., 2008).
NUCKS-1 is a substrate for casein kinase 2 (CK2),
cyclin dependant kinase-1 (Cdk1) and DNA-activated
Kinase in vitro and in vivo , therefore it appears to be
important for cell cycle progression (Grundt et al., 2004;
Wiśniewski et al., 2008).
Even though NUCKS-1 gene is ubiquitously expressed
in all mammalian tissues and is highly expressed in
multiple human cancers including the breast one (Naylor
et al., 2005), yet its precise role in breast tumorgenesis
relative to metabolic status and immunomodulatory
mechanisms operating in breast cancer remain largely
elusive. Here we checked NUCKS-1 mRNA expression
levels in breast cancer tissues from obese patients and
their allied controls as well as their circulating cytokines
profiles of LCN2, IL-6 and IL-12, to investigate the role
of NUCKS-1 overexpression in breast cancer relative to
immunometabolic status and the impact of this on disease
incidence and progression.
Materials and Methods
All patients have given their informed consent and the
study was institutionally approved by the Research Ethical
Committee of Faculty of Medicine, Tanta University,
Egypt. This study included 59 postmenopausal women
who were admitted to the surgical department of Tanta
University Hospital for breast surgeries. Subjects were
divided in to three age matched groups. Group I (n=10)
women who underwent surgical plastic mastectomy, were
recruited as controls non obese group, group II (n=10)
obese women who underwent surgical plastic mastectomy,
were recruited as controls obese group and group III
obese age matched 39 female patients that had primary
unilateral invasive breast cancer with no other primary
cancer and they underwent modified radical mastectomy
with no preoperative neoadjuvant treatment. According
to histological typing, grade II (n=24) and grade III
(n=15) were considered as G IIIa and G IIIb respectively.
All patients and controls were age matched women
employing the following exclusion criteria: Smokers,
diseases of the gastrointestinal tract, pancreas, kidney or
liver, acute infections and radio-or chemotherapy before
surgery. During the surgical procedures, tissue samples
were obtained, snap frozen and stored in liquid nitrogen
or preserved in buffered paraformaldehyde (PFA) until
furtherly processed.
Obesity assessment
Measurements of waist circumference (WC) in cm
(were measured with a flexible, non stretching tape mid way between the last rib and anterior superior iliac spine)
and calculation of body mass index (BMI) (weight in
kilograms divided by the square of the height in meters
(kg/m2) were carried out to assess obesity especially
abdominal obesity. These measurements were taken under
proper condition of wearing light clothes and no shoes.
All subjects enrolled in this study were subjected to
full clinical history, clinical examination of the breast,
radiological and histopathological investigations.
All chemicals and solvents used unless otherwise
described were purchased from Sigma (Sigma, Germany).
RNA extraction, cDNA synthesis and Real time PCR
Total RNA was extracted from breast tissues using
Isogene (Nippon Gene, Toyama, Japan) according to
manufacturer’s instructions. The integrity of total RNA
was checked by electrophoresis through 1% agarose gel.
RNA samples were then stored at -80°C. cDNA synthesis
was performed using the RevertAid H Minus First
Strand cDNA Synthesis kit (#K1632,Thermo Scientific
Fermentas, St. Leon-Ro, Germany) according to the
manufacturer’s instructions. Real-time PCR was carried
out with single stranded cDNAs.
PCR reactions were performed using Power
SYBR Green PCR Master Mix and 7500 Fast
Real-Time PCR System (Applied Biosystems, CA,
USA). Sequence specific primers were designed by
Primer3 software: (http://frodo.wi.mit.edu/cgi-bin/
primer3/primer3_www.cgi) as follows: NUCKS1
(No:NM_022731.4) GGGCAGTGAGGAAGAACAA
and TTGATGCCTTTGAAGCTGTG.
β-actin (No:NM_001101.3) TGGCATTG
CCGACAGGATGCAGAA, and CTCGTCA
TACTCCTGCTTGCTGAT. β-actin primers were used
as an internal control. Real-Time PCR was carried out,
in duplicate, by 40 cycles of 95°C for 10 sec and 60°C
for 1 min. Productions of the expected amplification
fragments without unanticipated products and primers
were confirmed by melting curve analysis. Comparative
Ct (threshold cycle) method was used to determine
relative amounts of the products, according to the
Applied Biosystems instructions. Conventional PCR was
performed with Conventional PCR was performed with
the DreamTaq polymerase (#EP0701, Thermo Scientific
Fermentas, St. Leon-Ro, Germany). All expression data
were normalized by dividing the target amount by the
amount of β-actin used as internal control for each sample.
Biochemical and immunoassays
An overnight fasting blood samples were obtained
from each subject immediately before the induction of
general anesthesia and centrifuged at 3,000 rpm for 10
min. The extracted plasma was aliquoted and stored at
-80°C till the assay time. For LCN2 assay random urine
samples were collected in sterile containers, centrifuged
20 minutes at 3000 rpm until no precipitate. The resultant
supernatant was removed and stored at-80oC for further
analysis. Fasting Blood Sugar (FBS) was measured by
the oxidase method (Biodiagnostic., Egypt), total Lipid
profile including total cholesterol (TC), triglycerides
(TAG) and high density lipoprotein cholesterol (HDL-C)

Asian Pacific Journal of Cancer Prevention, Vol 15, 2014839
DOI:http://dx.doi.org/10.7314/APJCP .2014.15.2.837
Effect of NUCKS-1 Overexpression on Cytokines Profiling in Obese Women with Breast Cancerwere measured by enzymatic-colorimetric methods
(Biodiagnostic., Egypt). Low density lipoprotein
cholesterol (LDL-C) concentration was calculated
according to Friedewald (Friedewald et al., 1972); LDL-
C=TC-[ (TG/5)+HDL- C] (mg/dl).
Enzyme linked immunosorbent assay (ELISA) was
used to detect plasma levels of Insulin (USCN Life
Science Inc, Wuhan, China), IL-6 (R and D Systems Inc.,
Minneapolis, MN, USA) and IL-12 (AviBion, AniBiotech,
Finland) and urinary LCN2 (Sunred Biological Technology
Co. Ltd, Shanghai, China) according to manufacturers’
instructions and read on microplate reader (Stat Fax®2100,
Fisher Bioblock Scientific, France), at 450nm with
correction wavelength set at 570nm. Insulin resistance was
assessed by the homeostatic model assessment (HOMA-
IR), calculated as: Fasting glycemia (mg/dl) * fasting
insulinemia (μIU/mL) /405 (Duseja et al., 2007).
Histopathology
Breast tissues fixed in PFA (4%), dehydrated, and
embedded in paraffin using standard procedures. 4μm
thick slices were stained with Hematoxylin & Eosin
(H&E) and microscopically examined (Leica Imaging
System LTD., Cambridge, England). Tumors were graded
according to the traditional system of grading used by the
International Union against Cancer (UICC) (Robbins et
al., 1995; Tavassoli 2003).
Statistical analysis
Results represented means± SD, multiple comparisons
were performed by one-way analysis of variance (ANOV A) followed by Tukey’s post-hoc test for multiple
comparisons. Comparison between any two groups was
analyzed by the unpaired Student’s t-test using. Analysis
was performed by Statistical Package for Social Sciences
(SPSS), version 14.0 for windows (SPSS, Chicago,
IL, USA). The difference was considered statistically
significant when p<0.05. Correlations were analyzed using
the Pearson test.
Results
Demographic and basic characteristics of the studied
subjects
The demographic and clinical characteristics
among all studied groups as well as the percentages of
histopathological grading of breast cancer group are
depicted in Table (1). There was no statistically significant
difference in age between all subjects under study,
however there were statistically significant differences
in BMI (kg/m2) and WC (cm) (p<0.05 for both). Using
multiple comparisons test (Tukey’s test), the values were
markedly higher in breast cancer group (G III) and obese
group (G II) when compared to lean control group (G
I) with statistically higher level in breast cancer group
(p<0.05).
Biochemical characteristics and cytokine profiling of the
studied subjects
Table (2) revealed statistically significant differences
in values of serum lipid profile, FBG, fasting insulin and
HOMA-IR index among all the studied groups (p<0.05
Table 1. Demographic and Clinical Characteristics among all Studied Groups as well as the Percentages of
Histopathological Grading of Breast Cancer Group
G I G II G III ANOV A test Tukey’s test
Mean±SD Mean±SD Mean±SD
n=10 n=10 n=39 F P P1 P2 P3
Age (years) 46.1+1.35 45.5+2.93 48.2+5.3 0.526 0.258 0.526 0.336 0.114
BMI (kg/m2) 22.36+2.7 38.6+4.8 41.9+5.7 3.336 0.024* 0.005* 0.006* 0.024*
WC (cm) 71.5+3.8 95.7+4.36 112.3+13.5 2.363 0.027* 0.023* 0.024* 0.030*
Grading (number) Grade II (24) – – 61.30% – – – – –
Grade III (15) – – 38.50% – – – – –
Data presented as means± SD. (S) statistically significant difference. P was calculated by one way ANOV A test followed by Tukey’s post-hoc test. P was considered
significant at <0.05.; *Significant; P1 comparison between group I and II; P2 comparison between group I and III; P3 comparison between group II and III
Table 2. Comparative Statistics of Different Biochemical and Molecular Findings among all the Studied Groups
G I G II G III ANOV A test Tukey’s test
Mean±SD Mean±SD Mean±SD
n=10 n=10 n=39 F P P1 P2 P3
TC (mg/dl) 112.2+6.3 162.5+7.5 215.3+7.5 6.336 0.001* 0.001* 0.028* 0.001*
TAG(mg/dl) 123.5+8.3 172.6+8.96 233.9+15.3 6.335 0.002* 0.001* 0.024* 0.002*
HDL-C(mg/dl) 64.2+6.8 42.3+8.3 39.5+7.2 5.336 0.008* 0.005* 0.001* 0.085
LDL-C (mg/dl) 53.8+7.4 71.6+3.9 136.5+20.1 4.417 0.028* 0.037* 0.001* 0.01*
FBG (mg/dl) 76.3+9.6 185.3+11.9 188.6+9.6 8.336 0.009* 0.001* 0.001* 0.527
Fasting Insulin (μIU/ml) 4.6+1.5 11.9+2.4 21.6+3.8 5.336 0.002* 0.005* 0.001* 0.020*
HOMA-IR index 1.20+0.36 5.10+0.9 10.5+2.6 3.336 0.007* 0.001* 0.001* 0.003*
Serum IL-6 (pg/ml) 4.2+0.95 7.3+1.3 35.6+10.8 3.336 0.002* 0.049* 0.001* 0.001*
Serum IL-12 (pg/ml) 58.6+3.5 174.6+22.3 210+36.5 11.3 0.001* 0.001* 0.001* 0.001*
urinary LCN2 (pg/L) 52.3+13.5 64.2+6.8 85.6+4.27 2.36 0.011* 0.015* 0.024* 0.047*
Relative NUCKS-1 mRNA expression 0.42+0.06 0.49+0.10 0.60+0.2 3.685 0.024* 0.224 0.024* 0.039*
Data presented as means± SD. (S) statistically significant difference. P was calculated by one way ANOV A test followed by Tukey’s post-hoc test. P was considered
significant at <0.05.; *Significant; P1 comparison between group I and II; P2 comparison between group I and III; P3 comparison between group II and III

Nema Ali Soliman et al
Asian Pacific Journal of Cancer Prevention, Vol 15, 2014840for all). Using multiple comparisons test (Tukey’s test),
the above mentioned values were markedly higher in G
III and G II when compared to G I (Table 2). However,
for HDL-C, and FBG there were statistically insignificant
differences in G III versus G II.
As for cytokine profiling, serum IL-6 and IL-12 as well
as urinary LCN2 levels were significantly increased in
obese women with and without cancer compared to their
allied lean controls (p<0.05) Table (2), with statistically
higher values in breast cancer group (Figure 1). The
attained significant differences in levels of previous
parameters reflect their intimate relation to breast cancer
progression. Moreover, according to histological grading
of breast cancer (Figure 3), Table (3) and (Figure 4a)
showed that there were statistically higher significant
differences in values of WC, TC, TAG, LDL-C, IL-6,
IL-12 and LCN2 in Grade III patients (G IIIb) when
compared to Grade II ones (G IIIa), (p<0.05). It is of
note that no significant difference between both groups
could be encountered for values of BMI, HDL-C, FBG, fasting insulin and HOMA-IR among the two subgroups;
highlightening the role of cytokine profiling and WC in
breast cancer progression.
NUCKS-1 mRNA expression in breast tissues
NUCKS-1 gene expression was significantly
upregulated in breast tissues from obese women with
cancer (by about 3 folds) as compared to obese and lean
controls (p=0.024), (Figure 2). NUCKS-1 expression was
noted to be higher in G II relative to lean G I, although that
increase was not statistically significant (p=0.224), Table
(2). According to histological grading (Figure 3), Table (3)
and figure (4b) showed that NUCKS-1 gene expression
was statistically higher in grade III (G IIIb) as compared
Figure 2. Relative NUCKS-1 Gene Expression in
Breast Tissues. Comparison of Relative NUCKS-1 mRNA
expression in breast tissues among all the studied groups. Data
represents means; error bars indicate±SD, for significance and
p values (Table 2)
Figure 3. Histological Grading of Breast Tissues Used
in This Study. Photomicrographs of breast tissues (A) healthy
(B) grade II and (C) grade III. (H&E, X20)
Figure 4. Comparison of Biochemical and Molecular
Findings in Breast Cancer Subgroups. a) Comparison of
BMI (kg/m2) and WC (cm) as well as serum levels of TC (mg/
dl), TAG (mg/dl) and HDL-C (mg/dl), LDL-C (mg/dl), FBG
(mg/dl), fasting insulin (µIU/ml), HOMA-IR index, IL-6 (pg/ml)
IL-12 (pg/ml) and urinary LCN2 (pg/L) between G IIIa and G
IIIb; b) Comparison of relative NUCKS-1 mRNA expression in
breast tissue between G IIIa and G IIIb. Data represents means;
error bars indicate±SD, for significance and p values (Table 3)
a
b Figure 1. Cytokine Profiling among All the Studied
Groups. a) Comparison of serum IL-6 and IL-12 among all the
studied groups; b) Comparison of urinary LCN2 among all the
studied groups. Data represents means; error bars indicate±SD,
for significance and p values (Table 2)
Serum IL-6 and IL-12 (pg/ml) Urinary LCN2 (pg/L)a
b

Asian Pacific Journal of Cancer Prevention, Vol 15, 2014841
DOI:http://dx.doi.org/10.7314/APJCP .2014.15.2.837
Effect of NUCKS-1 Overexpression on Cytokines Profiling in Obese Women with Breast Cancerwith grade II (G IIIa) subgroups of breast cancer group
(G III) using T-test reflecting their strong relation to breast
cancer progression.
Among the entire cohort of breast cancer patients,
NUCKS-1 expression correlated well with some obesity
related risk factors including WC, Fasting insulin,
HOMA-IR, lipid profile (except for HDL-C) as well as
cytokines profile (p<0.05 for all), Tables (4, 5, 6), where
a statistically significant positive correlation was found
between NUCKS-1 mRNA expression with IL-6, IL-
12 and LCN2, reflecting a strong relationship between
NUCKS-1 mRNA expression and cytokine profile
especially urinary LCN2 as a non invasive biomarker in
this study of breast cancer.Correlations of obesity with different biochemical and
molecular findings in breast cancer
Table (4) showed person correlation between BMI and
WC with different biochemical and molecular findings
in breast cancer patients where, statistically significant
positive correlation (p<0.05) was found between BMI
and WC with all parameters of lipid profile, except
HDL-C that showed significant negative correlation
(p<0.05) with WC and insignificant negative correlation
with BMI. FBG, fasting insulin and HOMA-IR showed
insignificant positive correlation with BMI and significant
positive correlation (p<0.05) with WC which may reflect
that BMI may not always provide accurate information
about the variation in body fat and body composition
that is associated with morbidity, moreover WC may be
used as a tool for identification and assessment of obesity.
Meanwhile, IL-6, IL-12 and LCN2 showed statistically
significant positive correlation (p<0.05) with BMI and
WC.
The attained results reflect a strong relationship of the
studied parameters to obesity and breast cancer incidence
and progression.
Discussion
Obesity is a known risk factor for breast cancer. It is
generally accepted that obese women have an increased
risk for postmenopausal, but not premenopausal, breast
cancer (Gaudet et al., 2013).
In this current study high values of BMI, WC, FBG,
fasting blood insulin as well as HOMA-IR index were
detected in obese patients than control ones. Moreover,
higher levels were observed in obese patients with
breast cancer than other groups, this in agreement with
prior studies of (Moschos and Mantzoros 2002; Parker
and Folsom, 2003), indicating the development of
insulin resistance with the incidence of breast cancer.
Additionally, Huffman and Barzilai (2009) reported that Table 3. Comparative Statistics of BMI (kg/m2)
and WC (cm) as Well as Different Biochemical and
Molecular Findings between among G IIIa and G IIIb
G IIIa G IIIb T-test
Mean±SD Mean±SD t p
n=24 n=15
BMI (kg/m2) 40.2±6.3 43.5±3.61 0.635 0.224
WC (cm) 105.6±8.3 134.5±11.8 5.336 0.009*
TC (mg/dl) 174.5±10.6 238.6±22.6 12.632 0.001*
TAG (mg/dl) 153.3±20.6 241.3±28.6 8.635 0.001*
HDL-C (mg/dl) 42.3±3.5 39.6±2.35 0.963 0.258
LDL-C (mg/dl) 139.5±12.9 159.8±17.6 3.159 0.020*
FBG (mg/dl) 182.6±13.5 186.6±14.3 0.635 0.241
Fasting Insulin (μIU/ml) 18.6±3.20 20.36±3.5 0.558 0.226
HOMA/IR 9.62±3.5 11.24±3.5 0.417 0.335
Serum IL-6(pg/ml) 25.71±3.5 74.25±5.6 12.336 0.001*
Serum IL-12 (pg/ml) 163.52±23.5 295.3±33.5 15.632 0.001*
urinary LCN2 (pg/L) 82.3±6.5 102.5±6.63 6.332 0.001*
Relative NUCKS-1 mRNA expression
0.5±0.1 0.7±0.2 3.225 0.047*
*Significant at p value<0.05
Table 4. Pearson Correlation between BMI and WC
with Different Biochemical and Molecular Findings in
Patient Group (G III)
BMI WC
r p r p
TAG (mg/dl) 0.532 0.001* 0.425 0.005*
TC (mg/dl) 0.495 0.006* 0.361 0.004*
LDL-C (mg/dl) 0.358 0.003* 0.5 0.002*
HDL-C (mg/dl) -0.119 0.577 -0.429 0.008*
FBG (mg/dl) 0.136 0.856 0.528 0.006*
Fasting insulin (μIU/ml) 0.084 0.667 0.471 0.028*
HOMA-IR 0.102 0.447 0.663 0.001*
IL-6 (pg/ml) 0.256 0.020* 0.354 0.017*
IL-12 (pg/ml) 0.305 0.047* 0.429 0.006*
LCN2 (pg/L) 0.663 0.001* 0.447 0.005*
Relative NUCKS-1 mRNA expression 0.203 0.336 0.529 0.001*
Values are Pearson correlation coefficients; *Significance at p<0.05
Table 5. Pearson Correlation between Different Biochemical and Molecular Findings in Patient Group (G III)
TAG TC LDL-C HDL-C FBG Fasting Insulin HOMA-IR
(mg/dl) (mg/dl) (mg/dl) (mg/dl) (mg/dl) (μIU/ml)
IL-6 (pg/ml) r 0.352 0.352 0.441 -0.142 0.211 0.421 0.274
p 0.025* 0.036* 0.004* 0.225 0.102 0.042* 0.024*
IL-12 (pg/ml) r 0.358 0.258 0.418 -0.107 0.14 0.522 0.424
p 0.024* 0.041* 0.022* 0.635 0.369 0.033* 0.001*
LCN2 (pg/L) r 0.523 0.532 0.337 -0.088 0.095 0.369 0.522
p 0.001* 0.001* 0.028* 0.635 0.756 0.018* 0.001*
Relative NUCKS-1 mRNA expression r 0.417 0.258 0.339 -0.147 0.064 0.574 0.366
p 0.001* 0.018* 0.007* 0.756 0.224 0.020* 0.003*
Values are Pearson correlation coefficients; *Significance at p<0.05.Table 6. Pearson Correlation between NUCKS-1
mRNA Expression and Cytokine Profile in Patient
Group (G III)
Serum IL-6 Serum IL-12 urinary LCN2
(pg/ml) (pg/ml) (pg/L)
Relative NUCKS-1 mRNA expression
r 0.441 0.526 0.228
p 0.008* 0.001* 0.030*
Values are Pearson correlation coefficients; *Significance at p<0.05

Nema Ali Soliman et al
Asian Pacific Journal of Cancer Prevention, Vol 15, 2014842accumulation of visceral fat represents a greater risk for
the development of increase secretion of inflammatory
adipokines, insulin resistance and dyslipidemia.
In the present study, obese patients with breast cancer
had manifest dyslipidaemia in line with this Hsu et al.,
2007; Abdelsalam et al., 2012, showed that alterations in
lipid profile levels showed a significant correlation with
breast cancer risk, disease status and treatment outcome.
Abdelsalam et al., 2012 reported that lipids might be
associated with cancers because they play a key role in
maintenance of cell integrity, cholesterol synthesis may
also produce various tumorigenic compounds and acts as
a precursor for the synthesis of many sex hormones linked
to increased risk of various cancers such as breast cancer.
Our current data revealed that increased serum level of
IL-6 was significantly higher in G III and G II as compared
to their allied controls. Interestingly higher levels
were noted in patients with advanced grading of breast
cancer (G IIIb) as reported by Gonullu et al., 2005 who
suggested a possible contribution of endogenous IL-6 and
hyperinsulinemia associated obesity to the development of
breast cancer in overweight or obese patients reflecting the
role insulin resistance in disease activity and prognosis.
Barton and Murphy 2001; Hussein et al., 2004 reported
that IL-6 may also stimulate cancer cells growth and
contribute to locoregional relapse as well as metastasis.
In harmony with our results, Smyth et al., 2004 showed
that permanent synthesis and release of this cytokines
lead to augmentation of their serum level that might
be utilized as a marker of immunity status and immune
system activation in obesity and obesity associated with
breast cancer for prognosis and monitoring of the course
of cancer associated obesity.
It is thought that 15 to 30 % of circulating IL-6 levels
derives from adipose tissue production in the absence of
an acute inflammation (Goyal et al., 2012). Moreover,
Goyal et al., 2012 reported that IL-6 is a potent pleiotropic
inflammatory cytokine that is considered a key growth-
promoting and antiapoptotic factor.
Haura et al., 2005 stated that most IL-6 target genes
are involved in cell cycle progression and suppression of
apoptosis, which underscores the importance of IL-6 in
tumorigenesis. This indicates that IL-6 acts in an autocrine
fashion to mediate oncogene-induced senescence. On the
other hand, the pool of IL-6 secreted to the extracellular
compartment by the senescent cells had promitogenic
activity, as it enhanced proliferation of tumor cells in a
paracrine fashion (Minamino et al., 2003).
We also found positive correlations between IL6
levels and BMI, WC, fasting insulin and HOMA-IR index
as well as also lipid profile except for HDL-C in obese
patients with breast cancer as reported in previous studies
of Dandona et al., 2004; Goyal et al., 2012.
Interleukin-12 (IL-12) is a heterodimeric class-I
helical cytokine that is mainly produced by dentritic
cells and macrophages and influences differentiation of
T helper 1 (Th1) immune cells, promoting in this way a
linkage between innate response and adaptive immunity
(Trinchieri, 2003). IL-12 has been recently suggested to
participate during development of obesity-related insulin
resistance in rodents, since it clearly increases in both the epididymal adipose tissue and adipose tissue-associated
proinflammatory macrophages in high-fat-diet-fed mice
(Strissel et al., 2010) thus; levels of IL-12 are increased
in overweight and obese individuals and show a strong
relationship with markers of low-grade inflammation
and obesity.
Recent experimental evidence from high-fat-diet-fed
mice suggests that IL-12 could have an additional role in
the systemic low-grade inflammation and the concomitant
advent of obesity-related disorders, such as insulin
resistance (Silswal et al., 2005), these results run hand in
hand with our data where serum IL-12 levels were higher
in obese patients with breast cancer particularly advanced
one (G IIIb).
For this reason, it is of much relevance to study
the systemic levels of IL-12 in humans that show
high metabolic risk, such as obese individuals. In this
sense, it has been previously reported that circulating
concentrations of IL-12 are significantly increased in
subjects with metabolic syndrome as reported by Surendar
et al. (2011).
Furthermore, peripheral blood mononuclear cells
(PBMCs) from type 2 diabetes (T2D) patients are
able to produce higher levels of IL-12 in response to
lipopolysaccharide stimulation than those cells from
healthy subjects (Wu et al., 2010).
Concomitantly, a recent study in a rural population
of Mexican women suggests that the risk of having
elevated serum IL-12 diminishes in women with high
plasma concentrations of zinc, a micronutrient that
has been related with a reduced risk for being obese
(Zavala et al., 2012). In this sense, our results reveal that
circulating concentrations of IL-12 increase parallel with
all parameters of obesity, including BMI, WC, fasting
insulin and HOMA-IR index as well as all parameters
of lipid profile except HDL-C levels in patient groups as
reported by Derin et al. (2007); Sharabiani et al. (2011);
Suárez-Álvarez et al. (2013) who stated that interleukins
are known to play a fundamental role in cancer as they
investigated the serum levels of IL-8 and IL-12, in breast
cancer patients, and proved their relationship with the
prognostic parameters and therapy. In harmony with our
results, Silswal et al. (2005) reported that serum levels
of IL-12 are highly elevated in breast cancer patients
and correlate with tumor progression. Assays for serum
levels of IL-12 can be used as predictive non-invasive tests
for tumor progression in breast cancer patients, it might
also promote tumor development and this can explains
its higher serum level in advanced breast cancer cohort.
In the present study urinary levels of LCN2 were
significantly higher in G III and G II in comparison to
controls G I with higher levels in obese patients with
advanced breast cancer (G IIIb). Our data suggest that
LCN2 may be added to the growing list of secreted
molecules that adipocytes use to modulate glucose
homeostasis and insulin resistance as a subsequent
complication of obesity for examples breast cancer.
In harmony with our results Wang et al. (2007); Auguet
et al. (2011) reported that LCN2 levels were elevated
in both circulation and adipose tissue of obese people
moreover; circulating LCN2 concentration was positively

Asian Pacific Journal of Cancer Prevention, Vol 15, 2014843
DOI:http://dx.doi.org/10.7314/APJCP .2014.15.2.837
Effect of NUCKS-1 Overexpression on Cytokines Profiling in Obese Women with Breast Cancercorrelated with insulin resistance index and inflammatory
markers.
The results of the present study revealed that urinary
concentration of LCN2 increase at the same time that with
parameters of obesity, including BMI, WC, and fasting
insulin levels as well as HOMA-IR and all parameters
of lipid profile except HDL-C levels in patients group as
reported by previous studies of Liu et al. (2011).
LCN2 has been associated with breast cancer. LCN2
gene is among the genes most highly associated with
estrogen receptor (ER) negative breast tumors, it is also
one of the genes that is most increased in the luminal
epithelial cells compared with myoepithelial cells (Jones
et al., 2004), a significant finding because the majority of
breast carcinomas are thought to arise from the luminal
epithelial cells (Bauer et al., 2008). Taken together, these
data suggested that LCN2 may actively participate in
breast cancer progression so its levels were higher in G
IIIb than G IIIa of breast cancer patients.
Roy et al. (2004), considered the possibility that LCN2
levels might be elevated in the urine of women with breast
cancer, its increase in the urine of women with metastatic
breast cancer, suggesting that LCN2 may have potential
as a noninvasive biomarker for advanced breast cancer.
The proposed mechanisms by which LCN2 promotes
growth and metastasis of breast cancer cells are multiple.
Association of LCN2 with matrix metalloproteinase-9
(MMP-9) was shown to induce allosteric activation of the
enzymatic activity of MMP-9 and to protect MMP-9 from
auto degradation (Cramer et al., 2012). LCN2 has also
been inferred as a direct regulator of expression of pro-
oncogenic factors necessary for mesenchymal transition
(Yang et al., 2009).
In this sense LCN2, a newly identified biomarker and
a potential therapeutic target for breast cancer, and the
possible mechanisms underlying its role in tumorigenesis
and metastasis as reported by Xiaohong et al. (2011);
Yang et al. (2013).
The results of the present study showed that increased
urinary level of LCN2 was associated with high levels of
serum IL-6 and IL-12 reflects that LCN2 is associated
with pro-inflammatory markers suggesting its potential
involvement in the low-grade chronic inflammation
accompanying obesity and its subsequent complications
as reported by previous studies of Law et al. (2010);
Auguet et al. (2011).
Gene expression profiling of breast tumors is a novel
tool in the effort to classify tumor subtypes, predict risk
of relapse, identify genes that mediate disease progression
and select optimal therapeutic options (Nguyen and
Massagué, 2007).
NUCKS-1 protein seems to be phosphorylated at
more than one site in all phases of the cell cycle, by CK-
2, which is implicated in cell growth and proliferation,
and the cyclin-dependent kinases (Cdk1, 2, 4, and 6) all
of which play important roles in regulation of the cell
cycle, in addition, NUCKS-1 is an in vitro substrate for
DNA-activated protein kinase, which is involved in DNA
repair (Wiśniewski et al., 2008).
NUCKS-1 from proliferating cells exhibits higher
affinity for DNA when compared to NUCKS-1 from non-proliferating cells, NUCKS proteins are known
and as the protein is highly expressed throughout the
mammalian kingdom and serves as a substrate for Cdk1
in vivo (Ostvold et al., 2001).
Our obtained results show that there is an increased
NUCKS-1 gene expression in breast cancer from obese
patients compared to normal tissues, with a statistically
significant increase in its expression in G IIIb than G IIIa
this is in line with Liu et al. (2007), who reported that
NUCKS-1 has been found to correlate with the invasive
gene signature (IGS) which consists of 186 genes,
overexpressed in breast cancer stem cells, compared to
normal breast epithelial cells.
The NUCKS-1 gene is located on chromosome 1q32.1.
There are several scientific reports utilizing array-based
Comparative Genomic Hybridization (CGH) techniques,
which show that there is a high frequency (59-74%) of gain
of chromosome region 1q.32 in breast cancer and this gain
is an early event in the cancer progression (Nyante et al.,
2004). In harmony with our results Drosos et al. (2009)
reported in his study that the quantitative, real-time RT-
PCR analysis of primary culture cells derived from benign
lesions and breast carcinomas revealed higher NUCKS-1
gene expression compared to normal and fibroadenoma
culture cells.
Moreover, there are a number of scientific data
indicating that NUCKS-1 could play a role in the DNA
damage response (Wiese et al., 2007). Furthermore,
NUCKS-1 is a substrate for ATM (ataxia telangiectasia
mutated) and ATR (ATM and Rad3-related) kinases
(Matsuoka et al., 2007), implying that NUCKS-1 is a cell
cycle related protein involved in DNA damage response.
Since the activation of the DNA damage checkpoint is
an early event in carcinogenesis (Gorgoulis et al., 2005).
In this sense, our results reveal that NUCKS-1 mRNA
expression increase at the same time with parameters of
obesity, including WC, fasting insulin and HOMA-IR
index as well as all parameters of lipid profile except
HDL-C in patient group.
To our knowledge there was no studies showed an
evaluation of NUCKS-1 protein expression in association
with cytokine profiling in obese breast cancer patients that
give a new linkage between cytokines milieu and the cell
cycle protein expression, important components in this
linkage are the cytokines produced by activated innate
immune cells that stimulate tumor growth and progression
as well as cell cycle regulation.
In summary, there is still much work to be done
on elucidating the mechanisms by which NUCKS-1
contributes to oncogenesis so that in the long term this
protein could serve as a potential prognostic marker for
breast cancer progression.
In conclusion, the risk of postmenopausal breast
cancer is significantly increased by obesity. Further, low
grade chronic inflammation, a hallmark of obesity, can
contribute to detrimental health effects including high
cancer incidence. Our goal is to understand the molecular
basis for obesity-breast cancer interactions. Elevated
IL-6 and IL-12 serum concentrations and urinary LCN2
level are strongly associated with breast cancer and they
also correlate with clinical stage of disease. This can be

Nema Ali Soliman et al
Asian Pacific Journal of Cancer Prevention, Vol 15, 2014844possibly used to diagnose obese women with breast cancer
and to identify patients with a poor prognosis who may
benefit from more aggressive management. The fact that
NUCKS-1 belongs to IGS demonstrates its important
role in breast cancer complicated by obesity but further
investigation is needed in order to elucidate whether the
observed overexpression in breast cancer is correlated
with oncogenic progression and poor prognosis or reflect a
preventive response to cancer-related genomic instability.

Acknowledgements
We would like to thank Dr Y Miura (Nagoya City
University, Japan) for critical reading of the manuscript,
Dr R Wasfy (Tanta University, Egypt) for histopathological
examination and Mrs N Mohamed (Tanta University,
Egypt) for technical help.
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DOI:http://dx.doi.org/10.7314/APJCP .2014.15.2.837
Effect of NUCKS-1 Overexpression on Cytokines Profiling in Obese Women with Breast Cancer025.050.075.0100.0
Newly diagnosed without treatment
Newly diagnosed with treatment
Persistence or recurrence
Remission
None
Chemotherapy
Radiotherapy
Concurrent chemoradiation
10.3
0
12.8
30.0
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20.3
10.1
6.3
51.7
75.0
51.1
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54.2
46.8
56.3
27.6
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30.0
31.3
23.7
38.0
31.3025.050.075.0100.0
Newly diagnosed without treatment
Newly diagnosed with treatment
Persistence or recurrence
Remission
None
Chemotherapy
Radiotherapy
Concurrent chemoradiation
10.3
0
12.8
30.0
25.0
20.3
10.1
6.3
51.7
75.0
51.1
30.0
31.3
54.2
46.8
56.3
27.6
25.0
33.1
30.0
31.3
23.7
38.0
31.3025.050.075.0100.0
Newly diagnosed without treatment
Newly diagnosed with treatment
Persistence or recurrence
Remission
None
Chemotherapy
Radiotherapy
Concurrent chemoradiation
10.3
0
12.8
30.0
25.0
20.3
10.1
6.3
51.7
75.0
51.1
30.0
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