Identification of Essential Genes and Drug Discovery in Bladder Cancer and Inflammatory Bowel Disease via Text Mining and Bioinformatics Analysis


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Abstract

Background:Bladder cancer (BCa) is the most common malignancy of the urinary system. Inflammation is critical in the occurrence and development of BCa. The purpose of this study was to identify key genes and pathways of inflammatory bowel disease in BCa through text mining technology and bioinformatics technology and to explore potential therapeutic drugs for BCa.

Methods:Genes associated with BCa and Crohn's disease (CD) were detected using the text mining tool GenClip3, and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape, and modular analysis was performed using the Molecular Complex Detection plugin (MCODE). Finally, the genes clustered in the first two modules were selected as core genes, and the drug-gene interaction database was used to discover potential therapeutic drugs.

Results:We identified 796 genes shared by \"Bladder cancer\" and \"Crohn's disease\" by text mining. Gene function enrichment analysis yielded 18 enriched GO terms and the 6 most relevant KEGG pathways. A PPI network with 758 nodes and 4014 edges was constructed, and 20 gene modules were obtained using MCODE. We selected the top two gene clusters as core candidate genes. We found that 3 out of 55 selected core genes could be targeted by 26 existing drugs.

Conclusions:The results indicated that CXCL12, FGF2 and FSCN1 are potential key genes involved in CD with BCa. Additionally, 26 drugs were identified as potential therapeutics for BCa treatment and management.

About the authors

Qingyuan Zheng

Department of Urology, Renmin Hospital of Wuhan University

Email: info@benthamscience.net

Liantao Guo

Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University

Email: info@benthamscience.net

Rui Yang

Department of Urology, Renmin Hospital of Wuhan University

Email: info@benthamscience.net

Zhiyuan Chen

Department of Urology, Renmin Hospital of Wuhan University

Email: info@benthamscience.net

Xiuheng Liu

Department of Urology, Renmin Hospital of Wuhan University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249. doi: 10.3322/caac.21660 PMID: 33538338
  2. Witjes, J.A.; Bruins, H.M.; Cathomas, R.; Compérat, E.M.; Cowan, N.C.; Gakis, G.; Hernández, V.; Linares Espinós, E.; Lorch, A.; Neuzillet, Y.; Rouanne, M.; Thalmann, G.N.; Veskimäe, E.; Ribal, M.J.; van der Heijden, A.G. European association of urology guidelines on muscle-invasive and metastatic bladder cancer: Summary of the 2020 guidelines. Eur. Urol., 2021, 79(1), 82-104. doi: 10.1016/j.eururo.2020.03.055 PMID: 32360052
  3. Zhang, C.; Liu, S.; Peng, L.; Wu, J.; Zeng, X.; Lu, Y.; Shen, H.; Luo, D. Does inflammatory bowel disease increase the risk of lower urinary tract tumors: A meta-analysis. Transl. Androl. Urol., 2021, 10(1), 164-173. doi: 10.21037/tau-20-1020 PMID: 33532306
  4. Geng, Z.; Geng, Q. Risk of urinary bladder cancer in patients with inflammatory bowel diseases: A meta-analysis. Front. Surg., 2021, 8, 636791. doi: 10.3389/fsurg.2021.636791 PMID: 34124132
  5. Ghandour, R.; Singla, N.; Lotan, Y. Treatment options and outcomes in nonmetastatic muscle invasive bladder cancer. Trends Cancer, 2019, 5(7), 426-439. doi: 10.1016/j.trecan.2019.05.011 PMID: 31311657
  6. Patel, V.G.; Oh, W.K.; Galsky, M.D. Treatment of muscle‐invasive and advanced bladder cancer in 2020. CA Cancer J. Clin., 2020, 70(5), 404-423. doi: 10.3322/caac.21631 PMID: 32767764
  7. Lobo, N.; Mount, C.; Omar, K.; Nair, R.; Thurairaja, R.; Khan, M.S. Landmarks in the treatment of muscle-invasive bladder cancer. Nat. Rev. Urol., 2017, 14(9), 565-574. doi: 10.1038/nrurol.2017.82 PMID: 28675174
  8. Koch, G.E.; Smelser, W.W.; Chang, S.S. Side effects of intravesical BCG and chemotherapy for bladder cancer: What they are and how to manage them. Urology, 2021, 149, 11-20. doi: 10.1016/j.urology.2020.10.039 PMID: 33181123
  9. Actis, G.C.; Pellicano, R.; Fagoonee, S.; Ribaldone, D.G. History of inflammatory bowel diseases. J. Clin. Med., 2019, 8(11), 1970. doi: 10.3390/jcm8111970 PMID: 31739460
  10. Berkowitz, L.; Schultz, B.M.; Salazar, G.A.; Pardo-Roa, C.; Sebastián, V.P.; Álvarez-Lobos, M.M.; Bueno, S.M. Impact of cigarette smoking on the gastrointestinal tract inflammation: Opposing effects in Crohn’s disease and ulcerative colitis. Front. Immunol., 2018, 9, 74. doi: 10.3389/fimmu.2018.00074 PMID: 29441064
  11. Pedersen, N.; Duricova, D.; Elkjaer, M.; Gamborg, M.; Munkholm, P.; Jess, T. Risk of extra-intestinal cancer in inflammatory bowel disease: Meta-analysis of population-based cohort studies. Am. J. Gastroenterol., 2010, 105(7), 1480-1487. doi: 10.1038/ajg.2009.760 PMID: 20332773
  12. Gakis, G. The role of inflammation in bladder cancer. Adv. Exp. Med. Biol., 2014, 816, 183-196. doi: 10.1007/978-3-0348-0837-8_8 PMID: 24818724
  13. Hsiao, Y.W.; Lu, T.P. Text-mining in cancer research may help identify effective treatments. Transl. Lung Cancer Res., 2019, 8(Suppl. 4), S460-S463. doi: 10.21037/tlcr.2019.12.20 PMID: 32038938
  14. Zhang, N.; Xu, W.; Wang, S.; Qiao, Y.; Zhang, X. Computational drug discovery in chemotherapy-induced alopecia via text mining and biomedical databases. Clin. Ther., 2019, 41(5), 972-980.e8. doi: 10.1016/j.clinthera.2019.04.003 PMID: 31030996
  15. Kirk, J.; Shah, N.; Noll, B.; Stevens, C.B.; Lawler, M.; Mougeot, F.B.; Mougeot, J.L.C. Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy. Support. Care Cancer, 2018, 26(8), 2695-2705. doi: 10.1007/s00520-018-4096-2 PMID: 29476419
  16. Wang, J.H.; Zhao, L.F.; Wang, H.F.; Wen, Y.T.; Jiang, K.K.; Mao, X.M.; Zhou, Z.Y.; Yao, K.T.; Geng, Q.S.; Guo, D.; Huang, Z.X. GenCLiP 3: Mining human genes’ functions and regulatory networks from PubMed based on co-occurrences and natural language processing. Bioinformatics, 2019, btz807. doi: 10.1093/bioinformatics/btz807 PMID: 31681951
  17. Kanehisa, M.; Goto, S.; Sato, Y.; Furumichi, M.; Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res., 2012, 40(D1), D109-D114. doi: 10.1093/nar/gkr988 PMID: 22080510
  18. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res., 2021, 49(D1), D605-D612. doi: 10.1093/nar/gkaa1074 PMID: 33237311
  19. Tang, Z.; Li, C.; Kang, B.; Gao, G.; Li, C.; Zhang, Z. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res., 2017, 45(W1), W98-W102. doi: 10.1093/nar/gkx247 PMID: 28407145
  20. Sulochana, S.P.; Syed, M.; Chandrasekar, D.V.; Mullangi, R.; Srinivas, N.R. Clinical drug-drug pharmacokinetic interaction potential of sucralfate with other drugs: Review and perspectives. Eur. J. Drug Metab. Pharmacokinet., 2016, 41(5), 469-503. doi: 10.1007/s13318-016-0335-4 PMID: 27086359
  21. Mossanen, M. The epidemiology of bladder cancer. Hematol. Oncol. Clin. North Am., 2021, 35(3), 445-455. doi: 10.1016/j.hoc.2021.02.001 PMID: 33958144
  22. Kappelman, M.D.; Farkas, D.K.; Long, M.D.; Erichsen, R.; Sandler, R.S.; Sørensen, H.T.; Baron, J.A. Risk of cancer in patients with inflammatory bowel diseases: A nationwide population-based cohort study with 30 years of follow-up evaluation. Clin. Gastroenterol. Hepatol., 2014, 12(2), 265-273.e1. doi: 10.1016/j.cgh.2013.03.034 PMID: 23602821
  23. De Marzo, A.M.; Platz, E.A.; Sutcliffe, S.; Xu, J.; Grönberg, H.; Drake, C.G.; Nakai, Y.; Isaacs, W.B.; Nelson, W.G. Inflammation in prostate carcinogenesis. Nat. Rev. Cancer, 2007, 7(4), 256-269. doi: 10.1038/nrc2090 PMID: 17384581
  24. Madanchi, M.; Zeitz, J.; Barthel, C.; Samaras, P.; Scharl, S.; Sulz, M.C.; Biedermann, L.; Frei, P.; Vavricka, S.R.; Rogler, G.; Scharl, M. Malignancies in patients with inflammatory bowel disease: A single-centre experience. Digestion, 2016, 94(1), 1-8. doi: 10.1159/000447259 PMID: 27318857
  25. Janssens, R.; Struyf, S.; Proost, P. The unique structural and functional features of CXCL12. Cell. Mol. Immunol., 2018, 15(4), 299-311. doi: 10.1038/cmi.2017.107 PMID: 29082918
  26. Nazari, A.; Khorramdelazad, H.; Hassanshahi, G. Biological/pathological functions of the CXCL12/CXCR4/CXCR7 axes in the pathogenesis of bladder cancer. Int. J. Clin. Oncol., 2017, 22(6), 991-1000. doi: 10.1007/s10147-017-1187-x PMID: 29022185
  27. Song, Y.; Jin, D.; Chen, J.; Luo, Z.; Chen, G.; Yang, Y.; Liu, X. Identification of an immune-related long non-coding RNA signature and nomogram as prognostic target for muscle-invasive bladder cancer. Aging, 2020, 12(12), 12051-12073. doi: 10.18632/aging.103369 PMID: 32579540
  28. Zhao, X.; Tang, Y.; Ren, H.; Lei, Y. Identification of prognosis-related genes in bladder cancer microenvironment across TCGA database. BioMed Res. Int., 2020, 2020, 1-13. doi: 10.1155/2020/9143695 PMID: 33204728
  29. Alessi, P.; Leali, D.; Camozzi, M.; Cantelmo, A.; Albini, A.; Presta, M. Anti-FGF2 approaches as a strategy to compensate resistance to anti-VEGF therapy: Long-pentraxin 3 as a novel antiangiogenic FGF2-antagonist. Eur. Cytokine Netw., 2009, 20(4), 225-234. doi: 10.1684/ecn.2009.0175 PMID: 20167562
  30. Youssef, R.F.; Kapur, P.; Mosbah, A.; Abol-Enein, H.; Ghoneim, M.; Lotan, Y. Role of fibroblast growth factor in squamous cell carcinoma of the bladder: Prognostic biomarker and potential therapeutic target. Urol. Oncol., 2015, 33(3), 111.e1-111.e7. doi: 10.1016/j.urolonc.2014.09.020 PMID: 25477183
  31. Zaravinos, A.; Volanis, D.; Lambrou, G.; Delakas, D.; Spandidos, D.A. Role of the angiogenic components, VEGFA, FGF2, OPN and RHOC, in urothelial cell carcinoma of the urinary bladder. Oncol. Rep., 2012, 28(4), 1159-1166. doi: 10.3892/or.2012.1948 PMID: 22895562
  32. Gao, R.; Zhang, N.; Yang, J.; Zhu, Y.; Zhang, Z.; Wang, J.; Xu, X.; Li, Z.; Liu, X.; Li, Z.; Li, J.; Kong, C.; Bi, J. Long non-coding RNA ZEB1-AS1 regulates miR-200b/FSCN1 signaling and enhances migration and invasion induced by TGF-β1 in bladder cancer cells. J. Exp. Clin. Cancer Res., 2019, 38(1), 111. doi: 10.1186/s13046-019-1102-6 PMID: 30823924
  33. Zhang, N.; Bi, X.; Zeng, Y.; Zhu, Y.; Zhang, Z.; Liu, Y.; Wang, J.; Li, X.; Bi, J.; Kong, C. TGF-β1 promotes the migration and invasion of bladder carcinoma cells by increasing fascin1 expression. Oncol. Rep., 2016, 36(2), 977-983. doi: 10.3892/or.2016.4889 PMID: 27350089
  34. Chiyomaru, T.; Enokida, H.; Tatarano, S.; Kawahara, K.; Uchida, Y.; Nishiyama, K.; Fujimura, L.; Kikkawa, N.; Seki, N.; Nakagawa, M. miR-145 and miR-133a function as tumour suppressors and directly regulate FSCN1 expression in bladder cancer. Br. J. Cancer, 2010, 102(5), 883-891. doi: 10.1038/sj.bjc.6605570 PMID: 20160723
  35. Xue, M.; Pang, H.; Li, X.; Li, H.; Pan, J.; Chen, W. Long non‐coding RNA urothelial cancer‐associated 1 promotes bladder cancer cell migration and invasion by way of the hsa‐miR‐145- ZEB 1/2- FSCN 1 pathway. Cancer Sci., 2016, 107(1), 18-27. doi: 10.1111/cas.12844 PMID: 26544536
  36. Daugherty, S.E.; Pfeiffer, R.M.; Sigurdson, A.J.; Hayes, R.B.; Leitzmann, M.; Schatzkin, A.; Hollenbeck, A.R.; Silverman, D.T. Nonsteroidal antiinflammatory drugs and bladder cancer: A pooled analysis. Am. J. Epidemiol., 2011, 173(7), 721-730. doi: 10.1093/aje/kwq437 PMID: 21367875
  37. Agrawal, U.; Kumari, N.; Vasudeva, P.; Mohanty, N.K.; Saxena, S. Overexpression of COX2 indicates poor survival in urothelial bladder cancer. Ann. Diagn. Pathol., 2018, 34, 50-55. doi: 10.1016/j.anndiagpath.2018.01.008 PMID: 29661728
  38. Wahli, W.; Michalik, L. PPARs at the crossroads of lipid signaling and inflammation. Trends Endocrinol. Metab., 2012, 23(7), 351-363. doi: 10.1016/j.tem.2012.05.001 PMID: 22704720
  39. Mondal, A.; Gandhi, A.; Fimognari, C.; Atanasov, A.G.; Bishayee, A. Alkaloids for cancer prevention and therapy: Current progress and future perspectives. Eur. J. Pharmacol., 2019, 858, 172472. doi: 10.1016/j.ejphar.2019.172472 PMID: 31228447
  40. Goonewardene, T.I.; Bozcuk, H.; Oliver, R.T.D.; Barua, J.; Nargund, V.; Philip, T.; Mair, G.; Gibbs, S. Phase 1/2 study of synchronous methotrexate, cisplatin, vincristine (MOPq10) chemotherapy and radiation for patients with locally advanced bladder cancer. Urol. Int., 2001, 67(4), 293-297. doi: 10.1159/000051006 PMID: 11741131
  41. Griffiths, G.; Hall, R.; Sylvester, R.; Raghavan, D.; Parmar, M.K. International phase III trial assessing neoadjuvant cisplatin, methotrexate, and vinblastine chemotherapy for muscle-invasive bladder cancer: Long-term results of the BA06 30894 trial. J. Clin. Oncol., 2011, 29(16), 2171-2177. doi: 10.1200/JCO.2010.32.3139 PMID: 21502557

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