Abstract Background Tribulus terrestris L. (TT) is one of the most common Chinese herbs and distributes in various regions in China. TT was first documented to treat breast cancer in Shen-Nong-Ben-Cao-Jing. However, the pharmacological activities of TT extract on liver cancer have not been reported. In this study, we investigated its anti-liver cancer activity and underlying mechanism. Methods Traditional Chinese Medicine Systems Pharmacology (TCMSP) and PharmMapper databases were used to obtain the active ingredients and the targets of TT. Genecards database was employed to acquire TT targets in liver cancer. Furthermore, Venny 2.1, Cytoscape 3.8.2, DAVID 6.8 software were utilized to analyze the relationship between TT and liver cancer. In vivo experiment: The animal model of liver cancer was established by injection of H22 cells into Balb/c mice. After five days, drugs were intragastrically administered to the mice daily for 10 days. Body weight, tumor size and tumor weight were recorded. Tumor inhibitory rate was calculated. Protein levels were examined by Western blotting. Pathological changes of liver cancer tissues were evaluated by HE and Tunel staining. Metabolomics study: LC-MS was used to analyze different metabolites between model and TTM groups. Results 12 active ingredients of TT, 127 targets of active ingredients, 17,378 targets of liver cancer, and 125 overlapping genes were obtained. And then, 118 items of GO biological processes (BP), 54 items of GO molecular function (MF), 35 items of GO cellular component (CC) and 128 pathways of KEGG were gotten (P < 0.05). Moreover, 47 differential metabolites were affirmed and 66 pathways of KEGG (P < 0.05) were obtained. In addition, after TT and sorafenib treatment, tumor size was markedly reduced, respectively, compared with model group. Tumor weight was significantly decreased and tumor inhibitory rate was more than 44% in TTM group. After TT treatment, many adipocytes, cracks between tumor cells and apoptosis were found. The levels of pro-Cathepsin B, Cathepsin B, Bax, Bax/Bcl2, Caspase3 and Caspase7 were markedly increased, but the level of Bcl2 was significantly reduced after TT treatment. Conclusion TT has a broad range of effects on various signaling pathways and biological processes, including the regulation of apoptosis. It exhibits antitumor activity in an animal model of liver cancer and activates the apoptotic pathway by decreasing Sph level. This study provides valuable information regarding the potential use of TT extract in the treatment of liver cancer and highlights the importance of investigating the underlying molecular mechanisms of traditional medicines for the development of new therapeutic drugs in liver cancer. Keywords: Liver cancer, Tribulus terrestris L., Sphingosine, Network pharmacology, Metabolomics 1. Introduction Liver cancer is a common malignant tumor worldwide. According to the report of International Agency for Research on Cancer (IARC) in 2020, China had the highest morbidity and mortality of liver cancer compared with the rest world [[32]1]. There were 0.91 million new liver cancer patients and 0.83 million deaths around the world in 2020. However, 0.41 million new patients and 0.39 million deaths were in China. In addition, there were higher morbidity or mortality in men than women. Liver cancer is divided into primary and secondary liver cancer. Primary liver cancer (PLC) is that cancer cells originate from the epithelial and mesenchymal tissue of liver and its morbidity accounts for 90% of liver cancer. Previous studies found that hepatocarcinogenesis, especially PLC, was a complex process induced by many factors, including hepatitis B virus, hepatitis C virus, aflatoxin and liver cirrhosis [[33][2], [34][3], [35][4]]. Clinically surgery is the first choice for early PLC because it provides only chance for cure but has limited applicability. Adjuvant therapy, including chemotherapy, radiation and targeted therapy, are used for advanced PLC patients. In addition, accumulated studies showed that traditional Chinese medicine (TCM) is an alternative treatment for PLC because of its effective therapeutic outcome and fewer side effects compared to chemo-radiotherapy [[36]5,[37]6]. For example, Bei-Jia-Jian-Wan and Xiao-Chai-Hu-Tang are two of the most frequently used Chinese herbal medicine for PLC. Other TCMs used for PLC include Hedyotis diffusea Willd. (Bai-Hua-She-She-Cao), Sculellaria barbata D. Don (Ban-Zhi-Lian), Salvia miltiorrhiza Bge (Dan-shen), Brucea javanica (L.). Merr. (Ya-Dan-Zi) and Iphigenia indica Kunth (Shan-Ci-Gu) [[38][5], [39][6], [40][7]]. Based on TCM theory, Tribulus terrestris L. (TT), a common herb and first documented in Shen-Nong-Ben-Cao-Jing, is able to calm liver and dispel melancholy (“Ping Gan Jie Yu in Chinese), promote blood circulation to dissipate wind (“Huo Xue Qu Feng” in Chinese), improve eyesight (“Ming Mu” in Chinese) and relieve itching (“Zhi Yang” in Chinese). Previous studies demonstrated the effects of TT on prostate health [[41]8], reproductive processes [[42]9], hypertensive renal injury [[43]10], inflammation, ovarian cells apoptosis and so on [[44]11]. But the study on anti-liver cancer of TT was hardly reported. According to pathogenic factors and pathogenesis of TCM, liver cancer were divided into different syndromes including stagnation of liver-qi and deficiency of spleen (“Gan Yu Pi Xu” in Chinese), stagnated vital energy clots the blood (“Qi Zhi Xue Yu” in Chinese), cold-dampness obstructing the spleen (“Han Shi Kun Pi” in Chinese), yin deficiency of liver and kidney (“Gan Shen Yin Xu” in Chinese) [[45]12]. Thus we speculated that there were close relations between liver cancer and TT because of its TCM effects. In this study, we demonstrated the anti-liver cancer effect of TT in vivo with effectiveness equivalent to even better than sorafinib. Additionally, we investigated the underlying molecular mechanisms of TT's anti-tumor effects using network pharmacology and metabolomics ([46]Fig. 1) and found the apoptotic pathway as a major target of TT in PLC. Overall, our study provides important insights into the potential use of TT as a therapeutic drug for PLC and highlights the importance of using multi-disciplinary approaches to investigate the mechanisms of action of traditional medicines like TT. Fig. 1. [47]Fig. 1 [48]Open in a new tab The design of this study. Illustration of experimental workflow: (A) Network pharmacology study of TT's anti-liver cancer effect including targets collection, construction of Herb-Ingredients-Targets-Disease network and PPI network, GO and KEGG pathway analysis. (B) Metabonomics study of TT's anti-liver cancer effect. (C) Descriptive and correlational analysis between sphingosine and apoptosis induced by TT. The partial figure was drawn by Figdraw software ([49]http://www.figdraw.com/static/index.html#/). 2. Materials and methods 2.1. Network pharmacology study 2.1.1. Screening of major active ingredients of TT and collection of potential targets The major active ingredients of TT were gotten from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database ([50]https://tcmsp-e.com/). Screening parameters were set with Oral bioavailability (OB) greater than or equivalent to 30% and drug-likeness (DL) greater than or equivalent to 0.18. The potential targets of the major active ingredients were obtained from the TCMSP and PharmMapper database (Norm Fit>0.9, [51]http://www.lilab-ecust.cn/pharmmapper/), which was used as a supplement for targets prediction. Subsequently, the information of acquired targets was standardized by Uniprot database ([52]https://www.uniprot.org/). 2.1.2. Targets collection of TT on liver cancer The targets of liver cancer were gotten from Genecards database ([53]https://www.genecards.org/) using “Homo sapiens” as species and “liver cancer” as keywords. In addition, the targets of drug and disease were mapped by Venny 2.1 online software ([54]https://bioinfogp.cnb.csic.es/tools/venny/) to predict potential targets of TT on liver cancer. 2.1.3. Construction of herb-ingredients-targets-disease network and protein-protein interaction (PPI) network The Herb-Ingredients-Targets-Disease network was constructed by using Cytoscape 3.8.2 software. PPI network was constructed by using String 11.0 online software ([55]https://www.string-db.org/) and Cytoscape 3.8.2 software. 2.1.4. Enrichment analysis of GO and KEGG pathway The enrichment analysis of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted by DAVID 6.8 online software ([56]https://david.abcc.ncifcrf.gov/). 2.2. In vivo experiment 2.2.1. TT aqueous extract TT was purchased from Jining Banger Chinese Herbal Medicine Co. Ltd. (Jining, Shandong, China) and morphologically authenticated by Professor Hong-Jie Liu at Jinan university. The quality of TT met the requirements of Chinese pharmacopoeia (2020). The raw TT was separately reflux-extracted thrice with 10-fold volume of distilled water for 1 h. The combined extracts were filtered, concentrated and then lyophilized with a Virtis Freeze Dryer (Qingdao creatrust electronic technology Co., Ltd., Qingdao, Shandong, China). The resulting extracts were weighed and the yield was calculated (TT yield: 16%). These crude extracts were conserved at 4 °C for further use. Tribuloside D was quantified to control the quality of the TT aqueous extract. HPLC analysis of TT aqueous extract showed tribuloside D content of 0.072 mg/g. The detailed information was described in our other study. 2.2.2. H22 liver cancer mice model and experimental design Male Balb/c mice (SPF, certificate number SCXK (Lu) 20190003), weighing 16–18 g, were purchased from Jinan pengyue laboratory animal breeding Co., Ltd. Mice were kept in the animal facility at clinical medical laboratory center under standard conditions at 21–23 °C with a relative humidity of 50–60%. All experimental procedures were approved by the University Committee on Research Practice in Jining No.1 People's Hospital (No.: JNRM-2021-DW-050). After adaptive breeding for five days, 50 mice were subcutaneously injected with 5 × 10^6 cells/mL × 100 μL H22 cells in left subaxillary region. After five days, mice were randomly divided into model group, sorafenib group, TT high dose (TTH) group, TT medium dose (TTM) group, TT low dose (TTL) group, ten mice in each group. Mice in sorafenib group were treated with sorafenib (20 mg/kg), while mice in TT groups were treated with TT aqueous extract (0.84 g/kg, 0.42 g/kg, 0.21 g/kg) for ten days by oral administration. During the experiment, body weight and tumor size（V = 0.52 × length × width^2, cm^3） were recorded once three or four days. At the end of the trial, tumor weight was recorded, tumor inhibitory rate was calculated by (W[model group]-W[drug group])/W[model group] × 100%, and then liquid nitrogen quick freezing tumor tissues were selected for metabonomics and Western blot. Finally, the tumor tissue was fixed in 4% paraformaldehyde for Hematoxylin and eosin (HE) staining and Tunel staining. 2.2.3. HE staining The tumor tissue was fixed in 4% paraformaldehyde and then embedded in paraffin. The paraffin block was cut into 5 μm thick slices. HE staining was performed as described [[57]13]. 2.2.4. Tunel staining The paraffin block was cut into 5 μm thick slices and then stained with Tunel staining according to the manufacturer's protocol. The kit was purchased from Beyotime Biotechnology (Shanghai, China). 2.2.5. Western blot analysis Western blot analysis was performed as previously described [[58]14]. The expression level of apoptosis-related proteins, including pro-cathepsin B, cathepsin B, Bax, Bcl2, caspase3 and caspase7 in tumor tissue, were detected with Western blot. In addition, the level of GAPDH in tumor was detected as control for quantitative analysis. Tumor tissues were homogenized in RIPA buffer and 1 mM phenylmethylsulfonyl fluoride and then incubated on ice for 0.5 h. Proteins were gotten by centrifuging at 14,000 g at 4 °C for 15 min and quantified using the Beyotime protein assay regent. And then, equal amounts of proteins (20 μg) mixed with loading buffer were denatured, separated, transferred membrane, blocked, incubated with primary and secondary antibodies, detected using ECL reagents. In the last, relative protein expression level was analyzed using IPP software. 2.2.6. Statistical analysis Results were presented as mean ± SEM. Data between multiple groups were analyzed by one-way analysis of variance (ANOVA) using SPSS (version 20.0) statistical analysis program, and then differences among means were analyzed using Dunnett's multiple comparisons test or post hoc analysis. Differences were considered significant at P < 0.05. 2.3. Metabonomics of tumor tissue 2.3.1. Sample preparation Tumor tissue in model and TTM groups were chosen for metabonomics (N = 5). * 1 mL of cold extraction solvent methanol/acetonitrile/H2O (2:2:1, v/v/v) was added to80 mg sample, and adequately vortexed. The lysate was homogenized by MP homogenizer and sonicated at 4 °C (30 min × 2) then centrifuged at 14,000 g for 20 min at 4 °C and the supernatant was dried in a vacuum centrifuge at 4 °C. For LC-MS analysis, the samples were re-dissolved in 100 μL acetonitrile/water (1:1, v/v) solvent. 2.3.2. LC-MS, data and Bioinformatics analysis For untargeted metabolomics of polar metabolites, extracts were analyzed using a quadrupole time-of-flight mass spectrometer (Sciex Triple TOF 6600) coupled to hydrophilic interaction chromatography via electrospray ionization in Shanghai Applied Protein Technology Co., Ltd. LC separation was on a ACQUIY UPLC BEH Amide column (2.1 mm × 100 mm × 1.7 μm, waters) using a gradient of solvent A (25 mM ammonium acetate and 25 mM ammonium hydroxide in water) and solvent B (acetonitrile). The gradient was 0–0.5 min: 5%A:95%B; 0.5–7 min: 5%A:95%B to 35%A:65%B; 7–8 min: 35%A:65%B to 60%A:40%B; 8–9 min: 60%A:40%B; 9–9.1 min: 60%A:40%B to 5%A:95%B; 9.1–12 min: 5%A:95%B. Flow rate was 0.5 mL/min, column temperature was 25 °C, auto sampler temperature was 4 °C, and injection volume was 2 μL. The mass spectrometer was operated in both negative ionizations and positive ionizations mode. The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30 psi, source temperature: 600 °C, Ion Spray Voltage Floating (ISVF) ±5500 v. In MS acquisition, the instrument was set to acquire over the m/z range 60–1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25–1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 ± 15 eV; declustering potential (DP), 60 V (+) and −60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10. The raw MS data were converted to MzXML files using ProteoWizard MS Convert before importing into freely available XCMS software. For peak picking, the following parameters were used: centWave m/z = 25 ppm, peakwidth = c (10, 60), prefilter = c (10, 100). For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5 were used. In the extracted ion features, only the variables having more than 50% of the nonzero measurement values in at least one group were kept. Compound identification of metabolites by MS/MS spectra with an in-house database established with available authentic standards. After normalized to total peak intensity, the processed data were uploaded into before importing into SIMCA-P (version 14.1, Umetrics, Umea, Sweden), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The 7-fold cross-validation and response permutation testing were used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Significance was determined using an unpaired Student's t-test. VIP value > 1 and P < 0.05 was considered as statistically significant. For KEGG pathway annotation, the metabolites were blasted against the online Kyoto Encyclopedia of Genes and Genomes (KEGG) database to retrieve their Cos and were subsequently mapped to pathways in KEGG11. The corresponding KEGG pathways were extracted. To further explore the impact of differentially expressed metabolites, enrichment analysis was performed. KEGG pathway enrichment analyses were applied based on the Fisher’ exact test, considering the whole metabolites of each pathway as background dataset. And only pathways with P-values under a threshold of 0.05 were considered as significant changed pathways. 3. Results 3.1. Screening of major active ingredients of TT and action target results In this study, 12 active ingredients were gotten from TCMSP database with the values of OB and DL greater than or equivalent to 30% and 0.18, respectively. The information of active ingredients was displayed in [59]Table 1. We found 127 targets by using TCMSP and PharmMapper database, 37 of which were from TT1, 3 from TT2, 62 from TT3, 8 from TT4, 16 from TT5, 27 from TT6, 25 from TT7, 49 from TT8, 18 from TT9, 5 from TT10, 1 from TT11, 4 from TT12. The target results were seen in [60]Fig. 2B. Table 1. The information of TT's active ingredients. Molecule Name MW (Da) OB (%) DL AlogP Hdon Hacc Caco-2 BBB FASA- HL TT1 isorhamnetin 316.28 49.60 0.31 1.755 4 7 0.31 −0.54 0.32 14.34 TT2 sitosterol 414.79 36.91 0.75 8.084 1 1 1.32 0.87 0.22 5.37 TT3 kaempferol 286.25 41.88 0.24 1.771 4 6 0.26 −0.55 0 14.74 TT4 (Z)-3-(4-hydroxy-3-methoxy-phenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylami de 313.38 118.35 0.26 2.859 3 5 0.51 −0.27 0 4.26 TT5 (2aR,2′R,4R,6aR,6bS,8aS,8bR,11aS,12aR,12bR)-4-((S)-2-(2,6-dimethylpheny l)propoxy)-5′,5′,6a,8a-tetramethyl-8-methylenedocosahydro-1H-spiro[pent aleno [2,1-a]phenanthrene-10,2′-pyran] 573 59.49 0.28 9.376 0 2 1.49 0.78 0.26 7.16 TT6 (3R,8S,9S,10R,13R,14R,17S)-17-((2S,5R)-5-ethyl-6-methylheptan-2-yl)-3-h ydroxy-10,13-dimethyl-3,4,8,9,10,11,12,13,14,15,16,17-dodecahydro-1H-cy clopenta[a]phenanthren-7(2H)-one 428.77 40.93 0.79 7.153 1 2 0.76 0.12 0.27 3.00 TT7 (3R,7R,8S,9S,10S,13R,14S,17R)-17-((2R,5S)-5-ethyl-6-methylheptan-2-yl)- 3,10-dimethyl-2,3,4,7,8,9,10,11,12,13,14,15,16,17-tetradecahydro-1H-cyc lopenta[a]phenanthren-7-ol 414.79 34.21 0.76 8.332 1 1 1.34 1.00 0.22 4.58 TT8 (Z)-3-(3,4-dihydroxyphenyl)-N-[2-(4-hydroxyphenyl)ethyl]acrylamide 299.35 113.25 0.24 2.608 4 5 0.41 −0.35 0.39 4.87 TT9 β-sitosterol-β-d-glucopyranoside 398.79 32.41 0.71 9.117 0 0 1.86 1.86 0.23 4.12 TT10 terrestriamide 327.36 114.09 0.29 2.424 3 6 0.18 −0.60 0.37 19.05 TT11 (2aR,2′S,4R,4′R,5′S,6aS,6bS,8aS,8bR,9S,11aR,12aR,12bR)-4,4′-dihydroxy-5 ′,6a,8a,9-tetramethylicosahydro-1H-spiro[pentaleno [2,1-a]phenanthrene-10,2′-pyran]-8(2H)-one 444.72 58.74 0.76 3.265 2 4 0.05 −0.70 0.21 4.77 TT12 (2aR,5S,6aS,6bS,8aS,8bS,11aS,12aR,12bR)-10-isopentyl-6a,8a,9-trimethyl- 2,2a,3,4,5,6,6a,6b,7,8,8a,8b,11a,12,12a,12b-hexadecahydro-1H-naphtho[2′ ,1':4,5]indeno [2,1-b]furan-5-ol 400.71 39.21 0.84 5.931 1 2 1.21 0.82 0.21 4.39 [61]Open in a new tab Fig. 2. [62]Fig. 2 [63]Open in a new tab Network construction between TT and liver cancer. (A) Potential targets of TT's active ingredients in the treatment of liver caner. Purple circle represents targets of liver cancer. Yellow circle represents targets of TT. There were 125 overlapping genes between liver cancer and TT. (B) The construction of herb-active ingredients-targets-disease network. Green node represents TT. Purple nodes represent the active ingredients of TT. Sky blue nodes represent the intersecting targets between herb and disease. Dark blue node represents liver cancer. (C) Protein-protein interaction (PPI) network of intersecting targets of core ingredients and liver cancer. Empty node represents a protein of unknown 3D structure. Filled nodes represent proteins of known or predicted 3D structures. Sky-blue lines represent known interactions from curated databases. Purplish red lines represent known interaction experimentally determined. Grass green lines represent the predicted interaction of gene neighborhood. Red lines represent the predicted interaction of gene fusion. Dark blue lines represent the predicted interaction of gene co-occurrence. Light green lines represent the relationship of textmining. Black lines represent the relationship of co-expression. Purple lines represent the relationship of protein homology. (For interpretation of the references to color in this figure