Regulation of Shaoyao Ruangan Mixture on Intestinal Flora in Mice With Primary Liver Cancer

Abstract

Background: Shaoyao Ruangan mixture (SRM) has been applied clinically for more than 20 years in Zhejiang Cancer Hospital to treat patients with primary liver cancer (PLC). Intestinal microecology plays an important role in the emergence of liver diseases. This study aimed to reveal connections among SRM, intestinal microbiota and PLC, and the potential targets of SRM for liver cancer. Methods: We established a control group, a PLC model group, and a treatment group of mice to analyze the inhibitory effect of SRM on PLC and its intestinal flora target. We also evaluated drug efficacy of SRM and analyzed specific changes in intestinal flora by 16S rDNA sequencing of stools. As the serum interleukin (IL)-10 level could be an independent prognostic factor for unresectable liver cancer, we detected IL-10 levels and analyzed their association with the abundance of specific bacteria. Results: Liver tumors in the treatment group were smaller and fewer than those in the model group (P = .046). The abundance of Bacteroides was significantly higher in the model group than that in the control group, while SRM significantly reduced the increasing abundance of Bacteroides in mice with PLC. We found that the IL-10 level was positively correlated with the abundance of Bacteroides. Conclusion: SRM can effectively inhibit the progression of PLC and increase Bacteroides abundance. In view of the association between Bacteroides and liver cancer and the significant positive correlation between Bacteroides and IL-10 levels, Bacteroides may be the target intestinal flora of SRM to inhibit PLC.

Keywords

Shaoyao Ruangan mixture primary liver cancer intestinal flora IL-10

Introduction

Approximately half of all new cases of primary liver cancer (PLC) and associated deaths worldwide occur in China.¹ Due to an insufficient understanding of drug mechanisms and targets, a large number of clinical trials fail.^2-4 The adaptability and stability of intestinal microbiota and their responsiveness to physiological and pathological changes make them possible biomarkers and therapeutic targets for tumors.⁵ Further exploration of intestinal microbiota promotes an understanding of the pathogenesis and prevention of hepatocellular carcinoma (HCC).⁶ For example, intestinal bacterial metabolites engage key pathways to promote inflammation, fibrosis, and genotoxicity in HCC; and a tumor-promoting microenvironment caused by gram-positive intestinal flora promotes the development of HCC.^7,8 Traditional Chinese medicine (TCM) is one of the oldest medical systems in the world and has been used in China for thousands of years.^9,10 The TCM preparation Shaoyao Ruangan mixture (SRM) made by Zhejiang Cancer Hospital has been applied clinically for more than 20 years.¹¹ A case series reported that SRM may improve the survival and quality of life of patients with advanced liver cancer.¹² This study aimed to investigate the inhibitory effect of SRM on PLC in mice and the regulation of specific intestinal microbiota in order to explore its target intestinal flora.

Materials and Methods

Preparation of SRM

Shaoyao Ruangan mixture is a medical preparation formulated by the Hangzhou Traditional Chinese Medicine Hospital under medicine preparation Approval Number Z20100018. The batch number of the preparation used in this experiment is 160624. SRM consists of the following Chinese herbal medicinals¹³: Herba Hedyotidis (herb of Hedyotis diffusa Willd., Family Rubiaceae), 86 g; Scutellariae Barbatae Herba (herb of Scutellaria barbata D, Don, Family Lamiaceae), 86 g; Paridis Rhizoma (rhizomes of Paris polyphylla Smith var. yunnanensis [Franch.] Hand.-Mazz., Family Liliaceae), 28.7 g; Tetrastigma hemsleyanum Diels et Gilg (root of Tetrastigma hemsleyanum Diels et Gilg, FamilyVitaceaae), 34.4 g; Paeoniae Radix Alba (root of Paeonia lactiflora Pall, Family Ranunculaceae), 34.4 g; Galli Gigerii Endothelium Corneum (inside the gizzard of Gallus gallus domesticus Brisson, Family Phasianidae), 25.8 g; Citri Reticulatae Pericarpium Viride (immature peel of Citrus reticulata Blanco, Family Rutaceae), 25.8 g; Citri Reticulatae Pericarpium (ripe peel of Citrus reticulata Blanco, Family Rutaceae), 25.8 g; Crataegi Fructus (ripe fruit of Crataegus pinnatifida Bge, Family Rosaceae), 86 g; Curcumae Radix (root of Curcuma wenyujin Y. H. Chen et C. Ling., Family Zingiberaceae), 25.8 g; Sparganii Rhizoma (tuber of Sparganium stoloniferum Buch.-Ham., Family Sparganiaceae), 25.8 g; Curcumae Rhizoma (rhizomes of Curcuma phaeocaulis Val., Family Zingiberaceae), 25.8 g; Imperatae Rhizoma (rhizomes of Imperata cylindrica Beauv. var. major [Nees] C. E. Hubb., Family Gramineae), 86 g; Gardeniae Fructus Praeparatus (ripe fruit of Gardenia jasminoides Ellis, Family Rubiaceae), 25.8 g; Lysimachiae Herba (herb of Lysimachia christinae Hance, Family Primulaaceae), 86 g; Ardisiae Japonicae Herba (herb of Ardisia japonica [Thunb.] Blume, Family Myrsinaceae, Nom. Conserv.), 86 g; Aristolochia mollissima Hance (herb of Aristolochia mollissima Hance, Family Aristolochiaceae), 86 g; Liquidambaris Fructus (ripe infructescence of Liquidambar formosana Hance, Family Hamamelidaceae), 34.4 g; and Agrimoniae Herba (ground part of Agrimonia pilosa Ledeb., Family Rosaceae), 86 g. The above-mentioned 19 Chinese herbal medicines were boiled twice with water, the first decoction for 120 minutes and the second decoction for 90 minutes. After filtration, the 2 decoctions were combined and precipitated. The supernatant was aspirated and concentrated to 1000 mL. Then, it was packed, sealed, and steam-sterilized.

Transgenic PLC Model and Groups

Mutation of the Ras proto-oncogene or activation of the Ras signaling pathway is an essential feature of liver cancer.¹⁴ The mechanism of Ras transformation in mice may differ from that in humans.¹⁵ There are 3 Ras family genes related to human cancer: H-Ras, K-Ras, and N-Ras. H-Ras12V has a higher transforming potential than either K-Ras12V or N-Ras12V in RIE-1 rat epithelial cell cultures.¹⁶ In 2005, Wang used a serum albumin enhancer and promoter to induce the specific expression of a mutant Ras oncogene (H-Ras12V) in hepatocytes and successfully established a mouse model of H-Ras12V transgenic liver cancer.¹⁷

The experiment included a control group, a model group, and a treatment group. Twenty-six 7-month-old SPF H-Ras12V transgenic male mice, established by Wang, were randomly assigned to the model group or the treatment group. These mice carried the H-Ras12V oncogene in the C57BL/6J genetic background. All mice weighed between 23.06 g and 31.00 g. The control group included 13 C57BL/6J male mice with weights ranging from 24.78 g to 34.61 g. The above-mentioned animals were bred and raised at the Experimental Animal Center of Dalian Medical University (Qualification No. 211003700000627). All mice were given a standard diet. Procedures involving animals and their care were conducted in conformity with the National Institutes of Health (NIH) guidelines (NIH Publication No. 85-23, revised 1996) and were approved by the Animal Care and Use Committee of the Zhejiang Cancer Hospital.

Treatment of PLC Mice

The mice in the treatment group were intragastrically administered 20 mg/kg body weight SRM decoction daily, while the mice in the control and model groups were intragastrically administered 20 mg/kg physiological saline every day. The entire treatment period lasted 12 weeks. The weight of each mouse was recorded once a week.

Model Assessment and Efficacy Evaluation

The model was assessed based on the general conditions of the mice and the liver tumor formation rate. The efficacy of SRM was evaluated by the following 2 aspects: (1) the general conditions of the mice, including fur condition, weight trend, and activity; and (2) the tumor characteristics, especially the size and number of liver tumors. Mice were sacrificed and dissected in the 12th week of the experiment. The diameter and number of liver tumors were recorded.

Determination of Serum Interleukin (IL)-10 Levels

The IL-10 test kit was used to detect serum IL-10 levels. After preparing all the reagents and standards, 50 µL sample diluent was added to each well. Within 15 minutes, 50 µL each of standards, samples, and controls were added to previously defined wells. Then, 50 µL detection antibody was added to each well. The antibodies were incubated for 3 hours at room temperature, and then, the plates were washed 6 times. After 45 minutes of incubation at room temperature, they were washed 6 times again. Then, 100 µL chromogenic substrate was added, and the plates were protected from light and incubated for 15 minutes at room temperature. Finally, 100 µL stop solution was added to each well. Within 30 minutes, the optical density was detected at a wavelength of 450 nm, with a reference wavelength of 570 nm or 630 nm.

Collection and Pretreatment of Intestinal Flora Samples

The stools of 7 randomly selected mice in each group were collected using sterile tweezers in the eighth week of the experiment. After swirling 0.1 g fecal specimens and 4 mL sterile phosphate-buffered saline (PBS; pH 7.4) for 5 minutes, each fecal specimen was centrifuged at 40× g for 8 minutes. The upper layers containing bacteria were collected. The swirling and centrifugation steps were repeated once. The supernatants were collected and centrifuged at 2000× g for 8 minutes. The obtained bacterial pellets were washed twice with PBS. Finally, the samples were stored in sterile EP tubes containing PBS at −80°C.

Sequencing of Intestinal Flora

Total DNA quality tests were performed by using a Thermo NanoDrop 2000 Ultraviolet Microspectrophotometer (Thermo Fisher Scientific Ltd.) and 1% agarose gel electrophoresis. The V3-V4 region of 16S rDNA was selected for amplification. The universal primers used were F341 and R806. The 5′ end of the universal primer was designed with the index sequence and linker sequence suitable for HiSeq 2500 PE250 sequencing: forward primer (5′-3′): ACTCCTACGGGRSGCAGCAG (F341); reverse primer (5′-3′): GGACTACVVGGGTATCTAATC (R806). To ensure the accuracy and efficiency of the amplification, polymerase chain reaction (PCR) was performed using the KAPA HiFi HotStart ReadyMix PCR kit (Sopachem Ltd.). The diluted genomic DNA served as a template. The PCR products were detected by 2% agarose gel electrophoresis and recovered by an AxyPrep DNA Gel Recovery Kit (Axygen Ltd.). Then, the library was examined by a Thermo NanoDrop 2000 Ultraviolet Microspectrophotometer and 2% agarose gel electrophoresis. After the quality of the library was confirmed, Qubit was used for library quantification. According to the quantity requirement for each sample, the appropriate amount of each reagent was mixed together. Finally, sequencing was performed using Illumina HiSeq PE250.

Processing of the Intestinal Flora Data

After quality control of the original data, reads were sorted by abundance from large to small. The operational taxonomic unit (OTU) was obtained by using USEARCH software to cluster the data with a standard of 97% similarity. Using QIIME software for alpha diversity analysis, the leveling parameters were determined. Then, 25 828 reads were randomly selected from each sample. A read was separately extracted from an OTU as the representative sequence. Using the Ribosomal Database Project classifier, the representative sequence was compared with the 16S database to perform taxa classification for each OTU. After classification, the OTU abundance table was made according to the number of sequences in each OTU. From this table, taxa abundance analysis, alpha diversity analysis, beta diversity analysis, and significant difference taxa analysis were performed.

Statistical Analysis

General Conditions of Mice and Tumor Characteristics

Measurement data are expressed as the mean and standard deviation ( $\bar{x} \pm s$ ). Data with a normal distribution were compared with the independent sample t test. Repeated measurement data were analyzed by analysis of variance. Statistical analysis of ordered grade data was weighted on the basis of the number and conducted by the Mann-Whitney test. Statistical analysis was completed by SPSS statistical software V 23.0 (SPSS, Chicago, IL). P value <.05 was considered statistically significant.

Intestinal Microbiota

Differences in taxa communities among samples were compared by the UniFrac distance distribution. Analysis of significantly different taxa was performed by using linear discriminant analysis Effect Size (LEfSe analysis). The Kruskal-Wallis test was used to identify genera with significant differences (P < .05) among groups. The top 30 taxa in terms of abundance at the genus level were selected, and the Spearman correlation test was used to analyze the relationships among the dominant taxa. The correlation between serum IL-10 levels and Bacteroides was analyzed by Pearson correlation.

Results

Model Assessment and Drug Efficacy Evaluation

General Animal Conditions

The mice in the control group were the most active and responsive. Meanwhile, they had glossier fur. The mice suffering from PLC had messier fur and slower responses. In the treatment group, the mice’s fur became smoother and they reacted more sensitively to their circumstances.

Tumor Characteristics

Multiple tumors appeared in the liver of mice in the model group and the treatment group. The longest diameter of the largest tumor in the model group was 11.80 ± 5.01 mm. The longest diameter of the largest tumor in the treatment group was 10.91 ± 3.60 mm. Liver tumors in the 2 groups were classified into 4 categories of <2 mm, 2 to 5 mm, 5 to 8 mm, and >8 mm. Tumors in the treatment group were fewer in number and smaller than those in the model group. As shown in Figure 1, there was a significant inhibitory effect of SRM on liver cancer development in mice.

Figure 1.

Size and number of tumors in the model group and the treatment group. After weighing the case by number, a Mann-Whitney test was performed (Z = −2.032, P = .042).

Changes in Body Weight Over Time

The body weight of mice in the control group showed a continuous and steady increase. The body weight of mice in the model group showed a slightly curved trend, first increasing and then gradually stabilizing before finally showing a downward trend. The body weight of mice in the treatment group was stable from beginning to end. The weight trend over time of each group is shown in Figure 2 as $\bar{x} \pm s$ . The difference in the body weight of mice in different groups was not statistically significant, but the difference in body weight over time was statistically significant. In addition, there was an interaction effect between mouse body weight and time in different groups. This finding indicated that SRM could maintain stable body weight with the progression of PLC.

Figure 2.

The body weight of the mice changed over time. Analysis of variance of repeated measurement data was used to examine the effects of time and group on mouse body weight and their interactions. The corrected intrasubject effect (time) was statistically significant (P = .003). The intersubject effect (group) test had a P value of .232, and there was no significant difference in mouse body weight in different groups. The interaction effect analysis revealed an interaction between group and time, which meant that group allocation influenced the trend in mouse body weight over time (P = .039).

Taxa Abundance Analysis

According to the taxa annotation results, the taxa abundance of each group was analyzed at the genus level. The top 5 genera with the highest abundance in the control group were Alloprevotella, Barnesiella, Clostridium XlVa, Bacteroides, and Alistipes. The top 5 genera in the model group with the highest abundance were Barnesiella, Alloprevotella, Bacteroides, Clostridium XlVa, and Saccharibacteria genus incertae sedis. Correspondingly, Barnesiella, Desulfovibrio, Clostridium XlVa, Saccharibacteria genus incertae sedis, and Bacteroides were the top 5 dominant taxa in the treatment group. The abundance of taxa and their proportions in the 3 groups are shown in Figure 3.

Figure 3.

Taxa abundance histogram at the genus level.

Analysis of Taxa With Significant Differences in Abundance

As shown in Figure 4A, the different genera between the control group and the model group were Bacteroides, Saccharibacteria genera incertae sedis, Parabacteroides, Flavonifractor, Akkermansia, and Allobaculum. The different genera between the model group and the treatment group were Alloprevotella, Bacteroides, Desulfovibrio, Turicibacter, and Clostridium XVIII, and more details can be found in Figure 4B. The abundance of Bacteroides increased significantly in mice with liver cancer and decreased after treatment with SRM. These important data can be seen in Figure 4C.

Figure 4.

Significantly different taxa at the genus level. The Kruskal-Wallis test was used to identify taxa with significant differences between groups (P < .05). *P < .05, **P < .01.

Correlation Between Serum IL-10 Level and Bacteroides Abundance

In these 3 groups of mice, the level of IL-10 was positively correlated with the abundance of Bacteroides, and the Pearson correlation test showed that this correlation was statistically significant (Figure 5).

Figure 5.

A scatter plot of serum interleukin (IL)-10 levels and Bacteroides abundance. The Pearson correlation coefficient was 0.837 (P < .05) in the control group, 0.856 (P < .05) in the model group, and 0.898 (P < .01) in the treatment group.

Spearman Correlation of Dominant Taxa

The top 30 genera with differential abundance were selected to explore the relationships among the dominant taxa. The Spearman correlation heat map was drawn by the corrplot package in R software. The important patterns and relationships among the dominant taxa were analyzed using this heat map. The Spearman correlation analysis between the control group and the model group is shown in Figure 6A. Taking P < .01 as the level of significance, there were significant positive correlations between Bacteroides and Parabacteroides and between Akkermansia and Escherichia. There were significant negative correlations between Bacteroides and Allobaculum, Akkermansia and Flavonifractor, and Escherichia and Saccharibacteria genus incertae sedis. Taking P < .05 as the level of significance, there were significant negative correlations between Alloprevotella and Clostridium XVIII, Alloprevotella and Turicibacter, and Bacteroides and Desulfovibrio, and there was a positive correlation between Clostridium XVIII and Turicibacter. The Spearman correlation analysis of the model group and the treatment group is shown in Figure 6B.

Figure 6.

(A) Spearman correlation analysis of the control group and the model group. (B) Spearman correlation analysis of the model group and the treatment group. Blue indicates a positive correlation, and red indicates a negative correlation. A darker color indicates a stronger correlation between taxa.

Discussion

Potential Intestinal Flora Target of SRM

Differences in intestinal flora are an important factor in tumor immunotherapy.¹⁸ The activity of antitumor drugs relates to the type of bacteria that live in the human intestinal tract.¹⁹ This study found that the intestinal microbiota of SRM-treated mice with liver cancer underwent specific changes. The abundance of Bacteroides in the model group increased significantly compared with that in the control group, while the abundance of Bacteroides in the treatment group decreased significantly compared with that in the model group.

Bacteroides is 1 of the 3 major intestinal genera in the human body.²⁰ The abundance of Bacteroides in the intestine increases significantly when people keep a high-fat and high-protein diet for a long period.²¹ Sphingolipid is a common feature of Bacteroides and an important factor in the ability of these organisms to become dominant bacteria in the intestine.²² Sphingolipids regulate multiple cellular processes, including proliferation, cell cycle, inflammatory pathways, and apoptosis. Some sphingolipids have opposing functions, and their metabolic dysfunction can lead to the loss of cell cycle control.²³ For example, the sphingolipid metabolite ceramide can regulate cell proliferation, differentiation, and apoptosis; reverse drug resistance; and inhibit tumors.²⁴ Studies have shown that Bacteroides fragilis of the genus Bacteroides is more abundant in patients with colorectal cancer than in healthy individuals and that Bacteroides fragilis causes carcinogenesis of the colon.^25,26 In addition to the genus Bacteroides, the genera Porphyromonas and Prevotella also have sphingolipids.²² Porphyromonas gingivalis of the genus Porphyromonas is an oral microbe that causes periodontitis and is associated with various tumors, such as colorectal cancer, non-Hodgkin’s lymphoma, oral cavity cancer, gastrointestinal cancer, and pancreatic cancer.^27,28 However, Prevotella has exactly the opposite clinical value. An increase in Prevotella abundance can promote the release of anti-inflammatory substances and ultimately inhibit liver cancer.²⁹ In conclusion, the unique structure of Bacteroides and its metabolites are closely related to liver cancer and other types of cancer.

SRM consists of 19 Chinese herbal medicinals. There are no reports of the effects of these medicinals on intestinal flora in animals or humans. Some researchers previously analyzed the arbuscular mycorrhizal fungi of Poncirus trifoliata and Citrus reticulata by SSU rDNA analysis, and 5 strains isolated from citrus orchards promote the early emergence and growth of citrus seedlings.^30,31 Another study found a high potential for the application of Streptomyces violascens MT7, an Indian indigenous, Loktak Lake soil isolate, and its extracellular metabolites as an effective eco-friendly alternative to synthetic fungicides for controlling toxigenic citrus and papaya-rotting fungi.³² However, there is no evidence to suggest that these Chinese herbal medicinals are linked to intestinal microecology.

Other Changes in Intestinal Flora in the Context of Liver Cancer

In addition to the significant increase in the abundance of Bacteroides, this study also found that the abundance of Akkermansia significantly decreased in mice with liver cancer. The abundance of Akkermansia is inversely related to mouse body weight, which contrasts the positive correlation of Bacteroides with high-fat and high-protein diets.³³ Recent studies have shown that Akkermansia muciniphila can improve immunotherapy for epithelial-derived tumors.³⁴ Therefore, the correlation between liver cancer and Akkermansia still needs further exploration.

Allobaculum is another intestinal flora that is negatively related to mouse body weight.³³ This study found a negative correlation between Allobaculum and Bacteroides. As obesity and a diet high in animal fat can increase the risk of liver cancer, the abundance of Allobaculum and the ratio of Allobaculum to Bacteroides in the pathogenesis of liver cancer require further study.³⁵

Correlation Between IL-10 Levels and Bacteroides Abundance

Serum IL-10 is closely related to PLC. It is not only a complementary tumor marker for patients with low α-fetoprotein levels but also an independent prognostic factor for unresectable HCC.^36,37 Increased IL-10 production accelerates the progression of chronic hepatitis B virus infection and the development of HCC.³⁸ A study assessing the association between the IL-10 polymorphisms IL-10-1082 (G/A), IL-10-592 (C/A), IL-10-819 (T/C), and HCC susceptibility found that the IL-10-592 A/C polymorphism may be associated with HCC among Asians.³⁹ Therefore, we evaluated IL-10 in mice with liver cancer.

Previous studies have found that IL-10 levels are positively correlated with the abundance of Bacteroides fragilis.⁴⁰ For example, Bacteroides fragilis could inhibit colitis by responding to IL-10 produced by CD4⁺ T-cells.⁴¹ Further studies found that Bacteroides fragilis protected against colitis induced by Helicobacter hepaticus by promoting IL-10 production by CD4⁺ T-cells.⁴² There was a significant positive correlation between IL-10 level and Bacteroides abundance in this study. On the one hand, Bacteroides abundance and serum IL-10 levels were significantly increased in mice with liver cancer compared with normal mice, which indicated their potential as auxiliary diagnostic markers. On the other hand, the abundance of Bacteroides specifically decreased after treatment with SRM. SRM plays a role in prolonging the survival time of patients with liver cancer, and the abundance of Bacteroides may also be an independent prognostic factor for liver cancer.

Conclusions

SRM could effectively inhibit the progression of tumors in mice with PLC and regulate their intestinal microecology. Bacteroides was regarded as the potential intestinal flora target of SRM. However, intestinal microecology is very complex and can adapt to many factors, such as diet, environment, physiology, and pathological conditions.⁵ Studying these adaptations requires systematic and complex evaluation methods. The existing research methods focus on intestinal flora diversity, bacterial structure, and differential species abundance; and 2 × 2 correlation analyses of differences in species and dominant species can be performed. However, the changes in intestinal microecology influence the relationships among many bacterial communities, which form a very complex network, and further improvement in microecological evaluation methods is required. The composition of compound TCM formulas is complex, so its mechanism of treating disease is difficult to fully clarify. Because of its systemic effects and complexity, intestinal microecology is a valuable system to evaluate when studying the antitumor mechanism of compound TCM formulas.

Supplemental Material

Supplementary_table – Supplemental material for Regulation of Shaoyao Ruangan Mixture on Intestinal Flora in Mice With Primary Liver Cancer

Supplemental material, Supplementary_table for Regulation of Shaoyao Ruangan Mixture on Intestinal Flora in Mice With Primary Liver Cancer by Hongde Zhen, Xiang Qian, Xiaoxuan Fu, Zhuo Chen, Aiqin Zhang and Lei Shi in Integrative Cancer Therapies

Footnotes

Acknowledgements

We want to express our gratitude to those who have contributed to this study. In particular, Professor Aiguo Wang of Dalian Medical University gave us an experimental technical guide, and Mr Qi Liu from Realbio Genomics Institute helped us with intestinal flora detection.

Authors’ Note

The raw materials of Figures 1 and 2 were listed in Supplementary Table 1 (available online); for Figures 3 and 4 in Supplementary Tables 2 and 3, and for in Supplementary Table 4. The information of samples, the abundance of taxa, and the level of serum interleukin-10 used to support the findings of this study are included in the supplementary information files.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the project from Zhejiang Provincial Administration of Traditional Chinese Medicine (No. 2016ZA037).

Supplemental Material

Supplemental material for this article is available online.

ORCID iD

Hongde Zhen

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