Abstract

   Cryptotanshinone (CT), an active component of the traditional Chinese
   medicine Salvia miltiorrhiza Bunge, exhibits a wide range of biological
   and pharmacological activities. Although the anticancer activity of CT
   is well known, the knowledge of its effect on the regulation of cancer
   cell metabolism is relatively new. The present study investigated the
   anticancer mechanism of CT in ovarian cancer with a focus on cancer
   metabolism. CCK8 assays, apoptosis assays, and cell cycle assays were
   conducted to reveal the growth-suppressive effect of CT on ovarian
   cancer A2780 cells. To explore the potential underlying mechanisms of
   CT, the changes in endogenous metabolites in A2780 cells before and
   after CT intervention were investigated using the gas
   chromatography–mass spectrometry (GC-MS) approach. A total of 28
   important potential biomarkers underwent significant changes, mainly
   involving aminoacyl-tRNA biosynthesis, energy metabolism, and other
   pathways. Changes in the ATP and amino acid contents were verified with
   in vitro and in vivo experiments. Our results indicate that CT may
   exert an anti-ovarian cancer effect by inhibiting ATP production,
   promoting the protein catabolic process, and inhibiting protein
   synthesis, which may lead to cell cycle arrest and apoptosis.

   Keywords: Cryptotanshinone, A2780 cells, ovarian cancer, cell
   metabolomics, GC-MS

1. Introduction

   Ovarian cancer is considered one of the most fatal cancers among
   malignant tumors [[38]1]. Difficulties in the early diagnosis of
   ovarian cancer due to a lack of effective screening strategies lead to
   high morbidity and mortality. The estimated numbers of new ovarian
   cancer cases and deaths in China were 55,342 and 37,519, accounting for
   17.63% and 18.10% of global cases, respectively [[39]2]. Surgery and
   chemotherapy are currently the mainstream treatment options for ovarian
   cancer. However, the prognosis of ovarian cancer remains very poor due
   to severe toxicity and the development of drug resistance against
   prevailing chemotherapy drugs [[40]3]. Therefore, the development of
   more potent anticancer drugs with fewer side effects and a novel
   mechanism of action is extremely important.

   Natural products have played and continue to play a significant role in
   the treatment of various ailments, including cancer. It is estimated
   that approximately 60% of all anticancer drugs available on the market
   at present are derived from natural products [[41]4,[42]5].
   Cryptotanshinone (CT) is a naturally occurring bioactive molecule of
   the quinine family that has received increased attention from the
   scientific community due to its impressive antitumor activity against a
   wide range of human cancers. The potential molecular mechanisms
   involved in the anticancer activity of CT include the inhibition of
   cell proliferation, migration, and invasion; the induction of
   apoptosis; the modulation of estrogen and androgen receptors; the
   reversion of drug resistance; and the inhibition of aberrantly
   activated cell survival signaling pathways [[43]6,[44]7,[45]8,[46]9].
   Apart from its anticancer activity as monotherapy, CT has been shown to
   improve the anticancer activity of various clinical anticancer drugs in
   combination therapy [[47]10]. Recently, CT has been reported to inhibit
   the growth of breast and ovarian cancer by regulating glucose
   metabolism [[48]11,[49]12]. However, the detailed mechanism of this
   metabolic regulation remains unknown.

   Aberrant metabolism is one of the hallmarks of cancer cells, and the
   metabolic pathways are important targets of cancer therapy [[50]13].
   Gas chromatography–mass spectrometry (GC-MS) has been extensively used
   in metabolomics and plays a significant role in the treatment of
   cancers [[51]14]. In this study, we aimed to investigate the
   alterations in the metabolic profiles of ovarian cancer cells after CT
   intervention and elucidate the mechanism of CT in ovarian cancer cells
   by identifying biomarkers and the corresponding metabolic pathways. The
   anticancer activity of CT was evaluated in A2780 ovarian cancer cells
   and a xenograft tumor model. This investigation provides ideas for
   studying antitumor mechanisms in traditional Chinese medicine.

2. Results

2.1. Effect of CT on Cell Viability Assay

   The CCK-8 results show that CT significantly inhibited the survival of
   A2780 cells in a dose-dependent manner ([52]Figure 1A). The IC[50]
   values of the A2780 cells treated with CT were 11.39 and 8.49 μM after
   24 and 48 h, respectively. Based on the IC[50] values and the living
   cell metabolites, a 10 μM CT dose and 24 h drug exposure time were
   selected for further metabolomics research.

Figure 1.

   [53]Figure 1
   [54]Open in a new tab

   CT inhibited growth of A2780 cells, promoted G1-phase cell cycle
   arrest, and induced apoptosis. (A) Cell viability of the A2780 cells
   untreated or treated with various concentrations of CT for 24 and 48 h.
   (B) Flow cytometry analysis of cell cycle profile of A2780 cells
   treated with CT (10 μM) for 24 h. The percentage of cells in the G1, S,
   or G2/M phases of the cell cycle is indicated. (C) Apoptotic rate of
   A2780 cells untreated or treated with CT (10 and 20 μM) for 24 h.
   Differences between two groups were analyzed using Student’s t-test.
   *** p < 0.001 compared with the control group. Data are expressed as
   mean ± SD (n = 3).

2.2. Analysis of Cell Cycle Profiles and Apoptosis

   Flow cytometry was employed to detect the apoptosis and cell cycle
   profiles of the control and CT treatment groups. Compared with the
   control group, the percentage of cells in the G1 phase in the CT
   treatment group significantly increased with a subsequent decrease in
   the S and G2/M phases ([55]Figure 1B). Moreover, CT significantly
   induced apoptosis in A2780 cells in a dose-dependent fashion
   ([56]Figure 1C).

2.3. GC-MS Cell Metabolomics Analysis

2.3.1. Discriminant Analysis of Model

   A total ion flow chromatogram of metabolites in both untreated and
   treated groups analyzed using GC-MS is shown in [57]Figure 2. After
   normalization and standardization, PCA was performed using SIMCA
   software ([58]Figure 3A). The QC samples were concentrated at the
   origin and clustered tightly, indicating that the method was stable and
   repeatable. Additionally, the CT treatment group and the blank group
   were significantly separated, indicating that the cell metabolites in
   the two groups were significantly different from each other.

Figure 2.

   [59]Figure 2
   [60]Open in a new tab

   GC-MS total ion chromatograms of the A2780 cells. (A) Control group.
   (B) CT-treated group.

Figure 3.

   [61]Figure 3
   [62]Open in a new tab

   Multivariate analysis derived from total ion chromatograms (n = 6). (A)
   Principal component analysis (PCA) score plot of QC, Con (control
   group), and CT (CT-treated group). (B) OPLS-DA score plots. (C) OPLS-DA
   validation plot: R^2 = 0.869; Q^2 = −0.502.

   Furthermore, orthogonal partial least squares discriminant analysis
   (OPLS-DA) was used to filter the irrelevant signals and obtain the
   OPLS-DA model ([63]Figure 3B). The values of the R^2Xcum, R^2Ycum, and
   Q^2Ycum fitting parameters were 0.694, 0.999, and 0.969, respectively,
   suggesting that the model had good stability and high predictability.
   To avoid over-fitting, cross-validation was used to test the quality of
   the model, and 200 permutation tests were performed on the model. After
   cross-validation, the intercepts of R^2 and Q^2 with the Y-axis were
   0.869 and −0.502, respectively, indicating that the model was not
   over-fitting ([64]Figure 3C).

2.3.2. Identification of Differential Metabolites

   [65]Table 1 shows the p-value and VIP value calculated with SIMCA. A
   total of 28 different metabolites were identified according to p < 0.05
   and VIP > 1. Aspartic acid, malic acid, N-acetyl-aspartic acid, and
   oleic acid were down-regulated via CT. Oleic acid, citric acid,
   deoxidization cytidine triphosphate, pantothenic acid, cysteine,
   serine, phenylalanine, tyrosine, threonine, valine, isoleucine,
   glutamic acid, glycine, putrescine, alanine, methionine, lysine,
   3-hydroxy proline, N-acetyl-glutamic acid and glutaminases,
   4-hydroxyproline, stearic acid, γ-aminobutyric acid, palmitic acid, and
   3-phosphoglyceric acid were up-regulated. [66]Figure 4 presents the
   heat map of the difference between the metabolite distributions of the
   blank group and the treatment group. Red represents the up-regulation
   of intracellular metabolite content, and blue represents
   down-regulation. The results show that there were significant
   differences in the endogenous metabolites between the cells treated
   with CT and the blank group.

Table 1.

   Different metabolites in cells after CT treatment.
   No. Name Average Rt (min) p Value VIP Fold Change Trend
   1 Aspartic acid 7.989 0.0000 1.68 0.40 ↓ ***
   2 Citric acid 9.746 0.0000 1.60 2.80 ↑ ***
   3 2′-Deoxycytidine -5’-triphosphate 7.765 0.0001 1.58 3.03 ↑ ***
   4 Pantothenic acid 10.642 0.0001 1.56 1.67 ↑ ***
   5 Malic acid 7.781 0.0001 1.53 0.65 ↓ ***
   6 Cysteine 8.224 0.0002 1.52 2.17 ↑ ***
   7 Serine 6.946 0.0003 1.50 2.21 ↑ ***
   8 Phenylalanine 8.676 0.0003 1.52 2.01 ↑ ***
   9 Valine 5.914 0.0003 1.50 1.78 ↑ ***
   10 Tyrosine 10.367 0.0004 1.50 2.35 ↑ ***
   11 Threonine 6.495 0.0005 1.46 1.76 ↑ ***
   12 Isoleucine 6.484 0.0006 1.47 1.87 ↑ ***
   13 L-Pyroglutamic acid 8.034 0.0009 1.47 1.83 ↑ ***
   14 Glycine 5.150 0.0009 1.45 1.58 ↑ ***
   15 Putrescine 9.314 0.0011 1.40 2.01 ↑ **
   16 Alanine 5.024 0.0013 1.41 1.61 ↑ **
   17 N-Acetyl-aspartic acid 8.819 0.0015 1.40 0.74 ↓ **
   18 Methionine 8.000 0.0023 1.37 1.67 ↑ **
   19 Lysine 10.265 0.0036 1.37 2.08 ↑ **
   20 Trans-3-hydroxy-proline 8.018 0.0043 1.30 2.31 ↑ **
   21 N-Acetyl-glutamic acid 9.397 0.0050 1.30 1.86 ↑ **
   22 Glutamine 9.074 0.0065 1.28 2.81 ↑ **
   23 Oleic acid 11.619 0.0067 1.27 0.77 ↓ **
   24 Trans-4-hydroxy-proline 8.040 0.0107 1.22 1.42 ↑ *
   25 Stearic acid 11.718 0.0174 1.14 1.10 ↑ *
   26 γ-Aminobutyric acid 3.781 0.0216 1.15 1.06 ↑ *
   27 Palmitic acid 10.813 0.0262 1.07 1.10 ↑ *
   28 3-Phosphoglycerate 9.688 0.0455 1.05 1.32 ↑ *
   [67]Open in a new tab

   Note: * p < 0.05, ** p < 0.01, and *** p < 0.001 compared with the
   control group. ↑: upward adjustment; ↓: downward adjustment.

Figure 4.

   [68]Figure 4
   [69]Open in a new tab

   Heat map of different cell metabolite distributions in control and
   CT-treated groups (n = 6).

2.3.3. Metabolic Pathway Analysis

   We imported the 28 differential metabolites into MetaboAnalyst 5.0 for
   online pathway analysis to study the changes in metabolic pathways
   after CT intervention [[70]15,[71]16]. The circles in the metabolic
   pathway diagram represent contribution values, and the larger the
   circle, the greater the contribution ([72]Figure 5A). The metabolic
   pathway analysis found that CT interfered with the biosynthesis as well
   as the metabolism of various molecules. As shown in [73]Figure 5B, CT
   interfered with the biosynthesis of aminoacyl-tRNA, pantothenic acid,
   CoA, arginine, valine, leucine, isoleucine, phenylalanine, tyrosine,
   tryptophan, and unsaturated fatty acids in A2780 cells. Similarly, CT
   also interfered with alanine, glutamic acid, aspartic acid, glycine,
   serine, threonine, glutamine, glutamate, phenylalanine, glyoxylic acid,
   dicarboxylic acid, cysteine, methionine, and glutathione metabolism.
   These pathways may be closely related to the therapeutic function of CT
   in ovarian cancer. To analyze the relationships between different
   metabolites, we combined the KEGG database with the related literature
   to establish a metabolic network diagram between 28 CT biomarkers and
   metabolic pathways ([74]Figure 6). The biomarkers of CT intervention in
   A2780 cells are mainly related to amino acid metabolism, and different
   metabolites participate in the tricarboxylic acid cycle (TCA) through
   the connections between intermediate products and acetyl-CoA.

Figure 5.

   [75]Figure 5
   [76]Open in a new tab

   Score plot for metabolic pathway analysis. (A) Pathway analysis of
   differential metabolites between control group and CT-treated group.
   (a) Phenylalanine, tyrosine, and tryptophan biosynthesis, (b) alanine,
   aspartate, and glutamate metabolism, (c) phenylalanine metabolism, (d)
   glycine, serine, and threonine metabolism, (e) glutathione metabolism.
   (B) Overview of the pathway enrichment.

Figure 6.

   [77]Figure 6
   [78]Open in a new tab

   Schematic diagram of the network of metabolic pathways associated with
   different metabolites. Red indicates downward adjustment; blue
   indicates upward adjustment.

2.4. Determination of Intracellular ATP and Amino Acid Contents

   The intracellular ATP contents of the A2780 cells were measured to
   assess the effect of CT on energy metabolism. It was shown that ATP
   decreased significantly after treatment with CT for 12 and 24 h. The
   ATP content was 16.23 nmol/mg in the blank group, while the ATP
   contents in the treated group were 4.66 nmol/mg and 2.23 nmol/mg after
   12 and 24 h, respectively ([79]Figure 7A).

Figure 7.

   [80]Figure 7
   [81]Open in a new tab

   Effects of CT on intracellular ATP and amino acid contents. (A) ATP
   contents in A2780 cells in the absence or presence of CT (10 μM) for 12
   and 24 h. (B) Amino acid contents in A2780 cells in the absence or
   presence of CT (10 μM) for 24 and 48 h. Differences between two groups
   were analyzed using Student’s t-test. *** p < 0.001 compared with the
   control group. Data are expressed as mean ± SD (n = 3).

   In addition, we compared the amino acid contents in the cells to
   preliminarily test the levels of amino acid metabolism. Compared with
   the control group, the amino acid contents in the CT-treated group were
   significantly up-regulated ([82]Figure 7B).

2.5. Effect of CT on A2780 Xenograft Tumor Growth

   Compared with the mean xenograft tumor volume in the blank groups, the
   volume of the transplanted tumor in the experimental group decreased
   significantly ([83]Figure 8A,B). We discovered that CT significantly
   reduced the volume and weight of subcutaneous xenograft tumors. The
   amino acid contents in both serum and tissue increased in the CT groups
   ([84]Figure 8C,D), whereas the ATP contents decreased ([85]Figure 8E).

Figure 8.

   [86]Figure 8
   [87]Open in a new tab

   Effects of CT on xenograft tumor growth and the amino acid and ATP
   contents. (A) Representative image of tumor growth in control and CT
   groups (n = 4). (B) Tumor growth curves in control and CT groups (n =
   4). (C) The effect of CT on amino acid contents in serum (n = 5). (D)
   The effect of CT on amino acid contents in tumor tissue (n = 5). (E)
   The effect of CT on ATP contents in tumor tissue (n = 3). Data are
   expressed as mean ± SD. Differences between two groups were analyzed
   using Student’s t-test. ** p < 0.01 and *** p < 0.001 compared with the
   control group.

3. Discussion

   The KEGG pathway enrichment analysis showed that CT had the most
   significant effects on the aminoacyl-tRNA biosynthetic pathway,
   suggesting that CT caused dysfunction in protein syntheses. It was
   demonstrated that CT attenuated tumor growth by activating the
   AMP-activated protein kinase (AMPK) signaling pathway. The activation
   of the AMPK signaling pathway leads to the suppression of mTOR
   activity, which is responsible for the inhibition of protein synthesis
   [[88]17,[89]18]. Altered aminoacyl-tRNA biosynthesis may cause
   oxidative stress, which may ultimately sabotage the cellular and
   metabolic activities of tumor cells [[90]19]. The disordered
   glutathione metabolism in the CT treatment group may be related to the
   increases in the intracellular glycine, cysteine, putrescine, and
   pyroglutamate contents. It is known that glutathione is involved in
   redox regulation and plays an important role in the antioxidant defense
   system and the oxidation-dependent regulation system [[91]20]. It was
   reported that serine promotes the survival and proliferation of cancer
   cells [[92]21] and regulates the cell anti-oxidant system [[93]22]. The
   increase in the serine content in the cells of the treatment group
   suggests that CT may disrupt the antioxidant system in A2780 cells.

   The results show that the malic acid, aspartic acid, and
   acetyl-aspartic acid contents in the cells of the treatment group were
   down-regulated. Citric acid and malic acid are important components of
   the TCA cycle. It has been revealed that the malate–aspartate shuttle
   promotes the transfer of NADH from the cytoplasm to mitochondria and
   oxidative phosphorylation, which is crucial for maintaining a high
   glycolysis rate and supporting the rapid growth of tumor cells
   [[94]23]. CT treatment was found to affect branched-chain amino acid
   metabolites, such as valine and isoleucine, which are not only involved
   in protein synthesis, but are also related to energy metabolism
   [[95]24]. In the treatment group, it was found that the metabolic
   pathways of glyoxylic acid and dicarboxylic acid, which are closely
   related to energy metabolism, were changed and may be related to the
   differentiation ability of tumor cells [[96]23]. Pantothenic acid, as
   an intermediate product of the TCA cycle, participates in the synthesis
   of acetyl-CoA [[97]25]. Our results show that the intracellular
   pantothenic acid content increased after CT treatment, which indicates
   that the cell energy metabolism was out of balance. The glycine,
   serine, and threonine contents increased in cells treated with CT. It
   is known that glycine, serine, and threonine provide energy via the
   conversion of pyruvate to acetyl-CoA to enter the TCA cycle [[98]26].
   The changes in the metabolites in the above pathways suggest that CT
   may affect the energy metabolism in A2780 cells, causing the imbalance
   between cell energy metabolism and cell activity inhibition.

   The experimental results show that the contents of aspartic acid and
   glutamic acid, which are the raw materials in the synthetic process of
   pyrimidine, increased in cells treated with CT [[99]27]. The increase
   in deoxycytidine triphosphate in the treatment group cells suggests
   that CT may affect the nucleotide metabolism in A2780 cells.

   To investigate the effects of CT on energy metabolism and amino acid
   metabolism, we further detected the ATP and amino acid contents in
   vitro and in vivo. CT treatment led to a significant reduction in
   intracellular ATP levels, which was also confirmed in vivo. Changes in
   the ATP contents were consistent with the results obtained in the
   metabolomics study. Numerous studies have confirmed that amino acid
   metabolism is deregulated in many cancers [[100]28,[101]29,[102]30].
   Amino acids are potential biomarkers of ovarian cancer and are related
   to tumor growth, playing an important role in cancerogenesis
   [[103]31,[104]32]. A previous study revealed abnormalities in the
   serum-free amino acid profiles of ovarian cancer patients [[105]33]. In
   our study, CT increased amino acid contents in A2780 cells as well as
   in the serum and tumor tissue of the xenograft models. This observation
   is consistent with the aforementioned report.

   We observed increased levels of glutamine in the CT group compared with
   the control group. Glutamine plays a vital role in the TCA cycle by
   providing a carbon source [[106]34]. It can be transformed into
   α-ketoglutarate, which is a TCA cycle intermediate for energy
   recycling. The up-regulation of glutamine after CT treatment may be
   attributed to the inhibition of glutamine metabolism and the TCA cycle.
   Glutamine metabolism is involved in both glycolysis and the TCA cycle.
   It has been demonstrated that CT inhibited glycolysis via the
   STAT3/SIRT3 signaling pathway in ovarian cancer cells [[107]12]. The
   inhibition of glycolysis may lead to a compensatory increase in
   glutamine [[108]35].

   The down-regulation of the intracellular oleic acid content and the
   up-regulation of the palmitic acid and stearic acid content suggest
   that CT may affect the lipid metabolism in A2780 cells.

   CT treatment increased the content of the neurotransmitter metabolite
   γ-aminobutyric acid in A2780 cells and decreased the content of
   N-acetyl-aspartic acid. Moreover, the increases in the phenylalanine
   and tyrosine contents in the treatment group may be related to
   neurotransmitter production [[109]36]. Neurotransmitters can play a key
   role in the regulation of tumor cells and can influence the formation
   and development of tumors. Experimental data have shown that
   γ-aminobutyric acid receptors are widely expressed in a variety of
   cancer tissues and cancer cells and are related to the progression of
   ovarian cancer [[110]1,[111]37,[112]38]. N-acetyl-aspartic acid is
   considered as a marker for the diagnosis of complex cystic masses such
   as benign teratoma, endometriosis, and tubo-ovarian abscesses
   [[113]39]. Furthermore, its ratio to choline has proved to be a
   reliable biomarker for differentiating between borderline and malignant
   epithelial ovarian tumors [[114]40]. Therefore, the antitumor effect of
   CT may be related to neurotransmitter disorders, and neurotransmitters
   such as γ-aminobutyric acid may offer potential as biomarkers.

4. Materials and Methods

4.1. Materials

   High-purity (≧98%) CT, Cell counting Kit-8 (CCK8), and the Annexin
   V-FITC Apoptosis Detection Kit were purchased from Dalian Meilun
   Biotechnology Co., Ltd. (Dalian, China). DMEM medium (high glucose) was
   purchased from Hyclone Company (Hyclone, Logan, UT, USA). Fetal bovine
   serum (FBS) was obtained from Gibco (Grand Island, NY, USA). The BCA
   Protein Assay Kit and propidium iodide were purchased from Shanghai
   Yeasen Biotechnology Co., Ltd. (Shanghai, China). RNaseA was obtained
   from Thermo Fisher Scientific Inc. (Waltham, MA, USA). The ATP Assay
   Kit was purchased from Shanghai Biyuntian Biotechnology Co., Ltd.
   (Shanghai, China). Dimethyl sulfoxide (DMSO) was purchased from Beijing
   Solarbio Science and Technology Co., Ltd. (Beijing, China). Myristic
   acid-1,2-^13C[2], N, O-Bis (trimethylsilyl) trifluoroacetamide (BSTFA),
   trimethylchlorosilane (TMCS), pyridine, and methoxamine were purchased
   from Sigma-Aldrich (St. Louis, MO, USA).

4.2. Cell Lines

   The A2780 Human ovarian cancer cell line was obtained from the European
   Collection of Authenticated Cell Cultures (ECACC). A2780 cells were
   cultured in DMEM supplemented with 10% FBS and cultured at 37 °C with
   5% CO[2].

4.3. Cell Proliferation Assay

   The A2780 cells were incubated in 96-well plates at 1 × 10^4 cells/well
   with 100 µL of DMEM. Following treatment with 4, 8, 12, 16, and 20 μM
   of CT for 24 and 48 h, 10 μL of CCK-8 solution was added to each well,
   and they were incubated at 37 °C for 1 h under dark conditions.
   Absorbance was measured at 450 nm with a microplate reader, and the
   cell survival rate and half inhibitory concentration (IC[50]) were
   calculated.

4.4. Apoptosis and Cell Cycle Analysis

   Apoptosis assays were conducted as follows: the cells were seeded into
   6-well tissue culture plates and treated with 0 and 10 μM of a
   CT-medicated medium for 24 h. The cells were harvested and resuspended
   in a binding buffer. Then, Annexin V-FITC and PI were added, and the
   cells were incubated at room temperature for 15 min in the dark
   according to the instructions. The cells were analyzed using flow
   cytometry after 200-mesh screening.

   The cell cycle analysis was conducted as follows: A2780 cells were
   treated with 0 and 10 μM of CT for 24 h. The cells were collected and
   resuspended in 300 μL of PBS. An amount of 700 µL of precooled absolute
   ethanol was slowly added to fix the cells at 4 °C for 3 h. After the
   samples were washed twice with PBS, 500 μL of a precooled PBS-Pi-RNaseA
   mixture (PI: 50 μg/mL; RNase A: 20 μg/mL) was added to each sample,
   which was then resuspended and incubated at room temperature for 15 min
   in the dark. The cells were analyzed using flow cytometry after
   200-mesh screening.

4.5. GC-MS Sample Preparation

   A2780 cells in the logarithmic growth phase were seeded in 6-well
   plates (5×10^5 cells/well) and divided into blank groups and
   experimental groups. The cells were treated with DMSO or CT (10 μM) for
   24 h. Six parallel wells were set up for each group. Then, the
   supernatant was discarded, and the cells were washed 3 times with
   pre-chilled PBS. Cellular metabolism was quenched by adding liquid
   nitrogen, and 300 μL of pure water was added for storage at −80 °C.
   Following 3 freeze–thaw cycles (freezing at −80 °C for 60 min and
   melting at 37 °C for 30 min), 20 μL of each sample was used to measure
   the protein concentration with a BCA kit. An amount of 400 μL of
   methanol solution (containing 4 μg of myristic acid-1,2-^13C[2] as an
   internal standard) was added to the remaining samples, and they were
   sonicated for 1 min. QC samples were prepared by vortex mixing 30 μL
   samples. The remaining samples and QC samples were centrifuged for 2
   min (14,000 rpm) at 4 °C. An amount of 300 μL of each supernatant was
   placed into a new tube and evaporated in a centrifugal concentrator, 30
   μL of a 40 mg/mL solution of methoxamine hydrochloride in pyridine was
   added, and the mixture was vortexed for 5 min. After oscillating in a
   constant-temperature oscillator for 1.5 h (300 rpm) at 30 °C, 30 μL of
   BSTFA solution containing 1% TMCS was added to the supernatant, and the
   mixture continued oscillating for 10 min at 4 °C (18,000 rpm). An
   amount of 50 μL of supernatant was removed and analyzed using GC-MS.
   The QC samples were analyzed before sample analysis and after every 4
   samples were analyzed to investigate the stability of the analysis
   system.

4.6. GC-MS Analysis Conditions

   GC-MS was performed using a Thermo Trace 1310 GC-MS system (Thermo
   Fisher Scientific, USA). First, 1 μL of each sample was injected and
   separated with a TG-5MS GC column (30 m × 0.25 mm, 0.25 μm; Thermo
   Fisher, USA) with a split ratio of 20:1. Helium (purity > 99.99%) was
   used as the carrier gas and maintained at a constant flow rate of 1.2
   mL/min. The initial oven temperature was held at 60 °C for 1 min, and
   then the temperature was increased to 320 °C at a rate of 20 °C/min,
   and finally maintained at 320 °C for 5 min. The electron energy was 70
   eV. The transmission line and ion source temperatures were 250 and 280
   °C, respectively. MS data were acquired in full-scan mode with a mass
   range of 50–500 m/z and the collection time was 3.5–19 min.

4.7. Data Processing and Analysis

   Collected GC/MS data were converted using an ABF Converter, and the
   MS-DIAL software was employed to identify and align the peaks. The
   FiehnLib database was used to match the chromatographic peaks with
   metabolites, and metabolites with a similarity greater than 80% were
   further matched with fragment ion peaks using the NIST library. The
   peak area was corrected with the internal standard peak area and
   protein concentration. SIMCA 14.0 software was applied to perform
   unsupervised principal component analysis (PCA) and supervised
   orthogonal partial least squares discriminant analysis (OPLS-DA)
   combined with orthogonal noise filtering (OSC). The data were
   normalized, and the different metabolites were screened according to
   VIP values (VIP > 1) and p-values (p < 0.05). The metabolites, peak
   values, and other information were imported into the MetaboAnalyst 5.0
   database for cluster analysis. After Pareto scaling, the Ward
   clustering algorithm was used to draw heat maps, and the hypergeometric
   test was used for path enrichment analysis. Metabolic pathways were
   screened according to p-values (p < 0.05).

4.8. Determination of Intracellular ATP Contents

   A2780 cells were seeded in 6-well plates and treated with 0 and 10 μM
   of CT for 12 and 24 h, respectively. An amount of 200 μL of lysate was
   added to each well to lysate the cells. Then, the supernatants were
   centrifuged for 5 min at 4 °C (12,000 rpm). The ATP concentrations were
   calculated with the ATP assay kit (Beyotime Biotechnology Co., Ltd.,
   Shanghai, China) according to the standard curve, and protein
   concentrations were determined using the BCA kit (Beyotime
   Biotechnology Co., Ltd., Shanghai, China).

4.9. Determination of Intracellular Amino Acid Contents

   A2780 cells were treated with 0 and 10 μM of CT for 24 and 48 h. The
   cells were harvested, and amino acid contents were determined using a
   micro amino acid content assay kit (Beijing Solarbio Science and
   Technology Co., Ltd., Beijing, China). The amino acid contents were
   normalized to the corresponding protein concentrations.

4.10. Mouse Xenograft Models

   Female BALB/c nude mice (5 weeks old; 16–18 g) were purchased from
   Jiangsu GemPharmatech Biotechnology Co., Ltd. (Nanjing, China). All the
   mice were kept in an SPF-level laboratory animal room and fed with
   sterilized food and water at the Animal Center of the Nanjing
   University of Chinese Medicine. The animal studies were approved by the
   Committee on Animal Research and Ethics of the Nanjing University of
   Chinese Medicine. All animal procedures were carried out following the
   National Institutes of Health Guide for the Care and Use of Laboratory
   Animals.

   A2780 cells in the logarithmic growth stage were selected to prepare
   cell suspensions with 2 × 10^7 cells/mL concentrations. To establish
   the A2780 human ovarian cancer xenograft tumor model, each nude mouse
   was subcutaneously inoculated with 100 μL of cell suspension in the
   left anterior axillary region. Tumor growth was observed 10 days after
   inoculation. The A2780 tumor-bearing mouse model was successfully
   established until the diameter of the tumor volume reached 2 mm. The
   mice were randomly divided into blank and experimental groups and
   received intraperitoneal injections containing 10 mg/kg of CT or normal
   saline every 3 days.

   The long diameters (a) and short diameters (b) of the tumors were
   measured every 2 days using vernier calipers. The tumor volumes were
   calculated using the following formula: V (mm^3) = 3.14ab^2/6. Tumor
   growth was observed continuously for 2 weeks, and tumor growth curves
   were drawn.

   After 2 weeks of inoculation, all nude mice were sacrificed, and blood
   was collected. The tumors were stripped and weighed, and 1 mL of blood
   was collected from the orbital veins. Serum was separated and stored at
   −80 °C. The amino acid contents in serum and tumor tissue were
   determined according to the instructions of the amino acid content
   assay kit. The ATP content in tumor tissue was determined according to
   the instructions of the ATP assay kit.

5. Conclusions

   In the present study, the inhibitory effect of CT was evaluated in
   A2780 human ovarian cancer cells and a xenograft tumor model. CT
   significantly induced cell cycle arrest and apoptosis in A2780 cells in
   vitro and inhibited tumor growth in vivo. The antitumor mechanism of CT
   was further clarified using GC-MS-based cellular metabolomics. The
   significantly altered metabolites were mainly associated with amino
   acid metabolism and energy metabolism, which are involved in the
   aminoacyl-tRNA biosynthesis pathway; alanine, aspartate, and glutamate
   metabolism; and pantothenate and CoA biosynthesis. Consistent with the
   metabolomics results, CT increased the amino acid contents and
   decreased the ATP contents in A2780 cells and xenograft tumors. This
   study reveals new insights into the underlying anticancer mechanism of
   CT and provides references for the discovery of ovarian cancer