Abstract
Background: Xihuang Wan (XHW), a purgative and detoxifying agent, is
commonly utilized in modern medicine as a treatment and adjuvant
therapy for various malignancies, including breast cancer, liver
cancer, and lung cancer. A clinical study demonstrated the potential
usefulness of the combination of XHW and gemcitabine as a therapy for
pancreatic cancer (PC), indicating that XHW’s broad-spectrum antitumor
herbal combination could be beneficial in the treatment of PC. However,
the precise therapeutic efficacy of XHW in treating pancreatic cancer
remains uncertain.
Aim: This study assessed the biological activity of XHW by optimizing
the therapeutic concentration of XHW (Xihuang pills, XHP). We performed
cell culture and developed an animal test model to determine whether
XHP can inhibit pancreatic cancer (PC). We also applied the well-known
widely targeted metabolomics analysis and conducted specific
experiments to assess the feasibility of our method in PC therapy.
Materials and Methods: We used UPLC/Q-TOF-MS to test XHP values to set
up therapeutic concentrations for the in vivo test model. SW1990
pancreatic cancer cells were cultured to check the effect the
anti-cancer effects of XHP by general in vitro cell analyses including
CCK-8, Hoechst 33258, and flow cytometry. To develop the animal model,
a solid tumor was subcutaneously formed on a mouse model of PC and
assessed by immunohistochemistry and TUNEL apoptosis assay. We also
applied the widely targeted metabolomics method following Western blot
and RT-PCR to evaluate multiple metabolites to check the therapeutic
effect of XHP in our cancer test model.
Results: Quantified analysis from UPLC/Q-TOF-MS showed the presence of
the following components of XHP: 11-carbonyl-β-acetyl-boswellic acid
(AKBA), 11-carbonyl-β-boswellic acid (KBA),
4-methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and
(1S-endo)-2-methyl-3-methylene-2-(4-methyl-3-3-pentenyl)-bicyclo
[2.2.1heptane]. The results of the cell culture experiments
demonstrated that XHP suppressed the growth of SW1990 PC cells by
enhancing apoptosis. The results of the animal model tests also
indicated the suppression effect of XHP on tumor growth. Furthermore,
the result of the widely targeted metabolomics analysis showed that the
steroid hormone biosynthesis metabolic pathway was a critical factor in
the anti-PC effect of XHP in the animal model. Moreover, Western blot
and RT-PCR analyses revealed XHP downregulated CYP3A4 expression as an
applicable targeted therapeutic approach.
Conclusion: The results of this study demonstrated the potential of XHP
in therapeutic applications in PC. Moreover, the widely targeted
metabolomics method revealed CYP3A4 is a potential therapeutic target
of XHP in PC control. These findings provide a high level of confidence
that XHP significantly acts as a CYP3A4 inhibitor in anti-cancer
therapeutic applications.
Keywords: pancreatic cancer, Xihuang pills, CYP3A4, widely targeted
metabolomics, steroid hormone biosynthesis
1 Introduction
Pancreatic cancer (PC) is one of the most significant neoplasms of the
gastrointestinal tract. The prevalence of risk factors, including those
related to stressful environments and the consumption of processed
foods has been increasing dramatically ([48]Rahib et al., 2014;
[49]Chen et al., 2016; [50]Moore and Donahue, 2019). Worldwide, PC
ranks first among cancer-related diseases and fourth and sixth in China
([51]Rahib et al., 2021). Despite treatments and delivery methods such
as chemotherapy, the survival rate of patients with PC is relatively
low, with <10% of patients surviving for 5 years or more ([52]He et
al., 2022). Moreover, increasing the quality of life of patients with
PC remains challenging.
Increasing attention has been paid to the use of herbal medicine such
as traditional Chinese medicine in the treatment of various diseases
including cancer. The advantages of herbal medicine-based therapy
include multi-targeting, multi-channeling, structural stability, high
safety, and minimal side effects ([53]Rawla et al., 2019; [54]Wang et
al., 2021). Herbs such as emodin, matridin, or triptolide showed
significant improvement in therapeutic applications and reduced the
adverse effects of radiotherapy in the treatment of PC ([55]Lang et
al., 2020; [56]Wei et al., 2011; [57]Chen et al., 2013; [58]Liu et al.,
2014). Moreover, f QYHJ and Fufangkushen are among those herbs used in
the successful therapy for PC ([59]Liu et al., 2003).
Excessive dampness, heat, and toxins together with unknown factors may
contribute to serious diseases. ([60]Jin and Wang., 2014). For
instance, the aforementioned factors are risk factors for PC formation;
however, more research is needed to verify their effects. The present
study tested the hypothesis that the use of Chinese herbal medicine for
the treatment of PC clears heat, removes toxins, resolves dampness, and
disperses accumulation. We herein introduce a detoxifying agent, XHW,
which comprises Niuhuang (Bos taurus domesticus Gmelin, artificial
Niuhuang in Chinese), Shexiang (Moschus berezovskii Flerov, Moschus
sifanicus Przewalski, Moschus moschiferus Linnaeus, artificial Shexiang
in Chinese), Ruxiang (Boszvellia carterii Birdw., Boswcllia
bhaurdajiana Birdw., Ruxiang in Chinese), and Moyao (Commiphora myrrha
Engl., Commiphora molmol Engl., Moyao in Chinese). XHW is primarily
utilized for the treatment of various malignant tumors, including
breast cancer ([61]Xu et al., 2020; [62]Wu and Wang., 2020), liver
cancer ([63]Zhou, Y., 2020; [64]Liu et al., 2010), and lung cancer
([65]He et al., 2020; [66]Chen, 2020; [67]Cen, 2017) Combination
treatment is among the significant therapeutic approaches in PC. A
previous study comparing chemotherapy alone and the combination of XHW
+ chemotherapy showed that the combination therapy showed significant
tumor inhibition and suppression of CA19-9, a PC biomarker. Reduced
chemotherapy-induced leukopenia was also observed ([68]Zhang et al.,
2010).
The drawbacks of Chinese herbs include their complexity in formulation,
which limits their wide applications in targeted therapy. However,
recent technological developments in metabolomics, proteomics,
genomics, and transcriptomics have provided new insights into the
potential use of herbal medicines for disease treatment ([69]Guo et
al., 2022). Therefore, interest is increasing regarding the therapeutic
applications of traditional Chinese medicine ([70]Zhang et al., 2022;
[71]Wu et al., 2022; [72]Pan et al., 2022).
In the present study, we formulated XHP, a novel agent derived from
XHW, and assessed its effects on the treatment of PC in an in vitro
cell culture and animal test model. We also followed up these findings
by applying widely targeted metabolomics screening for validation of
our target therapy together with Western blot and RT-PCR analyses. Our
results revealed that XHP inhibited CYP3A4, downregulating its
expression level and regulating related metabolic pathways in the
animal model. Our findings demonstrate CYP3A4 as a significant
therapeutic target and the clinical applications of Chinese herbal
medicines and their compounds.
2 Materials and methods
2.1 Animals and cells
Six-week-old male BALB/c nude mice (weight 20 ± 2 g) were obtained from
Beijing HFK Bioscience (Certificate of Conformity No. SYXK (chuan)
2008-100; China). Human SW1990 PC cells (Fuheng Biology, China) were
cultured in L-15 medium ([73]L20256, Fuheng Biology, China)
supplemented with 10% fetal bovine serum (AUS-01S-02, CellBox,
Australia) and 1% penicillin–streptomycin (15140122, Gibco, United
States), and maintained in an incubator at 37°C, 95% O[2]–5% CO[2]. All
experimental procedures were carried out in accordance with the ethical
principles for laboratory animals (R20220210-2) as stipulated by the
Science and Technology Department of Sichuan Province (Chengdu, China).
2.2 Drug preparation
XHP was supplied as samples from the Department of Pharmacy (Sichuan
Academy of Chinese Medicine Sciences). For mass analysis, one XHP
tablet was placed in a 10-mL volumetric flask and sonicated with
methanol–acetonitrile–water (4:4:2) for 30 min and filtered using a
0.22-μm microporous filter membrane. For in vivo animal studies, an
appropriate amount of XHP was crushed in a mortar and placed in a
plastic bottle in CMC-Na 0.5% (CMC-Na: hot water = 1:200) supplied by
KESHI (9004-32-4, China). The positive drug was gemcitabine
hydrochloride for injection (CTTQ Pharmaceutical, China), with saline
used as a solvent. For the cell assay, 3 g of XHP was placed in a 15 mL
centrifuge tube and dissolved in 12 mL of DMSO. After filtration, a
master batch of 250 mg/mL was obtained. The mother liquor was filtered
through a filter membrane (0.22 μm), diluted to the appropriate dose,
and stored at 4°C before use.
2.3 Chemicals and reagents
Cell Counting Kit-8 (CCK-8) was purchased from Biosharp (22082317,
China), Hoechst 33258 from Beyotime (C1011, China), annexin-V FITC
Apoptosis Kit from Beyotime (C1062L, China), antigen Ki67 from
Servicebio ([74]GB121141, China), 10% goat serum from Dr. D. Bio
(AR1009, China), secondary antibody (HRP-labeled goat anti-rabbit,
GB23303) and DAB kit from Beijing Zhongshan JinQiao Biological Co. Ltd.
(ZLI-9018, China), BCA Kit from Beyotime (P0009, China), antibody
CYP3A4 (rabbit) from CST (13384S, United States), GAPDH (mouse) and
biotinylated goat anti-mouse IgG (H + L) from ABclonal (AC033; AS033,
United States), biotinylated goat anti-rabbit IgG (H + L) from Affinity
Biosciences (S0001, United States), Molpure® Cell/Tissue Total RNA Kit
from YEASEN (19221ES50, China), PrimeScript RT reagent kit from Takara
(RR047A, China), and TB Green TM Premix Ex Taq^TM Ⅱ (Tli RNaseH Plus)
from Takara (RR820A, China).
2.4 UPLC/Q-TOF-MS for the qualitative detection of XHP components
We used a column system on an LC30A-UPLC device at the Shimadzu
Laboratory of Japan, which utilized a Kinetex XB-C18 column (100 mm ×
2.1 mm, 2.6 μm) with a mobile phase consisting of ultrapure water
(A)—acetonitrile (B), containing 0.1% formic acid The gradient elution
program started with 10% B for 1.00 min, followed by a change to 55% B
from 1.00 to 7.00 min, and then to 85% B from 7.00 to 18.00 min,
continuing until 22.00 min. Subsequently, the system was adjusted to
10% B from 22.5 min and continued until 25.00 min. The flow rate was
set at 250 μL-min^-1, and the column temperature was maintained at
30°C.
The Triple TOF 4600 Q-TOF-MS (AB SCIEX, United States) instrument uses
an ESI ion source with two types of ionization that allow for both
positive and negative ionization. The mass scan range of the instrument
was set between 100 and 1,000 m/z with the following gas pressure
settings: sheath gas, 0.38 MPa; auxiliary gas, 0.38 MPa; and curtain
gas, 0.17 MPa. Atomization was achieved using TOF/MS primary pre-scan
and the triggered secondary product Ion-IDA ion accumulation time,
which were set to 250 and 100 ms, respectively. Multi-mass-deficit
(MMDF) and dynamic background subtraction (DBS) were utilized as
secondary trigger conditions. The declustering voltage was set to 80 V,
and the collision energy (CE) was ±35 eV. The CES collision energies in
the positive and negative modes were 35 ± 15 eV and −25 ± 15 eV,
respectively. We determined the components of the XHP used in our
experiment based on reference materials, such as 11-carbonyl-β-acetyl
boswellic acid (AKBA), 11-carbonyl-β-boswellic acid (KBA),
4-methylene-2,8,8 -trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and
(1S-endo)-2-methyl-3-methylene-2-(4-methyl-3- pentenyl)-bicyclo
[2.2.1heptane, among others ([75]Yang et al., 2022). The XIC Manager
function in Peak View 1.2 (AB SCIEX, United States) was used for
preliminary screening of the compounds to gather information on
molecular ion peaks, secondary fragmentation ions, and retention times.
The compounds were then identified based on their accurate molecular
masses in conjunction with a secondary spectral fragmentation analysis.
2.5 Cell inhibition rates
We performed a CCK-8 assay to determine the inhibition by XHP of SW1990
PC cells. The cells were cultured in 96-well plates (n = 6) at a
density of 10^4 cells/well and incubated at 37°C in a 95% O[2], 5%
CO[2]incubator for 24 h. A blank control group was included and cells
were treated with XHP solution at gradient concentrations of
74.5 μg/mL, 92.7 μg/mL, 116.36 μg/mL, 145.45 μg/mL, and 181.82 μg/mL.
The cells were cultured for an additional 24, 48, and 72 h before
performing the CCK-8 assays. Spectroscopy analysis was conducted to
measure the optical density (OD) of the cells at 450 nm using an
RC/STEEVOLYZER enzyme marker (Tecan Sunrise, Switzerland).
2.6 Hoechst 33258 staining assay
Hoechst 33258 staining was used to observe changes in the cell
structure and apoptosis. In brief, SW1990 cells in logarithmic growth
phase were cultured in six-well plates at a density of 2 × 10 ^5
cells/well (n = 3) and exposed to different concentrations of XHP
(0 μmol/L, 74.5 μg/mL, 92.7 μg/mL, 116.36 μg/mL, 145.45 μg/mL, and
181.82 μg/mL) for 24 h. The treated cells were then washed with PBS (−)
twice and fixed at 4°C for 10 min with 1 mL of methanol per well. The
cells were then incubated with Hoechst 33258 dye for 10 min at room
temperature and washed 2–3 times with PBS (−). We then observed any
change in cell structure by T 5 A/H fluorescence microscopy (Carl
Zeiss, Germany). The resulting images were quantified using ImageJ
V1.8.0.
2.7 Flow cytometry
SW1990 cells were seeded in six-well plates and incubated at 37°C in a
95% O[2], 5% CO[2] incubator for 24 h. To assess the effect of XHP on
cell viability, five different concentrations of XHP and a control
group were established and incubated for 24 h. The supernatant was
collected from the six-well plates and the SW1990 cells were
dissociated using EDTA-free trypsin. The collected supernatant was then
added to the detached cells and centrifuged. The supernatant was
discarded, and the cells were washed twice with pre-cooled PBS before
being mixed and suspended for counting. Next, 10^5 cells per tube were
centrifuged at 1000 r/min for 5 min. The supernatant was discarded, and
195 µl of binding buffer was added and gently suspended. Subsequently,
5 µL of PI with 5 µI annexin V-FITC was added, the mixture was well
agitated, and the final solution was incubated for 15 min at room
temperature in the dark. Then, the solution was placed in an ice bath.
Apoptosis was measured within 1 h using a B4 flow cytometer (ACEA
NovoCyte, United States).
2.8 SW1990 PC cell xenograft model
SW1990 cells were cultured to a density of 1 × 10^7 cells/mL at 37°C in
a 95% O[2], 5% CO[2] incubator. Cells at ∼70–80% confluency were mixed
with PBS (8121733, Gibco, United States). To build a tumor-bearing
mouse model, 0.1 mL of cell suspension (approximately 2 × 10^6 tumor
cells) was subcutaneously injected near the right hind limb of nude
mice. We measured the volume of the solid tumors after approximately 1
week using the following formula:
[MATH:
v=a×b<
/mi>22 :MATH]
, where a is the length diameter and b is the short diameter. Once the
tumor size was approximately 62.5 mm^3, the mice were randomly divided
into the following five groups (n = 6): model group (which received an
equal volume of carboxymethylcellulose sodium, CMC-Na), 0.47 g/kg XHP
group, 0.93 g/kg XHP group, 1.87 g/kg XHP group, and gemcitabine group
(0.065 g/kg, as doses ≥0.0065 g/kg were lethal to mice). We report here
on the use of XHP as a reference for the clinical therapeutic
application at a specific dose based on a reported value of 1.233 g/kg,
which was derived from a standard body weight of 60 kg and a standard
body size factor of k = 12.33. We used a low dose of 0.47 g/kg and a
high dose of 1.87 g/kg based on the XHP ratio (0.76 times that of XHW).
Therefore, the medium dose was 0.93 g/kg. For the gemcitabine group, we
considered 0.065 g/kg, as ≥0.0065 g/kg is lethal to mice. Subsequently,
the XHP group received daily gavage administration, while the model
group was administered CMC-Na once daily for 12 days. In the
gemcitabine group, nude mice were intraperitoneally injected with
gemcitabine at a dose of 0.065 g/kg once every 6 days, for a total of
two doses. After completing the experiment, we measure the sizes of the
solid tumors every 3 days based on the tumor tissue mass using the
following formula: (
[MATH: Tumor inhibition rate%=
WMo
del−W<
/mi>XHPW¯×100% :MATH]
). To follow up the aforementioned solid mass measurement, the animals
were anesthetized by urethane injection. The tumor tissue was fixed in
4% paraformaldehyde (142174, Biosharp, China) and embedded in paraffin.
2.9 Immunocytochemical analysis of the antiproliferative activity of XHP
To perform the immunocytochemical assay, we followed the standard
method using cell nuclear antigen Ki67 (1:100). In brief, the collected
samples fixed in formalin were treated with 3% H[2]O[2] to block
endogenous peroxidase activity and then incubated with 10% goat serum
(1:9) before an overnight incubation at 4°C with anti-Ki67 antibodies.
Next, the samples were incubated with secondary antibodies. Finally,
staining was carried out using the DAB kit. The immunoreactivity was
then determined using a blinded method in which we counted the total
number of positive antigen cells in five high-power fields (40×) per
section.
2.10 TUNEL apoptosis
To check for apoptosis signs, the cells in the collected samples were
stained using the TUNEL (TdT-mediated dUTP nick end-labeling) apoptosis
detection kit followed by image processing using a BA210 digital
trinocular camera microscope (MOTIC, United States). The image
observation was performed for each sample at low magnification of three
representative areas (×400). The percentage of positive expression was
calculated using the Image-Pro Plus 6.0 image analysis system (Media
Cybernetics, United States).
2.11 Widely targeted metabolomics analysis
The metabolomics samples were prepared as described elsewhere. In
brief, samples stored at −80°C were first thawed on ice. The thawed
samples were homogenized using a grinder (30 HZ) for 20 s. Then, 400 μL
solution (Methanol: Water = 7:3, V/V) containing internal standard was
added to 20 mg of ground sample followed by vigorous agitation for
5 min. After incubating on ice for 15 min, the samples were centrifuged
at 12,000 rpm for 10 min (4°C). Next, 300 μL of supernatant was
collected and placed at −20°C for 30 min before centrifugation at
12,000 rpm for 3 min (4°C). Finally, 200 μL aliquots of supernatant
were transferred for further LC-MS analysis.
T3 UPLC: Extracted samples were analyzed on an LC-ESI-MS/MS system
(UPLC, ExionLC AD, [76]https://sciex.com.cn/; MS, QTRAP® System,
[77]https://sciex.com/) using the following conditions: UPLC: column,
Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); column
temperature, 40°C; flow rate: 0.4 mL/min; injection volume, 2 μL;
solvent system: water (0.1% formic acid), acetonitrile (0.1% formic
acid); and gradient program: 95:5 V/V at 0 min, 10:90 V/V at 11.0 min,
10:90 V/V at 12.0 min, 95:5 V/V at 12.1 min, and 95:5 V/V at 14.0 min.
ESI-QTRAP-MS/MS: Both LIT and triple quadrupole (QQQ) scans were set up
on a triple quadrupole-linear ion trap mass spectrometer system (QTRAP®
LC-MS/MS System). The scanner was fixed with an ESI turbo ion-spray
interface controlled by Analyst 1.6.3 software (Sciex). We set up
operation functions based on the following optimized conditions: source
temperature: 500°C; ion spray voltage (IS): 5500 V (positive), −4500 V
(negative); ion source gas I (GSI), gas II (GSII), and curtain gas
(CUR): 55, 60, and 25.0 psi, respectively. For instrument tuning and
mass calibration, we used 10 and 100 μmol/L polypropylene glycol
solutions in QQQ and LIT modes. The MRM transitions were monitored for
each period at a specific set.
2.12 Analysis of metabolomics data
We performed qualitative evaluations of the detected substances using
the MetWare database (MWDB) with reference to retention time (RT),
ion-pair information, and secondary spectral data followed by multiple
reaction monitoring (MRM) analysis utilizing triple quadrupole mass
spectrometry. Quality control analysis and mass spectrometry data were
processed using Analyst 1.6.3 software, while principal component
analysis (PCA) was performed using the statistical function prcomp
within the R programming language ([78]www.r-project.org) followed by
processing the data to UV (unit variance scaling). Heatmaps were
generated using the ComplexHeatmap package in R software to perform
hierarchical cluster analysis (HCA) of the metabolite accumulation
patterns across the samples. To select differential metabolites (VIP≥1
and |log2FC|≥1.0), orthogonal least squares-discriminant analysis
(OPLS-DA) was used to observe the classification of the two groups
([79]Thévenot et al., 2015). The identified metabolites were then
annotated using the KEGG compound database
([80]http://www.kegg.jp/kegg/compound/) and pasted into the KEGG
pathway database ([81]http://www.kegg.jp/kegg/pathway.html). We then
assessed the potential biological roles of the relevant differential
metabolites using the MetaboAnalyst enrichment analysis database
([82]http://www.metaboanalyst.ca/).
To build each metabolite and its optimized targeting, we used MetScape
([83]http://metscape.ncibi.org/) as described by [84]Gao et al. (2010)
and [85]Obi et al. (2016). To identify the interactions of proteins in
their internal structures, we built those interactions in STRING
([86]https://cn.string-db.org/) based on the aforementioned targets and
identified key targets using Cytoscape’s MCODE plugin and UALCAN online
database analysis ([87]Chandrashekar et al., 2017; [88]Feng et al.,
2019; [89]Chandrashekar et al., 2022).
2.13 Western blot
To perform Western blot analysis, each selected tumor was lysed in RIPA
solution (G2002, Servicebio, China) with steel beads in a KZ-III-F
high-speed cryogenic tissue grinder (Servicebio, China). We measured
the protein amount using a BCA protein quantification kit. In brief,
the lysed sample (100 µg) was loaded on 8%–12% polyacrylamide gels and
transferred to PVDF membranes. TBST buffer diluted with 5% skimmed milk
was then used to block the membranes for 1.5 h, followed by incubation
with CYP3A4 and GAPDH antibodies for 8–12 h, respectively. The
membranes were then incubated in biotinylated goat antibody IgG for
1 h.
2.14 RT-PCR
Reverse transcription PCR (RT-PCR) was performed using RNA extracted
from the cells using TRIzol reagent. The mRNA levels were measured in a
reaction using SYBR Green PCR mix, as previously described. The primers
for CYP3A4 (ttatgctcttcaccatgacccacag and
caatgctgcccttgttctctttgc) and β-actin
(ctacctcatgaagatcctgacc and cacagcttctctttgatgtcac) were
designed based on the NCBI database. The ΔΔCT method was used to
determine the relative gene expression levels with β-actin levels as a
reference, as previously described.
2.15 Statistical analysis
One-way ANOVA was performed using GraphPad Prism v6.0 and SPSS 23.0.
The data were expressed as means ± standard deviation (±SD). p < 0.05
was considered statistically significant.
3 Results
3.1 UPLC/Q-TOF-MS determination of the main components of XHP
As seen in [90]Figure 1A, total chromatograms for both positive and
negative ion modes were obtained according to the optimized condition
of the spectrometric instrument. Analysis of the XHP components was
based on all collected samples for the primary and secondary fragment
ions for the optimized experimental condition. The main components of
XHP identified by UPLC/Q-TOF-MS, were 11-carbonyl-β-acetyl boswellic
acid (AKBA), 11-carbonyl-β-boswellic acid (KBA), 4-methylene-2,8,8
-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and
[(1S-endo)-2-methyl-3-methylene-2-(4-methyl-3- pentenyl)-bicyclo 2.2.1]
heptane ([91]Table 1; [92]Figures 1B–E).
FIGURE 1.
[93]FIGURE 1
[94]Open in a new tab
UPLC/Q-TOF-MS for the determination of the main components of XHP (A).
TIC in positive and negative ion modes of XHP. (B) Primary and
secondary fragment ion plots of 11-carbonyl-β-acetyl boswellic acid
(AKBA). (C) Primary and secondary fragment ion plots of
11-carbonyl-β-boswellic acid (KBA). (D) Primary and secondary fragment
ion plots of 4-methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane.
(E) Primary and secondary fragment ion plots of
(1S-endo)-2-methyl-3-methylene-2-(4-methyl-3--3-pentenyl)-bicyclo
[2.2.1heptane.
TABLE 1.
UPLC/Q-TOF-MS data analysis.
Name Formula Mass (Da) Found at mass (Da) tR (min)
AKBA C[32]H[48]O[5] 513.3575 513.3485 20.28
KBA C[30]H[46]O[4] 471.3469 471.3389 17.10
4-Methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane C[15]H[24]
205.1951 205.1922 12.88
(1S-endo)-2-Methyl-3-methylene-2-(4-methyl-3--3-pentenyl)-bicyclo
[2.2.1heptane C[15]H[24] 205.1951 205.1922 18.92
[95]Open in a new tab
3.2 XHP inhibits SW1990 PC cell growth in vitro
To assess the significant anti-tumor effects of XHP on PC, we first
cultured SW1990 PC cells in the presence of various XHP concentrations
(74.5 μg/mL, 92.7 μg/mL, 116.36 μg/mL, 145.45 μg/mL, and 181.82 μg/mL).
Untreated cells in DMSO were used as the control group. At the end of
the experiment, we performed CCK-8 assays at different times (24, 48,
and 72 h). As shown in [96]Figure 2A XHP significantly inhibited PC
cells in dose- and time-dependent manners. The inhibition rates are
summarized in [97]Table 2. DMSO did not significantly impact the
results (p > 0.05). This finding demonstrates the anti-proliferative
effects of XHP on PC cells.
FIGURE 2.
[98]FIGURE 2
[99]Open in a new tab
Effect of XHP on PC tumor growth in vitro. (A) Concentration and
time-dependent inhibitory effect of XHP (92.7 μg/mL) on SW1990 cells
after 24 h of treatment. (B) Hoechst 33258 staining assays to evaluate
PC cell clonality after 24 h of XHP treatment. Scale bar, 50 µm. (C)
Induction of SW1990 cell apoptosis after XHP treatment for 24 h. The
apoptotic processes were evaluated by flow cytometry. **p < 0.01 and
****p < 0.0001.
TABLE 2.
Inhibition rate of SW1990 pancreatic cancer cells by Xihuang tablets
(±SD, n = 6).
Group Concentration (µg/mL) 24 h (%) 48 h (%) 72 h (%)
Blank 0.00 0.00 0.00 0.00
DMSO 0.4% 0.067 ± 3.697 1.119 ± 7.624 −1.262 ± 3.073
XHP 74.50 7.014 ± 2.679 26.308 ± 7.308**** 41.305 ± 7.385****
92.70 18.698 ± 8.638**** 38.330 ± 10.185**** 51.236 ± 9.727****
116.36 28.449 ± 4.610**** 66.041 ± 5.981**** 77.607 ± 6.457****
145.45 73.180 ± 2.265**** 90.036 ± 1.438**** 94.874 ± 1.248****
181.82 90.431 ± 0.924**** 96.571 ± 0.480**** 97.205 ± 0.443****
[100]Open in a new tab
Compared to blank, each group ****p < 0.0001.
3.3 XHP promotes apoptosis in SW1990 PC cells in vitro
[101]Figure 2B shows that the application of XHP had some pro-apoptotic
effects on SW1990 human PC cells. With increasing concentrations of XHP
(0 μg/mL, 74.5 μg/mL, 92.7 μg/mL, 116.36 μg/mL, 145.45 μg/mL, and
181.82 μg/mL), the apoptosis rate increased from 11.43% to 13.71%,
18.54%, 18.54%, 24.99%, 41.96%, and 53.34%, respectively ([102]Figure
2C). Our data suggest that the percentage of SW1990 PC cells undergoing
early apoptotic (annexin V + -PI-) and late apoptotic (annexin V +
-PI+) stages increased in a dose-dependent manner with increasing XHP
concentration. Notably, we observed a significant difference at
concentrations >116.36 μg/mL (p < 0.01). Taken together, our results
prove the pro-apoptotic properties of XHP in PC cells.
3.4 XHP inhibits the growth of PC xenograft models in vivo
To further analyze the anti-PC effects of XHP, we subcutaneously
injected SW1990 cells near the right hind limb of mice. Tumor volume
and weight were measured every 3 days after treatment with different
concentrations of XHP. The mice were euthanized after 12 days.
[103]Figures 3A–C show that XHP significantly reduced the tumor volume
compared to the model group (**p < 0.01, ****p < 0.0001). Moreover, the
tumor volume was lower at medium and high doses of XHP compared to
those in the positive control group administered intraperitoneal
gemcitabine hydrochloride (###p < 0.01, ###p < 0.001). No significant
differences in the body weight of mice were observed between the
positive control and XHP groups compared to the model group
([104]Figure 3D). Therefore, our data proved that XHP inhibited the
growth of PC in the mouse model. Additionally, the immunohistochemical
assay demonstrated that XHP significantly reduced the expression level
of Ki67 in a dose-dependent manner in the tumor tissues of PC mice
([105]Table 3; [106]Figure 3E) and showed stronger anti-proliferative
effects compared to the positive control group.
FIGURE 3.
[107]FIGURE 3
[108]Open in a new tab
Effect of XHP on PC tumor growth in vivo. (A) Tumor dissection in an
animal model of PC established by injecting an SW1990 cell suspension
into BALB/c nude mice. (B) Tumor volume was recorded every 3 days
during tumor growth. (C) Tumor weight was recorded after sacrifice. (D)
Body weight of mice during tumor growth was recorded every 3 days. (E)
Hematoxylin-stained nuclei (blue). DAB (brownish yellow) shows positive
expression. Scale bar: 40 µm, magnification: ×20. (F) Positive
expression: apoptotic cell nuclei light yellow or brownish yellow
color, negative expression (normal cell nuclei): blue or light blue
with white background. Scale bar: 10 µm, magnification: ×400. Compared
with the model group, each group *p < 0.05, **p < 0.01, ****p < 0.0001;
compared with the positive group, each XHP group ##p < 0.01, ###p <
0.001.
TABLE 3.
umbers of Ki67-positive positive control cells (±SD).
Group Sample Positive nuclei (
[MATH: x¯
:MATH]
± SD)
Model 3 89.44 ± 4.99
Positive control (0.065 g/kg) 3 55.89 ± 10.31^***
XHP (0.47 g/kg) 3 42.56 ± 4.35^****
XHP (0.93 g/kg) 3 31.33 ± 2.60^****##
XHP (1.87 g/kg) 3 14.89 ± 5.36^****###
[109]Open in a new tab
All groups compared to the model group, ***p < 0.001, ****p < 0.0001;
XHP all dose groups compared to the positive group, ##p < 0.05, ##p <
0.001.
3.5 XHP has pro-apoptotic effects on PC cells in vivo
TUNEL analysis was applied to evaluate the pro-apoptotic effect of XHP
on PC tumors ([110]Table 4; [111]Figure 3F). We observed that XHP
significantly affected the apoptotic rate in tumor tissues compared to
the model group (p < 0.0001). We also observed a dose-dependent
pro-apoptotic effect in the XHP group. The significant value in the XHP
group (at doses of 0.93 g/kg and 1.87 g/kg) showed higher apoptosis
rates in tumor tissues (p < 0.001). A significantly lower apoptosis
rate was observed in the 0.47 g/kg XHP group (p < 0.05).
TABLE 4.
Percentages of apoptotic cells in the tumor tissues of nude mice (%).
Statistical results (±SD).
Group Sample Positive nuclei (
[MATH: x¯
:MATH]
± SD)
Model 3 2.35 ± 0.44
Positive control (0.065 g/kg) 3 8.74 ± 0.44^****
XHP (0.47 g/kg) 3 6.66 ± 0.63^****#
XHP (0.93 g/kg) 3 12.78 ± 0.05^****###
XHP (1.87 g/kg) 3 19.28 ± 1.18^****####
[112]Open in a new tab
All groups compared to the model group, ****p < 0.0001; XHP: all dose
groups compared to the positive group, #p < 0.05, ###p < 0.001, ####p <
0.0001.
3.6 Widely targeted metabolomics analysis identifies differential metabolites
and metabolic pathways
To assess the effects of XHP on the metabolic mechanism of the PC
model, we identified and analyzed differential metabolites in mouse
tumor tissue. We then performed TIC of mass spectrometry for different
QC samples of the model and XHP groups. Our data showed higher degrees
of similarity in the curves for the total ion current of the metabolite
assay, indicating consistent retention times and peak intensities.
These results were in good accordance with the MS results when the same
sample was detected at different times ([113]Figure 4A). PCA analysis
([114]Figures 4B, C) and OPLS-DA ([115]Figures 4D, E) showed clear
metabolome separation between the model and XHP groups, Heatmaps were
then generated using the ComplexHeatmap package in R after UV (unit
variance scaling) processing, and hierarchical cluster analysis (HCA)
was applied to the accumulation patterns of 93 metabolites across
different samples. Of these metabolites, 36 were upregulated and 57
were downregulated ([116]Figure 5A). A volcano scatter plot
([117]Figure 5B) was generated to display the results of screening 63
differential metabolites based on the triple screening principle of
VIP≥1, FC ≥ 2 or FC ≤ 0.5, and p < 0.05. Each metabolite was
represented by a dot on the plot, with the degree of variation
indicated by different colors. Each row in the plot represented one
sample, and each column represented one metabolite. The top 20
differential metabolites are shown in [118]Table 5. The differential
metabolite results were subjected to KEGG pathway enrichment analysis
([119]Figures 5C, D), which identified five significantly enriched
metabolic pathways, including steroid hormone biosynthesis, cortisol
synthesis and secretion, Cushing’s syndrome steroid biosynthesis,
steroid biosynthesis, and inflammatory mediator regulation of TRP
channels. The top ten metabolites were annotated using the KEGG
database ([120]Kanehisa and Goto, 2000), which showed that the major
metabolic pathway was steroid hormone biosynthesis ([121]Figure 5E). To
further analyze the differential metabolites identified based on the
screening criteria, the significantly enriched KEGG metabolic pathways
were selected and all differential metabolites in these pathways were
clustered ([122]Figure 5F). The clustered metabolites included Ile-Tyr,
Leu-Tyr, tripterin, cortisol, urobilin, 11β-hydroxyprogesterone,
17α-hydroxyprogesterone, and desoxycortone, among others.
FIGURE 4.
[123]FIGURE 4
[124]Open in a new tab
Plots for multivariate statistical analysis (1). (A) Superimposed plot
of the total ion flow diagram (TIC plot) for the mass spectrometric
detection of QC samples. N, negative ion mode; P, positive ion mode.
(B) PCA-2D plot. PC1–PC3, first, second, and third principal
components, respectively. Percentage, interpretation rate of the
principal component to the data set. (C) PCA-3D plot: PC1–PC3, first,
second, and third principal components, respectively. (D) OPLS-DA score
plot. Horizontal coordinate, predicted principal component; horizontal
direction, gap between groups; vertical coordinate, orthogonal
principal component; vertical direction, gap within groups; percentage,
explanation rate of the component to the data set (R2X (cum) = 0.278;
R2Y (cum) = 0.982; Q2 (cum) = 0.719; pre = 1; ort = 1; p = 0.005). (E)
S-plot of OPLS-DA. Horizontal coordinate, covariance between the
principal component and the metabolite; vertical coordinate,
correlation coefficient between the principal component and the
metabolite. The closer the metabolite is to the upper right and lower
left corner, the more significant the difference. Red dots, VIP ≥ 1;
green dots, VIP ≤ 1. Each dot in the plot indicates a sample, with
samples from the same group represented using the same color.
FIGURE 5.
[125]FIGURE 5
[126]Open in a new tab
Plots for multivariate statistical analysis (2). (A) HCA diagram:
horizontal, sample name; vertical, differential metabolite information;
red, high content; green, low content; darker color, higher content;
clustering line on the left side of the diagram, metabolite clustering
line. (B) Volcano plot. Each point in the volcano plot represents a
metabolite. Green, downregulated; red, upregulated; gray, metabolites
detected but not significantly different; horizontal coordinate,
|log[2]FC| of the relative content difference of a metabolite in two
groups of samples. The larger the absolute value of the horizontal
coordinate, the larger the relative content difference between the two
groups of samples. Under the VIP + FC + p-value triple screening
condition: vertical coordinate, level of significance of the difference
(-logP-value); dot size, VIP value. (C) KEGG enrichment analysis of the
top 20 differential metabolites. Horizontal coordinate, rich factor for
each pathway, with larger values indicating greater enrichment;
vertical coordinate, pathway name (sorted by p-value), dot color,
p-value magnitude, with more red indicating more significant
enrichment. (D) Analysis of the overall changes in the KEGG pathways of
the top 20 differential metabolites. Vertical coordinate, differential
pathway name (sorted by p-value); horizontal coordinate, differential
abundance (DA) score; line segment length, absolute value of the DA
score; dot size at the end of the line segment, number of differential
metabolites in that pathway. Larger dots indicate a higher number of
metabolites. The colors of the line, and the dots reflect the magnitude
of the p-value, with closer to red indicating a smaller p-value and
closer to purple indicating a larger p-value. (E) Annotated graph of
metabolite function. Red, metabolite content significantly upregulated
in the experimental group; blue, metabolite detected but not
significantly changed; green, metabolite content significantly
downregulated in the experimental group. (F) Cluster analysis plot of
differential metabolites of the KEGG pathway. Horizontal coordinates,
sample names; vertical coordinates, differential metabolites; red, high
levels; green, low levels.
TABLE 5.
Top 20 ranked differential metabolites.
Index Formula Compound Class I VIP p-value FC Type
MEDN2000 C[15]H[22]N[2]O[4] Ile-Tyr Amino acid and its metabolites
1.75E+00 3.26E-02 1.44E+04 Up
MEDN1999 C[15]H[22]N[2]O[4] Leu-Tyr Amino acid and its metabolites
1.75E+00 3.26E-02 1.44E+04 Up
MEDP2286 C[29]H[38]O[4] Tripterin Benzene and substituted derivatives
2.23E+00 1.81E-03 8.88E+00 Up
MEDP0889 C[21]H[30]O[5] Cortisol Hormones and hormone-related compounds
2.31E+00 2.74E-04 6.82E+00 Up
MEDP1255 C[33]H[42]N[4]O[6] Urobilin Tryptamines, cholines, and
pigments 2.24E+00 2.42E-04 5.71E+00 Up
MEDP1709 C[21]H[30]O[3] 11β-Hydroxyprogesterone Hormones and
hormone-related compounds 2.11E+00 3.54E-03 4.51E+00 Up
MEDP1636 C[21]H[30]O[3] 17α-Hydroxyprogesterone Hormones and
hormone-related compounds 2.11E+00 3.54E-03 4.51E+00 Up
MEDP1635 C[21]H[30]O[3] Desoxycortone Hormones and hormone-related
compounds 2.11E+00 3.54E-03 4.51E+00 Up
MEDN0105 C[26]H[45]NO[7]S Taurocholic acid Bile acids 2.14E+00 3.48E-03
4.32E+00 Up
MEDP0315 C[9]H[9]NO[3] Hippuric acid Organic acid and its derivatives
1.89E+00 7.28E-03 3.63E+00 Up
MEDN1493 C[4]H[8]O[3] 3-Hydroxybutanoic acid Organic acid and its
derivatives 1.48E+00 3.38E-02 2.44E-01 Down
MEDN0749 C[22]H[32]O[4] 1-Mar FA 1.91E+00 2.97E-02 2.32E-01 Down
MEDN0790 C[22]H[32]O[4] PDX FA 1.91E+00 2.97E-02 2.32E-01 Down
MEDN0802 C[22]H[32]O[4] RvD5 FA 1.91E+00 2.97E-02 2.32E-01 Down
MEDN0771 C[20]H[30]O[3] 15-oxoETE FA 1.99E+00 4.47E-02 2.13E-01 Down
MEDN1444 C[22]H[32]O[3] 16-HDoHE FA 2.14E+00 3.48E-02 1.62E-01 Down
MEDN0375 C[18]H[30]O[3] 13-HOTrE FA 2.07E+00 2.10E-02 1.62E-01 Down
MEDN1442 C[22]H[32]O[3] 8-HDoHE FA 2.20E+00 3.69E-02 1.55E-01 Down
MEDN0769 C[22]H[32]O[3] 14(S)-HDHA FA 2.23E+00 2.61E-02 1.40E-01 Down
MEDN0754 C[22]H[32]O[3] (±)17-HDHA FA 2.25E+00 1.62E-02 7.94E-02 Down
[127]Open in a new tab
3.7 CYP3A4 is a key target for the anti-PC effects of XHP
Differential metabolites were selected based on the following criteria:
VIP value ≥ 1, fold change ≥2 or ≤0.5, and p ≤ 0.05. The known KEGG IDs
of the selected metabolites were obtained using MetScape (Cytoscape
plug-in). The ID information obtained in the previous step was then
imported into the system to construct a metabolite composition–target
network ([128]Figure 6A). A total of 64 relevant targets were
identified. A protein–protein interaction (PPI) network was constructed
using the STRING platform. Targets outside of the network were
excluded, and the remaining targets were imported into Cytoscape and
arranged based on their degree values ([129]Figure 6B). To further
refine the target selection, the MODE plug-in was used to obtain 13
significant targets with the highest scores (score = 7) from the
reciprocal map of targets. Additionally, KEGG enrichment analysis
revealed that these targets were significantly associated with the
steroid hormone biosynthesis metabolic pathway. The 13 targets were
subsequently analyzed in the UALCAN online database to identify key
targets, among which CYP3A4 was identified as a crucial target
([130]Figure 6C). Targets with p > 0.05 were considered non-significant
and were excluded from further analysis. Western blot (WB) experiments
indicated that XHP treatment resulted in the downregulation of CYP3A4
protein expression ([131]Figure 6D), while the RT-PCR experiments also
showed that XHP downregulated the mRNA expression of CYP3A4 in vivo.
These results suggested that CYP3A4 may be a useful key target for XHP
in the treatment of PC.
FIGURE 6.
[132]FIGURE 6
[133]Open in a new tab
CYP3A4 may be a candidate key target for XHP in the treatment of PC.
(A) Differential metabolite composition–target network diagram. Nodes,
relevant metabolites, and targets regulated by important metabolites;
edges, biochemical reactions. (B) PPI networks of the aforementioned
metabolite regulatory targets and screening of candidates by MCODE for
the highest scoring key targets. Darker colors and larger circles,
larger degree values. (C) CYP3A4 as a potential key target in PC
screened by the UALCAN online database (p < 0.05). (D) Expression
levels of CYP3A4 in XHP-treated and control pancreatic cancer (PC)
cells by both Western blot (WB) and reverse transcription polymerase
chain reaction (RT-PCR). *p < 0.05, ***p < 0.001, ****p < 0.0001.
4 Discussion
In the concept of biology, metabolites can be formed according to the
organism phenotype, in which cellular interconnection is key for
understanding the mechanisms to provide a more comprehensive view of
metabolite changes ([134]Kuang et al., 2021). Among the pathways
underlying metabolomics, targeted tumor therapy is considered one
successful therapy ([135]Dang et al., 2010; [136]Johnson et al., 2016).
Therefore, drug targeting and metabolic pathways for cancer treatment
are the core of therapeutic applications. Traditional Chinese medicine
indicates that the interaction of dampness, heat, and toxicity may
cause PC formation. In this context, XHP is primarily intended to
soften the knots and disperse the canker sores consistent with the
pathogenesis of PC. While previous studies demonstrated that the
combination of XHW and gemcitabine can alleviate clinical symptoms in
patients with PC, the direct anti-PC effects of XHW have not yet been
reported. To our knowledge, this study is the first to demonstrate the
significant efficacy of XHP in the treatment of PC. Moreover, we
identified new targets of PC using metabolomics by screening widely
targeted metabolites. The results of this analysis revealed that XHP
not only possesses anti-PC properties but also has potential inhibitory
effects on CYP3A4.
Our results showed that the administration of XHP had anti-PC cancer
effects. XHP directly inhibited SW1990 PC cell activity with
anti-proliferative and pro-apoptotic effects. Additionally, the animal
model showed that XHP significantly inhibited tumor growth. XHP is
composed of four Chinese herbal medicines; namely, Ruxiang, Moyao,
artificial Niuhuang, and artificial Shexiang. Our quality analysis
showed that XHP mainly consists of 11-carbonyl-β-acetyl boswellic acid
(AKBA), 11-carbonyl-β-boswellic acid (KBA), 4-methylene-2,8,8
-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and
(1S-endo)-2-methyl-3-methylene-2-(4-methyl-3- pentenyl)-bicyclo
[2.2.1heptane. In addition, AKBA and KBA have anti-inflammatory effects
by inhibiting the production of pro-inflammatory cytokines, such as
interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis
factor-α (TNF-α), and suppressing the activation of nuclear factor
kappa-light-chain-enhancer of activated B cells (NF-κB) ([137]Siddiqui,
2022). KBA induced apoptosis and cell cycle arrest at the G2-M phase in
non-small cell lung cancer H446 cells ([138]Agrawal et al., 2011). AKBA
significantly suppressed pro-inflammatory factors in tumor tissue and
inhibited biomarkers of tumor survival, proliferation, aggressiveness,
and angiogenesis, resulting in reduced growth and metastasis of human
colorectal cancer in vivo ([139]Huang et al., 2018). In addition, AKBA
hindered gastric cancer cell proliferation and migration and promoted
cell apoptosis through the PTEN/Akt/COX-2 signaling pathway ([140]Yadav
et al., 2012). Therefore, XHP exhibits significant anti-tumor effects.
Additionally, we found that the oral administration of XHP in the PC
mouse model greatly impacted the metabolic profiles. Cellular
metabolism represents a primary characteristic by orchestrating
aberrant metabolic reprogramming to fulfill the increased energy
requirements for sustained proliferation ([141]Sun et al., 2020).
Cancer cells can evade apoptosis through aberrant metabolic regulation
([142]Kumar et al., 2022). Our findings showed that XHP induced
alterations in the concentrations of multiple metabolites in PC mice,
suggesting that XHP may modulate metabolism. Within the XHP-treated
cohort, we observed notable modifications in the levels of metabolites
such as fatty acyls and hormones relative to those in the model group.
Genetic variation in pertinent genes can perturb steroid hormone
biosynthetic pathways and their receptors, thereby modifying an
individual’s susceptibility to gastric cancer ([143]Pan et al., 2022).
Based on this concept, the modulation of steroid hormone biosynthesis
may be a promising anti-tumor pathway; however, further studies are
needed.
We used MetScape to construct a metabolite component-target network
based on the differential metabolites identified through the
metabolomics analysis. Our results identified CYP3A4 as a potential key
target for XHP treatment of PC, which is implicated in the biosynthesis
of steroid hormones. The cytochrome P450 family (CYP) is a group of
proteins that require heme as a cofactor. While the liver is the
primary location of CYP-mediated drug metabolism, CYP enzymes are also
expressed at varying levels in extrahepatic tissues, particularly in
the small intestine, as well as the kidneys, lungs, and brain.
Moreover, CYP is expressed in a range of tumor tissues ([144]Cho et
al., 2012). CYP plays a crucial role in the metabolism of diverse
exogenous substances that can influence tumorigenesis by activating or
deactivating carcinogens, and are also closely associated with chemical
carcinogenesis ([145]Van Eijk et al., 2019). Cancer therapy depends on
the activity of the cytochrome P450 enzyme family, primarily carried
out by CYP3A4 and CYP3A5 ([146]Šemeláková et al., 2021). CYP3A4 is
involved in the metabolism of over 50% of clinically active drugs;
thus, its overexpression can lead to reduced efficacy and the
development of chemotherapeutic drug resistance, posing a major
challenge for patients with cancer ([147]Lehmann et al., 1998;
[148]Sevrioukova and Poulos., 2013). Clinically, while CYP3A inhibition
can present challenges such as inadvertent elevation of substrate drug
exposure, resulting in toxicity, it may also offer benefits. For
instance, inhibition can be advantageous because a substantial number
of drugs are rapidly metabolized by CYP3A, leading to inadequate
therapeutic plasma levels ([149]Loos et al., 2022). CYP3A4 is primarily
found in the liver and intestinal tissues. However, the expression
levels of CYP3A4 in the intestine are not correlated with those in the
liver, indicating the independent expression of CYP3A4 in different
tissues ([150]Ohno et al., 2007). Although CYP3A4 is not exclusively
associated with PC, according to the UALCAN database, CYP3A4 is highly
expressed in PC tissues and exhibits low expression in normal
pancreatic tissues. Our results further revealed that XHP can suppress
CYP3A4 expression, implying that CYP3A4 could be a therapeutic useful
target for PC treatment. However, additional investigations are
required to validate the specific mechanism of action of XHP in
regulating CYP3A4. Thus, XHP could serve not as only an effective
anti-PC therapeutic agent but also as a CYP3A4 inhibitor, similar to
drugs such as ritonavir and cobicistat ([151]Sevrioukova and Poulos,
2010; [152]Sherman et al., 2015). Additionally, XHP may play a critical
role in inhibiting resistance to chemotherapy drugs; however, further
validation is necessary to confirm this hypothesis.
Our further validation of specific marker protein levels of XHP pro-PC
apoptosis provides a more comprehensive understanding of the
therapeutic effects of XHP. Further exploration of the mechanism of
CYP3A4 inhibition by XHP may help to identify specific targets and
pathways for future drug development. In addition, investigating which
component of XHP is responsible for down-regulating CYP3A4 can inform
the development of more targeted and effective treatments for PC. Also,
combining XHP with PC chemotherapeutic agents and testing its ability
to inhibit chemotherapeutic drug resistance by suppressing CYP3A4
expression is an avenue for future research that could provide insights
into the potential clinical use of XHP as an adjunct therapy for the
treatment of PC.
5 Conclusion
Our results demonstrated that XHP hindered the proliferation of PC
cells and stimulated apoptosis both in vivo and in vitro. In addition,
the metabolomics analysis showed that XHP elicited significant anti-PC
effects by modulating the biosynthetic metabolic pathway of steroid
hormones. Furthermore, XHP is not only an effective therapeutic agent
against PC but may also function as a CYP3A4 inhibitor. In conclusion,
our investigation utilized the widely targeted metabolomics method to
screen for pivotal therapeutic targets through differential
metabolites, thereby providing novel insights for exploring the role of
herbal medicine and its compounding in disease treatment.
Acknowledgments