Identification of NQO1 as a target of herbal drug agrimol B in hepatocellular carcinoma
Dingyue Zhang A # , Lixia Dong A # , Wenyong Yang B * and Kui Wang A *A West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, P. R. China.
B Department of Neurosurgery, Medical Research Center, The Third People’s Hospital of Chengdu, The Affiliated Hospital of Southwest Jiaotong University, The Second Chengdu Hospital Affiliated to Chongqing Medical University, Chengdu, 610041, P. R. China.
Handling Editor: Mibel Aguilar
Australian Journal of Chemistry 76(8) 465-475 https://doi.org/10.1071/CH22255
Submitted: 2 December 2022 Accepted: 19 April 2023 Published: 24 May 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing.
Abstract
Agrimol B is a small molecule isolated from traditional Chinese herbal medicine with a potential anti-cancer effect. However, the molecular target of agrimol B remains unclear. In this report, we found that agrimol B inhibits the growth of hepatocellular carcinoma (HCC) cells. A combination of network pharmacology strategy, bioinformatics analysis, molecular docking and target validation experiments was performed to identify and verify the protein targets of agrimol B in HCC. Bioinformatics analysis suggests that the activity of agrimol B against HCC was related to a cellular response to chemical stress and oxidative stress, folate biosynthesis, the complement and coagulation cascade and FoxO signaling pathway. We further identified 10 core targets through network pharmacology analysis. Among them, NAD(P)H: quinone dehydrogenase 1 (NQO1) was screened as the most promising target based on a molecular docking analysis. The interaction between agrimol B and NQO1 was corroborated by a cellular thermal shift assay. In addition, agrimol B inhibited the growth of HCC cells by decreasing NQO1 activity. Taken together, we identified NQO1 as a molecular target of agrimol B, which provides a new insight into the anti-cancer mechanism of agrimol B in HCC.
Keywords: agrimol B, anticancer agents, hepatocellular carcinoma, herbal drugs, molecular docking, network pharmacology, NQO1, target identification.
References
[1] H Sung, J Ferlay, RL Siegel, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71, 209.| Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.Crossref | GoogleScholarGoogle Scholar |
[2] A Forner, M Reig, J Bruix, Hepatocellular carcinoma. Lancet 2018, 391, 1301.
| Hepatocellular carcinoma.Crossref | GoogleScholarGoogle Scholar |
[3] D Anwanwan, SK Singh, S Singh, et al. Challenges in liver cancer and possible treatment approaches. Biochim Biophys Acta Rev Cancer 2020, 1873, 188314.
| Challenges in liver cancer and possible treatment approaches.Crossref | GoogleScholarGoogle Scholar |
[4] A Bishayee, T Politis, AS Darvesh, Resveratrol in the chemoprevention and treatment of hepatocellular carcinoma. Cancer Treat Rev 2010, 36, 43.
| Resveratrol in the chemoprevention and treatment of hepatocellular carcinoma.Crossref | GoogleScholarGoogle Scholar |
[5] N Polachi, G Bai, T Li, et al. Modulatory effects of silibinin in various cell signaling pathways against liver disorders and cancer – A comprehensive review. Eur J Med Chem 2016, 123, 577.
| Modulatory effects of silibinin in various cell signaling pathways against liver disorders and cancer – A comprehensive review.Crossref | GoogleScholarGoogle Scholar |
[6] S Man, C Luo, M Yan, et al. Treatment for liver cancer: From sorafenib to natural products. Eur J Med Chem 2021, 224, 113690.
| Treatment for liver cancer: From sorafenib to natural products.Crossref | GoogleScholarGoogle Scholar |
[7] CH Jung, JH Kim, S Park, et al. Inhibitory effect of Agrimonia pilosa Ledeb. on inflammation by suppression of iNOS and ROS production. Immunol Invest 2010, 39, 159.
| Inhibitory effect of Agrimonia pilosa Ledeb. on inflammation by suppression of iNOS and ROS production.Crossref | GoogleScholarGoogle Scholar |
[8] CY Kim, QM Yu, HJ Kong, et al. Antioxidant and Anti-Inflammatory Activities of Agrimonia pilosa Ledeb. Extract. Evid Based Complement Alternat Med 2020, 2020, 8571207.
| Antioxidant and Anti-Inflammatory Activities of Agrimonia pilosa Ledeb. Extract.Crossref | GoogleScholarGoogle Scholar |
[9] KJ Nho, JM Chun, HK Kim, Agrimonia pilosa ethanol extract induces apoptotic cell death in HepG2 cells. J Ethnopharmacol 2011, 138, 358.
| Agrimonia pilosa ethanol extract induces apoptotic cell death in HepG2 cells.Crossref | GoogleScholarGoogle Scholar |
[10] S Wang, Q Zhang, Y Zhang, et al. Agrimol B suppresses adipogenesis through modulation of SIRT1-PPAR gamma signal pathway. Biochem Biophys Res Commun 2016, 477, 454.
| Agrimol B suppresses adipogenesis through modulation of SIRT1-PPAR gamma signal pathway.Crossref | GoogleScholarGoogle Scholar |
[11] SST Hnit, R Ding, L Bi, et al. Agrimol B present in Agrimonia pilosa Ledeb impedes cell cycle progression of cancer cells through G0 state arrest. Biomed Pharmacother 2021, 141, 111795.
| Agrimol B present in Agrimonia pilosa Ledeb impedes cell cycle progression of cancer cells through G0 state arrest.Crossref | GoogleScholarGoogle Scholar |
[12] D Ross, D Siegel, The diverse functionality of NQO1 and its roles in redox control. Redox Biol 2021, 41, 101950.
| The diverse functionality of NQO1 and its roles in redox control.Crossref | GoogleScholarGoogle Scholar |
[13] Y Yang, J Zheng, M Wang, et al. NQO1 promotes an aggressive phenotype in hepatocellular carcinoma via amplifying ERK-NRF2 signaling. Cancer Sci 2021, 112, 641.
| NQO1 promotes an aggressive phenotype in hepatocellular carcinoma via amplifying ERK-NRF2 signaling.Crossref | GoogleScholarGoogle Scholar |
[14] Y Yang, Y Zhang, Q Wu, et al. Clinical implications of high NQO1 expression in breast cancers. J Exp Clin Cancer Res 2014, 33, 14.
| Clinical implications of high NQO1 expression in breast cancers.Crossref | GoogleScholarGoogle Scholar |
[15] Y Ma, J Kong, G Yan, et al. NQO1 overexpression is associated with poor prognosis in squamous cell carcinoma of the uterine cervix. BMC Cancer 2014, 14, 414.
| NQO1 overexpression is associated with poor prognosis in squamous cell carcinoma of the uterine cervix.Crossref | GoogleScholarGoogle Scholar |
[16] L Lin, J Sun, Y Tan, et al. Prognostic implication of NQO1 overexpression in hepatocellular carcinoma. Hum Pathol 2017, 69, 31.
| Prognostic implication of NQO1 overexpression in hepatocellular carcinoma.Crossref | GoogleScholarGoogle Scholar |
[17] S Yang, J Zhang, Y Yan, et al. Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Atractylodes macrocephala Koidz. for the Treatment of Chronic Gastritis. Front Pharmacol 2019, 10, 1629.
| Network Pharmacology-Based Strategy to Investigate the Pharmacologic Mechanisms of Atractylodes macrocephala Koidz. for the Treatment of Chronic Gastritis.Crossref | GoogleScholarGoogle Scholar |
[18] SI Berger, R Iyengar, Network analyses in systems pharmacology. Bioinformatics 2009, 25, 2466.
| Network analyses in systems pharmacology.Crossref | GoogleScholarGoogle Scholar |
[19] N Singh, L Chaput, BO Villoutreix, Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2021, 22, 1790.
| Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Crossref | GoogleScholarGoogle Scholar |
[20] A Bender, DW Young, JL Jenkins, et al. Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint. Comb Chem High Throughput Screen 2007, 10, 719.
| Chemogenomic data analysis: prediction of small-molecule targets and the advent of biological fingerprint.Crossref | GoogleScholarGoogle Scholar |
[21] X Wang, Y Shen, S Wang, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res 2017, 45, W356.
| PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database.Crossref | GoogleScholarGoogle Scholar |
[22] J Piñero, JM Ramírez-Anguita, J Saüch-Pitarch, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res 2020, 48, D845.
| The DisGeNET knowledge platform for disease genomics: 2019 update.Crossref | GoogleScholarGoogle Scholar |
[23] G Stelzer, N Rosen, I Plaschkes, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 2016, 54, 1.30.1.
| The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses.Crossref | GoogleScholarGoogle Scholar |
[24] AP Davis, TC Wiegers, RJ Johnson, et al. Comparative Toxicogenomics Database (CTD): update 2023. Nucleic Acids Res 2022, 51, D1257.
| Comparative Toxicogenomics Database (CTD): update 2023.Crossref | GoogleScholarGoogle Scholar |
[25] Y Xiong, Y Hu, L Chen, et al. Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed Panax notoginseng Using Network Pharmacology Coupled With Multivariate Data Analyses. Front Pharmacol 2019, 9, 1514.
| Unveiling Active Constituents and Potential Targets Related to the Hematinic Effect of Steamed Panax notoginseng Using Network Pharmacology Coupled With Multivariate Data Analyses.Crossref | GoogleScholarGoogle Scholar |
[26] CH Chin, SH Chen, HH Wu, et al. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 2014, 8, S11.
| cytoHubba: identifying hub objects and sub-networks from complex interactome.Crossref | GoogleScholarGoogle Scholar |
[27] T Khan, AJ Lawrence, I Azad, et al. Computational Drug Designing and Prediction Of Important Parameters Using in silico Methods- A Review. Curr Comput Aided Drug Des 2019, 15, 384.
| Computational Drug Designing and Prediction Of Important Parameters Using in silico Methods- A Review.Crossref | GoogleScholarGoogle Scholar |
[28] KY Hsin, S Ghosh, H Kitano, Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLoS One 2013, 8, e83922.
| Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology.Crossref | GoogleScholarGoogle Scholar |
[29] Á Bartha, B Győrffy, TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues. Int J Mol Sci 2021, 22, 2622.
| TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues.Crossref | GoogleScholarGoogle Scholar |
[30] MJ Goldman, B Craft, M Hastie, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 2020, 38, 675.
| Visualizing and interpreting cancer genomics data via the Xena platform.Crossref | GoogleScholarGoogle Scholar |
[31] O Menyhárt, Á Nagy, B Győrffy, Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma. R Soc Open Sci 2018, 5, 181006.
| Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma.Crossref | GoogleScholarGoogle Scholar |
[32] DM Molina, R Jafari, M Ignatushchenko, et al. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science 2013, 341, 84.
| Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay.Crossref | GoogleScholarGoogle Scholar |
[33] MY Huang, LL Zhang, J Ding, et al. Anticancer drug discovery from Chinese medicinal herbs. Chin Med 2018, 13, 35.
| Anticancer drug discovery from Chinese medicinal herbs.Crossref | GoogleScholarGoogle Scholar |
[34] R Zhang, X Zhu, H Bai, et al. Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment. Front Pharmacol 2019, 10, 123.
| Network Pharmacology Databases for Traditional Chinese Medicine: Review and Assessment.Crossref | GoogleScholarGoogle Scholar |
[35] M Fujita, M-JM Chen, DR Siwak, et al. Proteo-genomic characterization of virus-associated liver cancers reveals potential subtypes and therapeutic targets. Nat Commun 2022, 13, 6481.
| Proteo-genomic characterization of virus-associated liver cancers reveals potential subtypes and therapeutic targets.Crossref | GoogleScholarGoogle Scholar |
[36] ZL He, H Zheng, H Lin, et al. Overexpression of polo-like kinase1 predicts a poor prognosis in hepatocellular carcinoma patients. World J Gastroenterol 2009, 15, 4177.
| Overexpression of polo-like kinase1 predicts a poor prognosis in hepatocellular carcinoma patients.Crossref | GoogleScholarGoogle Scholar |
[37] R Wang, C Yin, XX Li, et al. Reduced SOD2 expression is associated with mortality of hepatocellular carcinoma patients in a mutant p53-dependent manner. Aging 2016, 8, 1184.
| Reduced SOD2 expression is associated with mortality of hepatocellular carcinoma patients in a mutant p53-dependent manner.Crossref | GoogleScholarGoogle Scholar |
[38] Q Luo, Y Li, Y Lai, et al. The role of NF-κB in PARP-inhibitor-mediated sensitization and detoxification of arsenic trioxide in hepatocellular carcinoma cells. J Toxicol Sci 2015, 40, 349.
| The role of NF-κB in PARP-inhibitor-mediated sensitization and detoxification of arsenic trioxide in hepatocellular carcinoma cells.Crossref | GoogleScholarGoogle Scholar |
[39] CJ Lord, A Ashworth, PARP inhibitors: Synthetic lethality in the clinic. Science 2017, 355, 1152.
| PARP inhibitors: Synthetic lethality in the clinic.Crossref | GoogleScholarGoogle Scholar |
[40] H Van Remmen, Y Ikeno, M Hamilton, et al. Life-long reduction in MnSOD activity results in increased DNA damage and higher incidence of cancer but does not accelerate aging. Physiol Genomics 2003, 16, 29.
| Life-long reduction in MnSOD activity results in increased DNA damage and higher incidence of cancer but does not accelerate aging.Crossref | GoogleScholarGoogle Scholar |
[41] F de Braud, S Cascinu, G Spitaleri, et al. A phase I, dose-escalation study of volasertib combined with nintedanib in advanced solid tumors. Ann Oncol 2015, 26, 2341.
| A phase I, dose-escalation study of volasertib combined with nintedanib in advanced solid tumors.Crossref | GoogleScholarGoogle Scholar |
[42] I El Dika, HY Lim, WP Yong, et al. An Open-Label, Multicenter, Phase I, Dose Escalation Study with Phase II Expansion Cohort to Determine the Safety, Pharmacokinetics, and Preliminary Antitumor Activity of Intravenous TKM-080301 in Subjects with Advanced Hepatocellular Carcinoma. Oncologist 2019, 24, 747.
| An Open-Label, Multicenter, Phase I, Dose Escalation Study with Phase II Expansion Cohort to Determine the Safety, Pharmacokinetics, and Preliminary Antitumor Activity of Intravenous TKM-080301 in Subjects with Advanced Hepatocellular Carcinoma.Crossref | GoogleScholarGoogle Scholar |
[43] HZ Zhou, HQ Zeng, D Yuan, et al. NQO1 potentiates apoptosis evasion and upregulates XIAP via inhibiting proteasome-mediated degradation SIRT6 in hepatocellular carcinoma. Cell Commun Signal 2019, 17, 168.
| NQO1 potentiates apoptosis evasion and upregulates XIAP via inhibiting proteasome-mediated degradation SIRT6 in hepatocellular carcinoma.Crossref | GoogleScholarGoogle Scholar |
[44] X Wang, Y Liu, A Han, et al. The NQO1/p53/SREBP1 axis promotes hepatocellular carcinoma progression and metastasis by regulating Snail stability. Oncogene 2022, 41, 5107.
| The NQO1/p53/SREBP1 axis promotes hepatocellular carcinoma progression and metastasis by regulating Snail stability.Crossref | GoogleScholarGoogle Scholar |
[45] M Dimri, A Humphries, A Laknaur, et al. NAD(P)H Quinone Dehydrogenase 1 Ablation Inhibits Activation of the Phosphoinositide 3-Kinase/Akt Serine/Threonine Kinase and Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase Pathways and Blocks Metabolic Adaptation in Hepatocellular Carcinoma. Hepatology 2020, 71, 549.
| NAD(P)H Quinone Dehydrogenase 1 Ablation Inhibits Activation of the Phosphoinositide 3-Kinase/Akt Serine/Threonine Kinase and Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase Pathways and Blocks Metabolic Adaptation in Hepatocellular Carcinoma.Crossref | GoogleScholarGoogle Scholar |