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Current Computer-Aided Drug Design

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ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

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Research Article

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

Author(s): Samvedna Singh, Aman Chandra Kaushik, Himanshi Gupta, Divya Jhinjharia and Shakti Sahi*

Volume 20, Issue 6, 2024

Published on: 12 October, 2023

Page: [955 - 973] Pages: 19

DOI: 10.2174/1573409920666230914100826

Price: $65

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.

Keywords: Pancreatic cancer, potential drug target, differential gene expression, anti-cancer drugs, nilotinib, molecular dynamic simulations.

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[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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/10.2217/fon-2018-0468] [PMID: 30547682]
[35]
Markham, A.; Keam, S.J. Selumetinib: First approval. Drugs, 2020, 80(9), 931-937.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/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.
[http://dx.doi.org/10.1186/s13046-021-01919-5] [PMID: 33794967]

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