Identification of Prognostic Markers and Potential Therapeutic Targets using Gene Expression Profiling and Simulation Studies in Pancreatic Cancer


Cite item

Full Text

Abstract

Background:Pancreatic ductal adenocarcinoma (PDAC) has a 5-year relative survival rate of less than 10% making it one of the most fatal cancers. A lack of early measures of prognosis, challenges in molecular targeted therapy, ineffective adjuvant chemotherapy, and strong resistance to chemotherapy cumulatively make pancreatic cancer challenging to manage

Objective:The present study aims to enhance understanding of the disease mechanism and its progression by identifying prognostic biomarkers, potential drug targets, and candidate drugs that can be used for therapy in pancreatic cancer.

Methods:Gene expression profiles from the GEO database were analyzed to identify reliable prognostic markers and potential drug targets. The disease's molecular mechanism and biological pathways were studied by investigating gene ontologies, KEGG pathways, and survival analysis to understand the strong prognostic power of key DEGs. FDA-approved anti-cancer drugs were screened through cell line databases, and docking studies were performed to identify drugs with high affinity for ARNTL2 and PIK3C2A. Molecular dynamic simulations of drug targets ARNTL2 and PIK3C2A in their native state and complex with nilotinib were carried out for 100 ns to validate their therapeutic potential in PDAC.

Results:Differentially expressed genes that are crucial regulators, including SUN1, PSMG3, PIK3C2A, SCRN1, and TRIAP1, were identified. Nilotinib as a candidate drug was screened using sensitivity analysis on CCLE and GDSC pancreatic cancer cell lines. Molecular dynamics simulations revealed the underlying mechanism of the binding of nilotinib with ARNTL2 and PIK3C2A and the dynamic perturbations. It validated nilotinib as a promising drug for pancreatic cancer.

Conclusion:This study accounts for prognostic markers, drug targets, and repurposed anti-cancer drugs to highlight their usefulness for translational research on developing novel therapies. Our results revealed potential and prospective clinical applications in drug targets ARNTL2, EGFR, and PI3KC2A for pancreatic cancer therapy.

About the authors

Samvedna Singh

School of Biotechnology, Gautam Buddha University

Email: info@benthamscience.net

Aman Kaushik

Wuxi School of Medicine, Jiangnan University

Email: info@benthamscience.net

Himanshi Gupta

School of Biotechnology, Gautam Buddha University

Email: info@benthamscience.net

Divya Jhinjharia

School of Biotechnology, Gautam Buddha University

Email: info@benthamscience.net

Shakti Sahi

School of Biotechnology, Gautam Buddha 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. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2021. CA Cancer J. Clin., 2021, 71(1), 7-33. doi: 10.3322/caac.21654 PMID: 33433946
  3. Waddell, N.; Pajic, M.; Patch, A.M.; Chang, D.K.; Kassahn, K.S.; Bailey, P.; Johns, A.L.; Miller, D.; Nones, K.; Quek, K.; Quinn, M.C.J.; Robertson, A.J.; Fadlullah, M.Z.H.; Bruxner, T.J.C.; Christ, A.N.; Harliwong, I.; Idrisoglu, S.; Manning, S.; Nourse, C.; Nourbakhsh, E.; Wani, S.; Wilson, P.J.; Markham, E.; Cloonan, N.; Anderson, M.J.; Fink, J.L.; Holmes, O.; Kazakoff, S.H.; Leonard, C.; Newell, F.; Poudel, B.; Song, S.; Taylor, D.; Waddell, N.; Wood, S.; Xu, Q.; Wu, J.; Pinese, M.; Cowley, M.J.; Lee, H.C.; Jones, M.D.; Nagrial, A.M.; Humphris, J.; Chantrill, L.A.; Chin, V.; Steinmann, A.M.; Mawson, A.; Humphrey, E.S.; Colvin, E.K.; Chou, A.; Scarlett, C.J.; Pinho, A.V.; Giry-Laterriere, M.; Rooman, I.; Samra, J.S.; Kench, J.G.; Pettitt, J.A.; Merrett, N.D.; Toon, C.; Epari, K.; Nguyen, N.Q.; Barbour, A.; Zeps, N.; Jamieson, N.B.; Graham, J.S.; Niclou, S.P.; Bjerkvig, R.; Grützmann, R.; Aust, D.; Hruban, R.H.; Maitra, A.; Iacobuzio-Donahue, C.A.; Wolfgang, C.L.; Morgan, R.A.; Lawlor, R.T.; Corbo, V.; Bassi, C.; Falconi, M.; Zamboni, G.; Tortora, G.; Tempero, M.A.; Gill, A.J.; Eshleman, J.R.; Pilarsky, C.; Scarpa, A.; Musgrove, E.A.; Pearson, J.V.; Biankin, A.V.; Grimmond, S.M. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature, 2015, 518(7540), 495-501. doi: 10.1038/nature14169 PMID: 25719666
  4. Falzone, L.; Lupo, G.; Rosa; Crimi, S.; Anfuso, C.D.; Salemi, R.; Rapisarda, E.; Libra, M.; Candido, S. Identification of novel micrornas and their diagnostic and prognostic significance in oral cancer. Cancers, 2019, 11(5), 610. doi: 10.3390/cancers11050610 PMID: 31052345
  5. Jia, D.; Li, S.; Li, D.; Xue, H.; Yang, D.; Liu, Y. Mining TCGA database for genes of prognostic value in glioblastoma microenvironment. Aging, 2018, 10(4), 592-605. doi: 10.18632/aging.101415 PMID: 29676997
  6. Pan, J.; Zhou, H.; Cooper, L.; Huang, J.; Zhu, S.; Zhao, X.; Ding, H.; Pan, Y.; Rong, L. LAYN is a prognostic biomarker and correlated with immune infiltrates in gastric and colon cancers. Front. Immunol., 2019, 10, 6. doi: 10.3389/fimmu.2019.00006 PMID: 30761122
  7. Feng, H.; Gu, Z.Y.; Li, Q.; Liu, Q.H.; Yang, X.Y.; Zhang, J.J. Identification of significant genes with poor prognosis in ovarian cancer via bioinformatical analysis. J. Ovarian Res., 2019, 12(1), 35. doi: 10.1186/s13048-019-0508-2 PMID: 31010415
  8. Clough, E.; Barrett, T. The gene expression omnibus database. In: Methods in Molecular Biology; Humana Press: New York, NY, 2016; pp. 93-110. doi: 10.1007/978-1-4939-3578-9_5
  9. Selga, E.; Oleaga, C.; Ramírez, S.; de Almagro, M.C.; Noé, V.; Ciudad, C.J. Networking of differentially expressed genes in human cancer cells resistant to methotrexate. Genome Med., 2009, 1(9), 83. doi: 10.1186/gm83 PMID: 19732436
  10. Barry, S.; Chelala, C.; Lines, K.; Sunamura, M.; Wang, A.; Marelli-Berg, F.M.; Brennan, C.; Lemoine, N.R.; Crnogorac-Jurcevic, T. S100P is a metastasis-associated gene that facilitates transendothelial migration of pancreatic cancer cells. Clin. Exp. Metastasis, 2013, 30(3), 251-264. doi: 10.1007/s10585-012-9532-y PMID: 23007696
  11. Zhang, X.; Liu, Y.; Zhang, Z.; Tan, J.; Zhang, J.; Ou, H.; Li, J.; Song, Z. Multi-omics analysis of anlotinib in pancreatic cancer and development of an anlotinib-related prognostic signature. Front. Cell Dev. Biol., 2021, 9, 649265. doi: 10.3389/fcell.2021.649265 PMID: 33748143
  12. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 2015, 43(7), e47-e47. doi: 10.1093/nar/gkv007 PMID: 25605792
  13. Gene Ontology Consortium. The Gene Ontology (GO) project in 2006. Nucleic Acids Res., 2006, 34(90001), D322-D326. doi: 10.1093/nar/gkj021 PMID: 16381878
  14. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57. doi: 10.1038/nprot.2008.211 PMID: 19131956
  15. Xie, Z.; Bailey, A.; Kuleshov, M.V.; Clarke, D.J.B.; Evangelista, J.E.; Jenkins, S.L.; Lachmann, A.; Wojciechowicz, M.L.; Kropiwnicki, E.; Jagodnik, K.M.; Jeon, M.; Ma’ayan, A. Gene set knowledge discovery with enrichr. Curr. Protoc., 2021, 1(3), e90. doi: 10.1002/cpz1.90 PMID: 33780170
  16. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 2017, 45(D1), D353-D361. doi: 10.1093/nar/gkw1092 PMID: 27899662
  17. Nagy, Á.; Lánczky, A.; Menyhárt, O.; Győrffy, B. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci. Rep., 2018, 8(1), 9227. doi: 10.1038/s41598-018-27521-y PMID: 29907753
  18. Tang, Z.; Kang, B.; Li, C.; Chen, T.; Zhang, Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res., 2019, 47(W1), W556-W560. doi: 10.1093/nar/gkz430 PMID: 31114875
  19. Barretina, J.; Caponigro, G.; Stransky, N.; Venkatesan, K.; Margolin, A.A.; Kim, S.; Wilson, C.J.; Lehár, J.; Kryukov, G.V.; Sonkin, D.; Reddy, A.; Liu, M.; Murray, L.; Berger, M.F.; Monahan, J.E.; Morais, P.; Meltzer, J.; Korejwa, A.; Jané-Valbuena, J.; Mapa, F.A.; Thibault, J.; Bric-Furlong, E.; Raman, P.; Shipway, A.; Engels, I.H.; Cheng, J.; Yu, G.K.; Yu, J.; Aspesi, P., Jr; de Silva, M.; Jagtap, K.; Jones, M.D.; Wang, L.; Hatton, C.; Palescandolo, E.; Gupta, S.; Mahan, S.; Sougnez, C.; Onofrio, R.C.; Liefeld, T.; MacConaill, L.; Winckler, W.; Reich, M.; Li, N.; Mesirov, J.P.; Gabriel, S.B.; Getz, G.; Ardlie, K.; Chan, V.; Myer, V.E.; Weber, B.L.; Porter, J.; Warmuth, M.; Finan, P.; Harris, J.L.; Meyerson, M.; Golub, T.R.; Morrissey, M.P.; Sellers, W.R.; Schlegel, R.; Garraway, L.A. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 2012, 483(7391), 603-607. doi: 10.1038/nature11003 PMID: 22460905
  20. Yang, W.; Soares, J.; Greninger, P.; Edelman, E.J.; Lightfoot, H.; Forbes, S.; Bindal, N.; Beare, D.; Smith, J.A.; Thompson, I.R.; Ramaswamy, S.; Futreal, P.A.; Haber, D.A.; Stratton, M.R.; Benes, C.; McDermott, U.; Garnett, M.J. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res., 2012, 41(D1), D955-D961. doi: 10.1093/nar/gks1111 PMID: 23180760
  21. Smirnov, P.; Safikhani, Z.; El-Hachem, N.; Wang, D.; She, A.; Olsen, C.; Freeman, M.; Selby, H.; Gendoo, D.M.A.; Grossmann, P.; Beck, A.H.; Aerts, H.J.W.L.; Lupien, M.; Goldenberg, A.; Haibe-Kains, B. PharmacoGx: An R package for analysis of large pharmacogenomic datasets. Bioinformatics, 2016, 32(8), 1244-1246. doi: 10.1093/bioinformatics/btv723 PMID: 26656004
  22. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242. doi: 10.1093/nar/28.1.235 PMID: 10592235
  23. Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem in 2021: New data content and improved web interfaces. Nucleic Acids Res., 2021, 49(D1), D1388-D1395. doi: 10.1093/nar/gkaa971 PMID: 33151290
  24. Tian, W.; Chen, C.; Lei, X.; Zhao, J.; Liang, J. CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res., 2018, 46(W1), W363-W367. doi: 10.1093/nar/gky473 PMID: 29860391
  25. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791. doi: 10.1002/jcc.21256 PMID: 19399780
  26. Abraham, M.J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J.C.; Hess, B.; Lindahl, E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX, 2015, 1-2, 19-25. doi: 10.1016/j.softx.2015.06.001
  27. Stroet, M.; Caron, B.; Visscher, K.M.; Geerke, D.P.; Malde, A.K.; Mark, A.E. Automated topology builder version 3.0: Prediction of solvation free enthalpies in water and hexane. J. Chem. Theory Comput., 2018, 14(11), 5834-5845. doi: 10.1021/acs.jctc.8b00768 PMID: 30289710
  28. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph., 1996, 14(1), 33-38, 27-28. doi: 10.1016/0263-7855(96)00018-5 PMID: 8744570
  29. The PyMOL Molecular Graphics System, Version 1.8. Schrödinger, LLC; , 2015, 1, p. 8.
  30. Kumari, R.; Kumar, R.; Lynn, A. g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. J. Chem. Inf. Model., 2014, 54(7), 1951-1962. doi: 10.1021/ci500020m PMID: 24850022
  31. Yan, H.H.; Jung, K.H.; Son, M.K.; Fang, Z.; Kim, S.J.; Ryu, Y.L.; Kim, J.; Kim, M.H.; Hong, S.S. Crizotinib exhibits antitumor activity by targeting ALK signaling not c-MET in pancreatic cancer. Oncotarget, 2014, 5(19), 9150-9168. doi: 10.18632/oncotarget.2363 PMID: 25193856
  32. Abdelgalil, A.A.; Al-Kahtani, H.M.; Al-Jenoobi, F.I. Chapter four - Erlotinib. Profiles of drug substances, Excipients and related methodology; Elsevier, 2020, 45, pp. 93-117. doi: 10.1016/bs.podrm.2019.10.004
  33. Wu, Z.; Gabrielson, A.; Hwang, J.J.; Pishvaian, M.J.; Weiner, L.M.; Zhuang, T.; Ley, L.; Marshall, J.L.; He, A.R. Phase II study of lapatinib and capecitabine in second-line treatment for metastatic pancreatic cancer. Cancer Chemother. Pharmacol., 2015, 76(6), 1309-1314. doi: 10.1007/s00280-015-2855-z PMID: 26507197
  34. Sacha, T.; Saglio, G. Nilotinib in the treatment of chronic myeloid leukemia. Future Oncol., 2019, 15(9), 953-965. doi: 10.2217/fon-2018-0468 PMID: 30547682
  35. Markham, A.; Keam, S.J. Selumetinib: First approval. Drugs, 2020, 80(9), 931-937. doi: 10.1007/s40265-020-01331-x PMID: 32504375
  36. Rascio, F.; Spadaccino, F.; Rocchetti, M.T.; Castellano, G.; Stallone, G.; Netti, G.S.; Ranieri, E. The pathogenic role of PI3K/AKT pathway in cancer onset and drug resistance: An updated review. Cancers, 2021, 13(16), 3949. doi: 10.3390/cancers13163949 PMID: 34439105
  37. Conway, J.R.W.; Herrmann, D.; Evans, T.R.J.; Morton, J.P.; Timpson, P. Combating pancreatic cancer with PI3K pathway inhibitors in the era of personalised medicine. Gut, 2019, 68(4), 742-758. doi: 10.1136/gutjnl-2018-316822 PMID: 30396902
  38. Falasca, M.; Hamilton, J.R.; Selvadurai, M.; Sundaram, K.; Adamska, A.; Thompson, P.E.; Class, I.I. Class II phosphoinositide 3-kinases as novel drug targets. J. Med. Chem., 2017, 60(1), 47-65. doi: 10.1021/acs.jmedchem.6b00963 PMID: 27644332
  39. Gulluni, F.; Martini, M.; De Santis, M.C.; Campa, C.C.; Ghigo, A.; Margaria, J.P.; Ciraolo, E.; Franco, I.; Ala, U.; Annaratone, L.; Disalvatore, D.; Bertalot, G.; viale, G.; Noatynska, A.; Compagno, M.; Sigismund, S.; Montemurro, F.; Thelen, M.; Fan, F.; Meraldi, P.; Marchiò, C.; Pece, S.; Sapino, A.; Chiarle, R.; Di Fiore, P.P.; Hirsch, E. Mitotic spindle assembly and genomic stability in breast cancer require PI3K-C2α scaffolding function. Cancer Cell, 2017, 32(4), 444-459.e7. doi: 10.1016/j.ccell.2017.09.002 PMID: 29017056
  40. Payne, S.N.; Maher, M.E.; Tran, N.H.; Van De Hey, D.R.; Foley, T.M.; Yueh, A.E.; Leystra, A.A.; Pasch, C.A.; Jeffrey, J.J.; Clipson, L.; Matkowskyj, K.A.; Deming, D.A. PIK3CA mutations can initiate pancreatic tumorigenesis and are targetable with PI3K inhibitors. Oncogenesis, 2015, 4(10), e169-e169. doi: 10.1038/oncsis.2015.28 PMID: 26436951
  41. Mehra, S.; Deshpande, N.; Nagathihalli, N. Targeting PI3K pathway in pancreatic ductal adenocarcinoma: Rationale and progress. Cancers, 2021, 13(17), 4434. doi: 10.3390/cancers13174434 PMID: 34503244
  42. Mortazavi, M.; Moosavi, F.; Martini, M.; Giovannetti, E.; Firuzi, O. Prospects of targeting PI3K/AKT/mTOR pathway in pancreatic cancer. Crit. Rev. Oncol. Hematol., 2022, 176, 103749. doi: 10.1016/j.critrevonc.2022.103749 PMID: 35728737
  43. Wang, Z.; Liu, T.; Xue, W.; Fang, Y.; Chen, X.; Xu, L.; Zhang, L.; Guan, K.; Pan, J.; Zheng, L.; Qin, G.; Wang, T. ARNTL2 promotes pancreatic ductal adenocarcinoma progression through TGF/BETA pathway and is regulated by miR-26a-5p. Cell Death Dis., 2020, 11(8), 692. doi: 10.1038/s41419-020-02839-6 PMID: 32826856
  44. Wang, S.; Ma, X.; Ying, Y.; Sun, J.; Yang, Z.; Li, J.; Jin, K.; Wang, X.; Xie, B.; Zheng, X.; Liu, B.; Xie, L. Upregulation of ARNTL2 is associated with poor survival and immune infiltration in clear cell renal cell carcinoma. Cancer Cell Int., 2021, 21(1), 341. doi: 10.1186/s12935-021-02046-z PMID: 34217271
  45. Grapa, C.M.; Mocan, T.; Gonciar, D.; Zdrehus, C.; Mosteanu, O.; Pop, T.; Mocan, L. Epidermal growth factor receptor and its role in pancreatic cancer treatment mediated by nanoparticles. Int. J. Nanomed., 2019, 14, 9693-9706. doi: 10.2147/IJN.S226628 PMID: 31849462
  46. Chiramel, J.; Backen, A.; Pihlak, R.; Lamarca, A.; Frizziero, M.; Tariq, N.A.; Hubner, R.; Valle, J.; Amir, E.; McNamara, M. Targeting the epidermal growth factor receptor in addition to chemotherapy in patients with advanced pancreatic cancer: A systematic review and meta-analysis. Int. J. Mol. Sci., 2017, 18(5), 909. doi: 10.3390/ijms18050909 PMID: 28445400
  47. Qing, L.; Qing, W. Development of epidermal growth factor receptor targeted therapy in pancreatic cancer. Minerva Chir., 2018, 73(5), 488-496. doi: 10.23736/S0026-4733.18.07512-0 PMID: 29397631
  48. Mahajan, U.M.; Li, Q.; Alnatsha, A.; Maas, J.; Orth, M.; Maier, S.H.; Peterhansl, J.; Regel, I.; Sendler, M.; Wagh, P.R.; Mishra, N.; Xue, Y.; Allawadhi, P.; Beyer, G.; Kühn, J.P.; Marshall, T.; Appel, B.; Lämmerhirt, F.; Belka, C.; Müller, S.; Weiss, F.U.; Lauber, K.; Lerch, M.M.; Mayerle, J. Tumor-specific delivery of 5-fluorouracil–incorporated epidermal growth factor receptor-targeted aptamers as an efficient treatment in pancreatic ductal adenocarcinoma models. Gastroenterology, 2021, 161(3), 996-1010.e1. doi: 10.1053/j.gastro.2021.05.055 PMID: 34097885
  49. Oliveira-Cunha, M.; Newman, W.G.; Siriwardena, A.K. Epidermal growth factor receptor in pancreatic cancer. Cancers, 2011, 3(2), 1513-1526. doi: 10.3390/cancers3021513 PMID: 24212772
  50. Uribe, M.L.; Marrocco, I.; Yarden, Y. EGFR in cancer: Signaling mechanisms, drugs, and acquired resistance. Cancers, 2021, 13(11), 2748. doi: 10.3390/cancers13112748 PMID: 34206026
  51. Kakarala, K.K.; Jamil, K. Identification of novel allosteric binding sites and multi-targeted allosteric inhibitors of receptor and non-receptor tyrosine kinases using a computational approach. J. Biomol. Struct. Dyn., 2022, 40(15), 6889-6909. doi: 10.1080/07391102.2021.1891140 PMID: 33682622
  52. Yoshizawa, T.; Uchibori, K.; Araki, M.; Matsumoto, S.; Ma, B.; Kanada, R.; Seto, Y.; Oh-hara, T.; Koike, S.; Ariyasu, R.; Kitazono, S.; Ninomiya, H.; Takeuchi, K.; Yanagitani, N.; Takagi, S.; Kishi, K.; Fujita, N.; Okuno, Y.; Nishio, M.; Katayama, R. Microsecond-timescale MD simulation of EGFR minor mutation predicts the structural flexibility of EGFR kinase core that reflects EGFR inhibitor sensitivity. NPJ Precis. Oncol., 2021, 5(1), 32. doi: 10.1038/s41698-021-00170-7 PMID: 33863983
  53. Todsaporn, D.; Mahalapbutr, P.; Poo-arporn, R.P.; Choowongkomon, K.; Rungrotmongkol, T. Structural dynamics and kinase inhibitory activity of three generations of tyrosine kinase inhibitors against wild-type, L858R/T790M, and L858R/T790M/C797S forms of EGFR. Comput. Biol. Med., 2022, 147, 105787. doi: 10.1016/j.compbiomed.2022.105787 PMID: 35803080
  54. Li, D.D.; Wu, T.T.; Yu, P.; Wang, Z.Z.; Xiao, W.; Jiang, Y.; Zhao, L.G. Molecular dynamics analysis of binding sites of epidermal growth factor receptor kinase inhibitors. ACS Omega, 2020, 5(26), 16307-16314. doi: 10.1021/acsomega.0c02183 PMID: 32656454
  55. Chen, K.E.; Tillu, V.A.; Chandra, M.; Collins, B.M. Molecular basis for membrane recruitment by the PX and C2 domains of class II phosphoinositide 3-kinase-C2α. Structure, 2018, 26(12), 1612-1625.e4. doi: 10.1016/j.str.2018.08.010 PMID: 30293811
  56. Moberly, J.G.; Bernards, M.T.; Waynant, K.V. Key features and updates for Origin 2018. J. Cheminform., 2018, 10(1), 5. doi: 10.1186/s13321-018-0259-x PMID: 29427195
  57. Anuar, N.F.S.K.; Wahab, R.A.; Huyop, F.; Amran, S.I.; Hamid, A.A.A.; Halim, K.B.A.; Hood, M.H.M. Molecular docking and molecular dynamics simulations of a mutant Acinetobacter haemolyticus alkaline-stable lipase against tributyrin. J. Biomol. Struct. Dyn., 2021, 39(6), 2079-2091. doi: 10.1080/07391102.2020.1743364 PMID: 32174260
  58. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin., 2022, 72(1), 7-33. doi: 10.3322/caac.21708 PMID: 35020204
  59. Reichardt, P.; Montemurro, M. Clinical experience to date with nilotinib in gastrointestinal stromal tumors. Semin. Oncol., 2011, 38(Suppl. 1), S20-S27. doi: 10.1053/j.seminoncol.2011.01.015 PMID: 21419932
  60. Prerna, K.; Dubey, V.K. Repurposing of FDA-approved drugs as autophagy inhibitors in tumor cells. J. Biomol. Struct. Dyn., 2022, 40(13), 5815-5826. doi: 10.1080/07391102.2021.1873862 PMID: 33467992
  61. Meng, L.; Zhao, P.; Hu, Z.; Ma, W.; Niu, Y.; Su, J.; Zhang, Y. Nilotinib, a tyrosine kinase inhibitor, suppresses the cell growth and triggers autophagy in papillary thyroid cancer. Anticancer. Agents Med. Chem., 2022, 22(3), 596-602. doi: 10.2174/1871520621666210402110331 PMID: 33797387
  62. Wang, S.; Xie, Y.; Bao, A.; Li, J.; Ye, T.; Yang, C.; Yu, S. Nilotinib, a Discoidin domain receptor 1 (DDR1) inhibitor, induces apoptosis and inhibits migration in breast cancer. Neoplasma, 2021, 68(5), 972-982. doi: 10.4149/neo_2021_201126N1282 PMID: 34263649
  63. Weigel, M.T.; Rath, K.; Alkatout, I.; Wenners, A.S.; Schem, C.; Maass, N.; Jonat, W.; Mundhenke, C. Nilotinib in combination with carboplatin and paclitaxel is a candidate for ovarian cancer treatment. Oncology, 2014, 87(4), 232-245. doi: 10.1159/000363656 PMID: 25116401
  64. Bao, S.; Zheng, H.; Ye, J.; Huang, H.; Zhou, B.; Yao, Q.; Lin, G.; Zhang, H.; Kou, L.; Chen, R. Dual targeting EGFR and STAT3 with erlotinib and alantolactone co-loaded PLGA nanoparticles for pancreatic cancer treatment. Front. Pharmacol., 2021, 12, 625084. doi: 10.3389/fphar.2021.625084 PMID: 33815107
  65. Kenney, C.; Kunst, T.; Webb, S.; Christina, D., Jr; Arrowood, C.; Steinberg, S.M.; Mettu, N.B.; Kim, E.J.; Rudloff, U. Phase II study of selumetinib, an orally active inhibitor of MEK1 and MEK2 kinases, in KRASG12R-mutant pancreatic ductal adenocarcinoma. Invest. New Drugs, 2021, 39(3), 821-828. doi: 10.1007/s10637-020-01044-8 PMID: 33405090
  66. Suda, T.; Tsunoda, T.; Uchida, N.; Watanabe, T.; Hasegawa, S.; Satoh, S.; Ohgi, S.; Furukawa, Y.; Nakamura, Y.; Tahara, H. Identification of secernin 1 as a novel immunotherapy target for gastric cancer using the expression profiles of cDNA microarray. Cancer Sci., 2006, 97(5), 411-419. doi: 10.1111/j.1349-7006.2006.00194.x PMID: 16630140
  67. Miyoshi, N.; Ishii, H.; Mimori, K.; Sekimoto, M.; Doki, Y.; Mori, M. SCRN1 is a novel marker for prognosis in colorectal cancer. J. Surg. Oncol., 2010, 101(2), 156-159. doi: 10.1002/jso.21459 PMID: 20039278
  68. Geisler, C.; Gaisa, N.T.; Pfister, D.; Fuessel, S.; Kristiansen, G.; Braunschweig, T.; Gostek, S.; Beine, B.; Diehl, H.C.; Jackson, A.M.; Borchers, C.H.; Heidenreich, A.; Meyer, H.E.; Knüchel, R.; Henkel, C. Identification and validation of potential new biomarkers for prostate cancer diagnosis and prognosis using 2D-DIGE and MS. BioMed Res. Int., 2015, 2015, 1-23. doi: 10.1155/2015/454256 PMID: 25667921
  69. Li, L.; Yang, K.; Ye, F.; Xu, Y.; Cao, L.; Sheng, J. Abnormal expression of TRIAP1 and its role in gestational diabetes mellitus related pancreatic β cells. Exp. Ther. Med., 2021, 21(3), 187. doi: 10.3892/etm.2021.9618 PMID: 33488796
  70. Qian, W.; Chen, K.; Qin, T.; Xiao, Y.; Li, J.; Yue, Y.; Zhou, C.; Ma, J.; Duan, W.; Lei, J.; Han, L.; Li, L.; Shen, X.; Wu, Z.; Ma, Q.; Wang, Z. The EGFR-HSF1 axis accelerates the tumorigenesis of pancreatic cancer. J. Exp. Clin. Cancer Res., 2021, 40(1), 25. doi: 10.1186/s13046-020-01823-4 PMID: 33422093
  71. Troiani, T.; Martinelli, E.; Capasso, A.; Morgillo, F.; Orditura, M.; De Vita, F.; Ciardiello, F. Targeting EGFR in pancreatic cancer treatment. Curr. Drug Targets, 2012, 13(6), 802-810. doi: 10.2174/138945012800564158 PMID: 22458527
  72. Amelia, T.; Kartasasmita, R.E.; Ohwada, T.; Tjahjono, D.H. Structural insight and development of EGFR tyrosine kinase inhibitors. Molecules, 2022, 27(3), 819. doi: 10.3390/molecules27030819 PMID: 35164092
  73. Margaria, J.P.; Ratto, E.; Gozzelino, L.; Li, H.; Hirsch, E. Class II PI3Ks at the intersection between signal transduction and membrane trafficking. Biomolecules, 2019, 9(3), 104. doi: 10.3390/biom9030104 PMID: 30884740
  74. Lo, W.T.; Zhang, Y.; Vadas, O.; Roske, Y.; Gulluni, F.; De Santis, M.C.; Zagar, A.V.; Stephanowitz, H.; Hirsch, E.; Liu, F.; Daumke, O.; Kudryashev, M.; Haucke, V. Structural basis of phosphatidylinositol 3-kinase C2α function. Nat. Struct. Mol. Biol., 2022, 29(3), 218-228. doi: 10.1038/s41594-022-00730-w PMID: 35256802
  75. Awasthi, N.; Kronenberger, D.; Stefaniak, A.; Hassan, M.S.; von Holzen, U.; Schwarz, M.A.; Schwarz, R.E. Dual inhibition of the PI3K and MAPK pathways enhances nab-paclitaxel/gemcitabine chemotherapy response in preclinical models of pancreatic cancer. Cancer Lett., 2019, 459, 41-49. doi: 10.1016/j.canlet.2019.05.037 PMID: 31153980
  76. Ciuffreda, L.; Del Curatolo, A.; Falcone, I.; Conciatori, F.; Bazzichetto, C.; Cognetti, F.; Corbo, V.; Scarpa, A.; Milella, M. Lack of growth inhibitory synergism with combined MAPK/PI3K inhibition in preclinical models of pancreatic cancer. Ann. Oncol., 2017, 28(11), 2896-2898. doi: 10.1093/annonc/mdx335 PMID: 28666315
  77. Lu, M.; Huang, L.; Tang, Y.; Sun, T.; Li, J.; Xiao, S. ARNTL2 knockdown suppressed the invasion and migration of colon carcinoma: Decreased SMOC2-EMT expression through inactivation of PI3K/AKT pathway. Am. J. Transl. Res., 2020, 12(4), 1293-1308.
  78. Cash, E.; Sephton, S.; Woolley, C.; Elbehi, A.M.; R i, A.; Ekine-Afolabi, B.; Kok, V.C. The role of the circadian clock in cancer hallmark acquisition and immune-based cancer therapeutics. J. Exp. Clin. Cancer Res., 2021, 40(1), 119. doi: 10.1186/s13046-021-01919-5 PMID: 33794967

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers