Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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Identification and Validation of Novel Biomarkers and Potential Targeted Drugs in Cholangiocarcinoma: Bioinformatics, Virtual Screening, and Biological Evaluation

  • Wang, Jiena (Department of Pharmacy, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University) ;
  • Zhu, Weiwei (Department of Pharmacy, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University) ;
  • Tu, Junxue (Department of Pharmacy, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University) ;
  • Zheng, Yihui (Department of Pharmacy, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University)
  • Received : 2022.07.18
  • Accepted : 2022.09.08
  • Published : 2022.10.28
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Abstract

Cholangiocarcinoma (CCA) is a complex and refractor type of cancer with global prevalence. Several barriers remain in CCA diagnosis, treatment, and prognosis. Therefore, exploring more biomarkers and therapeutic drugs for CCA management is necessary. CCA gene expression data was downloaded from the TCGA and GEO databases. KEGG enrichment, GO analysis, and protein-protein interaction network were used for hub gene identification. miRNA were predicted using Targetscan and validated according to several GEO databases. The relative RNA and miRNA expression levels and prognostic information were obtained from the GEPIA. The candidate drug was screened using pharmacophore-based virtual screening and validated by molecular modeling and through several in vitro studies. 301 differentially expressed genes (DEGs) were screened out. Complement and coagulation cascades-related genes (including AHSG, F2, TTR, and KNG1), and cell cycle-related genes (including CDK1, CCNB1, and KIAA0101) were considered as the hub genes in CCA progression. AHSG, F2, TTR, and KNG1 were found to be significantly decreased and the eight predicted miRNA targeting AHSG, F2, and TTR were increased in CCA patients. CDK1, CCNB1, and KIAA0101 were found to be significantly abundant in CCA patients. In addition, Molport-003-703-800, which is a compound that is derived from pharmacophores-based virtual screening, could directly bind to CDK1 and exhibited anti-tumor activity in cholangiocarcinoma cells. AHSG, F2, TTR, and KNG1 could be novel biomarkers for CCA. Molport-003-703-800 targets CDK1 and work as potential cell cycle inhibitors, thereby having potential for consideration for new chemotherapeutics for CCA.

Keywords

References

  1. Khan SA, Tavolari S, Brandi G. 2019. Cholangiocarcinoma: Epidemiology and risk factors. Liver Int. 39 Suppl 1: 19-31. https://doi.org/10.1111/liv.14095
  2. Blechacz B. 2017. Cholangiocarcinoma: Current knowledge and new developments. Gut Liver 11: 13-26. https://doi.org/10.5009/gnl15568
  3. Rodrigues PM, Olaizola P, Paiva NA, Olaizola I, Agirre-Lizaso A, Landa A, et al. 2021. Pathogenesis of cholangiocarcinoma. Annu. Rev. Pathol. 16: 433-463. https://doi.org/10.1146/annurev-pathol-030220-020455
  4. Cardinale V, Bragazzi MC, Carpino G, Torrice A, Fraveto A, Gentile R, et al. 2013. Cholangiocarcinoma: increasing burden of classifications. Hepatobiliary Surg. Nutr. 2: 272-280.
  5. Valle JW, Kelley RK, Nervi B, Oh DY, Zhu AX. 2021. Biliary tract cancer. Lancet 397: 428-444. https://doi.org/10.1016/S0140-6736(21)00153-7
  6. Gigante E, Paradis V, Ronot M, Cauchy F, Soubrane O, Ganne-Carrie N, et al. 2021. New insights into the pathophysiology and clinical care of rare primary liver cancers. JHEP Rep. 3: 100174. https://doi.org/10.1016/j.jhepr.2020.100174
  7. Coelho R, Silva M, Rodrigues-Pinto E, Cardoso H, Lopes S, Pereira P, et al. 2017. CA 19-9 as a marker of survival and a predictor of metastization in cholangiocarcinoma. GE Port. J. Gastroenterol. 24: 114-121. https://doi.org/10.1159/000452691
  8. Rizvi S, Khan SA, Hallemeier CL, Kelley RK, Gores GJ. 2018. Cholangiocarcinoma - evolving concepts and therapeutic strategies. Nat. Rev. Clin. Oncol. 15: 95-111. https://doi.org/10.1038/nrclinonc.2017.157
  9. Banales JM, Marin JJG, Lamarca A, Rodrigues PM, Khan SA, Roberts LR, et al. 2020. Cholangiocarcinoma 2020: the next horizon in mechanisms and management. Nat. Rev. Gastroenterol. Hepatol. 17: 557-588. https://doi.org/10.1038/s41575-020-0310-z
  10. Lee YT, Tan YJ, Oon CE. 2018. Molecular targeted therapy: Treating cancer with specificity. Eur. J. Pharmacol. 834: 188-196. https://doi.org/10.1016/j.ejphar.2018.07.034
  11. Bayat A. 2002. Science, medicine, and the future: Bioinformatics. BMJ 324: 1018-1022. https://doi.org/10.1136/bmj.324.7344.1018
  12. Manzoni C, Kia DA, Vandrovcova J, Hardy J, Wood NW, Lewis PA, et al. 2018. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief. Bioinform. 19: 286-302. https://doi.org/10.1093/bib/bbw114
  13. Pereira CA, Saye M, Reigada C, Silber AM, Labadie GR, Miranda MR, et al. 2020. Computational approaches for drug discovery against trypanosomatid-caused diseases. Parasitology 147: 611-633. https://doi.org/10.1017/S0031182020000207
  14. Iqbal D, Rehman MT, Bin Dukhyil A, Rizvi SMD, Al Ajmi MF, Alshehri BM, et al. 2021. High-throughput screening and molecular dynamics simulation of natural product-like compounds against Alzheimer's disease through multitarget approach. Pharmaceuticals (Basel) 14: 937. https://doi.org/10.3390/ph14090937
  15. Batool M, Ahmad B, Choi S. 2019. A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20: 2783. https://doi.org/10.3390/ijms20112783
  16. Alamri MA, Alamri MA. 2019. Pharmacophore and docking-based sequential virtual screening for the identification of novel Sigma 1 receptor ligands. Bioinformation 15: 586-595. https://doi.org/10.6026/97320630015586
  17. Chen Z, Li HL, Zhang QJ, Bao XG, Yu KQ, Luo XM, et al. 2009. Pharmacophore-based virtual screening versus docking-based virtual screening: a benchmark comparison against eight targets. Acta Pharmacol. Sin. 30: 1694-1708. https://doi.org/10.1038/aps.2009.159
  18. Clough E, Barrett T. 2016. The gene expression omnibus database. Methods Mol. Biol. 1418: 93-110. https://doi.org/10.1007/978-1-4939-3578-9_5
  19. Wang Z, Jensen MA, Zenklusen JC. 2016. A practical guide to the cancer genome atlas (TCGA). Methods Mol. Biol. 1418: 111-141. https://doi.org/10.1007/978-1-4939-3578-9_6
  20. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. 2015. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43: e47. https://doi.org/10.1093/nar/gkv007
  21. Robinson MD, McCarthy DJ, Smyth GK. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26: 139-140. https://doi.org/10.1093/bioinformatics/btp616
  22. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. 2013. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 41: D991-995.
  23. Huang da W, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4: 44-57. https://doi.org/10.1038/nprot.2008.211
  24. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. 2017. The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45: D362-D368. https://doi.org/10.1093/nar/gkw937
  25. Su G, Morris JH, Demchak B, Bader GD. 2014. Biological network exploration with Cytoscape 3. Curr. Protoc. Bioinformatics 47: 8.13-1-24.
  26. Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. 2014. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 8 Suppl 4: S11. https://doi.org/10.1186/1752-0509-8-S4-S11
  27. 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 Res. 45: W98-W102. https://doi.org/10.1093/nar/gkx247
  28. Agarwal V, Bell GW, Nam JW, Bartel DP. 2015. Predicting effective microRNA target sites in mammalian mRNAs. Elife 4: e05005. https://doi.org/10.7554/eLife.05005
  29. Wong NW, Chen Y, Chen S, Wang X. 2018. OncomiR: an online resource for exploring pan-cancer microRNA dysregulation. Bioinformatics 34: 713-715. https://doi.org/10.1093/bioinformatics/btx627
  30. Sunseri J, Koes DR. 2016. Pharmit: interactive exploration of chemical space. Nucleic Acids Res. 44: W442-448. https://doi.org/10.1093/nar/gkw287
  31. Wood DJ, Korolchuk S, Tatum NJ, Wang LZ, Endicott JA, Noble MEM, et al. 2019. Differences in the conformational energy landscape of CDK1 and CDK2 suggest a mechanism for achieving selective CDK inhibition. Cell. Chem. Biol. 26: 121-130 e125. https://doi.org/10.1016/j.chembiol.2018.10.015
  32. Molvarec A, Kalabay L, Derzsy Z, Szarka A, Halmos A, Stenczer B, et al. 2009. Preeclampsia is associated with decreased serum alpha(2)-HS glycoprotein (fetuin-A) concentration. Hypertens. Res. 32: 665-669. https://doi.org/10.1038/hr.2009.79
  33. Palta S, Saroa R, Palta A. 2014. Overview of the coagulation system. Indian J. Anaesth. 58: 515-523. https://doi.org/10.4103/0019-5049.144643
  34. Wieczorek E, Ozyhar A. 2021. Transthyretin: from structural stability to osteoarticular and cardiovascular diseases. Cells 10: 1768. https://doi.org/10.3390/cells10071768
  35. Peyrou M, Cereijo R, Quesada-Lopez T, Campderros L, Gavalda-Navarro A, Linares-Pose L, et al. 2020. The kallikrein-kinin pathway as a mechanism for auto-control of brown adipose tissue activity. Nat. Commun. 11: 2132. https://doi.org/10.1038/s41467-020-16009-x
  36. Zamolodchikov D, Duffield M, Macdonald LE, Alessandri-Haber N. 2022. Accumulation of high molecular weight kininogen in the brains of Alzheimer's disease patients may affect microglial function by altering phagocytosis and lysosomal cathepsin activity. Alzheimers Dement. 18: 1919-1929. https://doi.org/10.1002/alz.12531
  37. Feinstein DI. 2015. Disseminated intravascular coagulation in patients with solid tumors. Oncology (Williston Park) 29: 96-102.
  38. Chen D, Wu H, He B, Lu Y, Wu W, Liu H, et al. 2019. Five hub genes can be the potential DNA methylation biomarkers for cholangiocarcinoma using bioinformatics analysis. Onco Targets Ther. 12: 8355-8365. https://doi.org/10.2147/OTT.S203342
  39. Ding L, Cao J, Lin W, Chen H, Xiong X, Ao H, et al. 2020. The roles of cyclin-dependent kinases in cell-cycle progression and therapeutic strategies in human breast cancer. Int. J. Mol. Sci. 21: 1960. https://doi.org/10.3390/ijms21061960
  40. Strzalka W, Ziemienowicz A. 2011. Proliferating cell nuclear antigen (PCNA): a key factor in DNA replication and cell cycle regulation. Ann. Bot. 107: 1127-1140. https://doi.org/10.1093/aob/mcq243
  41. De March M, Barrera-Vilarmau S, Crespan E, Mentegari E, Merino N, Gonzalez-Magana A, et al. 2018. p15PAF binding to PCNA modulates the DNA sliding surface. Nucleic Acids Res. 46: 9816-9828. https://doi.org/10.1093/nar/gky723
  42. Hanahan D, Weinberg RA. 2011. Hallmarks of cancer: the next generation. Cell 144: 646-674. https://doi.org/10.1016/j.cell.2011.02.013
  43. Roskoski R, Jr. 2019. Cyclin-dependent protein serine/threonine kinase inhibitors as anticancer drugs. Pharmacol. Res. 139: 471-488. https://doi.org/10.1016/j.phrs.2018.11.035
  44. Kommalapati A, Tella SH, Borad M, Javle M, Mahipal A. 2021. FGFR inhibitors in oncology: Insight on the management of toxicities in clinical practice. Cancers (Basel) 13: 2968. https://doi.org/10.3390/cancers13122968