Abstract
Health-strengthening (Fu-Zheng) herbs is a representative type of
traditional Chinese medicine (TCM) widely used for cancer treatment in
China, which is in contrast to pathogen eliminating (Qu-Xie) herbs.
However, the commonness in the biological basis of health-strengthening
herbs remains to be holistically elucidated. In this study, an
innovative high-throughput research strategy integrating computational
and experimental methods of network pharmacology was proposed, and 22
health-strengthening herbs were selected for the investigation.
Additionally, 25 pathogen-eliminating herbs were included for
comparison. First, based on network-based, large-scale target
prediction, we analyzed the target profiles of 1446 TCM compounds.
Next, the actions of 166 compounds on 420 antitumor or immune-related
genes were measured using a unique high-throughput screening strategy
by high-throughput sequencing, referred to as HTS^2. Furthermore, the
structural information and the antitumor activity of the compounds in
health-strengthening and pathogen-eliminating herbs were compared.
Using network pharmacology analysis, we discovered that: (1)
Functionally, the predicted targets of compounds from health
strengthening herbs were enriched in both immune-related and antitumor
pathways, similar to those of pathogen eliminating herbs. As a case
study, galloylpaeoniflorin, a compound in a health strengthening herb
Radix Paeoniae Alba (Bai Shao), was found to exert antitumor effects
both in vivo and in vitro. Yet the inhibitory effects of the compounds
from pathogen eliminating herbs on tumor cells proliferation as a whole
were significantly stronger than those in health-strengthening herbs (p
< 0.001). Moreover, the percentage of assay compounds in
health-strengthening herbs with the predicted targets enriched in the
immune-related pathways (e.g., natural killer cell mediated
cytotoxicity and antigen processing and presentation) were
significantly higher than that in pathogen-eliminating herbs (p <
0.05). This finding was supported by the immune-enhancing effects of a
group of compounds from health-strengthening herbs indicated by
differentially expressed genes in the HTS^2 results. (2) Compounds in
the same herb may exhibit the same or distinguished mechanisms in
cancer treatment, which was demonstrated as the compounds influence
pathway gene expressions in the same or opposite directions. For
example, acetyl ursolic acid and specnuezhenide in a
health-strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi) both
upregulated gene expressions in T cell receptor signaling pathway.
Together, this study suggested greater potentials in tumor immune
microenvironment regulation and tumor prevention than in direct killing
tumor cells of health-strengthening herbs generally, and provided a
systematic strategy for unveiling the commonness in the biological
basis of health-strengthening herbs in cancer treatment.
Keywords: traditional Chinese medicine, health strengthening herb,
cancer treatment, network pharmacology, network target, high-throughput
analysis
1. Introduction
China has a long history of using traditional Chinese medicine (TCM)
for treating cancer [[38]1]. A large amount of medication experience
and clinical cases have been accumulated by TCM practitioners, which
makes TCM contribute greatly to the development of China’s national
health status. According to an urban basic medical insurance survey of
inpatient use of health services in China from 2008 to 2010, 42.4% of
oncology patients have used antineoplastic TCMs in the Chinese national
medical insurance catalogue [[39]2]. With increasing scientific
evidence in biological, chemical, and medical research, as well as
clinical trials, the use of traditional Chinese medicine in cancer
treatment is gradually being recognized as a complementary and
alternative therapy all over the world [[40]3,[41]4,[42]5,[43]6].
Traditionally, TCM adopts a relative and holistic point of view in
cancer treatment. The clinical treatment strategy by strengthening
health reflects the characteristics of focusing on regulatory effects
instead of the antagonistic effects of TCM, and embodies the classical
therapeutic theory that “pathogenic-qi cannot invade the body if
health-qi remains strong” in the Canon of Internal Medicine (Huangdi
Neijing). Therefore, an in-depth exploration on the antitumor effects
exerted by health- strengthening herbs is meaningful and urgently
needed. TCMs in the Chinese national medical insurance catalogue (2017)
for oncology treatment are officially divided into two categories,
including antitumor TCM and adjuvant TCM for tumors, which contain 40
TCM prescriptions in total [[44]7]. Additionally, some
health-strengthening prescriptions are widely applied in cancer
treatment, such as Sijunzi decoction in colorectal cancer [[45]8],
Shenqi Fuzheng injection in colorectal cancer and breast cancer
[[46]9,[47]10], Shenling Baizhu San in gastric cancer [[48]11], and
Buzhong Yiqi decoction in colorectal and lung cancer [[49]12,[50]13].
The wide application and the distinctive therapeutic strategy of
health-strengthening herbs have given rise to the growing research
interests in the investigation on the effects and the underlying
mechanisms of health-strengthening herbs in cancer treatment. A large
amount of research effort has been put into the studies on the
biological basis of health-strengthening TCM from a variety of
perspectives, such as their immune and metabolic regulatory effects.
For example, previous studies on various health strengthening formulae
(e.g., Shenqi Fuzheng injection, Danggui Buxue decoction, Huangqi
Jianzhong decoction, and Liu-wei-di-huang pill) revealed their
protective effect on immune functions in cancer therapy
[[51]14,[52]15,[53]16,[54]17]. Metabolic regulatory function is also
involved in the antitumor effects of health-strengthening formulae,
suggested by the pharmacological studies on the Liu-wei-di-huang pill,
Jianpi Yiqi decoction, and Yishen Gukang decoction
[[55]17,[56]18,[57]19]. Considering the situation that more studies
emphasize the immune regulatory effects of health strengthening herbs,
in this study, we took the immunological effects of health
strengthening herbs as an example to explore the commonness in their
biological basis of in cancer treatment. Despite the great efforts in
the research field, the understanding of the antitumor mechanism of
health-strengthening medicine is not clear enough [[58]20,[59]21]. The
solution is constrained by the following three interrelated factors:
the complex composition of TCM, the lack of target information of TCM,
and the complex biological system involved in cancer development. To
further promote the application of TCM in the treatment of cancer,
proposing a comprehensive analysis strategy for exploring the impact of
TCM from a holistic point of view is urgently needed.
An increasing amount of evidence indicates that TCM may exert
therapeutic effects by targeting a variety of biomolecules [[60]22].
However, due to the complexity of the ingredients and the limitations
in the application of experimental methods, the targets of many TCM
compounds are still unclear [[61]23]. It has been proposed that TCM
formulae and herbs impact the network of targets in complex diseases,
such as cancer [[62]24,[63]25,[64]26,[65]27], and researchers may
investigate the systemic effects of drugs on biological networks. The
systematic concept is consistent with the multitarget characteristic of
TCM and makes it suitable for studying the complex mechanism of TCM
[[66]28,[67]29]. The advent of the big data era, the continuous
accumulation of omics data, and the progress of bioinformatics methods
provide strong support for the development of network pharmacology
[[68]30]. As a core concept in network pharmacology, network targets
have changed the current research mode of “single target” and provided
a potential research strategy for analyzing the biological basis of TCM
from the perspective of networks and guiding the discovery of new
active ingredients in TCM [[69]31].
The development of high-throughput transcriptional assay technologies
provides researchers with a comprehensive viewpoint for exploring the
effect of compounds on gene expression. High-throughput methods are an
integral part of pharmacological studies and have led to many
achievements in biomedical fields [[70]32]. High-throughput
experimental methods, together with other genomic technologies, enables
a comprehensive and systematic approach to the biological basis of
medicine. TCM is widely recognized as a holistic treatment to diseases
[[71]13] and the mechanisms of TCM in cancer treatment are still
unclear. Hopefully, the development of high-throughput methods will
shed light on deciphering the comprehensive mechanism of TCM in cancer
treatment. Here, a unique high-throughput screening strategy by
high-throughput sequencing, referred to as HTS^2 [[72]33], was adopted
for investigating the biological basis of 166 TCM compounds in cancer
simultaneously. In the HTS^2 assay, we added the compound library to
the cell line and obtained a large-scale and quantitative
transcriptional profiling in cells by detecting the signals of the
designed gene probes.
To approach the systematic mechanisms of TCM compounds for cancer
treatment, we combined network pharmacology prediction methods with
HTS^2 assay methods in our data analysis process. In this research, the
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways involved in the
antitumor or immunological activities of the TCM compounds are
identified [[73]34]. Taking advantage of the prediction and assay
results, we conducted a systematic investigation on the antitumor
mechanism of compounds in the two types of herbs (health-strengthening
and pathogen-eliminating herbs), compounds in the same herb, and
compounds that regulate the same pathway. Our study also revealed a
potential bioactive compound, galloylpaeoniflorin, for cancer therapy,
which may exhibit its efficacy via both regulating immune-related and
antitumor pathways.
Together, by combining target prediction and high-throughput assay,
this study proposed a systematic overview on the biological basis
underlying the pharmacological effects of health strengthening herbs in
cancer treatment. Despite the need for further investigation, it was
indicated that health-strengthening herbs may provide researchers with
a valuable candidate library for tumor immune regulatory and tumor
preventive drug development.
2. Results
2.1. The Prediction and Examination of Potential Targets of by Literature
Mining and the HTS^2 Assay
Due to the complex composition of TCM and the lack of corresponding
target records, the potential target lists of TCM compounds were
obtained by using a computational prediction method, drugCIPHER-CS
[[74]12]. Literature mining based on text searching was conducted to
verify the reliability of the target prediction results. The
co-occurrence of the compound and target appearing in one or more
abstracts was used to define the association between them. For each
investigated TCM compound, we searched the PubMed database by its name
in the abstracts and counted the total item number of the search
results. Since the numbers of the related reports of the different TCM
compounds varied greatly, which would influence the following analysis
results (e.g., the percentage of predicted targets verified by
literature), we only selected the compounds with adequate related
reports (total item number ranging from 500 to 1000) for the
verification of target prediction results. All the related abstracts of
the selected compounds were downloaded, and text-processing codes were
programed for extracting the biomolecules mentioned in the abstracts.
The results from literature mining were then verified via manual
examination by deleting the false positive responses.
After literature mining, we obtained the biomolecules mentioned in the
abstracts related to the compounds in [75]Figure 1A. Additionally, the
differentially expressed genes (DEGs) after treatment with these TCM
compounds in the cell line were achieved using the HTS^2 assay. We
examined whether a predicted target was directly or indirectly related
with the biomolecules in the literature or DEGs in the HTS^2 assay. The
indirect relationship was established if the predicted target was in
the upstream in a KEGG pathway of the biomolecules in the abstracts or
DEGs, or if they were related by protein–protein interaction (PPI) in
the HPRD, BIND, IntAct, MINT, or OPHID database
[[76]35,[77]36,[78]37,[79]38,[80]39]. The cover rate demonstrated in
[81]Figure 1A stands for the percentage of the predicted targets
supported by literature or the HTS^2 assay. The cover rate was
calculated as
[MATH:
|The predicted targets <
mi>related to the<
mtext> reported targets
(or DEGs)||The predicted targets
|×100%
:MATH]
. The results ([82]Figure 1A) indicated that the predicted target lists
of TCM compounds for cancer basically covered 75%–90% of the
biomolecules in the literature and had a relatively strong reliability.
In addition, some potential targets were verified by the HTS^2
experimental results, which demonstrated that the HTS^2 assay may be an
alternative method for exploring the novel biological functions of TCM
compounds.
Figure 1.
[83]Figure 1
[84]Open in a new tab
Evaluation of the target prediction results based on the literature and
the HTS^2 assay results. (A) The ratio of the predicted targets of the
TCM compounds covered by the literature and the assay results. The
cover rate was calculated as
[MATH:
|The predicted targets <
mi>related to the<
mtext> reported targets
(or DEGs)||The predicted targets
|×100%
:MATH]
. (B) Some targets of wogonin, which were not related to biomolecules
in the literature or the DEGs in the HTS^2 assay, were in the same
pathway or connected via the protein–protein interaction (PPI) in
STRING with the biomolecules in the literature or the DEGs in the HTS^2
assay for wogonin. (C) Literature verification of the KEGG pathways
related to the TCM compounds for cancer treatment with target
enrichment and HTS^2 evidence. Error bars represent the precision and
recall rates of different TCM compounds. The precision was
[MATH:
|The predicted relevant
pathways related to
the reported relavant pathways||The predicted relevant pathways|×100% :MATH]
. The recall rate was
[MATH:
|The predicted relevant
pathways related to
the reported relavant pathways||The reported relevant pathways|×
100% :MATH]
. Data represent mean ± SD.
In [85]Figure 1A, we found that 87% of the predicted targets of
wogonin, a representative compound from Radix Scutellariae (Huang Qin),
were supported with literature or assay evidence. As for the other 13%
of the potential targets of wogonin predicted by drugCIPHER-CS, some of
them were connected to the biomolecules in the literature or the DEGs
in the HTS^2 assay by an additional indirect mapping, as shown in
[86]Figure 1B. The indirect mapping in [87]Figure 1B represented
protein–protein interaction (PPI) in the STRING database [[88]40] or
relations via pathways in KEGG.
Next, to further analyze the mechanism of the action of the compounds,
we performed KEGG pathway enrichment analysis based on the target
prediction results. We checked the enrichment p-values of the targets
in 26 cancer hallmarks (immune-excluded) and immune-related KEGG
pathways. The literature verification of the pathways with HTS^2 or
enrichment evidence was conducted manually by reading the papers in the
PubMed database. If a significantly enriched pathway was related to the
bioactivity records in the published papers in the PubMed database
[[89]41] or included enough DEGs (the cut-off value was set as between
one to five DEGs and the robustness of the threshold was measured in
[90]Figure 1C) in the HTS^2 assay results, then it would be considered
as a predicted related pathway with supports from literature records or
HTS^2 assay results. As shown in [91]Figure 1C, a series of cut-off
values (from one to five DEGs in a pathway in the HTS^2 assay) was set
to examine the robustness of the literature verification results. Our
results indicated that the cut-off values had little influence on the
precision rate for the related pathways determined using an HTS^2
assay, and the recall rate scaled from 97% to 72% as the cut-off
changed. The precision and recall rate of the significantly enriched
KEGG pathways (p < 0.05, false discovery rate (fdr) adjusted) were
approximately 60% and 70%, respectively, which indicated the
reliability of the pathway prediction results. By comprehensively
considering both the precision and recall results, we selected the
pathways with three or more DEGs in the HTS^2 assay as the ones with
support from the assay data for further investigation.
2.2. Target Prediction and Assay Results Indicate that the Two Types of TCM
Herbs May Regulate Several Key Biological Processes in Cancer Treatment,
Including Antitumor and Immune Modulation
Historically, TCM encompasses a two-way philosophy in cancer treatment
in that it is involved in both health strengthening and pathogen
elimination. TCMs applied in curing cancer are classified as
health-strengthening or pathogen-eliminating herbs according to their
therapeutic effects. However, the biological functions of the two types
of anti-cancer TCMs have not yet been elucidated. Therefore, we hoped
to identify the regulated pathways of both types of TCM herbs by taking
advantage of network pharmacology prediction and the HTS^2 assay. As
shown in [92]Figure 2A, 1446 compounds were selected for target
prediction, including 655 compounds in health-strengthening herbs, 667
compounds in pathogen-eliminating herbs, and 124 compounds in both
types of herbs. In [93]Figure 2B, the structural similarities among the
compounds were measured by applying a principle component analysis
(PCA) method to the compound 2-D structure information from the ChEMBL
database [[94]42]. A total of 881-dimensional CADD Group
Chemoinformatics Tools and User Services (CACTVS) substructures in
PubChem [[95]43] were adopted to encode the structures of the
investigated TCM compounds into binary vectors. The PCA analysis was
conducted by using the princomp function in the R packages stats v3.2.2
(RStudio, Boston, MA, USA) under the environment of RStudio v1.1.447
[[96]44]. As demonstrated in [97]Figure 2B, the compounds in
health-strengthening and pathogen-eliminating herbs may contain similar
substructures. This result was consistent with the fact that
health-strengthening and pathogen-eliminating herbs contained multiple
compounds with common herbal chemical types, such as saponins,
flavonoids, and alkaloids. For further unveiling the structural basis
of the two clouds of compounds in [98]Figure 2B, we examined the 10
CACTVS substructures that contributed the most to the first and the
second feature vectors in the PCA analysis. The selection of the
feature vectors was consistent with the two dimensions depicted in
[99]Figure 2B. The contributions were determined by the absolute values
of the coefficients of the first two feature vectors. C:CC=C, C=C-C:C,
O-C-C:C, C:C-C:C, and O-C-C:C-C were the five strongest positive
features, and ≥ 3 any ring size 6, ≥ 4 any ring size 6,
C(-C)(-C)(-H)(-O), C(-C)(-H)(-O), and [#1]-C-O-[#1] were the five
strongest negative features. Therefore, the left cloud of compounds was
more likely to contain the substructures among the negative features
and the right cloud of compounds was more likely to contain the
substructures among the positive features in [100]Figure 2B. As shown
in [101]Figure 2C, it was found that the average structure similarity
score of the specific compounds in the two types of herbs was
significantly higher than that between the TCM compounds and
antineoplastic Western drugs (p < 0.001). The structure similarity
analysis was conducted by calculating Tanimoto coefficients [[102]45]
between the 881-dimensional CACTVS substructures of the compounds.
Figure 2.
[103]Figure 2
[104]Open in a new tab
An overview of types of TCM applied in the research on their
structures, inhibitory effects on tumor cells, and regulated
bioactivities, based on public data, target prediction, and the HTS^2
assay. (A) The number of compounds in the two types of TCM herbs in
cancer treatment applied in the target prediction. (B) PCA analysis on
structure of compounds in the two types of herbs in cancer treatment.
(C) The structure similarity comparison between the compounds from
health-strengthening herbs and pathogen-eliminating herbs and that
between the TCM compounds and antineoplastic Western drugs via Tanimoto
coefficients. Data represent median ± interquartile range. Statistical
analysis was performed using a Kolmogorov–Smirnov (KS) test. *** p <
0.001. (D) The number of compounds from the two types of TCM herbs
applied in the HTS^2. (E) The inhibitory effects of compounds in the
two types of herbs on proliferation of tumor cell lines in public
bioactivity databases. Data represent median ± interquartile range.
Statistical analysis was performed using a Wilcoxon rank sum test. ***
p < 0.001. (F) The regulated immune and cancer hallmarks
(immune-excluded) pathways of several TCM compounds supported using
target prediction and the HTS^2 assay. (G) Expression data of several
genes in the cell cycle and T cell receptor signaling pathways after
TCM compound treatments.
Among the 1446 TCM compounds applied in target prediction, 166
compounds were used in the HTS^2 assay, including 67 compounds in
health-strengthening herbs, 66 compounds in pathogen-eliminating herbs,
and 33 compounds in both types of herbs ([105]Figure 2D). In the HTS^2
assay, approximately 3000 HCT116 colorectal cancer cells were seeded in
each well of a 384-well plate for 24 h. Then the TCM compound library
were added to the cells for 24 h to achieve a transcriptional profile
after compounds treatment. Eight dimethyl sulfoxide (DMSO) replicates
were also added in the wells as controls. After obtaining the gene
probe reads, we performed gene expression normalization by using 18
stable genes in in colorectal cancer ([106]GSE44076, [107]GSE44861,
[108]GSE53295, and [109]GSE53965 in the Gene Expression Omnibus (GEO)
database [[110]46]). The normalized expression of a gene was defined as
the reads of the gene probe divided by the median number of the reads
of 18 housekeeping genes. The fold change was calculated as the
normalized gene expression after the drug treatment divided by the
median number of normalized gene expression after the eight DMSO
replicates treatment. For each TCM compound, genes with a fold change >
2 were considered DEGs.
To explore the inhibitory effects on proliferation in the different
cell lines of compounds from health-strengthening and
pathogen-eliminating herbs, we analyzed the half-maximal inhibitory
concentration (IC50) and the half-maximal inhibitory concentration on
cell growth (GI50) data of the TCM compounds collected from the ChEMBL
database or literature. The median IC50 (or GI50) was calculated as the
median number of all the human cell line specific experiments in ChEMBL
and literature records after the TCM compound treatment. These two
metrics were considered as the same and were merged in the analysis.
Even though the compounds from pathogen-eliminating herbs exhibited the
lower median IC50s (or GI50s) than compounds from health-strengthening
herbs in [111]Figure 2E, the results indicated that compounds in
health-strengthening herbs might be anti-proliferative to tumor cells.
The IC50 and GI50 data were also adopted as reference concentrations in
the
(3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophen
yl)-2H-tetrazolium (MTS) assay, a cell survival rate measurement assay,
and these bioactivity data were then applied in setting the
concentrations of the TCM compounds in the HTS^2 assay after the manual
adjustment.
In [112]Figure 2F, target prediction and HTS^2 assay suggested that
some compounds may regulate immune and cancer hallmarks
(immune-excluded) pathways simultaneously. As shown in [113]Figure 2G,
in the cell cycle pathway, the expression of the CDK4, CDK6, and CCND1
genes in the HTS^2 assay were reduced after treatment with several
compounds in the two types of herbs, while the gene expressions in T
cell receptor signaling pathways were upregulated after treatment with
several TCM compounds from the two types of herbs. The percentage of
the experimental compounds in health-strengthening herbs with predicted
targets enriched in the immune-related pathways was significantly
higher than that in pathogen eliminating herbs (e.g., 37.3% and 22.7%,
respectively, in antigen processing and presentation, and 65.7% and
47.0%, respectively, in natural killer cell mediated cytotoxicity)
(Fisher exact test, p < 0.05). In [114]Table 1, the regulated pathways
of several compounds from health-strengthening and pathogen-eliminating
herbs predicted using target enrichment and the HTS^2 assay results
were listed. These results indicated that TCM herbs, no matter their
therapeutic classification, may exhibit therapeutic effects in cancer
treatment via both immune regulation and other antitumor functions.
Table 1.
Several KEGG pathways predicted to be regulated by compounds from
health-strengthening and pathogen-eliminating herbs using target
enrichment and the HTS^2 assay.
KEGG Pathway Herb Type Herb Compounds Enrichment p-value DEG Number
Apoptosis Fu-Zheng Radix Angelicae sinensis
(Shan Yao) Batatasin IV
Dioscin 2.1 × 10^−4
1.1 × 10^−2 13
23
Qu-Xie Rhizoma Curcumae
(E Zhu) Curcumin
Isocurcumenol 1.7 × 10^−3
1.7 × 10^−3 11
31
vascular endothelial growth factor (VEGF) signaling pathway Fu-Zheng
Fructus Schisandrae
(Wu Wei Zi) Schisanhenol
Gomisin J 3.2 × 10^−3
3.2 × 10^−3 14
7
Qu-Xie Radix et Rhizoma Rhei
(Da Huang) Chrysaron
Rhein 2.3 × 10^−6
3.2 × 10^−3 9
5
Cell cycle Fu-Zheng Fructus Ligustri lucidi
(Nv Zhen Zi) Ligustroflavone
Specnuezhenide 2.4 × 10^−3
1.5 × 10^−3 4
10
Qu-Xie Cortex Moutan
(Gan Chan Pi) Cinobufagin
Telocinobufagin 1.5 × 10^−3
2.3 × 10^−4 20
26
T cell receptor signaling pathway Fu-Zheng Poria
(Fu Ling) Pachymic Acid
Poricoic Acid B 8.7 × 10^−7
7.8 × 10^−5 5
8
Qu-Xie Cortex Magnoliae officinali
(Huang Qin) Baicalein
Wogonin 7.8 × 10^−5
3.8 × 10^−3 18
15
Toll-like receptor signaling pathway Fu-Zheng Radix Astragali
(Huang Qi) Astragaloside A
Formononetin 4.1 × 10^−3
4.1 × 10^−3 5
3
Qu Xie Venenum Bufonis
(Chan Su) Bufarenogin
Cinobufagin 4.1 × 10^−3
4.1 × 10^−3 15
20
Nucleotide-binding oligomerization domain (NOD)-like receptor signaling
pathway Fu-Zheng Radix Ginseng
(Ren Shen) Ginsenoside Rh3
Protopanaxadiol 4.9 × 10^−5
4.9 × 10^−5 23
15
Qu-Xie Fructus Bruceae
(Ya Dan Zi) Bruceantin
Bruceine D 1.6 × 10^−3
3.0 × 10^−4 7
14
[115]Open in a new tab
2.3. HTS^2 Assay Results Show that Health-Strengthening Medicine May Regulate
Tumor Immunity Via Promoting NK Cell Activity and Tumor Cell Antigen
Presentation
NK cells recognize and kill tumor cells containing the mutated gene
fragments [[116]47]. In the tumor environment, the NK cell activity is
inhibited by the biological function of the tumor cells [[117]48]. In
[118]Figure 3A, some TCM compounds from health-strengthening herbs with
the potential biological functions of promoting NK cell activity were
listed at the top, as indicated by the HTS^2 assay results. For
instance, ginsenoside Re, a compound in Radix Ginseng (Ren Shen), was
suggested to exert an NK cell activation effect by upregulating the
expression of genes involved in degranulation and NK cell-related
cytokines release.
Figure 3.
[119]Figure 3
[120]Open in a new tab
The regulatory effects on immune-related pathways induced by a group of
compounds from health-strengthening herbs. The dotted lines linked the
TCM compounds and the DEGs after compound treatment in the HTS^2 assay.
(A) The HTS^2 assay results showed that compounds from health
strengthening herbs upregulated the biomolecules in the NK cell
mediated cytotoxic pathway. (B) The HTS^2 assay results showed that
compounds from health strengthening herbs upregulated the biomolecules
involved in the MHC-I antigen processing and presentation pathway.
Antigen processing and presentation plays an important role in tumor
immunity [[121]49]. In the tumor environment, the antigen presentation
functions of tumor cells are relatively suppressed [[122]50].
Therefore, the immune system may not fully recognize and kill tumor
cells. The analysis of the HTS^2 assay results revealed that several
health strengthening herbs may comprehensively increase the level of
antigen presentation in tumor cells by promoting intracellular
synthesis of main histocompatibility complex class I (MHC-I) molecules,
improving antigen processing efficiency, and combining tumor cell
biopeptides with MHC-I molecules ([123]Figure 3B). For example,
sinnamaldehyde, a compound in Cortex Cinnamomi (Rou Gui), was indicated
to upregulate antigen processing and MHC biosynthesis related genes, as
shown in [124]Figure 3B. Additionally, as demonstrated in [125]Figure
3B, some compounds that may enhance the NK cell activities may also
improve the levels of antigen processing and presentation in tumor
cells.
2.4. Target Prediction and HTS^2 Assay Results Show that Compounds in the
Same Herb May Exhibit Different Patterns in Modulating Antitumor or Immune
Processes
To further identify how the combinations of the TCM compounds in the
same herb may regulate the same biological process, we extracted the
HTS^2 results of the TCM compounds that were predicted to regulate the
same pathway. Next, we examined the expression data of the genes in the
predicted regulated pathways. After the integration of the HTS^2
results from the TCM compounds and the gene information in the KEGG
pathways, we concluded that the TCM compounds in the same herb may have
interactions in several patterns in regulating the same KEGG pathway.
As shown in [126]Figure 4, the selected TCM compounds may regulate the
same pathway in the same or opposite directions to induce the final
effects, as indicated by the HTS^2 assay results. For instance,
kaempferol and kaempferide are two compounds from the
health-strengthening herb Fructus Corni (Shan Zhu Yu). After analyzing
the HTS^2 assay results of kaempferol and kaempferide, we discovered
that they have similar molecular patterns for inhibiting cell cycles,
as shown in [127]Figure 4B. Additionally, paeonol and
galloylpaeoniflorin, two compounds in Radix Paeoniae Alba (Bai shao),
were indicated to influence gene expression in the mitogen-activated
protein kinase (MAPK) signaling pathway in the opposite directions
([128]Figure 4C). These results suggested that compounds in the same
herb may exhibit similar or distinguished mechanisms in cancer
treatment. The compounds with different mechanisms may have the
potential bioactivities in treating different types of tumors.
Figure 4.
[129]Figure 4
[130]Open in a new tab
The HTS^2 assay results indicated that within the same pathway,
compounds from the same herb may influence gene expression in the same
cancer hallmarks (immune-excluded) or immune-related KEGG pathway in
the same or opposite directions, as shown in the three boxes. (A)
Compounds from the same herb may simultaneously upregulate the gene
expression in the same pathway. (B) Compounds in the same herb may
simultaneously downregulate the gene expression in the same pathway.
(C) Compounds from the same herb may regulate the gene expression in
the same pathway in opposite directions.
2.5. Prediction and Assay Results Imply that Compounds in One
Health-Strengthening Herb or a Single Compound May Exert Antitumor and
Immune-Related Functions Simultaneously for Cancer Therapy
Considering the multicompound characteristics of TCM herbs, it is
necessary to examine the biological functions of different compounds
from one herb in treating cancer. As we know, Radix Paeoniae Alba (Bai
Shao) and Radix Sophorae flavescentis (Ku Shen) are two typical
health-strengthening and pathogen-eliminating herbs that are widely
used for cancer treatment [[131]2]. After target prediction and KEGG
pathway enrichment, we extracted the mRNA expression induced by the
compounds from Radix Paeoniae Alba and Radix Sophorae flavescentis in
the HTS^2 assay. The target enrichment results of the pathways with the
assay evidence are shown in [132]Figure 5A and [133]Figure 5B. T cell
receptor signaling pathway, B cell receptor signaling pathway, Th17
cell differentiation, MAPK signaling pathway, mTOR signaling pathway,
and some other pathways were regulated by several compounds from Radix
Paeoniae Alba, including 1,2,3,4,6-pentagalloylglucose, albiflorin,
coumarin, galloylpaeoniflorin, paeoniflorin, and paeonol ([134]Figure
5A). The target enrichment and HTS^2 assay results suggested that all
the compounds in [135]Figure 5A might regulate both immune-related and
other antitumor pathways. The similar pattern was found in the target
enrichment results of the compounds from Radix Sophorae flavescentis.
As shown in [136]Figure 5B, nine compounds from Radix Sophorae
flavescentis were selected for the HTS^2 assay, and all of them may
influence at least one immune-related pathway and one other antitumor
pathway.
Figure 5.
[137]Figure 5
[138]Open in a new tab
Identification of the immune regulatory and other antitumor biological
functions of compounds from Radix Paeoniae Alba (Bai Shao) and Radix
Sophorae flavescentis (Ku Shen). (A,B) The comprehensive functional
characterization of the compounds in Radix Paeoniae Alba (A) or Radix
Sophorae flavescentis (B) using the pathway enrichment analysis based
on target prediction results. The white blanks represent pathways not
regulated by the compounds according to HTS^2 assay results. (C) The
pathway regulation effects of galloylpaeoniflorin, a compound in Radix
Paeoniae Alba (Bai Shao) using the target enrichment and HTS^2 assay. A
subnetwork representing the expression of the predicted targets of
galloylpaeoniflorin in the cell cycle pathway was shown. (D) Inhibitory
effects of galloylpaeoniflorin on tumor cell proliferation using the
cell lines of several tumor types, as assessed using an MTT assay. The
experiment was done once in triplicate. (E) Inhibitory effects of
galloylpaeoniflorin on tumor growth in H22 mice. The assay was
performed on seven BALB/c/nu nude female mice injected with the H22
tumor for each group. ** p < 0.01, *** p < 0.001, compared with the
solvent control group. Statistical analysis was performed using
multiple t tests. Data represent mean ± SD.
One of the compounds from Radix Paeoniae Alba, galloylpaeoniflorin, had
no relevant antitumor records or any other biological activity records.
The target prediction and HTS^2 assay results indicated that
galloylpaeoniflorin might regulate several immune-related pathways
(i.e., T cell receptor signaling pathway, Th17 cell differentiation,
and B cell receptor signaling pathway), and cancer hallmarks pathways
(i.e., MAPK signaling pathway, cell cycle, and mTOR signaling pathway)
([139]Figure 5C). For instance, the relative expression of genes in the
cell cycle in the HTS^2 assay was shown in the right part of
[140]Figure 5C, and the expression of the biomolecules listed in the
subnetwork were reduced by galloylpaeoniflorin. Moreover, we examined
the antitumor effects of galloylpaeoniflorin both in vitro and in vivo
([141]Figure 5D,E). As shown in [142]Figure 5D, galloylpaeoniflorin
effectively inhibited the proliferation of several cell lines (IC50 <
40µg/mL), including HCT116 (a colorectal cancer cell line), B16F10 (a
melanoma cell line), MCF-7 (a breast cancer cell line), and NCI-H460 (a
lung cancer cell line) cells. Different solvents were applied (DMSO and
ethanol) in the top and the bottom of [143]Figure 5D and the results
demonstrated robust inhibitory effects on the tumor cell lines induced
by galloylpaeoniflorin. The in vitro inhibitory effects of
galloylpaeoniflorin in [144]Figure 5D on not only the HTC116 cells,
which was adopted in the HTS^2 assay, but also several cell lines of
varied cancer types indicated its antineoplastic potentials in treating
different tumors. The experiment in [145]Figure 5D was done once in
triplicate. Our in vivo results confirmed that the tumor weight of the
H22 liver tumor mice was significantly reduced after the treatment with
galloylpaeoniflorin ([146]Figure 5E). Twenty-one BALB/c/nu nude female
mice injected with H22 tumor were utilized for the in vivo assay.
3. Discussion
Health-strengthening medicine is seen as a representative application
of the classical philosophy of TCM in cancer therapy. It is reported
that, unlike Western medicine, which may exhibit direct killing effects
on tumor cells, health-strengthening medicine is developed to treat
cancer by systematically regulating the tumor microenvironment
[[147]51,[148]52,[149]53,[150]54]. According to the traditional
efficacy of TCM, health-strengthening herbs can be classified into
different categories, such as yin-nourishing (Zi-Yin) herbs and
qi-tonifying (Yi-Qi) herbs. In this research, as a first step, we
treated health-strengthening herbs as a whole rather than the
subcategories for the following network pharmacological analysis.
Several biological processes may be involved in the regulatory effects
of health-strengthening medicine, including some immune-related
bioactivities. This finding is in accord with the previous studies on
the comprehensive anti-tumor mechanisms of nuciferine, a compound from
a yin-nourishing health-strengthening herb, Nelumbo nucifera Gaertn (He
Ye) [[151]55]. Additionally, some classical TCM formulae with the
health-strengthening efficacy are clinically proven to enhance the
innate immunological function (e.g., the killing abilities of NK cells)
and the sensitivity of immune system to tumor cells [[152]56,[153]57].
These results were consistent with our findings that
health-strengthening medicine might regulate the immune function in
multiple aspects, including NK-cell-mediated cytotoxicity and antigen
processing and presentation. However, the biological activities of
health strengthening medicine have not been fully elucidated.
Here, based on the target prediction and the HTS^2 assay results, we
analyzed the potential bioactivities of compounds from
health-strengthening herbs. Several pathogen-eliminating herbs were
also included in the research paradigm for comparison. Our prediction
and assay results suggested that compounds from both types of TCMs may
regulate both immune and cancer hallmarks (immune-excluded) pathways.
This finding was consistent with the multitarget characteristic of TCM
compounds. We further investigated the differences between these two
types of TCMs and discovered that the overall inhibitory effects of the
compounds in pathogen-eliminating herbs on tumor cells were
significantly stronger than those in health-strengthening herbs. The
results were consistent with the traditional understanding that
pathogen-eliminating herbs tend to target the tumor directly. However,
the traditional therapeutic advantages of health-strengthening herbs on
the immune system need to be further explored by adopting immunological
experimental results, and our high-throughput assay was conducted on
tumor cells. Additionally, we revealed a group of compounds from the
same herb that influences pathway gene expression in the same or
different directions. The compounds with opposite influences on pathway
gene expression may be explained by the different underlying mechanisms
in cancer treatment. Therefore, it was suggested that they may be
applied for treating different types of tumors.
In addition, galloylpaeoniflorin, a compound from a
health-strengthening herb Radix Paeoniae Alba (Bai Shao) was predicted
to impact several pathways that are significant in tumor development,
including T cell receptor signaling pathway, B cell receptor signaling
pathway, cell cycle, and mTOR signaling pathway. The regulatory effects
were further supported by the HTS^2 gene expression profile of the
related genes in the pathways after the treatment with
galloylpaeoniflorin. In fact, the antitumor effects of Radix Paeoniae
Alba and its total glucosides were found in recent research, and the
mechanism may be related to the inhibitory effects on the cell cycle of
tumor cells [[154]58,[155]59]. Notably, to the best of our knowledge,
there is no activity record of galloylpaeoniflorin. Therefore, we
examined and initially validated the antitumor effects of
galloylpaeoniflorin both in vitro and in vivo. More work should be
conducted for further evaluating the antitumor ability as well as
unveiling the potential mechanism of galloylpaeoniflorin.
In this work, we divided cancer-related pathways into two categories:
immune pathways and other cancer hallmarks for separate investigations.
The distinction was made for the following reason: in the aspect of
clinical treatment, immunotherapy and other approaches (e.g., targeted
approaches and cytotoxic agents) are distinguished treatment options
available in cancer treatment [[156]60]. That was because the
mechanisms that cancer immunotherapy are based on differ greatly from
those of other approaches in cancer therapy [[157]61]. Even though
immune evasion is one of cancer’s hallmarks, it characterizes the
responses using the immune system [[158]62]. Also, this distinction was
consistent with the Anatomical Therapeutic Chemical (ATC) drug
classification, a drug classification system based on pharmacological
and anatomical properties by WHO, in which antineoplastic (L01) and
immunomodulating (L03 and L04) agents are independent drug catalogues
[[159]63].
To be mentioned, previous studies indicated that health-strengthening
herbs may exert their therapeutic effects in cancer treatment in
multiple aspects (e.g., immunity and metabolism)
[[160]14,[161]15,[162]16,[163]17,[164]18,[165]19]. In this study, the
immunological efficacy was selected as an example and more work is
needed for a comprehensive understanding of their efficacy in other
aspects, such as the metabolic regulation effects. Moreover, TCM
syndrome (Zheng) is an essential concept in the TCM theory [[166]64].
It is reported that TCM syndromes correlates with treatment response to
TCM in cancer therapy [[167]65]. Therefore, future studies on the
therapeutic effects of health strengthening herbs cancers with
different TCM syndromes would further promote the understanding of the
biological basis of health-strengthening herbs.
According to the analysis results in this study, health-strengthening
herbs may exhibit both immune-regulatory and antitumor effects. Taking
into consideration the generally weaker antitumor effects (IC50 or
GI50) in vitro of compounds in health strengthening herbs than that in
pathogen-eliminating herbs in public records, the high percentage of
selected compounds in health-strengthening herbs related to
immune-related pathways, and the good safety of health-strengthening
herbs, it was indicated that health-strengthening herbs may have more
pharmacological potential in preventing tumors and improving a
tumor-immune microenvironment, compared to directly killing tumor
cells.
In contrast to previous studies, our study features the usage of
high-throughput computational and experimental methods for a more
comprehensive understanding of underlying mechanism of
health-strengthening herbs. HTS^2 may significantly promote the
parallel processing of candidate compounds and genes, and has been
applied in drug screening [[168]66]. However, this study only provided
in vitro large-scale experimental results of the HTS^2 assay on one
cell line, HCT116, and it does not represent cells of the immune
system. Therefore, most of the results in this manuscript are
hypothesis-generating and more experimental studies are needed to
further explore the bioactivities regulated by TCM compounds. Still,
the research strategy in this study does provided avenues for
large-scale experimentation. Hopefully, our study may help reveal the
biological basis of health-strengthening herbs, a characteristic herb
type which has been used for a long period by TCM practitioners, and
shed light on the future researches on anti-tumor drugs by unveiling
the wisdom of TCM.
4. Materials and Methods
4.1. TCM Compounds Data Preparation
We collected compound information of 47 TCM herbs (including 22
health-strengthening herbs and 25 pathogen-eliminating herbs for
comparison) that are widely used in cancer therapy from the Chinese
national medical insurance catalogue and commonly used prescriptions
(e.g., Liu-wei-di-huang pill, Buzhong Yiqi decoction, and Sijunzi
decoction) ([169]Supplementary Table S1). The 22 health-strengthening
herbs include different categories, such as yin-nourishing (Zi-Yin)
herbs and qi-tonifying (Yi-Qi) herbs, and the 25 pathogen-eliminating
herbs include categories, such as heat clearing and detoxifying
(Qing-Re-Jie-Du) herbs, and blood activating and stasis dissolving
(Huo-Xue-Hua-Yu) herbs, according to the traditional classification
based on TCM efficacy. Together, 1446 TCM compounds with PubChem
records were collected, including 655 compounds from
health-strengthening herbs, 667 compounds from pathogen-eliminating
herbs, and 124 compounds from both types of herbs.
4.2. Analysis Workflow Based on Network Pharmacology
In this study, we proposed an approach based on network pharmacology to
study the antitumor mechanisms of health-strengthening medicine. The
network pharmacology approach was applied for predicting the potential
targets of the TCM compounds and to visualize the analysis results as
networks in this manuscript. After the TCM compounds data preparation,
we predicted the potential targets of the TCM compounds by utilizing an
algorithm based on the correlation between the pharmacological network
and genomic network. The prediction results were analyzed via
literature mining, the HTS^2 assay, public assay data, and in vitro and
in vivo experiments. Taking advantage of the prediction and analysis
results, we analyzed the molecular functional patterns of
health-strengthening herbs ([170]Figure 6).
Figure 6.
[171]Figure 6
[172]Open in a new tab
The network analysis workflow for understanding the effects of
health-strengthening medicine compounds in cancer treatment.
4.3. Literature Mining
The literature mining was performed via text searching and no algorithm
was applied in the process. By literature mining, we aimed to obtain
the related biomolecules for each TCM compound and to compare the
results with the predicted targets. The co-occurrence of compound and
target appearing in one or more abstracts was used to define the
association of them.
We searched the PubMed database using the name of each TCM compound in
the abstracts and recorded the number of returned search results. In
this study, we selected the compounds with an adequate number of search
results (between 500 to 1000) for further analysis. The interval was
necessary because the number of the related literature varied greatly,
and this would have impacts on the following analysis (e.g., the
percentage of predicted targets verified by literature records). All
the related abstracts of the selected compounds were downloaded, and
text-processing codes were programed for extracting the biomolecules
mentioned in the abstracts. If a biomolecule co-occurred in the
abstract with the compound name, then we considered that the
biomolecule was related to the compound.
The results from the literature mining were then verified via a manual
examination by deleting the false positive responses. Then, the results
were used as the verification set for target prediction and HTS^2 assay
results.
4.4. Target Prediction for the TCM Compounds Applied in Cancer Treatment
The potential targets of the TCM compounds were predicted by
drugCIPHER-CS [[173]24] using Matlab 2016a (MathWorks, Natick, MA, USA)
[[174]67], a network-based target prediction method. Using a liner
regression model, this method correlates pharmacological and genomic
spaces for predicting the drug targets. In this method, the likelihood
of a candidate compound targeting a specific protein can be described
as a concordance score between the structural similarity vector of the
candidate compound and drugs in DrugBank [[175]68] and the drug-protein
closeness vector based on PPI. According the article on drugCIPHER-CS,
the accuracy of target prediction is 77.3% when the top 100
biomolecules were chosen to form a potential target profile, as
measured using cross-validation. Therefore, in this study, the top 100
biomolecules in the prediction result list were selected as the
potential target list of each TCM compound. The PPI network was
constructed by combining the PPI information recorded in HPRD, BIND,
IntAct, MINT, and OPHID in May 2011
[[176]35,[177]36,[178]37,[179]38,[180]39], and it contained 137,037
PPIs for 13,388 human proteins. Drug–protein interactions were
retrieved from DrugBank associated to the PubChem database in May 2015
[[181]43]. Drug structural similarity vectors were the Tanimoto
coefficients [[182]45] based on 881-dimensional CACTVS substructures in
PubChem.
Moreover, to measure the reliability of target prediction results,
biomolecules mentioned in the literature and DEGs in HTS^2 results were
collected. If a predicted target is directly or indirectly related to a
biomolecule which co-occurred with the compound in the literature or to
a DEG in the HTS^2 assay after treatment with the compound, it is
considered to be supported by the literature mining and HTS^2 assay.
The indirect relationship was established if the predicted target in in
the upstream in a KEGG pathway of the biomolecules in the abstracts or
DEGs, or if they are related via PPI. The cover rate was calculated as
[MATH:
|The predicted targets <
mi>related to the<
mtext> reported targets
(or DEGs)||The predicted targets
|×100%
:MATH]
.
4.5. KEGG Pathway Enrichment Analysis
We performed the KEGG pathway enrichment analysis for the predicted
targets of the TCM compounds applied in cancer therapy in order to
identify their biological functions. We used a hypergeometric test for
enrichment analysis. We performed target enrichment under the
background of 13388 human proteins and checked the p-values of the
pathways to see if they were significantly enriched. The enrichment
p-values of 26 pathways from cancer hallmarks and immune-related
pathways in the KEGG database were examined. The enrichment analysis
was performed using RStudio v1.1.447 and an open source programming
language, Ruby 2.3.0. The significantly enriched KEGG pathways (p <
0.05, fdr adjusted) were retained for further research.
4.6. Chemical Space Analysis
CACTVS substructures in PubChem were adopted in the chemical space
analysis. In the analysis, we used 881-dimensional substructure binary
vectors to encode the investigated TCM compounds. A PCA analysis was
conducted using the princomp function in the R packages stats v3.2.2
under the environment of RStudio v1.1.447 [[183]44]. The Tanimoto
coefficients of the binary vectors of the TCM compounds in the two
types of herbs and antineoplastic Western drugs were calculated.
4.7. Network Visualization
Network visualization was performed using Cytoscape v3.6.0 (National
Resource for Network Biology, Bethesda, MD, USA) [[184]69]. For
visualization, the KEGG, HPRD, BIND, IntAct, MINT, and OPHID databases
were used for providing pathway and PPI information.
4.8. Cell Culture
HCT116 (a colorectal cancer cell line), B16F10 (a melanoma cell line),
MCF-7 (a breast cancer cell line), and NCI-H460 (a lung cancer cell
line) cells were obtained from the cell center of Chinese Academy of
Medical Sciences and Peking Union Medical College (CAMS and PUMC). The
cells were cultured in an incubator with 5% CO[2] at 37 °C. Dulbecco’s
modified eagle medium (DMEM) containing 10% fetal bovine serum, 100
U/mL penicillin and 100 g/mL streptomycin were applied for the
incubation.
4.9. The HTS^2 Assay
The HTS^2 assay is a high-throughput screening strategy that enables a
large-scale and quantitative analysis of gene transcriptional profiles
in cells [[185]33]. In the HTS^2 assay, approximately 3000 HCT116
colorectal cancer cells were seeded in each well of a 384-well plate
for 24 h. After that, the TCM compounds library was added to the cells
for another 24 h, including eight DMSO replicates as negative controls.
The HTS^2 assay was then conducted to obtain the transcriptional
profiles of the designed gene probes. The cells were lysed in GentLys
buffer (Nanopure, Beijing, China). The instrumentation of the HTS^2
assay was an automated liquid handling system, which contained the
Agilent Bravo automated liquid handling platform (Agilent, Santa Clara,
CA, USA) and the Agilent bench robot (Agilent, Santa Clara, CA, USA).
By RNA annealing, selection and ligation, the instrumentation
automatically performed the HTS^2 assay. Pooled pairs of
oligonucleotides targeting selected gene probes by
streptavidin-magnetic beads and the biotinylated oligo-dT were used.
Then, the paired oligonucleotides were ligated using T4 DNA ligase and
were amplified using Polymerase Chain Reaction (PCR). Using unique
bar-coded primer in Illumina flowcells, the HTS^2 assay allowed a
high-throughput transcriptional profiling of up to 1400 genes from 2000
samples.
4.9.1. Selection and Preparation of the TCM Compounds for HTS^2
The selection of the TCM compounds for the HTS^2 assay were performed
considering their recorded biological activity data and the offer lists
provided by the suppliers. The bioactivity data (IC50 and GI50) of the
TCM compounds were collected from ChEMBL or via manual literature
searching. The median IC50 and GI50 of a compound were achieved via
calculating the median number in all human cell line specific
experiments in ChEMBL and literature records. The two metrics, IC50 and
GI50 were used as the same metrics in the analysis. Then, we chose the
compounds with an adequate antitumor activity (median IC50 or GI50 <
100 µM) as candidate compounds for the HTS^2 assay. The candidate
compounds were further selected considering the product availability of
the suppliers.
The TCM compounds were dissolved in DMSO. The concentrations were
preliminarily set as the median IC50 and were adjusted afterwards based
on the cell survival rate using MTS assay for the HCT116 cells to meet
the standard for the HTS^2 assay (cell survival rate > 70%). Detailed
information about the compounds applied in the HTS^2 assay (e.g.,
PubChem Compound Identifier (CID), supplier, and purity) was presented
in [186]Table S2.
4.9.2. The Gene Selection and Probe Design of the HTS^2 Assay
A total of 420 genes were selected to form a gene set for the HTS^2
assay. The gene set contained immune-related and other
antitumor-related genes. The selection of the 420 genes were achieved
in three steps. First, the genes were selected from databases and by
predictions to form a cancer-related gene lists, including genes in
pathways in cancer (hsa05200) in KEGG, genes in colorectal cancer
(hsa05210) in KEGG, the colorectal cancer related genes in OMIM (MIM
Number: 114500) [[187]70], the targets of antineoplastic drugs in
DrugBank, and the colorectal cancer related genes predicted using
CIPHER, a phenotype-gene network based algorithm [[188]71,[189]72]. The
reason why we selected some genes related to colorectal cancer was that
the HCT116 cell line applied in the following HTS^2 assay was a
colorectal cancer cell line.
Then, we referred to the public gene expression profiles in the GEO
database for selecting a reliable set of genes with adequate expression
levels. Here, we selected two profiles of samples from patients with
colorectal cancer ([190]GSE44076 and [191]GSE44861) and two profiles of
the HCT116 cell line ([192]GSE53295 and [193]GSE53965) for measuring
the gene expression. A gene was selected if it had at least three of
the above profiles in its expression data and if the expression ranked
10% to 60% in the detected gene set in at least one profile.
Additionally, we selected 30 housekeeping genes that meet the following
standards: (1) it was not in the cancer-related gene lists achieved in
the first step; (2) the expression of the gene was detected in the four
gene expression profiles ([194]GSE44076, [195]GSE44861, [196]GSE53295,
and [197]GSE53965), and was not a DEG in any one of these profiles; and
(3) the expression of the gene ranked 10% to 60% in the gene sets of at
least one profile. The moderate ranking was to ensure that the gene
expression was neither too high or too low, which may impair the
credibility of the HTS^2 assay results.
At last, the probes for the genes were designed, and 420 genes with
efficient probes, were selected for the HTS^2 assay, including 18
housekeeping genes. Sequences of 10 probes used in the HTS^2 assay were
provided in [198]Table S3.
4.9.3. HTS^2 Data Processing
First, the reads were mapped to the probe sequences, and three
mismatches for each were permitted. The raw experimental data of HTS^2
assay after treatment with DMSO and several TCM compounds were provided
in [199]Table S4. The numbers in [200]Table S4 were the reads of gene
probes using the HTS^2 assay, which represent the abundance of genes.
The HTS^2 data was normalized by the expression of 18 stable
housekeeping genes. The normalized gene expression was computed with
raw reads of the gene after the drug treatment and the median number of
raw reads of 18 housekeeping genes after the drug treatment.
Second, to identify the DEGs for each TCM compound, we calculated the
fold change of the tested genes as the normalized gene expression after
the drug treatment divided by the median number of normalized gene
expression after the eight DMSO replicates treatments. For each TCM
compound, genes with fold change > 2 were considered DEGs.
Third, to evaluate the reliability of the transcriptional profile, we
calculated the Pearson correlations among the normalized
transcriptional data after treatment with the eight DMSO replicates.
The results are demonstrated in [201]Figure S1. The correlations ranged
from 0.84 to 0.99, which indicated the reliability of the assay
results.
4.10. Cell Viability Assay
For exploring the antitumor effects of galloylpaeoniflorin, we further
performed an MTT cell viability assay on different tumor cells. The
HCT116, B16F10, MCF-7, and NCI-H460 cell lines were seeded in a 96-well
plate before the drug treatment. Various concentrations of
galloylpaeoniflorin dissolved in DMSO and ethanol respectively were
added to the cells after incubation for 24 h. To assess the IC50s of
the cell lines, the MTT cell viability assay was conducted after
incubation for another 120 h. The IC50s were achieved by fitting the
dose-response curve. The MTT assay was done once in triplicate.
4.11. Animal Studies
Twenty-one six-weeks-old BALB/c/nu nude female mice obtained from the
Vital River Laboratories (Vital River Laboratories, Beijing, China) and
were used for the xenograft experiments. H22 cells were injected into
the left flank of the mice. When the tumor volume reached the size of
100–250 mm^3, the mice were randomly separated into three groups and
were administered an oral dose of galloylpaeoniflorin (40 mg/kg/day or
80 mg/kg/day) or vehicle control (1 × solution with cremophor
EL/ethanol/water (12.5:12.5:75)). The mice were sacrificed at the end
of the treatment period. The tumor volume was measured and weighted for
the analysis.
The animal experiments were conducted in accordance with the guidelines
for the care and use of laboratory animals. The work was approved by
the Animal Care Committee of Chinese Academy of Medical Sciences and
Peking Union Medical Colleges (Beijing, China) (Permit Number: SYXK
2015-0025).
4.12. Statistical Analysis
Data are shown as the means ± standard deviation (SD) ([202]Figure 1C
and [203]Figure 5E) and median ± interquartile range ([204]Figure 2C
and [205]Figure 2E). Multiple types of data were used in the manuscript
and various statistical analysis methods were applied. Statistical
analysis was performed using Kolmogorov-Smirnov (KS) test in
[206]Figure 2C, Wilcoxon rank sum test in [207]Figure 2E and Student
t-tests in [208]Figure 5E. The significance levels were set at * p <
0.05, ** p < 0.01, and *** p < 0.001.
5. Conclusions
In conclusion, in this study, we performed a network-based analysis of
health-strengthening medicine by integrating a series of methods,
including target prediction, literature mining, the HTS^2 experiment,
and some low-throughput assays. The expression levels of 420 genes,
associated with tumor growth and immune functions, were detected after
a parallel treatment of 166 TCM compounds. By combining evidence from
different sources, we helped further uncover the biological basis of
health-strengthening medicine. We concluded that health-strengthening
herbs, might regulate both immune-related and antitumor pathways,
similar to pathogen-eliminating herbs. A typical case was demonstrated
by Radix Paeoniae Alba (Bai Shao), a health-strengthening herb widely
used in TCM cancer treatment. Galloylpaeoniflorin, a compound from
Radix Paeoniae Alba, was predicted to regulate several essential
biological processes in cancer development, and its antitumor effect
was preliminarily proven both in vivo and in vitro. Additionally, some
TCM compounds in the same herb were indicated to regulate pathway gene
expression with similar or different patterns, suggesting the urgent
need for further in-depth studies on TCM prescriptions. For instance,
acetyl ursolic acid and specnuezhenide, two compounds in a health
strengthening herb Fructus Ligustri lucidi (Nv Zhen Zi), both
upregulated gene expressions in T cell signaling pathway in HTS^2
assay. In summary, this study provided a new research strategy for
explaining the biological basis of health-strengthening herbs, and
further suggested the tumor immune regulatory and tumor preventive
potentials of health-strengthening herbs.
Acknowledgments