% Options for packages loaded elsewhere \PassOptionsToPackage{unicode}{hyperref} \PassOptionsToPackage{hyphens}{url} % \documentclass[ ]{article} \usepackage{amsmath,amssymb} \usepackage{lmodern} \usepackage{iftex} \ifPDFTeX \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \usepackage{textcomp} % provide euro and other symbols \else % if luatex or xetex \usepackage{unicode-math} \defaultfontfeatures{Scale=MatchLowercase} \defaultfontfeatures[\rmfamily]{Ligatures=TeX,Scale=1} \fi % Use upquote if available, for straight quotes in verbatim environments \IfFileExists{upquote.sty}{\usepackage{upquote}}{} \IfFileExists{microtype.sty}{% use microtype if available \usepackage[]{microtype} \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts }{} \makeatletter \@ifundefined{KOMAClassName}{% if non-KOMA class \IfFileExists{parskip.sty}{% \usepackage{parskip} }{% else \setlength{\parindent}{0pt} \setlength{\parskip}{6pt plus 2pt minus 1pt}} }{% if KOMA class \KOMAoptions{parskip=half}} \makeatother \usepackage{xcolor} \IfFileExists{xurl.sty}{\usepackage{xurl}}{} % add URL line breaks if available \IfFileExists{bookmark.sty}{\usepackage{bookmark}}{\usepackage{hyperref}} \hypersetup{ hidelinks, pdfcreator={LaTeX via pandoc}} \urlstyle{same} % disable monospaced font for URLs \usepackage{longtable,booktabs,array} \usepackage{calc} % for calculating minipage widths % Correct order of tables after \paragraph or \subparagraph \usepackage{etoolbox} \makeatletter \patchcmd\longtable{\par}{\if@noskipsec\mbox{}\fi\par}{}{} \makeatother % Allow footnotes in longtable head/foot \IfFileExists{footnotehyper.sty}{\usepackage{footnotehyper}}{\usepackage{footnote}} \makesavenoteenv{longtable} \usepackage{graphicx} \makeatletter \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth\else\Gin@nat@width\fi} \def\maxheight{\ifdim\Gin@nat@height>\textheight\textheight\else\Gin@nat@height\fi} \makeatother % Scale images if necessary, so that they will not overflow the page % margins by default, and it is still possible to overwrite the defaults % using explicit options in \includegraphics[width, height, ...]{} \setkeys{Gin}{width=\maxwidth,height=\maxheight,keepaspectratio} % Set default figure placement to htbp \makeatletter \def\fps@figure{htbp} \makeatother \setlength{\emergencystretch}{3em} % prevent overfull lines \providecommand{\tightlist}{% \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} \setcounter{secnumdepth}{-\maxdimen} % remove section numbering \ifLuaTeX \usepackage{selnolig} % disable illegal ligatures \fi \author{} \date{} \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. 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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}