The Role of the Cell Surface Heparan Sulfate Proteoglycan Syndecan-3 In Breast Cancer Pathophysiology

1. Introduction

Breast cancer is the second most common cancer and the fourth leading cause of cancer mortality worldwide [1,2]. Among women, it is the most frequently diagnosed cancer and the leading cause of cancer-related deaths globally [1,2]. The disease encompasses a heterogeneous group of malignant tumors with diverse morphologies, biological behaviors, clinical presentations, and prognoses [3]. It is classified based on standardized pathomorphological criteria, including the histological type of tumor, histological degree of malignancy, stage according to the TNM classification, the expression of estrogen- and progesterone-hormone-receptors, expression of human-epidermal-growth-factor-receptor (HER2), and the cellular proliferation index Ki-67 [4].

Recent research has highlighted the role of the extracellular matrix (ECM) in cancer progression [5]. The ECM is a complex network of macromolecules, that assembles into three-dimensional supramolecular structures with distinct biochemical and biomechanical properties [6,7]. ECM dynamics play key roles in physiological tissue development and homeostasis but may become dysregulated, once its composition is altered [6,7,8].

Proteoglycans are major structural components of the ECM and influence both cellular behavior and matrix properties through direct and indirect interactions with cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes, and glycoproteins [9,10,11]. The most abundant subtype of proteoglycans is heparan sulfate proteoglycans (HSPGs), which are components of the cell surface and of the basement membrane that separates endothelial and epithelial cells from the underlying connective tissue [12]. Cell surface HSPGs serve as ECM receptors, and are classified into two major groups: syndecans, comprising four distinct members (SDC1 to SDC4), and glypicans, encompassing six members (GPC1 to GPC6) [11,13]. In healthy adult tissue, SDC1 is predominantly expressed in epithelia and plasma cells, syndecan-2 (SDC2) in endothelia and fibroblasts, SDC3 in neuronal and musculoskeletal tissue, while syndecan-4 (SDC4) is expressed heterogeneously [13,14]. Syndecans regulate multiple cellular signaling pathways due to their ability to act as coreceptors with signaling receptors, as well as endocytosis receptors [13]. In cancer, as well as in the cancer-surrounding stroma, syndecans can be abnormally expressed [15]. Syndecans play distinct roles in cancer pathogenesis depending on the cancer type and stage, either promoting or inhibiting cancer progression by influencing cell proliferation, migration, adhesion, invasion, and metastasis [15,16]. Several syndecan-targeting therapeutic approaches have been developed in recent years, including monoclonal antibodies, peptide inhibitors, biomolecule mimetics, and heparan sulfate mimetics or inhibitors that interfere with syndecan exosomal packaging [16].

SDC3 has previously been associated with the pathogenesis of ovarian cancer [17], pancreatic cancer [18], and renal cell carcinoma [19]. However, little is known about the potential role of SDC3 in breast cancer pathogenesis. To address our research question, we first performed an in-silico analysis utilizing large public datasets. Subsequently, we conducted in vitro experiments using the human breast cancer cell lines MDA-MB-231 and MCF-7, in which SDC3 downregulation was achieved by a single siRNA-sequence, and a Pool-of 30 sequences against SDC3.

Our findings suggest that SDC3 may influence critical cancer hallmarks associated with breast tumorigenesis and cancer progression. We propose that SDC3 may regulate the activity of the proto-oncogene tyrosine-protein kinase Src (SRC) as well as other genes involved in crucial breast cancer signaling pathways, thereby affecting human breast cancer cell behavior.

2. Materials and Methods

2.1. The Human Protein Atlas, TNM Plot, and Kaplan–Meier Plot Analysis

The gene expression of SDC3 in healthy as well as pathological tissues was assessed using The Human Protein Atlas database (The Human Protein Atlas, https://www.proteinatlas.org, accessed September 10th, 2024), which integrates antibody-based imaging, mass spectrometry-based proteomics, transcriptomics, and systems biology technologies [20].

Additionally, the TNMplot database (TNMplot, https://tnmplot.com, accessed September 10th^, 2024) was utilized, which includes public gene expression data by gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO), RNA-seq data from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) [21]. To investigate the expression of SDC3 in breast cancer, the tools Gene Expression Comparison, Gene Chip Data, the gene symbol SDC3, and breast tissue were selected on the TNMplot website.

The open-access online tool Kaplan-Meier Plotter (Kaplan-Meier Plotter, www.kmplot.com, accessed September 10th, 2024) [22] was used, to conduct a survival analysis of SDC3 expression in breast cancer. Kaplan-Meier Plotter analysis was performed with mRNA gene chip data. The gene symbol SDC3 (Affymetrix ID: 202898_at) and relapse-free-survival (RFS; n=4929) was chosen on the website, the median was selected as the cut-off value, and JetSet best probe option was applied, to ensure the selection of the optimal probe set [22].

2.2. Cell Culture

The human breast cancer cell lines MDA-MB-231, MCF-7, T47D, BT20, BT474, BT549, SK-BR3, HCC1806, MDA-MB-453, and MDA-MB-468 were purchased from ATCC/LGC Promochem (Teddington, United Kingdom). SUM-149 cells were purchased from BIOIVT (West Sussex, UK). MCF-7 cells were cultured in RPMI medium (PAN-Biotech GmbH, Aidenbach, Germany), containing 10% fetal calf serum (FCS) and 1% penicillin/ streptomycin (P/S), at 37°C and 6.5% CO₂. SUM-149 cells were cultured in Ham's Nutrient Mixture F12 (cat. N66589) supplemented with 5 % FCS, 1 % P/S, 5µg of insulin (cat. I9278) and 1µg/mL hydrocortisone (cat. H4001) in a humidified incubator with 5 % CO₂ at 37 °C. All other cell lines were cultured in DMEM medium (Sigma-Aldrich, St. Louis, USA), containing 10% fetal calf serum (FCS) (PAN-Biotech GmbH, Aidenbach, Germany) and 1% P/S (Sigma-Aldrich, St. Louis, USA), at 37°C and 7.5% CO₂.

2.3. siRNA Transfection

MDA-MB-231 and MCF-7 cells were cultured in growth media at a density of 3 × 10⁵ cells/well of a six-well plate for 24 h. Next, single siRNA and siPool transfection was performed, using the Silencer Select Pre-designed siRNA (#s228527) (Ambion Life Technologies, Carlsbad, USA), targeting exon 3 of human SDC3 mRNA (NM_014654.3) at 20 nM final concentration, and the siPool (Lot. #SDC3-003) (siTools GmbH, Planegg-Martinsried, Germany), targeting 30 sequences of SDC3 (NCBI gene ID #9672) at 2.5 nM final concentration to silence SDC3, along with a Silencer Select Negative Control (ID#4390844) (Ambion Life Technologies, Carlsbad, USA), and siPool Negative Control (Lot. #N000-c1-058) (siTools GmbH, Planegg-Martinsried, Germany). The transfection mixture of 1 mL contained 840 μL Opti-MEM media (Cat. #31985070, Thermo Fisher Scientific, Waltham, USA) and Dharmafect (Cat. #T-2001-01, Dharmacon, Lafayette, USA), 80 μL SDC3 siRNA or SDC3 siPool or negative control siRNA or negative control siPool/Opti-MEM, and 80 μL 2.5% Dharmafect/Opti-MEM solution according to the manufacturer’s protocol (Dharmacon). The knockdown efficiency was confirmed by quantitative real-time PCR (qRT-PCR).

2.4. MTT Cell Viability Assay

The MTT metabolic cell viability assay was conducted on a 96-well plate usingMDA-MB-231 and MCF-7 cells 48 hours post-transfection. With a multichannel pipette, 100μl of pre-warmed culture medium without phenol red (DMEM/ RPMI with 10% FCS, 1% penicillin/ streptomycin w/o phenol red; PAN-Biotech GmbH, Aidenbach, Germany) was first added to each well from row B to row H, except for row A. Next, 10,000 cells per well were added to the first two rows, A and B. A dilution series was created by suspending and transferring 100μl of the cell suspension from row B to each of the lower rows, continuing until row G. The dilution series consisted of 10,000 cells/ well in row A, 5,000 cells/ well in row B, 2,500 cells/ well in row C, 1,250 cells/ well in row D, 625 cells/ well in row E, 312 cells/ well in row F, and 156 cells/ well in row G. The last row H contained a cell-free blank sample. Each sample was triplicated to generate mean values. The plate was incubated for 96 hours at 37°C. After incubation, the medium was removed and 20μl of 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) (Cat. # M2128-1G) at a concentration of 5 mg/ml was added. The cells were incubated for 4 hours at 37°C. Subsequently, 100μl of MTT stop buffer containing 10% (w/v) sodium dodecyl sulfate (SDS) (Cat. # 3599286) and 50% (v/v) N,N-Dimethyl formamide), pH 4.7 was added per well to stop the reaction and dissolve the formazan crystals. The final incubation was performed for 24 hours in the dark at room temperature. The photometric analysis was carried out at 570/650 nm with the ELISA-Reader VersaMax (Molecular Devices, San José, USA).

2.5. Cell Cycle Analysis

24 hours post-transfection, MDA-MB-231, and MCF-7 cells were serum-starved for 24 hours. The cells were then harvested by Trypsin/EDTA (PAN-Biotech GmbH, Aidenbach, Germany), and subjected to the cell cycle analysis using DAPI (4', 6'-diamidino-2-phenylindole) (Cat. #D9542, Sigma-Aldrich, St. Louis, USA). The cell pellet was resuspended in 1 mL of DAPI solution (Cytecs, Münster, Germany), after 5 min of incubation at room temperature, the cells were analyzed using the CyFlow space flow cytometer (PARTEC, Münster, Germany), and the Guava easyCyte 3 HT reader (Merck Millipore; Billerica, MA, USA). Excitation of the cells was carried out with a 375nm UV laser, and fluorescence emissions were measured at 455nm in FL4.

2.6. Hanging Drop Assay for 3D Sphere Formation

The hanging drop method was utilized to generate three-dimensional spheroids [23]. MDA-MB-231 and MCF-7 cells were harvested by Trypsin/EDTA 48 hours post-transfection, washed, and resuspended in culture medium. A total of 12 drops, each containing 20 000 cells per condition, were placed on the top lid of a petri dish. The bottom chamber was filled with 10ml PBS (Sigma-Aldrich, St. Louis, USA), the petri dish was sealed, and spheroid formation was stimulated by gravity. The sealed petri dish was incubated at 37°C. Images of the spheroids were taken on day 4 and day 7 of the experiments with an Axiovert (Zeiss, Jena, Germany) bright field light microscope (magnification x5). Quantification of the core spheroid area was carried out with the program ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.net/ij/, 1997-2018).

2.7. Cell Migration Assay

24 hours post-transfection, MDA-MB-231, and MCF-7 cells were harvested by Trypsin/EDTA, washed, resuspended in culture medium, and subjected to the cell migration assay. 30,000 MDA-MB-231 cells and 75,000 MCF-7 cells were seeded per migration chamber (Cat. #353097, Corning Inc., Corning, USA). The migration chambers were placed in the empty wells of a six-well plate and incubated for 24 hours at 37°C. Subsequently, the culture medium inside the chambers was removed, and 500μl of culture medium without FCS was supplemented. Cell migration was induced, by adding 750μl of culture medium containing 5% FCS to each well of the 6-well plate. The plates were incubated for another 24 hours at 37°C. MDA-MB-231 cells were fixed and stained 24 hours after the medium change, while the same procedures were performed on MCF-7 cells 72 hours after the medium change. For staining, the chambers were removed from the six-well plate and placed in methanol (Merck KGaA, Darmstadt, Germany) for five minutes, then washed with PBS for one minute, and subsequently immersed in 1% toluidine blue staining solution (Cat. #89640, Sigma-Aldrich, St. Louis, USA) for six minutes. The chambers were left to dry for 24 hours, protected from light. Images were taken with the Axiovert (Zeiss, Jena, Germany) bright-field microscope (magnification x10), and quantification of the experiments was performed with the program ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, Maryland, USA, https://imagej.net/ij/, 1997-2018).

2.8. Total RNA Extraction and Quantitative Real-Time PCR

Total RNA extraction from SDC3-silenced MDA-MB-231 and MCF-7 cells was performed 72 h after transfection with the InnuPREP RNA mini kit (Analytik Jena, Jena, Germany) following the manufacturer’s instructions. Likewise, RNA was prepared from a panel of breast cancer cell lines under standard culture conditions to determine basal expression levels of SDC3. Reverse transcription of mRNA (from 1 µg of total RNA) into cDNA was carried out with the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, USA). Quantitative real-time PCR (qRT-PCR) for the cell lines was performed using the SYBR Green qPCR Primer Assay (Biolegio, Nijmegen, The Netherlands) and Takyon ROX SYBR MasterMix blue dTTP (Eurogentec, Lüttich, Belgien) in a 7300 real-time PCR detection system (Applied Biosystems, Foster City, USA). Primer sequences are shown in Supplementary Table S1. The basal gene expression levels of SDC3 were assessed with the 2−∆∆Ct method after normalization to Actin gene expression as an internal control [24]. In our analysis, the relative gene expression was expressed as fold change and set in relation to the negative control.

2.9. Western Blotting

24 hours post-transfection, MDA-MB-231 and MCF-7 cells were serum-starved for 48 hours and simultaneously treated with 50ng/ml human recombinant TFPI (Lot: 6D30L4530, Sigma-Aldrich, St. Louis, USA). The cells were lysed 72 hours post-transfection and 48 hours after stimulation with 200 µL/well of protein extraction buffer (Cell Signaling Technology, Danvers, USA). The protein samples were separated by SDS-polyacrylamide gel electrophoresis under reducing conditions and subsequently transferred to a nitrocellulose membrane. The membrane was blocked with 5% skim milk in TBST buffer, pH 7.6. The primary antibodies (phospho-SRC, rabbit monoclonal, 1:1000, Cat.#2101, Cell Signaling Technology, Danvers, USA; SRC, rabbit monoclonal, 1:1000, Cat.#2108, Cell Signaling Technology, Danvers, USA; and GAPDH, mouse monoclonal, 1:5000, Santa Cruz Biotechnologies, Dallas, USA) were diluted in 5% BSA in TBST buffer and incubated with the membranes overnight at 4°C. The membranes were first probed with phospho-SRC antibody and reprobed with the additional antibodies after stripping with 0.2 M glycine, pH 2.5, using the procedure indicated above. Following primary antibody incubation, the membranes were washed 3× with TBST and subsequently incubated with the secondary goat anti-rabbit or goat anti-mouse antibodies (1:5000, Merck KGaA, Darmstadt, Germany) in 5% skim milk for 1 hour at room temperature. The membranes were subjected to a chemiluminescence ECL reaction with the SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific, Waltham, USA) and detected with a Fusion SL Chemiluminescence Imaging System (Vilber Lourmat, Eberhardzell, Germany) device.

2.10. In-Silico Protein Interaction Network Analysis

STRING database version 12.0 (STRING, https://string-db.org, accessed September 13th, 2024) was utilized to conduct an in-silico analysis of known and predicted protein-protein interaction networks related to SDC3. These interactions include both physical and functional interactions. Data from the STRING database is sourced from automated text mining of scientific literature, computational interaction predictions from co-expression, conserved genomic contexts, as well as primary databases of interaction experiments [25]. To visualize the interactions of SDC3 with proteins of different signaling pathways, the protein name (Sdc3, cell surface proteoglycan) and organism (homo sapiens) were chosen on the STRING database website. The ten most significantly enriched GO terms (p < 0.05) were selected in the biological processes, molecular function, cellular component, and KEGG pathways categories. All the adjusted statistically significant values of the GO-terms were negative 10-base log-transformed.

2.11. Statistical Analysis

For the statistical analysis, Microsoft Excel (Microsoft Corporation, Redmond, USA) and GraphPad (GraphPad Software Inc., Boston, USA) were used. The unpaired two-tailed Student-t-test or unpaired t-test with Welch´s correction was utilized for two-way comparisons. The level of significance was set as p ≤ 0.05. Results with p ≤ 0.05 were considered statistically significant (*), while p ≤ 0.01 was described as very significant (**), and p ≤ 0.001 as highly significant (***). The graphs depict mean values, with error bars indicating the standard error of the mean (SEM). In each analysis, three independent experimental replicates were included (n = 3).

3. Results

3.1. SDC3 Is Overexpressed in Breast Cancer

The analysis of The Human Protein Atlas database (The Human Protein Atlas, https://www.proteinatlas.org, accessed September 10th, 2024) revealed that SDC3 RNA expression was elevated in breast invasive carcinoma, as well as in other types of tumors, such as glioblastoma, and cutaneous melanoma (Figure 1A). The Human Protein Atlas furthermore included images of immunohistochemically stained breast cancer tissue samples, visualizing SDC3 protein expression. The staining was performed with the antibody #HPA017087 (Sigma-Aldrich, St. Louis, USA), and reported as low, or medium, based on conventional immunohistochemistry profiling of breast tissue, indicating that SDC3 is differentially expressed in breast cancer tissues (Figure 1B, Table 1). SDC3 expression was largely present within the tumor compared to the stroma, and showed a membranous and cytoplasmic staining pattern.

The open-access tool TNM-plot (TNMplot, https://tnmplot.com, accessed September 10th, 2024) was used to compare SDC3 gene expression in normal breast tissue, breast cancer tissue, and metastatic tissue (Figure 2A). TNM-plot analysis of gene chip data of normal tissue (n = 242), breast cancer tissue (n = 7569), and metastatic tissue (n = 82) demonstrated that SDC3 RNA was significantly upregulated in breast cancer tissue (Dunn-test P = 5.45e-17), compared to normal tissue. However, SDC3 RNA expression was not significantly altered in metastatic tissue (Dunn-test P = 1.28e-01), in relation to normal tissue.

3.2. SDC3 Expression Affects the Prognosis and Survival of Breast Cancer Patients

To investigate, whether SDC3 expression influences the relapse-free survival (RFS) of breast cancer patients, a Kaplan-Meier Plotter analysis was conducted, using the KMplot website (KMplot, https://kmplot.com/analysis/, accessed September 10^th, 2024). All breast cancer samples included in the analysis were stratified by receptor status, molecular subtypes, lymph node status, grade, and treatment (Figure 2B, Table 2). Our Kaplan-Meier Plotter analysis revealed that high SDC3 RNA expression levels served as a protective factor for the relapse-free survival of breast cancer patients overall. This correlation applied to Her2-negative-, St. Gallen subtype luminal A-, St. Gallen subtype luminal B-, St. Gallen subtype basal-, PAM50 subtype luminal B-, PAM50 subtype basal-, lymph node-negative-, and Grade 2- tumors. High SDC3 expression was furthermore associated with better relapse-free survival in breast cancer patients following neoadjuvant chemotherapy.

3.3. SDC3 RNA Expression Varies in Breast Cancer Cell Lines of Different Classification

qRT-PCR analysis was performed, to measure the gene expression levels of SDC3 in seven different human breast cancer cell lines. The cell lines represent different molecular breast cancer subtypes, including the luminal A (MCF-7), luminal B (BT474), HER2-positive (SKBR3, MDA-MB 453), triple-negative A (basal-like) (MDA-MB 468, HCC1806, BT20), and triple-negative B (normal-like/claudin-low) (MDA-MB-231, BT549, SUM149 subtype [26]. Our qRT-PCR analysis demonstrated that SDC3 gene expression levels varied substantially across the breast cancer cell lines investigated (Figure 3A). For our further functional studies, we selected the widely used MDA-MB-231 as one of the highest, and MCF-7 as one of the lowest SDC3-expressing cell lines [27]. MDA-MB-231 cells model the triple-negative breast cancer subtype, which is characterized by the absence of estrogen- (ER-), progesterone- (PR-), and Her2- (Her2-) receptors [26]. MCF-7 cells are classified as the luminal A breast cancer subtype, as they express estrogen (ER+), and progesterone receptors (PR+), but not Her2-receptors (Her2-) [26].

Figure 2. SDC3 is highly expressed in tumors and correlates with better survival. (A) SDC3 expression varies across breast cancer progression stages. Gene chip analysis reveals significantly increased SDC3 mRNA levels in primary tumors and metastatic lesions, compared to normal breast tissue. (B) SDC3 expression is associated with the relapse-free survival (RFS) of breast cancer patients. Kaplan-Meier relapse-free survival curves are plotted based on: all breast cancer patients (n =4929), HER2 negative status (n = 4047), St. Gallen luminal A status (n = 2277), Grade 2 (n = 1177), and neoadjuvant chemotherapy (n = 402). Log-rank p values and hazard ratios (HRs; 95% confidence interval in parentheses) are shown.

Figure 2. SDC3 is highly expressed in tumors and correlates with better survival. (A) SDC3 expression varies across breast cancer progression stages. Gene chip analysis reveals significantly increased SDC3 mRNA levels in primary tumors and metastatic lesions, compared to normal breast tissue. (B) SDC3 expression is associated with the relapse-free survival (RFS) of breast cancer patients. Kaplan-Meier relapse-free survival curves are plotted based on: all breast cancer patients (n =4929), HER2 negative status (n = 4047), St. Gallen luminal A status (n = 2277), Grade 2 (n = 1177), and neoadjuvant chemotherapy (n = 402). Log-rank p values and hazard ratios (HRs; 95% confidence interval in parentheses) are shown.

Table 3. Correlation between SDC3 RNA expression and RFS of breast cancer patients.

Classification	Status	Cases	Hazard Ratio	P value
All breast cancer patients		4929	0.75 (0.68 - 0.83)	Log-rank P = 1.7e-08
Estrogen receptor (ER)	Positive	2561	0.87 (0.75 – 1.02)	Log-rank P = 0.079
	Negative	796	0.81 (0.64 – 1.03)	Log-rank P =0.086
Progesterone receptor (PR)	Positive	926	0.95 (0.71 – 1.26)	Log-rank P = 0.72
	Negative	925	0.91 (0.72 – 1.14)	Log-rank P = 0.41
Her2	Positive	882	0.85 (0.68 – 1.06)	Log-rank P = 0.15
	Negative	4047	0.73 (0.65 – 0.81)	Log-rank P = 3.6e-08
ER, PR, Her2	Negative	392	0.9 (0.63 – 1.29)	Log-rank P = 0.56
St. Gallen subtype	Luminal A	2277	0.77 (0.65 – 0.91)	Log-rank P = 0.0016
	Luminal B	1419	0.71 (0.59 – 0.85)	Log-rank P = 0.00015
	Her2 positive	315	0.9 (0.64 – 1.29)	Log-rank P = 0.6
	Basal	846	0.72 (0.58 – 0.9)	Log-rank P = 0.004
PAM50 subtype	Luminal A	1809	0.88 (0.72 – 1.08)	Log-rank P = 0.23
	Luminal B	1353	0.83 (0.7 – 0.99)	Log-rank P = 0.038
	Her2 positive	695	0.86 (0.68 – 1.1)	Log-rank P = 0.23
	Basal	953	0.69 (0.56 – 0.86)	Log-rank P = 7e-04
Lymph node	Positive	1656	0.97 (0.82 – 1.15)	Log-rank P = 0.75
	Negative	2368	0.74 (0.63 – 0.87)	Log-rank P = 0.00027
Grade	1	397	1.12 (0.73 – 2)	Log-rank P = 0.47
	2	1177	0.74 (0.59 – 0.92)	Log-rank P = 0.0057
	3	1300	0.88 (0.73 – 1.06)	Log-rank P = 0.16
Treatment with chemotherapy	Neoadjuvant	402	0.6 (0.41 – 0.88)	Log-rank P = 0.009
	Adjuvant	458	0.93 (0.67 – 1.3)	Log-rank P = 0.68
Treatment with endocrine therapy		867	0.86 (0.66 – 1.12)	Log-rank P = 0.25
Treatment with chemotherapy and endocrine therapy		510	1.22 (0.81 – 1.83)	Log-rank P = 0.34

Table 3. Correlation between SDC3 RNA expression and RFS of breast cancer patients.

Classification	Status	Cases	Hazard Ratio	P value
All breast cancer patients		4929	0.75 (0.68 - 0.83)	Log-rank P = 1.7e-08
Estrogen receptor (ER)	Positive	2561	0.87 (0.75 – 1.02)	Log-rank P = 0.079
	Negative	796	0.81 (0.64 – 1.03)	Log-rank P =0.086
Progesterone receptor (PR)	Positive	926	0.95 (0.71 – 1.26)	Log-rank P = 0.72
	Negative	925	0.91 (0.72 – 1.14)	Log-rank P = 0.41
Her2	Positive	882	0.85 (0.68 – 1.06)	Log-rank P = 0.15
	Negative	4047	0.73 (0.65 – 0.81)	Log-rank P = 3.6e-08
ER, PR, Her2	Negative	392	0.9 (0.63 – 1.29)	Log-rank P = 0.56
St. Gallen subtype	Luminal A	2277	0.77 (0.65 – 0.91)	Log-rank P = 0.0016
	Luminal B	1419	0.71 (0.59 – 0.85)	Log-rank P = 0.00015
	Her2 positive	315	0.9 (0.64 – 1.29)	Log-rank P = 0.6
	Basal	846	0.72 (0.58 – 0.9)	Log-rank P = 0.004
PAM50 subtype	Luminal A	1809	0.88 (0.72 – 1.08)	Log-rank P = 0.23
	Luminal B	1353	0.83 (0.7 – 0.99)	Log-rank P = 0.038
	Her2 positive	695	0.86 (0.68 – 1.1)	Log-rank P = 0.23
	Basal	953	0.69 (0.56 – 0.86)	Log-rank P = 7e-04
Lymph node	Positive	1656	0.97 (0.82 – 1.15)	Log-rank P = 0.75
	Negative	2368	0.74 (0.63 – 0.87)	Log-rank P = 0.00027
Grade	1	397	1.12 (0.73 – 2)	Log-rank P = 0.47
	2	1177	0.74 (0.59 – 0.92)	Log-rank P = 0.0057
	3	1300	0.88 (0.73 – 1.06)	Log-rank P = 0.16
Treatment with chemotherapy	Neoadjuvant	402	0.6 (0.41 – 0.88)	Log-rank P = 0.009
	Adjuvant	458	0.93 (0.67 – 1.3)	Log-rank P = 0.68
Treatment with endocrine therapy		867	0.86 (0.66 – 1.12)	Log-rank P = 0.25
Treatment with chemotherapy and endocrine therapy		510	1.22 (0.81 – 1.83)	Log-rank P = 0.34

3.4. SDC3 Depletion Affects the Metabolic Activity and Cell Cycle of Human MDA-MB-231 and MCF-7 Breast Cancer Cells

Building on our in-silico analysis, which suggested a potential role of SDC3 in breast cancer pathogenesis, we conducted in vitro experiments with SDC3-depleted human MDA-MB-231 and MCF-7 breast cancer cells. We studied the impact of SDC3 downregulation in vitro using the siRNA and siPool knockdown approach, which were quantified by qRT-PCR and analyzed in relation to the cells transfected with the negative control (Figure 3B). In MDA-MB-231 cells, we observed a significant downregulation of approx. 80% of SDC3 expression, with both SDC3 siRNA (p ≤ 0.01) and SDC3 siPool (p ≤ 0.001) knockdown approaches. In MCF-7 cells, we detected a significant downregulation of approx. 50% of SDC3 expression, in both SDC3 siRNA (p ≤ 0.001) and SDC3 siPool (p ≤ 0.001) transfected cells.

We then analyzed the effect of SDC3 depletion on cell viability, utilizing the metabolic MTT assay (Figure 3C). The analysis revealed that the metabolic activity of MDA-MB-231 cells following siRNA-mediated SDC3 knockdown was significantly decreased by approx. 63% (p ≤ 0.01), while it was reduced by approx. 22% (p ≤ 0.05) following SDC3 knockdown with the siPool. A similar effect was observed in MCF-7 cells, where the metabolic activity was significantly decreased by approx. 32% (p ≤ 0.01) upon siRNA-mediated SDC3 knockdown. A non-significant reduction in metabolic activity was observed following SDC3 depletion with the siPool.

To investigate the potential role of SDC3 on the cell cycle, a flow cytometry analysis with transfected MDA-MB-231 and MCF-7 cells was performed (Figure 3D). Significant changes in the G1- and S-phase of the cell cycle were observed in MDA-MB-231 cells transfected with the SDC3 siRNA, as significantly more cells accumulated in the G1-phase (80.7%; p ≤ 0.01), while significantly fewer cells were detected in the S-phase (8.2%; p ≤ 0.01), compared to the negative control (G1-phase: 66.4%; S-phase: 20.0%). The G2/M-phase was not altered in MDA-MB-231 cells following transfection with the SDC3 siRNA. By contrast, MDA-MB-231 cells transfected with the siPool did not exhibit alterations of the cell cycle, and the proportions of cells detected in the G1-, S-, and G2/M phases were similar to those of the negative control. The cell cycle of MCF-7 cells transfected with the SDC3 siRNA and siPool was not significantly altered in our analysis.

3.5. SDC3 Depletion Affects Three-Dimensional Spheroid Growth of Human MDA-MB-231 and MCF-7 Breast Cancer Cells

We analyzed the effect of SDC3 downregulation on the growth of three-dimensional (3D) spheroids in a hanging drop assay, as a readout of stem cell activity [23,28,29]. MDA-MB-231 cells transfected with the negative control formed compact, round spheroids from day 4 until day 7. In comparison, SDC3 siRNA-transfected MDA-MB-231 cells formed less cohesive spheroids that exhibited loose, irregular shapes on day 4. By day 7, the spheroids started to dissolve, single cells drifted away from the edges and spread throughout the culture medium. Quantification of the core spheroid area showed that siRNA-mediated SDC3 depletion resulted in a significant increase (p ≤ 0.05) in sphere size on day 4, while a significant decrease (p ≤ 0.05) was visible on day 7. Interestingly, MDA-MB-231 cells transfected with the siPool formed compact, round spheroids from day 4 until day 7 and did not disintegrate in a similar manner as MDA-MB-231 cells transfected with the SDC3 siRNA. On day 4, the sphere size of the SDC3 siPool-transfected cells was non-significantly altered, however, a significant decrease (p ≤ 0.001) was detected by day 7 (Figure 4A).

MCF-7 cells transfected with the negative control formed spheroids of irregular shapes with several side extensions and holes from day 4 until day 7. This morphology was also observed in MCF-7 cells transfected with the SDC3 siRNA on day 4. However, by day 7, SDC3 siRNA-transfected MCF-7 cells formed spheroids of round, compact shapes. The sphere size of MCF-7 cells transfected with the SDC3 siRNA was non-significantly altered on day 4 and significantly decreased (p ≤ 0.001) on day 7, in comparison to the negative control. A similar trend was observed for MCF-7 cells transfected with the SDC3 siPool. On day 4, the spheroids of siPool-transfected MCF-7 cells exhibited irregular shapes with several side extensions and holes, and by day 7 the spheroids were round and compact. The sphere size of MCF-7 cells transfected with the SDC3 siPool was non-significantly altered on day 4, by day 7 it was significantly decreased (p ≤ 0.05), compared to the negative control (Figure 4B).

3.6. SDC3 Depletion Affects Cell Migration of Human MDA-MB-231 and MCF-7 Breast Cancer Cells

The cell migration assay was conducted with siRNA-transfected MDA-MB-231 and MCF-7 cells. The migration of MDA-MB-231 cells was significantly decreased by 68% (p ≤ 0.01) following SDC3 knockdown, in comparison to the negative control. MCF-7 cells displayed little migratory behavior in general, cell migration was not significantly altered upon SDC3 knockdown, compared to the negative control (Figure 5A).

3.7. SDC3 Depletion Affects the RNA Expression of Target Genes Associated with Relevant Signaling Pathways in Breast Cancer

qRT-PCR was performed with transfected MDA-MB-231 and MCF-7 cells to explore the effects of SDC3 depletion on the expression of several target genes associated with relevant signaling pathways in breast cancer. The gene expression of SDC1 (Sdc1) and SDC4 (Sdc4) was evaluated in SDC3-depleted cells, to determine potential compensatory expression changes. Moreover, the expression of target genes associated with notch-signaling (Notch1, HES1), wnt-signaling (WNT5A, TCF7L1), hedgehog-signaling (Gli1, Gli2, Gli3), epithelial-to-mesenchymal-transition (Twist, Snail1, Snail2, CDH1, Vimentin), matrix-metalloproteinases (MMP1, MMP2, MMP9), angiogenesis (VEGF-A), inflammation (IL-8), basement membranes (COL4A2), and CD44-signaling (CD44) was investigated. These target genes were chosen for our analysis, as previous studies have shown the involvement of other syndecan family members in these pathways [14,16,17,30,31,32,33,34,35].

In MDA-MB-231 cells, the most consistent changes in gene expression were observed for Gli2, Twist, MMP1, MMP2, and MMP9 following SDC3 knockdown with the siRNA and siPool. While Gli2, twist, and MMP9 were downregulated upon SDC3 knockdown, an upregulation was observed for MMP1 and MMP2 (Figure 5B, Supp. A). In MCF-7 cells, siRNA- and siPool-mediated SDC3 knockdown resulted in consistent upregulation of HES1, CDH1, and VEGF-A (Figure 5C, Supp. B). Notably, SDC3 knockdown with the siPool led to an upregulation of SDC4 in MCF7 cells (Supp. B).

3.8. SDC3 Depletion Affects the Activation of SRC of Human MDA-MB-231 and MCF-7 Breast Cancer Cells

To further evaluate potential mechanisms by which SDC3 influences cell viability, cell-cycle phases, stemness-associated factors, and cell migration mainly in MDA-MB-231 and in MCF-7 breast cancer cells, we examined the effect of SDC3 downregulation on the potential activation of Src proto-oncogene tyrosine-protein kinase (pSRC), as well as on the expression of its total form (SRC). Src has previously been associated with SDC3 function in a neurobiological context [68]. Moreover, Src is a key regulator of several oncogenic signaling pathways and has been implicated in promoting tumor angiogenesis, metabolic reprogramming, and metastasis in various cancers [69,70,71,72].

Western blot analysis was performed with MDA-MB-231 and MCF-7 cells, following transfection with the negative control, SDC3 siRNA, and SDC3 siPool, and additional incubation with tissue-factor-pathway-inhibitor (TFPI) (Figure 5D). TFPI has been previously linked to SDC1 in breast cancer [36], and SDC3 in vascular and cancer cells [37].

MDA-MB-231 cells transfected with the SDC3 siRNA exhibited reduced phosphorylation of Src (pSRC), while the expression of the total form (SRC) was not altered in comparison to the negative control. A similar observation was made for MDA-MB-231 cells transfected with the SDC3 siRNA and treated with TFPI. MDA-MB-231 cells transfected with the siPool did not show activation or expression changes of pSRC and SRC, respectively, in comparison to the negative control. However, MDA-MB-231 cells transfected with the SDC3 siPool and treated with TFPI exhibited a slight downregulation of pSRC, but not of SRC. MCF-7 cells transfected with the siRNA showed visible downregulation of both pSRC and SRC. The same effect was observed in MCF-7 cells transfected with the SDC3 siRNA and treated with TFPI. By contrast, the expression of the pSRC only was reduced in MCF-7 cells following transfection with the SDC3 siPool. Similarly, this effect was detected in MCF-7 cells transfected with the SDC3 siPool and treated with TFPI (Figure 5D).

3.9. STRING Functional Enrichment Analysis

To investigate the molecular interactions between SDC3 and key proteins involved in signaling pathways in cancer, we performed an in silico functional interaction analysis using the open-access tool STRING database (STRING, https://string-db.org, accessed September 13th, 2024) [25]. We developed a virtual overview of our research findings, as depicted in Figure 6A. Our analysis revealed direct connections between SDC3, proto-oncogene tyrosine-protein kinase Src (SRC), fibroblast growth factor 2 (FGF2), and matrix metalloproteinase 7 (MMP7). Through SRC and FGF2, SDC3 was shown to interact with vascular endothelial growth factor receptor 1 (FLT1), vascular endothelial growth factor receptor 2 (KDR), vascular endothelial growth factor B (VEGFB), and vascular endothelial growth factor C (VEGFC). Indirect connections between SDC3 and cadherin-1 (CDH1) through SRC, FGF2, and MMP7 were furthermore established. Moreover, multiple interactions between SDC3 and the matrix metalloproteinases-1, -2, -9, and -14 (MMP1, MMP2, MMP9, and MMP14), as well as the metalloproteinase inhibitor-2 (TIMP2) and -4 (TIMP4) were visualized. Figure 6B depicts the gene ontology (GO) of the interconnections between SDC3 and the presented markers in Figure 6A. The biological processes category included mechanisms like cell migration, transmembrane receptor protein tyrosine kinase signaling, vascular endothelial growth receptor signaling, chemotaxis, vascular endothelial growth factor signaling, tube morphogenesis, and angiogenesis (Figure 6B, blue). In terms of molecular function, SDC3 was linked to metalloendopeptidase-, vascular endothelial factor receptor-, chemoattractant-, protein tyrosine kinase-, transmembrane receptor tyrosine kinase-, and serine-type endopeptidase activity. Furthermore, SDC3 was associated with the functions of protein binding, growth factor receptor binding, catalytic activity acting on a protein, and integrin binding (Figure 6B, green). The cellular component analysis demonstrated that SDC3 was related to the extracellular region, -space, and -matrix, as well as to other cellular components, such as cytoplasmic vesicles, sorting endosomes, platelets alpha granule lumen, endomembrane systems and endosomes (Figure 6B, orange). KEGG-pathway analysis revealed interconnections between SDC3 and the Rap1-signaling, focal-adhesion-signaling, Ras-signaling, Relaxin-signaling, MAPK-signaling, EGFR-tyrosine-kinase-inhibitor-resistance, PI3K-Akt-signaling, and adherens-junctions-pathways. Also was associated with pathways-related-to-proteoglycans-in-cancer, -to-bladder-cancer, -to-melanoma, -to-fluid-shear-stress-and-atherosclerosis, and to-gastric-cancer signaling (Figure 6C).

4. Discussion

Breast cancer is the most frequently diagnosed malignant tumor in women worldwide [1,2]. Its incidence is rising across all regions globally, despite significant advancements in early detection and treatment development [4]. Enhancing therapeutic strategies and identifying reliable predictive and prognostic markers therefore remain key priorities in ongoing research [4].

Syndecans are integral cell surface heparan sulfate proteoglycans that influence multiple signaling pathways, regulating cell proliferation, adhesion, motility, angiogenesis, wound repair, and inflammation [14]. In cancer, syndecans can be aberrantly expressed and are implicated in tumorigenesis and metastasis [16]. Among all members of the syndecan family, SDC3 remains the least studied to date, with limited data currently available on its dysregulation in human malignancies [38].

Our in-silico analysis of the TNM-plot-, and The Human Protein Atlas-database revealed that SDC3 mRNA expression was significantly upregulated in breast cancer tissue, in comparison to normal breast tissue. In breast cancer metastasis, no significant alterations in SDC3 expression were detected, which might suggest that the molecule plays distinct roles in the early and late stages of breast tumor development. Interestingly, the analysis of The Human Protein Atlas demonstrated that SDC3 mRNA and protein are mainly localized in the mitochondria and nucleoplasm of human cells. Since a protein´s function is often closely tied to its precise location within tissues, cells, and subcellular structures, we suggest that the spatial distribution of SDC3 may be of relevance to breast tumor biology [20]. Using the Kaplan-Meier-Plotter online tool, we explored the association between SDC3 and the relapse-free survival (RFS) of breast cancer patients. High SDC3 expression was significantly linked to improved RFS (n = 4929), particularly in the HER2-negative, luminal A, luminal B, and basal subtype, as well as in patients receiving neoadjuvant chemotherapy, suggesting a potential stage-specific role for SDC3 in breast cancer metastasis. Supporting this theory, a study on ovarian cancer found that SDC3 RNA expression was higher in primary tumors compared to metastatic sites, further indicating the potential association of the molecule with early-stage cancer progression [17].

SDC3 expression has previously been observed in various cancer cell lines (i.e. bladder cancer, hepatocellular carcinoma, mammary carcinoma, ovarian cancer, pancreatic cancer, prostate carcinoma, renal cell carcinoma, and glioma cell lines) [39], and in cells of the tumor microenvironment (TME), specifically tumor-associated macrophages and endothelial cells [39]. Interestingly, the molecule has been recently identified as a modulator of macrophage function, aiming at supporting a pro-inflammatory and anti-tumor phenotype [40]. SDC3 defective macrophages were found to exhibit distinctive gene expression patterns, resulting in impaired tumor cell phagocytosis and increased tumor cell proliferation [40].

However, the distinct SDC3 expression patterns in each cancer type and its correlation with cancer progression largely remain unexplored. Previous studies on SDC3 in breast cancer have yielded mixed results [40,41]. Wu et al. (2013) found in an immunohistochemical and in situ hybridization study on 159 breast cancer patients that GFRα1 (GDNF family receptor alpha-1) and GFRα3 (GDNF family receptor alpha-3) (which, like SDC3, bind Artemin/ARTN) were better predictors of breast cancer progression than SDC3 [41]. In contrast, Jiang et al. (2021) included SDC3 in a seven-gene glycolysis-related signature (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) that predicted poorer prognosis in high-risk breast cancer patients in an analysis of The Cancer Genome Atlas mRNA profiling datasets [42]. Notably, Kaplan-Meier analysis in their study showed that higher SDC3 expression alone was associated with a better prognosis, in accordance with our study [42]. Therefore, the difference in methodology (immunohistochemistry vs. transcriptomics) and the size of the sample collectives (being substantially higher in the transcriptomic studies) may have accounted for the discrepancies.

Building upon our in-silico findings, we conducted several in vitro experiments using the human breast cancer cell lines MDA-MB-231 and MCF-7. Gene silencing of SDC3 was achieved by two independent siRNA-, and siPool-knockdown approaches. Subsequently, several functional assays were performed to assess the metabolic activity, cell cycle progression, 3D-spheroid formation, and cell migration in both SDC3-depleted and control MDA-MB-231 and MCF-7 cells. Surprisingly, in contrast to the positive prognostic value of high SDC3 expression, SDC3 depletion in breast cancer cells in vitro resulted in a reduction of pathogenetic phenotypes. This may suggest a differential role for cancer cell-autonomous SDC3 and SDC3 in the tumor microenvironment, as the public gene expression datasets are derived from whole tumor containg tumor cells as well as cells of the tumor microenvironment (cancer-associated fibroblasts, infiltrating immune cells, blood vessel derived endothelial and smooth muscle cells). Indeed, SDC3 expressed by marcophages infiltrating tumor tissue may contribute to tumor cell phagocytosis and decreased tumor cell proliferation, thus exhibiting an antitumoral effect [40].

With respect to breast cancer cell-autonomous SDC3, our MTT analysis demonstrated that SDC3-depletion in both MDA-MB-231 and MCF-7 cells resulted in a significant reduction of metabolic activity. This finding is supported by a study on ovarian cancer, which reported that SDC3 knockdown significantly reduced cell viability in SKOV3 ovarian cancer cells [17]. We can only speculate if our MTT assay findings align with the analysis of The Human Protein Atlas, possibly suggesting a link between SDC3, cellular metabolism, and its mitochondrial localization.

Our cell cycle analysis revealed that siRNA-mediated knockdown of SDC3 caused G1-phase accumulation and reduced S-phase entry, particularly in MDA-MB-231 cells. SDC3 depletion by siPool-transfection did not yield similar effects, possibly due to differences in knockdown efficiency or compensatory mechanisms. We did not observe any effects of SDC3 depletion on apoptosis in our experimental model (results not shown). SDC3 has been previously identified as a regulator of the cell cycle in cerebellar granule cell precursors (CGCPs), where it was shown to interact with sonic hedgehog (Shh) signaling to modulate cell cycle exit and differentiation [43]. Additionally, loss of SDC3 was found to impair the cell cycle progression of satellite cells (SCs), the adult skeletal muscle progenitors, resulting in reduced progenitor cell expansion, delayed onset of differentiation, increased cell death, and diminished self-renewal capacity [44].

Breast cancer progression, treatment resistance, and relapse are greatly influenced by the activities of cancer stem cells (CSCs)[45]. CSCs possess multiple self-renewal and differentiation abilities that support tumor heterogeneity Previous studies have shown that heparan sulfate proteoglycans can modulate breast stem cell properties [46,47,48]. Therefore, we evaluated the effect of SDC3 depletion on the growth of three-dimensional (3D) spheroids in a hanging drop assay, a method considered suitable for studying stem cell biology in vitro [28,29]. SDC3 knockdown in both MDA-MB-231 and MCF-7 cells resulted in altered spheroid morphology and a reduction in spheroid size, compared to the negative controls. This may suggest a potential role for SDC3 in enforcing CSC attributes. SDC3 has been previously shown to play a role in regulating physiological stem cell function as well as cell adhesion [44,49,50]. The molecule has been implicated in bone-marrow-derived-mesenchymal stem cell (MSC) biology [49], in stem cell-related notch-signaling during myogenesis [44], and in the differentiation of neural stem cells (NSCs) [50]. In ovarian cancer, a link between SDC3 and the CSC phenotype has already been established [17]. It was demonstrated that SDC3 depletion in SKOV3 and CAOV3 cells led to significant changes in spheroid morphology, along with altered RNA expression of several stemness-related constituents of the notch-, wnt- and hedgehog pathway [17]. Similarly, breast cancer initiation and progression were found to be influenced by a dysregulated notch-, hedgehog-, and wnt- signaling [51]. Our qRT-PCR analysis revealed that Gli2 (associated with hedgehog-signaling) in MDA-MB-231 cells and HES1 (associated with notch-signaling) in MCF-7 cells were significantly altered following SDC depletion. These findings may suggest that SDC3 plays a role in regulating notch- and hedgehog-signaling pathways across different cancer types.

Syndecans are important mediators of cancer cell migration, due to their ability to bind different growth factors, chemokines, morphogens, and extracellular matrix components [14,16,52]. In our in vitro migration assay, siRNA-mediated SDC3 depletion led to reduced cell migration in both MDA-MB-231 and MCF-7 cells. This effect was statistically significant in MDA-MB-231 cells, but non-significant in MCF-7 cells. Since MCF-7 cells form tight cell-cell junctions and are more differentiated than MDA-MB-231 cells [26], this finding may be attributable to inherent differences in the characteristics of these cell lines.

SDC3 was reported to play a role in cell motility in both inflammatory and neurobiological contexts [53,54,55]. The molecule was found to inhibit leukocyte migration in vitro and to reduce disease severity in animal models of rheumatoid arthritis [56]. Furthermore, SDC3 deficiency has been shown to impair neural cell migration and to perturb the laminar structure of the cerebral cortex during brain development [53]. Interestingly, it was observed that SDC3 interacts with the glial cell line-derived-neurotropic factor family ligands (GFL) with the involvement of Src kinase activation, thereby mediating cell spreading and neurite outgrowth [54].

To further investigate the involvement of SDC3 in cell migration, invasion, and epithelial-to-mesenchymal transition (EMT), we examined its role at the molecular level using qRT-PCR analysis. EMT is defined as a cellular program, in which epithelial cells acquire a mesenchymal phenotype that enables cell migration and invasion of secondary sites [57]. Our analysis revealed that SDC3 depletion in both MDA-MB-231 and MCF-7 cells significantly impacted the expression of EMT-markers. SDC3 knockdown resulted in the downregulation of Twist in MDA-MB-231 cells, as well as in increased expression of CDH1 in MCF7 cells. Furthermore, SDC3 depletion significantly upregulated MMP1, and downregulated MMP9 in MDA-MB-231 cells, suggesting a multifunctional role of the molecule in modulating migration and invasion in breast cancer [58,59]. SDC3 has been previously associated with MMP-mediated shedding in neurobiological contexts [60]. Predictive algorithms have suggested roles for MMP2 and MMP9 at multiple cleavage sites of SDC3, however, the exact mechanisms and protease in SDC3 shedding have not yet been confirmed experimentally [61,62].

Moreover, we aimed to explore the effect of SDC3 on angiogenesis at the molecular level. Syndecans have previously been shown to mediate VEGF-associated signaling and regulate angiogenesis [32,63,64]. In our qRT-PCR analysis, we observed that SDC3 knockdown upregulated VEGF-A expression in MCF-7 cells. SDC3 has been reported to suppress endothelial cell migration, indicating a potential anti-angiogenic function of the molecule [65]. On the other hand, thrombin-cleaved SDC3 fragments were found to induce endothelial hyperpermeability, having pro-angiogenic effects [66]. It has been revealed that hypoxia activates the hypoxia-inducible factor 1-alpha (HIF1α)/ vascular endothelial growth factor A (VEGF-A) axis in breast cancer to promote angiogenesis [67]. Moreover, hypoxia-induced angiogenic HIF1α/VEGF-A signaling was related to SDC3 upregulation in hypoxic tumors, which further implicates SDC3 in breast tumor growth and vascular remodeling [39].

To evaluate the underlying mechanisms by which SDC3 influences cell viability, cell-cycle regulation, stemness, and migration in breast cancer, we investigated the effects of SDC3 depletion on the activation of proto-oncogene tyrosine-protein kinase Src (pSRC, SRC). Src has been previously linked to SDC3 function in a neurobiological context [68]. Our results showed a trend of reduction in the activation of Src in MDA-MB-231 and MCF-7 cells, suggesting that SDC3 may regulate Src signaling and thereby potentially influence breast cancer pathogenesis. Src has been shown to be involved in multiple signaling pathways, such as PI3K/AKT and HIF1α, and to impact tumor angiogenesis, metabolism, and metastasis [69,70,71,72]. Of note, Src has also been associated with activating signal transduction pathways leading to the expression of HIF and its downstream genes, providing a molecular mechanism for tumor cells to adapt to a hypoxic environment [70]. HIF regulates several pro-tumorigenic genes, including those involved in angiogenesis (i.e. VEGFA, IL-8), glycolytic cellular metabolism (i.e. LDHA), extracellular matrix remodeling (i.e. MMPs), epithelial-to-mesenchymal transition (i.e. CDH1), metastasis (i.e. TWIST1), and cell motility and adhesion (i.e. integrins) [73].

To summarize our key research findings, we developed a virtual overview of the signaling pathways affected by SDC3 using the STRING database. Our analysis revealed direct connections between SDC3, proto-oncogene tyrosine-protein kinase Src (SRC), fibroblast growth factor 2 (FGF2), and matrix metalloproteinase 7 (MMP7). Further indirect associations were established between SDC3 and vascular endothelial growth factor receptor 1 (FLT1), vascular endothelial growth factor receptor 2 (KDR), vascular endothelial growth factor B (VEGFB), vascular endothelial growth factor C (VEGFC), cadherin-1 (CDH1) as well as matrix metalloproteinases-1, -2, -9 and -14 (MMP1, MMP2, MMP9, and MMP14).

5. Conclusions

We suggest that breast cancer cell-derived SDC3 contributes to several cancer hallmarks associated with breast tumorigenesis and progression. Our molecular analysis may provide a mechanistic explanation for the functional changes observed upon SDC3 depletion. Future research should further elucidate SDC3-related signal transduction pathways in the context of breast cancer and a possible antitumoral effect of SDC3 of the tumor microenvironment, which may contribute to its positive predictive value in transcriptome-based survival analysis. Based on our findings and existing literature, an association between the molecule and the previously described pathways appears likely. Further investigation, using advanced preclinical models, could provide valuable insights into its potential as a therapeutic target or biomarker in breast cancer.