Abstract
Many individual herbs and herbal formulae have been demonstrated to
provide safe and effective treatment for pancreatic ductal
adenocarcinoma (PDAC); however, the therapeutic mechanisms underlying
their effects have not been fully elucidated. A total of 114 herbal
formulae comprising 216 single herbal medicines used to treat PDAC were
identified. Cluster analysis revealed a core prescription including
four herbs [Glycyrrhizae Radix et Rhizome (Gan Cao), Codonopsis Radix
(Dang Shen), Citri Reticulatae Pericarpium (Chen Pi), and Pinelliae
Rhizoma (Ban Xia)] in combination to treat PDAC, and 295, 256, 141, and
365 potential targets were screened for each of these four herbs,
respectively. PDAC-related proteins (n = 2940) were identified from the
DisGeNET database. Finally, 44 overlapping targets of herbs and PDAC
were obtained, representing potential targets of the herbal medicines
for PDAC treatment. GO enrichment analysis indicated that targets
common to herbs and PDAC primarily functioned in response to steroid
hormones. KEGG pathway enrichment analysis indicated that the herbs may
prevent PDAC by influencing apoptotic, p53, and PI3K/Akt signaling
pathways. Further, molecular docking analysis indicated that of
identified bioactive compounds, stigmasterol, phaseol, perlolyrine,
shinpterocarpin, and licopyranocoumarin have good binding ability with
proteins involved in responses to steroid hormones, while stigmasterol,
phaseol, perlolyrine, and DIOP have good binding ability with
PTGS2(also known as COX-2), ESR1, ESR2, AR, and PGR. The anti-PDAC
activity of herbal medicines may be mediated via regulation of proteins
with roles in responses to steroid hormones. This study provides
further evidence supporting the potential for use of herbal medicines
to treat PDAC.
Subject terms: Cancer, Computational biology and bioinformatics
Introduction
Despite decades of research, the outcomes of patients with pancreatic
ductal adenocarcinoma (PDAC) remain poor. Compared with favorable
prognosis tumors, PDAC is characterized by its high degree of
malignancy, insidious onset, no typical symptoms, many anatomical
sites, low resection rate, high recurrence rate and poor prognosis.
PDAC patients have an average 5-year survival rate of less than 10%,
and less than 3% for patients with advanced or metastatic diseasee. The
incidence of PDAC continues to increase and there is an urgent need to
improve survival rates.
Complementary and alternative medicines that are effective for PDAC
patients include gene therapy, immunotherapy, targeted therapy,
neoadjuvant therapy and natural medicine/herbal medicine^[40]1. Among
complementary and alternative medicines, natural products/herbal
medicines, such as Chinese herbal medicines, have become the choice of
more advanced cancer patients due to their good therapeutic effects and
fewer side effects^[41]1. For example, Ukrain(NSC-631570) is a
semisynthetic compound of thiophosphoric acid and the alkaloid
chelidonine derived from the plant Chelidonium majus, a common weed in
Europe and western Asia. It has been shown to be effective against a
range of cancers, including PDAC trials^[42]2–[43]4. It is also
important to mention that large amounts of polyphenols (curcumin,
quercetin, green tea flavanols, resveratrol and triacetylresveratrol)
have been shown to have potent antitumor, anti-inflammatory,
antioxidant and pro-apoptotic effects on various human cancers,
especially on PDAC models^[44]5–[45]7.
In the United States, about 50–60% of cancer patients use agents from
plants entirely or in combination with traditional treatment regimens
such as chemotherapy and radiation therapy^[46]8. Gemcitabine, a
nucleoside analogue, is considered to be one of the most important
chemotherapeutic agents for the treatment of PDAC. However, due to the
development of chemical resistance, it shows low reaction rate and
disease free survival. Over the past few years, many plants and plant
derivatives have been used in combination with gemcitabine, showing
promising anticancer results by targeting many signaling pathways PDAC
models in vitro and in vivo^[47]9,[48]10. The combined use of herbal
medicines with conventional chemotherapy and radiotherapy can improve
the anti-cancer efficacy and reduce the side effects. Therefore, the
development of new anti-PDAC drugs based on herbal medicines has a good
prospect of application^[49]11.
Many individual herbs and herb formulae can provide safe and effective
treatment for PDAC^[50]12–[51]14, and recent studies support a better
prognosis for patients with PDAC who receive Chinese medicine treatment
compared with those undergoing conventional treatment alone^[52]15.
Herbal formulations, including Chinese herbs, are derived from
thousands of years of human experience and practical application;
however, which specific herbs are beneficial for patients with PDAC
requires further investigation. To release the full potential of herbal
medicine for cancer therapy and broaden its application, the molecular
mechanisms underlying the therapeutic activity of herbal medicine
formulae in PDAC must be further explored.
The current study was designed to explore the herb combinations and
underlying molecular mechanisms of traditional Chinese medicine
prescriptions used to treat PDAC. In total, 114 herbal formulae tested
for the treatment of PDAC in randomized controlled experiments were
identified. The most frequently used herbs and the associations between
use of different herbs were determined. The molecular mechanism
underlying the treatment of PDAC with four core herbs frequently used
in combination [Glycyrrhizae Radix et Rhizome (Gan Cao), Codonopsis
Radix (Dang Shen), Citri Reticulatae Pericarpium (Chen Pi), and
Pinelliae Rhizoma (Ban Xia)] was further investigated. Enrichment
analysis of pathways and gene ontologies were applied to 44 protein
targets common to the core herbs and PDAC. The results suggested that
the four core herbs may function in PDAC treatment by influencing
responses to steroid hormones, as well as apoptotic, p53, and PI3K/Akt
signaling pathways. Molecular auto-dock analysis was used to validate
predicted herb compound-target interactions.
Materials and methods
Data Sources and selection criteria
PDAC treatment-related classical prescriptions were retrieved from the
published literature by screening the CNKI ([53]https://www.cnki.net/)
and PubMed ([54]https://pubmed.ncbi.nlm.nih.gov/) databases.
Only publications meeting the following criteria were selected:
randomized controlled or clinical controlled experiments to investigate
the treatment of PDAC; diagnostic criteria meeting the “Guidelines for
the Diagnosis and Treatment of Pancreatic Cancer in China”, formulated
by the Chinese Medical Association; the treatment evaluation standard
adopted was an internationally recognized universal standard; symptoms
or indicators of PDAC were improved or cured following the application
of traditional Chinese medicine prescriptions.
A dataset of standardized names was then compiled, by substituting all
of the polysemes, synonyms, and acronyms of the herbs, according to
Chinese Pharmacopoeia (2020 edition) and Chinese Materia Medica. The
frequencies of occurrence of single herbs were calculated.
Association rule and cluster analysis
Apriori algorithm-based association rule analysis was conducted and the
results plotted using SPSS Modeler software. The support degree
indicates the probability of simultaneous occurrence of two drugs, and
the confidence degree (A → B) represents the probability of occurrence
of medicine B under the condition of occurrence of medicine A. An
association network diagram of 24 drugs was constructed.
Hierarchical clustering of the 24 herbs used most frequently was
conducted using R.
Establishment of a database of core herb target genes and PDAC-related genes
The Traditional Chinese Medicine Systems Pharmacology Database and
Analysis Platform (TCMSP; [55]https://tcmsp-e.com/) was searched and
the chemical components and target genes of herbs retrieved.
PDAC-related genes were collected from the DisGeNET database
([56]https://www.disgenet.org/home/) using the keywords “Pancreatic
carcinoma” or “Pancreatic Ductal Adenocarcinoma”.
A protein–protein interaction (PPI) Network Map was conducted by
entering targets common to retrieved herbs and PDAC into the STRING
online database ([57]https://string-db.org/); species, “Homo sapiens”.
The resulting “tsv” file was imported into Cytoscape 3.9.1 software for
further analysis of the core network.
Gene ontology and KEGG pathway enrichment analysis
The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathway enrichment analysis was performed with R^[58]16,[59]17. The
results are displayed in bubble charts with homemade R scripts. Network
construction was generated using the network visualization software,
Cytoscape (ver. 3.5.0).
Molecular docking
Download the two-dimensional (2D) structure diagram of the compound
from the PubChem database, import it into Chem3D software, draw the
three-dimensional structure diagram of the compound, optimize the
energy, and save the structure in mol2 format. Next, import the files
into AutoDockTools-1.5.6 software, add charging information and display
rotatable keys, and save them in pdbqt format. Download the protein
crystal structures corresponding to the core target genes from the PDB
database, import PyMOL software to remove water molecules and
heteromolecules, import Auto-DockTools-1.5.6 software to add hydrogen
atoms, save them in pdbqt format. These compounds were used as ligands,
and the proteins were used as receptors for molecular docking analysis.
Autodock vina-1.1.2 ([60]http://vina.scripps.edu/) was used to estimate
the binding ability of the molecules and targets. Results were analyzed
and interpreted using PyMOL software and Discovery Studio 3.5 Client.
Results
High frequency herbal medicines and association rule analysis for herb
combinations
Of the 216 herbal medicines included in the 114 prescriptions
identified by literature screening, the total frequency of drug use was
1613 times (Supplementary Table [61]1). Atractylodis macrocephalae
Rhizoma (Bai Zhu), Poria (Fu Ling), Hedyotis diffusa Willd. (Bai Hua
She She Cao), Astragali Radix (Huang Qi), and Glycyrrhizae Radix Et
Rhizoma (Gan Cao) were the herbal medicines most frequently used in the
clinic (Fig. [62]1). Twenty-four Chinese medicines used more than 15
times were identified (Fig. [63]2A). These results suggest that those
herbs were preferred for treatment of PDAC.
Figure 1.
[64]Figure 1
[65]Open in a new tab
The top 10 herbs most frequently applied in treatment of PDAC and their
chief chemical components with well-defined pharmacological activities.
Chemical structures were downloaded from the Traditional Chinese
Medicine Systems Pharmacology database and PubChem
([66]https://pubchem.ncbi.nlm.nih.gov/). The chemical component of
Poria (Fu Ling) shows the main chain of pachyman, including 50
[MATH: β :MATH]
-(1 → 3) bound glucose units. “A” represent additional 47
[MATH: β :MATH]
-(1 → 3) bound glucose units.
Figure 2.
[67]Figure 2
[68]Open in a new tab
Frequently used herbal medicines and association rule analysis of herb
combinations. (A) Frequencies of individual herbs. (B) Network diagram
of association rules among the pharmacological mechanisms of herbs of
interest.
Chinese herbal medicine compatibility refers to the purposeful
combination of two or more herbs, according to clinical requirements
and pharmacodynamic effects, and is the main method used for clinical
drug application and the basis for the composition of Chinese herbal
medicine prescriptions.
Association rule analysis was carried out for 24 medicines used at high
frequency using an apriori algorithm. We focused on two parameters:
support and confidence level, where support was set as ≥ 20% and
confidence level as ≥ 80% to obtain the top 10 herb pairs and suitable
association rules (Table [69]1). The association between Atractylodis
macrocephalae rhizoma (Bai Zhu) and Poria (Fu Ling) had the highest
degree of support (52.63%), while those of Glycyrrhizae radix et
rhizoma (Gan Cao) and Atractylodis macrocephalae rhizoma (Bai Zhu) with
Poria (Fu Ling) had the highest confidence level (92%). The resulting
association network diagram is presented in Fig. [70]2B.
Table 1.
Apriori algorithm-based association rules for herbs used to treat PDAC.
No Association rules Support (%) Confidence (%)
1 Atractylodis macrocephalae rhizoma (Bai Zhu) ⇒ Poria (Fu Ling) 52.63
81.67
2 Poria (Fu Ling) ⇒ Atractylodis macrocephalae rhizoma (Bai Zhu) 51.75
83.05
3 Scutellariae barbatae herba (Ban Zhi Lian) ⇒ Hedyotisdiffusa Willd
(Bai Hua She She Cao) 28.07 81.25
4 Citri reticulatae pericarpium (Chen Pi) ⇒ Poria (Fu Ling) 26.32 80.00
5 Astragali radix (Huang Qi) and atractylodis macrocephalae rhizoma
(Bai Zhu) ⇒ Poria (Fu Ling) 26.32 80.00
6 Astragali radix (Huang Qi) and Poria (Fu Ling) ⇒ Atractylodis
macrocephalae rhizome (Bai Zhu) 25.44 82.76
7 Codonopsis radix (Dang Shen) and atractylodis macrocephalae rhizoma
(Bai Zhu) ⇒ Poria (Fu Ling) 22.81 88.46
8 Glycyrrhizae radix et rhizoma (Gan Cao) and atractylodis
macrocephalae rhizoma (Bai Zhu) ⇒ Poria (Fu Ling) 21.93 92.00
9 Coicis semen (Yi Yi Ren) and atractylodis macrocephalae rhizoma (Bai
Zhu) ⇒ Poria (Fu Ling) 21.93 82.00
10 Hedyotisdiffusa willd (Bai Hua She She Cao) and Poria (Fu
Ling) ⇒ Atractylodis macrocephalae rhizoma (Bai Zhu) 21.05 91.67
[71]Open in a new tab
Rules for combination of herbal medicines based on cluster analysis
Clustering classification is widely used to determine the compatibility
of herbs and the rules for combination of different Chinese medicines.
Here, we applied hierarchical cluster analysis to identify the core
herbs that are used in combination for treatment of PDAC. The 24 drugs
mentioned in the previous section at the highest frequency were
classified into five categories according to traditional Chinese
medicine theory (Fig. [72]3). Based on compatibility rules and clinical
experience, the core prescription used for PDAC treatment included four
herbs: Glycyrrhizae Radix et Rhizome (Gan Cao), Codonopsis Radix (Dang
Shen), Citri Reticulatae Pericarpium (Chen Pi), and Pinelliae Rhizoma
(Ban Xia). Our results showed that these four herbs, which are
frequently used in the clinic, are often used in combination to treat
PDAC.
Figure 3.
[73]Figure 3
[74]Open in a new tab
Unsupervised hierarchical cluster analysis of the 24 most frequently
used herbs.
Identification of potential targets of core prescription for PDAC treatment
To investigate the possible mechanism underlying the core prescriptions
used to treat PDAC, the targets of the four selected herbs were
obtained from the TCMSP database. In total, 295, 256, 141, and 365
potential targets were identified for Glycyrrhizae Radix et Rhizome
(Gan Cao), Codonopsis Radix (Dang Shen), Citri Reticulatae Pericarpium
(Chen Pi), and Pinelliae Rhizoma (Ban Xia), respectively. Importantly,
84 targets were shared by the four herbs, and these were defined as the
core prescription targets. Target proteins were associated with tumors
and apoptosis (e.g., TP53, TNF, BAX, BCL2, CASP3, and CASP9, among
others).
In addition, 2940 PDAC-related proteins were identified from the
DisGeNET database. Among the 84 core prescription targets, 44
overlapped with proteins in the 2940 PDAC-related group (hypergeometric
p value < 9.04e−23; Fig. [75]4A). The 44 common proteins identified as
both targets of the herbs and related to PDAC were considered to
represent likely targets of the herbal medicines during PDAC treatment.
Figure 4.
[76]Figure 4
[77]Open in a new tab
Targets of core prescriptions used for PDAC treatment. (A) Molecules in
common between herb targets and PDAC-associated proteins. (B) PPI
network diagram of the common targets of the four core herbs used to
treat PDAC. The PPI network contains 44 nodes and 297 edges. Circles
represent protein targets; orange circles indicate higher degree
values. The node size of gene targets is proportional to the number of
degrees.
These 44 shared proteins were imported into STRING, and an Herb–PDAC
target PPI network constructed using Cytoscape (Fig. [78]4B). From this
PPI network, several nodes (TNF, AKT1, TP53, HSP90AA1, MMP9, JUN,
CASP3, and IL6) had high degree values.
GO and KEGG enrichment analysis
To elucidate the potential molecular mechanisms by which core
prescriptions act on PDAC, GO biological process and KEGG pathway
enrichment analyses were performed using the 44 identified core
proteins.
The top 10 enriched GO biological process terms were determined
(Fig. [79]5A), and analysis showed that the targets were closely
related to processes involved in responses to steroid hormones and
apoptotic signaling pathways. The most significantly enriched KEGG
pathways included those involved in cancer, hepatitis B, apoptosis, p53
signaling, and PI3K/Akt signaling (Fig. [80]5B).
Figure 5.
[81]Figure 5
[82]Open in a new tab
Functional analysis of common targets. (A) GO enrichment analysis of
putative targets. (B) Target–GO network terms. (C) KEGG pathway
enrichment analysis of putative targets. (D) Target–Signaling pathway
network. Pink diamond nodes represent main signaling pathways and blue
circle nodes refer to putative common targets of the four core herbs
used for treatment of PDAC. Node size is proportional to the number of
degrees.
Target–GO term and Target–KEGG pathway networks were then constructed,
based on the targets involved in each GO term or KEGG pathway. The
Target–GO network comprised 46 nodes and 138 edges (Fig. [83]5C). The
majority of targets were primarily implicated in responses to steroid
hormones and apoptotic signaling pathways. In addition, the targets
participating in the largest number of terms were PTGS2 (also known as
COX-2), AKT1, and TNF, which were involved in 10, 9, and 8 GO terms,
respectively. The Target–KEGG network included 36 nodes and 126 edges
(Fig. [84]5D).
The steroid hormone response genes expression level between tumor and
normal samples were extracted from GEPIA^[85]18. Surprisingly, mRNA
expression of PTGS2 was specifically significantly upregulated in 3
types of cancer samples (among 30 types of cancer) including PDAC
samples compared with normal samples (FC > 2, p < 0.01, Fig. [86]6A,
Supplementary Fig. [87]1A). Importantly, patients with high PTGS2
expression in their tumors had poor prognosis compared to patients with
low PTGS2 expression (p = 0.012, Fig. [88]6B).
Figure 6.
[89]Figure 6
[90]Open in a new tab
PTGS2 is highly expressed in PDAC and is associated with disease free
survival. (A) The expression profile of PTGS2 from the TCGA Research
Network ([91]http://cancergenome.nih.gov/). Data were presented by box
plots. n = 179 for PDAC tissues and n = 171 for adjacent normal
tissues. TPM, transcripts per million (B) Kaplan–Meier survival curves
comparing PDAC patients with high (80%) and low (20%) expression of
PTGS2. HR, hazard ratio.
The results suggest that the mechanism of action of core prescriptions
for treatment of PDAC involves stimulation of responses to steroid
hormones and apoptotic.
Molecular docking
To evaluate whether active compounds from core prescription components
that possess good pharmacokinetic properties could bind directly to
proteins involved in responses to steroid hormone, we applied molecular
docking analysis to explore potential binding modes. The top 10
compounds with highest oral bioavailability and drug-likeness values
for each herb were identified as active compounds, and included
flavonoids, alkaloids, amino acids, steroids, and volatile oils, among
other substances (Supplementary Table [92]2).
As shown in Fig. [93]7A, stigmasterol could bind to PTGS2 with the
lowest binding energy (− 10.2 kcal/mol). The binding site of
stigmasterol in PTGS2 was GLY-225. Further, the binding sites for
phaseol in PTGS2 were TYR-130 and VAL-47 and the binding energy for
phaseol with PTGS2 was − 10.1 kcal/mol (Fig. [94]7B). These results
suggest that stigmasterol and phaseol could directly bind to PTGS2.
Figure 7.
[95]Figure 7
[96]Open in a new tab
Schematic 3D representation of molecular docking models, active sites,
and binding distances. Binding modes of: stigmasterol to PTGS2 (PDB
id:5ikq) (A), phaseol to PTGS2 (B), perlolyrine to ESR1 (PDB id: 1a52)
(C), DIOP to ESR2 (PDB id: 3ols) (D), phaseol to AR (PDB id:1e3g) (E),
and licopyranocoumarin to PGR(PDB id: 3g8o) (F).
Furthermore, perlolyrine could bind to ESR1 with a binding energy of
− 8.8 kcal/mol and DIOP bind to ESR2 with the same binding energy
(Fig. [97]7C,D). Notably, phaseol and AR were able to bind with a free
binding energy of − 8.6 kcal/mol, while the free binding energy of
licopyranocoumarin with PGR was − 9.7 kcal/mol (Fig. [98]7E,F).
These results indicate that several active compounds from the four
identified medicines could bind to proteins that function in responses
to steroid hormones.
Discussion
Plant-based medicines and plant-derived products remain the main source
of therapeutics for much of the world’s population. Traditional Chinese
medicine involves combinations of numerous co-occurring biologically
active compounds, and is a valuable source of therapeutic drugs that
have been used for a relatively long period of history^[99]19. The
medicinal compatibility model can reduce the number of targets used to
identify active ingredients, which makes the arbitrariness of the drug
discovery process more efficient and effective^[100]20. Indeed, more
than 60% of current anticancer chemotherapeutic drugs used in the
clinic were initially developed from natural products/herbal
medicines^[101]21.
The current study determined effective herbal prescriptions for PDAC
treatment by conducting a comprehensive analysis, based on clinical
cases, integration of association rules, cluster analysis, network
pharmacology, bioinformatic methods, and molecular auto-dock
analysis.The results indicate that Atractylodis macrocephalae Rhizoma
(Bai Zhu), Poria (Fu Ling), Hedyotis diffusa Willd. (Bai Hua She She
Cao), Astragali Radix (Huang Qi), and Glycyrrhizae Radix Et Rhizoma
(Gan Cao) are the herbs most frequently used for PDAC treatment.
Importantly, several recent studies have suggested that herb
combinations including Atractylodis macrocephalae Rhizoma (Bai Zhu),
Poria (Fu Ling), Astragali Radix (Huang Qi), and Glycyrrhizae Radix Et
Rhizoma (Gan Cao) can significantly improve symptoms in patients with
cancer and attenuate metastatic potential^[102]22,[103]23. The
effectiveness of evidence-based strategies in selecting herbs for
further treatment can be determined by their efficacy. To our best
knowledge, this is the first report of a potential core combination of
herbs used in prescriptions for treatment of PDAC.
Data accumulated from several studies have demonstrated that the use of
herbs in combination may have greater pharmacological efficacy than the
use of herbs singly^[104]24–[105]26. The current research reveals that
combinations including four herbs used in Chinese medicine formulae,
including Glycyrrhizae Radix et Rhizome (Gan Cao), Codonopsis Radix
(Dang Shen), Citri Reticulatae Pericarpium (Chen Pi), and Pinelliae
Rhizoma (Ban Xia), are potentially useful for the treatment of PDAC.
To further investigate the molecular mechanisms underlying the effects
of combinations use of these four core herbs for treatment of PDAC, we
selected 44 common targets shared by the four core herbs and PDAC, that
were identified via network pharmacology analyses. GO enrichment
analysis indicated that the 44 drug and disease targets in common were
involved in modulation of cellular processes involved in responses to
steroid hormones and apoptotic signaling pathways. Based on KEGG
pathway enrichment analysis, our data further reveal that various
oncogenic pathways with significant roles in the pathology of PDAC are
significantly enriched for the selected common targets, including
cancer, hepatitis B, apoptosis, p53 signaling, and PI3K/Akt signaling
pathways.
The sex steroid hormones, estrogen, androgen, and progestin, all have
functional roles in the healthy pancreas. PDAC, which is a highly
lethal disease, has a higher prevalence in men than women. Together,
these factors may suggest a potential role for sex hormone regulation
or dysregulation in pancreatic carcinogenesis. In fact, in addition to
PTGS2, the mRNA expression of AR was also significantly upregulated in
PDAC samples (and other five cancers) compared with normal samples
(FC > 1.6, p < 0.01, Supplementary Fig. [106]1B).
The results of molecular docking showed that stigmasterol and phaseol
had high affinity for PTGS2. PTGS2 is an inducible enzyme with vital
roles in the pathophysiological processes of PDAC^[107]27. Importantly,
there is evidence that PTGS2 is an attractive target in PDAC because it
is highly upregulated and participates in anti-apoptotic
mechanisms^[108]28. Our data indicate that perlolyrine can bind to ESR1
and DIOP to ESR2 with binding energies of − 8.8 kcal/mol. A recently
published study revealed that the G protein-coupled estrogen receptor
(GPER) inhibits PDAC^[109]29. It was demonstrated that GPER acts as a
tumor suppressor in cancers that are not classically considered hormone
responsive, suggesting that GPER activity may contribute to biological
differences between the sexes that influence cancer progression and
response to modern therapies^[110]29. An additional study showed that
there is an association between thyroid hormone supplementation and
PDAC invasion^[111]30.
In summary, the results of the current study suggest that four core
herbs may play important roles in treating PDAC by regulating proteins
that function in response to steroid hormones. Hence, bioactive
compounds, such as stigmasterol, phaseol, perlolyrine, and DIOP, may
exert anti-cancer effects against PDAC.
Conclusion
This study reveals the core prescription used for PDAC treatment
included four herbs: Glycyrrhizae Radix et Rhizome (Gan Cao),
Codonopsis Radix (Dang Shen), Citri Reticulatae Pericarpium (Chen Pi),
and Pinelliae Rhizoma (Ban Xia). The anti-PDAC activity of core
prescription may be mediated via regulation of proteins with roles in
responses to steroid hormones. This study provides further evidence
supporting the potential for use of herbal medicines to treat PDAC.
Supplementary Information
[112]Supplementary Table 1.^ (77.5KB, xlsx)
[113]Supplementary Table 2.^ (156.8KB, xlsx)
[114]Supplementary Figure 1.^ (409.5KB, pdf)
Abbreviations
AMR
Atractylodis Macrocephalae Rhizoma (Bai Zhu)
AR
Astragali Radix (Huang Qi)
ASR
Angelicae Sinensis Radix (Dang Gui)
BR
Bupleuri Radix (Chai Hu)
CF
Crataegi Fructus (Shan Zha)
CoR
Corydalis Rhizoma (Yan Hu Suo)
CRh
Curcumae Rhizoma (E Zhu)
CRhi
Coptidis Rhizoma (Huang Lian)
CR
Codonopsis Radix (Dang Shen)
CRP
Citri Reticulatae Pericarpium (Chen Pi)
CS
Coicis Semen (Yi Yi Ren)
FFA
Fruitof Fiverleaf Akebia (Ba Yue Zha)
GGEC
Galli Gigerii Endothelium Corneum (Ji Nei Jin)
GO
Gene ontology
GRER
Glycyrrhizae Radix Et Rhizoma (Gan Cao)
HFG
Hordei Fructus Germinatus (Mai Ya)
HW
Hedyotis diffusa Willd (Bai Hua She She Cao)
KEGG
Kyoto encyclopedia of genes and genomes
ML
Medicated Leaven (Shen Qu)
PDAC
Pancreatic ductal adenocarcinoma
Por
Poria (Fu Ling)
PR
Pinelliae Rhizoma (Ban Xia)
PRa
Pseudostellariae Radix (Tai Zi Shen)
PRA
Paeoniae Radix Alba (Bai Shao)
RRER
Rhei Radix Et Rhizoma (Da Huang)
SBH
Scutellariae Barbatae Herba (Ban Zhi Lian)
SMRER
Salviae Miltiorrhizae Radix Et Rhizoma (Dan Shen)
Author contributions
C.H. responsible for concept and design. Z.Z., J.W. and B.L. are
responsible for data analysis and interpretation. C.H. drafted the
paper; C.H. and J.X. supervised the study; all authors participated in
the analysis and interpretation of data and approved the final paper.
Funding
This work was supported by the National Natural Science Foundation of
China [31801061, 81803651].
Data availability
The original contributions presented in the study are included in the
article/Supplementary Material, further inquiries can be directed to
the corresponding authors.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
These authors contributed equally: Zhiyi Zhang, Juan Wang, Bingying Liu
and Yu Liu.
Contributor Information
Jie Xin, Email: xinjiezhongyao@126.com.
Chunxiang Hao, Email: Chunxianghao@outlook.com.
Supplementary Information
The online version contains supplementary material available at
10.1038/s41598-022-14174-1.
References