You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background {#Sec1}
==========

Breast cancer is the first in incidence and the second in mortality among women \[[@CR1]\]. Currently, a reduction in overall cancer death rates is due to a combination of earlier diagnosis and treatment improvement \[[@CR1]\]. Nevertheless, breast cancer remains a worldwide health problem. The lack of more informative biomarkers to discriminate among patients according to the prognosis and clinical course complicates the choice of therapeutic strategy.

Metastasis development is the main death factor attributed to breast cancer \[[@CR2]\]. The organized breakdown of the extracellular matrix (ECM) by matrix metalloproteinases (MMPs) is an essential event in the complex metastatic cascade \[[@CR3]\]. Inhibitors of MMPs, known as tissue inhibitors of MMPs (TIMPs), and the membrane-associated MMP inhibitor RECK are also critical for controlling the maintenance of the ECM integrity due to their regulation of MMP function \[[@CR4]--[@CR7]\].

Elevated MMP expression and activity levels have been associated with poor prognosis in several cancer types \[[@CR8]--[@CR10]\]. Surprisingly, a higher expression of MMP inhibitors, such as TIMPs, correlates with a worse outcome for breast cancer patients \[[@CR11]--[@CR14]\]. This ambiguous nature of TIMPs may be due to the wide variety of TIMP functions, which are independent of their activities as MMP inhibitors \[[@CR6]\]. However, this finding remains unclear.

RECK, another type of MMP inhibitor, has been shown to decrease cellular invasion \[[@CR15]\], most likely by inhibiting the enzymatic activity of MMP-2, MMP-9 and MMP-14 \[[@CR16], [@CR17]\]. Additionally, RECK has been shown to decrease MMP-9 mRNA levels \[[@CR18]\]. The potential prognostic power of RECK has already been reported for other tumors, such as pancreatic, liver, lung and colorectal cancers \[[@CR19]--[@CR23]\], where RECK expression is inversely correlated with MMP expression during tumor progression \[[@CR23]\]. Thus, high expression levels of RECK correlate with a better clinical outcome \[[@CR23]\].

However, in breast cancer, the RECK expression profile is controversial and remains unclear \[[@CR24]--[@CR27]\]. Moreover, no functional analysis in human mammary tumors has been previously reported. A retrospective study using 119 cases of breast cancer samples, suggests, similar to other cancer types, that a low expression of RECK indicates a shorter survival rate for patients with invasive breast cancer \[[@CR24]\]. The available data indicated that lower RECK mRNA levels were expressed in tumor tissue samples than in normal breast tissue samples, and higher levels were found in invasive human breast cancer cells than in less aggressive ones \[[@CR24]--[@CR26]\].

In the present study, we aimed to evaluate the prognostic significance of RECK in breast cancer patient outcome. The RECK protein expression levels were analyzed in a large series (1040) of breast cancer cases using immunohistochemistry of tissue microarrays (TMAs). Furthermore, we investigated RECK function in mammary neoplasia using RECK-silenced human breast cancer cells, obtained by RNA interference methodology. Here, we present evidence supporting that, although RECK acts as a negative regulator of MMP-9 and invasion, its levels of expression fail to correlate with patient survival rates.

Methods {#Sec2}
=======

Patients and methods {#Sec3}
--------------------

All breast samples were obtained from patients diagnosed and treated at the A. C. Camargo Hospital, São Paulo, Brazil. TMAs were arranged from a series of 1040 cases of primary breast carcinoma from patients diagnosed between 1980 and 1999. Duplicate 1 mm cores from tumors were used in the TMAs. The median age of the patients was 55 years (range: 26--90 years). The tumor tissues were removed at surgery. The A. C. Camargo Hospital Ethics Committee approved this research (Process number: 1648/12) and waived the need for informed consent term.

Vimentin and pan-cytokeratin were used as tissue immunoreactivity and staining controls. RECK staining was performed using a monoclonal antibody diluted 1:200 (Cell Signaling, Beverly, MA, USA). Several patient and tumor characteristic data were available, including age, menopausal status, tumor size, lymph node stage, SBR stage (Scarff-Bloom-Richardson, histological classification method) and TNM tumor stage. The expression state of several biomarkers, such as estrogen receptor (ER), progesterone (PR), Her-2 and cytokeratins (CK5, CK6, CK8, CK14 and CK18), was also evaluated by two distinct pathologists.

The ScanScope XT device was used for slide scanning. The percentage of cells positively stained for RECK, and its staining pattern in the TMA core, was determined using the Spectrum Plus computational program, by a trained person. For each subject, at least three measurements were obtained, from two distinct slides, with samples represented in duplicate. The average of these three values was considered as the RECK immunoreactivity for each subject. RECK immunoreactivity values greater than the median were considered as positive (or high expression).

Cell lines and reagents {#Sec4}
-----------------------

Six breast cell lines (S1, MCF-7, T47D, T4-2, MDA-MB-231 and Hs578T) with distinct tumorigenic and aggressiveness levels were used in this study \[[@CR26]\]. The broad-spectrum MMP inhibitor (GM6001) was purchased from Millipore (Billerica, MA, USA).

Viral transduction and shRNA experiments {#Sec5}
----------------------------------------

For the inhibition of RECK expression, five different specific shRNA sequences (sequentially numbered from shRECK23 through shRECK27) were tested. These vectors and the structural ones belonged to the Mission® shRNA system (Sigma-Aldrich, St. Louis, MO, USA). Viral recombinant particles were generated by co-transfection of the 293FT virus packer cell line with these recombinant lentiviral vectors. Transduced cells were selected by the addition of 2 μg/mL puromycin.

Quantitative RT-PCR studies {#Sec6}
---------------------------

Total RNA was extracted from cell lines using the RNeasy Mini Kit (Qiagen, Germantown, MD, USA). Its integrity and quantity were evaluated using a NanoDrop™ 1000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). For cDNA synthesis, the SuperScript® III Reverse Transcriptase Kit was used (Life Technologies, Carlsbad, CA, USA). Quantitative RT-PCR was carried out using QuantiTect SYBR Green RT-PCR Kit (Qiagen). The primers used to amplify RECK, MMP-9 and the endogenous control (18S ribosomal RNA) were obtained from a validated primer panel available from Qiagen (QuantiTect Primer Assays). Each experiment was carried out in duplicate. The relative expression levels were calculated according to the 2^-∆∆Ct^ method \[[@CR28]\].

Western blotting {#Sec7}
----------------

Total extracts were prepared from each cell culture line. Equal protein amounts from each extract were fractionated by SDS-PAGE and then electro-transferred to nitrocellulose membranes. After blockade with BSA, these membranes were incubated with RECK (Cell Signaling) and Lamin A/C (Santa Cruz, Santa Cruz, CA, USA) antibodies, diluted at 1:1000 and 1:200, respectively. Immunoreactive proteins were detected with the appropriate secondary horseradish peroxidase-conjugated antibody (GE Healthcare, Little Chalfont, UK) and visualized using the SuperSignal West Femto Chemiluminescent Substrate (Thermo Fisher Scientific). Quantitative densitometry of the electrophoretic band images was carried out using AlphaEaseFC software.

Gelatin zymography assays {#Sec8}
-------------------------

Gelatin zymography of the conditioned media was used to assess the *in vitro* enzymatic activity levels of MMP-9 in RECK-silenced and scrambled MDA-MB-231 cells. These samples were fractionated by 10 % SDS-polyacrylamide gel electrophoresis co-polymerized with the enzyme substrate, 0.1 % denatured type I collagen (gelatin type A; Sigma, St. Louis, MO, USA). After electrophoresis, the gels were washed at room temperature with 2.5 % Triton X-100, incubated for 48 h at 37 °C in substrate buffer containing 50 mM Tris buffer (pH 8.5) and 10 mM CaCl~2~. Gels were stained with Coomassie Blue R-250 (Sigma) and destained with 40 % methanol (Merck) and 10 % acetic acid (Merck) in water. Gelatinolytic activity was visualized as negatively staining bands. Each independent experiment was performed in duplicate. Quantitative densitometry of the electrophoretic band images was performed using the AlphaEaseFC software.

*In vitro* invasion assays {#Sec9}
--------------------------

To assess the role of RECK in cellular invasiveness potential, RECK-deficient or scrambled control MDA-MB-231 cells were plated on top of Matrigel-coated 8-μm-pore transwells (BD Bioscience, Franklin Lakes, NJ, USA). These cells were maintained in the presence of GM6001 or its vehicle (DMSO) in serum-free medium. The medium present in the bottom chamber was supplemented with 10 % bovine serum, used as a chemo-attractant. Cells were allowed to invade for 36 h. Cells remaining at the top chamber were removed, and those present at the bottom of the filter were stained and fixed with Coomassie Blue 0.125 % in methanol:acetic acid:H~2~O (45:10:45, v/v/v) for 15 min. Using the ImageJ® program, the relative cellular invasion was quantified from the images obtained (10× objective lens) under each experimental condition. Non-tumorigenic S1 mammary cells were used as a negative control. Triplicate wells were utilized per condition in each independent experiment.

Statistical analysis {#Sec10}
--------------------

Statistical analysis of data from patients was performed using R (<http://www.r-project.org/>) \[[@CR29]\]. Chi-square tests were used to assess the association between RECK immunoreactivity and different clinico-pathological parameters. P-values were corrected for multiple tests by the False Discovery Rate procedure \[[@CR30]\]. Kaplan-Meier (KM) plots were used for overall (OS) and disease-free (DFS) survival analysis, and the log-rank test was used to compare curves separated according to RECK expression. For KM analysis, 3 of 1040 subjects were identified as being outliers by Tukey's criterion; thus, they were removed from the data analysis. Cox proportional hazards regression was used to estimate hazard ratios (HRs). Subjects for whom no information on the analyzed covariates was available, were removed, with 940 of the 1040 subjects remaining for the Cox regression analysis.

Data obtained using the cell lines model were analyzed using the GraphPad Prism 5.0 program. In this case, statistical significance was determined using the one way ANOVA variance analysis and the Tukey-Kramer test. The results were presented as the mean ± standard error of the mean. A p-value less than 0.05 was considered to be statistically significant.

Results {#Sec11}
=======

RECK protein expression levels in human breast cells correlates with invasiveness {#Sec12}
---------------------------------------------------------------------------------

In several tissue models, RECK expression was shown to be inversely related to oncogenesis \[[@CR15], [@CR31], [@CR32]\], being expressed in normal tissues but not in many tumor cell lines and tumor samples \[[@CR15], [@CR32]\]. However, in breast cancer, the RECK expression profile remains uncertain and controversial \[[@CR24]--[@CR27]\]. To better understand this divergence, we quantified the RECK protein levels in a panel of breast cell lines with distinct aggressiveness levels, including non-tumorigenic (S1) cells, and non-invasive (MCF-7 and T47D) and invasive (T4-2, MDA-MB-231 and Hs578T) cancer cell lines (Fig. [1](#Fig1){ref-type="fig"}). We observed that more invasive cells (T4-2, MDA-MB-231 and Hs578T) displayed significantly higher (*p* \< 0.05, *p* \< 0.01 and *p* \< 0.001, respectively) RECK protein levels than non-invasive breast cancer lineages (MCF-7 and T47D). Furthermore, RECK protein was significantly overexpressed in the highly invasive Hs578T cells (*p* \< 0.001) compared with the non-malignant ones (Fig. [1](#Fig1){ref-type="fig"}). The doublet bands detected were possibly a result of RECK glycosylation \[[@CR33]\]. Therefore, our results suggest that, unexpectedly, human breast cancer cells with a higher invasive potential express elevated levels of RECK (Fig. [1](#Fig1){ref-type="fig"}).Fig. 1Invasive breast cancer cells express higher levels of RECK than non-invasive and non-tumorigenic ones. Western blot assays were performed to analyze RECK protein expression levels in a panel of human breast cells. The lamin A/C protein level was used as the loading control and for normalization. The results are presented as the means ± standard error from three independent experiments. \*, *p* \< 0.05, \*\*, *p* \< 0.01 and \*\*\**p* \< 0.001

RECK down-modulates invasion and MMP-9 in highly invasive breast cancer cells {#Sec13}
-----------------------------------------------------------------------------

To further clarify the specific role of RECK in mammary neoplasia, we evaluated the functional activity of RECK. To this end, MDA-MB-231 cells, a highly invasive human breast cancer cell line, were used as a study model. The RECK expression level in MDA-MB-231 cells was silenced using different shRNA sequences (shRECK oligos numbered 23 through 27). Their ability to silence RECK was confirmed by qRT-PCR and Western blot assays (Fig. [2a](#Fig2){ref-type="fig"}). All of the sequences tested could decrease RECK expression at both the mRNA and protein levels. Subsequent functional assays were performed using shRECK26-transduced cells in which RECK protein expression was reduced by 80 % relative to the scrambled control (Fig. [2a](#Fig2){ref-type="fig"}).Fig. 2RECK functional analysis in the highly invasive MDA-MB-231 cells supports the role of RECK as an MMP inhibitor and negative modulator of cellular invasion. **a** RECK mRNA and protein expression levels in the MDA-MB-231 cells infected with lentiviral particles carrying different shRNA sequences specific for RECK inhibition (shRECK 23 through 27) or the scrambled control. The 18S ribosomal RNA expression and β-actin protein levels were used as endogenous controls in qRT-PCR and Western blot assays, respectively. **b** Transwell™ invasion assays were performed to assess the invasion capacity of scrambled control and RECK-deficient (shRECK26) cells that were treated with or without GM6001 (broad-spectrum MMP inhibitor). **c** MMP-9 mRNA and protein expression (pro-enzyme and active forms) levels in control (scrambled) and RECK-silenced cells quantified by qRT-PCR and Zymography assay, respectively. The 18S ribosomal RNA expression was used as the endogenous control in qRT-PCR assays. Proliferation analysis of scrambled and shRECK 26 cells by (**d**) colony formation assays and (**e**) growth curves. The results are presented as the means ± standard error from two independent experiments. \*\*, *p* \< 0.01 and \*\*\*, *p* \< 0.001, all versus control (MDA-MB-231 scrambled cells)

First, we addressed the role of RECK in the invasion of MDA-MB-231 cells. To this end, the *in vitro* invasion capacities of scrambled control and RECK-downmodulated cells were compared in Transwell™ assays (Fig. [2b](#Fig2){ref-type="fig"}). Our results indicate that the reduction of RECK led to a significant increase (*p* \< 0.01) in MDA-MB-231 invasion. Treatment with a broad-spectrum MMP inhibitor (GM6001) suggests that this effect of RECK on the cell invasive capacity is largely MMP dependent because GM6001 could significantly (*p* \< 0.01) reverse the RECK silencing effect in MDA-MB-231 cells (Fig. [2b](#Fig2){ref-type="fig"}). Additionally, we found that RECK-silenced cells express significantly higher levels of both MMP-9 (*p* \< 0.01) mRNA and its pro-enzyme (*p* \< 0.01) than scrambled control cells (Fig. [2c](#Fig2){ref-type="fig"}). Importantly, no influence of RECK was observed in MDA-MB-231 cell proliferation, as indicated in Fig. [2](#Fig2){ref-type="fig"} (d and e). These results suggest that although its expression is correlated with increased breast cancer cell aggressiveness, RECK still serves as a negative regulator of cellular invasion and MMP-9 expression in MDA-MB-231 cells.

RECK expression in breast tumor tissue samples and its association with clinico-pathological parameters {#Sec14}
-------------------------------------------------------------------------------------------------------

Previous reports have supported the potential role of RECK as a molecular marker for the prognosis of several cancer types (23). However, the association between RECK expression and the outcome of breast cancer patients remains unclear. We evaluated RECK protein expression levels in a large series (*n* = 1040) of breast cancer cases by immunohistochemistry on TMAs. The staining pattern obtained for RECK in mammary tissue samples derived from benign proliferation, ductal carcinoma *in situ* (DCIS) and invasive tumors is presented in Fig. [3](#Fig3){ref-type="fig"}. In all of these distinct breast tissues, RECK was predominantly expressed in epithelial cells, while the stromal cells were negative.Fig. 3Representative immunohistochemistry images of the RECK staining profile detected in breast tissue samples show RECK expression in epithelial cells. **a** Benign proliferation, (**b**) ductal carcinoma *in situ* (DCIS) and (**c**) invasive tumor samples analyzed in tissue microarray cores

All of the breast tumor samples included in this analysis were classified as positive or negative for RECK considering the intensity and percentage of stained cells ([Methods](#Sec2){ref-type="sec"}). To evaluate the correlation between RECK expression and different clinico-pathological parameters, Chi-squared tests were performed (Table [1](#Tab1){ref-type="table"}). Except for histological grade (*p* = 0.0008) and TNM stage (*p* = 0.0114), there was no correlation between RECK and most of the analyzed factors (age, menopausal status, tumor size, lymph node stage, recurrence and ER/PR status).Table 1Association between RECK expression and clinicopathological parametersParameterRECK negative no. cases (%)RECK positive no. cases (%)*χ*2p-valueAge  \< 50 years195 (18.8 %)191 (18.4 %)0.0210.8848  ≥ 50 years324 (31.3 %)326 (31.5 %)Menopausal status Premenopausal206 (20.1 %)197 (19.1 %)0.0820.7752 Postmenopausal312 (30.4 %)312 (30.4 %)Tumor size  \< 5 cm252 (25.5 %)268 (27.1 %)1.4940.2216  ≥ 5 cm247 (24.9 %)223 (22.5 %)Lymph node stage Negative (N0)169 (16.6 %)160 (15.7 %)0.2430.6222 Positive (N1, N2, N3)341 (33.5 %)348 (34.2 %)Recurrence Negative257 (25.1 %)259 (25.3 %)0.0630.8014 Positive258 (25.2 %)250 (24.4 %)ER/PR status ER+/PR+208 (22.3 %)204 (22.0 %)8.0010.046 ER+/PR-125 (13.5 %)86 (9.3 %) ER-/PR+8 (0.9 %)11 (1.2 %) ER-/PR-135 (14.5 %)151 (16.3 %)Histological grade 186 (8.3 %)73 (7.1 %)14.257**0.0008** 2315 (30.5 %)273 (26.4 %) 3116 (11.2 %)170 (16.5 %)TNM stage 139 (3.8 %)17 (1.6 %)11.069**0.0114** 2A and 2B201 (19.5 %)204 (19.7 %) 3A, 3B, and 3C240 (23.2 %)240 (23.2 %) 439 (3.8 %)54 (5.2 %)*χ*2, Chi-square test coefficient; ER/PR, estrogen receptor/progesterone receptorp-values considered significant after FDR correction were highlighted in bold

RECK is not a prognostic indicator for breast cancer patients {#Sec15}
-------------------------------------------------------------

To determine whether RECK would be a useful marker for the survival rate or could serve as a prognostic indicator, we performed several sets of analyses. The association between RECK and the overall (OS) and disease-free (DFS) survival was analyzed using Kaplan-Meier curves (Fig. [4](#Fig4){ref-type="fig"}). RECK expression did not predict any significant differences in OS and DFS (Fig. [4](#Fig4){ref-type="fig"}) among all of the 1040 breast cancer cases analyzed.Fig. 4RECK does not a predictive value for the survival of breast cancer patients. Differences in the overall (**a**) and disease-free survival (**b**) based on RECK immunoreactivity intensity of the diagnosed tumors were assessed using Kaplan-Meier plots

The prognostic significance of RECK for specific cohorts of patients was further evaluated. The tumor samples were classified according to molecular subtypes, namely luminal A, luminal B, Her2 type and basal-like/triple negative, which, in turn, were determined by the status of crucial breast cancer biomarkers (ER, PR and Her2). Samples were excluded from the analysis once the parameters required for subtype discrimination were not available. Thus, the predictive value of RECK was tested in 344 cases of luminal A, 19 cases of luminal B, 81 cases of Her2 type and 174 cases of Triple-negative/basal-like breast cancer. Regardless of each molecular subtype examined, differences in the RECK protein levels were not associated with significant changes in OS (Fig. [5](#Fig5){ref-type="fig"}) or DFS (Fig. [6](#Fig6){ref-type="fig"}).Fig. 5RECK is not associated with the overall survival (OS) of breast cancer patients regardless of the molecular subtype examined. Kaplan-Meier plots were obtained for OS analysis stratified by RECK expression in patients diagnosed with (**a**) luminal A, (**b**) luminal B, (**c**) Her2 type and (**d**) basal-like breast tumorsFig. 6RECK is not a prognostic marker of disease-free survival (DFS) for patients diagnosed with different breast cancer molecular subtypes. Kaplan-Meier plot estimate of DFS stratified by RECK expression in breast cancer patients diagnosed with (**a**) luminal A, (**b**) luminal B, (**c**) Her2 type and (**d**) basal-like tumors

The presence of tumor cells in axillary lymph nodes is also critical for the staging and prognosis of breast cancer, in addition to providing a more assertive therapy choice. Survival analyses were also performed considering two distinct groups: lymph node-negative (*n* = 330) or -positive (*n* = 690) patients. However, independently of lymph node status, RECK remained unable to predict the OS and DFS of these patients (Fig. [7](#Fig7){ref-type="fig"}). Multivariate Cox regression analysis demonstrated that RECK did not provide independent prognostic information for breast cancer patient survival (Tables [2](#Tab2){ref-type="table"} and [3](#Tab3){ref-type="table"}), strengthening the already reported evidence that this MMP inhibitor is not a relevant biomarker for breast cancer.Fig. 7RECK is not related to survival of breast cancer patients regardless of the lymph node status. Kaplan-Meier curves of overall (**a** and **b**) and disease-free (**c** and **d**) survival stratified by RECK expression in breast cancer patients classified as lymph node negative (**a** and **c**) or positive (**b** and **d**) at diagnosisTable 2Cox regression for overall survival analysis in the entire patient cohortEntire cohort (*n* = 940)UnivariateMultivariate^a^Prognostic factorHR95 % CIp-valueHR95 % CIp-valueRECK (low *vs.* high)0.950.67--1.370.80.910.63--1.310.63Age (continuous)1.011.01--1.02\<0.011.011.01--1.02\<0.01Tumor size (continuous)1.051.02--1.07\<0.011.031.01--1.060.02Histological grading (3 *vs.* 1--2)1.271.14--1.40\<0.011.241.11--1.38\<0.01TNM stage (IV *vs.* I--III)1.050.98--1.130.1510.93--1.070.96Nodal status (positive *vs.* negative)1.531.34--1.76\<0.011.511.32--1.74\<0.01ER (positive *vs.* negative)0.880.77--1.010.080.960.81--1.130.6PR (positive *vs.* negative)0.880.77--1.000.050.960.82--1.120.59Her2 (positive vs. negative)1.221.01--1.480.041.010.82--1.250.91RECK. REversion-inducing Cysteine-rich protein with Kazal motifs; ER. estrogen receptor; PR. progesterone receptor; Her2. human epidermal growth factor 2^a^Adjusted for all other variables in the tableTable 3Cox regression for disease-free survival analysis in the entire patient cohortEntire cohort (*n* = 940)UnivariateMultivariate^a^Prognostic factorHR95 % CIp-valueHR95 % CIp-valueRECK (low *vs.* high)0.980.69-1.380.890.950.64--1.310.63Age (continuous)1.011.00--1.01\<0.011.011.00--1.02\<0.01Tumor size (continuous)1.081.06--1.11\<0.011.061.03--1.09\<0.01Histological grading (3 *vs.* 1--2)1.281.16--1.42\<0.011.261.13--1.41\<0.01TNM stage (IV *vs.* I--III)1.131.05--1.22\<0.011.070.99--1.140.08Nodal status (positive *vs.* negative)1.571.37--1.80\<0.011.511.31--1.74\<0.01ER (positive *vs.* negative)0.890.78--1.020.111.040.87--1.230.68PR (positive *vs.* negative)0.850.75--0.970.010.920.79--1.080.3Her2 (positive vs. negative)1.160.96--1.400.130.960.78--1.180.71RECK. REversion-inducing Cysteine-rich protein with Kazal motifs; ER. estrogen receptor; PR. progesterone receptor; Her2. human epidermal growth factor 2^a^Adjusted for all other variables in the table

Discussion {#Sec16}
==========

Since its first report, RECK down-regulation has been linked to tumor progression \[[@CR15], [@CR32]\], and it is considered to be an adequate biomarker for a better clinical course for patients diagnosed with various tumors \[[@CR23]\]. Nevertheless, in this study, we obtained surprising evidence that RECK is not associated with breast cancer patient survival. Initially, our results may seem contradictory to previously published data. However, despite the extensive amount of work addressing the RECK prognostic value for prostate, lung and pancreatic tumors, RECK function and expression profile in breast cancer remain an open question. The few preceding papers on human mammary gland assessed RECK in a reduced number of samples and, in most cases, only at the transcriptional level \[[@CR24]--[@CR27]\]. Moreover, these prior studies are controversial. Some of them corroborate the typical RECK down-modulation as a hallmark of tumor aggressiveness \[[@CR24], [@CR25]\]. Conversely, another report demonstrated not only higher RECK levels in invasive breast cancer cell lines than in the non-invasive one but also a positive correlation between RECK and MMP expression in tumor tissue samples \[[@CR26]\]. In the present work, we measured RECK protein levels both in a panel of human breast cell lines and in a large series (1040) of breast cancer cases, allowing a more significant conclusion.

Additionally, we performed a functional analysis of RECK in a breast cancer cell model. Despite the differential expression profile displayed by RECK in breast cancer samples, we confirmed that it performs similar functions in MDA-MB-231 cells to those previously described in other models. In this cell culture model, RECK continues to act as an invasion inhibitor via MMP down-modulation even in highly invasive human breast cancer cells. Although, at first, it may seem conflicting, we hypothesize that, to restore the balance between proteases and their inhibitors, cells respond by inducing RECK. In fact, an increase in both MMPs and TIMPs has been described as a central and crucial hallmark of tumor progression. Although this may be a mechanism to maintain the protease/inhibitor ratio in balance, this cellular response mechanism cannot block disease progression.

Conclusions {#Sec17}
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In contrast to other tumor types, RECK expression cannot predict breast cancer patient survival, although its inhibitory function in invasion has been confirmed. Despite the well-known cancer heterogeneity, several studies are still aiming to identify a universal tumor marker and draw an overall conclusion. However, in agreement with the emerging concept of personalized cancer therapy, general conclusions should be avoided, and more tumor-specific biomarkers should be sought.

CK

:   Cytokeratin

DFS

:   Disease-free survival

ECM

:   Extracellular matrix

ER

:   Estrogen receptor

KM

:   Kaplan-Meier

MMP

:   Matrix metalloproteinase

OS

:   Overall survival

PR

:   Progesterone receptor

qRT-PCR

:   Quantitative reverse transcription polymerase chain reaction

RECK

:   Reversion-inducing cysteine-rich protein with Kazal motifs

TIMP

:   Tissue inhibitors of matrix metalloproteinases

TMA

:   Tissue microarray

**Competing interests**

The authors declare that they have no competing interests.

**Authors' contributions**

LRG was responsible for most of the experimental work, results interpretation and manuscript preparation. AF performed the statistical analysis and assisted in data interpretation. JDM and LL participated in the study design and data discussion. FAS executed the immunohistochemistry staining on TMA blocks. MCS was involved in the study design, results interpretation and supervision. All authors read and approved the final manuscript.

We thank Professor Mina J. Bissell (Lawrence Berkeley National Laboratory, Life Science Division, Berkeley, CA, USA) for the supervision of all cell culture data presented in this work. We are also grateful to Dr. Bissell for important suggestions and helpful comments. We are deeply grateful for the excellent technical assistance provided by Zizi de Mendonça, Sandra Regina de Souza, Débora Cristina da Costa, Ricardo Krett de Oliveira and Marluce Mantovani. This work was supported by Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP), Conselho Nacional de Pesquisa (CNPq), Financiadora de Estudos e Projetos (FINEP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Banco Nacional de Desenvolvimento Social e Econômico (BNDES -- FUNTEC), Departamento de Ciência e Tecnologia em Saúde - Ministério da Saúde (DECIT-MS) and Ministério da Ciência, Tecnologia e Inovação (MCTI).