Skip to main content

Advertisement

SpringerLink
Log in

Anticancer Effect of Active Component of Astragalus Membranaceus Combined with Olaparib on Ovarian Cancer Predicted by Network-Based Pharmacology

  • Original Article
  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

In China, a traditional Chinese medicine formulation called astragalus membranaceus (AM) has been utilised for more than 20 years to treat tumors with extraordinary effectiveness. The fundamental mechanisms, nevertheless, are still not well understood. The aim of this study is identifying its possible therapeutic targets and to evaluate the effects of AM in combination with a PARP inhibitor (olaparib) in the treatment of BRCA wild-type ovarian cancer. Significant genes were collected from Therapeutic Target Database and Database of Gene-Disease Associations. The components of AM were analyzed using the Traditional Chinese Medicine System Pharmacology (TCMSP) database to screen the active ingredients of AM based on their oral bioavailability and drug similarity index. In order to find intersection targets, Venn diagrams and STRING website diagrams were employed. STRING was also used to create a protein-protein interaction network. In order to create the ingredient-target network, Cytoscape 3.8.0 was used. DAVID database was utilized to carry out enrichment and pathway analyses. The binding ability of the active compounds of AM to the core targets of AM-OC was verified with molecular docking using AutoDock software. Experimental validations, including cell scratch, cell transwell, cloning experiment, were conducted to verify the effects of AM on OC cells. A total of 14 active ingredients of AM and 28 AM-OC-related targets were screened by network pharmacology analysis. The ten most significant Gene Ontology (GO) biological function analyses, as well as the 20 foremost Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways were selected. Moreover, molecular docking results showed that bioactive compound (quercetin) demonstrated a good binding ability with tumor protein p53 (TP53), MYC, vascular endothelial growth factor A (VEGFA), phosphatase and tensin homolog (PTEN), AKT serine/threonine kinase 1 (AKT1) and cyclin D1 (CCND1) oncogenes. According to experimental methods, in vitro OC cell proliferation and migration appeared to be inhibited by quercetin, which also increased apoptosis. In addition, the combination with olaparib further enhanced the effect of quercetin on OC. Based on network pharmacology, molecular docking, and experimental validation, the combination of PARP inhibitor and quercetin enhanced the anti-proliferative activity in BRCA wild-type ovarian cancer cells, which supplies the theoretical groundwork for additional pharmacological investigation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availability

Not applicable.

References

  1. Doubeni, C. A., Doubeni, A. R., & Myers, A. E. (2016). Diagnosis and management of ovarian cancer. American Family Physician, 93(11), 937–944.

    PubMed  Google Scholar 

  2. Bonadio, R. C., & Estevez-Diz, M. (2021). Perspectives on PARP inhibitor combinations for ovarian cancer. Frontiers in Oncology, 11, 754524. https://doi.org/10.3389/fonc.2021.754524

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Curtin, N. J., & Szabo, C. (2020). Poly(ADP-ribose) polymerase inhibition: Past, present and future. Nature Reviews Drug Discovery, 19(10), 711–736. https://doi.org/10.1038/s41573-020-0076-6

    Article  PubMed  CAS  Google Scholar 

  4. Javle, M., & Curtin, N. J. (2011). The role of PARP in DNA repair and its therapeutic exploitation. British Journal of Cancer, 105(8), 1114–1122. https://doi.org/10.1038/bjc.2011.382

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Hodgson, D. R., Dougherty, B. A., Lai, Z., Fielding, A., Grinsted, L., Spencer, S., O’Connor, M. J., Ho, T. W., Robertson, J. D., Lanchbury, J. S., Timms, K. M., Gutin, A., Orr, M., Jones, H., Gilks, B., Womack, C., Gourley, C., Ledermann, J., & Barrett, J. C. (2018). Candidate biomarkers of PARP inhibitor sensitivity in ovarian cancer beyond the BRCA genes. British Journal of Cancer, 119(11), 1401–1409. https://doi.org/10.1038/s41416-018-0274-8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Rose, M., Burgess, J. T., O’Byrne, K., Richard, D. J., & Bolderson, E. (2020). PARP inhibitors: Clinical relevance, mechanisms of action and tumor resistance. Frontiers in Cell and Developmental Biology, 8, 564601. https://doi.org/10.3389/fcell.2020.564601

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pujade-Lauraine, E., Ledermann, J. A., Selle, F., Gebski, V., Penson, R. T., Oza, A. M., Korach, J., Huzarski, T., Poveda, A., Pignata, S., Friedlander, M., Colombo, N., Harter, P., Fujiwara, K., Ray-Coquard, I., Banerjee, S., Liu, J., Lowe, E. S., Bloomfield, R., & Pautier, P. (2017). Olaparib tablets as maintenance therapy in patients with platinum-sensitive, relapsed ovarian cancer and a BRCA1/2 mutation (SOLO2/ENGOT-Ov21): A double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncology, 18(9), 1274–1284. https://doi.org/10.1016/S1470-2045(17)30469-2

    Article  PubMed  CAS  Google Scholar 

  8. Moore, K., Colombo, N., Scambia, G., Kim, B. G., Oaknin, A., Friedlander, M., Lisyanskaya, A., Floquet, A., Leary, A., Sonke, G. S., Gourley, C., Banerjee, S., Oza, A., González-Martín, A., Aghajanian, C., Bradley, W., Mathews, C., Liu, J., Lowe, E. S., … DiSilvestro, P. (2018). Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. New England Journal of Medicine, 379(26), 2495–2505. https://doi.org/10.1056/NEJMoa1810858

    Article  PubMed  CAS  Google Scholar 

  9. Xiang, Y., Guo, Z., Zhu, P., Chen, J., & Huang, Y. (2019). Traditional chinese medicine as a cancer treatment: Modern perspectives of ancient but advanced science. Cancer Medicine, 8(5), 1958–1975. https://doi.org/10.1002/cam4.2108

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liu, Y., Yang, S., Wang, K., Lu, J., Bao, X., Wang, R., Qiu, Y., Wang, T., & Yu, H. (2020). Cellular senescence and cancer: Focusing on traditional chinese medicine and natural products. Cell Proliferation, 53(10), e12894. https://doi.org/10.1111/cpr.12894

    Article  PubMed  PubMed Central  Google Scholar 

  11. Wang, K., Chen, Q., Shao, Y., Yin, S., Liu, C., Liu, Y., Wang, R., Wang, T., Qiu, Y., & Yu, H. (2021). Anticancer activities of TCM and their active components against tumor metastasis. Biomedicine & Pharmacotherapy, 133, 111044. https://doi.org/10.1016/j.biopha.2020.111044

    Article  CAS  Google Scholar 

  12. Ge, S., Xing, Q., Zhang, A., & Wang, Y. (2021). Effect of traditional chinese medicine (TCM) on survival, quality of life, and immune function in patients with ovarian carcinoma: A protocol for systematic review and meta analysis. Medicine, 100(2), e23904. https://doi.org/10.1097/MD.0000000000023904

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Wang, R., Sun, Q., Wang, F., Liu, Y., Li, X., Chen, T., Wu, X., Tang, H., Zhou, M., Zhang, S., Xiao, Y., Huang, W., Wang, C. C., & Li, L. (2019). Efficacy and safety of Chinese herbal medicine on ovarian cancer after reduction surgery and adjuvant chemotherapy: A systematic review and Meta-analysis. Frontiers in Oncology, 9, 730. https://doi.org/10.3389/fonc.2019.00730

    Article  PubMed  PubMed Central  Google Scholar 

  14. Zhang, Y. M., Zhang, Z. Y., & Wang, R. X. (2020). Protective mechanisms of quercetin against myocardial ischemia reperfusion injury. Frontiers in Physiology, 11, 956. https://doi.org/10.3389/fphys.2020.00956

    Article  PubMed  PubMed Central  Google Scholar 

  15. Li, Y., Chen, M., Xu, Y., Yu, X., Xiong, T., Du, M., Sun, J., Liu, L., Tang, Y., & Yao, P. (2016). Iron-mediated lysosomal membrane permeabilization in ethanol-induced hepatic oxidative damage and apoptosis: protective effects of quercetin. Oxidative Medicine and Cellular Longevity, 2016, 4147610. https://doi.org/10.1155/2016/4147610

  16. Ferreira, P. E., Lopes, C. R., Alves, A. M., Alves, ÉP., Linden, D. R., Zanoni, J. N., & Buttow, N. C. (2013). Diabetic neuropathy: An evaluation of the use of quercetin in the cecum of rats. World Journal of Gastroenterology, 19(38), 6416–6426. https://doi.org/10.3748/wjg.v19.i38.6416

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang, R. E., Hunt, C. R., Chen, J., & Taylor, J. S. (2011). Biotinylated quercetin as an intrinsic photoaffinity proteomics probe for the identification of quercetin target proteins. Bioorganic & Medicinal Chemistry, 19(16), 4710–4720. https://doi.org/10.1016/j.bmc.2011.07.005

    Article  CAS  Google Scholar 

  18. Wang, M., Chen, X., Yu, F., Zhang, L., Zhang, Y., & Chang, W. (2022). The targeting of noncoding RNAs by Quercetin in Cancer Prevention and Therapy. Oxidative Medicine and Cellular Longevity, 2022, 4330681. https://doi.org/10.1155/2022/4330681

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Ru, J., Li, P., Wang, J., Zhou, W., Li, B., Huang, C., Li, P., Guo, Z., Tao, W., Yang, Y., Xu, X., Li, Y., Wang, Y., & Yang, L. (2014). TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. Journal of Cheminformatics, 6, 13. https://doi.org/10.1186/1758-2946-6-13

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Zhang, W., Chen, Y., Jiang, H., Yang, J., Wang, Q., Du, Y., & Xu, H. (2020). Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology. Talanta, 211, 120710. https://doi.org/10.1016/j.talanta.2020.120710

    Article  PubMed  CAS  Google Scholar 

  21. Pundir, S., Martin, M. J., & O’Donovan, C. (2017). UniProt protein knowledgebase. Methods in Molecular Biology (Clifton N J), 1558, 41–55. https://doi.org/10.1007/978-1-4939-6783-4_2

    Article  PubMed  CAS  Google Scholar 

  22. Rebhan, M., Chalifa-Caspi, V., Prilusky, J., & Lancet, D. (1997). GeneCards: Integrating information about genes, proteins and diseases. Trends in Genetics: TIG, 13(4), 163. https://doi.org/10.1016/s0168-9525(97)01103-7

    Article  PubMed  CAS  Google Scholar 

  23. Piñero, J., Bravo, À, Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., & Furlong, L. I. (2017). DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research, 45(D1), D833–D839. https://doi.org/10.1093/nar/gkw943

    Article  PubMed  CAS  Google Scholar 

  24. Koscielny, G., An, P., Carvalho-Silva, D., Cham, J. A., Fumis, L., Gasparyan, R.,Hasan, S., Karamanis, N., Maguire, M., Papa, E., Pierleoni, A., Pignatelli, M., Platt,T., Rowland, F., Wankar, P., Bento, A. P., Burdett, T., Fabregat, A., Forbes, S.,Gaulton, A., … Dunham, I. (2017). Open Targets: a platform for therapeutic target identification and validation. Nucleic Acids Research, 45(D1), D985–D994. https://doi.org/10.1093/nar/gkw1055

  25. Jia, A., Xu, L., & Wang, Y. (2021). Venn diagrams in bioinformatics. Briefings in Bioinformatics, 22(5), bbab108. https://doi.org/10.1093/bib/bbab108

    Article  PubMed  CAS  Google Scholar 

  26. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. https://doi.org/10.1101/gr.1239303

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., & Snel, B. (2003). STRING: A database of predicted functional associations between proteins. Nucleic Acids Research, 31(1), 258–261. https://doi.org/10.1093/nar/gkg034

    Article  CAS  Google Scholar 

  28. Dennis, G., Jr, Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003). DAVID: Database for annotation, visualization, and integrated discovery. Genome Biology, 4(5), P3.

    Article  PubMed  Google Scholar 

  29. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Research, 40(Database issue), D109–D114. https://doi.org/10.1093/nar/gkr988

    Article  PubMed  CAS  Google Scholar 

  30. Pilipović, A., Mitrović, D., Obradović, S., & Poša, M. (2021). Docking-based analysis and modeling of the activity of bile acids and their synthetic analogues on large conductance Ca2 + activated K channels in smooth muscle cells. European Journal of Gynaecological Oncology, 25(23), 7501–7507. https://doi.org/10.26355/eurrev_202112_27449

  31. Dong, Y., Zhao, Q., & Wang, Y. (2021). Network pharmacology-based investigation of potential targets of astragalus membranaceous-angelica sinensis compound acting on diabetic nephropathy. Scientific Reports, 11(1), 19496. https://doi.org/10.1038/s41598-021-98925-6

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Yan, C., & Zou, X. (2015). Predicting peptide binding sites on protein surfaces by clustering chemical interactions. Journal of Computational Chemistry, 36(1), 49–61. https://doi.org/10.1002/jcc.23771

    Article  PubMed  CAS  Google Scholar 

  33. Pereira, J. C., Caffarena, E. R., & Dos Santos, C. N. (2016). Boosting docking-based virtual screening with deep learning. Journal of Chemical Information and Modeling, 56(12), 2495–2506. https://doi.org/10.1021/acs.jcim.6b00355

    Article  PubMed  CAS  Google Scholar 

  34. Xiao, S. M., Bai, R., & Zhang, X. Y. (2016). [Genomic research of traditional chinese medicines in vivo metabolism]. Zhongguo Zhong Yao Za Zhi, 41(22), 4103–4111. https://doi.org/10.4268/cjcmm20162204

    Article  PubMed  Google Scholar 

  35. Yang, Y., Zhang, Z., Li, S., Ye, X., Li, X., & He, K. (2014). Synergy effects of herb extracts: Pharmacokinetics and pharmacodynamic basis. Fitoterapia, 92, 133–147. https://doi.org/10.1016/j.fitote.2013.10.010

    Article  PubMed  CAS  Google Scholar 

  36. Wang, Z., Wan, H., Tong, X., He, Y., Yang, J., Zhang, L., Shao, C., Ding, Z., Wan, H., & Li, C. (2021). An integrative strategy for discovery of functional compound combination from traditional chinese medicine: Danhong Injection as a model. Biomedicine & Pharmacotherapy, 138, 111451. https://doi.org/10.1016/j.biopha.2021.111451

    Article  CAS  Google Scholar 

  37. Hu, X. Q., Sun, Y., Lau, E., Zhao, M., & Su, S. B. (2016). Advances in synergistic combinations of chinese Herbal Medicine for the treatment of Cancer. Current Cancer Drug Targets, 16(4), 346–356. https://doi.org/10.2174/1568009616666151207105851

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Qi, F., Zhao, L., Zhou, A., Zhang, B., Li, A., Wang, Z., & Han, J. (2015). The advantages of using traditional chinese medicine as an adjunctive therapy in the whole course of cancer treatment instead of only terminal stage of cancer. Bioscience Trends, 9(1), 16–34. https://doi.org/10.5582/bst.2015.01019

    Article  PubMed  CAS  Google Scholar 

  39. Zhu, X., Wu, Z., Cao, Y., Gao, R., Zhang, X., & Li, J. (2021). Efficacy and safety of TCM therapies combined with hyperthermic intraperitoneal chemotherapy for peritoneal metastasis of gastric cancer: A protocol for systematic review and meta-analysis. Medicine, 100(4), e24337. https://doi.org/10.1097/MD.0000000000024337

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Xiao, Z., Chen, Z., Han, R., Lu, L., Li, Z., Lin, J., Hu, L., Huang, X., & Lin, L. (2021). Comprehensive TCM treatments combined with chemotherapy for advanced non-small cell lung cancer: A randomized, controlled trial. Medicine, 100(18), https://doi.org/10.1097/MD.0000000000025690

  41. Zhang, G. L., Pan, M., Wang, Y. Z., Huang, J. X., Gu, G. S., Wang, Y., Wu, Q., Yao, L. T., Xie, H. R., & Hu, X. J. (2021). [Regulation effect of myeloid leukemia No.1 chinese Herb Medicine prescription combined with chemotherapy on Th17 cells in bone marrow of patients with Acute myeloid leukemia]. Zhongguo Shi Yan Xue Ye Xue Za Zhi, 29(2), 328–332. https://doi.org/10.19746/j.cnki.issn.1009-2137.2021.02.004

    Article  PubMed  Google Scholar 

  42. Li, S., Sun, Y., Huang, J., Wang, B., Gong, Y., Fang, Y., Liu, Y., Wang, S., Guo, Y., Wang, H., Xu, Z., & Guo, Y. (2020). Anti-tumor effects and mechanisms of Astragalus membranaceus (AM) and its specific immunopotentiation: Status and prospect. Journal of Ethnopharmacology, 258, 112797. https://doi.org/10.1016/j.jep.2020.112797

    Article  PubMed  CAS  Google Scholar 

  43. Tang, S. M., Deng, X. T., Zhou, J., Li, Q. P., Ge, X. X., & Miao, L. (2020). Pharmacological basis and new insights of quercetin action in respect to its anti-cancer effects. Biomedicine & Pharmacotherapy, 121, 109604. https://doi.org/10.1016/j.biopha.2019.109604

    Article  CAS  Google Scholar 

  44. Vafadar, A., Shabaninejad, Z., Movahedpour, A., Fallahi, F., Taghavipour, M., Ghasemi, Y., Akbari, M., Shafiee, A., Hajighadimi, S., Moradizarmehri, S., Razi, E., Savardashtaki, A., & Mirzaei, H. (2020). Quercetin and cancer: New insights into its therapeutic effects on ovarian cancer cells. Cell & Bioscience, 10, 32. https://doi.org/10.1186/s13578-020-00397-0

    Article  CAS  Google Scholar 

  45. Shafabakhsh, R., & Asemi, Z. (2019). Quercetin: A natural compound for ovarian cancer treatment. Journal of Ovarian Research, 12(1), 55. https://doi.org/10.1186/s13048-019-0530-4

    Article  PubMed  PubMed Central  Google Scholar 

  46. Ren, M. X., Deng, X. H., Ai, F., Yuan, G. Y., & Song, H. Y. (2015). Effect of quercetin on the proliferation of the human ovarian cancer cell line SKOV-3 in vitro Experimental and Therapeutic Medicine, 10(2), 579–583. https://doi.org/10.3892/etm.2015.2536

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Domcke, S., Sinha, R., Levine, D. A., Sander, C., & Schultz, N. (2013). Evaluating cell lines as tumour models by comparison of genomic profiles. Nature Communications, 4, 2126. https://doi.org/10.1038/ncomms3126

    Article  PubMed  CAS  Google Scholar 

  48. Zhong, Q., Hu, Z., Li, Q., Yi, T., Li, J., & Yang, H. (2019). Cyclin D1 silencing impairs DNA double strand break repair, sensitizes BRCA1 wildtype ovarian cancer cells to olaparib. Gynecologic Oncology, 152(1), 157–165. https://doi.org/10.1016/j.ygyno.2018.10.027

    Article  PubMed  CAS  Google Scholar 

  49. Li, H., Liu, Z. Y., Wu, N., Chen, Y. C., Cheng, Q., & Wang, J. (2020). PARP inhibitor resistance: The underlying mechanisms and clinical implications. Molecular Cancer, 19(1), 107. https://doi.org/10.1186/s12943-020-01227-0

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Pillay, N., Tighe, A., Nelson, L., Littler, S., Coulson-Gilmer, C., Bah, N., Golder, A., Bakker, B., Spierings, D., James, D. I., Smith, K. M., Jordan, A. M., Morgan, R. D., Ogilvie, D. J., Foijer, F., Jackson, D. A., & Taylor, S. S. (2019). DNA replication vulnerabilities render ovarian Cancer cells sensitive to poly(ADP-Ribose) glycohydrolase inhibitors. Cancer Cell, 35(3), 519-533e8. https://doi.org/10.1016/j.ccell.2019.02.004

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Lee, E. K., & Matulonis, U. A. (2020). Emerging drugs for the treatment of ovarian cancer: A focused review of PARP inhibitors. Expert Opinion on Emerging Drugs, 25(2), 165–188. https://doi.org/10.1080/14728214.2020.1773791

    Article  PubMed  CAS  Google Scholar 

  52. Boussios, S., Karihtala, P., Moschetta, M., Karathanasi, A., Sadauskaite, A., Rassy, E., & Pavlidis, N. (2019). Combined strategies with poly (ADP-Ribose) polymerase (PARP) inhibitors for the treatment of ovarian cancer: a literature review. Diagnostics (Basel Switzerland), 9(3), 87. https://doi.org/10.3390/diagnostics9030087

    Article  PubMed  CAS  Google Scholar 

  53. Slade, D. (2020). PARP and PARG inhibitors in cancer treatment. Genes & Development, 34(5–6), 360–394. https://doi.org/10.1101/gad.334516.119

    Article  CAS  Google Scholar 

  54. Su, C., Haskins, A. H., Omata, C., Aizawa, Y., & Kato, T. A. (2017). PARP inhibition by Flavonoids Induced selective cell killing to BRCA2-Deficient cells. Pharmaceuticals (Basel Switzerland), 10(4), 80. https://doi.org/10.3390/ph10040080

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Rather, R. A., & Bhagat, M. (2020). Quercetin as an innovative therapeutic tool for cancer chemoprevention: Molecular mechanisms and implications in human health. Cancer Medicine, 9(24), 9181–9192. https://doi.org/10.1002/cam4.1411

    Article  PubMed  CAS  Google Scholar 

  56. Reyes-Farias, M., & Carrasco-Pozo, C. (2019). The anti-cancer effect of quercetin: molecular implications in cancer metabolism. International Journal of Molecular Sciences, 20(13), 3177. https://doi.org/10.3390/ijms20133177

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China

    Yang Liu, Jie Li & Jie Jiang

  2. Department of Gynecology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China

    Yang Liu

  3. School of Laboratory Animal & Shandong Laboratory Animal Center, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250118, Shandong, China

    Zhongkun Guo

  4. Maternal and Child Health Hospital of Shandong Province, Jinan, 250014, Shandong, China

    Fangfang Lang

Authors
  1. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongkun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fangfang Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jie Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally.

Corresponding authors

Correspondence to Jie Li or Jie Jiang.

Ethics declarations

Ethics Approval

All work has been done under the guidelines of Institutional Ethics Committee.

Consent to Participate

All authors have their consent to participate.

Consent for Publication

All authors have their consent to publish their work.

Competing Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

ESM 1

(JPG 3.56 MB)

ESM 2

(JPG 0.99 MB)

ESM 3

(JPG 1.77 MB)

ESM 4

(JPG 1.42 MB)

ESM 5

(JPG 2.34 MB)

ESM 6

(JPG 1.13 MB)

ESM 7

(JPG 1.78 MB)

ESM 8

(JPG 1.55 MB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Y., Guo, Z., Lang, F. et al. Anticancer Effect of Active Component of Astragalus Membranaceus Combined with Olaparib on Ovarian Cancer Predicted by Network-Based Pharmacology. Appl Biochem Biotechnol 195, 6994–7020 (2023). https://doi.org/10.1007/s12010-023-04462-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-023-04462-5

Keywords

Search

Navigation