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. 2026 Feb 26;159(1):257–268. doi: 10.1002/ijc.70401

Metabolome analysis identified exogenous cholesterol within lipid rafts that activate the Akt/mTOR signaling pathway in epithelial ovarian cancer

Hitomi Sakaguchi‐Mukaida 1, Kosuke Hiramatsu 1,✉, Tatsuo Masuda 1, Mamoru Kakuda 1, Satoshi Nakagawa 1, Tadashi Iwamiya 1, Shinya Matsuzaki 1, Tetsuji Naka 2, Yutaka Ueda 1, Tadashi Kimura 3, Michiko Kodama 1
PMCID: PMC13140013  PMID: 41748499

Abstract

Epithelial ovarian cancer (EOC) is a highly aggressive malignancy with poor prognosis. Thus, new treatment options are needed. Recently, lipid metabolism in EOC has been highlighted. However, the specific lipid molecules activating lipid metabolism remain unclear. This study aimed to elucidate lipid metabolism in EOC and evaluate the potential of its inhibition as a therapeutic approach. We obtained high‐fat diet (HFD)‐fed mouse serum and performed metabolome analysis to identify lipid molecules contributing to cell proliferation of EOC. We also analyzed which signaling pathway was activated by the lipid molecule. Finally, we demonstrated the inhibition of lipid metabolism in EOC cells. HFD significantly promoted tumor growth of EOC cells in vivo, and HFD‐fed mouse serum promoted EOC cell proliferation in vitro. Metabolome analysis identified cholesterol (C27H46O) as a key molecule in HFD‐fed mouse serum. Cholesterol (C27H46O) activated the Akt/mTOR signaling pathway in vitro. Cholesterol (C27H46O) is an important component of lipid rafts, and its inhibitor, which extracts cholesterol (C27H46O) from lipid rafts, inactivated the Akt/mTOR signaling pathway and suppressed subsequent EOC cell proliferation. Exogenous cholesterol (C27H46O) contributed to cell proliferation of EOC via the lipid rafts—Akt/mTOR signaling pathway, and its inhibition undoubtedly presents a novel therapeutic strategy for EOC.

Keywords: cholesterol, epithelial ovarian cancer, high‐fat diet, lipid metabolism, metabolome analysis

What's New?

Epithelial ovarian cancer (EOC) is a severe malignancy with few effective treatment options. While the mechanisms remain unclear, changes in lipid metabolism may fuel tumor growth and EOC progression, offering a possible path for new treatments. Here, the authors examined serum and cells from EOC mice fed high‐fat diet to identify lipids and pathways that promote ovarian cancer cell proliferation. Exogenous cholesterol was shown to drive EOC cell proliferation by activating Akt/mTOR signaling through lipid rafts in the cell membrane. Disruption of this pathway markedly reduced EOC cell growth, highlighting the therapeutic potential of targeting lipid raft‐mediated cholesterol signaling.

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Abbreviations

BSA

bovine serum albumin

D‐PBS

Dulbecco's phosphate‐buffered saline

EGFR

epidermal growth factor receptor

EOC

epithelial ovarian cancer

FBS

fetal bovine serum

HCA

hierarchical cluster analysis

HCD

high‐cholesterol (C27H46O) diet

HFD

high‐fat diet

LC‐TOFMS

liquid chromatography time‐of‐flight mass spectrometry

LDs

lipid droplets

LSR

lipolysis‐stimulated lipoprotein receptor

MβCD

methyl‐β‐cyclodextrin

ND

normal diet

PC

principal component

PCA

principal component analysis

PLS

partial least squares

RPMI

Roswell Park Memorial Institute

SC

subcutaneously

SCID

severe combined immunodeficient' mice

SDS‐PAGE

sodium dodecyl sulfate‐polyacrylamide gel electrophoresis

VIP score

variable importance in projection score

1. INTRODUCTION

Epithelial ovarian cancer (EOC) is the most aggressive gynecological malignancy. In 2025, EOC is expected to be the sixth most lethal malignant neoplasm in the United States. 1 According to the annual estimates of the American Cancer Society, approximately 20,890 new diagnoses and 12,730 deaths due to EOC are predicted to occur in 2025. 1 Early stage detection of EOC remains challenging; consequently, nearly 80% of patients are diagnosed at an advanced stage (stage III or IV), leading to a poor prognosis, as outlined in the US National Comprehensive Cancer Network (NCCN) guidelines, which involves optimal surgery followed by platinum‐based chemotherapy. 2 However, many patients develop chemoresistance, leading to recurrence. Although molecular targeted therapies, including poly(ADP‐ribose) polymerase inhibitors and angiogenesis inhibitors, have emerged, the prognosis of EOC remains still poor, with a long‐term survival rate of 20% or less. 3 Therefore, there is an unmet need to develop novel treatment strategies.

Previously, we identified the lipolysis‐stimulated lipoprotein receptor (LSR) as a target protein that regulates lipid metabolism in EOC. This study demonstrated that the inhibition of lipid metabolism by anti‐LSR antibody suppressed the proliferation of EOC cells. 4 In terms of lipid metabolism, it has been reported that high‐fat diet (HFD) is associated with the poor prognosis of many cancers, including colorectal, breast, and prostate cancers, suggesting that lipid metabolism is crucial in many cancers and may serve as a new therapeutic target. 5 , 6 , 7 However, the lipid molecules contributing to EOC cell proliferation remain unclear.

Recent advancements in metabolomic analysis have revealed various metabolites supporting cancer progression. 8 Among these, lipid molecules, particularly cholesterol (C27H46O), have been implicated in promoting aggressive cancer behavior. 9 Cholesterol (C27H46O) has essential functions in human cells, including maintaining cell membrane integrity, storing intracellular lipids as lipid droplets (LDs), and regulating signal transduction. 10 Within cell membranes, cholesterol (C27H46O) regulates the rigidity, fluidity, and permeability of the lipid bilayer and also plays an important role in the formation of lipid rafts. 11

Lipid rafts are the microdomains of cell membrane that are rich in cholesterol (C27H46O) and sphingolipids, functioning as platforms for intracellular signal transduction, lipid metabolism, and transport of cholesterol (C27H46O) or phospholipids. 12 Dysfunction of lipid rafts has been linked to various conditions, including B‐cell hematologic malignancies, neurodegenerative diseases such as Alzheimer's disease, inflammatory diseases, infections, and cancer. 13 Particularly, in cancer cells, lipid rafts in cell membrane increase and enhance cholesterol (C27H46O) uptake, which is thought to be one of the important functions that promote cell proliferation and progression. 12 , 14 Since cholesterol (C27H46O) plays a crucial role in cancer cell proliferation and progression, targeting cholesterol (C27H46O) metabolism has emerged as a promising anticancer strategy. However, despite extensive research, anticancer treatments aimed at inhibiting cholesterol (C27H46O) biosynthesis and uptake or disrupting lipid rafts in EOC have yet to be clinically implemented.

The objectives of this study were as follows: (i) to identify the specific lipid molecules that promote EOC celproliferation using metabolome analysis, (ii) to elucidate the lipid metabolic pathways activated by these lipid molecules, and (iii) to evaluate the effects of inhibiting these lipid metabolic pathways using various anticancer agents and assess their impact on EOC cell proliferation. This study aimed to establish a novel therapeutic approach for EOC by targeting the lipid metabolism, potentially paving the way for innovative and effective therapeutic strategies.

2. MATERIALS AND METHODS

2.1. Cell lines

Two human EOC cell lines, KURAMOCHI derived from high‐grade serous carcinoma (RRID: CVCL_1345) and RMG‐I derived from clear cell carcinoma (RRID: CVCL_1662), were obtained from the Japanese Collection of Research Bioresources (Osaka, Japan) and were cultured in Roswell Park Memorial Institute (RPMI) 1640 with L‐Gln (with Phenol Red) liquid (Nacalai tesque, Kyoto, Japan) supplemented with 10% fetal bovine serum (FBS) (Gibco, Grand Island, NY, USA).

Cells were cultured at 37°C under a humidified atmosphere with 5% CO2. All cell lines were authenticated using short tandem repeat (STR) profiling within the last 3 years to confirm their identity. Cells were regularly screened for mycoplasma with the use of a MycoAlert Mycoplasma Detection Kit (Lonza, Basel, Switzerland) and used less than 3 months after resuscitation. All experiments were performed with mycoplasma‐free cells.

2.2. Western blot analysis

To capture cell proteins, cells were seeded on 10 cm dishes at the concentration of 1.5 × 106 cells for KURAMOCHI cells or 2.0 × 106 cells for RMG‐I cells and incubated in RPMI 1640 medium with 1% FBS (1% FBS‐RPMI) for starvation. After 24 h of incubation, the medium was replaced with one of the following: (i) 1% FBS‐RPMI and (ii) 500 μM cholesterol (C27H46O) with 1% FBS‐RPMI, (iii) 0.3 mM methyl‐β‐cyclodextrin (MβCD; #C4555, from Sigma‐Aldrich, St. Louis, MO, USA), or (iv) a combination of 500 μM cholesterol (C27H46O) and 0.3 mM MβCD. Cells were treated at 37°C for 30 min, collected according to the manufacturer's protocols, and the extracted proteins were quantified using bovine serum albumin (BSA) as a standard.

Western blot analysis was performed as previously described using sodium dodecyl sulfate‐polyacrylamide gel electrophoresis (SDS‐PAGE). We acquired the following specific antibodies: anti‐Akt (#9272), anti‐phospho‐Akt (Ser473) (#9271), anti‐mTOR (7C10) (#2983), anti‐phospho‐mTOR (Ser2448) (D9C2) (#5536), from Cell Signaling Technology (Danvers, MA, USA); and anti‐GAPDH (sc‐25,778) from Santa Cruz Biotechnology (Dallas, TX, USA). Immunoblots were quantified using a ChemiDoc Touch MP imaging system (Bio‐Rad, Hercules, CA, USA). Each experiment was independently conducted at least three times.

2.3. Cell proliferation assay

Cells were seeded in 96‐well microplates at 2000 cells per well with 1% FBS for starvation, and after incubation for 24 h, the medium was replaced with the target medium as needed. Cell viability was assessed using the WST‐8 assay at 24 h post‐medium change, according to the manufacturer's protocol. All the results were assessed using a Multiskan FC microplate photometer (Thermo Fisher Scientific, Waltham, MA, USA). In the mouse serum experiment, after 24 h of incubation with 1% FBS‐RPMI, the medium was replaced with RPMI 1640 supplemented with 5% serum from normal diet (ND)‐ or HFD‐fed mice obtained under the same conditions as those used for metabolome analysis. In the experiment with cholesterol (C27H46O) and its inhibition by MβCD, after incubation for 24 h with 1% FBS‐RPMI, the medium was replaced with the following reagents: (i) 1% FBS‐RPMI as control, (ii) 500 μM cholesterol (C27H46O) + 1% FBS‐RPMI, (iii) 0.3 mM MβCD + 1% FBS‐RPMI, and (iv) 500 μM cholesterol (C27H46O) + 0.3 mM MβCD + 1% FBS‐RPMI. Each experiment was independently conducted at least three times.

2.4. Cytological immunofluorescence staining

KURAMOCHI and RMG‐I cells were seeded in 96‐well microplates at a concentration of 5 × 104 cells per well in 1% FBS‐RPMI. After incubation for 24 h, the medium was replaced with either 1% FBS‐RPMI or 1 mM cholesterol (C27H46O) + 1% FBS‐RPMI, and the cells were further cultured for 24 h. Following incubation, cells were washed with Dulbecco's phosphate‐buffered saline (D‐PBS) and fixed in 10% paraformaldehyde for 12 h at room temperature. After washing with D‐PBS, to detect the accumulation of intracellular LDs, LDs were stained with the Lipi‐Red dye (Dojindo, Kumamoto, Japan, #LD03) and incubated at 37°C for 30 min in the 5% CO2. Cells were then washed with PBS, mounted on slides using the mounting medium, and nuclei were stained with Hoechst 33258 (Dojindo, #23491‐45‐4) diluted 1:2000 in PBS for 10 min at room temperature. The Lipi‐Red working and Hoechst stock solution were prepared according to the manufacturer's protocol. Finally, the cells were washed with PBS and observed using a fluorescence microscope BZ‐X800 (KEYENCE, Osaka, Japan), and images were captured using BZ‐X Analyzer software (KEYENCE).

2.5. Animal experiments

In this study, 6‐week‐old female CB‐17/Icr‐scid/scid Jcl mice (severe combined immunodeficient' mice [SCID] mice) (CREA Japan, Inc., Tokyo, Japan) were used for implantation of human EOC. For subcutaneous xenograft experiments, SCID mice were subcutaneously (SC) injected with 5 × 106 KURAMOCHI and RMG‐I cells in a total volume of 100 μL of PBS/Matrigel (Becton Dickinson; San Jose, CA, USA) (1:1, v/v). Tumors were measured twice a week and tumor volumes were calculated using the following formula: tumor volume (mm3) = length × width2 × 0.5. When the mean tumor volume reached approximately 100 mm3, the mice were divided into two groups and assigned to the following diet groups: ND or HFD.

To assess the effect of HFD, mice with KURAMOCHI and RMG‐I cells were fed with ND (PicoLab® 5053 diet, irradiated; LabDiet, St. Louis, MO, USA) or HFD (D12492; Research Diets, New Brunswick, NJ, USA) for 25 and 17 days, respectively. At the end of the feeding period, the mice were sacrificed, and tumors were resected for further analysis.

To evaluate the effect of high‐cholesterol (C27H46O) diet (HCD), mice with RMG‐I tumors were fed with ND (MFG [Oriental Yeast, Tokyo, Japan]) or HCD (10% fat, with 5% consisting of cholesterol (C27H46O); Oriental Yeast) for 39 days, and then the mice were sacrificed and tumors were resected.

Serum samples were collected for the purpose of metabolome analysis from non‐tumor bearing mice fed with ND (PicoLab® 5053) or HFD (D12492) for 2 weeks. Fractionation of serum lipoprotein was measured using Fuji Dri‐Chem (Fujifilm, Tokyo, Japan).

2.6. Metabolite extraction

One hundred microliter of serum was added to 300 μL of 1% formic acid/acetonitrile containing internal standards (H3304‐1002) (Human Metabolome Technologies [HMT], Yamagata, Japan) at 0°C to suppress enzymatic activity. The mixture was centrifuged at 2300 × g, 4°C for 5 min and then filtered using a hybrid SPE‐phospholipid cartridge (Hybrid SPE‐Phospholipid 30 mg/mL) (SUPELCO, Bellefonte, PA, USA) to remove phospholipids. Subsequently, the filtrate was evaporated to dryness under nitrogen and reconstituted in 100 μL of 50% isopropanol (1:1, v/v) for metabolomic analysis at HMT.

2.7. Metabolome analysis (LC‐basic scan)

Metabolome analysis was conducted according to HMT's LC‐Basic Scan package using liquid chromatography time‐of‐flight mass spectrometry (LC‐TOFMS) based on previously described methods. 15 Briefly, LC‐TOF‐MS analysis was performed using an Agilent 1260 HPLC system equipped with an Agilent 6230 time‐of‐flight mass spectrometer (Agilent Technologies, Inc., Santa Clara, CA, USA). The systems were controlled using an Agilent MassHunter Workstation Data Acquisition system (Agilent Technologies) and connected by an ODS column (2 mm i.d. × 50 mm, 2 μm). The spectrometer was scanned from m/z 100 to 1700 and peaks were extracted using MasterHands, an automatic integration software (Keio University, Yamagata, Japan), in order to obtain peak information including mass‐to‐charge ratio (m/z), peak area, and retention time (RT). 16 Signal peaks corresponding to isotopomers, adduct ions, and other product ions of known metabolites were excluded and the remaining peaks were annotated according to HMT metabolite database based on their m/z values and RTs. The areas of the annotated peaks were normalized to the internal standards and sample amounts to obtain the relative levels of each metabolite. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed using the proprietary MATLAB and R programs, respectively. 17

2.8. Metabolite detection and quantification

Metabolite profiling was conducted using LC‐TOF‐MS in both positive and negative ion modes. Ten mouse serum samples were analyzed, resulting in the detection of 210 peaks (115 in positive mode and 95 in negative mode). Peaks were matched to candidate metabolites using HMT metabolite library. The analysis was performed under the specified conditions for both modes, and the detected peaks were automatically integrated using MasterHands ver. 2.18.0.1 (developed by Keio University). Peaks with a signal‐to‐noise ratio (S/N) greater than three were automatically extracted, and the m/z, peak area, and RT were recorded. Peak area values were converted into relative areas for further analysis. Adduct (such as Na + and K+) and fragment (such as dehydrated or deammoniated ions) ions were excluded from the analysis; however, some compound‐specific adducts and fragments remained uneliminated because of their complexity.

2.9. Data processing

Alignment and matching of peaks across samples were performed based on m/z and RT values. Detected peaks were compared to all metabolites in the HMT metabolite library, using an allowable RT error of ±0.3 min and an m/z error of ±25 ppm. If a single candidate metabolite matched multiple peaks, a suffix was added to distinguish between the peaks. Metabolites above the limit of detection (LOD) of at least 43% (in total 210 metabolites, 74 without HMDBIDs [The Human Metabolome Database Identifications] and 45 without NDs were excluded) of all samples were used for statistical analysis. As a preprocessing step for the data, eps (≅0) is used as a substitute for ND and standardization (μ = 0, σ = 1) is performed. Raw data were analyzed using PCA, partial least squares (PLS) discriminant analysis, and heatmap generation through MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). All data were normalized to the peak levels prior to further analysis.

2.10. Statistical analysis and graphical illustrations

The data are shown as means ± standard deviations for in vitro analysis and means ± standard errors for in vivo analysis. We used Student's t‐test for the cell proliferation assay. p‐Values <.05 were considered statistically significant. All statistical analyses were performed using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Graphical illustrations were created with BioRender.com (https://www.biorender.com).

3. RESULTS

3.1. High‐fat diet promotes tumor growth and the proliferation of EOC cells in vivo and in vitro

To investigate the effect of diet on tumor growth in EOC, KURAMOCHI, and RMG‐I cell lines were SC injected into SCID mice (Figure 1A). After the tumor volume reached approximately 100 mm3, the mice were fed ND or HFD for 25 days (KURAMOCHI) and 17 days (RMG‐I). The average tumor volume of KURAMOCHI xenografts in HFD‐fed mice was 1553 ± 133 mm3 as compared to 767 ± 87 mm3 in ND‐fed mice at identical time points (p < .05) (Figure 1B). Similarly, the average tumor volume of RMG‐I xenografts in HFD‐fed mice was 1077 ± 21 mm3 as compared to 487 ± 66 mm3 in ND‐fed mice at identical time points (p < .05) (Figure 1B). These results indicated that HFD promoted tumor growth in EOC cell lines.

FIGURE 1.

FIGURE 1

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High‐fat diet promotes tumor growth and cell proliferation in epithelial ovarian cancer cells. (A) Severe combined immunodeficient (SCID) mice were subcutaneously (SC) injected with 5 × 106 KURAMOCHI or RMG‐I cells. Once the tumor volume reached approximately 100 mm3, the mice were assigned to either normal diet (ND, 13 kcal% fat) or high‐fat diet (HFD, 60 kcal% fat) and fed for 25 days (KURAMOCHI) and 17 days (RMG‐I). The tumor volume was measured twice a week throughout the experimental period. (B) HFD significantly promoted tumor growth in KURAMOCHI and RMG‐I cells in SCID mice compared to that of ND‐fed mice. The mean tumor volume in KURAMOCHI was 1553 ± 133 mm3 in HFD‐fed mice and 767 ± 87 mm3 in ND‐fed mice (p = .024), whereas in RMG‐I, it was 1077 ± 21 mm3 in HFD‐fed mice and 487 ± 66 mm3 in ND‐fed mice (p = .0017). (C) Serum samples were collected from non‐tumor‐bearing SCID mice that were fed either ND or HFD for 2 weeks, following the protocol described in Section 2. (D) Cell proliferation assay after administration of ND‐ or HFD‐fed mouse serum. HFD‐fed mouse serum significantly enhanced the proliferation of KURAMOCHI and RMG‐I cells compared to ND‐fed mouse serum (p < .001).

Next, to assess the tumor‐promoting effect of mouse serum in vitro, we obtained mouse serum after feeding mice with ND or HFD. We fed non‐tumor‐bearing SCID mice for 2 weeks and collected serum from ND (n = 10) and HFD (n = 10)‐fed mice (Figure 1C). Serum extracted from ND‐ or HFD‐fed mice was administered to KURAMOCHI and RMG‐I cells in vitro. After 24 h of incubation, HFD‐fed mouse serum significantly promoted cell proliferation in vitro compared to ND‐fed mouse serum (p < .05) (Figure 1D). These results demonstrated that HFD promoted tumor growth in EOC and lipid molecules in HFD‐fed mouse serum contributed to the proliferation of EOC cells.

3.2. Cholesterol (C27H46O) is a key molecule contributing to EOC cell proliferation in HFD‐fed mouse serum

To analyze the differences between ND‐ or HFD‐fed mouse serum and detect the specific lipid molecules responsible for promoting cell proliferation with HFD, we conducted a comprehensive metabolite profile in the serum of non‐tumor‐bearing ND‐ or HFD‐fed mice by metabolome analysis. According to the same procedure and schedule as in the experimental design (Figure 1C), serum samples were collected from non‐tumor‐bearing ND‐ (n = 5) and HFD‐ (n = 5) fed mice. Metabolome analysis was performed by LC‐TOFMS. Based on the m/z and RT values of the substances registered in the HMT Metabolite Library, we detected 210 peaks (115 positive and 95 negative) (data not shown) and assigned candidate compounds. We used 91 metabolites for subsequent analysis, excluding those not detected and those without HMDBID.

To reduce dimensionality and visualize variations in the metabolomic data, PCA was performed on the 91 selected metabolites. Each ellipse in the PCA plot indicated the type of diet and the distribution of metabolite variance. The first principal component (PC1) explained 46.5% of the variance (among the 91 metabolites obtained from 10 samples), the second principal component (PC2) explained 32.6% of variance; a total of 79.1% of the variance was explained by the first two principal components. These results suggest that important features were well captured in the current analysis. The metabolomic profiles of ND‐ and HFD‐fed mouse serum were different in PCA (Figure 2A). For the unsupervised statistical analysis of these samples, we performed hierarchical clustering analysis using 91 metabolites. Cluster analysis demonstrated that serum samples from ND‐ and HFD‐fed mice were completely divided into each cluster (Figure 2B). In addition, to detect which metabolites contribute to the proliferation of EOC in HFD‐fed mouse serum, we performed PLS discriminant analysis. In PLS discriminant analysis, important molecules were valued with variable importance in projection score (VIP score) (Figure 2C). Many metabolites showed high scores in HFD‐fed mouse serum (Figure S1), and cholesterol (C27H46O) was the lipid molecule with the highest score (Figure 2D). These findings suggest that cholesterol (C27H46O) in HFD‐fed mouse serum plays a critical role in promoting EOC cell proliferation, highlighting its potential as a key metabolic factor in EOC progression.

FIGURE 2.

FIGURE 2

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Metabolome analysis with the serum of non‐tumor bearing normal diet (ND)‐ or high‐fat diet (HFD)‐fed mice. (A) Principal component analysis (PCA) shows apparent different metabolic profiles between ND‐ or HFD‐fed mouse serum. The first principal component (PC1), accounting for 46.5% of the total variance, captures the most significant variation in the dataset. The second principal component (PC2), accounting for 32.6% of the variance, represents additional variation orthogonal to PC1. Metabolites from ND‐ or HFD‐fed mouse serum form distinct clusters along the PC1 axis, suggesting that metabolites of HFD‐fed mouse serum induce substantial metabolic changes compared to metabolites of ND‐fed mouse serum. (B) Unsupervised hierarchical clustering using metabolites from ND‐ (N1–N5) and HFD‐ (F1–F5) fed mouse serum. Each row and column represent metabolites and samples, respectively. The analysis revealed two distinct major clusters, completely separating ND and HFD samples, indicating clear metabolic differences associated with dietary intervention. (C) Partial least squares (PLS) discriminant analysis revealed the significant metabolites in ND‐ or HFD‐fed mouse serum, ranked with descending order of variable importance in projection (VIP) scores along the primary PLS components. The metabolites numbers shown on the left correspond to the metabolites listed on the table of the figure. Based on normalized values, the gradient on the right indicates the relative abundance of these metabolites, with red representing higher abundance and blue representing relatively lower abundance. (D) Metabolome analysis showed that cholesterol (C27H46O) levels were significantly higher in HFD‐fed mouse serum compared to ND‐fed mouse serum (p < .0001), indicating a higher abundance of cholesterol (C27H46O) associated with HFD.

3.3. Cholesterol (C27H46O) promotes EOC cell proliferation and activates Akt/mTOR signaling pathway

As per previous reports, high cholesterol (C27H46O) in serum of patients with cancer contributed to cancer progression of colon and breast cancers. 18 , 19 To investigate the function of cholesterol (C27H46O) in EOC, we administered cholesterol (C27H46O) to EOC cell lines in vitro. By cell proliferation assay, we demonstrated that cholesterol (C27H46O) significantly promoted the cell proliferation of EOC cell lines (KURAMOCHI and RMG‐I) (p < .05) (Figure 3A). Although these experiments were performed with 1% FBS, cholesterol (C27H46O) utilization was enhanced at lower FBS concentrations (data not shown), suggesting that the lipid requirement of EOC cells is increased in the nutrient‐poor tumor microenvironment.

FIGURE 3.

FIGURE 3

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Cholesterol (C27H46O) promotes cell proliferation and induces accumulation of intracellular lipid droplets in epithelial ovarian cancer (EOC) cells. (A) KURAMOCHI and RMG‐I cells were incubated in Roswell Park Memorial Institute (RPMI) 1640 medium with 1% fetal bovine serum (FBS) (1% FBS‐RPMI) for starvation. After 24 h of incubation, the medium was replaced with 500 mM cholesterol (C27H46O) + 1% FBS‐RPMI for 24 h, and cell proliferation was assessed using the WST‐8 assay. Cholesterol (C27H46O) significantly promotes cell proliferation of KURAMOCHI and RMG‐I cells (p < .01 and p < .001, respectively). (B) KURAMOCHI and RMG‐I cells were cultured in the same manner as in (A). Western blot analysis demonstrated that cholesterol (C27H46O) strongly promoted phosphorylation of Akt and mTOR in KURAMOCHI and RMG‐I cells. (C) Representative images of immunofluorescence staining. Cells were seeded and cultured following the protocol described in the Materials and Methods section. In both KURAMOCHI and RMG‐I cell lines, panels on the left side represent the control group, whereas panels on the right side represent the cholesterol (C27H46O) group in which cells were cultured with cholesterol (C27H46O)‐supplemented medium. Nuclei shown on the top were stained with Hoechst 33258 (blue staining), and intracellular lipid droplets (LDs) shown in the middle were stained with Lipi‐Red (red staining, indicated by arrows). The cholesterol (C27H46O) group contains more intracellular LDs than the control group. In other words, these results suggest that cholesterol (C27H46O) promotes intracellular LDs accumulation in both EOC cell lines. Bar, 20 μm. Abbreviations: ADP, adenosine diphosphate; AEA, anandamide (arachidonoylethanolamide); GAPDH, Glyceraldehyde‐3‐phosphate dehydrogenase; n.s.: not significant; p‐mTOR, phosphorylated mammalian target of rapamycin; p‐Akt, phosphorylated protein kinase B (Akt); RRID, Research Resource Identifier; t‐Akt, total protein kinase B (Akt); t‐mTOR, total mammalian target of rapamycin (mechanistic target of rapamycin); 15(S)‐HETE‐3, 15(S)‐hydroxyeicosatetraenoic acid‐3.

In previous study, Akt/mTOR signaling pathway was activated by cholesterol (C27H46O) in prostate cancer, and we analyzed the expression of Akt/mTOR signaling pathway related proteins by western blot. 20 After administration of cholesterol (C27H46O), the phosphorylation of Akt and mTOR were increased in KURAMOCHI and RMG‐I cells (Figure 3B), suggesting that cholesterol (C27H46O) activated Akt/mTOR signaling pathway in EOC.

3.4. Cholesterol (C27H46O) promotes the accumulation of intracellular lipid droplets

Although we confirmed that cholesterol (C27H46O) enhanced the cell proliferation of EOC and activated the Akt/mTOR signaling pathway, its impact on intracellular LDs remained unclear. To investigate this, we stained LDs using Lipi‐Red and observed that the accumulation of intracellular LDs in KURAMOCHI and RMG‐I cells was significantly increased following cholesterol (C27H46O) administration, particularly under low FBS conditions (Figure 3C). This finding indicates that cholesterol (C27H46O) enhances the accumulation of intracellular LDs, which may be utilized to overcome metabolic stress via lipid metabolism.

3.5. Lipid rafts inhibitor (MβCD) inactivated Akt/mTOR signaling pathway and inhibited subsequent cell proliferation

To further investigate the direct effect of cholesterol (C27H46O) on Akt/mTOR signaling pathway, we focused on lipid rafts. Lipid rafts are the membrane microdomains highly enriched in cholesterol (C27H46O) and sphingolipids and act as scaffolds for signaling pathways such as Akt/mTOR in the cell membrane. 21 A recent study has demonstrated that lipid rafts are potential therapeutic targets for cancer. 22 Cholesterol (C27H46O) significantly promoted cell proliferation of EOC cells (p < .01 and p < .0001, respectively), and MβCD showed no effect on cell proliferation in single agent treatment. However, MβCD strongly suppressed cell proliferation of EOC cells under cholesterol (C27H46O) administration (p < .0001, respectively), suggesting that MβCD inhibited cholesterol‐induced cell proliferation (Figure 4A). Moreover, western blot analysis demonstrated that cholesterol (C27H46O) strongly promoted phosphorylation of Akt and mTOR in EOC cells. Although MβCD did not promote phosphorylation of Akt and mTOR in single agent therapy, it suppressed cholesterol‐induced phosphorylation of Akt and mTOR, suggesting that MβCD disrupted lipid rafts, inactivated the Akt/mTOR signaling pathway, and inhibited subsequent cell proliferation (Figure 4B).

FIGURE 4.

FIGURE 4

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Methyl‐β‐cyclodextrin (MβCD) disrupted lipid rafts, inactivated Akt/mTOR signaling pathway, and inhibited subsequent cell proliferation. (A) KURAMOCHI and RMG‐I cells were incubated in Roswell Park Memorial Institute (RPMI) 1640 medium with 1% fetal bovine serum (FBS) (1% FBS‐RPMI) for starvation. After 24 h of incubation, the medium was replaced with (i) 1% FBS‐RPMI as control, (ii) 500 mM cholesterol (C27H46O) + 1% FBS‐RPMI, (iii) 0.3 mM MβCD + 1% FBS‐RPMI, and (iv) 500 mM cholesterol (C27H46O) + 0.3 mM MβCD + 1% FBS‐RPMI for 24 h, and cell proliferation was assessed using the WST‐8 assay. Cholesterol (C27H46O) significantly promoted cell proliferation of KURAMOCHI and RMG‐I cells (p < .01 and p < .0001, respectively), and MβCD showed no effect on cell proliferation in single agent treatment. However, MβCD strongly suppressed cell proliferation of KURAMOCHI and RMG‐I cells under cholesterol (C27H46O) administration (p < .0001, respectively), suggesting that MβCD inhibited cholesterol‐induced cell proliferation. (B) KURAMOCHI and RMG‐I cells were cultured in the same manner as in (A). Western blot analysis demonstrated that cholesterol (C27H46O) strongly promoted phosphorylation of Akt and mTOR in KURAMOCHI and RMG‐I cells. Same as (A), MβCD did not promote phosphorylation of Akt and mTOR in single agent therapy; however, it suppressed cholesterol‐induced phosphorylation of Akt and mTOR, suggesting that MβCD disrupted lipid rafts, inactivated Akt/mTOR signaling pathway and inhibited subsequent cell proliferation.

3.6. High‐cholesterol (C27H46O) diet promotes tumor growth in EOC cells

To further validate the effect of cholesterol (C27H46O) in vivo, we developed HCD and fed mice with ND or HCD (Figure 5A). Mice SC injected with RMG‐I cells were fed either ND or HCD for 39 days after the average tumor volume reached approximately 100 mm3. The average tumor volume of RMG‐I cells xenografts in HFD‐fed mice was 1264 ± 90 mm3 as compared to 815 ± 48 mm3 in ND‐fed mice at identical time points (p < .05) (Figure 5B). These results demonstrate that dietary exogenous cholesterol (C27H46O) promotes tumor progression in EOC, further supporting its role as a key metabolic factor in EOC growth.

FIGURE 5.

FIGURE 5

Open in a new tab

High‐cholesterol diet (HCD) promotes tumor growth in EOC. (A) Severe combined immunodeficient (SCID) mice were subcutaneously (SC) injected with 5 × 106 RMG‐I cells. Once the tumor volume reached approximately 100 mm3, mice were assigned to either normal diet (ND, 4.5% fat) or HCD (5% cholesterol in 10% fat). Tumor volume was measured twice per week over a period of 39 days. (B) HCD significantly promoted tumor growth of RMG‐I in SCID mice compared to ND. (The mean tumor volume in HCD‐fed mice was 1264 ± 90 and 815 ± 48 mm3 in ND‐fed mice; p = .008).

4. DISCUSSION

In this study, we demonstrated that HFD promoted tumor growth in EOC, and cholesterol (C27H46O) in HFD‐fed mouse serum activated the Akt/mTOR signaling pathway via lipid rafts and subsequent cell proliferation. Moreover, MβCD inhibited lipid metabolism in EOC. To the best of our knowledge, this is the first report to identify the following findings: (i) identify high cholesterol (C27H46O) levels in HFD‐fed mouse serum using metabolome analysis; (ii) demonstrate the activation of lipid metabolic pathways by cholesterol (C27H46O); (iii) propose a novel therapeutic approach for EOC by targeting cholesterol metabolism.

Omics analysis is a comprehensive approach that integrates multiple biological datasets, including genome, transcriptome, proteome, and metabolome. 23 , 24 This method has been widely used to elucidate the complex molecular mechanisms underlying cancer. 24 , 25 This method enables a comprehensive understanding of the molecular profiles of cancer, mechanisms of carcinogenesis and cancer progression, discovery of prognostic markers, establishment of diagnostic methods, development of new treatments, and investigation of drug resistance mechanisms. 24 , 25 , 26 Unlike genomic, transcriptomic, and proteomic analyses that indirectly infer metabolic activity, metabolome analysis reflects real‐time biochemical changes and facilitates a deeper understanding of cancer metabolism and potential therapeutic targets. 27

In cancer cells, metabolic characteristics are dramatically altered to adapt to the tumor microenvironment, a phenomenon known as metabolic reprogramming. The tumor microenvironment is often nutrient‐deficient; thus, cancer cells reprogram their metabolic pathways to maintain cell proliferation and survival in nutrient‐deficient environments. 28 , 29 In metabolic reprogramming, lipid metabolism is an important process against lack of oxygen and nutrients to obtain energy and develop cell membranes characterized by increased lipid uptake, lipid synthesis, fatty acid oxidation, and lipid storage. 30 , 31 In EOC, lipid metabolic reprogramming is a key driver of tumor progression, facilitating cell growth and survival by regulating the energy supply and lipid metabolism. 32 These findings highlight the critical role of lipid metabolism as a potential therapeutic target in EOC.

Various lipid species, including cholesterol, fatty acids, phospholipids, and sphingolipids, have been implicated in tumor progression in many cancers. 21 , 33 , 34 In ovarian cancer, the activation of fatty acid metabolism has been reported. In fact, our metabolome analysis showed that oleic acid and arachidonic acid are highly detected in HFD mouse serum. Interestingly, our data demonstrated that the VIP score of cholesterol (C27H46O) was higher than these fatty acids, suggesting that cholesterol (C27H46O) acts as a key driver of lipid metabolism in EOC.

Cholesterol (C27H46O) is a critical molecule in lipid metabolism and plays a fundamental role in cellular homeostasis, modulating membrane composition, regulating intracellular signaling cascades, and participating in key metabolic pathways. 35 Studies have shown that compared to untransformed cells, cholesterol (C27H46O) synthesis is increased in cancer cells. 36 Additionally, cholesterol (C27H46O) serves multiple functions in tumor biology, including acting as a structural component of plasma membranes, serving as a precursor for steroid hormone biosynthesis, and promoting lipid rafts formation. 35 Lipid rafts are defined as dynamic, cholesterol (C27H46O)‐ and sphingolipid‐enriched microdomains within the cell membrane that regulate membrane fluidity and permeability, facilitate receptor clustering including epidermal growth factor receptor (EGFR), leading to enhanced activation of oncogenic pathways such as the Akt/mTOR signaling pathway. 37

In this study, we confirmed that cholesterol (C27H46O) induced cell proliferation was accompanied by the activation of the Akt/mTOR signaling pathway via lipid rafts function and facilitates tumor cell growth. Previous studies have demonstrated that the Akt/mTOR signaling pathway is activated by cholesterol (C27H46O) in prostate, breast, and colon cancers. 21 , 33 , 34 In EOC, our report is the first evidence that exogenous cholesterol (C27H46O) activates the Akt/mTOR signaling pathway. Moreover, we show that disruption of lipid rafts by removing cholesterol (C27H46O) from the cell membrane inactivates the Akt/mTOR signaling pathway, suggesting a potential therapeutic strategy targeting lipid rafts. There are several approaches to remove cholesterol (C27H46O) within lipid rafts. 50 One is to restrict exogenous cholesterol (C27H46O) uptake from dietary or extracellular sources, and the other is to inhibit the synthesis and utilization of cholesterol (C27H46O) in cancer cells, such as with MβCD and statins. For the former approach, we hypothesized that disrupting lipid rafts could be achieved using a cholesterol (C27H46O)‐removing agent.

Diet has been recognized as a factor influencing cancer prognosis. 38 However, in EOC, there is limited evidence regarding the impact of diet on survival outcomes, and the role of cholesterol (C27H46O) in cancer progression remains poorly understood. 39 , 40 , 41 Nonetheless, our findings suggest that HFD promotes tumor progression. In the present study, cholesterol (C27H46O) was elevated in HFD‐fed mouse serum compared to ND‐fed mouse serum, suggesting that cholesterol (C27H46O) contributes to cancer cell growth, whereas HFD did not affect the concentration of lipoprotein that contains cholesterol (C27H46O) (Figure S2). These findings indicate that lipoprotein concentration alone is insufficient to assess these effects, highlighting the need for further investigation.

For inhibition of cholesterol (C27H46O) synthesis, statins, originally developed as HMG‐CoA reductase inhibitors for the treatment of cardiovascular diseases, have emerged as potential anticancer agents. 42 Several preclinical and epidemiological studies have suggested that statins have cancer‐preventive and therapeutic effects in prostate, colorectal, breast, and other cancers. 43 , 44 , 45 Statins markedly improve the prognosis of patients with EOC. 46 Statins have also been reported to inhibit angiogenesis, induce apoptosis, and overcome drug resistance. 47 Although this study focused on exogenous cholesterol (C27H46O), comprehensive inhibition of cellular cholesterol (C27H46O) utilization requires not only inhibition of exogenous cholesterol (C27H46O) uptake but also inhibition of endogenous cholesterol (C27H46O) synthesis. MβCD is a commonly used lipid rafts inhibitor that extracts cholesterol (C27H46O) from lipid rafts in the cell membrane and directly disrupts the integrity of lipid rafts in vitro and in vivo. 48 , 49 However, lipid rafts inhibitor has not been clinically established yet. From this perspective, it is rational to use statins, which are already in clinical use, and combining statins with lipid rafts inhibitor may result in even stronger antitumor effects.

This study had several strengths. To the best of our knowledge, this is the first study to analyze important lipid molecules by metabolome analysis and compare ND‐ and HFD‐fed mouse serum. As far as we have investigated, no studies have identified lipid molecules in serum affected by HFD and evaluated their effects on cancer cells in vitro and in vivo.

We also analyzed the function of cholesterol (C27H46O) in lipid rafts in EOC cell membranes and demonstrated that inhibition could be a new treatment option for EOC. Moreover, although functional analysis was not performed in this study, other lipid molecules related to lipid metabolism in EOC were identified by metabolome analysis, and investigation of these lipid molecules could contribute to the analysis of lipid metabolic pathways in EOC.

This study has certain limitations. Although many studies have already shown that cholesterol (C27H46O) is an important molecule that composes lipid rafts, how cholesterol (C27H46O) is uptaken into cells has not been elucidated in previous reports, nor has it been clarified in this study.

In addition, our metabolome analysis showed that many metabolites are highly detected in HFD mouse serum; however, we have not yet analyzed the interaction of these metabolites. Systematic metabolism is not controlled by a single molecule; it is necessary to evaluate the complex metabolic activities of multiple molecules. Furthermore, because extremely high cholesterol (C27H46O) concentrations were set in vitro and in vivo experiments, it is unknown what cholesterol (C27H46O) levels actually affect cell proliferation of EOC. In addition, it remains to be proven whether the antitumor effect of cholesterol (C27H46O) inhibition selectively acts on cancer cells in vivo.

Our findings suggest that targeting cholesterol (C27H46O) metabolism represents a promising therapeutic approach to EOC. Inhibition of cholesterol (C27H46O) synthesis and uptake, and disruption of lipid rafts inactivates signaling pathways and inhibits tumor growth.

The present study clearly demonstrated the changes in metabolic profiles in serum due to HFD by metabolome analysis, among which cholesterol (C27H46O) plays an important role in tumor growth. Previous studies have suggested a link between lipid metabolism and tumor progression; however, the specific molecular mechanisms underlying this link are unknown. In this study, we demonstrated that cholesterol (C27H46O) activates the Akt/mTOR signaling pathway via lipid rafts and promotes tumor growth as shown in the graphical abstract. We additionally showed that lipid rafts inhibition by MβCD inhibited tumor growth, presenting a potential new therapeutic strategy that is different from the conventional inhibition of cholesterol (C27H46O) synthesis by statins. These lipid‐targeting strategies exploit the unique metabolic dependencies of EOC cells and may lead to the development of selective and effective therapeutic options to improve patient outcomes.

5. CONCLUSIONS

Metabolome analysis revealed that HFD increased serum cholesterol (C27H46O) and it is an important molecule for EOC in cell proliferation via lipid rafts and the Akt/mTOR signaling pathway. In addition, inhibition of lipid rafts significantly suppressed EOC cell proliferation. Inhibition of the lipid metabolism pathway via cholesterol (C27H46O) may be a new therapeutic approach for EOC treatment.

AUTHOR CONTRIBUTIONS

HS‐M, KH, and YU conceived and designed the study. TN contributed to the study concept and design, gave advice on the analytic approach to be taken. HS‐M, KH, MK, SN, SM, TI, and TM analyzed the data. HS‐M, KH created the figures and tables and interpreted the results. HS‐M, KH, MK, SN, IT, YU drafted the manuscript and HS‐M, KH, TM, SM, TN, TK, and MK edited with others. HS‐M and KH revised the manuscript. KH is the corresponding authors of the study. All authors have read and agreed to the published version of the manuscript. All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

CONFLICT OF INTEREST STATEMENT

All authors state no conflict of interest and have received no payment in preparation of this study.

ETHICS STATEMENT

All animal experiments were conducted according to the Institutional Ethical Guidelines for Animal Experimentation of Osaka University, reported in accordance with Animal Research and following the Reporting of In Vivo Experiments guidelines (Assurance Number 21007‐A).

Supporting information

Data S1. Supporting Information.

IJC-159-257-s001.pdf (253.2KB, pdf)

ACKNOWLEDGMENTS

We are grateful to the members of the Department of Obstetrics and Gynecology, Pathology Laboratory, Graduate School of Medicine, Osaka University for their valuable discussions and suggestions throughout this project. We also thank Naoto Mukaida, Hazuki Abe and Kanako Sakiyama for their technical and administrative assistance in the preparation of this manuscript. Especially, Masashi Akada helped with many additional experiments in revised manuscript. Additionally, we would like to acknowledge Editage (https://www.editage.jp/) for their English language editing.

DATA AVAILABILITY STATEMENT

The raw metabolomic data generated in this study have been deposited in the Metabolomics Workbench under Study ID ST004065, Project ID PR002552 (datatrack ID 6140), and are publicly accessible via DOI: https://doi.org/10.21228/M8S25M. All other relevant data that support the findings of this study are available from the corresponding author upon request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1. Supporting Information.

IJC-159-257-s001.pdf (253.2KB, pdf)

Data Availability Statement

The raw metabolomic data generated in this study have been deposited in the Metabolomics Workbench under Study ID ST004065, Project ID PR002552 (datatrack ID 6140), and are publicly accessible via DOI: https://doi.org/10.21228/M8S25M. All other relevant data that support the findings of this study are available from the corresponding author upon request.

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