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1 , Department of Biological Sciences2 Purdue Cancer Center, Purdue University, West Lafayette, Indiana 47907, USA
(Correspondence should be addressed to I G Camarillo; Email: ignacio{at}bilbo.bio.purdue.edu)
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Abstract |
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 Top Abstract IntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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Introduction |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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In addition to stimulating proliferation in mammary epithelia, leptin has been shown to inhibit apoptosis, promote cell invasion, and modulate the extracellular matrix (ECM) in other cell types (Castellucci et al. 2000, Han et al. 2001, Saxena et al. 2003, 2004, Garofalo & Surmacz 2006, Ogunwobi & Beales 2006). Each of these events are known to play a significant role in tumor growth and progression (Hanahan & Weinberg 2000, Tlsty & Coussens 2006), but the regulation of these processes by leptin in cancer is not well understood. Toward determining the broader influence of leptin on mammary tumor cells, and to gain an insight into the mechanisms involved, this is the first study to use a customized gene microarray approach for identifying leptin-regulated genes in human breast cancer. Leptin up-regulated many genes associated with cell cycle and proliferation, DNA synthesis, and ECM in Michigan Cancer Foundation-7 (MCF-7) cells. Expression of genes associated with decreasing proliferation and apoptosis were down-regulated in response to leptin. These results indicate that leptin induces proliferation, modifies ECM, and suppresses apoptosis to promote breast cancer cell growth and survival. The stimulation of MCF-7 cell proliferation by leptin has been demonstrated in other reports (Dieudonne et al. 2002, Okumura et al. 2002, Somasundar et al. 2003, Catalano et al. 2004); however, its effect on regulating apoptotic/anti-apoptotic and ECM genes in breast cancer has not been previously described. The robust influence of leptin on ECM genes is of particular interest, given the increased recognition that the microenvironment significantly impacts tumor progression (Eckhardt et al. 2005, Tlsty & Coussens 2006). As there is gaining interest in targeting leptin action for novel therapeutic strategies (Garofalo et al. 2006, Gonzalez et al. 2006, Surmacz 2007), the identification of leptin-regulated genes described here begins to provide valuable insights into the mechanistic links between obesity, breast cancer incidence, and morbidity.
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Materials and Methods |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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MCF-7 human breast cancer epithelial cells were obtained from American Type Culture Collection (ATCC Manassas, VA, USA). The cells were maintained routinely in Roswell Park Memorial Institute (RPMI) 1640 media (ATCC) supplemented with 10% fetal calf serum (ATCC), 100 U/ml penicillin G, and 0.1 mg/ml streptomycin sulfate at 37 °C in a humidified, 5% CO2, 95% air atmosphere. Human recombinant leptin was purchased from the National Hormone and Peptide Program (Harbor-UCLA Medical Center, Torrance, CA, USA).
RNA extraction
The MCF-7 cell line was routinely maintained in RPMI 1640 media and supplemented with 10% fetal calf serum for 2 days. The cells were then serum starved for 24 h and treated with media containing 500 or 0 ng/ml leptin (control). This concentration of leptin stimulates MCF-7 cell proliferation and has been used in other in vitro signaling studies (Dieudonne et al. 2002, Hu et al. 2002, Somasundar et al. 2003, Fruhbeck 2006, Perera et al. 2008). RNA was isolated from these cells after 6 or 24 h of treatment using the RNeasy Micro kit (Qiagen). The integrity of RNA was verified by ethidium bromide staining of agarose gels and by an optical density (OD) absorption ratio of OD 260 nm/OD 280 nm >1.9. This RNA was used for microarray analysis and real-time PCR.
DualChip microarrays
Total RNA from control- and leptin-treated cells was used to generate cDNA (cMaster labeling kit; Eppendorf, Hamburg, Germany), which was labeled with biotin, according to the recommendations by Eppendorf. cDNA was hybridized to a DualChip human cancer array, containing oligonucleotide probes for 281 cancer-associated genes. After hybridization, detection was carried out by incubating the chips with Cy3-conjugated IgG anti-biotin antibody (Jackson ImmunoResearch Laboratories, West Grove, PA, USA), and scanning by ScanArray 5000 scanner (Packard BioChip, Technologies, Billerica, MA, USA) using three different photomultiplier tube (PMT) settings (low, middle, and high gains). To ensure high-quality results, the Eppendorf DualChip human cancer array was equipped with several controls that allow verification of cDNA synthesis efficiency, hybridization efficiency, and signal linearity. In addition, the chips also contained oligonucleotide probes for many housekeeping genes (HKGs) that allowed normalization of generated ratios. Array quantification was done by GenePix 6.0 software (Axon Instruments, Union City, CA, USA). Average signals (median) and background signals (mean) from all three datasets (different PMT settings) were transferred into Eppendorf DualChip evaluation software 2.0. A signal was accepted if the average intensity after background subtraction was at least twice higher than local background. Very bright intensities (saturated signals indicating highly expressed genes) were defined as unquantifiable as they underestimated the intensity ratios. These were excluded from quantitative analysis. A two-step normalization procedure was employed using internal standards and HKGs. Each microarray slide consists of two identical microarrays, printed side by side, to ensure reproducibility. Leptin- and control-treated samples were used side by side in each array slide. Each array consists of three replicate probes per gene. At least three different sets of control (0 ng/ml leptin treated) and 500 ng/ml leptin-treated MCF-7 cells were used for gene analyses for each time point. MCF-7 cells belonging to the same passage number were used in both microarray and real-time PCR experiments.
Identification of leptin-regulated genes
DualChip evaluation software (Eppendorf) was used to identify leptin-regulated genes. The variance of the normalized set of HKGs (except those affected by the tested condition) was used to generate a confidence interval to test the significance of the gene expression ratios (de Longgueville et al. 2002). The statistical algorithms of the software are based on a test that was originally developed by Chen et al. 1997. This model assumes that intensities are distributed according to Gaussian distribution. Based on these assumptions, a formula was developed for the coefficient of variation (CV) and confidence intervals. The CV is estimated based on the subset of the ratios of HKGs selected for normalization, which are stable between test and reference samples and have ratios distributed around a value of 1. The significance of the ratios of gene expression is established using the confidence interval computed from this statistical model: ratios outside the 95% (99%) confidence interval were determined to be significantly (highly significantly) different. Acceptable gene expression measurements had signal intensities higher than twice the local background, as well as higher than the mean of the negative hybridization controls plus twice their standard deviation. Different combinations of housekeeping gene sets were evaluated to obtain the optimal CV. From each group of HKGs, the means and standard deviations were calculated and the confidence interval of the Gaussian curve was determined. Subsequently, borderlines/limits were set during the calculation process, where the values are defined as being significant/highly significant relative to these limits. Limits are applied in order to minimize the possibility of obtaining false-positive (e.g., non-regulated genes are called regulated) and false-negative (e.g., regulated genes are called non-regulated) results. The result of these thresholds is that no ratio below 1.49489 is determined to be significant and all ratios above 1.812389 are defined as significant (1/1.49489 and 1/1.812389 for down-regulated genes).
Verification of gene expression data via real-time PCR
Leptin-regulated genes of interest, identified by microarray, were verified using real-time PCR. For real-time PCR, four to five sets of MCF-7 mRNA, which were different from the mRNA employed in microarray, were used. About three to four up- and down-regulated genes identified by microarray analysis was verified for each time point (6 and 24 h) using real-time PCR. One microgram of total RNA was reverse transcribed with MMLV reverse transcriptase using random hexamers (Promega), according to the manufacturer's instructions. Real-time quantitative RT-PCR analysis was performed using 10 ng of reverse-transcribed total RNA with 20 pmol/µl of both sense and antisense primers and SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA) in a final reaction volume of 30 µl. An ABI PRISM 7700 Sequence Detection System Instrument (Applied Biosystems) was used for the amplification. β-Actin was used in each experiment to control for variability in the initial quantities of cDNA. Relative quantification for a gene was expressed as a fold change over the control gene (not treated with leptin). Fold change was calculated using the difference between cycle threshold (Ct) value of the gene and control gene using the formula 2–
CtA–CtB (comparative CT method/Ct). PCR was performed using specific primers (described in Table 1). Cycling conditions consisted of an initial denaturation step of 95 °C for 10 min as a ‘hot start’ followed by 40 cycles of 95 °C for 15 s at the noted annealing temperature for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min. In addition, real-time PCR was used for evaluating the time course (Fig. 2) of the regulation of cell cycle and apoptosis genes identified from microarray analysis.
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To determine the role of transforming growth factor-β (TGFB1) in leptin-induced MCF-7 cell proliferation, the cells were initially seeded onto 12-well plates at 6x104 cells/well in RPMI 1640 containing 10% FBS. After 24 h, subconfluent cells were synchronized by serum starving for 24 h in RPMI media. The cells were then incubated in the media containing either 10% FBS, 10% FBS plus leptin (500 ng/ml), TGFB1 (100 pM), or 10% FBS plus leptin and TGFB1. A parallel experiment was performed in which synchronized MCF-7 cells were incubated in serum-free media with the same individual or combined hormone treatments. In each study, 24 h after treatment, total viable cells were counted with a hemocytometer. For each treatment, the % change in cell number, relative to its initial counts, was calculated. In the initial experiments, several concentrations of leptin were tested using cell counting and fluorescence activated cell sorting (FACS) analysis, and it was determined that 500 ng/ml leptin induces proliferation of MCF-7 cells (Perera et al. 2008) and, therefore, was used in this study. In accordance with this, various other studies examining the influence of leptin in breast cancer cell proliferation have used 500 ng/ml or a similar concentration. As circulating leptin levels range from 10 to 60 ng/ml, the use of 500 ng/ml leptin to elicit a cellular response is likely a reflection of elevated local hormone concentrations that can occur in vivo. In support of this, it has been demonstrated that hormone levels in tissue microenvironments can be five- to tenfold higher, compared with circulating concentrations (Stefanczyk-Krzymowska et al. 1998, Dieudonne et al. 2002, Hu et al. 2002, Okumura et al. 2002, Somasundar et al. 2003).
Apoptosis assay
To examine the effect of leptin on MCF-7 apoptosis, the cells were grown on coverslips in RPMI 1640 media containing 10% fetal calf serum. Cells at 
60% confluence were growth arrested in serum-free media. After 24 h, the cells were incubated in the media, in the presence (500 ng/ml) or absence of leptin. After leptin treatment for 6 and 24 h, the coverslips were removed and the cells were evaluated for apoptosis via TUNEL assay (terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling). TUNEL assay was performed using the TMR red in situ cell death detection system (Roche Diagnostics), according to the manufacturer's specifications. Briefly, the cells were washed with 1xPBS and fixed for 20 min in 4% paraformaldehyde at room temperature. The fixed cells were washed twice with 1xPBS+50 mM glycine for 20 min. The cells were then blocked for 2 h in a blocking buffer (PBS, 0.1% Triton X-100, and 1% BSA) and treated with TUNEL reagent and incubated at 37 °C in a humidified environment for 1 h. On completion of incubation, the cells were washed three times with the blocking buffer and evaluated by fluorescence microscopy. To quantify in situ TUNEL assay fluorescence, total number of cells and the number of apoptotic cells in each field were counted and the percentage of apoptotic cells calculated. A minimum of 1000 cells per slide were counted in random fields. The cells treated with serum for 24 h or the cells exposed to UV for 2 h (to induce DNA strand breaks) were used as negative and positive apoptosis controls respectively.
Statistical analysis
Values from real-time PCR, MCF-7 proliferation, and apoptosis assays were expressed as means±S.E.M. from at least three different experiments (each with n=3–5). Statistical analysis was performed using ANOVA and Student's t-test using Microsoft Excel or Statview 5.0 software (SAS Institute Inc., Cary, NC, USA). A value of P<0.05 was considered statistically significant.
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Results |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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The purpose of this study was to gain an insight into the mechanisms by which leptin influences tumor growth. Toward this, MCF-7 hormone-dependent human cancerous mammary epithelial cells were treated with leptin, and leptin-responsive genes were identified by microarray. To examine the time-dependent regulation of gene expression, MCF-7 cells were treated with or without leptin for 6 and 24 h. A fold change in expression was calculated for each gene at each treatment time point. The significance of the ratios for the genes identified by microarray analysis was established using the confidence interval computed from the statistical model, where ratios above the 95% confidence interval are considered as statistically significant. The cut-off criteria for significance included that the fold change was at least 1.5 or greater compared with control (0 ng/ml leptin sample).
Out of the 281 cancer genes analyzed, 217 were found to be non-significant or produced signal intensities too low to be detected. A total of 64 different genes were significantly differentially expressed, where 35 were up-regulated and 29 down-regulated by leptin. A total of 75 genes were regulated at either 6 or 24 h, with 11 of these genes changed at both time points (Table 2). Thus, for the most part, the early, when compared with late, categories represent different sets of genes. Some of the leptin-responsive genes identified here have been reported by others. As described in Table 3, the majority of these previously identified leptin-regulated genes have been in cell types other than breast cells.
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Time-course analyses were performed to gain an insight into the general pattern of leptin gene stimulation. Time-course patterns of leptin-regulated gene expression were evaluated by assigning microarray-identified genes to one of three categories: 1) early regulated (6 h), 2) early and late regulated (6 and 24 h), or 3) late regulated (24 h). When grouped in this manner, 25 genes were early regulated, 11 genes were regulated at early and late time points, and the majority of the genes (50) were regulated at the late time point. These expression patterns indicate that leptin has a transient effect on most of the genes it regulates. This effect was verified by subsequent real-time PCR experiments. At the early time point, there were 16 stimulated and 9 inhibited genes (total 25 genes). At the late time point, the number of stimulated and inhibited genes was similar.
Functional categories of novel leptin-regulated genes
The major functional categories of leptin-regulated genes include cell cycle, proliferation, apoptosis, cell adhesion/ECM, structural, growth factors/hormones/cytokines, receptors, signal transduction, transcription, protein binding/modification, DNA repair/synthesis, and tumor suppressor genes. The majority of these genes have not been previously reported to be regulated by leptin. Out of the 64 leptin-regulated genes identified, 16 were associated with cell cycle or proliferation, 9 with apoptosis, and 7 with cell adhesion/ECM (Table 2). A major trend observed was that leptin regulated cell cycle/proliferation, apoptosis, and DNA repair/synthesis genes mainly at the early time point but cell adhesion/ECM proteins, growth factors/cytokines, and receptor genes were mainly regulated at the late time point. These data suggest that leptin promotes mammary tumor formation initially by inducing cell cycle progression and proliferation followed by modulating angiogenesis, cell adhesion, and ECM to promote a more aggressive tumor phenotype.
Leptin regulates cell cycle, proliferation, and apoptosis genes
Reports focusing on individual cell-signaling molecules have demonstrated that leptin stimulates breast cancer cell proliferation (Hu et al. 2002, Somasundar et al. 2003, Garofalo & Surmacz 2006). However, the underlying mechanisms of this leptin effect have not been studied using a global genomic approach. Here, we find that a substantially greater proportion of genes involved in cell cycle, compared with other categories, were induced by leptin. Cyclins D1, A2, and G and cyclin-dependent kinase 2 (CDK2) were increased by leptin, suggesting that leptin is important in promoting cell cycle progression by altering the cyclin expression (Table 2). Leptin also regulated the cell cycle by altering the expression of inhibitors of G1-specific CDK–cyclin complexes including cyclin-dependent kinase inhibitor (CDKN1A) (p21), CDKN1B (p27), and CDKN2A (p16). In addition, leptin-induced MT3, a gene associated with poor tumor prognosis, and replication factor C2 (RFC2), important in DNA synthesis. The expression of MT3, CDK2, PDAP1, cyclin A2, growth factor receptor bound protein 2 (GRB2), and CD82 were verified using real-time PCR (Fig. 1A and B). Additionally, leptin induced the expression of anti-apoptotic genes BCL2 and survivin, and reduced the expression of many apoptotic genes such as TRAIP, IGF1R, and TRADD.
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Leptin regulates cell adhesion, ECM, and cytoskeleton genes
The effect of leptin on the expression of cell adhesion, ECM, and cytoskeleton genes has not been evaluated in breast cancer cells. Leptin up-regulated the expression of many ECM genes, including connective tissue growth factor (CTGF), villin 2, and basigin (BSG; Table 2). Overexpression of these genes is associated with breast cancer progression and metastasis, but has not been correlated with obesity or leptin. The effect of leptin on BSG and CTGF expression was confirmed using real-time PCR (Fig. 1B). Many of the cell adhesion, ECM and cytoskeleton genes were regulated at the late time point (24 h), in contrast to the early effect of leptin on cell cycle genes.
Leptin regulates genes encoding DNA repair/synthesis and transcription
Herein, we demonstrate that leptin up-regulates genes involved in DNA repair/synthesis, including thymidylate synthetase, O-6-methylguanine-DNA methyltransferase (MGMT ), polymerase 
(POLA2), and ubiquitin-conjugating enzyme E2 (UBE2A). Leptin also regulated the expression of transcription factors lipopolysaccharide-induced TNF factor (LITAF), purine rich element binding protein A (PURA), and TRAF family member associated NFKB activator (TANK) (Table 2). The expression of LITAF was initially suppressed (6 h) and then substantially induced at 24 h. Real-time PCR data for LITAF expression at 24 h was consistent with microarray analysis (Fig. 1B).
Leptin regulates growth factors, cytokines, and receptors
Leptin regulated the expression of growth factors, cytokines, and receptors mainly at the late time point in MCF-7 cells (Table 2). Interestingly, leptin down-regulated TGFB2, TGFB3, and IGFBP2, factors that are known to suppress mammary epithelial proliferation. Leptin also induced hyaluronan-mediated motility receptor (HMMR) and suppressed insulin-like growth factor-I (IGF1) receptor (IGF1R) and erythropoietin receptor, genes involved in tumor cell migration and metastasis. To determine the significance of leptin's suppression of TGFB1, the role of TGFB1 in leptin-stimulated MCF-7 cell proliferation was tested. As described in the Materials and Methods section, synchronized MCF-7 cells were incubated for 24 h in media containing either leptin (500 ng/ml), TGFB1, leptin and TGFB1 combined, or no hormone supplements. These experiments were done in two ways, where synchronized MCF-7 cells were incubated in serum-free media or 10% FBS media with each media containing hormone treatments described. As shown in Fig. 3, leptin induced a significant increase (40–60%), while TGFB1 caused a reduction (30%) in cell numbers under the serum-free and 10% FBS incubation conditions. When leptin and TGFB1were given in combination, TGFB1 completely abrogated leptin-induced proliferation in both serum-free and FBS conditions. Identification of these leptin-regulated growth factor genes begins to suggest potential mechanisms by which leptin promotes tumor aggressiveness.
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In these microarray studies, several apoptosis-related genes were regulated by leptin. To support the idea that leptin promotes tumor cell viability by suppressing apoptosis, the anti-apoptotic effect of leptin was quantified by TUNEL assay (Roche Diagnostics). Briefly, MCF-7 cells were incubated for 6 and 24 h in media without leptin or containing 500 ng/ml leptin. Via TUNEL, the percentage of labeled apoptotic nuclei was calculated. Treatment with 500 ng/ml leptin for 24 h lead to a significant sixfold reduction in apoptotic cells (2±0.41%), compared with 0 ng/ml leptin treatment (12±1.5%; Fig. 4). Subsequently, a notable correlation is observed between the time courses of the TUNEL assay and the gene expression levels. In real-time PCR verification of leptin-regulated genes (Fig. 2), we measured a sevenfold increase in survivin at 3 h and a tenfold increase in BCL2 at 6 h. The timing and sizeable induction of these anti-apoptotic genes by leptin place them in the early regulated category. The timing of early increased levels of survivin and BCL2 transcripts correlates well with the TUNEL assay (Fig. 4), where leptin significantly reduces apoptosis at 24 h, but not at 6 h.
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Discussion |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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In the studies described here, a novel microarray system was exploited to determine the networks of genes regulated by leptin in human breast cancer cells. We show that leptin influences numerous cancer-associated genes, the majority of which have not been identified as leptin regulated, and some of which have been previously reported. In general, we found that leptin regulates the greatest number of genes in the cell cycle/proliferation, apoptosis, and cell adhesion/ECM categories. We also demonstrate that leptin has a transient effect on most of the genes it regulates, a result verified via PCR time-course experiments. Potential mechanisms for the genes regulated at early-only time points may be a rapid increase in protein production leading to quick negative feedback on transcription. For late-only regulated genes, this may result from the downstream effects of leptin. In support of this, our recent report using proteomics approaches reveals that leptin regulates the levels of other cytokine growth factors in MCF-7 cells (Perera et al. 2008). At present, the mechanisms of leptin's transient regulatory effects are not known; however, the influence of leptin on proliferation and cell survival is becoming evident. Thus, further studies defining leptin's transcription regulatory processes should yield valuable clues into its role in tumorigenesis.
Leptin regulates expression of cell cycle and apoptosis genes
Prior in vitro studies show that leptin induces proliferation of cancerous mammary epithelial cells, including MCF-7 cells, and have focused on individual signaling molecules as mediators of leptin action (Dieudonne et al. 2002, Hu et al. 2002, Okumura et al. 2002, Somasundar et al. 2003). The present microarray work indicates that leptin impacts proliferation by affecting many genes, in particular through up-regulating cell cycle progression and suppressing cell cycle inhibitor genes. Cell cycle progression genes up-regulated by leptin included various cyclins. Expression of CDKNI, which are mediators of G1-specific CDK–cyclin complexes, was also regulated by leptin. CDKN1A (p21), which has anti-apoptotic properties and contributes to cell cycle progression, was induced by leptin (Weiss et al. 2003). Leptin substantially decreased the expression of CDKN1B (p27), a molecule well recognized as a tumor suppressor and whose degradation plays a pivotal role in cell cycle progression (Sherr & Roberts 1999). Leptin also up-regulated CDKN2A (p16) expression, a molecule that accumulates as cells age and whose overexpression is associated with more aggressive breast tumors (Milde-Langosch et al. 2001). Interestingly, leptin stimulated a 40-fold induction in metallothionein 3 (MT3) gene expression. Metallothioneins are low-molecular-weight metal-binding proteins associated with proliferation, are overexpressed during breast cancer, and serve as potential biomarkers for poor cancer prognosis (Sens et al. 2001, Bay et al. 2006).
Included as one of the hallmarks of cancer (Hanahan & Weinberg 2000), uncontrolled proliferation is achieved from an imbalance between cell cycle progression and apoptosis. This array analysis provides evidence on the involvement of leptin in promoting tumor cell viability by regulating genes that suppress apoptosis. More specifically, leptin induced the expression of anti-apoptotic gene BCL2 (Martinez-Arribas et al. 2007) and reduced the expression of other apoptotic genes (TNF receptor associated factor (TRAF) interacting protein, IGF1R, and TNFRSF1A associated via death domain (TRADD)) involved in the tumor necrosis factor (TNF)-induced apoptotic pathway. Binding of TNF to its receptor leads to the recruitment of an intracellular death-inducing signaling complex (DISC), consisting of at least six different members that include TRADD, TRAF1, and TRAF2 to cause apoptosis (Wajant et al. 2003). Additionally, leptin-stimulated expression of survivin, an inhibitor of apoptosis, is linked with radiation- and drug-resistant cancers (Xia et al. 2006, Zhang et al. 2006). These data suggest that, along with effecting expression of cell cycle components, leptin inhibits apoptosis and promotes cell survival through regulating several apoptosis-associated genes. This inhibitory effect of leptin in breast cancer cell apoptosis has not been previously reported and was verified via TUNEL assay. The early and sizeable induction of the anti-apoptotic genes, survivin and BCL2, which we verified by PCR, correlates very well with our TUNEL data where we demonstrate leptin significantly reduces apoptosis at 24 h but not at 6 h. This delay in leptin's anti-apoptotic effect likely stems from the time necessary for translational processes to produce sufficient protein levels for altering signaling dynamics. Taken together, the timing of increased survivin and BCL2 levels with the effect of leptin on MCF-7 apoptosis suggests that these molecules are important mediators of leptin-induced cell survival.
Leptin regulates genes encoding DNA repair/synthesis and transcription
Although prior reports demonstrate leptin stimulates DNA synthesis and transcription in MCF-7 cells (Dieudonne et al. 2002, Okumura et al. 2002), the mechanisms involved in this process are not well understood. It has been shown that leptin activates the cell-signaling molecules STAT3, MAPK3/1, protein kinase B (Akt), glycogen synthase kinase 3 (GSK-3), PKC, and the transcription factor JUN during mammary epithelial cell proliferation (Dieudonne et al. 2002, Hu et al. 2002, Okumura et al. 2002, Catalano et al. 2003, 2004, Fruhbeck 2006). In accordance with these studies, some leptin-regulated signaling genes previously identified were also found via microarray. In addition, we reveal several other novel leptin-regulated genes related to DNA repair/synthesis and cell-signaling molecules, including LITAF, PURA, and TANK. Identification of these genes begins to define additional signaling pathways for leptin-induced breast cancer cell proliferation.
Leptin regulates ECM and cytoskeleton genes
Overexpression of ECM is associated with cancer cell-invasive potential and metastatic tumors (Eckhardt et al. 2005, Tlsty & Coussens 2006). Leptin increases the expression of the ECM gene collagen in liver, mesangial, and trophoblast cells (Castellucci et al. 2000, Han et al. 2001, Saxena et al. 2003). Here, we observe that leptin up-regulates the expression of the ECM genes, CTGF, villin 2, and basigin in MCF-7 cells. CTGF is linked to breast cancer mortality and can contribute to tumor aggressiveness by serving as an angiogenic factor, stimulating ECM deposition and promoting breast cancer metastasis to bone (Kondo et al. 2002, Minn et al. 2005). Villin 2, also known as ezrin, is involved in cell adhesion, motility, and survival (Elliott et al. 2005). Basigin, an inducer of ECM metalloproteinase, promotes tumor growth, invasion, and breast cancer metastasis (Yang et al. 2006). Hence, the up-regulation of CTGF, villin 2, and basigin by leptin may represent mechanisms central to breast cancer progression and morbidity in obese patients. Favoring the idea that leptin leads to elevated ECM in breast cancer, our group has recently described that mammary tumors in obese rats have higher levels of collagen 1, when compared with tumors of lean rats. Collagen 1 content, quantified by CARS advanced imaging, was correlated with tumor aggressiveness, the predominant tumor phenotype in obese rats (Le et al. 2007). We have also shown that in MCF-7 cells leptin induces a substantial increase in collagens at the protein and mRNA levels (Perera et al. 2008 and unpublished data). A related study demonstrates that in mice bearing MCF-7 xenograft tumors, administration of leptin promotes tumor growth and stimulated E-cadherin, an adhesion molecule implicated in cell proliferation and survival (Mauro et al. 2007). Collectively, these in vitro and in vivo data suggest that leptin influences various tumor cell behaviors including adhesion, migration, and metastases through modulating the microenvironment and more importantly begins to uncover the mediators of these actions. Interestingly, our time course of microarray gene expression profile categories suggests that leptin promotes mammary tumor formation by initially inducing cell cycle/proliferation genes followed by regulating angiogenesis, cell adhesion, and the ECM.
Leptin regulates growth factor, cytokine, and receptor genes
Even though leptin itself is a cytokine, little is known regarding its influence on the production of other autocrine/paracrine factors. These studies show that in tumor cells leptin regulates the genes for other growth factors and receptors at the late time points evaluated. Leptin reduced the expression of TGFB1 and IGFBP2 genes, peptides known for their growth-inhibitory effects in mammary epithelial cells (Nam et al. 1997, Kaaks 2001, Pereira et al. 2004, Kibbey et al. 2006, Buck & Knabbe 2006, Frommer et al. 2006). Initial studies assigned dual and opposing roles for TGFB1 in tumor cells, both as a growth suppressor and as a component of the cell invasion signal cascade. Toward clarifying this ambiguity, recent work conveys that the transition of TGFB1 from tumor inhibitor to enhancer is estrogen receptor (ER) dependent, with TGFB1 impeding growth in ER-positive tumor cells, such as MCF-7 (Buck & Knabbe 2006). Herein, we determine the functional significance of leptin's effect on TGFB1 suppression by demonstrating that exogenous TGFB1 completely abrogates leptin-induced MCF-7 cell proliferation. We also show that leptin significantly suppresses MCF-7 cell apoptosis. Based on previous studies showing that TGFB1 promotes apoptosis in various mammary tumor cell lines (Tobin et al. 2001, Li et al. 2005, Souchelnytskyi 2005), it is highly likely that TGFB1 regulation is an important mechanism in leptin's anti-apoptotic effects as well. Overall, these are the first studies to describe a mechanistic relationship between leptin, TGFB1, and breast cancer. The ability of leptin to reduce IGFBP2 expression may contribute to breast tumorigenesis by two mechanisms. First, extracellular IGFBP2 competes with cell surface IGF receptors for binding to IGF1, a potent mitogen in tumor cells (Kaaks 2001, Kibbey et al. 2006). Thus, lowered levels of antagonistic IGFBP2, which have been observed in obese subjects (Nam et al. 1997), may lead to greater availability of IGF1 to its receptor resulting in tumor cell proliferation. Second, IGFBP2 suppression by leptin can support tumor progression, given that IGFBP2 induces apoptosis and inhibits migration of breast cancer cells in a ligand-independent manner (Pereira et al. 2004, Frommer et al. 2006).
Here, we also demonstrate that leptin regulates expression of receptors and induces the HMMR gene and reduced erythropoietin receptor (EPOR). The presence of EPO receptors in breast cancer cells has only recently been established (Lester et al. 2005). Initial reports indicate that EPOR mediates an anti-apoptotic effect of EPO (Hardee et al. 2006); therefore, the reduction in EPOR expression by leptin was unexpected. Increased HMMR expression by leptin can contribute greatly to migratory potential, as the binding of hyaluronan (HA) to HMMR has been shown to be important for metastasis of breast cancer cells to lymph nodes (Bose & Masellis 2005). These data suggest that leptin promotes breast cancer progression through exploiting pathways of other growth factors and receptors.
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Conclusion |
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Declaration of interest |
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TopAbstractIntroductionMaterials and MethodsResultsDiscussionConclusionDeclaration of interestReferences |
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References |
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Received in final form 28 July 2008
Accepted 20 August 2008
Made available online as an Accepted Preprint 20 August 2008
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