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. 2019 Oct 4;33(12):13710–13721. doi: 10.1096/fj.201901388R

Pitavastatin slows tumor progression and alters urine-derived volatile organic compounds through the mevalonate pathway

Luqi Wang *,†, Yue Wang *,†, Andy Chen †, Meghana Teli †, Rika Kondo †,‡, Aydin Jalali †, Yao Fan *,†, Shengzhi Liu †, Xinyu Zhao †,§, Amanda Siegel ¶,‖, Kazumasa Minami ‡, Mangilal Agarwal ¶,#, Bai-Yan Li *,1, Hiroki Yokota *,†,¶,#,**,††,2
PMCID: PMC6894072  PMID: 31585508

Abstract

Bone is a frequent site of metastasis from breast cancer, and a desirable drug could suppress tumor growth as well as metastasis-linked bone loss. Currently, no drug is able to cure breast cancer–associated bone metastasis. In this study, we focused on statins that are known to inhibit cholesterol production and act as antitumor agents. After an initial potency screening of 7 U.S. Food and Drug Administration–approved statins, we examined pitavastatin as a drug candidate for inhibiting tumor and tumor-induced bone loss. In vitro analysis revealed that pitavastatin acted as an inhibitor of tumor progression by altering stress to the endoplasmic reticulum, down-regulating peroxisome proliferator–activated receptor γ, and reducing Snail and matrix metalloproteinase 9. In bone homeostasis, it blocked osteoclast development by suppressing transcription factors c-Fos and JunB, but stimulated osteoblast mineralization by regulating bone morphogenetic protein 2 and p53. In a mouse model, pitavastatin presented a dual role in tumor inhibition in the mammary fat pad, as well as in bone protection in the osteolytic tibia. In mass spectrometry–based analysis, volatile organic compounds (VOCs) that were linked to lipid metabolism and cholesterol synthesis were elevated in mice from the tumor-grown placebo group. Notably, pitavastatin-treated mice reduced specific VOCs that are linked to lipid metabolites in the mevalonate pathway. Collectively, the results lay a foundation for further investigation of pitavastatin’s therapeutic efficacy in tumor-induced bone loss, as well as VOC-based diagnosis of tumor progression and treatment efficacy.—Wang, L., Wang, Y., Chen, A., Teli, M., Kondo, R., Jalali, A., Fan, Y., Liu, S., Zhao, X., Siegel, A., Minami, K., Agarwal, M., Li, B.-Y., Yokota, H. Pitavastatin slows tumor progression and alters urine-derived volatile organic compounds through the mevalonate pathway.

Keywords: breast cancer, VOC, osteoclasts, bone metastasis

Statins are potent inhibitors of 3-hydroxy-3-methyl-glutaryl-CoA (HMG-CoA) reductase, a rate-limiting enzyme for synthesizing cholesterol (1). They are clinically used for lowering cholesterol and reducing cardiovascular diseases (2, 3) because blocking HMG-CoA reductase prevents production of mevalonate, an intermediate product required for synthesis of cholesterol (4). Blocking mevalonate production has additionally been shown, in both preclinical and clinical studies, to lead to antitumor activity (5–7). A potential mechanism of statin’s antitumor action is suggested to relate to the stimulatory role of mevalonate in tumor proliferation and protein prenylation (8). The mevalonate pathway is essential in cholesterol synthesis and lipid metabolism and also affects tumor progression (9), and prenylation adds hydrophobic molecules to proteins that are vital for tumor growth (10). Thus, the use of statins to suppress mevalonate may also inhibit tumor growth.

Bone is a frequent site of metastasis from breast cancer (11), and it is desirable to develop a drug that may suppress tumor growth and reduce metastasis-linked bone loss. However, no therapeutic drugs are currently available to cure breast cancer–associated bone metastasis. The mevalonate pathway is a target of bisphosphonates that are used to prevent bone loss for patients with osteoporosis (12–14). Bisphosphonates inhibit conversion of mevalonate to farnesyl pyrophosphate, which plays a critical role in protein prenylation (15). Because the mevalonate pathway is a common target of both statins and bisphosphonates, an intriguing question is whether any of the known statins alone may act not only as an antitumor agent but also as a bone-protective agent (16, 17). In this study, we tested Food and Drug Administration–approved statin molecules not only for potency in tumor reduction but also for their effect on bone protection by regulating stress to the endoplasmic reticulum, lipid metabolism, osteoclast maturation, osteoblast differentiation, and physiologic changes in urine.

We previously investigated agents that modulate dopaminergic signaling and checkpoint kinases to determine whether they displayed both antitumor and bone-protective action (18–21). Although these studies found agents that may inhibit tumor growth and induce bone-protective action, those agents may also produce substantial side effects in neurotransmitter or cell cycling regulation. By contrast, it is reported that statins are relatively well tolerated with few adverse effects (22). In the present study, we conducted a prescreening of 7 existing statins and focused on the role of pitavastatin (Livalo), a moderately lipophilic statin with high bioavailability (23). The primary hypothesis we tested herein is that pitavastatin is capable of effectively reducing tumor growth in the mammary pad and protecting bone from tumor-induced osteolysis.

To investigate antitumor and bone-protective actions, we employed a pair of mouse models for breast cancer and bone metastasis. In those models, 4T1.2 mammary tumor cells were injected into the mammary fat pad (mammary tumor) and the proximal tibia (tumor-induced osteolysis). The treatment effects were evaluated by determining tumor size, conducting micro–computed tomography (μCT) imaging for trabecular bone, mechanical testing for bone stiffness, and histology. We evaluated the expression of Snail and matrix metalloproteinase 9 (MMP9), which are linked to epithelial-mesenchymal transition and cellular migration (24–26). We also evaluated the expression of peroxisome proliferator–activated receptors [peroxisome proliferator–activated receptor γ (PPAR-γ)], which are involved in the metabolism of glucose triglyceride and lipoproteins (27), as well as the proliferation of cancer cells (28). Furthermore, we evaluated pitavastatin’s role in the stress to the endoplasmic reticulum, focusing on the phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) with protein kinase R–like endoplasmic reticulum kinase (Perk), a kinase specific to eIF2α. In our previous studies on dopamine signaling and checkpoint kinases, apoptosis of breast cancer cells was induced with the elevation of phosphorylated (p-)eIF2α (18–21). To examine pitavastatin’s effect on bone cells, we determined expression of nuclear factor of activated T cells cytoplasmic 1 (NFATc1; transcription factor for osteoclastogenesis), AP-1 transcription factors (c-Fos and JunB), bone morphogenetic protein 2 (BMP-2), and p53 and evaluated the development of osteoclasts and osteoblasts.

Besides evaluation of pitavastatin’s actions for tumor inhibition and bone protection, we conducted mass spectrometry–based analysis of urine-derived volatile organic compounds (VOCs). VOCs are small chemicals (e.g., alcohols, aldehydes, and ketones) found in body fluids and have been considered indicators of a variety of health conditions, including cancers (29–31). Among the common sources of VOCs, urine is a noninvasive option relatively easy to collect and store. In this study, we used urine-derived VOCs as a potential novel diagnostic tool for evaluating the efficacy of pitavastatin as an antitumor agent. We hypothesized that compared with the placebo animals with no treatment, administration of pitavastatin alters levels of VOCs that are linked to the mevalonate pathway.

MATERIALS AND METHODS

Cell culture

4T1.2 and 4T1 mouse mammary tumor cells (obtained from Dr. R. Anderson, Peter MacCallum Cancer Institute, Melbourne, VIC, Australia) (32) and MDA-MB-231 human breast cancer cells [American Type Culture Collection (ATCC), Manassas, VA, USA] (33) were cultured in DMEM. RAW264.7 preosteoclast cells (ATCC) (34) and MC3T3 osteoblast-like cells (MilliporeSigma, Burlington, MA, USA) (35) were grown in α-minimum essential medium (MEM). Complete culture medium contained 10% fetal bovine serum and antibiotics (50 U/ml penicillin and 50 μg/ml streptomycin; Thermo Fisher Scientific, Waltham, MA, USA). Cells were maintained at 37°C and 5% CO2 in a humidified incubator.

Cellular viability was examined using a 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay (Thermo Fisher Scientific) with the procedure previously described (20). An ethynyl deoxyuridine (EdU) cell proliferation assay (Thermo Fisher Scientific) was also employed to evaluate cellular DNA proliferation (20). To examine pitavastatin’s inhibitory effect on MMP9, its activity in culture medium was determined using aSensoLyte 520 MMP9 Assay Kit (AnaSpec, Fremont, CA, USA). To examine pitavastatin’s osteogenic capability, MC3T3 osteoblast cells were grown in the osteogenic medium (70 μg/ml ascorbic acid, 5 mM β-glycerophosphate) for 5 wk, and the effect of pitavastatin on mineralization was evaluated using alizarin red staining (MilliporeSigma) (21).

Three-dimensional spheroid assay

Cells (5000 cells/well for 4T1.2 cells, and 3000 cells/well for RAW264.7 cells) were cultured in Ultra-Low Attachment 96-Well Plates (S-Bio, Hudson, NH, USA) to form 3-dimensional spheroids. The IncuCyte Zoom real-time imaging microscope was employed to capture live-cell images every hour using fluorescent staining (Incucyte CytoLight Green and Red; Essen Bioscience, Ann Arbor, MI, USA). The cell viability was measured with CellTiter-Glo 3D Cell Viability Assay (Promega, Madison, WI, USA).

Two-dimensional motility assay

To evaluate 2-dimensional motility, a wound-healing scratch assay was conducted as previously described (18). In brief, cells were plated in 12-well plates, and scratching was performed using a plastic tip. The areas newly occupied with cells in the scratched zone were determined using optical images, which were scanned with Adobe Photoshop (CS2; Adobe Systems, San Jose, CA, USA) and quantified with ImageJ (National Institutes of Health, Bethesda, MD, USA).

Osteoclast differentiation and pit formation assay

The osteoclast differentiation assay was conducted using 30 ng/ml of receptor activator of NF-κB ligand (RANKL) in the presence and absence of pitavastatin. Adherent cells were fixed and stained with a tartrate resistant acid phosphate (TRAP)-Staining Kit (MilliporeSigma). TRAP-positive multinucleated cells (>3 nuclei) were identified as mature osteoclasts (19). A pit formation assay was conducted using 24-well culture plates coated with hydroxyapatite (36). After 6 d, cells were removed with 5% sodium hypochlorite, and images of the wells were captured with a phase-contrast inverted microscope. Using ImageJ, the areas of the wells no longer coated with hydroxyapatite were measured.

RNA interference and Western blot analysis

RNA interference was conducted using small interfering RNA (siRNA) specific to PPAR-γ (151218; Thermo Fisher Scientific), with a negative siRNA (Silencer Select #1; Thermo Fisher Scientific) as a nonspecific control. Cells were transiently transfected with siRNA using Lipofectamine RNAiMax (Thermo Fisher Scientific) in Opti-MEM I medium, and the medium was replaced by regular culture medium after 24 h. For Western blotting, cells were lysed in a radio-immunoprecipitation assay buffer with protease inhibitors (Santa Cruz Biotechnology, Dallas, TX, USA) and phosphatase inhibitors (Calbiochem, Billerica, MA, USA). We used antibodies against c-Fos, MMP9, NFATc1, Perk (Santa Cruz Biotechnology), caspase-3, cleaved caspase-3, eIF2α, p-eIF2α, JunB, LC3A/B II, microtubule-associated proteins 1A/1B light chain 3B II (LC3A/B II), p-Perk, PPAR-γ, Snail (Cell Signaling Technology, Danvers, MA, USA), Rac1, ras-related C3 butulinum toxin substrate 1 (Rac1) (MilliporeSigma), and β-actin (MilliporeSigma).

Animal model

The experimental procedures were approved by the Indiana University Animal Care and Use Committee and were in compliance with the Guiding Principles in the Care and Use of Animals endorsed by the American Physiologic Society (Rockville, MD, USA). In the mouse model of mammary tumor (18), 23 BALB/c female mice (∼6 wk; Harlan Laboratories, Indianapolis, IN, USA) received subcutaneous injections of 4T1.2 cells (3 × 105 cells in 50 μl PBS) to the mammary fat pad. Pitavastatin (2 mg/kg body weight) was administered s.c. into the area of the cell injection every day, whereas the placebo animals received a vehicle control. The animals were euthanized on d 22. In the mouse model of osteolysis (19), 24 BALB/c female mice received intratibial injection of 4T1.2 cells (2.5 × 105 cells in 20 μl PBS) to the right tibia. Pitavastatin was administered daily via intraperitoneal injection at 4 or 8 mg/kg body weight, and the animals were euthanized on d 21. A whole-body X-ray of each mouse was taken, and the tibiae were harvested for mechanical testing and μCT imaging.

Mechanical testing and μCT imaging

Mechanical testing was conducted using an ElectroForce loading device (TA Instruments, New Castle, DE, USA). A sinusoidal force (1 N peak-to-peak force) was applied at 1 Hz, and the slope of the force-displacement relationship was determined as tibial stiffness. μCT was performed using Skyscan 1172 (Bruker-MicroCT; Bruker, Billerica, MA, USA) (20). The harvested bone samples were wrapped in parafilm to maintain hydration and placed in a plastic tube and oriented vertically. Scans were performed at pixel size 8.99 μm. Using manufacturer-provided software, the images were reconstructed (nRecon v.1.6.9.18; Bruker) and analyzed (CTan v.1.13). We determined bone volume normalized by total volume and cortical bone mineral density of the proximal tibia segment (1 mm thick) from the growth plate to the distal section.

Analysis of VOCs

In total, 39 urine samples (50 μl or more from each mouse) were collected from 3 groups of mice (20 samples from normal control, 8 samples from placebo, and 11 samples from 8 mg/kg pitavastatin-treated). The normal control samples were collected on d 1, whereas the placebo and pitavastatin-treated samples were collected on d 17 and 20, respectively. Next, 50 μl of urine was divided into aliquots and incubated with 8 M guanidine hydrochloride in a 1:1 ratio at room temperature for 1 h. VOCs in mouse urine headspace were collected by solid-phase microextraction and injected into an Agilent 7890A gas chromatograph coupled to a 7200 Accurate Mass Quadruple Time of Flight Mass Spectrometer (Agilent Technologies, Santa Clara, CA, USA) using a method previously reported (37–39). Mass spectra for each sample were deconvoluted and spectrally aligned using MassHunter Quantitative Profinder software (B.08.00; Agilent Technologies). We identified 748 VOCs in total that were present in 50% or more samples in at least 1 of the 3 groups. Relative abundances were calculated and log2 transformed, and hierarchical clustering analysis (40) and principal component analysis (41, 42) were conducted in MatLab (R2017a; MathWorks, Natick, MA, USA). Compounds were identified using Agilent Unknown Analyzer and NIST14 (http://nistmassspeclibrary.com/).

Compliance with ethical standards

All applicable international, national, and institutional guidelines for the care and use of animals were followed.

Statistical analysis

The data were expressed as means ± sd. One-way ANOVA was employed to examine statistical significance among groups, and Fisher’s protected least significant difference was conducted as a post hoc test for the pairwise comparisons. Statistical significance was assumed when P < 0.05.

RESULTS

Variations among 7 statins for inhibition of 4T1.2 tumor and RAW264.7 preosteoclasts

We first evaluated the efficacy of 7 statins (pitavastatin, simvastatin, fluvastatin, iovastatin, atorvastatin, pravastatin, and rosuvastatin) as potential inhibitors of cellular viability of 4T1.2 tumor cells and RAW264.7 preosteoclasts. Of note, 4T1.2 cells are variants of 4T1 cells derived from metastasized bone. In the MTT-based assay, 3 statin agents (pitavastatin, simvastatin, and fluvastatin) significantly reduced the number of 4T1.2 cells and RAW264.7 preosteoclasts at 5 and 10 μM (Supplemental Fig. S1A, B). At 10 μM, pitavastatin was most effective in inhibiting 4T1.2 cells, whereas simvastatin was most effective in reducing RAW264.7 cells. Between pitavastatin and simvastatin, MC3T3 osteoblast cells were less inhibited by pitavastatin (Supplemental Fig. S1C). Four other statins elevated the number of RAW264.7 cells at 5 and/or 10 μM. Our primary objective was to identify a drug candidate that suppresses growth and differentiation of tumor cells and preosteoclasts that also promotes differentiation of osteoblasts. Pitavastatin was identified as the top candidate and was hereafter focused on in comparison with simvastatin and pravastatin.

Pitavastatin’s suppression of proliferation and migration of tumor cells

We first evaluated pitavastatin’s effects on several tumor cells. In response to 1–10 μM pitavastatin, we observed a reduction in EdU-based cellular proliferation and MTT-based cellular viability (Fig. 1A, B). Cellular images indicated that it induced apoptotic cell death (Fig. 1C) and elevated cleaved caspase-3 (Fig. 1D). Of note, an autophagy marker, LC3A/B II, was not significantly altered. We further evaluated its efficacy in inhibiting the proliferation and migration of 4T1.2 cells as well as MDA-MB-231 breast cancer cells. In a 3-dimensional spheroid assay, pitavastatin decreased the cross-sectional area of spheroids and 3-dimensional-based cell viability (Fig. 1E, F). Pitavastatin also reduced cellular viability and proliferation of MDA-MB-231 cells in MTT and EdU assays and elevated cleaved caspase-3 (Fig. 1G–I). In a wound-healing assay, migration of 4T1.2 cells was reduced by 2, 5, and 10 μM pitavastatin (Fig. 2A, B).

Figure 1.

Figure 1

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Responses of 4T1.2 mammary tumor cells and MDA-MB-231 breast cancer cells to pitavastatin. A) MTT-based relative viability of 4T1.2 cells in response to 1, 2, 5, and 10 μM pitavastatin (n = 3). B) EdU-based cellular proliferation of 4T1.2 cells in response to 1, 2, 5, and 10 μM pitavastatin (d 2, n = 3). C) Representative images of 4T1.2 cells in response to 10 μM pitavastatin for 1 d. The arrows indicate dying cells. D) Dose-dependent elevation of cleaved caspase-3 in 4T1.2 cells in response to 1, 2, 5, and 10 μM pitavastatin for 1 d. E) Shrinkage of 4T1.2 tumor spheroids by 10 and 20 μM pitavastatin. F) Reduction in 3-dimensional (3D) cell viability of 4T1.2 tumor spheroids based on an EdU cell proliferation assay. G) Elevation of cleaved caspase-3 in MDA-MB-231 cells in response to 2, 5, and 10 μM pitavastatin for 1 d. H) MTT-based relative viability of MDA-MB-231 cells in response to 2, 5, and 10 μM pitavastatin (d 2, n = 3). I) EdU-based cellular proliferation of MDA-MB-231 cells in response to 1, 2, and 5 μM pitavastatin (d 2, n = 3). Casp, caspase; CN, control; Pita, pitavastatin. *P < 0.05, **P < 0.01.

Figure 2.

Figure 2

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Effects of pitavastatin in migration and proliferation of 4T1.2 and 4T1 cells. A, B) Reduction in cellular migration of 4T1.2 cells by pitavastatin based on a wound-healing assay (n = 3). C, D) Reduction in 4T1.2 tumor size by daily injection of 2 mg/kg pitavastatin in a mouse model of mammary tumor (n = 8). E) Regulation of PPAR-γ in 4T1.2 and 4T1 cells. F) Regulation of Snail and MMP9 in 4T1.2 and 4T1 cells. G, H) Regulation of p-Perk, p-eIF2α, and Rac1 in response to 2, 5, and 10 μM pitavastatin at 8 and 24 h. CN, control; Pita, pitavastatin. *P < 0.05, **P < 0.01.

Reduction of mammary tumors in vivo by administration of pitavastatin

To evaluate pitavastatin’s effect on inhibiting tumor progression, we conducted in vivo experiments by inoculating 4T1.2 mammary tumor cells in the mammary fat pad. In this mouse model of mammary tumor, tumor size in the mammary fat pad was significantly reduced by daily injection of 2 mg/kg pitavastatin (Fig. 2C). The mean tumor weight was determined as 0.12 g (placebo, n = 8) and 0.04 g (pitavastatin, n = 7; P < 0.05) (Fig. 2D). To determine a potential mechanism of pitavastatin’s action, we examined expression of genes linked to lipid metabolism (PPAR-γ) and cellular migration (Snail and MMP9). Pitavastatin down-regulated PPAR-γ in 4T1.2 and 4T1 cells, indicating that it has an inhibitory role in lipid metabolism (Fig. 2E). Expression of Snail and MMP9 was also reduced, suggesting that pitavastatin can suppress the migratory capability of 4T1.2 cells (Fig. 2F). Pitavastatin’s up-regulation of p-Perk and p-eIF2α (Fig. 2G) is consistent with the antitumor effect by the elevated phosphorylation of eIF2α (21) and down-regulation of Rac1 (Fig. 2H).

Pitavastatin-driven development of osteoblasts

Because our primary aim was to determine pitavastatin’s effects not only on inhibition of tumor growth but also on bone loss, we next evaluated MTT-based relative viability of MC3T3 osteoblasts. The result showed that pitavastatin reduced MC3T3 viability (Supplemental Fig. S2A). At lower concentrations (1 and 2 μM), pitavastatin promoted differentiation of osteoblasts and mineral deposition in the osteogenic medium (Supplemental Fig. S2B, C). Pitavastatin at 1 μM up-regulated BMP-2, a stimulator of bone formation, and down-regulated p53, an inhibitor of bone formation (Supplemental Fig. S2D).

Effect of mevalonate on pitavastatin’s action to tumor cells

Statins are known to block HMG-CoA reductase, reducing production of mevalonate, an intermediate product in cholesterol synthesis. Pitavastatin-driven reduction in 4T1.2 cellular viability was significantly suppressed by 100 μM mevalonate (Fig. 3A) and down-regulation of PPAR-γ (Fig. 3B). Consistent with the decrease in MMP9 expression, 10 μM pitavastatin also reduced the activity of MMP9 (Fig. 3C). This reduction was mediated by PPAR-γ because partial silencing of PPAR-γ suppressed pitavastatin-driven reduction (Fig. 3D, E). In a 3-dimensional spheroid assay, both pitavastatin and simvastatin significantly reduced cross-sectional area of 4T1.2 tumor spheroids (Fig. 3F). Pamidronate (Aredia) is a bisphosphonate that inhibits cholesterol synthesis downstream of mevalonate. It reduced the level of PPAR-γ, but it did not alter the levels of Snail, MMP9, or p-eIF2α (Fig. 3G, H). Unlike pitavastatin, pamidronate up-regulated the level of Rac1, which may contribute to cellular migration (Fig. 3I).

Figure 3.

Figure 3

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Mevalonate-mediated effects of pitavastatin. A) Suppression of pitavastatin-driven reduction in cellular viability by mevalonate (n = 3). B) Suppression of pitavastatin’s effect on PPAR-γ by mevalonate. C) Reduction of MMP9 activity by 10 μM pitavastatin (n = 3). D, E) Suppression of pitavastatin-driven down-regulation of expression and activity of MMP9 by partial silencing of PPAR-γ by RNA interference (n = 3). NC indicates nonspecific control siRNA. F) Shrinkage of 4T1.2 tumor spheroids (red) by pitavastatin and simvastatin (n = 3). G) Regulation of PPAR-γ in response to 2, 5, and 10 μM Aredia. H, I) Regulation of Snail, MMP9, p-eIF2α, and Rac1 in response to 2, 5, and 10 μM Aredia. Are, Aredia (pamidronate); CN, control; Me, mevalonate; Pita, pitavastatin; Sim, simvastatin. * P < 0.05, **P < 0.01.

Pitavastatin’s suppression of proliferation and differentiation of preosteoclasts

So far, we showed pitavastatin’s effects on tumor cells and osteoblasts, as well as its linkage to the mevalonate pathway. We next examined its effect on bone-resorbing osteoclasts using RAW264.7 and bone marrow–derived preosteoclasts. In the MTT assay, pitavastatin reduced cellular viability of bone marrow–derived preosteoclasts (Fig. 4A) and decreased expression of NFATc1 (Fig. 4B). In bone marrow–derived preosteoclasts, pitavastatin reduced the number of TRAP-positive multinucleated osteoclast cells (Fig. 4C, D).

Figure 4.

Figure 4

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Suppression of proliferation and differentiation of RAW264.7 and bone marrow–derived preosteoclasts by pitavastatin. A) MTT-based relative viability of bone marrow–derived cells in response to pitavastatin (n = 3). B) Reduction in NFATc1 in RANKL-stimulated bone marrow–derived preosteoclasts by pitavastatin for 1 d. C, D) Reduction in the number of TRAP-positive multinucleated cells in RANKL-stimulated bone marrow–derived cells by pitavastatin for 5 d (n = 3). E) MTT-based relative viability of RAW264.7 preosteoclasts in response to 1–10 μM pitavastatin (d 2, n = 3). F) Up-regulation of cleaved caspase-3 and LC3A/B II in RAW264.7 cells in response to 2–5 μM pitavastatin for 1 d. casp, caspase. G) Down-regulation of NFATc1, c-Fos, and JunB in RANKL-stimulated RAW264.7 cells by 2–10 μM pitavastatin for 1 d. H, I) Reduction in the area of pit formation in bone marrow–derived cells by 2–10 μM pitavastatin for 5 d (n = 3). J) Effect of mevalonate on size of 3-dimensional RAW264.7 spheroids, respectively. K) Suppression of pitavastatin-driven reduction in cellular viability of RAW264.7 cells by mevalonate (n = 3). L) Expression of NFATc1, c-Fos, and JunB in RAW264.7 cells in response to pitavastatin in the presence and absence of mevalonate. CN, control; Me, mevalonate; Pita, pitavastatin. *P < 0.05, **P < 0.01.

In RAW264.7 cells, pitavastatin inhibited their viability by elevating cleaved caspase-3 as well as LC3A/B II (autophagy marker) (Fig. 4E, F). It down-regulated expression of NFATc1, a master transcription factor for osteoclastogenesis, in RANKL-stimulated RAW264.7 cells (Fig. 4G) and decreased the area of pit formation (bone degradation) (Fig. 4H, I). Furthermore, it shrank RAW264.7 spheroids (Supplemental Fig. S3A, B) and reduced the number of TRAP-positive multinucleated osteoclast cells (Supplemental Fig. S3C, D). As expected, shrinkage of RAW264.7 spheroids by pitavastatin was suppressed by 100 μM mevalonate (Fig. 4J). Mevalonate significantly suppressed pitavastatin-driven reduction of viability of RAW264.7 cells, pitavastatin-driven down-regulation of NFATc1, as well as c-Fos and JunB, which promote NFATc1 expression (Fig. 4K, L).

Comparison of pitavastatin, simvastatin, pravastatin, and pamidronate

Whereas statins and bisphosphonates inhibit synthesis of cholesterol via mevalonate signaling, the efficacy of each of these agents as antitumor agents significantly differs. Using 4T1.2 tumor cells, the effects of 3 statins (pitavastatin, simvastatin, and pravastatin) and pamidronate (Aredia) were compared. Pamidronate has been used as a supportive medication to treat cancer-driven hypercalcemia. Simvastatin down-regulated PPAR-γ, Snail, and MMP9 (Supplemental Fig. S4A). Pravastatin, which did not inhibit cellular viability of 4T1.2 cells, down-regulated PPAR-γ with slightly elevated Snail and MMP9 (Supplemental Fig. S4B). Simvastatin and pravastatin up-regulated the level of p-eIF2α (Supplemental Fig. S4C). Compared wiith pitavastatin, the efficacy of pamidronate in inhibiting proliferation and migration of tumor cells was impaired. Compared with pitavastatin, MTT-based cellular viability of 4T1.2 cells was significantly reduced in response to 2–10 μM pamidronate (Aredia), but to a lesser degree (Supplemental Fig. S4D).

Efficacy of pitavastatin in the mouse model of osteolysis in the tibia

To evaluate the effect of pitavastatin on preventing tumor-linked bone loss, we employed a mouse model of osteolysis and induced osteolytic lesions in the right tibia by intratibial injection of 4T1.2 cells (Fig. 5A). Daily administration of 8 mg/kg pitavastatin significantly elevated mechanical strength of the tibia by increasing stiffness, although a lower dose of 4 mg/kg did not show protection of osteolytic bone (Fig. 5B). MicroCT images illustrate that destruction of the proximal tibia in the placebo group was alleviated by 8 mg/kg pitavastatin (Fig. 5C, D). Consistent with 3-dimensional reconstructed images, pitavastatin protected cortical bone in the proximal tibia from osteolysis (Fig. 5E) and significantly increased bone volume as well as bone mineral density (Fig. 5F). Histologic images of the proximal tibia show that pitavastatin-treated bone presented an intact growth plate with a lower number of osteolytic lesions than that in the placebo (Fig. 5G).

Figure 5.

Figure 5

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Efficacy of pitavastatin in the mouse model of osteolysis in the tibia. A) Representative X-ray images of the control tibia and pitavastatin-treated tibia (8 mg/kg). B) Elevated stiffness (N/mm) of the tibia by daily administration of 8 mg/kg pitavastatin (n = 7). C) Three-dimensional reconstruction of the proximal tibia. D) Cross sections of the proximal tibia (top section = growth plate section; and bottom section = 1 mm distal to the top section). E) Percent continuity of the cortical section at the top and bottom sections in the proximal tibia (n = 7). F) Percent ratio of bone volume to total volume (BV/TV) and cortical bone mineral density (BMD) in the proximal tibia [1-mm zone (C), n = 7). G) Histologic images of the proximal tibia of the placebo and pitavastatin-treated samples. CN, control; Pita, pitavastatin. *P < 0.05, **P < 0.01.

Differential expression of urine-derived VOCs

In urine analysis for the 3 groups of mice (normal control, placebo, and 8 mg/kg pitavastatin-treated), we focused on 119 VOCs that were differentially expressed in the normal control and placebo groups. Hierarchical clustering analysis illustrates that the VOC profiles for the placebo group is significantly different from those in the normal control and pitavastatin-treated groups (Fig. 6A). As noted in the key, up-regulated VOCs are shown in red, whereas those down-regulated are in green. Principal component analysis using 20 VOCs (Table 1) showed that along the first principal component axis, the placebo group was separated from the normal control and pitavastatin-treated groups (Fig. 6B). Along this axis, 9 VOCs enriched in the placebo group were positioned in the left domain (red), whereas 11 VOCs were in the right domain (green) (Fig. 6C). The pitavastatin-treated group did not present VOC #1 (N,N-dimethylethan-1-amine), which is linked to acetophenone and lipid metabolism in the Kyoto Encyclopedia of Genes and Genomes (KEGG) methane metabolism pathway, or VOC #9 (3-methylbutan-2-one), which is a toxic compound (43). Furthermore, the pitavastatin-treated group did not present VOC #4 [(6E)-7,11-dimethyl-3-methylidenedodeca-1,6,10-triene] and #8 (1-phenylethanone), which are linked to triterpenoid synthesis and ethylbenzene degradation pathways, respectively. Triterpenoids (C30H48O7S) are precursors to all steroids including cholesterol (44), and ethylbenzene dehydrogenase is involved in an anoxic cholesterol pathway (45).

Figure 6.

Figure 6

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Clustering analysis and principal component analysis of VOCs, and the proposed mechanism. A) Clustering analysis of 119 VOCs in the normal control, placebo, and pitavastatin-treated groups. B) Principal component analysis of 3 groups (green, normal control; red, placebo; blue, 8 mg/kg pitavastatin-treated) with 20 VOCs. C) Principal component analysis of 20 VOCs, enriched in the placebo (red) and control (green) or pitavastatin-treated (blue) groups.

TABLE 1.

VOCs in principal component analysis

ID VOC name (IUPAC) Metabolic pathway
Placebo samples
 1 N,N-dimethylethan-1-amine Methane metabolism
 2 1,2,4-Trimethylcyclohexane
 3 1,4-Dihydronaphthalene
 4 (6E)-7,11-Dimethyl-3-methylidenedodeca-1,6,10-triene Triterpenoid synthesis
 5 1-Propan-2-ylpyrrolo[2,3-b]pyridine
 6 (E)-oct-3-en-2-one
 7 2-Ethylhex-2-enal
 8 1-Phenylethanone Ethylbenzene degradation
 9 3-Methylbutan-2-one Toxic compound
Pitavastatin-treated samples
 10 2-Methylbut-3-en-2-ol
 11 3-Propylcyclopentene
 12 4'-Hydroxy-3′-methoxyacetophenone, propyl ether
 13 2,6-Dimethyl-7-octen-4-one
 14 [2,2,4-Trimethyl-3-(2-methylpropanoyloxy)pentyl] 2-methylpropanoate
 15 3,7-Dimethyloct-6-enyl hexanoate
 16 4-Methylsulfanylphenol
 17 2-Methyl-1-methylidene-3-prop-1-en-2-ylcyclopentane
 18 6-Methylheptane-2,4-dione
 19 1,4-Dimethyl-2-ethenylbenzene
 20 1-(4-Hydroxy-2-methylphenyl)ethanone
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ID, identifier; IUPAC, International Union of Pure and Applied Chemistry.

DISCUSSION

This study demonstrates that pitavastatin suppresses proliferation and migration of tumor cells and inhibits bone-resorbing activity of preosteoclasts. In tumor cells, pitavastatin down-regulated signaling pathways mediated via mevalonate and PPAR-γ, leading to a partial reversal of epithelial-mesenchymal transition by inhibiting Snail and MMP9. It also up-regulated Perk, which phosphorylates eIF2α in response to stress to the endoplasmic reticulum. In preosteoclast cells, pitavastatin down-regulated c-Fos, JunB, and NFATc1 and suppressed osteoclast maturation and osteolytic activity. Notably, it acted in the opposite direction on osteoblasts by regulating BMP-2 and stimulating their differentiation. Animal experiments showed that administration of pitavastatin significantly reduced the weight of tumors in the mammary fat pad and improved bone strength as well as structural integrity in tumor-invaded osteolytic bone. Pitavastatin was more potent than pamidronate (Aredia), a bisphosphonate, in suppressing genes involved in tumor proliferation and inhibiting osteoclastogenesis.

Hierarchical clustering and principal component analysis showed that urine-derived VOCs indicate differences between the normal and placebo controls. VOCs detected in this study may be linked to integrity of lipid membranes because benzene derivatives and intermediates of ketosis can be generated in the process of membrane destruction (46). Alternatively, some VOCs may be derived as bacterial metabolites in the gut or metastasized bone tissues (47, 48). Although the mechanism of the differential profiles of VOCs is yet to be clarified, the signature VOCs in Table 1 are linked to pitavastatin’s action via mevalonate, which is a precursor in cholesterol synthesis.

Pitavastatin is a U.S. Food and Drug Administration–approved drug to lower cholesterol and lipid levels by inhibiting HMG-CoA reductase in the mevalonate pathway. Among 9 VOCs enriched in the tumor-grown placebo group and not in the pitavastatin-treated group, 3 VOCs [N,N-dimethylethan-1-amine, (6E)-7,11-dimethyl-3-methylidenedodeca-1,6,10-triene, and 1-phenylethanone] were associated with cholesterol synthesis and lipid metabolism. Thus, the result in the urine-based VOCs is consistent with the therapeutic action of pitavastatin in blocking mevalonate-driven cholesterol synthesis and regulating lipid metabolism. Furthermore, the results herein indicate the possibility of employing VOCs in the urine as diagnostic markers to evaluate the efficacy of other chemotherapeutic agents. Further analysis is necessary to identify a group of specific VOC markers in clinical studies.

The results herein are consistent with pitavastatin’s dual actions mediated by the mevalonate pathway (Fig. 7). Pitavastatin-driven down-regulation of MMP9 was suppressed by partial silencing of PPAR-γ, which is elevated in many tumors (49). Pitavastatin’s efficacy was compared with simvastatin (50), pravastatin (23), and pamidronate (51). Simvastatin can also suppress PPAR-γ, Snail, and MMP9, but it inhibits osteoblast proliferation stronger than pitavastatin. Pravastatin’s effects were less potent because Snail and MMP9 increased only slightly. Pamidronate reduced MTT-based viability of 4T1.2 tumor cells and down-regulated PPAR-γ, but no effects on Snail, MMP9, and eIF2α were detected. Notably, pitavastatin inhibited differentiation of RAW264.7 preosteoclasts and bone marrow–derived primary preosteoclasts, but it promoted differentiation of bone-forming osteoblasts. Down-regulation of c-Fos and JunB in preosteoclasts was consistent with their stimulatory role in NFATc1 expression (52), whereas up-regulation of BMP-2 in osteoblasts agreed with its role in bone formation (53). Collectively, among the selected agents, pitavastatin was demonstrated to most effectively induce a dual effect on tumor and bone via the mevalonate pathway.

Figure 7.

Figure 7

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Proposed mechanism of pitavastatin’s action on osteoclasts and mammary tumor cells.

The observed involvement of eIF2α regulation is common to dopaminergic signaling and cell cycle regulation, which also suppress tumor growth and bone loss. We have previously reported that the action of an agonist of dopamine receptor D1 is in part mediated by elevating p-eIF2α (18). It stimulated dopamine- and cAMP-regulated phosphoprotein, which inhibits protein phosphate 1 and elevates p-eIF2α. The elevated p-eIF2α was shown to suppress tumor cell migration via inhibition of Rac1 (54) and prevent bone resorption (55). In cell cycle regulation, inhibition of checkpoint kinase 2 elevated p-eIF2α via Perk, an eIF2α-selective kinase (21). Inhibition of the mevalonate pathway is reported to activate Perk and induce stress to the endoplasmic reticulum (56).

The results herein establish a link among pitavastatin’s action, tumor suppression, and bone protection, though there are several limitations that should be noted. This study used mouse-derived transfected cell lines of osteoblasts and osteoclasts, mouse-derived tumor cells, primary bone marrow cells, and human breast cancer cells. Primary human breast cancer cells with different types at various stages should be used to translate these findings toward clinical relevance.

In summary, this study explores the therapeutic role of pitavastatin in an integrated bone-tumor pathogenesis using a mouse model of osteolysis associated with breast cancer. Pitavastatin is shown to be highly effective in suppression of tumor growth and bone degradation among inhibitors of the mevalonate pathway. Furthermore, urine-derived VOC analysis suggested linkage of pitavastatin’s action to cholesterol synthesis and lipid metabolism, suggesting a mechanistic rationale for exploiting VOC-based noninvasive diagnosis and treatment efficacy. Further studies might be directed to determine the appropriate dose of pitavastatin for balancing the treatment of bone metastasis with the regulation of cholesterol synthesis and to develop a novel diagnostic tool with urine-based VOCs. Pitavastatin can be administered simultaneously with existing chemotherapeutic agents.

ACKNOWLEDGMENTS

The authors thank Ali Daneshkhah, Paul Grocki, and Mark Woollam (all from Indiana University–Purdue University Indianapolis) for technical support, and Hisako Masuda (Indiana University, Kokomo, IN, USA) for data analysis. This study was supported, in part, by funds from 100 Voices of Hope (to H.Y.) and the U.S. National Institutes of Health (NIH)/National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR052144) and NIH/National Cancer Institute (R03CA238555 to H.Y.). The authors declare no conflicts of interest.

Glossary

μCT

micro–computed tomography

BMP-2

bone morphogenetic protein 2

EdU

ethynyl deoxyuridine

eIF2α

eukaryotic translation initiation factor 2α

HMG-CoA

3-hydroxy-3-methyl-glutaryl-CoA

MMP9

matrix metalloproteinase 9

MTT

3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide

NFATc1

nuclear factor of activated T cells cytoplasmic 1

Perk

protein kinase R–like endoplasmic reticulum kinase

PPAR-γ

peroxisome proliferator–activated receptor γ

RANKL

receptor activator of NF-κB ligand

siRNA

small interfering RNA

TRAP

tartrate resistant acid phosphate

VOC

volatile organic compound

Footnotes

This article includes supplemental data. Please visit http://www.fasebj.org to obtain this information.

AUTHOR CONTRIBUTIONS

M. Agarwal, B.-Y. Li, and H. Yokota designed the study; L. Wang, Y. Wang, A. Chen, M. Teli, R. Kondo, A. Jalali, Y. Fan, S. Liu, X. Zhao, A. Siegel, and K. Minami collected and interpreted data; A. Chen, B.-Y. Li, and H. Yokota drafted the manuscript; and all authors reviewed the manuscript and approved the final draft.

Supplementary Material

This article includes supplemental data. Please visit http://www.fasebj.org to obtain this information.

Click here for additional data file. (863.9KB, docx)

REFERENCES

  • 1.Corsini A., Bellosta S., Baetta R., Fumagalli R., Paoletti R., Bernini F. (1999) New insights into the pharmacodynamic and pharmacokinetic properties of statins. Pharmacol. Ther. 84, 413–428; erratum: 86, 199 [DOI] [PubMed] [Google Scholar]
  • 2.Davies J. T., Delfino S. F., Feinberg C. E., Johnson M. F., Nappi V. L., Olinger J. T., Schwab A. P., Swanson H. I. (2016) Current and emerging uses of statins in clinical therapeutics: a review. Lipid Insights 9, 13–29 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chou R., Dana T., Blazina I., Daeges M., Jeanne T. L. (2016) Statins for prevention of cardiovascular disease in adults: evidence report and systematic review for the US preventive services task force. JAMA 316, 2008–2024 [DOI] [PubMed] [Google Scholar]
  • 4.Buhaescu I., Izzedine H. (2007) Mevalonate pathway: a review of clinical and therapeutical implications. Clin. Biochem. 40, 575–584 [DOI] [PubMed] [Google Scholar]
  • 5.Sopková J., Vidomanová E., Strnádel J., Škovierová H., Halašová E. (2017) The role of statins as therapeutic agents in cancer. Gen. Physiol. Biophys. 36, 501–511 [DOI] [PubMed] [Google Scholar]
  • 6.Iannelli F., Lombardi R., Milone M. R., Pucci B., De Rienzo S., Budillon A., Bruzzese F. (2018) Targeting mevalonate pathway in cancer treatment: repurposing of statins. Recent Patents Anticancer Drug Discov. 13, 184–200 [DOI] [PubMed] [Google Scholar]
  • 7.Iarrobino N. A., Gill B., Bernard M. E., Mishra M. V., Champ C. E. (2018) Targeting tumor metabolism with statins during treatment for advanced-stage pancreatic cancer. [E-pub ahead of print] Am. J. Clin. Oncol. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Kobayashi Y., Kashima H., Rahmanto Y. S., Banno K., Yu Y., Matoba Y., Watanabe K., Iijima M., Takeda T., Kunitomi H., Iida M., Adachi M., Nakamura K., Tsuji K., Masuda K., Nomura H., Tominaga E., Aoki D. (2017) Drug repositioning of mevalonate pathway inhibitors as antitumor agents for ovarian cancer. Oncotarget 8, 72147–72156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Göbel A., Browne A. J., Thiele S., Rauner M., Hofbauer L. C., Rachner T. D. (2015) Potentiated suppression of Dickkopf-1 in breast cancer by combined administration of the mevalonate pathway inhibitors zoledronic acid and statins. Breast Cancer Res. Treat. 154, 623–631 [DOI] [PubMed] [Google Scholar]
  • 10.Chimento A., Casaburi I., Avena P., Trotta F., De Luca A., Rago V., Pezzi V., Sirianni R. (2019) Cholesterol and its metabolites in tumor growth: therapeutic potential of statins in cancer treatment. Front. Endocrinol. (Lausanne) 9, 807 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Brook N., Brook E., Dharmarajan A., Dass C. R., Chan A. (2018) Breast cancer bone metastases: pathogenesis and therapeutic targets. Int. J. Biochem. Cell Biol. 96, 63–78 [DOI] [PubMed] [Google Scholar]
  • 12.Dunford J. E. (2010) Molecular targets of the nitrogen containing bisphosphonates: the molecular pharmacology of prenyl synthase inhibition. Curr. Pharm. Des. 16, 2961–2969 [DOI] [PubMed] [Google Scholar]
  • 13.Drake M. T., Clarke B. L., Khosla S. (2008) Bisphosphonates: mechanism of action and role in clinical practice. Mayo Clin. Proc. 83, 1032–1045 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Whitaker M., Guo J., Kehoe T., Benson G. (2012) Bisphosphonates for osteoporosis--where do we go from here? N. Engl. J. Med. 366, 2048–2051 [DOI] [PubMed] [Google Scholar]
  • 15.Baron R., Ferrari S., Russell R. G. (2011) Denosumab and bisphosphonates: different mechanisms of action and effects. Bone 48, 677–692 [DOI] [PubMed] [Google Scholar]
  • 16.Rogers M. J. (2000) Statins: lower lipids and better bones? Nat. Med. 6, 21–23 [DOI] [PubMed] [Google Scholar]
  • 17.Jadhav S. B., Jain G. K. (2006) Statins and osteoporosis: new role for old drugs. J. Pharm. Pharmacol. 58, 3–18 [DOI] [PubMed] [Google Scholar]
  • 18.Minami K., Liu S., Liu Y., Chen A., Wan Q., Na S., Li B. Y., Matsuura N., Koizumi M., Yin Y., Gan L., Xu A., Li J., Nakshatri H., Yokota H. (2017) Inhibitory effects of dopamine receptor D1 agonist on mammary tumor and bone metastasis. Sci. Rep. 7, 45686 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Liu S., Fan Y., Chen A., Jalali A., Minami K., Ogawa K., Nakshatri H., Li B. Y., Yokota H. (2018) Osteocyte-driven downregulation of snail restrains effects of Drd2 inhibitors on mammary tumor cells. Cancer Res. 78, 3865–3876 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Liu S., Liu Y., Minami K., Chen A., Wan Q., Yin Y., Gan L., Xu A., Matsuura N., Koizumi M., Liu Y., Na S., Li J., Nakshatri H., Li B. Y., Yokota H. (2018) Inhibiting checkpoint kinase 1 protects bone from bone resorption by mammary tumor in a mouse model. Oncotarget 9, 9364–9378 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wang L., Wang Y., Chen A., Jalali A., Liu S., Guo Y., Na S., Nakshatri H., Li B. Y., Yokota H. (2018) Effects of a checkpoint kinase inhibitor, AZD7762, on tumor suppression and bone remodeling. Int. J. Oncol. 53, 1001–1012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Jiang P., Mukthavaram R., Chao Y., Nomura N., Bharati I. S., Fogal V., Pastorino S., Teng D., Cong X., Pingle S. C., Kapoor S., Shetty K., Aggrawal A., Vali S., Abbasi T., Chien S., Kesari S. (2014) In vitro and in vivo anticancer effects of mevalonate pathway modulation on human cancer cells. Br. J. Cancer 111, 1562–1571 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Catapano A. L. (2012) Pitavastatin: a different pharmacological profile. Clin. Lipidol. 7, 3–9 [Google Scholar]
  • 24.Nieto M. A. (2002) The snail superfamily of zinc-finger transcription factors. Nat. Rev. Mol. Cell Biol. 3, 155–166 [DOI] [PubMed] [Google Scholar]
  • 25.Wieczorek E., Jablonska E., Wasowicz W., Reszka E. (2015) Matrix metalloproteinases and genetic mouse models in cancer research: a mini-review. Tumour Biol. 36, 163–175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Fan M., Xu Y., Hong F., Gao X., Xin G., Hong H., Dong L., Zhao X. (2016) Rac1/β-catenin signalling pathway contributes to trophoblast cell invasion by targeting snail and MMP9. Cell. Physiol. Biochem. 38, 1319–1332 [DOI] [PubMed] [Google Scholar]
  • 27.Han L., Shen W. J., Bittner S., Kraemer F. B., Azhar S. (2017) PPARs: regulators of metabolism and as therapeutic targets in cardiovascular disease. Part II: PPAR-β/δ and PPAR-γ. Future Cardiol. 13, 279–296 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nomura S., Nakajima A., Ishimine S., Matsuhashi N., Kadowaki T., Kaminishi M. (2006) Differential expression of peroxisome proliferator-activated receptor in histologically different human gastric cancer tissues. J. Exp. Clin. Cancer Res. 25, 443–448 [PubMed] [Google Scholar]
  • 29.Lavra L., Catini A., Ulivieri A., Capuano R., Baghernajad Salehi L., Sciacchitano S., Bartolazzi A., Nardis S., Paolesse R., Martinelli E., Di Natale C. (2015) Investigation of VOCs associated with different characteristics of breast cancer cells. Sci. Rep. 5, 13246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Khalid T., Aggio R., White P., De Lacy Costello B., Persad R., Al-Kateb H., Jones P., Probert C. S., Ratcliffe N. (2015) Urinary volatile organic compounds for the detection of prostate cancer. PLoS One 10, e0143283 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Wang C., Sun B., Guo L., Wang X., Ke C., Liu S., Zhao W., Luo S., Guo Z., Zhang Y., Xu G., Li E. (2014) Volatile organic metabolites identify patients with breast cancer, cyclomastopathy, and mammary gland fibroma. Sci. Rep. 4, 5383 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lelekakis M., Moseley J. M., Martin T. J., Hards D., Williams E., Ho P., Lowen D., Javni J., Miller F. R., Slavin J., Anderson R. L. (1999) A novel orthotopic model of breast cancer metastasis to bone. Clin. Exp. Metastasis 17, 163–170 [DOI] [PubMed] [Google Scholar]
  • 33.Cailleau R., Young R., Olivé M., Reeves W. J., Jr (1974) Breast tumor cell lines from pleural effusions. J. Natl. Cancer Inst. 53, 661–674 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Raschke W. C., Baird S., Ralph P., Nakoinz I. (1978) Functional macrophage cell lines transformed by Abelson leukemia virus. Cell 15, 261–267 [DOI] [PubMed] [Google Scholar]
  • 35.Kodama H. A., Amagai Y., Sudo H., Kasai S., Yamamoto S. (1981) Establishment of a clonal osteogenic cell line from newborn mouse calvaria. Jpn. J. Oral Biol. 23, 899–901 [Google Scholar]
  • 36.Miyazaki T., Miyauchi S., Anada T., Imaizumi H., Suzuki O. (2011) Evaluation of osteoclastic resorption activity using calcium phosphate coating combined with labeled polyanion. Anal. Biochem. 410, 7–12 [DOI] [PubMed] [Google Scholar]
  • 37.Kwak J., Grigsby C. C., Rizki M. M., Preti G., Köksal M., Josue J., Yamazaki K., Beauchamp G. K. (2012) Differential binding between volatile ligands and major urinary proteins due to genetic variation in mice. Physiol. Behav. 107, 112–120 [DOI] [PubMed] [Google Scholar]
  • 38.Siegel A. P., Daneshkhah A., Hardin D. S., Shrestha S., Varahramyan K., Agarwal M. (2017) Analyzing breath samples of hypoglycemic events in type 1 diabetes patients: towards developing an alternative to diabetes alert dogs. J. Breath Res. 11, 026007 [DOI] [PubMed] [Google Scholar]
  • 39.Woollam M., Teli M., Angarita-Rivera P., Liu S., Siegel A. P., Yokota H., Agarwal M. (2019) Detection of volatile organic compounds (VOCs) in urine via gas chromatography-mass spectrometry QTOF to differentiate between localized and metastatic models of breast cancer. Sci. Rep. 9, 2526 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Murtagh F., Contreras P. (2012) Algorithms for hierarchical clustering: an overview. WIREs Data Mining Knowl. Discov. 2, 86–97 [Google Scholar]
  • 41.Lan C. H., Huang Y. L., Ho S. H., Peng C. Y. (2014) Volatile organic compound identification and characterization by PCA and mapping at a high-technology science park. Environ. Pollut. 193, 156–164 [DOI] [PubMed] [Google Scholar]
  • 42.Liu Y., Vincenti M. P., Yokota H. (2005) Principal component analysis for predicting transcription-factor binding motifs from array-derived data. BMC Bioinformatics 6, 276 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Kanehisa M., Goto S. (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Summons R. E., Bradley A. S., Jahnke L. L., Waldbauer J. R. (2006) Steroids, triterpenoids and molecular oxygen. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 951–968 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Chiang Y. R., Ismail W., Heintz D., Schaeffer C., Van Dorsselaer A., Fuchs G. (2008) Study of anoxic and oxic cholesterol metabolism by Sterolibacterium denitrificans. J. Bacteriol. 190, 905–914 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Gago-Dominguez M., Jiang X., Castelao J. E. (2007) Lipid peroxidation, oxidative stress genes and dietary factors in breast cancer protection: a hypothesis. Breast Cancer Res. 9, 201 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Garrett W. S. (2015) Cancer and the microbiota. Science 348, 80–86 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Wang H., Altemus J., Niazi F., Green H., Calhoun B. C., Sturgis C., Grobmyer S. R., Eng C. (2017) Breast tissue, oral and urinary microbiomes in breast cancer. Oncotarget 8, 88122–88138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Kotta-Loizou I., Giaginis C., Theocharis S. (2012) The role of peroxisome proliferator-activated receptor-γ in breast cancer. Anticancer. Agents Med. Chem. 12, 1025–1044 [DOI] [PubMed] [Google Scholar]
  • 50.Ahern T. P., Lash T. L., Damkier P., Christiansen P. M., Cronin-Fenton D. P. (2014) Statins and breast cancer prognosis: evidence and opportunities. Lancet Oncol. 15, e461–e468 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Coleman R. E. (2000) Optimising treatment of bone metastases by Aredia(TM) and Zometa(TM). Breast Cancer 7, 361–369 [DOI] [PubMed] [Google Scholar]
  • 52.Hamamura K., Chen A., Tanjung N., Takigawa S., Sudo A., Yokota H. (2015) In vitro and in silico analysis of an inhibitory mechanism of osteoclastogenesis by salubrinal and guanabenz. Cell. Signal. 27, 353–362 [DOI] [PubMed] [Google Scholar]
  • 53.Kamiya N. (2012) The role of BMPs in bone anabolism and their potential targets SOST and DKK1. Curr. Mol. Pharmacol. 5, 153–163 [DOI] [PubMed] [Google Scholar]
  • 54.Xu W., Wan Q., Na S., Yokota H., Yan J. L., Hamamura K. (2015) Suppressed invasive and migratory behaviors of SW1353 chondrosarcoma cells through the regulation of Src, Rac1 GTPase, and MMP13. Cell. Signal. 27, 2332–2342 [DOI] [PubMed] [Google Scholar]
  • 55.Hamamura K., Tanjung N., Yokota H. (2013) Suppression of osteoclastogenesis through phosphorylation of eukaryotic translation initiation factor 2 alpha. J. Bone Miner. Metab. 31, 618–628 [DOI] [PubMed] [Google Scholar]
  • 56.Ghavami S., Yeganeh B., Stelmack G. L., Kashani H. H., Sharma P., Cunnington R., Rattan S., Bathe K., Klonisch T., Dixon I. M., Freed D. H., Halayko A. J. (2012) Apoptosis, autophagy and ER stress in mevalonate cascade inhibition-induced cell death of human atrial fibroblasts. Cell Death Dis. 3, e330 [DOI] [PMC free article] [PubMed] [Google Scholar]

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