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\begin{document}

DDX43 mRNA expression and protein levels in relation to

clinicopathological profile of breast cancer

\begin{quote}
\textbf{Abstract:}\\
Background: Breast cancer (BC) is the most often diagnosed cancer in
women globally. Cancer cells appear to rely heavily on RNA helicases.
DDX43 is one of DEAD- box RNA helicase family members.But, the
relationship between clinicopathological, prognostic significance, in
different BC subtypes and DDX43 expression remains unclear. Our aim,
therefore, is to assess the clinicopathological and prognostic
significance in relation to DDX43 protein and mRNA expression. Materials
and Methods: A total of 80 females newly diagnosed with BC and 20
control females, that were age-matched, were recruited for this study.
DDX43 protein levelswere measured by ELISA technique. We used a
real-time polymerase chain reaction quantification (real-time PCR) to
measure the levels of DDX43 mRNA expression. Levels of DDX43 protein and
mRNA expression within BC patients were compared to those of control
subjects and correlated with clinicopathological data. Results: The mean
normalized serum levels of DDX43 protein were slightly higher in control
than in both benign and malignant groups, but this result was
non-significant. The mean normalized level of DDX43mRNA expression was
higher in control than in both benign and malignant cases, although the
results were not statistically significant and marginally significant
respectively. Moreover, the mean normalized level of DDX43 mRNA
expression was significantly higher in benign than in malignant cases.
In malignant cases, low DDX43 protein expression was linked to higher
nuclear grade and invasive duct carcinoma (IDC), whereas high mRNA
expression was linked to a poor prognosis. Conclusion: Our study
explored DDX43 as a cancer marker in human breast cancer. It has the
potential to be used in clinical settings as a disease progression
marker.

\textbf{Keywords:} mRNA expression, protein expression, breast cancer,
RNA helicases, DDX43, clinical outcome.

\textbf{Introduction:}\\
The epithelial cells that line the breast's ducts and lobules
proliferate malignantly in breast cancer (BC) (1).

An estimated 2.3 million new cases of breast cancer in women will occur
in 2020, making it the most common cancer among women (11.7\% of all
cancers). It is the fifth biggest cause of cancer death worldwide. The
most prevalent type of cancer in women is breast cancer, affecting one
out of every four cases and killing one out of every six deaths. In the
vast majority of countries, it is the most prevalent cause of death and
the first cancer in terms of incidence. Breast cancer rates are rapidly
rising in transitional South American, African, and Asian countries, as
well as in highincome Asian countries (Japan and the Republic of Korea),
where they have been historically low (2). According to GLOBOCAN data,
new cases and deaths of breast cancer constitute 32.4\% and 22.4\% of
female cancer cases in 2020 in Egypt (3).

A gene expression profile is used to divide breast cancer into five
subtypes luminal A, luminal B, normal-like, basal-like, and human
epidermal growth factor receptor 2 (HER2)-amplified (HER2-positive).
Incidence, prognosis, diagnosis age, and treatment response vary widely
within these intrinsic subgroups. Triple-negative breast cancer (TNBC),
is a subtype of breast cancer that does not have hormone
\end{quote}

1

\begin{quote}
receptors (estrogen receptor (ER), progesterone receptor (PR), and
HER2), unlike the others; accounting for about 10\%--15\% of overall
breast cancers. Comparatively, when it comes to other subtypes of breast
cancer, TNBC has a poor prognosis, is prone to distant metastasis, and
has a high recurrence rate (4).

To unwind double-stranded RNA (dsRNA) molecules, ribonucleic acid (RNA)
helicases enzymes hydrolyze a nucleoside triphosphate (NTP). The RNA
helicase known as the DEAD-box, which belongs to the helicase
superfamily 2 (SF2), is the most common form of nucleic acid helicase.
DEAD-box proteins, so named because of their distinctive
Asp--Glu--Ala--Asp (D-E-A-D) motif, have been identified in almost all
organisms, including bacteria, fungi, and viruses, as well as humans.
These proteins have been discovered to be ATP-dependent RNA helicases,
which are responsible for RNA structural changes in a range of
biological processes. They have been found to be important in
practically every phase of RNA metabolism involving reformation of
complexes of RNAs and ribonucleoprotein (RNP).. DEAD-box proteins
contribute to these processes by stimulating the production of perfect
RNA structures on a regular basis, rather than by processive RNA
unwinding, or by mediating RNA--protein association/dissociation.The
DEAD-box proteins all have the same structurally conserved core region
that is required for helicase action. Because they are synchronized with
a variety of cellular processes, A broader role than RNA duplex
unwinders has been demonstrated for DEAD-box helicases. Along with RNA
unwinding and ATPase functions activities, DEAD-box helicases cause the
creation of RNA duplexes and unique forms of the RNA-protein complex.
They can also serve as assembly platforms for bigger RNP complexes, as
well as metabolite sensors, as previously stated (5). All kinds of
RNA-related metabolic processes, including translation, pre-mRNA
splicing and turnover, are carried out by DEAD-box helicases. They also
play a role in the production of pre-ribosomal/ribosomal subunits (6).

Remarkably, cancer cells appear to depend intensely on RNA helicases
tosatisfy the augmented overall protein production request as well as to
promote survival by translating some pro-oncogenic mRNAs. RNA helicases
play other parts in cancer biology by regulating transcription as well
as alternative splicing (for example DDX5, and DDX17), ribosomal
biogenesis (for example DDX5, DDX21, and DDX43), mRNA transport (for
example DDX5, DDX3), miRNA regulation (for example DDX3, DDX5) as well
as apoptosis (for example DDX3, RHA), and other processes (7).

In a human sarcoma cell line, researchers found the cancer/testis
antigen gene DDX43, commonly known as HAGE (helicase antigen gene). A
73-kDa protein from ATP-dependent RNA helicase DEAD-box family members
is encoded by this gene, which is located on chromosome 6 (6q12-q13). In
tumors, mRNA levels of DDX43 are not less than 100 times greater than in
normal tissues. Many types of cancerous cells contain varying amounts of
DDX43 such as the brain, bladder, esophagus, colon, breast, stomach,
small intestine, lung, liver, and kidney, whereas normal tissues contain
either no protein or very little protein. About one-fifth of individuals
with acute myeloid leukemia (AML), more than half of chronic myeloid
leukemia (CML) patients, and more than 40 \% of those with multiple
myeloma have a high level of DDX43 expression. The presence of high
levels of DDX43 overexpression in diverse malignancies suggests that it
could be a feasible cancer therapeutic target. DDX43, a helicase
belonging to the DEAD-box family, has been discovered to have nucleic
acid unwinding activity by \textbf{Yadav et al.} and others. Because
DDX43 is overexpressed in a
\end{quote}

2

\begin{quote}
wide range of malignancies, making it a potential biomarker or
therapeutic target. To predict anthracycline treatment response in
breast cancer, expression of DDX43 could be used as an indication and
prognostic marker. Melanoma tumor development and progression are aided
by DDX43 expression. CML and acute myeloid leukemia both have high
levels of DDX43 expression. Higher DDX43 expression correlates with
higher stage and metastatic progression (8).

It had been formerly revealed that protein expression of the Helicase
Antigen (DDX43) is (a) common in ER negative BC, (b) is substantially
associated with aggressive clinicopathological features in BC, as well
as (c) may be a predictor of chemotherapy response (9).

Though, the association between clinicopathological, prognostic
significance within various BC subtypes and DDX43 expression remains
unclear. There are also few studies about DDX43, its related pathways
and possible role in cancer. We therefore aim to assess the
clinicopathological and prognostic significance in different BC subtypes
and DDX43 expression.

Our study has the following goals: (a) assess the DDX43 protein levels
and mRNA expression in BC patients compared to control subjects (b)
evaluate the correlation between DDX43 protein levels and mRNA
expression levels (c) investigate the possible relationships between
both DDX43 protein, mRNA levels and clinicopathological parameters (d)
examine the potential interacting proteins with DDX43 protein via one of
the commonly used protein-protein interaction databases.

\textbf{Patients and methods:}\\
\underline{Ethics Statement:}\\
The study design was agreed by the Baheya Research Center and the
National Research Center Ethical Committees in compliance with the
Declaration of Helsinki's ethical standards. In addition, before being
included in the study, each subject was required to complete an informed
consent form.

\underline{Study Population and specimen Collection:}\\
An observational case control study was employed. Random unrelated 80
female patients newly diagnosed with BC aged 27-74 years who presented
to the outpatient clinic of Baheya Centre for Early Detection and
Treatment of Breast Cancer were recruited for this study between March
2019 and February 2020. Age and sex matched 20 control subjects were
included if they had not any clinical symptoms or suspecting data
indicating a history of illness of BC or any type of cancer.

Inclusion criteria for patients included diagnosis with BC either
malignant or benign. Any concomitant type of cancer other than breast
cancer was a criterion for exclusion for both patients and controls.

Based on morphologic analysis of tumor samples, histopathologic
diagnosis and tumor grading were performed using the World Health
Organization categorization of breast cancers, fifth edition (2019).
Examining the removed specimens allowed the pathologic stage to be
established. The seventh version of tumor-node-metastasis (TNM)
classification of the American Joint Committee on Cancer (AJCC) is used
(10). A senior pathologist evaluated and confirmed all histopathologic
data.
\end{quote}

3

\begin{quote}
As a result, to determine ER and PR positivity, samples with 1 \% to
100\% of tumour nuclei positive for ER or PR are deemed positive.

Metastatic workups for all patients included a chest radiograph, abdomen
sonar and bone scan as well as complete history, and clinical
examination. Biopsies were performed on all patients for
histopathological diagnosis and testing of hormone receptors (ER, PR),
and HER2 by Ventana Bench Mark XT Autostainer). A senior pathologist
reviewed the ER, PR, and HER2 data and reported it in accordance with
the new guidelines issued by the American Society of Clinical Oncology/
the College of American Pathologists in 2018. As a result, for defining
ER and PR positivity, samples with 1\% to 100\% of tumor nuclei positive
for ER or PR are considered positive (11).

Molecular subtypes of breast cancer were divided into five groups:
luminal A (ER+, PgR+ or PgR-, HER2-, and low Ki-67 index), luminal B
(HER2 -) (ER+, PgR+ or PgR-, HER2-, and high Ki-67 index), Luminal B
(HER2+) (ER+, PgR+ or PgR-, and HER2+), HER2 (ER-, PgR-, and HER2+), and
basal-like (ER-, PgR-, and HER2-) (12).

Five milliliters of peripheral blood were drawn from all subjects prior
to any treatment, 2ml was collected for RNA extraction into K3 EDTA
coated vacuum tubes, while the remaining 3 ml was collected into serum
vacuum tubes with clot activator and instantly centrifuged to isolate
the serum. Serum samples were split into aliquots and kept at -40°C
until the assay.

\underline{Measurement of serum helicase antigen gene (DDX43):}\\
A quantitative double-antibody sandwich enzyme-linked immunosorbent
(ELISA) test was used to measure the serum levels of DDX43 using a human
monoclonal antibody and following the instructions provided by the
manufacturer. A commercial kit was provided by Assay Kit Co., USA. Using
the standard calibration curve, we determined the DDX43 levels in the
serum specimens.

\underline{RNA extraction and cDNA synthesis:}\\
Samples of blood were processed using the QIAamp RNA Blood Mini Kit
(Qiagen, Germany) to get their total RNA content. A Nanodrop ND-1000
spectrophotometer (Thermo Fisher Scientific, USA) was used to assess RNA
concentrations and quality. High-Capacity cDNA Reverse High-Capacity
cDNA Reverse Transcription Kit (Applied Biosystems,Thermo Fisher
Scientific, USA) was used to reverse-transcribe the RNA to cDNA.
Following the manufacturer's instructions, 10 µl of total RNA was used
to synthesize cDNA.

\underline{Quantitative real‑time PCR (q real-time PCR) from blood
samples:}\\
Custom primers (Thermo Fisher) for the helicase antigen gene (DDX43)
(forward, 5´ -GGAGATCGGCCATTGATAGA-3 ´and reverse, 5´ -

GGATTGGGGATAGGTCGTTT-3´ and the housekeeping gene hypoxanthine
phosphoribosyl transferase 1 (HPRT1) (forward, 5´
-TGACACTGGCAAAACAATGCA-3´ and reverse, 5´ -GGTCCTTTTCACCAGCAAGCT-3´ and
Maxima SYBR Green/ROX qPCR Master Mix (2X) were then used in
quantitative real-time PCR on Applied Biosystems StepOne Real-Time PCR
System (Thermo Fisher Scientific Inc, USA). A total of 12.5 μl
comprising 2.5 μl of cDNA (≤200 ng), 1 μl of primer mix (the forward and
reverse primers were dissolved in water in 25 pmol/ml concentrations),
6.25 μl of SYBR Green master mix, and 2.65 μl of water were used in the
polymerase chain reaction.
\end{quote}

4

\begin{quote}
The PCR conditions were as follows: an activation step at 95°C for 10
minutes followed by 40 cycles of denaturation at 95°C for 15 seconds,
annealing at 60°C for 30 seconds, and extension at 72°C for 30 seconds.
Melt curves analysis was performed. Housekeeping gene values were
averaged and calculations were made to normalize expression of gene of
interest using comparative CT method (2- ΔΔCT method) (13).

\textbf{Statistical analysis:}\\
In order to perform the statistical analysis, we used the Statistical
Package for the Social Sciences (SPSS, version 16). The t-test was used
to examine the significance of the relationships between DDX43 levels
and clinicopathological parameters, and a 95\% confidence interval was
determined. P-values less than 0.05 were considered statistically
significant, while those between 0.05 and 0.1 were considered marginally
significant.

\textbf{Results:}\\
\underline{Study Population characteristics}\\
A total of 100 samples were analyzed, (Table 1). Eight samples from BC
patients and two samples from controls were excluded because results
from the quantitative real‑time PCR analysis were undetermined. The
serum levels of DDX43 protein and DDX43 mRNA expression were assessed in
control, benign as well as malignant cases. The mean normalized serum
levels of DDX43 protein were slightly higher in control than in both
benign and malignant groups, but this result was non-significant
(p=0.77, p=0.68). The mean normalized level of DDX43mRNA expression was
1.5times higher in control than in benign cases, although this result
was marginally significant (p=0.14). Also, the mean normalized level of
DDX43 mRNA expression was three-times higher in control than in
malignant cases, although this result was not statistically significant
(p=0.51). But the mean normalized level of DDX43 mRNA expression was
significantly higher in benign than in malignant cases, (p=0.016). Also,
using non-parametric test (Mann-Whitney test), the mean level of DDX43
mRNA expression was significantly higher in benign than in malignant
cases (p=0.02), figure (1).

\underline{Serum DDX43 protein levels, mRNA expression of DDX43 and
other parameters} We then compared the serum DDX43 protein levels and
its mRNA expression in the 60 malignant breast cancer patients in
subgroups according to body mass index, menopausal status and
pathological parameters, (table 3).

The mean normalized serum level of DDX43 protein was significantly lower
in IDC tumor type than in both ILC and ICC tumor types (p=0.042,
p=0.006). The mean normalized serum level of DDX43 protein was higher in
patients with tumor size (T2) than tumor size (T1), but the result was
marginally significant (p=0.147). While, the mean normalized serum level
of DDX43 protein was lower in ILC tumor type than in ICC tumor type but
the result was marginally significant (p=0.152). Also, the mean
normalized serum level of DDX43 protein was higher in mitosis score 2
than mitosis score 3, but the result was marginally significant
(p=0.137). Similarly, the mean normalized serum level of DDX43 protein
was higher in nuclear grade 2 than nuclear grade 3, but the result was
marginally significant (p=0.088).

The mean normalized level of DDX43mRNA expression was considerably
higher in tumor grade 3 than tumor grade 2, (p=0.037). In addition, in
tumor grade 3 the mean normalized level of DDX43 mRNA expression was
higher than in tumor grade 1,but the result was marginally significant
(p=0.056). While, the mean normalized
\end{quote}

5

\begin{quote}
level of DDX43mRNA expression was lower in mitosis score 1 than in both
mitosis scores 2 \&3. But the result was marginally significant
(p=0.129, p=0.108). Also, the mean normalized level of DDX43mRNA
expression was higher in nuclear grade 3 than nuclear grade 2, but the
result was marginally significant (p=0.085). The mean normalized level
of DDX43mRNA expression was lower in the estrogen receptorpositive
patients than in estrogen receptor-negative patients. But the result was
marginally significant (p=0.168). Whereas, the mean normalized DDX43mRNA
expression level was higher in patients with triple-negative phenotype
than in other patients, but the result was marginally significant
(p=0.187).

DDX43 was predicted to have three protein interacting networks (PINs)
using the STRING data (https://stringdb.org/cgi/)(human database) under
high-confidence (with a minimal interaction score of 0.700), and the
results showed three functional partners: Sarcoma antigen 1 (SAGE1),
Melanoma-associated antigen 1 (MAGEA1), and a probable ATP-dependent
RNA-helicase; DEAD-box helicase 53 (DDX53) (17). These four proteins had
correlation networks based on curated data and experimentally validated
data. The "more" button on the STRING interface was used to add an
additional 20 nodes/protein to the initial network of three proteins.
The functional partner proteins with DDX43 are listed in the table, and
the confidence cutoff value for interaction linkages has been adapted to
0.700, figure 2A, accompanying table 2B. As a consequence of the above
findings, we used Kyoto Encyclopedia of Genes and Genomes (KEGG) mapping
to map DDX43 protein to molecular interaction/reaction/relation networks
(KEGG pathway maps, BRITE hierarchies and KEGG modules) revealed no
pathways mediated by DDX43. This shows that there is no evidence for
such pathways.KEGG pathway is based on the curated and verified
information. So, it appears that such information is not proven.

We used Expression Atlas to obtain most of the reported mRNA expression
of DDX43 in various types of cancer. It is shown in figure 3 (18).

\textbf{Discussion:}\\
DDX43 is an ATP-dependent dual helicase that relaxes both RNA and DNA
molecules, according to \textbf{Talwar et al.} This demonstrates its
putative cellular roles, which may be linked to its overexpression in
malignancies. In the presence of Mg2+ and ATP, it catalyzes the
unwinding process most effectively. Interestingly, DDX43 relaxes RNA
substrates without adhering to a rigid movement mechanism, while it
relaxes DNA in a unidirectional manner (only in the 3' to 5' direction).
They suggested that DDX43 is designed to function on short duplex
molecules, but its precise involvement in DNA metabolism has to be
investigated further (19).

While the DDX43 gene has been proposed as a possible oncogene because of
its high expression in in various types of cancer, details about its
precise physical function are scarce.

\underline{Serum protein levels and mRNA expression of DDX43 in the
studied groups}\\
The mean normalized serum DDX43 protein level was slightly higher in
control than in both benign and malignant groups, but this result was
non-significant. To our knowledge, there are few information about DDX43
protein levels in serum. This may be as it is not predicted to be
definitely secreted to blood and thus not analyzed (20). However, our
results regardingserum DDX43 level were somewhat similar to a previous
study. DDX43 protein was found in a range of tumor tissues, which
include the brain, bladder, esophagus, colon, breast, stomach, small
intestine, lung, liver, and kidney, but not in normal tissues or at
extremely low levels, according to this study. It showed low staining of
DDX43 protein in breast cancer tissues like serum DDX43
\end{quote}

6

\begin{quote}
protein level in malignant breast cancer patients stated in our study
(21). But in our study the serum DDX43 protein level in normal subjects
was higher than in both benign and malignant groups.

As we know, DDX43 mRNA expression in breast cancer patients has been
studied before. We have found for the first time that DDX43 expression
in the blood of malignant breast cancer patients is decreased. Unlike
other studies that have found it overexpressed in a number of solid
tumors, including the brain, salivary gland, lung, colon, and prostate
cancers, as well as hematologic malignancies (e.g., chronic myeloid
leukemia and multiple myeloma) (22, 23).

However, most of information arelimited to the detection of DDX43 at the
messenger RNA level. Because of factors like as the fluctuating
stability of distinct mRNA molecules, the translation regulatory
mechanism, proteasomal degradation, and post-translational
modifications, inconsistencies between mRNA and protein levels are
prevalent (21).

Our data show that DDX43 may have a tumor suppressive role. as well as
its mRNA expression and/or serum protein levels are potential biomarkers
for disease progression in human breast cancer.

\underline{Serum protein levels and mRNA expression of DDX43 in relation
to clinicopathological parameters}\\
The mean normalized serum DDX43 protein level was significantly lower in
IDC tumor type than in both ILC and ICC tumor types.

This is consistent with \textbf{Mathieu et al.} who reported little
protein level in situ expression of DDX43 in breast cancer tissue
(invasive ductal carcinoma) (21).

In various studies comparing clinical and pathological aspects,
metastatic sites and survival rates of IDC and ILC, most of the results
was contradictory. Nevertheless, most research found no significant
difference regarding most clinicopathological features, treatment
aspects as well as survival was found between the IDC and ILC groups.
But regarding vascular invasion, it was lower in ILC than in IDC and the
difference was significant (24). This means that IDC has a poor
prognosis than ILC.

Although both ICC and IDC belong to a family of low-grade breast cancer,
\textbf{Zhang et al.} found that ICC was related to a number of
advantageous prognostic aspects than IDC (25). This proposes that lower
serum DDX43 protein levels may be linked to types of breast cancer with
a bad prognosis or with a poor prognosis.

While, the mean normalized serum DDX43 protein level was lower in ILC
tumor type than in ICC tumor type but the result was marginally
significant. Though, ICC is a separate subtype of invasive breast
carcinoma, it is a carcinoma with a specifically convenient prognosis
and similar low-grade tumor nuclear characteristics (26). The lowerserum
DDX43 protein level in ILC tumor type than in ICC tumor type may
indicate promising prognosis and similar low-grade tumor nuclear
characteristics of ICC type than ILC type.

Transcription, translation, protein, and mRNA turnover all influence
gene expression. The lower protein/mRNA ratio could be linked to
increased mRNA levels due to augmented transcription and reduced mRNA
degradation, as well as lower protein levels due to reduced translation
and enhanced protein breakdown. Dysregulation of protein translation is
implicated in the growth and progression of numerous tumor types.
\textbf{Du et al.} found that LumA ILC produces less protein than LumA
IDC when they analyzed the gene expression profiles of luminal A ILC
(LumA ILC) and luminal A IDC. LumA ILC showed a higher immunological
response, poorer general metabolism, lower protein translation rate,
cell proliferation, and were further bioenergetically inactive than LumA
ILC, according to their findings (27). The
\end{quote}

7

\begin{quote}
described lower general metabolism and protein synthesis in LumA ILC may
support the lowerserum DDX43 protein level in ILC than in ICC.

The mean normalized serum DDX43 protein level was higher in patients
with tumor size (T2) than tumor size (T1), but the result was marginally
significant.

In two studies, \textbf{Wo et al.} and \textbf{Hernandez-Aya et al.}
demonstrated two intriguing clinical findings regarding the link between
tumor size and lymph node status and clinical prognosis (28,29). These
publications point to a less-than-ideal relationship between tumor size
and its propensity to infiltrate distant organs and lymph nodes. In
cases of broad lymph node involvement, extremely small tumors, according
to \textbf{Wo et al.}, may represent a more aggressive subtype than
larger tumors with equivalent lymph node contribution (28). According to
\textbf{Hernandez-Aya et al.} second's study, the number of positive
lymph nodes may have no effect on prognosis, independent of tumor size,
once there is proof of lymph node contribution, in triple-negative
breast cancers (29). Thus, tumor size T1 may correlate to poor clinical
outcome or worse prognosis which could explain the lower serum DDX43
protein level.

Also, the mean normalized serum DDX43 protein level was higher in
mitosis score 2 than mitosis score 3, but the result was marginally
significant.

According to numerous studies, the mitotic count is a critical element
in determining histological grade. Regardless of the technique utilised,
\textbf{Van Diest et al.} observed that higher proliferation is closely
associated with a bad prognosis. In primary invasive breast cancer, many
groups from around the world many countries have established the
predictive significance of mitotic counting through future studies (30).

Evaluation of mitotic figures, represented as the mitotic activity index
(MAI) has been proven to be a substantial independent predictive factor
(31). This also supports that low serum DDX43 protein level is
associated with worse prognosis in mitosis score 3 than mitosis score 2.

Similarly, the mean normalized serum DDX43 protein level was higher in
nuclear grade 2 than nuclear grade 3, but the result was marginally
significant.

Nuclear atypia or nuclear pleomorphism in histopathologic pictures of
breast cancer is crucial for predicting the breast cancer grading (32).
In breast cancer , nuclear grade is additionally known prognostic
component. In \textbf{Yang et al.} study, nuclear scoring linked
positively with proliferative activity, specifically, the proliferation
index of well-differentiated tumors is likely to be low, whereas the
index of poorly differentiated tumors is likely to be high. Also,
nuclear pleomorphism, cell differentiation, and mitotic frequency are
all aspects of cell morphology that are included in nuclear grade. As a
result, nuclear grade might propose a range of molecular measures
mirrored in cellular morphology (33). These studies together with our
result support that serum DDX43 protein level negatively associated with
the more aggressive the tumor cells tend to be.

Sarcomas express DDX43 as a tumor-specific gene, and it has been linked
to bad clinical consequences in patients with breast cancer (22).

The mean normalized level of DDX43mRNA expression was greater in tumor
grade 3 than in tumor grades 2 and 1. The result was statistically
significant regarding grade 2 vs. grade 3 but it was marginally
significant regarding grade 1 vs. grade 3.

Tumor grade is a classification system for tumors according to the
aberration of the tumor cells and tissue under a microscope. It's a
measure of how quickly a tumor will develop and spread. In general, a
lower grade suggests a better prognosis. A highergrade malignancy may
grow and spread faster, requiring immediate or more antagonistic therapy
(34).
\end{quote}

8

\begin{quote}
\textbf{Schwartz et al.} examination shows that the histologic grade in
breast cancer is still a predictive factor despite alterations in tumor
size and number of positive lymph nodes. As the histologic grade
increased from G1 to G3 for each grouping of T and N, the survival rates
progressively reduced as the tumor size as well as number of entailed
nodes increased for every grade level (35). This supports that higher
DDX43 mRNA expression is linked to poor diagnosis.

While, the mean normalized level of DDX43mRNA expression was lower in
mitosis score 1 than in both mitosis scores 2 \&3. But the result was
marginally significant. Also, the mean normalized level of DDX43mRNA
expression was higher in nuclear grade 3 than nuclear grade 2, but the
result was marginally significant.

These results are also compatible with DDX43 mRNA expression in relation
to tumor grade. All supports that higher DDX43 mRNA expression is linked
to poor diagnosis.

They agree with \textbf{Abdel-Fatah et al.} finding that :DDX43+
expression is considerably linked to aggressive clinicopathological and
high proliferation metrics are also a new independent predictor of poor
prognosis (36).

The mean normalized levels of DDX43mRNA expression were lower in the
estrogen receptor-positive patients than in estrogen receptor-negative
patients, but the result was marginally significant. Whereas, the mean
normalized levels of DDX43 mRNA expression were higher in patients with
triple-negative phenotype, as well the result was marginally
significant.

Both results are compatible with what \textbf{Abdel-Fatah et al.} study
showed. Their study stated that DDX43+expression was related to
ER-negative expression along with other aggressive characteristics, such
as TNBC, overexpression of the HER2 gene, high grade and proliferation.
Though, more functional studies to verify this initial info are
necessary (36).

According to patent (WO2013144616) which focused on the use of DDX43 for
screening BC patients. It showed similar findings to \textbf{Abdel-Fatah
et al.} study. It reported that increased expression of DDX43 correlates
substantially with aggressive clinicopathological parameters, as high
proliferation, absence of concurrent expression of ER, progesterone
receptor as well as HER2 (triple negative), as well as overexpression of
both EGFR and HER2. Taking into account the restrictions of the present
chemotherapy, the inventors state moreover DDX43 as a possible target to
treat cancer with a DDX43-specific chemotherapeutic agent antigen or
DDX43 specific antibody. Therefore, the DDX43 level can function as a
biomarker in the outline of the contemporary diagnosis as well as
pharmacological therapy of patients with BC (37).

Low DDX43 protein expression was linked to a bad clinical result in our
study, but high mRNA levels were linked to a poor prognosis. These
results are in contrast to \textbf{Abdel-Fatahet al.} results in early
primary TNBC patients, which informed that high DDX43 protein expression
was related to poor clinical consequence, while high DDX43 mRNA levels
were linked to good outcome. Differences in anthracycline treatment may
be to blame for these contradictory results. The majority of those whose
protein expression was evaluated did not receive anthracycline
chemotherapy, whereas most of those in which mRNA expression was
examined did. But they cannot eliminate the likelihood that the
incompatible impact of protein and mRNA on survival may also owing to an
inaccurate relationship between protein and mRNA levels, provided that
mRNA readings could be used to forecast just around 40\% of cellular
protein levels. In their study they stated DDX43 expression was a
hopeful predictive biomarker in TNBC and it seems to predict advantage
from adjuvant and
\end{quote}

9

\begin{quote}
neoadjuvant chemotherapy based on anthracyclines (38). A significant
reason for distinction of our results from those of
\textbf{Abdel-Fatahet al.} may also as a result of few TNBC patients 7
versus 53 non-TNBC patients.

One more explanation to our results regarding low DDX43
proteinassociated with high mRNA level can be concluded from \textbf{Liu
et al.} review. \textbf{Liu et al.} concluded that transcript levels
alone are insufficient for predicting protein levels in a variety of
contexts and elucidating genotype-phenotype correlations, and that
high-quality gene expression data at diverse levels are essential for
the whole interpreting of biological processes. Biology's core dogma
firmly connects DNA, RNA, and protein. There is, however, no minor
connection between the concentration of a transcript and the
concentration(s) of the protein(s) generated from a specific location.
Systematic studies measuring transcripts and proteins at genomic scales
discovered the value of many factors more than transcript concentration
in determining a protein's expression level. These consist of (1)
translation rates; (2) translation rate modulation; (3) protein
half-life modulation; (4) protein synthesis delay and (5) Transport of
proteins. Therefore, protein and mRNA quantities from the same area or
cell type should not be directly compared. Protein levels are mainly
influenced by transcript concentrations, as a minimum at the majority
level and under stable-state settings. The most significant idea is that
post-transcriptional activities may result in greater departures from an
ideal relationship during highly energetic stages such as cellular
differentiation or stress response. This is undeniably true in the case
of breast cancer (39).

Since DDX43 gene is down-regulated in the blood of patients with breast
cancer relative to normal subjects, this may be due to DDX43 gene
mutation in breast cancer. This mutation needs further investigation to
reveal it. Other than regulating translation, abnormal DDX43 expression
could play a role in oncogenesis. Therefore, more investigation is
required to better understand the role of DDX43 in both translation
start and oncogenesis. As several researches have emphasized the
relation between disease-associated variants in regulatory DNA and
breast cancer and additional kinds of cancer. So, a hallmark of cancer
that definitely contributes to further modify expression or sequences in
regulatory regions is the genome instability which could promote tumor
progression (40).

The noticeable inconsistency between the behaviors claimed to promote
tumor growth versus our hypothesized suppressor effect might be
explained in a variety of ways. Initially, many of the earlier DDX43
investigations were focused on the cellular functional link while DDX43
expression and signaling pathways have not been explored. So
comprehensive expression-cellular impact association studies are
mandatory to establish their functional link.

Although, \textbf{Bourgeois et al.} review stated that DDX43 is one of
RNA helicases which are engaged in translation activation, they found
that their exact roles remain equivocal (41). This report is supported
by our results of higher DDX43 expression in normal subjects compared to
breast cancer patients.

Another explanation for the down-regulation of DDX43 gene expression in
the blood of patients with breast cancer may be attributable to low Mg2+
levels in the selected cancer patients. Many studies stated that the
pathophysiology of numerous diseases, involving cancer is caused by
imbalance of magnesium homeostasis. New interesting studies link
magnesium as well as Mg2+ transporters to characteristic and
complementary abilities that allow tumor development and metastatic
propagation have supported the crucial concept of magnesium as a main
controller of cell proliferation (42). Low Mg2+ levels may affect DDX43
gene expression as in the
\end{quote}

10

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 14\tabcolsep) * \real{0.12}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
presence of Mg2+, it most efficiently catalyzes the unwinding reaction.
However, this hypothesis requires investigating Mg2+ levels as well as
the relating factors.

Cancer/testis antigens (CTAs) had been initially known as proteins with
epitopes that caused cell-mediated immune responses in cancer patients
and because of their particular distribution of expression in
malignancies, are likely perfect candidates for tumor-specific
immunotherapy. DDX43 might will be a perfect tumor vaccine because it
has been recognized highly immunogenic as a complete antigen as well as
numerous MHC class I/IIDDX43-derived immunogenic peptides have been
discovered. Furthermore, the demethylating compound
5-aza-2-deoxycytidine, which is currently an essential medication
utilized in the treatment of some types of cancer, could induce the
DDX43 expression. Consequently, DDX43-targeted immunotherapy could hold
promise as a new approach to enhance the efficiency of DNA methylation
inhibitors, particularly in older patients who are unable to endure the
severe side effects of chemotherapy (43).

Protein interacting networks (PINs) of DDX43 and its mRNA expression in
diseases Proteins, as principal elements of biological function, were
found in early biological investigations to establish the phenotypic of
all organisms. By the arrival of molecular biology, it has been adopted
that proteins have interactions with each other as well as other
molecules (e.g., DNA, RNA) that intervene organismal systems, metabolic
and signaling pathways, along with cellular activities processes.
Systematic studies of protein interaction networks have been verified to
be particularly significant for interpreting the associations between
network structure as well as function, discover finding new protein
function, recognizing functionally coherent modules, and conserved
molecular interaction forms. As proteins have major role in biological
function, their interactions establish cellular and molecular mechanisms
that regulate healthy as well as diseased states in organisms. This
sequentially can evaluate approaches for prevention, diagnosis, and
treatment. Protein networks are beneficial resources to recognize novel
pathways to acquire basic information of diseases. As discovering
disease-associated interaction proteins can help us in identifying
possibly fascinating disease-associated gene candidates. Thus, protein
interactions could be utilized to rank gene candidates in researches
examining the genetic foundation of disease. Disease networks can
enhance drug design by defining main nodes as possible therapeutic
targets. Cancer is a complicated disease, and several genes have been
stated to entail in the development of cancers. A systematic examination
of cancer proteins in the human protein-protein interaction network
might supply major biological information for revealing the molecular
mechanisms of cancer and, likely, other complex diseases. A
protein-protein interaction (PPI) network denotes a track by which we
have this chance to systematically recognize disease-related genes from
the relations between genes with like functions. (44).

STRING database predictions revealed three proteins with functional
links to DDX43: SAGE1: Sarcoma antigen 1, MAGEA1: Melanoma-associated
antigen 1 and DDX53: Probable ATP-dependent RNA helicase; DEAD-box
helicase 53. The evidence pointing to a functional connection between
DDX43 and SAGE1 include co-Expression in other organisms and co-mention
in Pubmed Abstracts. The evidence pointing to a functional relationship
between DDX43 and MAGEA1 include comention in Pubmed Abstracts. While
the evidence suggesting a functional link
\end{quote}
\end{minipage} & & & & & & & \\
\midrule
\endhead
between & DDX43 & and & DDX53 & include & cooccurence & Across &
Genomes, \\
\bottomrule
\end{longtable}

\begin{quote}
Experimental/Biochemical Data and co-mention in Pubmed Abstracts (17).

\textbf{Maheswaran et al.} demonstrated that in normal tissues SAGE1
antigen display a germ cell-specific expression pattern, but was not
expressed in breast and lung cancer.
\end{quote}

11

\begin{quote}
This result proposed that SAGE1 cancer/testis antigen is not hopeful
targets for breast and lung cancer immunotherapy (45). There are few
studies that report SAGE1 expression in some types of cancer, but there
are no studies that examine its role /function in these types or in
breast cancer.

Several MAGE proteins are unusually expressed in many cancer types. In
the majority of cancer types examined, for example lung and melanoma
cancer, the projecting role for MAGEs is inducing carcinogenesis.Though,
\textbf{Zhao et al.} study showed that MAGEA1 primarily has a
suppressive effect in the breast cancer cell lines studied.The research
from \textbf{Zhao et al.} showed that MAGEA1 inhibited cell growth in
addition to migration in the breast cancer cell lines evaluated (46).
This study results resemble to a great degree our results regarding
DDX43 underexpression in BC patients compared to normal subjects that
are inconsistent with previous studies reporting its overexpression in
BC.

Also, MAGEA1 is commonly expressed in triple-negative breast cancer,
according to \textbf{Raghavendra et al.} (47). This is similar to DDX43
expression in this type of BC which supports common pathways and
mechanisms regulating this type of BC.

\textbf{Mi et al.}targeted MAGE-A1 in a breast cancer cell line and
noticed that let-7a miRNA suppressed cellular proliferation, migration
as well as invasion by targeting MAGE-A1 in breast cancer (48). A
similar mechanism may be responsible for the tumor suppressor effect of
DDX43.

Earlier explorations have showed that definite RNA helicases including
DDX1, DDX5 (p68), DDX53 (CAGE) are involved in tumor cell progress and
proliferation in numerous types of malignancies (43).

In many human cancer tissues, comprising breast cancer, \textbf{Cho et
al.} study revealed high rates of promoter hypomethylation.
Hypomethylation of CAGE at CpG locations in nearly all cancer cell lines
supports that the growing malignancy may have chosen to hypomethylate
CAGE (49).

According to reports, DDX43 interacts with DDX53 and DHX15. The current
findings imply that the exact activity of cancer-related DEAD-box
proteins like DDX3X is affected by their interaction partners and the
tumor or background environment (50).DDX43 has been shown to have
ATP-dependent RNA helicase activity, whereas DDX53 has yet to be
biochemically studied (51).

The reported promoter hypomethylation mechanism regulating DDX53
resembles that reported in DDX43 but in different types of cancer. This
supports that DDX43 may have a role in breast cancer different from
DDX53 role.

Figure (3) of the reported mRNA expression of DDX43 in various types of
cancer showed that the expression level is detected in normal blood,
normal breast and normal breast adjacent to breast adenocarcinoma but
the expression level in breast adenocarcinoma and invasive lobular
carcinoma is below the cutoff. This is consistent with our results that
reported higher DDX43 mRNA expression in normal subjects than in BC
patients.

The limitations of our investigation comprised the small sample size
specially of the control group that could have contributed to the lack
of a statistically significant relationship between DDX43 and certain
clinicopathological parameters. Many of the results that came close to
statistical significance may have been affected by a larger cohort
analysis. To ensure consistency, protein expression was quantified in
addition to DDX43 mRNA transcript levels. Lack of data regarding protein
expression of the studied gene is also another limitation. Finally, the
possible protein interacting networks of DDX43 together with the
underlying molecular mechanisms in breast cancer were elucidated using
bioinformatics analyses only. As a result, additional in
\end{quote}

12

\begin{quote}
vitro and in vivo research studies are needed to verify the findings of
the present study.

\textbf{Conclusion}\\
In contrast to numerous documented tumor-promoting effects of DDX43 in
breast cancer and other cancer types, our investigation found that it
may have a tumorsuppressive effect in human breast cancer. It has
potential to be used as a marker of disease progression. Real-time PCR
methodology was used to analyze blood \emph{DDX43} mRNA expression in a
case control study of both benign and malignant breast cancer cases in
comparison to healthy people which is a strength of our study. This
proposes a non-invasive method for breast cancer prognosis. More study
is needed to validate the DDX43's tumor-suppressing role in human breast
cancer. Moreimmunohistochemistry studies, in vitro and in vivo tests,
and larger validation studies are needed to validate the influence of
DDX43 expression on human breast cancer pathogenesis and its worth in
prognosis and gene therapy. To examine the role and signaling pathways
of DDX43 in breast cancer cells, genetic, biochemical, and cell biology
studies should be carried out.

\textbf{Acknowledgements}\\
The Authors wish to thank Baheya foundation for early detection and
treatment of Breast Cancer for supporting this work.

\textbf{Conflicts of Interest}\\
The Authors declare that they have no competing interests in regard to
this study.

\textbf{Data availability (data transparency)}\\
The datasets generated during and/or analyzed during the current study
are not publicly available but are available from the corresponding
author on reasonable request.

\textbf{Author contributions}\\
Conception :Noha Nagah Amer\\
Interpretation or analysis of data :Mahmoud M Kamel, Rabab Khairat, Noha
Nagah Amer and Amal Hammad.

Preparation of the manuscript:Noha Nagah Amer andAmal Hammad.

Revision for important intellectual content:Rabab Khairat and Noha Nagah
Amer Supervision: Mahmoud M Kamel and Rabab Khairat.

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2021{]}. & Available & from: &
https://www.cancer.gov/about-cancer/diagnosis- \\
\bottomrule
\end{longtable}

\begin{quote}
\underline{staging/prognosis/tumor-grade-fact-sheet}).

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\textbf{37. Wiese M, Pajeva IK.} DDX43, the helicase antigen as a
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\begin{longtable}[]{@{}lllllll@{}}
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\endhead
mRNA & Abundance. & Cell. & 2016 & Apr & 21;165(3):535-50. & doi: \\
\bottomrule
\end{longtable}

\begin{quote}
10.1016/j.cell.2016.03.014.
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\textbf{43. Lin J, Chen Q, Yang J, Qian J, Deng ZQ, Qian W, Chen XX, Ma
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16

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may predict a favorable outcome in acute myeloid leukemia. Leuk Res.
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\textbf{45. Maheswaran E, Pedersen CB, Ditzel HJ, Gjerstorff MF.} Lack
of ADAM2,

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
CALR3 and SAGE1 Cancer/Testis Antigen Expression in Lung and Breast
\end{quote}
\end{minipage} & & & & & & \\
\midrule
\endhead
Cancer. & PLoS & One. & 2015 & Aug & 7;10(8):e0134967. & doi: \\
\bottomrule
\end{longtable}

\begin{quote}
10.1371/journal.pone.0134967.
\end{quote}

\textbf{46. Zhao J, Wang Y, Mu C, Xu Y, Sang J.} MAGEA1 interacts with
FBXW7 and

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
regulates ubiquitin ligase-mediated turnover of NICD1 in breast and
ovarian
\end{quote}
\end{minipage} & & & & & & \\
\midrule
\endhead
cancer & cells. & Oncogene. & 2017 & Aug & 31;36(35):5023-5034. &
doi: \\
\bottomrule
\end{longtable}

\begin{quote}
10.1038/onc.2017.131.

\textbf{47. Raghavendra A, Kalita-de Croft P, Vargas AC, Smart CE,
Simpson PT,Saunus JM, Lakhani SR.} Expression of MAGE-A and NY-ESO-1
cancer/testis antigens is enriched in triple-negative invasive breast
cancers. Histopathology. 2018 Jul;73(1):68-80. doi: 10.1111/his.13498.
\end{quote}

\textbf{48. Mi Y, Liu F, Liang X, Liu S, Huang X, Sang M, Geng C.} Tumor
suppressor let-

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 10\tabcolsep) * \real{0.17}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
7a inhibits breast cancer cell proliferation, migration and invasion by
targeting
\end{quote}
\end{minipage} & & & & & \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
MAGE-A1.
\end{quote}
\end{minipage} & Neoplasma. & 2019 & Jan & 15;66(1):54-62. & doi: \\
\bottomrule
\end{longtable}

\begin{quote}
10.4149/neo\_2018\_180302N146.

\textbf{49. Cho B, Lee H, Jeong S, Bang YJ, Lee HJ, Hwang KS, Kim HY,
Lee YS, KangGH, Jeoung DI.} Promoter hypomethylation of a novel
cancer/testis antigen gene CAGE is correlated with its aberrant
expression and is seen in premalignant stage of gastric carcinoma.
Biochem Biophys Res Commun. 2003 Jul 18;307(1):52-63. doi:
10.1016/s0006-291x(03)01121-5.
\end{quote}

\textbf{50. Wu H, Zhai LT, Chen PY, Xi XG.} DDX43 prefers single strand
substrate and its

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 12\tabcolsep) * \real{0.14}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
full binding activity requires physical connection of all domains.
Biochem
\end{quote}
\end{minipage} & & & & & & \\
\midrule
\endhead
Biophys

10.1016/ & \begin{minipage}[t]{\linewidth}\raggedright
Res

\begin{quote}
.bbrc.20
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Commun.

9.09.114.
\end{quote}
\end{minipage} & 2019 & Dec & 10;520(3):594-599. & doi: \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{51. Xing}
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Z, Ma}
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{WK, Tran}
\end{quote}
\end{minipage} & \textbf{EJ.} &
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
The DDX5/Dbp2 subfamily of DEAD-
\end{quote}
\end{minipage} & & \\
\bottomrule
\end{longtable}

\begin{quote}
box RNA helicases. Wiley Interdiscip Rev RNA. 2019 Mar;10(2):e1519. doi:
10.1002/wrna.1519.

\textbf{Figure legends:}

\textbf{Figure (1):} Fold change expression of DDX43 (DDX43) gene
relative to the reference gene (HPRT1), relative to the expression in
the control samples. Bar heights indicate mean expression of the gene in
several samples in the studied groups. Error bars indicate standard
deviation of the fold changes in each group. One asterisk indicates
statistically significant difference between the means of malignant
group compared to the mean of the benign group.

\textbf{Figure (2):} STRING database predicted protein interacting
networks (PINs) of DDX43 (DDX43, Probable ATP-dependent RNA helicase
DDX43; DEAD-box helicase 43). (\textbf{A}) The network began with three
proteins and was further expanded by an additional 20 nodes/protein, by
using the ``more'' key on the STRING interface. The network nodes are
proteins. The edges represent the predicted functional associations. The
edge may be drawn with up to 7 differently colored lines representing
the existence of the seven types of evidence used in predicting the
associations. Red line - indicates the presence of fusion evidence.
Green line - neighborhood evidence. Blue line - cooccurrence evidence.
Purple line - experimental evidence. Yellow line - textmining evidence.
Light blue line - database evidence. Black line - coexpression evidence.
Line thickness indicates the strength of data support. (Abbreviations:
SAGE1: Sarcoma antigen 1, MAGEA1:
\end{quote}

17

\begin{quote}
Melanoma-associated antigen 1, DDX53: Probable ATP-dependent RNA
helicase; DEAD-box helicase 53, VEGFA: Vascular endothelial growth
factor A, SLC43A1: Large neutral amino acids transporter small subunit
3; Sodium-independent, CTAG1A: Cancer/testis antigen 1A, CTAG1B:
Cancer/testis antigen 1B, CTAG2: Cancer/testis antigen 2, PRAME:
PReferentially expressed Antigen in Melanoma, PMEL: Melanocyte protein,
BAGE5: B melanoma antigen 2, TYR: Tyrosinase, GAGE1: Cancerassociated
gene 1 protein; Cancer antigen 1, GAGE12F: G antigen 12F, GAGE2A: G
antigen 2A , GAGE13: G antigen 13, CSAG1: Putative
chondrosarcoma-associated gene 1 protein, MAGEC1: Melanoma-associated
antigen C1, MAGEC2: Melanoma-associated antigen C2, SPA17: Sperm surface
protein Sp17, PAGE5: Prostate-associated gene 5 protein; P antigen
family member 5, SSX2: Synovial sarcoma, X breakpoint 2, PRSS50:
Probable threonine protease.) (B) The accompanying table shows all
interacting proteins withDDX43, and the confidence cutoff value for
interaction linkages has been adapted to 0.700 as the high scoring link.

\textbf{Figure (3):} mRNA Expression of DDX43 in Alzheimer's disease and
various types of cancer compared to normal conditions. Expression level
in normal blood genotype tissue expression (GTEx) is 0.5 Transcripts Per
Million (TPM), expression level in breast adenocarcinoma and invasive
lobular carcinoma is below the cutoff, expression level in normal breast
adjacent to breast adenocarcinoma is 1 TPM, expression level in normal
breast (GTEx) is 3 TPM (18).
\end{quote}

18

\includegraphics[width=14.70833in,height=12in]{vertopal_1c026a3853da4b14beba0ffc0b8ceee2/media/image1.png}

\includegraphics[width=7.02083in,height=6.10417in]{vertopal_1c026a3853da4b14beba0ffc0b8ceee2/media/image2.png}

\includegraphics[width=10.45833in,height=5.5in]{vertopal_1c026a3853da4b14beba0ffc0b8ceee2/media/image3.png}

\includegraphics[width=14.70833in,height=12in]{vertopal_1c026a3853da4b14beba0ffc0b8ceee2/media/image4.png}

\begin{quote}
\textbf{Table 1:} Clinical characteristics of studied groups (n = 90).
\end{quote}

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{Characteristics}
\end{quote}
\end{minipage} & \textbf{Control gp}

(n = 18) & \textbf{Benign gp}

(n = 12) & \textbf{Malignant}

\textbf{gp}

(n = 60) & \textbf{P-value} \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Age} (years) (mean ± SD)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
48.94±10.42 48.94±10.42
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
36.09±8.10 36.09±8.10
\end{quote}
\end{minipage} & 52.07±10.24

52.07±10.24 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.262\\
0.002\\
0.0001
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Body mass index} (BMI), kg/m2 (mean ± SD)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
32.62±7.23 32.62±7.23
\end{quote}
\end{minipage} & 27.94±3.49 & 34.48±6.44 &
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.319\\
0.390
\end{quote}
\end{minipage} \\
& & & & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Diabetes n (\%)\\
Yes\\
No\\
NA\\
Diabetes medications Yes\\
No\\
NA\\
Any medication n (\%) Yes\\
No\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
15 (25\%)\\
40 (66.7\%) 5 (8.3\%)

15 (25\%)\\
40 (66.7\%) 5 (8.3\%)

22 (36.7\%) 33 (55\%)\\
5 (8.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Family History of Cancer} n (\%)\\
No\\
Yes\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
41 (68.3\%) 10 (16.7\%)9 (15\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Menopausal status} n (\%) Pre\\
Post\\
NA
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
7 (38.9\%) 11 (61.1)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
10 (83.3\%) 1 (8.3\%)\\
1 (8.3\%)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
26 (43.3\%) 34 (56.7\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Pathological parameters}
\end{quote}
\end{minipage} & & & & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor size (continuous)} n (\%)\\
T1 (\textless2 cm)\\
T2 (2--5 cm)\\
T3-4 (\textgreater5 cm)\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
8 (13.3\%)\\
20 (33.3\%) 10 (16.7\%) 22 (36.7\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor-node-metastasis (TNM) stage} n (\%)\\
Stage 0 - Tis, N0, M0.

Stage I - T1-T2, N0, M0.

Stage II - T2-T4, N0, M0.

Stage III - T1-T4, N1-N3, M0. Stage IV - T1-T4, N1-N3, M1.

NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2 (3.4\%)\\
20 (33.3\%)0 (0\%)\\
20 (33.3\%)0 (0\%)\\
18 (30\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Lymph node stage} n (\%) Negative
\end{quote}
\end{minipage} & & & 23 (38.3\%) & \\
\bottomrule
\end{longtable}

1

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
Positive (1--3 nodes) Positive (\textgreater3 nodes) NA
\end{quote}
\end{minipage} & & & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
11 (18.3\%) 10 (16.7\%) 16 (26.7\%)
\end{quote}
\end{minipage} & \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor typen (\%)}\\
Invasive duct carcinoma (IDC) Invasive lobular carcinoma(ILC)\\
Invasive Cribriform carcinoma (ICC)\\
Mixed\\
Ductal carcinoma in situ(DCIS)\\
Others\\
Fibroadenomas\\
Phyllodes Tumors\\
Benign others
\end{quote}
\end{minipage} & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
6 (50\%)\\
2 (16.7\%) 4 (33.3\%)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
36 (60\%) 8 (13.3\%) 5 (8.3\%)\\
4 (6.7\%)\\
3 (5\%)\\
4 (6.7\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Location} n (\%)\\
Right\\
Left\\
Not specified
\end{quote}
\end{minipage} & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
6 (50\%)\\
5 (41.7\%) 1 (8.3\%)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
27 (45\%)\\
32 (53.3\%) 1 (1.7\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Mitosis score} n (\%) 1\\
2\\
3\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
6 (10\%)\\
43 (71.7\%) 8 (13.3\%)\\
3 (5\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tubular formation score} n (\%)\\
1\\
2\\
3\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2 (3.3\%)\\
27 (45\%)\\
26 (43.3\%) 5 (8.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Nuclear grade} n (\%) 1\\
2\\
3\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
3 (5\%)\\
39 (65\%) 15 (25\%) 3 (5\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Histological grade} n (\%) 1\\
2\\
3\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
4 (6.7\%)\\
36 (60\%)\\
16 (26.7\%) 4 (6.7\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor grade} n (\%) Grade 1 (low)\\
Grade 2 (intermediate) Grade 3 (high)
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
7 (11.7\%)\\
36 (60\%)\\
17 (28.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Hormonal receptors}\\
Oestrogen receptor n (\%) Negative\\
Positive\\
Progesterone receptor n (\%)
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
9 (15\%)\\
51 (85\%)
\end{quote}
\end{minipage} & \\
\bottomrule
\end{longtable}

2

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
Negative\\
Positive\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
10 (16.7\%) 49 (81.7\%) 1 (1.6\%)
\end{quote}
\end{minipage} & \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Proliferation/cell cycle}\\
\textbf{regulators} Ki67 LI n (\%)\\
Low (immunostaining \textless10\%) High (immunostaining ≥10\%) NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
16 (26.7\%) 44 (73.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{HER2 Family} n (\%) HER2\\
Negative\\
Positive\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
52 (86.7\%)6 (10\%)\\
2 (3.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Triple-negative phenotype} n (\%)\\
No\\
Yes
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
52 (86.67\%) 8 (13.33\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Nottingham prognostic index (NPI)} n (\%)\\
Good prognostic group (⩽3.4) Moderate group (3.41--5.4)\\
Poor group (\textgreater5.41)\\
\textbf{NA}
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
5 (8.3\%) 27 (45\%) 4 (6.7\%) 24(40\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Biological classes} Luminal A\\
Luminal B\\
Luminal N\\
HER2+/ER−\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
29 (48.3\%) 20 (33.3\%)8 (13.4)\\
1 (1.7)\\
2 (3.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{NPI+ for the biological}\\
\textbf{classes}\\
n (\%)\\
Good prognostic group (⩽3.4) Moderate group (3.41--5.4)\\
Poor group (\textgreater5.41)\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
28 (46.67\%) 8 (13.33\%) 1 (1.67\%)\\
23 (38.33\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Chemotherapy} n (\%)\\
Yes (after blood sampling) No\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
38 (63.3\%) 10 (16.7\%) 12 (20 \%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Peritumoural}\\
\textbf{lymphovascular invasion} absent\\
suspicious\\
present\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
41 (68.3\%) 6 (10.0\%)\\
8 (13.3\%)\\
5 (8.3\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{DCIS in specimen}
\end{quote}
\end{minipage} & & & 24 (40\%) & \\
\bottomrule
\end{longtable}

3

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
absent\\
present\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
27 (45\%) 9 (15\%)
\end{quote}
\end{minipage} & \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Microcalcification} absent\\
present\\
NA
\end{quote}
\end{minipage} & & & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
31 (51.7\%) 11 (18.3\%) 18 (30\%)
\end{quote}
\end{minipage} & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
mRNA expression of DDX43 (mean ± SD)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
3.33±6.64 3.33±6.64
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
3.1±3.57 3.1±3.57
\end{quote}
\end{minipage} & 1.47±1.75

1.47±1.75 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.507\\
0.141\\
0.016
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Serum DDX43 protein levels (ng/L)(mean ± SD)
\end{quote}
\end{minipage} & 2148.4±202

5.69

2148.4±202

5.69 & 1902.3±140

1.47 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1905.8±174 0.05
\end{quote}
\end{minipage} & 0.682

0.776 \\
\bottomrule
\end{longtable}

\begin{quote}
NA: not available\\
\underline{Tumor-node-metastasis (TNM) stage:} Stage 0 - Indicates
carcinoma in situ. Tis, N0, M0, Stage I - Localized cancer. T1-T2, N0,
M0, Stage II - Locally advanced cancer, early stages. T2-T4, N0, M0,
Stage III - Locally advanced cancer, late stages. T1-T4, N1-N3, M0,
Stage IV - Metastatic cancer. T1-T4, N1-N3, M1 (14).The NPI was
calculated using the following formula: NPI=histological grade (1--3)
+LN stage (1--3; 1=negative, 2=1--3 nodes positive, 3=⩾4 nodes positive)
+ (tumour size/cm × 0.2) (15).
\end{quote}

4

\begin{quote}
\textbf{Table 2:} NPI+ formulae for the biological classes (16).
\end{quote}

\begin{longtable}[]{@{}ll@{}}
\toprule
\endhead
* & \\
\bottomrule
\end{longtable}

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 2\tabcolsep) * \real{0.50}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 2\tabcolsep) * \real{0.50}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{Class}
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{NPI+ formula}
\end{quote}
\end{minipage} \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Luminal A
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
(0.8 × Mitosis) + (0.5 x Size) + (1.8 × Nodal ratio*)
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Luminal N
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
(0.8 × Tubules) + (0.6 × Stage)
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Luminal B
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
(0.7 × Mitosis) + (1.0 × Nodal ratio)
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
HER2+/ER+
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
(0.5 × Size) + (0.9 × Stage)
\end{quote}
\end{minipage} \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
HER2+/ER−
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
(0.9 × Stage) − (0.6 × Nodal ratio)
\end{quote}
\end{minipage} \\
\bottomrule
\end{longtable}

\begin{quote}
Number of nodes positive/Total number of nodes.
\end{quote}

1

\begin{quote}
\textbf{Table 3:} Serum HAGE protein levelsand mRNA expression of HAGE
in patients with breast cancer according to subgroups.
\end{quote}

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{Characteristics}
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{Serum DDX43 protein levels} (ng/L) (mean ±SD)
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{P}\\
\textbf{value}
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{mRNA expressionof DDX43}\\
(mean ± SD)
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
\textbf{P}\\
\textbf{value}
\end{quote}
\end{minipage} \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Body mass index, kg/m2} (n)≤26kg/m2 (6)\\
\textgreater26kg/m2 (53)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1801.3±1297.4 1719.8±1432.03
\end{quote}
\end{minipage} & 0.801 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.48±2.58\\
1.46±1.69
\end{quote}
\end{minipage} & 0.388 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Menopausal status} (n) Pre (26)\\
Post (34)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2112±2674.89\\
1798.2±1032.69
\end{quote}
\end{minipage} & 0.813 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.22±1.46\\
1.66 ±1.95
\end{quote}
\end{minipage} & 0.589 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Pathological parameters}
\end{quote}
\end{minipage} & & & & \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor size (continuous)} (n)\\
T1 (\textless2 cm) (8) vs. T2 (2--5 cm) (20)

T2 (2--5 cm) (20) vs. T3-4 (\textgreater5 cm) (10)

T1 (\textless2 cm) (8) vs. T3-4 (\textgreater5 cm) \textbf{(}10)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1415.1±1066.13 2920.2±2352.15

2920.2±2352.15 1782.4±1155.41

1415.1±1066.13 1782.4±1155.41
\end{quote}
\end{minipage} & 0.147

0.422

0.610 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.82±1.7\\
1.51±2.02

1.51±2.02\\
1.49±1.16

1.82±1.7\\
1.49±1.16
\end{quote}
\end{minipage} & 0.372

0.479

0.822 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor-node-metastasis}\\
\textbf{(TNM) stage} (n)\\
Stage I - T1-T2, N0 (20)\\
Stage III - T1-T4, N1-N3 (20)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1888.1±1210.09 2522.1±2783.93
\end{quote}
\end{minipage} & 0.678 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.34±1.57\\
1.87±1.75
\end{quote}
\end{minipage} & 0.481 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Lymph node stage} (n)\\
Negative (23) vs. positive (1--3 nodes) (11)

Positive (1--3 nodes) (11) vs. positive (\textgreater3 nodes) (10)

Negative (23) vs. positive (\textgreater3 nodes) (10)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1884.8±1171.75 2724.5±3560.79

2724.5±3560.79 2387.2±2507.66

1884.8±1171.75 2387.2±2507.66
\end{quote}
\end{minipage} & 0.788

0.974

0.677 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.21±1.51\\
1.81±2.09

1.81±2.09\\
1.7±1.44

1.21±1.51\\
1.7±1.44
\end{quote}
\end{minipage} & 0.565

0.880

0.453 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor type} (n)\\
IDC (36) vs. ILC (8)

IDC (36) vs. ICC (5)

ILC (8) vs. ICC (5)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1177±934.84* 2103.1±936.43

1177±934.84* 4955±4284.34

2103.1±936.43 4955±4284.34
\end{quote}
\end{minipage} & 0.042

0.006

0.152 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.46±1.68\\
1.73±2.21

1.46±1.68\\
1.15±1.28

1.73±2.21\\
1.15±1.28
\end{quote}
\end{minipage} & 0.789

0.793

0.705 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Mitosis score} (n)\\
1 (6) vs. 2 (43)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
962.33±623.22 2271.8±1948.29
\end{quote}
\end{minipage} & 0.213 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.67±0.88\\
1.67±1.93
\end{quote}
\end{minipage} & 0.129 \\
\bottomrule
\end{longtable}

1

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
2 (43) vs. 3 (8)

1 (6) vs. 3 (8)
\end{quote}
\end{minipage} & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
2271.8±1948.29 1142.1±870.3

962.33±623.22 1142.1±870.3
\end{quote}
\end{minipage} & 0.137

0.863 & \begin{minipage}[b]{\linewidth}\raggedright
\begin{quote}
1.67±1.93\\
1.29±1.34

0.67±0.88\\
1.29±1.34
\end{quote}
\end{minipage} & 0.849

0.108 \\
\midrule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tubular formation score} (n) 2 (27)\\
3 (26)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2052.9±1741.02 1495±1040.19
\end{quote}
\end{minipage} & 0.308 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.32±1.37\\
1.59±2.01
\end{quote}
\end{minipage} & 0.969 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Nuclear grade} (n) 2 (39)\\
3 (15)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2252.1±1944.33 1273.1±1167.98
\end{quote}
\end{minipage} & 0.088 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.22±1.36\\
2.3±2.55
\end{quote}
\end{minipage} & 0.085 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Histological grade} (n) 2 (36)\\
3 (16)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1993.5±1645.52 1432.8±1107.12
\end{quote}
\end{minipage} & 0.260 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.5±1.68\\
1.82±2.22
\end{quote}
\end{minipage} & 0.413 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Tumor grade}(n)\\
Grade 1 (7) vs. grade 2 (36)

Grade 2 (36) vs. grade 3 (17)

Grade 1 (7) vs. grade 3 (17)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2807.8±3467.99 2005.5±1643.38

2005.5±1643.38 1447.3±1056.78

2807.8±3467.99 1447.3±1056.78
\end{quote}
\end{minipage} & 0.641

0.262

0.312 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.86±0.82\\
1.3±1.62

1.3±1.62\\
2.06±2.17*

0.86±0.82\\
2.06±2.17
\end{quote}
\end{minipage} & 0.805

0.037

0.056 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Hormonal receptors}\\
Oestrogen receptor (n)\\
Negative (9)\\
Positive (51)\\
Progesterone receptor (n) Negative (10)\\
Positive (49)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1249.1±1054.67 2015.2±1819.24

1142.1±979.04 2063.8±1831.55
\end{quote}
\end{minipage} & 0.358

0.217 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2.07±2.21\\
1.36±1.66

2.35±2.37\\
1.31±1.58
\end{quote}
\end{minipage} & 0.168

0.254 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{HER2 Family}(n) HER2\\
Negative (52)\\
Positive (6)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1996.6±1855.12 1243.1±614.13
\end{quote}
\end{minipage} & 0.535 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.61±1.84\\
0.56±0.31
\end{quote}
\end{minipage} & 0.233 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
Triple-negative phenotype (n) No (53)\\
Yes (7)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2015.2±1819.24 1249.1±1054.67
\end{quote}
\end{minipage} & 0.358 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.35±1.63\\
2.36±2.47
\end{quote}
\end{minipage} & 0.187 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{NPI} (n)\\
Good prognostic group (5) vs.

Moderate group (27)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1749.1±1325.29 2470.0±2228.24
\end{quote}
\end{minipage} & 0.660 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
0.85±0.79\\
1.77±1.89
\end{quote}
\end{minipage} & 0.333 \\
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{Biological classes}\\
Luminal A (29) vs. luminal B (20)

Luminal B (20) vs. Luminal N (8)

Luminal A (29) vs. Luminal N (8)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1652.9±1671.2 2357.7±2119.7

2357.7±2119.7 1882.3±1193.6

1652.9±1671.2 1882.3±1193.6
\end{quote}
\end{minipage} & 0.281

0.817

0.587 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.45±1.74\\
1.53±1.66

1.53±1.66\\
1.77±2.37

1.45±1.74\\
1.77±2.37
\end{quote}
\end{minipage} & 0.518

0.865

0.782 \\
\bottomrule
\end{longtable}

2

\begin{longtable}[]{@{}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}
  >{\raggedright\arraybackslash}p{(\columnwidth - 8\tabcolsep) * \real{0.20}}@{}}
\toprule
\endhead
\begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
\textbf{NPI+ for the biological classes} n (\%)\\
Good prognostic group (28) vs.

moderate group (8)
\end{quote}
\end{minipage} & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
2484.1±2180.21 1849.0±1121.39
\end{quote}
\end{minipage} & 0.716 & \begin{minipage}[t]{\linewidth}\raggedright
\begin{quote}
1.78±1.91\\
1.03±1.11
\end{quote}
\end{minipage} & 0.321 \\
\bottomrule
\end{longtable}

3

\end{document}
