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Table of Contents
Year : 2022  |  Volume : 8  |  Issue : 1  |  Page : 77-86

Integrated miRNA and mRNA analysis identified potential mechanisms and targets of qianggan extracts in preventing nonalcoholic steatohepatitis

1 China-Canada Center of Research for Digestive Diseases, Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
2 Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, University of Ottawa, Ottawa, Canada
3 China-Canada Center of Research for Digestive Diseases, Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine; School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, China

Date of Submission05-Dec-2020
Date of Acceptance25-Mar-2021
Date of Web Publication07-Jan-2022

Correspondence Address:
Dr. Ming-Zhe Zhu
China-Canada Center of Research for Digestive Diseases, Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine; School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/wjtcm.wjtcm_48_21

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Objective: Qianggan (QG) extract is a patented traditional Chinese medicine that has been widely used for the clinical treatment of nonalcoholic steatohepatitis (NASH). However, its mechanism remains unclear. Methods: The efficacy of QG was evaluated in mice with methionine-and-choline-deficient diet-induced NASH by measuring serum alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase levels and by H and E staining of liver sections. Microarray and bioinformatics analyses were performed to obtain hepatic microRNA (miRNA) and mRNA expression profiles and to mine potential mechanisms and therapeutic targets. Furthermore, representative miRNA and mRNA expression levels were validated by quantitative real-time polymerase chain reaction (qRT-PCR). Results: QG extract significantly improved NASH. Twelve differentially expressed miRNAs and 1124 differentially changed mRNAs were identified as potential targets of QG extract. Integrated analysis detected 976 miRNA–mRNA regulatory pairs, and networks including 11 miRNAs and 427 mRNAs were constructed by Cytoscape. Hub nodes including miR-7050-5p, miR-212-3p, Bcl2l11, and Kras were filtered out. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that 427 mRNAs were enriched in pathways including apoptotic process, immune response, FoxO signaling pathway, and natural killer cell-mediated cytotoxicity. We also constructed a protein–protein interaction network with 254 nodes, and identified hub genes including Kras, Fasl, and Ncam1. Finally, the results of qRT-PCR were in good accordance with microarray data. Conclusion: This study identified important hub miRNAs and mRNAs involved in the mechanism of QG extract and which might provide potential therapeutic targets for patients with NASH.

Keywords: Fatty liver; gene regulatory networks; microarray analysis; Qianggan extract; Traditional Chinese Medicine

How to cite this article:
Huang J, Li M, Zhoua WJ, Yao ZM, Ji G, Zhang L, Zhu MZ. Integrated miRNA and mRNA analysis identified potential mechanisms and targets of qianggan extracts in preventing nonalcoholic steatohepatitis. World J Tradit Chin Med 2022;8:77-86

How to cite this URL:
Huang J, Li M, Zhoua WJ, Yao ZM, Ji G, Zhang L, Zhu MZ. Integrated miRNA and mRNA analysis identified potential mechanisms and targets of qianggan extracts in preventing nonalcoholic steatohepatitis. World J Tradit Chin Med [serial online] 2022 [cited 2023 Jun 2];8:77-86. Available from: https://www.wjtcm.net/text.asp?2022/8/1/77/336834

  Introduction Top

Nonalcoholic steatohepatitis (NASH) represents a relatively severe form of nonalcoholic fatty liver disease (NAFLD), which broadly defined by the presence of steatosis with inflammation and progressive fibrosis, ultimately leading to cirrhosis and even hepatocellular carcinoma.[1]

Numerous studies have investigated the pathophysiology and treatment of NASH;[2],[3] however, its pathogenesis remains unclear and there is currently no consensus for its treatment. The agents that targeted metabolic and downstream processes including lipid metabolism, apoptosis, and fibrosis had been considered as potential therapies for NASH.[4] Therefore, herbal medicines have been widely used to prevent or treat NASH. For example, Baicalin could ameliorate NASH possibly by attenuating lipid accumulation and its anti-inflammation and anti-apoptosis effects in mice,[5] while Aristolochia manshuriensis Kom ethyl acetate extract protected mice against high-fat diet-induced NASH by regulating kinase phosphorylation.[6] Several clinical studies have demonstrated the efficacy of Qianggan (QG) capsules (consisting of 16 Chinese herbs including Radix Astragali, Radix Salviae miltiorrhizae, and Radix Angelicae sinensis) for treating NAFLD/NASH.[7],[8],[9] However, their underlying molecular mechanisms and therapeutic targets remain obscure.

MicroRNAs (miRNAs), short non-coding RNAs (20–24 nucleotides), could suppress gene expression, and the altered levels of certain miRNA molecules suggest that they may play crucial regulatory roles in certain disorders.[10] Numerous previous studies have indicated miRNAs and mRNAs were involved in complicated diseases including cancer, diabetes, and NAFLD/NASH.[11],[12],[13] Meanwhile, microarray technology has been widely used to uncover the molecular mechanisms in treating diseases. For example, a microarray analysis revealed that berberine (a natural alkaloid derived from Coptis chinensis) depressed multiple myeloma cells partially through suppressing miRNA clusters and related targeted mRNAs, which implicated that the miR-99a ~ 125b cluster might serve as a potential target to treat multiple myeloma.[14] Another microarray analysis revealed that the traditional Chinese medicine Jiang Tang Xiao Ke granules exerted antidiabetic properties by regulating mRNAs and miRNAs in pancreatic tissue.[15]

In the present study, we constructed a mouse model of NASH and examined the hepatic miRNA and mRNA expression profiles by microarray analysis. We filtered out differentially expressed miRNAs and mRNAs, identified miRNA and mRNA regulatory networks, and validated hub genes involved in the effects of QG extract in NASH. The results of this study may further our understanding of the mechanisms of NASH and help to identify the potential therapeutic targets of QG extract.


Preparation of Qianggan extract

QG extract is composed of 16 herbs: Artemisia capillaris Thunb (2.5 parts), Radix isatidis seu Baphicacanthii (1.25 parts), Angelica sinensis (Oliv.) Diels (1.25 parts), Cynanchum otophyllum (1.25 parts), Salvia miltiorrhiza Bge (2.5 parts), Curcuma longa L. (1.25 parts), Astragalus membranaceus (Fisch.) Bunge. (2.5 parts), Codonopsis pilosula (Franch.) Nannf. (1.25 parts), Alisma plantago-aquatica Linn. (1.25 parts), Polygonatum sibiricum (1.25 parts), Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey. (1.25 parts), common yam rhizome (1.25 parts), Crataegus pinnatifida Bunge (1 part), Massa Medicata Fermentata (1 part), Gentiana macrophylla Pall. (1 part), and Glycyrrhiza uralensis Fisch (1 part). All the medicinal materials were prepared by water extraction, condensing, and drying. The ratio of extracted to raw herbal material was 30%.

Animals and sample collection

Male C57BL/6 mice (6 weeks old) were purchased from Shanghai SLAC Laboratory Animal Co. Ltd. (China) and maintained in a temperature- and humidity-controlled room. Mice were randomly divided into normal, methionine-and-choline-deficient (MCD), and QG groups (n = 12 per group) fed with chow diet, MCD diet, and MCD diet supplemented with QG extract (0.4 g/kg/day), respectively. The extracts were administrated by gavage. Moreover, animals were allowed ad libitum to food and water during the experiment.

The mice were weighed and sacrificed after 4 weeks, and samples were harvested as described previously.[16] Briefly, blood samples were collected from the abdominal aorta and centrifuged to harvest serum samples. Liver samples were quickly collected and weighed. Pieces of liver tissues were harvested and fixed in 10% neutral-buffered formalin. The left liver samples were quickly frozen in liquid nitrogen and stored at −80°C. All animal procedures were approved by the Animal Experiment Ethics Committee of Shanghai University of Traditional Chinese Medicine (Approval number 201703014).

Efficacy of Qianggan extract in nonalcoholic steatohepatitis mice

As previously described,[16] the ratio of liver weight to body weight was calculated for each group. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) were analyzed using commercial kits (Wako, Richmond, VA, USA) following the manufacturer's instructions. Liver samples were fixed in 10% formalin for 48 h, embedded in paraffin, sectioned at 4 μm, stained with H and E, and evaluated using a light microscope at ×200 magnification.

MicroRNA and mRNA expression profiles

Total RNA was prepared using TaKaRa RNAiso Plus (TaKaRa, Japan) according to the manufacturer's instructions, and the RNA integrity was checked using an Bioanalyzer 2100 (Agilent Technologies, USA). Total RNA was further purified using an RNeasy mini kit (Qiagen, Germany) and RNase-Free DNase (Qiagen). Purified RNA samples were labeled and hybridized using a Mouse ceRNA microarray (SBC, China) and Mouse miRNA (8 × 60 K) V21.0 microarray (Agilent Technologies) according to the manufacturers' instructions, to yield expression profiles of mRNA and miRNA, respectively. After hybridization, the microarrays were scanned using a microarray scanner (Agilent Technologies) to obtain the raw data, which were normalized using R package limma.

Identification of differentially expressed microRNAs and mRNAs

Differentially expressed miRNAs (DEMs) and mRNAs (DEGs) were filtered out based on a threshold of 1.2-fold change (ratio between groups based on average signal values) and a P value (Student's t-test) <0.05. DEMs and DEGs between pairwise groups (MCD vs. normal and QG vs. MCD) were presented as volcano plots, constructed by plotting the –log10 (P) on the y-axis and log2 (fold change) on the x-axis.[17] Overlapping DEMs and DEGs between the MCD versus normal and QG versus MCD groups were identified by a Venn diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) and used for further analysis.

Construction of microRNA-mRNA regulatory networks

Regulatory networks were constructed to reveal the correlations between the identified DEMs and DEGs, as described previously.[17] Briefly, target genes of DEMs were first predicted based on the miRWalk database. The overlapping genes between DEM target genes and DEGs were then obtained from a Venn diagram and used to screen miRNA–mRNA pairs; miRNA regulatory networks were finally visualized using Cytoscape (version 3.7.0).

MicroRNA regulatory network analysis

The degree represents connections among nodes, with a higher degree indicating more connections. Node degrees were calculated using the Cytoscape plug-in CytoNCA to reveal the centralities of the networks and to obtain hub miRNAs or mRNAs (degree ≥ 8). DEGs involved in the regulatory networks were subjected to further analysis. Gene ontology (GO) enrichment was carried out using DAVID 6.8 (https://david.ncifcrf.gov/) using the default background, and Kyoto encyclopedia of genes and genomes (KEGG) pathways were analyzed using the Cytoscape plug-in ClueGO, to elucidate the biological functions. Protein–protein interaction (PPI) network analysis was performed using the STRING database (https://string-db.org/) and visualized with Cytoscape software.

Quantitative real-time polymerase chain reaction assay

Quantitative real-time polymerase chain reaction (qRT-PCR) experiments were conducted according to the method that described previously.[17] Briefly, cDNA was reverse transcribed using a GoScript™ Reverse Transcriptase kit (Promega, Madison, WI, USA), and qPCR was performed using a PowerUp™ SYBRTM Green Master Mix (Applied Biosystems, TX, USA), following the manufacturer's protocols. The cycling conditions used in the StepOnePlus™ Real-Time PCR System (Applied Biosystems) were as follows: initial denaturation at 95°C for 2 min, followed by 40 cycles of 95°C for 15 s, annealing at 60°C for 15 s, and final extension at 72°C for 1 min. miRNA expression was quantified using the 2ΔΔCt method.[18] The mRNA and miRNA primers were synthesized by Shanjing Biotech Co., Ltd. (Shanghai, China) and the sequences are listed in [Table 1] (miRNAs) and [Table 2] (mRNAs).
Table 1: Primer information for polymerase chain reaction verification in microRNAs

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Table 2: Primer information for polymerase chain reaction verification in microRNAs

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Statistical analysis

Data were expressed as mean ± standard deviation and analyzed by one-way analysis of variance using SPSS v18.0 (IBM Corp., Armonk, NY, USA). Correlation analyses between qRT-PCR and microarray data were performed using MedCalc® statistical software. A P < 0.05 was considered statistically significant.[19]

  Results Top

Effects of Qianggan extract on nonalcoholic steatohepatitis

The efficacy of QG extract on NASH has been reported in our previous study.[16] Briefly, MCD-diet mice showed obvious hepatic steatosis and inflammation, as typical characteristics of NASH. However, steatosis and inflammatory infiltration in the liver were attenuated after 4-week supplementation with QG extract. Body and liver weights were significantly decreased in MCD diet compared with normal mice, but there was no significant difference between mice in the QG and MCD groups. There was no significant difference in the ratio of liver weight to body weight among the groups. Serum AST, ALT, and ALP levels were significantly increased in MCD mice compared with normal mice, but levels were restored in mice treated with QG extract.

Hepatic microRNA and mRNA expression profiles

We identified 68 DEMs and 9340 DEGs in the MCD versus normal mice, and 37 DEMs and 1935 DEGs in the QG versus MCD mice, based on the cutoff of 1.2-fold change and P < 0.05 [Figure 1]. The Venn diagram identified 12 overlapping DEMs and 1124 overlapping DEGs. The expression patterns of the overlapping DEMs and DEGs differed between MCD versus normal and QG versus MCD pairs, indicating the efficacy of the QG extract. For example, miR-22-5p and miR-212-3p were decreased in the MCD (vs. normal) but increased in the QG (vs. MCD) groups. MiR-1187 and miR-5622-3p were upregulated in the MCD (vs. normal) but downregulated in the QG (vs. MCD) groups. Ncam1 and Kras expression levels were increased in the MCD (vs. normal) but decreased in the QG (vs. MCD) groups. The overlapping DEMs are listed in [Table 3] and representative overlapping DEGs are presented in [Figure 2].
Figure 1: Volcano plots of hepatic mRNAs and miRNAs. The plots were constructed by plotting the -log10 (P) on the y-axis and log2 (fold change) on the x-axis. Red blots represent increase, green blots represent decrease, and gray blots represent no significant difference in miRNAs or mRNAs. Volcano plots of mRNAs in MCD versus normal (a) and QG versus MCD groups (b). Volcano plots of miRNAs in MCD versus normal (c) and QG versus MCD groups (d). miRNAs: MicroRNAs, MCD: Methionine-and-choline-deficient, QG: Qianggan

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Figure 2: Expression patterns of representative overlapping DEGs. Gene symbols were plotted on the y-axis and log2 (fold change) was plotted on the x-axis. Red bars represent expression in MCD versus normal and blue bars represent expression in QG versus MCD groups. MCD: Methionine-and-choline-deficient, QG: Qianggan, DEGs: Differentially expressed mRNAs

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Table 3: List of 12 overlapped differentially expressed microRNAs

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MicroRNA-mRNA regulatory networks

To clarify the functions of the overlapping DEMs, we predicted their target genes and obtained 974 miRNA–mRNA regulatory pairs. For example, miR-7050-5p was downregulated in the MCD (vs. normal) and upregulated in the QG (vs. MCD) group, whereas its target genes (including Ccl8, Kras, and Ncam1) were upregulated in the MCD (vs. normal) and downregulated in the QG (vs. MCD) groups. We also constructed miRNA regulatory networks consisting of 11 miRNAs and 427 mRNAs [Figure 3]. CytoNCA revealed that each miRNA negatively regulated more than 26 mRNAs and miR-7050-5p targeted 152 mRNAs. We also noted that 43 mRNAs (including Bcl2l11, Kras, and Ncam1) correlated with more than four miRNAs.
Figure 3: MiRNA–mRNA regulatory networks. Ellipses represent mRNAs, triangles represent miRNAs, and yellow ellipses represent mRNAs that were negatively regulated by more than four miRNAs. MiRNA: MicroRNA

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Furthermore, GO enrichment was conducted to reveal main biological functions of the mRNAs in the regulatory networks. The top 20 terms (including protein transport, apoptotic process, and immune response) are presented in [Figure 4]a. KEGG pathway analysis was performed to reveal the main pathways using ClueGO. A series of KEGG pathways (including protein processing in endoplasmic reticulum, Foxo signaling pathway, and natural killer cell-mediated cytotoxicity) were enriched, and several hub genes (including Kras, Akt3, and Fasl) among the pathways were identified [Figure 4]b. In addition, PPI analysis was performed to depict mRNA interactions and to mine hub genes. A network was constructed including 254 nodes, and 48 hub genes (including Kras, Fasl, and Ncam1) were identified with relatively higher degrees [Figure 5].
Figure 4: Functional enrichment of DEGs involved in miRNA regulatory networks. (a) Top 20 enriched GO terms. X-axis indicates gene count number and y-axis indicates GO terms. (b) KEGG pathway analysis. Each node is a KEGG pathway item. Node size indicates pathway significance, edges between nodes indicate overlapped genes among pathways, and different colors of nodes indicate different functional groups. DEGs: Differentially expressed mRNAs, GO: Gene ontology, KEGG: Kyoto encyclopedia of genes and genomes

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Figure 5: PPI networks of DEGs involved in miRNA regulatory networks. A PPI network with 254 nodes was constructed. Red nodes represent hub genes with higher degrees (≥8). PPI: Protein–protein interaction, DEGs: Differentially expressed mRNAs, MiRNA: MicroRNA

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Validation by quantitative real-time polymerase chain reaction

A total of 15 representative RNAs were selected to perform validation experiments using qRT-PCR. The microarray and qRT-PCR data of the 15 representative RNAs were presented in [Supplemental Figure 1] and [Supplemental Figure 2], respectively. Correlation analyses between qRT-PCR and microarray data were performed, which revealed six RNAs with statistically significant correlation [Supplemental Table 1]. Furthermore, fold changes of RNAs between pairwise groups (MCD vs. normal or QG vs. MCD) were calculated based on average expression values. The trend of the qRT-PCR results and microarray data were in good consistence [Figure 6], thus partially validating the reliability of the present findings.
Figure 6: QRT-PCR validation of representative RNAs. The x-axis indicates RNA names and the y-axis indicates fold changes of RNAs calculated by average expression values. Blue bars indicate qRT-PCR results, and red line with points indicates microarray data. Data from MCD versus normal (a) and from QG versus MCD groups (b). MCD: Methionine-and-choline-deficient, QG: Qianggan, qRT-PCR: Quantitative real-time polymerase chain reaction

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  Discussion Top

The present study demonstrated the efficacy of QG extract in mice with NASH. We also provided miRNA and mRNA expression profiles related to QG treatment, identified QG-targeted DEMs and DEGs, constructed miRNA–mRNA regulatory networks, and verified a series of hub genes involved in the mechanism of QG action in NASH.

Previous studies identified numerous miRNAs related to NAFLD. Expression changes in miRNAs (e.g., miR-29c, miR-34a, miR-155, and miR-200b) were reported to contribute to the development of NASH,[20] while another study indicated that miR-122, miR-192, and miR-21 might serve as biomarkers in patients with NASH.[21] In the present study, we identified 11 DEMs, including miR-6997-5p, miR-22-5p, miR-7050-5p, miR-212-3p, miR-3098-5p, miR-27b-3p, miR-1904, miR-7058-5p, miR-6965-5p, miR-1187, and miR-5622-3p, which might play crucial roles in the efficacy of QG extract against NASH. Among these, circulating miR-22-5p has been revealed as a potential biomarker for drug-induced steatosis in NAFLD patients.[22] MiR-212-3p could activate hepatic stellate cells and the transforming growth factor-β pathway and might be used as a potential liver fibrosis therapeutic target.[23] In addition, miR-27b-3p was reported to suppress the activation of macrophage in chronic liver injury.[24] In the current study, QG extract regulated expression levels of miR-22-5p, miR-212-3p, and miR-27b-3p, which might partially explain its efficacy in NASH and identify its therapeutic targets.

We constructed miRNA–mRNA regulatory networks to further reveal the biological functions of the obtained DEMs. GO and KEGG pathway enrichment demonstrated that DEGs (involved in regulatory networks) were enriched in multiple functions including apoptotic process and immune response. Apoptotic process was shown to be related with NASH progression, and pharmacologic agents targeting apoptosis might be used for NASH treatment.[25] The contribution of the immune response to NAFLD progression is also critical, by accelerating and magnifying the extent of injury, while paradoxically facilitating the resolution of inflammation and fibrosis in NASH.[26] Further understanding of apoptotic process and immune response in the progression of NAFLD and identify their potential regulator may achieve novel therapeutic targets in NASH.

MiR-451 has been reported to negatively regulated inflammation or apoptosis-related cytokines including interleukin-8 and tumor necrosis factor (TNF)-α, which implicated its role in preventing NAFLD progression.[27] Meanwhile, miRNA-26a could suppress immune response regulators IL-17 and IL-6 and thus affect NAFLD development in a mouse model.[28] In the present study, 11 miRNAs negatively regulated numerous mRNAs. Among these, miR-7050-5p negatively regulated 152 mRNAs, including Bcl2l11, Cd244, Klrc1, and Slamf7. However, there is currently no information on the function of miR-7050-5p, which may be speculated from its target genes. Bcl2l11 (also known as Bim, a proapoptotic protein) has been reported to be activated by c-Jun N-terminal kinase, resulting in Bax activation and enhanced apoptosis, which in turn promotes the development of NASH.[29] CD244 variants were shown to be involved in cirrhosis and hepatocellular carcinoma progression.[30] KLRC1 (also known as NKG2A), an inhibitory receptor on natural killer cells, was significantly increased in patients with hepatitis and hepatocellular carcinoma,[31],[32],[33] while Slamf7 was shown to be related with the modulation of hepatic innate immune cells and hepatitis liver injury.[34] The present data were largely consistent with previous studies. Expression levels of Bcl2l11, Cd244, Klrc1, and Slamf7 were elevated in NASH mice but attenuated by QG extract. Further thorough and comprehensive investigations of miR-7050-5p and its target genes might indicate novel therapeutic targets for NASH.

We also carried out an integrated analysis of miRNA regulatory networks, and KEGG pathway and PPI analyses, and identified several important hub genes including Kras, Ncam1, and Fasl, which might implicate their regulation by QG extract. Ras proto-oncogenes are central regulators of intracellular signal transduction pathways, and Kras mutations have been demonstrated in liver fibrosis and progression of hepatocellular carcinoma.[35],[36] Previous studies also reported that NCAM1 (also known as CD56) expression was increased in NAFLD patients with moderate or severe steatosis, and natural killer-like T cells were involved in fibrosis severity.[37],[38] Fas is a transmembrane protein, which belongs to TNF receptors superfamily, and its ligation with Fas ligand (FasL) could initiate cell apoptosis by activating a caspase cascade.[39] Increased levels of Fas/Fasl have been identified as important factors contributing to hepatic steatosis in murine models.[40],[41] In the current study, mRNA levels of Kras, Ncam1, and Fasl were markedly increased in NASH mice and decreased by QG extract. We also observed that their expression levels might be negatively regulated by several miRNAs including miR-6997-5p, miR-7058-5p, and miR-1904. Further functional investigations of the hub genes and their negative regulators may help to identify promising new treatments for NASH.

  Conclusion Top

QG extract could be a potential candidate drug for managing NASH, and the identified hub miRNAs and mRNAs might be targets of QG extract for treating NASH. These results broaden our knowledge of the mechanisms of QG extract in NASH and provide potential therapeutic targets.


This work was supported by grants from the National Science and Technology Major Project (No. 2017ZX09301-068), the National Natural Science Foundation of China (No. 81620108030), and the Shanghai Innovation Project of TCM (No. ZYKC201601005).

Financial support and sponsorship

This work was done in Institute of Digestive Diseases, Longhua Hospital, China-Canada Center of Research for Digestive Diseases, Shanghai University of Traditional Chinese Medicine. This work was supported by grants from the National Science and Technology Major Project (No. 2017ZX09301-068), National Natural Science Foundation of China (No. 81620108030), and the Shanghai innovation project of TCM (No. ZYKC201601005).

Conflicts of interest

There are no conflicts of interest.

  References Top

Yamauchi A, Kamiyoshi A, Koyama T, Iinuma N, Yamaguchi S, Miyazaki H, et al. Placental extract ameliorates non-alcoholic steatohepatitis (NASH) by exerting protective effects on endothelial cells. Heliyon 2017;3:e00416.  Back to cited text no. 1
Said A, Ghufran A. Epidemic of non-alcoholic fatty liver disease and hepatocellular carcinoma. World J Clin Oncol 2017;8:429-36.  Back to cited text no. 2
Gasanova OV, Sarkisova EO, Chumak AA, Ovsyannikova LM, Nosach OV, Alohina LM, et al. Comparative characteristics of hepatoprotectors used for the treatment of non alcoholic steatohepatitis associated with herpesvirus infection in sufferers of the Chornobyl accident. Probl Radiac Med Radiobiol 2017;22:339-52.  Back to cited text no. 3
Banini BA, Sanyal AJ. Current and future pharmacologic treatment of nonalcoholic steatohepatitis. Curr Opin Gastroenterol 2017;33:134-41.  Back to cited text no. 4
Zhang J, Zhang H, Deng X, Zhang N, Liu B, Xin S, et al. Baicalin attenuates non-alcoholic steatohepatitis by suppressing key regulators of lipid metabolism, inflammation and fibrosis in mice. Life Sci 2018;192:46-54.  Back to cited text no. 5
Kwak DH, Kim JS, Chang KT, Choo YK. Aristolochia manshuriensis Kom ethyl acetate extract protects against high-fat diet-induced non-alcoholic steatohepatitis by regulating kinase phosphorylation in mouse. J Vet Sci 2016;17:279-87.  Back to cited text no. 6
Chen ZX, Zhang SJ, Yun LR. Clinical study of treatment nonalcoholic fatty liver with qianggan capsule. Zhongguo Zhong Yao Za Zhi 2006;31:1739-41.  Back to cited text no. 7
Gu S, Huang MX. Clinical observation of Qianggan capsule in the treatment of non-alcoholic of non-alcoholic fatty liver fibrosis. Zhonghua Gan Zang Bing Za Zhi 2011;19:791-2.  Back to cited text no. 8
Li L, Zhang XJ, Lan Y, Xu L, Zhang XZ, Wang HH. Treatment of non-alcoholic fatty liver disease by Qianggan Capsule. Chin J Integr Med 2010;16:23-7.  Back to cited text no. 9
Martinez B, Peplow PV. MicroRNAs in Parkinson's disease and emerging therapeutic targets. Neural Regen Res 2017;12:1945-59.  Back to cited text no. 10
[PUBMED]  [Full text]  
Robinson JM, Henderson WA. Modelling the structure of a ceRNA-theoretical, bipartite microRNA-mRNA interaction network regulating intestinal epithelial cellular pathways using R programming. BMC Res Notes 2018;11:19.  Back to cited text no. 11
Miao C, Zhang G, Xie Z, Chang J. MicroRNAs in the pathogenesis of type 2 diabetes: New research progress and future direction. Can J Physiol Pharmacol 2018;96:103-12.  Back to cited text no. 12
Zhang YJ, Hu Y, Li J, Chi YJ, Jiang WW, Zhang F, et al. Roles of microRNAs in immunopathogenesis of non-alcoholic fatty liver disease revealed by integrated analysis of microRNA and mRNA expression profiles. Hepatobiliary Pancreat Dis Int 2017;16:65-79.  Back to cited text no. 13
Feng M, Luo X, Gu C, Li Y, Zhu X, Fei J. Systematic analysis of berberine-induced signaling pathway between miRNA clusters and mRNAs and identification of mir-99a ∼125b cluster function by seed-targeting inhibitors in multiple myeloma cells. RNA Biol 2015;12:82-91.  Back to cited text no. 14
Mo FF, An T, Zhang ZJ, Liu YF, Liu HX, Pan YY, et al. Jiang tang xiao ke granule play an anti-diabetic role in diabetic mice pancreatic tissue by regulating the mRNAs and MicroRNAs associated with PI3K-Akt signaling pathway. Front Pharmacol 2017;8:795.  Back to cited text no. 15
Zhu M, Li M, Zhou W, Yang Y, Li F, Zhang L, et al. Qianggan extract improved nonalcoholic steatohepatitis by modulating lncRNA/circRNA immune ceRNA networks. BMC Complement Altern Med 2019;19:156.  Back to cited text no. 16
Zhu M, Wang Q, Zhou W, Liu T, Yang L, Zheng P, et al. Integrated analysis of hepatic mRNA and miRNA profiles identified molecular networks and potential biomarkers of NAFLD. Sci Rep 2018;8:7628.  Back to cited text no. 17
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001;25:402-8.  Back to cited text no. 18
Zhu M, Hao S, Liu T, Yang L, Zheng P, Zhang L, et al. Lingguizhugan decoction improves non-alcoholic fatty liver disease by altering insulin resistance and lipid metabolism related genes: A whole trancriptome study by RNA-Seq. Oncotarget 2017;8:82621-31.  Back to cited text no. 19
Pogribny IP, Starlard-Davenport A, Tryndyak VP, Han T, Ross SA, Rusyn I, et al. Difference in expression of hepatic microRNAs miR-29c, miR-34a, miR-155, and miR-200b is associated with strain-specific susceptibility to dietary nonalcoholic steatohepatitis in mice. Lab Invest 2010;90:1437-46.  Back to cited text no. 20
Becker PP, Rau M, Schmitt J, Malsch C, Hammer C, Bantel H, et al. Performance of Serum microRNAs -122, -192 and -21 as Biomarkers in Patients with Non-Alcoholic Steatohepatitis. PLoS One 2015;10:e0142661.  Back to cited text no. 21
López-Riera M, Conde I, Tolosa L, Zaragoza Á, Castell JV, Gómez-Lechón MJ, et al. New microRNA biomarkers for drug-induced steatosis and their potential to predict the contribution of drugs to non-alcoholic fatty liver disease. Front Pharmacol 2017;8:3.  Back to cited text no. 22
Zhu J, Zhang Z, Zhang Y, Li W, Zheng W, Yu J, et al. MicroRNA-212 activates hepatic stellate cells and promotes liver fibrosis via targeting SMAD7. Biochem Biophys Res Commun 2018;496:176-83.  Back to cited text no. 23
Li W, Chang N, Tian L, Yang J, Ji X, Xie J, et al. miR-27b-3p, miR-181a-1-3p, and miR-326-5p are involved in the inhibition of macrophage activation in chronic liver injury. J Mol Med (Berl) 2017;95:1091-105.  Back to cited text no. 24
Golabi P, Bush H, Younossi ZM. Treatment strategies for nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Clin Liver Dis 2017;21:739-53.  Back to cited text no. 25
Narayanan S, Surette FA, Hahn YS. The immune landscape in nonalcoholic steatohepatitis. Immune Netw 2016;16:147-58.  Back to cited text no. 26
Hur W, Lee JH, Kim SW, Kim JH, Bae SH, Kim M, et al. Downregulation of microRNA-451 in non-alcoholic steatohepatitis inhibits fatty acid-induced proinflammatory cytokine production through the AMPK/AKT pathway. Int J Biochem Cell Biol 2015;64:265-76.  Back to cited text no. 27
He Q, Li F, Li J, Li R, Zhan G, Li G, et al. MicroRNA-26a-interleukin (IL)-6-IL-17 axis regulates the development of non-alcoholic fatty liver disease in a murine model. Clin Exp Immunol 2017;187:174-84.  Back to cited text no. 28
Mota M, Banini BA, Cazanave SC, Sanyal AJ. Molecular mechanisms of lipotoxicity and glucotoxicity in nonalcoholic fatty liver disease. Metabolism 2016;65:1049-61.  Back to cited text no. 29
Peng H, Li QL, Hou SH, Hu J, Fan JH, Guo JJ. Association of genetic polymorphisms in CD8+ T cell inhibitory genes and susceptibility to and progression of chronic HBV infection. Infect Genet Evol 2015;36:467-74.  Back to cited text no. 30
Cho H. Phenotypic characteristics of natural killer cells in acute hepatitis. J Microbiol 2013;51:247-51.  Back to cited text no. 31
Jiang JH, Zhou ZF, Li JY, Ye YB, Chen Q. Expression and significance of the NK cell receptors in primary hepatocellular carcinoma and paracancerous tissues. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 2012;28:529-32.  Back to cited text no. 32
Thoens C, Berger C, Trippler M, Siemann H, Lutterbeck M, Broering R, et al. KIR2DL3+NKG2A-natural killer cells are associated with protection from productive hepatitis C virus infection in people who inject drugs. J Hepatol 2014;61:475-81.  Back to cited text no. 33
Li Y, Cao G, Zheng X, Wang J, Wei H, Tian Z, et al. CRACC-CRACC interaction between Kupffer and NK cells contributes to poly I:C/D-GalN induced hepatitis. PLoS One 2013;8:e76681.  Back to cited text no. 34
Nguyen AT, Emelyanov A, Koh CH, Spitsbergen JM, Lam SH, Mathavan S, et al. A high level of liver-specific expression of oncogenic Kras(V12) drives robust liver tumorigenesis in transgenic zebrafish. Dis Model Mech 2011;4:801-13.  Back to cited text no. 35
Chung SI, Moon H, Ju HL, Cho KJ, Kim DY, Han KH, et al. Hepatic expression of Sonic Hedgehog induces liver fibrosis and promotes hepatocarcinogenesis in a transgenic mouse model. J Hepatol 2016;64:618-27.  Back to cited text no. 36
Adler M, Taylor S, Okebugwu K, Yee H, Fielding C, Fielding G, et al. Intrahepatic natural killer T cell populations are increased in human hepatic steatosis. World J Gastroenterol 2011;17:1725-31.  Back to cited text no. 37
Kalyuzhin OV, Ponezheva ZB, Semenova IV, Khokhlova ON, Serebrovskaya LV, Guseva TS, et al. Lymphocyte subpopulations, levels of interferon, and expression of their receptors in patients with chronic hepatitis B and C: Correlation with the species of viruses and the degree of liver fibrosis. Ter Arkh 2017;89:14-20.  Back to cited text no. 38
Volpe E, Sambucci M, Battistini L, Borsellino G. Fas-fas ligand: Checkpoint of T cell functions in multiple sclerosis. Front Immunol 2016;7:382.  Back to cited text no. 39
Kourouma A, Keita H, Duan P, Quan C, Bilivogui KK, Qi S, et al. Effects of 4-nonylphenol on oxidant/antioxidant balance system inducing hepatic steatosis in male rat. Toxicol Rep 2015;2:1423-33.  Back to cited text no. 40
Li CP, Li JH, He SY, Li P, Zhong XL. Roles of Fas/Fasl, Bcl-2/Bax, and Caspase-8 in rat nonalcoholic fatty liver disease pathogenesis. Genet Mol Res 2014;13:3991-9.  Back to cited text no. 41


  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]

  [Table 1], [Table 2], [Table 3]

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