Skip to main content
SpringerLink
Log in

Homology Modeling, Screening, and Identification of Potential FOXO6 Inhibitors Curtail Gastric Cancer Progression: an In Silico Drug Repurposing Approach

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

Abstract

Gastric cancer is the world’s second leading cause of cancer-related fatalities, with the epidemiology changing over the previous several decades. FOXOs are the O subfamily of the forkhead box (FOX) transcription factor family, which consists of four members: FOXO1, FOXO3, FOXO4, and FOXO6. FOXO6 mRNA and protein levels are increased in gastric cancer tissues. FOXO6 forced overexpression enhances gastric cancer cell growth, while knockdown decreases proliferation. In our study, the GEPIA, Kaplan-Meier, KEGG, and STRING databases were used to determine FOXO6 mRNA expression, overall survival ratio, interactive pathways, and top 10 associated proteins in gastric cancer respectively. Due to the lack of a solved structure for FOXO6, homology modeling was performed to obtain a 3D structure model, and we used anti-cancer drugs and small molecules to target FOXO6 for identifying a potential selective FOXO6 inhibitor. The chemical composition of the proteins and ligands has a significant impact on docking procedure performance. With this in mind, a critical evaluation of the performance of three regularly used docking routines was carried out: MVD, AutoDock Vina in PyRx, and ArgusLab. The binding affinities, docking scores, and intermolecular interactions were used as assessment criteria. In the study, the porfimer sodium showed excellent binding affinity to the FOXO6 protein. The major three docking software packages were used to analyze the scoring/H-bonding energy and intermolecular interactions. Based on the results, we concluded that FOXO6 was upregulated in gastric cancer and the ligand porfimer sodium emerges as a promising potential FOXO6 inhibitor to curtail gastric cancer progression.

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

Similar content being viewed by others

Data Availability

Data supporting the productivity of this investigation are available from the corresponding author upon request.

References

  1. Dikshit, R. P., Mathur, G., Mhatre, S., & Yeole, B. B. (2011). Epidemiological review of gastric cancer in India. Indian Journal of Medical and Paediatric Oncology, 32, 3. https://doi.org/10.4103/0971-5851.81883

    Article  PubMed  PubMed Central  Google Scholar 

  2. Global Cancer Observatory Available online: https://gco.iarc.fr/ (accessed on Jan 30, 2023).

  3. Morgan, E., Arnold, M., Camargo, M. C., Gini, A., Kunzmann, A. T., Matsuda, T., Meheus, F., Verhoeven, R. H. A., Vignat, J., Laversanne, M., et al. (2022). The current and future incidence and mortality of gastric cancer in 185 countries, 2020–40: A population-based modelling study. eClinicalMedicine, 47, 101404. https://doi.org/10.1016/j.eclinm.2022.101404

    Article  PubMed  PubMed Central  Google Scholar 

  4. Barad, A. K., Mandal, S. K., Harsha, H. S., Sharma, B. M., & Singh, T. S. (2014). Gastric cancer—A clinicopathological study in a tertiary care centre of North-eastern India. Journal of Gastrointestinal Oncology, 5, 142–147. https://doi.org/10.3978/J.ISSN.2078-6891.2014.003

    Article  PubMed  PubMed Central  Google Scholar 

  5. Servarayan Murugesan, C., Manickavasagam, K., Chandramohan, A., Jebaraj, A., Jameel, A. R. A., Jain, M. S., & Venkataraman, J. (2018). Gastric cancer in India: Epidemiology and standard of treatment. Updates in surgery, 70, 233–239. https://doi.org/10.1007/S13304-018-0527-3

    Article  PubMed  Google Scholar 

  6. Zali, H., Rezaei-Tavirani, M., & Azodi, M. (2011). Gastric cancer: Prevention, risk factors and treatment. Gastroenterology and Hepatology from Bed to Bench, 4, 175.

    PubMed  PubMed Central  Google Scholar 

  7. Wroblewski, L. E., Peek, R. M., Jr., & Wilson, K. T. (2010). Helicobacter pylori and gastric cancer: Factors that modulate disease risk. Clinical Microbiology Reviews, 23, 713. https://doi.org/10.1128/CMR.00011-10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ishaq, S., & Nunn, L. (2015). Helicobacter pylori and gastric cancer: A state of the art review. Gastroenterology and Hepatology from Bed to Bench, 8, S6.

    PubMed  PubMed Central  Google Scholar 

  9. Kim, D. H., Zhang, T., Lee, S., & Dong, H. H. (2013). FoxO6 in glucose metabolism. Journal of Diabetes, 5, 233. https://doi.org/10.1111/1753-0407.12027

    Article  CAS  PubMed  Google Scholar 

  10. Jiramongkol, Y., & Lam, E. W.-F. (2020). FOXO transcription factor family in cancer and metastasis. Cancer and Metastasis Reviews, 39, 681–709. https://doi.org/10.1007/S10555-020-09883-W

    Article  CAS  PubMed  Google Scholar 

  11. Wang, J.-H., Tang, H., Li, X.-S., Zhang, X.-L., Yang, X.-Z., Zeng, L.-S., Ruan, Q., Huang, Y.-H., Liu, G.-J., Wang, J., et al. (2017). Elevated FOXO6 expression correlates with progression and prognosis in gastric cancer. Oncotarget, 8, 31682. https://doi.org/10.18632/ONCOTARGET.15920

    Article  PubMed  PubMed Central  Google Scholar 

  12. Qinyu, L., Long, C., Zhen-Dong, D., Min-Min, S., Wei-Ze, W., Wei-Ping, Y., & Cheng-Hong, P. (2013). FOXO6 promotes gastric cancer cell tumorigenicity via upregulation of C-myc. FEBS Letters, 587, 2105–2111. https://doi.org/10.1016/J.FEBSLET.2013.05.027

    Article  PubMed  Google Scholar 

  13. Berry, M., Fielding, B., & Gamieldien, J. (2015). Practical considerations in virtual screening and molecular docking. Elsevier Inc.

    Book  Google Scholar 

  14. Huang, S. Y., & Zou, X. (2010). Advances and challenges in protein-ligand docking. International Journal of Molecular Sciences, 11, 3016–3034. https://doi.org/10.3390/ijms11083016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tang, Z., Li, C., Kang, B., Gao, G., Li, C., & Zhang, Z. (2017). GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45, W98–W102. https://doi.org/10.1093/NAR/GKX247

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kaplan-Meier plotter [Gastric] Available online: https://kmplot.com/analysis/index.php?p=service&cancer=gastric (accessed on Feb 28, 2023).

  17. Nagy, Á., Lánczky, A., Menyhárt, O., & Gyorffy, B. (2018). Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Scientific Reports, 8, 9227. https://doi.org/10.1038/S41598-018-27521-Y

    Article  PubMed  PubMed Central  Google Scholar 

  18. Szász, A. M., Lánczky, A., Nagy, Á., Förster, S., Hark, K., Green, J. E., Boussioutas, A., Busuttil, R., Szabó, A., & Gyorffy, B. (2016). Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients. Oncotarget, 7, 49322–49333. https://doi.org/10.18632/ONCOTARGET.10337

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017). KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Research, 45, D353. https://doi.org/10.1093/NAR/GKW1092

    Article  CAS  PubMed  Google Scholar 

  20. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27. https://doi.org/10.1093/NAR/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., et al. (2021). The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research, 49, D605–D612. https://doi.org/10.1093/NAR/GKAA1074

    Article  CAS  PubMed  Google Scholar 

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

  23. Wheeler, D. L., Chappey, C., Lash, A. E., Leipe, D. D., Madden, T. L., Schuler, G. D., Tatusova, T. A., & Rapp, B. A. (2000). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 28, 10–14. https://doi.org/10.1093/nar/28.1.10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Biasini, M., Bienert, S., Waterhouse, A., Arnold, K., Studer, G., Schmidt, T., Kiefer, F., Cassarino, T. G., Bertoni, M., Bordoli, L., et al. (2014). SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research, 42, 1–7. https://doi.org/10.1093/nar/gku340

    Article  CAS  Google Scholar 

  25. SAVESv6.0—Structure validation server Available online: https://saves.mbi.ucla.edu/ (accessed on Jun 25, 2021).

  26. Laskowski, R. A., MacArthur, M. W., Moss, D. S., & Thornton, J. M. (1993). PROCHECK: A program to check the stereochemical quality of protein structures. Journal of Applied Crystallography, 26, 283–291. https://doi.org/10.1107/s0021889892009944

    Article  CAS  Google Scholar 

  27. Hooft, R. W., Vriend, G., & Sander, C. (1996). Abola EE Errors in protein structures. Nature, 381, 272.

    Article  CAS  PubMed  Google Scholar 

  28. BIOVIA Discovery Studio—BIOVIA—Dassault Systèmes® Available online: https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/biovia-discovery-studio/ (accessed on Jul 21, 2021).

  29. PyMOL | pymol.org Available online: https://pymol.org/2/ (accessed on Oct 13, 2021).

  30. Ali, A., Al-, S., Rehab, H., & Hamoodah, G. (2016). Transition metal complexes with tridentate ligand: Preparation, spectroscopic characterization, thermal analysis and structural studies. Baghdad Science Journal, 13. https://doi.org/10.21123/bsj.2016.13.4.0770

  31. PubChem Available online: https://pubchem.ncbi.nlm.nih.gov/ (accessed on Jun 24, 2021).

  32. BIDD Available online: http://bidd.group/ (accessed on Oct 13, 2021).

  33. ChemDraw—PerkinElmer Available online: https://perkinelmerinformatics.com/products/research/chemdraw/ (accessed on Jun 24, 2021).

  34. Hyper Available online: https://hyper.com/ (accessed on Jun 24, 2021).

  35. Daoud, I., Melkemi, N., Salah, T., & Ghalem, S. (2018). Combined QSAR, molecular docking and molecular dynamics study on new acetylcholinesterase and butyrylcholinesterase inhibitors. Computational Biology and Chemistry, 74, 304–326. https://doi.org/10.1016/j.compbiolchem.2018.03.021

    Article  CAS  PubMed  Google Scholar 

  36. Sadeghi, F., Afkhami, A., Madrakian, T., & Ghavami, R. (2021). Computational study to select the capable anthracycline derivatives through an overview of drug structure-specificity and cancer cell line-specificity. Chemical Papers, 75, 523–538. https://doi.org/10.1007/s11696-020-01321-z

    Article  CAS  Google Scholar 

  37. Hocquet, A., & Langgård, M. (1998). An evaluation of the MM+ force field. Journal of Molecular Modeling, 4, 94–112. https://doi.org/10.1007/s008940050128

    Article  CAS  Google Scholar 

  38. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30, 2785. https://doi.org/10.1002/JCC.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein–ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905–919. https://doi.org/10.1038/nprot.2016.051

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455. https://doi.org/10.1002/JCC.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dallakyan, S., & Olson, A. J. (2015). Small-molecule library screening by docking with PyRx. Chemical Biology: Methods and Protocols, 1263, 243–250. https://doi.org/10.1007/978-1-4939-2269-7_19

    Article  CAS  Google Scholar 

  42. Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., & Taylor, R. D. (2003). Improved protein-ligand docking using GOLD. Proteins, 52, 609–623. https://doi.org/10.1002/PROT.10465

    Article  CAS  PubMed  Google Scholar 

  43. Bitencourt-Ferreira, G., & de Azevedo, W. F. (2019). Molecular docking simulations with ArgusLab. Docking Screens for Drug Discovery, 203–220. https://doi.org/10.1007/978-1-4939-9752-7_13

  44. McGann, M. (2012). FRED and HYBRID docking performance on standardized datasets. Journal of Computer-Aided Molecular Design, 26, 897–906. https://doi.org/10.1007/S10822-012-9584-8

    Article  CAS  PubMed  Google Scholar 

  45. Rarey, M., Kramer, B., Lengauer, T., & Klebe, G. (1996). A Fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology, 261, 470–489. https://doi.org/10.1006/JMBI.1996.0477

    Article  CAS  PubMed  Google Scholar 

  46. Abagyan, R., Totrov, M., & Kuznetsov, D. (1994). ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation. Journal of Computational Chemistry, 15, 488–506. https://doi.org/10.1002/JCC.540150503

    Article  CAS  Google Scholar 

  47. Kusumaningrum, S., Budianto, E., Kosela, S., Sumaryono, W., & Juniarti, F. (2014). The molecular docking of 1,4-naphthoquinone derivatives as inhibitors of Polo-like kinase 1 using Molegro Virtual Docker. Journal of Applied Pharmaceutical Science, 4, 47–53. https://doi.org/10.7324/JAPS.2014.4119

    Article  Google Scholar 

  48. Bitencourt-Ferreira, G., & de Azevedo, W. F. (2019). Molegro virtual docker for docking. In Methods in molecular biology (Vol. 2053, pp. 149–167). Humana Press Inc..

    Google Scholar 

  49. Dawood, S., Zarina, S., & Bano, S. (2014). Docking studies of antidepressants against single crystal structure of tryptophan 2, 3-dioxygenase using Molegro Virtual Docker software. Pakistan Journal of Pharmaceutical Sciences, 27, 1529–1539.

    CAS  PubMed  Google Scholar 

  50. Hafeez, A., Saify, Z. S., Naz, A., Yasmin, F., & Akhtar, N. (2013). Molecular docking study on the interaction of riboflavin (Vitamin B 2 ) and cyanocobalamin (Vitamin B 12 ) coenzymes. Journal of Computational Medicine, 2013, 1–5. https://doi.org/10.1155/2013/312183

    Article  Google Scholar 

  51. Singh, D. B., Gupta, M. K., Singh, D. V., Singh, S. K., & Misra, K. (2013). Docking and in silico ADMET studies of noraristeromycin, curcumin and its derivatives with Plasmodium falciparum SAH hydrolase: A molecular drug target against malaria. Interdisciplinary Sciences: Computational Life Sciences, 5, 1–12. https://doi.org/10.1007/S12539-013-0147-Z/METRICS

    Article  PubMed  Google Scholar 

  52. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 23, 3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  53. Benet, L. Z., Hosey, C. M., Ursu, O., & Oprea, T. I. (2016). BDDCS, the rule of 5 and drugability graphical abstract HHS public access. Advanced Drug Delivery Reviews, 101, 89–98. https://doi.org/10.1016/j.addr.2016.05.007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. SwissADME Available online: http://www.swissadme.ch/ (accessed on Jul 3, 2021).

  55. Pires, D. E., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pinzi, L., & Rastelli, G. (2019). Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20, 4331. https://doi.org/10.3390/IJMS20184331

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bothara, K. G., Patil, A. U., & Sexena, A. (1998). Importance of docking studies in drug design. Indian Journal of Pharmaceutical Sciences, 60, 333.

    CAS  Google Scholar 

  58. Martin, Y. C. (2005). A bioavailability score. Journal of Medicinal Chemistry, 48, 3164–3170. https://doi.org/10.1021/JM0492002/ASSET/IMAGES/MEDIUM/JM0492002N00001.GIF

    Article  CAS  PubMed  Google Scholar 

  59. Abdullahi, S. H., Uzairu, A., Shallangwa, G. A., Uba, S., & Umar, A. B. (2022). Computational modeling, ligand-based drug design, drug-likeness and ADMET properties studies of series of chromen-2-ones analogues as anti-cancer agents. Bulletin of the National Research Centre, 46(1), 1. https://doi.org/10.1186/S42269-022-00869-Y

    Article  Google Scholar 

  60. Isyaku, Y., Uzairu, A., & Uba, S. (2020). Computational studies of a series of 2-substituted phenyl-2-oxo-, 2-hydroxyl- and 2-acylloxyethylsulfonamides as potent anti-fungal agents. Heliyon, 6, e03724. https://doi.org/10.1016/J.HELIYON.2020.E03724

    Article  PubMed  PubMed Central  Google Scholar 

  61. Scopus preview—Scopus—Document details—The effect of cytochrome P450 metabolism on drug response, interactions, and adverse effects Available online: https://www.scopus.com/record/display.uri?eid=2-s2.0-34547663626&origin=inward&txGid=267ea611c047cee3c547669b48f0915c (accessed on Mar 4, 2023).

  62. Daina, A., & Zoete, V. (2016). A BOILED-egg to predict gastrointestinal absorption and brain penetration of small molecules. ChemMedChem, 11, 1117–1121. https://doi.org/10.1002/CMDC.201600182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ibrahim, M. T., Uzairu, A., Shallangwa, G. A., & Uba, S. (2020). In-silico activity prediction and docking studies of some 2, 9-disubstituted 8-phenylthio/phenylsulfinyl-9h-purine derivatives as Anti-proliferative agents. Heliyon, 6, e03158. https://doi.org/10.1016/J.HELIYON.2020.E03158

    Article  PubMed  PubMed Central  Google Scholar 

  64. Yeşilkaynak, T., Nur Özkömeç, F., Çeşme, M., Demirdöğen, R. E., Sezer, C. V., Kutlu, H. M., & Emen, F. M. (2023). Novel thiourea derivative compounds: Thermal behavior, biological evaluation, Hirshfeld surfaces and frontier orbitals analyses, in silico ADMET profiling and molecular docking studies. Journal of Molecular Structure, 1280, 135086. https://doi.org/10.1016/J.MOLSTRUC.2023.135086

    Article  Google Scholar 

  65. Scopus preview—Scopus—Document details—hERG K(+) channels: Structure, function, and clinical significance. Available online: https://www.scopus.com/record/display.uri?eid=2-s2.0-84866679694&origin=inward&txGid=e8aea4cb13925e662a7133efc74a3ea9 (accessed on Mar 4, 2023).

  66. Lokhande, K. B., Tiwari, A., Gaikwad, S., Kore, S., Nawani, N., Wani, M., Swamy, K. V., & Pawar, S. V. (2023). Computational docking investigation of phytocompounds from bergamot essential oil against Serratia marcescens protease and FabI: Alternative pharmacological strategy. Computational Biology and Chemistry, 104, 107829. https://doi.org/10.1016/J.COMPBIOLCHEM.2023.107829

    Article  CAS  PubMed  Google Scholar 

  67. Liu, Y., Ao, X., Jia, Y., Li, X., Wang, Y., & Wang, J. (2022). The FOXO family of transcription factors: Key molecular players in gastric cancer. Journal of Molecular Medicine, 100, 997–1015. https://doi.org/10.1007/S00109-022-02219-X

    Article  CAS  PubMed  Google Scholar 

  68. Eun Kim, M., Sik Lee, J., & Hyun Kim, D. (2022). FoxO6-mediated TXNIP induces hepatic steatosis through NLRP3 inammasome activation in vivo and in vitro. Research Square. https://doi.org/10.21203/rs.3.rs-2156677/v1

    Book  Google Scholar 

  69. Zhang, L., Zhang, Y., Zhou, M., Wang, S., Li, T., Hu, Z., & Jin, C. (2021). Role and mechanism underlying FoxO6 in skeletal muscle in vitro and in vivo. International Journal of Molecular Medicine, 48, 1–8. https://doi.org/10.3892/IJMM.2021.4976/HTML

    Article  Google Scholar 

  70. Rothenberg, S. M., Concannon, K., Cullen, S., Boulay, G., Turke, A. B., Faber, A. C., Lockerman, E. L., Rivera, M. N., Engelman, J. A., Maheswaran, S., et al. (2015). Inhibition of mutant EGFR in lung cancer cells triggers SOX2-FOXO6-dependent survival pathways. Elife, 4, e06132. https://doi.org/10.7554/eLife.06132.001

    Article  PubMed  PubMed Central  Google Scholar 

  71. Qinyu, L., Long, C., Zhen-Dong, D., Min-Min, S., Wei-Ze, W., Wei-Ping, Y., & Cheng-Hong, P. (2013). FOXO6 promotes gastric cancer cell tumorigenicity via upregulation of C-myc. FEBS Letters, 587, 2105–2111. https://doi.org/10.1016/J.FEBSLET.2013.05.027

    Article  PubMed  Google Scholar 

  72. Van Der Heide, L. P., Jacobs, F. M. J., Burbach, J. P. H., Hoekman, M. F. M., & Smidt, M. P. (2005). FoxO6 transcriptional activity is regulated by Thr26 and Ser184, independent of nucleo-cytoplasmic shuttling. Biochemical Journal, 391, 623. https://doi.org/10.1042/BJ20050525

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Li, Q., Tang, H., Hu, F., & Qin, C. (2019). Silencing of FOXO6 inhibits the proliferation, invasion, and glycolysis in colorectal cancer cells. Journal of Cellular Biochemistry, 120, 3853–3860. https://doi.org/10.1002/JCB.27667

    Article  CAS  PubMed  Google Scholar 

  74. Hu, H. J., Zhang, L. G., Wang, Z. H., & Guo, X. X. (2015). FoxO6 inhibits cell proliferation in lung carcinoma through up-regulation of USP7. Molecular Medicine Reports, 12, 575–580. https://doi.org/10.3892/MMR.2015.3362/HTML

    Article  CAS  PubMed  Google Scholar 

  75. Yu, X., Gao, X., Mao, X., Shi, Z., Zhu, B., Xie, L., Di, S., & Jin, L. (2020). <p>Knockdown of FOXO6 inhibits glycolysis and reduces cell resistance to paclitaxel in HCC cells via PI3K/Akt signaling pathway</p>. OncoTargets and Therapy, 13, 1545–1556. https://doi.org/10.2147/OTT.S233031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhang, Z., Zhou, L., Xie, N., Nice, E. C., Zhang, T., Cui, Y., & Huang, C. Overcoming cancer therapeutic bottleneck by drug repurposing. Signal Transduction and Targeted Therapy, 5(1), 113. https://doi.org/10.1038/s41392-020-00213-8

  77. Drug repurposing | Anticancerfund Available online: https://www.anticancerfund.org/en/drug-repurposing (accessed on Jan 31, 2023).

  78. Rodrigues, R., Duarte, D., & Vale, N. (2022). Drug repurposing in cancer therapy: Influence of patient’s genetic background in breast cancer treatment. Int. J. Mol. Sci., 23. https://doi.org/10.3390/IJMS23084280

  79. Nwogu, C., Kloc, A., Attwood, K., Bshara, W., Durrani, F., & Pandey, R. (2021). Porfimer sodium versus PS785 for photodynamic therapy (PDT) of lung cancer xenografts in mice. Journal of Surgical Research, 263, 245–250. https://doi.org/10.1016/J.JSS.2020.12.067

    Article  CAS  PubMed  Google Scholar 

  80. Porfimer sodium: Uses, interactions, mechanism of action | DrugBank Online Available online: https://go.drugbank.com/drugs/DB00707 (accessed on Jan 31, 2023).

Download references

Acknowledgements

Thankful to the Department of Pharmaceutical Biotechnology, Andhra University, and the Department of Genetics and Biotechnology, Osmania University for providing support and encouragement in bioinformatics/software facilities.

Author information

Author notes

  1. Sneha Malleswari Poleboyina and Pavan Kumar Poleboyina equally contributed to this paper.

Authors and Affiliations

  1. Department of Pharmaceutical Biotechnology, AU College of Pharmaceutical Sciences, Andhra University, Visakhapatnam, Andhra Pradesh, 530003, India

    Sneha Malleswari Poleboyina & Girijasankar Guntuku

  2. Department of Genetics & Biotechnology, University College of Science, Osmania University, Hyderabad, Telangana, 500007, India

    Pavan Kumar Poleboyina & Smita C. Pawar

Authors
  1. Sneha Malleswari Poleboyina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pavan Kumar Poleboyina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Smita C. Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Girijasankar Guntuku
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GSG, SCP, PKP, and SMP conceptualized, designed interpreted data, and edited the manuscript. SMP and PKP conducted the study. All authors have approved the manuscript in its current form.

Corresponding authors

Correspondence to Smita C. Pawar or Girijasankar Guntuku.

Ethics declarations

Ethical Approval

Not applicable

Consent to Participate

Not applicable

Consent for Publication

Not applicable

Conflict of Interest

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.

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

Poleboyina, S.M., Poleboyina, P.K., Pawar, S.C. et al. Homology Modeling, Screening, and Identification of Potential FOXO6 Inhibitors Curtail Gastric Cancer Progression: an In Silico Drug Repurposing Approach. Appl Biochem Biotechnol 195, 7708–7737 (2023). https://doi.org/10.1007/s12010-023-04490-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12010-023-04490-1

Keywords

Search

Navigation