{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T21:49:06Z","timestamp":1772315346573,"version":"3.50.1"},"reference-count":92,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2022]]},"abstract":"<jats:p xml:lang=\"fr\">&lt;abstract&gt;\n\t\t\t&lt;p&gt;This study mainly used The Cancer Genome Atlas (TCGA) RNA sequencing dataset to screen prognostic snoRNAs of acute myeloid leukemia (AML), and used for the construction of prognostic snoRNAs signature for AML. A total of 130 AML patients with RNA sequencing dataset were used for prognostic snoRNAs screenning. SnoRNAs co-expressed genes and differentially expressed genes (DEGs) were used for functional annotation, as well as gene set enrichment analysis (GSEA). Connectivity Map (CMap) also used for potential targeted drugs screening. Through genome-wide screening, we identified 30 snoRNAs that were significantly associated with the prognosis of AML. Then we used the step function to screen a prognostic signature composed of 14 snoRNAs (SNORD72, SNORD38, U3, SNORA73B, SNORD79, SNORA73, SNORD12B, SNORA74, SNORD116-12, SNORA65, SNORA14, snoU13, SNORA75, SNORA31), which can significantly divide AML patients into high- and low-risk groups. Through GSEA, snoRNAs co-expressed genes and DEGs functional enrichment analysis, we screened a large number of potential functional mechanisms of this prognostic signature in AML, such as phosphatidylinositol 3-kinase-Akt, Wnt, epithelial to mesenchymal transition, T cell receptors, NF-kappa B, mTOR and other classic cancer-related signaling pathways. In the subsequent targeted drug screening using CMap, we also identified six drugs that can be used for AML targeted therapy, they were alimemazine, MG-262, fluoxetine, quipazine, naltrexone and oxybenzone. In conclusion, our current study was constructed an AML prognostic signature based on the 14 prognostic snoRNAs, which may serve as a novel prognostic biomarker for AML.&lt;\/p&gt;\n\t\t&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2022112","type":"journal-article","created":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T10:25:45Z","timestamp":1641551145000},"page":"2424-2452","source":"Crossref","is-referenced-by-count":12,"title":["Integrative genomic analysis of a novel small nucleolar RNAs prognostic signature in patients with acute myelocytic leukemia"],"prefix":"10.3934","volume":"19","author":[{"given":"Rui","family":"Huang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Department of Hematology, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiwen","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaochuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2022112-1","doi-asserted-by":"publisher","unstructured":"A. Khwaja, M. Bjorkholm, R. E. Gale, R. L. Levine, C. T. Jordan, G. Ehninger, et al., Acute myeloid leukaemia, <i>Nat. Rev. Dis. Primers<\/i>, <b>2<\/b> (2016), 16010. https:\/\/doi.org\/10.1038\/nrdp.2016.10","DOI":"10.1038\/nrdp.2016.10"},{"key":"key-10.3934\/mbe.2022112-2","doi-asserted-by":"publisher","unstructured":"E. Estey, H. Dohner, Acute myeloid leukaemia, <i>Lancet<\/i>, <b>368<\/b> (2006), 1894-1907. https:\/\/doi.org\/10.1016\/S0140-6736(06)69780-8","DOI":"10.1016\/S0140-6736(06)69780-8"},{"key":"key-10.3934\/mbe.2022112-3","doi-asserted-by":"publisher","unstructured":"L. Bullinger, K. Dohner, E. Bair, S. Frohling, R. F. Schlenk, R. Tibshirani, et al., Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia, <i>N. Eng. J. Med.<\/i>, <b>350<\/b> (2004), 1605-1616. https:\/\/doi.org\/10.1056\/NEJMoa031046","DOI":"10.1056\/NEJMoa031046"},{"key":"key-10.3934\/mbe.2022112-4","doi-asserted-by":"publisher","unstructured":"E. Papaemmanuil, M. Gerstung, L. Bullinger, V. I. Gaidzik, P. Paschka, N. D. Roberts, et al., Genomic classification and prognosis in acute myeloid leukemia, <i>N. Eng. J. Med.<\/i>, <b>374<\/b> (2016), 2209-2221. https:\/\/doi.org\/10.1056\/NEJMoa1516192","DOI":"10.1056\/NEJMoa1516192"},{"key":"key-10.3934\/mbe.2022112-5","doi-asserted-by":"publisher","unstructured":"C. C. Coombs, M. S. Tallman, R. L. Levine, Molecular therapy for acute myeloid leukaemia, <i>Nat. Rev. Clin. Oncol.<\/i>, <b>13<\/b> (2016), 305-318. https:\/\/doi.org\/10.1038\/nrclinonc.2015.210","DOI":"10.1038\/nrclinonc.2015.210"},{"key":"key-10.3934\/mbe.2022112-6","doi-asserted-by":"publisher","unstructured":"J. W. Tyner, C. E. Tognon, D. Bottomly, B. Wilmot, S. E. Kurtz, S. L. Savage, et al., Functional genomic landscape of acute myeloid leukaemia, <i>Nature<\/i>, <b>562<\/b> (2018), 526-531. https:\/\/doi.org\/10.1038\/s41586-018-0623-z","DOI":"10.1038\/s41586-018-0623-z"},{"key":"key-10.3934\/mbe.2022112-7","doi-asserted-by":"publisher","unstructured":"S. Abelson, G. Collord, S. W. K. Ng, O. Weissbrod, N. M. Cohen, E. Niemeyer, et al., Prediction of acute myeloid leukaemia risk in healthy individuals, <i>Nature<\/i>, <b>559<\/b> (2018), 400-404. https:\/\/doi.org\/10.1038\/s41586-018-0317-6","DOI":"10.1038\/s41586-018-0317-6"},{"key":"key-10.3934\/mbe.2022112-8","doi-asserted-by":"publisher","unstructured":"S. C. Meyer, R. L. Levine, Translational implications of somatic genomics in acute myeloid leukaemia, <i>Lancet Oncol.<\/i>, <b>15<\/b> (2014), e382-394. https:\/\/doi.org\/10.1016\/S1470-2045(14)70008-7","DOI":"10.1016\/S1470-2045(14)70008-7"},{"key":"key-10.3934\/mbe.2022112-9","doi-asserted-by":"publisher","unstructured":"T. Bratkovic, J. Bozic, B. Rogelj, Functional diversity of small nucleolar RNAs, <i>Nucleic Acids Res.<\/i>, <b>48<\/b> (2020), 1627-1651. https:\/\/doi.org\/10.1093\/nar\/gkz1140","DOI":"10.1093\/nar\/gkz1140"},{"key":"key-10.3934\/mbe.2022112-10","doi-asserted-by":"publisher","unstructured":"J. Ni, A. L. Tien, M. J. Fournier, Small nucleolar RNAs direct site-specific synthesis of pseudouridine in ribosomal RNA, <i>Cell<\/i>, <b>89<\/b> (1997), 565-573. https:\/\/doi.org\/10.1016\/s0092-8674(00)80238-x","DOI":"10.1016\/s0092-8674(00)80238-x"},{"key":"key-10.3934\/mbe.2022112-11","doi-asserted-by":"publisher","unstructured":"V. Chikne, K. S. Rajan, M. Shalev-Benami, K. Decker, S. Cohen-Chalamish, H. Madmoni, et al., Small nucleolar RNAs controlling rRNA processing in Trypanosoma brucei, <i>Nucleic Acids Res.<\/i>, <b>47<\/b> (2019), 2609-2629. https:\/\/doi.org\/10.1093\/nar\/gky1287","DOI":"10.1093\/nar\/gky1287"},{"key":"key-10.3934\/mbe.2022112-12","doi-asserted-by":"publisher","unstructured":"L. Xing, X. Zhang, X. Zhang, D. Tong, Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer, <i>J. Cell Phys.<\/i>, <b>235<\/b> (2020), 8071-8084. https:\/\/doi.org\/10.1002\/jcp.29462","DOI":"10.1002\/jcp.29462"},{"key":"key-10.3934\/mbe.2022112-13","doi-asserted-by":"publisher","unstructured":"Y. Zhao, Y. Yan, R. Ma, X. Lv, L. Zhang, J. Wang, et al., Expression signature of six-snoRNA serves as novel non-invasive biomarker for diagnosis and prognosis prediction of renal clear cell carcinoma, <i>J. Cell Mol. Med.<\/i>, <b>24<\/b> (2020), 2215-2228. https:\/\/doi.org\/10.1111\/jcmm.14886","DOI":"10.1111\/jcmm.14886"},{"key":"key-10.3934\/mbe.2022112-14","doi-asserted-by":"crossref","unstructured":"L. Huang, X. Z. Liang, Y. Deng, Y. B. Liang, X. Zhu, X. Y. Liang, et al., Prognostic value of small nucleolar RNAs (snoRNAs) for colon adenocarcinoma based on RNA sequencing data, <i>Pathol. Res. Pract.<\/i>, <b>216<\/b> (2020), 152937. https: \/\/doi.org\/10.1016\/j.prp.2020.152937","DOI":"10.1016\/j.prp.2020.152937"},{"key":"key-10.3934\/mbe.2022112-15","doi-asserted-by":"publisher","unstructured":"J. Gong, Y. Li, C. J. Liu, Y. Xiang, C. Li, Y. Ye, et al., A pan-cancer analysis of the expression and clinical relevance of small nucleolar RNAs in human cancer, <i>Cell Rep.<\/i>, <b>21<\/b> (2017), 1968-1981. https:\/\/doi.org\/10.1016\/j.celrep.2017.10.070","DOI":"10.1016\/j.celrep.2017.10.070"},{"key":"key-10.3934\/mbe.2022112-16","doi-asserted-by":"crossref","unstructured":"The Cancer Genome Atlas Research Network, Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia, <i>N. Eng. J. Med.<\/i>, <b>368<\/b> (2013), 2059-2074. <a href=\"https:\/\/doi.org\/10.1056\/NEJMoa1301689\" target=\"_blank\">https:\/\/doi.org\/10.1056\/NEJMoa1301689<\/a>","DOI":"10.1056\/NEJMoa1301689"},{"key":"key-10.3934\/mbe.2022112-17","doi-asserted-by":"publisher","unstructured":"M. D. Robinson, D. J. McCarthy, G. K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, <i>Bioinformatics<\/i>, <b>26<\/b> (2010), 139-140. https:\/\/doi.org\/10.1093\/bioinformatics\/btp616","DOI":"10.1093\/bioinformatics\/btp616"},{"key":"key-10.3934\/mbe.2022112-18","doi-asserted-by":"publisher","unstructured":"R. Huang, X. Liao, Q. Li, Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia, <i>Oncol. Targets Ther.<\/i>, <b>10<\/b> (2017), 5243-5254. https:\/\/doi.org\/10.2147\/OTT.S147717","DOI":"10.2147\/OTT.S147717"},{"key":"key-10.3934\/mbe.2022112-19","doi-asserted-by":"publisher","unstructured":"X. Liao, X. Wang, K. Huang, C. Yang, T. Yu, C. Han, et al., Genome-scale analysis to identify prognostic microRNA biomarkers in patients with early stage pancreatic ductal adenocarcinoma after pancreaticoduodenectomy, <i>Cancer Manage. Res.<\/i>, <b>10<\/b> (2018), 2537-2551. https:\/\/doi.org\/10.2147\/CMAR.S168351","DOI":"10.2147\/CMAR.S168351"},{"key":"key-10.3934\/mbe.2022112-20","doi-asserted-by":"publisher","unstructured":"X. Liao, X. Wang, K. Huang, C. Han, J. Deng, T. Yu, et al., Integrated analysis of competing endogenous RNA network revealing potential prognostic biomarkers of hepatocellular carcinoma, <i>J. Cancer<\/i>, <b>10<\/b> (2019), 3267-3283. https:\/\/doi.org\/10.7150\/jca.29986","DOI":"10.7150\/jca.29986"},{"key":"key-10.3934\/mbe.2022112-21","doi-asserted-by":"publisher","unstructured":"W. H. Da, B. T. Sherman, R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, <i>Nat. Protoc.<\/i>, <b>4<\/b> (2009), 44-57. https:\/\/doi.org\/10.1038\/nprot.2008.211","DOI":"10.1038\/nprot.2008.211"},{"key":"key-10.3934\/mbe.2022112-22","doi-asserted-by":"publisher","unstructured":"V. K. Mootha, C. M. Lindgren, K. F. Eriksson, A. Subramanian, S. Sihag, J. Lehar, et al., PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, <i>Nat. Genet.<\/i>, <b>34<\/b> (2003), 267-273. https:\/\/doi.org\/10.1038\/ng1180","DOI":"10.1038\/ng1180"},{"key":"key-10.3934\/mbe.2022112-23","doi-asserted-by":"publisher","unstructured":"A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, <i>Proc. Nat. Acad. Sci. U. S. A.<\/i>, <b>102<\/b> (2005), 15545-15550. https:\/\/doi.org\/10.1073\/pnas.0506580102","DOI":"10.1073\/pnas.0506580102"},{"key":"key-10.3934\/mbe.2022112-24","doi-asserted-by":"publisher","unstructured":"A. Liberzon, C. Birger, H. Thorvaldsdottir, M. Ghandi, J. P. Mesirov, P. Tamayo, The molecular signatures database (MSigDB) hallmark gene set collection, <i>Cell Syst.<\/i>, <b>1<\/b> (2015), 417-425. https:\/\/doi.org\/10.1016\/j.cels.2015.12.004","DOI":"10.1016\/j.cels.2015.12.004"},{"key":"key-10.3934\/mbe.2022112-25","doi-asserted-by":"publisher","unstructured":"A. Liberzon, A. Subramanian, R. Pinchback, H. Thorvaldsdottir, P. Tamayo, J. P. Mesirov, Molecular signatures database (MSigDB) 3.0, <i>Bioinformatics<\/i>, <b>27<\/b> (2011), 1739-1740. https:\/\/doi.org\/10.1093\/bioinformatics\/btr260","DOI":"10.1093\/bioinformatics\/btr260"},{"key":"key-10.3934\/mbe.2022112-26","doi-asserted-by":"publisher","unstructured":"J. Lamb, The connectivity map: a new tool for biomedical research, <i>Nat. Rev. Cancer<\/i>, <b>7<\/b> (2007), 54-60. https:\/\/doi.org\/10.1038\/nrc2044","DOI":"10.1038\/nrc2044"},{"key":"key-10.3934\/mbe.2022112-27","doi-asserted-by":"publisher","unstructured":"J. Lamb, E. D. Crawford, D. Peck, J. W. Modell, I. C. Blat, M. J. Wrobel, et al., The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease, <i>Science<\/i>, <b>313<\/b> (2006), 1929-1935. https:\/\/doi.org\/10.1126\/science.1132939","DOI":"10.1126\/science.1132939"},{"key":"key-10.3934\/mbe.2022112-28","doi-asserted-by":"crossref","unstructured":"E. W. Sayers, J. Beck, J. R. Brister, E. E. Bolton, K. Canese, D. C. Comeau, et al., Database resources of the national center for biotechnology information, <i>Nucleic Acids Res.<\/i>, <b>48<\/b> (2020), D9-D16. <a href=\"https:\/\/doi.org\/10.1093\/nar\/gkz899\" target=\"_blank\">https:\/\/doi.org\/10.1093\/nar\/gkz899<\/a>","DOI":"10.1093\/nar\/gkz899"},{"key":"key-10.3934\/mbe.2022112-29","doi-asserted-by":"crossref","unstructured":"S. Kim, J. Chen, T. Cheng, A. Gindulyte, J. He, S. He, et al., PubChem 2019 update: improved access to chemical data, <i>Nucleic Acids Res.<\/i>, <b>47<\/b> (2019), D1102-D1109. <a href=\"https:\/\/doi.org\/10.1093\/nar\/gky1033\" target=\"_blank\">https:\/\/doi.org\/10.1093\/nar\/gky1033<\/a>","DOI":"10.1093\/nar\/gky1033"},{"key":"key-10.3934\/mbe.2022112-30","doi-asserted-by":"crossref","unstructured":"M. Kuhn, D. Szklarczyk, A. Franceschini, M. Campillos, C. V. Mering, L. J. Jensen, et al., STITCH 2: an interaction network database for small molecules and proteins, <i>Nucleic Acids Res.<\/i>, <b>38<\/b> (2010), D552-556. <a href=\"https:\/\/doi.org\/10.1093\/nar\/gkp937\" target=\"_blank\">https:\/\/doi.org\/10.1093\/nar\/gkp937<\/a>","DOI":"10.1093\/nar\/gkp937"},{"key":"key-10.3934\/mbe.2022112-31","doi-asserted-by":"crossref","unstructured":"M. Kuhn, C. V. Mering, M. Campillos, L. J. Jensen, P. Bork, STITCH: interaction networks of chemicals and proteins, <i>Nucleic Acids Res.<\/i>, <b>36<\/b> (2008), D684-688. <a href=\"https:\/\/doi.org\/10.1093\/nar\/gkm795\" target=\"_blank\">https:\/\/doi.org\/10.1093\/nar\/gkm795<\/a>","DOI":"10.1093\/nar\/gkm795"},{"key":"key-10.3934\/mbe.2022112-32","doi-asserted-by":"crossref","unstructured":"D. Szklarczyk, A. Santos, C. V. Mering, L. J. Jensen, P. Bork, M. Kuhn, STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data, <i>Nucleic Acids Res.<\/i>, <b>44<\/b> (2016), D380-384. <a href=\"https:\/\/doi.org\/10.1093\/nar\/gkv1277\" target=\"_blank\">https:\/\/doi.org\/10.1093\/nar\/gkv1277<\/a>","DOI":"10.1093\/nar\/gkv1277"},{"key":"key-10.3934\/mbe.2022112-33","doi-asserted-by":"crossref","unstructured":"K. Yoshihara, M. Shahmoradgoli, E. Martinez, R. Vegesna, H. Kim, W. Torres-Garcia, et al., Inferring tumour purity and stromal and immune cell admixture from expression data, <i>Nat. Commun.<\/i>, <b>4<\/b> (2013), 2612. <a href=\"https:\/\/doi.org\/10.1038\/ncomms3612\" target=\"_blank\">https:\/\/doi.org\/10.1038\/ncomms3612<\/a>","DOI":"10.1038\/ncomms3612"},{"key":"key-10.3934\/mbe.2022112-34","doi-asserted-by":"publisher","unstructured":"B. Chen, M. S. Khodadoust, C. L. Liu, A. M. Newman, A. A. Alizadeh, Profiling tumor infiltrating immune cells with CIBERSORT, <i>Methods Mol. Biol.<\/i>, <b>1711<\/b> (2018), 243-259. https:\/\/doi.org\/10.1007\/978-1-4939-7493-1_12","DOI":"10.1007\/978-1-4939-7493-1_12"},{"key":"key-10.3934\/mbe.2022112-35","doi-asserted-by":"publisher","unstructured":"Y. Benjamini, D. Drai, G. Elmer, N. Kafkafi, I. Golani, Controlling the false discovery rate in behavior genetics research, <i>Behav. Brain Res.<\/i>, <b>125<\/b> (2001), 279-284. https:\/\/doi.org\/10.1016\/s0166-4328(01)00297-2","DOI":"10.1016\/s0166-4328(01)00297-2"},{"key":"key-10.3934\/mbe.2022112-36","doi-asserted-by":"publisher","unstructured":"P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, <i>Genome Res.<\/i>, <b>13<\/b> (2003), 2498-2504. https:\/\/doi.org\/10.1101\/gr.1239303","DOI":"10.1101\/gr.1239303"},{"key":"key-10.3934\/mbe.2022112-37","doi-asserted-by":"crossref","unstructured":"D. Otasek, J. H. Morris, J. Boucas, A. R. Pico, B. Demchak, Cytoscape automation: empowering workflow-based network analysis, <i>Genome Biol.<\/i>, <b>20<\/b> (2019), 185. <a href=\"https:\/\/doi.org\/10.1186\/s13059-019-1758-4\" target=\"_blank\">https:\/\/doi.org\/10.1186\/s13059-019-1758-4<\/a>","DOI":"10.1186\/s13059-019-1758-4"},{"key":"key-10.3934\/mbe.2022112-38","doi-asserted-by":"crossref","unstructured":"D. Ronchetti, L. Mosca, G. Cutrona, G. Tuana, M. Gentile, S. Fabris, et al., Small nucleolar RNAs as new biomarkers in chronic lymphocytic leukemia, <i>BMC Med. Genomics<\/i>, <b>6<\/b> (2013), 27. <a href=\"https:\/\/doi.org\/10.1186\/1755-8794-6-27\" target=\"_blank\">https:\/\/doi.org\/10.1186\/1755-8794-6-27<\/a>","DOI":"10.1186\/1755-8794-6-27"},{"key":"key-10.3934\/mbe.2022112-39","doi-asserted-by":"publisher","unstructured":"E. Bignotti, S. Calza, R. A. Tassi, L. Zanotti, E. Bandiera, E. Sartori, et al., Identification of stably expressed reference small non-coding RNAs for microRNA quantification in high-grade serous ovarian carcinoma tissues, <i>J. Cell Mol. Med.<\/i>, <b>20<\/b> (2016), 2341-2348. https:\/\/doi.org\/10.1111\/jcmm.12927","DOI":"10.1111\/jcmm.12927"},{"key":"key-10.3934\/mbe.2022112-40","doi-asserted-by":"publisher","unstructured":"L. H. Mao, S. Y. Chen, X. Q. Li, F. Xu, J. Lei, Q. L. Wang, et al., LncRNA-LALR1 upregulates small nucleolar RNA SNORD72 to promote growth and invasion of hepatocellular carcinoma, <i>Aging (Albany NY)<\/i>, <b>12<\/b> (2020), 4527-4546. https:\/\/doi.org\/10.18632\/aging.102907","DOI":"10.18632\/aging.102907"},{"key":"key-10.3934\/mbe.2022112-41","doi-asserted-by":"publisher","unstructured":"F. G. Lafaille, O. Harschnitz, Y. S. Lee, P. Zhang, M. L. Hasek, G. Kerner, et al., Human SNORA31 variations impair cortical neuron-intrinsic immunity to HSV-1 and underlie herpes simplex encephalitis, <i>Nat. Med.<\/i>, <b>25<\/b> (2019), 1873-1884. https:\/\/doi.org\/10.1038\/s41591-019-0672-3","DOI":"10.1038\/s41591-019-0672-3"},{"key":"key-10.3934\/mbe.2022112-42","doi-asserted-by":"publisher","unstructured":"H. Davanian, A. Balasiddaiah, R. Heymann, M. Sundstrom, P. Redenstrom, M. Silfverberg, et al., Ameloblastoma RNA profiling uncovers a distinct non-coding RNA signature, <i>Oncotarget<\/i>, <b>8<\/b> (2017), 4530-4542. https:\/\/doi.org\/10.18632\/oncotarget.13889","DOI":"10.18632\/oncotarget.13889"},{"key":"key-10.3934\/mbe.2022112-43","doi-asserted-by":"crossref","unstructured":"I. Nepstad, K. J. Hatfield, I. S. Gronningsaeter, H. Reikvam, The PI3K-Akt-mTOR signaling pathway in human acute myeloid leukemia (AML) cells, <i>Int. J. Mol. Sci.<\/i>, <b>21<\/b> (2020), 2907. <a href=\"https:\/\/doi.org\/10.3390\/ijms21082907\" target=\"_blank\">https:\/\/doi.org\/10.3390\/ijms21082907<\/a>","DOI":"10.3390\/ijms21082907"},{"key":"key-10.3934\/mbe.2022112-44","doi-asserted-by":"publisher","unstructured":"L. Herschbein, J. L. Liesveld, Dueling for dual inhibition: Means to enhance effectiveness of PI3K\/Akt\/mTOR inhibitors in AML, <i>Blood Rev.<\/i>, <b>32<\/b> (2018), 235-248. https:\/\/doi.org\/10.1016\/j.blre.2017.11.006","DOI":"10.1016\/j.blre.2017.11.006"},{"key":"key-10.3934\/mbe.2022112-45","doi-asserted-by":"publisher","unstructured":"J. Bertacchini, N. Heidari, L. Mediani, S. Capitani, M. Shahjahani, A. Ahmadzadeh, et al., Targeting PI3K\/AKT\/mTOR network for treatment of leukemia, <i>Cell Mol. Life Sci.<\/i>, <b>72<\/b> (2015), 2337-2347. https:\/\/doi.org\/10.1007\/s00018-015-1867-5","DOI":"10.1007\/s00018-015-1867-5"},{"key":"key-10.3934\/mbe.2022112-46","doi-asserted-by":"publisher","unstructured":"Y. Su, X. Li, J. Ma, J. Zhao, S. Liu, G. Wang, et al., Targeting PI3K, mTOR, ERK and Bcl-2 signaling network shows superior antileukemic activity against AML ex vivo, <i>Biochem. Pharmacol.<\/i>, <b>148<\/b> (2018), 13-26. https:\/\/doi.org\/10.1016\/j.bcp.2017.11.022","DOI":"10.1016\/j.bcp.2017.11.022"},{"key":"key-10.3934\/mbe.2022112-47","doi-asserted-by":"publisher","unstructured":"Y. Tabe, A. Tafuri, K. Sekihara, H. Yang, M. Konopleva, Inhibition of mTOR kinase as a therapeutic target for acute myeloid leukemia, <i>Expert Opin. Ther. Targets<\/i>, <b>21<\/b> (2017), 705-714. https:\/\/doi.org\/10.1080\/14728222.2017.1333600","DOI":"10.1080\/14728222.2017.1333600"},{"key":"key-10.3934\/mbe.2022112-48","doi-asserted-by":"publisher","unstructured":"N. Guo, M. Azadniv, M. Coppage, M. Nemer, J. Mendler, M. Becker, et al., Effects of neddylation and mTOR inhibition in acute myelogenous leukemia, <i>Transl. Oncol.<\/i>, <b>12<\/b> (2019), 602-613. https:\/\/doi.org\/10.1016\/j.tranon.2019.01.001","DOI":"10.1016\/j.tranon.2019.01.001"},{"key":"key-10.3934\/mbe.2022112-49","doi-asserted-by":"publisher","unstructured":"J. Wu, G. Hu, Y. Dong, R. Ma, Z. Yu, S. Jiang, et al., Matrine induces Akt\/mTOR signalling inhibition-mediated autophagy and apoptosis in acute myeloid leukaemia cells, <i>J. Cell Mol. Med.<\/i>, <b>21<\/b> (2017), 1171-1181. https:\/\/doi.org\/10.1111\/jcmm.13049","DOI":"10.1111\/jcmm.13049"},{"key":"key-10.3934\/mbe.2022112-50","doi-asserted-by":"crossref","unstructured":"Y. Feng, L. Wu, mTOR up-regulation of PFKFB3 is essential for acute myeloid leukemia cell survival, <i>Biochem. Biophys. Res. Commun.<\/i>, <b>483<\/b> (2017), 897-903.","DOI":"10.1016\/j.bbrc.2017.01.031"},{"key":"key-10.3934\/mbe.2022112-51","doi-asserted-by":"publisher","unstructured":"J. Bertacchini, C. Frasson, F. Chiarini, D. D'Avella, B. Accordi, L. Anselmi, et al., Dual inhibition of PI3K\/mTOR signaling in chemoresistant AML primary cells, <i>Adv. Biol. Regul.<\/i>, <b>68<\/b> (2018), 2-9. https:\/\/doi.org\/10.1016\/j.jbior.2018.03.001","DOI":"10.1016\/j.jbior.2018.03.001"},{"key":"key-10.3934\/mbe.2022112-52","doi-asserted-by":"publisher","unstructured":"V. Stavropoulou, S. Kaspar, L. Brault, M. A. Sanders, S. Juge, S. Morettini, et al., MLL-AF9 expression in hematopoietic stem cells drives a highly invasive AML expressing EMT-related genes linked to poor outcome, <i>Cancer Cell<\/i>, <b>30<\/b> (2016), 43-58. https:\/\/doi.org\/10.1016\/j.ccell.2016.05.011","DOI":"10.1016\/j.ccell.2016.05.011"},{"key":"key-10.3934\/mbe.2022112-53","doi-asserted-by":"publisher","unstructured":"T. J. Zhang, J. D. Zhou, J. C. Ma, Z. Q. Deng, Z. Qian, D. M. Yao, et al., CDH1 (E-cadherin) expression independently affects clinical outcome in acute myeloid leukemia with normal cytogenetics, <i>Clin. Chem. Lab. Med.<\/i>, <b>55<\/b> (2017), 123-131. https:\/\/doi.org\/10.1515\/cclm-2016-0205","DOI":"10.1515\/cclm-2016-0205"},{"key":"key-10.3934\/mbe.2022112-54","doi-asserted-by":"crossref","unstructured":"S. Wu, Y. Du, J. Beckford, H. Alachkar, Upregulation of the EMT marker vimentin is associated with poor clinical outcome in acute myeloid leukemia, <i>J. Transl. Med.<\/i>, <b>16<\/b> (2018), 170. <a href=\"https:\/\/doi.org\/10.1186\/s12967-018-1539-y\" target=\"_blank\">https:\/\/doi.org\/10.1186\/s12967-018-1539-y<\/a>","DOI":"10.1186\/s12967-018-1539-y"},{"key":"key-10.3934\/mbe.2022112-55","doi-asserted-by":"publisher","unstructured":"L. Zhong, J. Chen, X. Huang, Y. Li, T. Jiang, Monitoring immunoglobulin heavy chain and T-cell receptor gene rearrangement in cfDNA as minimal residual disease detection for patients with acute myeloid leukemia, <i>Oncol. Lett.<\/i>, <b>16<\/b> (2018), 2279-2288. https:\/\/doi.org\/10.3892\/ol.2018.8966","DOI":"10.3892\/ol.2018.8966"},{"key":"key-10.3934\/mbe.2022112-56","doi-asserted-by":"publisher","unstructured":"A. G. Chapuis, D. N. Egan, M. Bar, T. M. Schmitt, M. S. McAfee, K. G. Paulson, et al., T cell receptor gene therapy targeting WT1 prevents acute myeloid leukemia relapse post-transplant, <i>Nat. Med.<\/i>, <b>25<\/b> (2019), 1064-1072. https:\/\/doi.org\/10.1038\/s41591-019-0472-9","DOI":"10.1038\/s41591-019-0472-9"},{"key":"key-10.3934\/mbe.2022112-57","doi-asserted-by":"publisher","unstructured":"H. J. Stauss, S. Thomas, M. Cesco-Gaspere, D. P. Hart, S. A. Xue, A. Holler, et al., WT1-specific T cell receptor gene therapy: improving TCR function in transduced T cells, <i>Blood Cells Mol. Dis.<\/i>, <b>40<\/b> (2008), 113-116. https:\/\/doi.org\/10.1016\/j.bcmd.2007.06.018","DOI":"10.1016\/j.bcmd.2007.06.018"},{"key":"key-10.3934\/mbe.2022112-58","doi-asserted-by":"publisher","unstructured":"Y. Wang, A. V. Krivtsov, A. U. Sinha, T. E. North, W. Goessling, Z. Feng, et al., The Wnt\/beta-catenin pathway is required for the development of leukemia stem cells in AML, <i>Science<\/i>, <b>327<\/b> (2010), 1650-1653. https:\/\/doi.org\/10.1126\/science.1186624","DOI":"10.1126\/science.1186624"},{"key":"key-10.3934\/mbe.2022112-59","doi-asserted-by":"crossref","unstructured":"A. M. Gruszka, D. Valli, M. Alcalay, Wnt signalling in acute myeloid leukaemia, <i>Cells<\/i>, <b>8<\/b> (2019), 1403. <a href=\"https:\/\/doi.org\/10.3390\/cells8111403\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cells8111403<\/a>","DOI":"10.3390\/cells8111403"},{"key":"key-10.3934\/mbe.2022112-60","doi-asserted-by":"crossref","unstructured":"F. J. Staal, F. Famili, L. G. Perez, K. Pike-Overzet, Aberrant Wnt signaling in leukemia, <i>Cancers (Basel)<\/i>, <b>8<\/b> (2016), 78. <a href=\"https:\/\/doi.org\/10.3390\/cancers8090078\" target=\"_blank\">https:\/\/doi.org\/10.3390\/cancers8090078<\/a>","DOI":"10.3390\/cancers8090078"},{"key":"key-10.3934\/mbe.2022112-61","doi-asserted-by":"publisher","unstructured":"A. Valencia, J. Roman-Gomez, J. Cervera, E. Such, E. Barragan, P. Bolufer, et al., Wnt signaling pathway is epigenetically regulated by methylation of Wnt antagonists in acute myeloid leukemia, <i>Leukemia<\/i>, <b>23<\/b> (2009), 1658-1666. https:\/\/doi.org\/10.1038\/leu.2009.86","DOI":"10.1038\/leu.2009.86"},{"key":"key-10.3934\/mbe.2022112-62","doi-asserted-by":"publisher","unstructured":"E. A. Griffiths, S. D. Gore, C. Hooker, M. A. McDevitt, J. E. Karp, B. D. Smith, et al., Acute myeloid leukemia is characterized by Wnt pathway inhibitor promoter hypermethylation, <i>Leuk. Lymphoma<\/i>, <b>51<\/b> (2010), 1711-1719. https:\/\/doi.org\/10.3109\/10428194.2010.496505","DOI":"10.3109\/10428194.2010.496505"},{"key":"key-10.3934\/mbe.2022112-63","doi-asserted-by":"publisher","unstructured":"C. Gasparini, C. Celeghini, L. Monasta, G. Zauli, NF-kappaB pathways in hematological malignancies, <i>Cell Mol. Life Sci.<\/i>, <b>71<\/b> (2014), 2083-2102. https:\/\/doi.org\/10.1007\/s00018-013-1545-4","DOI":"10.1007\/s00018-013-1545-4"},{"key":"key-10.3934\/mbe.2022112-64","doi-asserted-by":"publisher","unstructured":"M. Breccia, G. Alimena, NF-kappaB as a potential therapeutic target in myelodysplastic syndromes and acute myeloid leukemia, <i>Expert Opin. Ther. Targets<\/i>, <b>14<\/b> (2010), 1157-1176. https:\/\/doi.org\/10.1517\/14728222.2010.522570","DOI":"10.1517\/14728222.2010.522570"},{"key":"key-10.3934\/mbe.2022112-65","doi-asserted-by":"publisher","unstructured":"M. C. Bosman, J. J. Schuringa, E. Vellenga, Constitutive NF-kappaB activation in AML: Causes and treatment strategies, <i>Crit. Rev. Oncol. Hematol.<\/i>, <b>98<\/b> (2016), 35-44. https:\/\/doi.org\/10.1016\/j.critrevonc.2015.10.001","DOI":"10.1016\/j.critrevonc.2015.10.001"},{"key":"key-10.3934\/mbe.2022112-66","doi-asserted-by":"publisher","unstructured":"J. Zhou, Y. Q. Ching, W. J. Chng, Aberrant nuclear factor-kappa B activity in acute myeloid leukemia: from molecular pathogenesis to therapeutic target, <i>Oncotarget<\/i>, <b>6<\/b> (2015), 5490-5500. https:\/\/doi.org\/10.18632\/oncotarget.3545","DOI":"10.18632\/oncotarget.3545"},{"key":"key-10.3934\/mbe.2022112-67","doi-asserted-by":"publisher","unstructured":"C. H. Choi, H. Xu, H. Bark, T. B. Lee, J. Yun, S. I. Kang, et al., Balance of NF-kappaB and p38 MAPK is a determinant of radiosensitivity of the AML-2 and its doxorubicin-resistant cell lines, <i>Leuk. Res.<\/i>, <b>31<\/b> (2007), 1267-1276. https:\/\/doi.org\/10.1016\/j.leukres.2006.11.006","DOI":"10.1016\/j.leukres.2006.11.006"},{"key":"key-10.3934\/mbe.2022112-68","doi-asserted-by":"publisher","unstructured":"A. Volk, J. Li, J. Xin, D. You, J. Zhang, X. Liu, et al., Co-inhibition of NF-kappaB and JNK is synergistic in TNF-expressing human AML, <i>J. Exp. Med.<\/i>, <b>211<\/b> (2014), 1093-1108. https:\/\/doi.org\/10.1084\/jem.20130990","DOI":"10.1084\/jem.20130990"},{"key":"key-10.3934\/mbe.2022112-69","doi-asserted-by":"publisher","unstructured":"M. C. Bosman, H. Schepers, J. Jaques, A. Z. Brouwers-Vos, W. J. Quax, J. J. Schuringa, et al., The TAK1-NF-kappaB axis as therapeutic target for AML, <i>Blood<\/i>, <b>124<\/b> (2014), 3130-3140. https:\/\/doi.org\/10.1182\/blood-2014-04-569780","DOI":"10.1182\/blood-2014-04-569780"},{"key":"key-10.3934\/mbe.2022112-70","doi-asserted-by":"crossref","unstructured":"M. Ma, X. Wang, N. Liu, F. Shan, Y. Feng, Low-dose naltrexone inhibits colorectal cancer progression and promotes apoptosis by increasing M1-type macrophages and activating the Bax\/Bcl-2\/caspase-3\/PARP pathway, <i>Int. Immunopharmacol.<\/i>, <b>83<\/b> (2020), 106388. <a href=\"https:\/\/doi.org\/10.1016\/j.intimp.2020.106388\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.intimp.2020.106388<\/a>","DOI":"10.1016\/j.intimp.2020.106388"},{"key":"key-10.3934\/mbe.2022112-71","doi-asserted-by":"crossref","unstructured":"N. Liu, M. Ma, N. Qu, R. Wang, H. Chen, F. Hu, et al., Low-dose naltrexone inhibits the epithelial-mesenchymal transition of cervical cancer cells in vitro and effects indirectly on tumor-associated macrophages in vivo, <i>Int. Immunopharmacol.<\/i>, <b>86<\/b> (2020), 106718. <a href=\"https:\/\/doi.org\/10.1016\/j.intimp.2020.106718\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.intimp.2020.106718<\/a>","DOI":"10.1016\/j.intimp.2020.106718"},{"key":"key-10.3934\/mbe.2022112-72","doi-asserted-by":"publisher","unstructured":"A. C. Menezes, M. Carvalheiro, J. M. P. F. de Oliveira, A. Ascenso, H. Oliveira, Cytotoxic effect of the serotonergic drug 1-(1-Naphthyl)piperazine against melanoma cells, <i>Toxicol. Int.<\/i> <i>Vitro<\/i>, <b>47<\/b> (2018), 72-78. https:\/\/doi.org\/10.1016\/j.tiv.2017.11.011","DOI":"10.1016\/j.tiv.2017.11.011"},{"key":"key-10.3934\/mbe.2022112-73","doi-asserted-by":"crossref","unstructured":"G. G. Wei, L. Gao, Z. Y. Tang, P. Lin, L. B. Liang, J. J. Zeng, et al., Drug repositioning in head and neck squamous cell carcinoma: An integrated pathway analysis based on connectivity map and differential gene expression, <i>Pathol. Res. Pract.<\/i>, <b>215<\/b> (2019), 152378. <a href=\"https:\/\/doi.org\/10.1016\/j.prp.2019.03.007\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.prp.2019.03.007<\/a>","DOI":"10.1016\/j.prp.2019.03.007"},{"key":"key-10.3934\/mbe.2022112-74","doi-asserted-by":"publisher","unstructured":"J. Takezawa, Y. Ishimi, K. Yamada, Proteasome inhibitors remarkably prevent translesion replication in cancer cells but not normal cells, <i>Cancer Sci.<\/i>, <b>99<\/b> (2008), 863-871. https:\/\/doi.org\/10.1111\/j.1349-7006.2008.00764.x","DOI":"10.1111\/j.1349-7006.2008.00764.x"},{"key":"key-10.3934\/mbe.2022112-75","doi-asserted-by":"publisher","unstructured":"P. G. Richardson, C. Mitsiades, T. Hideshima, K. C. Anderson, Bortezomib: proteasome inhibition as an effective anticancer therapy, <i>Annu. Rev. Med.<\/i>, <b>57<\/b> (2006), 33-47. https:\/\/doi.org\/10.1146\/annurev.med.57.042905.122625","DOI":"10.1146\/annurev.med.57.042905.122625"},{"key":"key-10.3934\/mbe.2022112-76","doi-asserted-by":"publisher","unstructured":"I. Zavrski, C. Naujokat, K. Niemoller, C. Jakob, U. Heider, C. Langelotz, et al., Proteasome inhibitors induce growth inhibition and apoptosis in myeloma cell lines and in human bone marrow myeloma cells irrespective of chromosome 13 deletion, <i>J. Cancer Res. Clin. Oncol.<\/i>, <b>129<\/b> (2003), 383-391. https:\/\/doi.org\/10.1007\/s00432-003-0454-6","DOI":"10.1007\/s00432-003-0454-6"},{"key":"key-10.3934\/mbe.2022112-77","unstructured":"W. X. Wang, B. H. Kong, P. Li, K. Song, X. Qu, B. X. Cui, et al., Effect of extracellular signal regulated kinase signal pathway on apoptosis induced by MG262 in ovarian cancer cells, <i>Zhonghua Fu Chan Ke Za Zhi<\/i>, <b>43<\/b> (2008), 690-694"},{"key":"key-10.3934\/mbe.2022112-78","doi-asserted-by":"publisher","unstructured":"J. Y. Wu, S. S. Lin, F. T. Hsu, J. G. Chung, Fluoxetine inhibits DNA repair and NF-kB-modulated metastatic potential in non-small cell lung cancer, <i>Anticancer Res.<\/i>, <b>38<\/b> (2018), 5201-5210. https:\/\/doi.org\/10.21873\/anticanres.12843","DOI":"10.21873\/anticanres.12843"},{"key":"key-10.3934\/mbe.2022112-79","doi-asserted-by":"crossref","unstructured":"L. C. Hsu, H. F. Tu, F. T. Hsu, P. F. Yueh, I. T. Chiang, Beneficial effect of fluoxetine on anti-tumor progression on hepatocellular carcinoma and non-small cell lung cancer bearing animal model, <i>Biomed. Pharmacother.<\/i>, <b>126<\/b> (2020), 110054. <a href=\"https:\/\/doi.org\/10.1016\/j.biopha.2020.110054\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.biopha.2020.110054<\/a>","DOI":"10.1016\/j.biopha.2020.110054"},{"key":"key-10.3934\/mbe.2022112-80","unstructured":"A. R. Mun, S. J. Lee, G. B. Kim, H. S. Kang, J. S. Kim, S. J. Kim, Fluoxetine-induced apoptosis in hepatocellular carcinoma cells, <i>Anticancer Res.<\/i>, <b>33<\/b> (2013), 3691-3697"},{"key":"key-10.3934\/mbe.2022112-81","doi-asserted-by":"crossref","unstructured":"D. Sun, L. Zhu, Y. Zhao, Y. Jiang, L. Chen, Y. Yu, et al., Fluoxetine induces autophagic cell death via eEF2K-AMPK-mTOR-ULK complex axis in triple negative breast cancer, <i>Cell Prolif.<\/i>, <b>51<\/b> (2018), e12402. <a href=\"https:\/\/doi.org\/10.1111\/cpr.12402\" target=\"_blank\">https:\/\/doi.org\/10.1111\/cpr.12402<\/a>","DOI":"10.1111\/cpr.12402"},{"key":"key-10.3934\/mbe.2022112-82","doi-asserted-by":"publisher","unstructured":"A. M. Kabel, A. A. Elkhoely, Ameliorative potential of fluoxetine\/raloxifene combination on experimentally induced breast cancer, <i>Tissue Cell<\/i>, <b>48<\/b> (2016), 89-95. https:\/\/doi.org\/10.1016\/j.tice.2016.02.002","DOI":"10.1016\/j.tice.2016.02.002"},{"key":"key-10.3934\/mbe.2022112-83","doi-asserted-by":"publisher","unstructured":"M. Bowie, P. Pilie, J. Wulfkuhle, S. Lem, A. Hoffman, S. Desai, et al., Fluoxetine induces cytotoxic endoplasmic reticulum stress and autophagy in triple negative breast cancer, <i>World J. Clin. Oncol.<\/i>, <b>6<\/b> (2015), 299-311. https:\/\/doi.org\/10.5306\/wjco.v6.i6.299","DOI":"10.5306\/wjco.v6.i6.299"},{"key":"key-10.3934\/mbe.2022112-84","doi-asserted-by":"publisher","unstructured":"T. M. Khing, W. W. Po, U. D. Sohn, Fluoxetine enhances anti-tumor activity of paclitaxel in gastric adenocarcinoma cells by triggering apoptosis and necroptosis, <i>Anticancer Res.<\/i>, <b>39<\/b> (2019), 6155-6163. https:\/\/doi.org\/10.21873\/anticanres.13823","DOI":"10.21873\/anticanres.13823"},{"key":"key-10.3934\/mbe.2022112-85","doi-asserted-by":"publisher","unstructured":"P. P. Khin, W. W. Po, W. Thein, U. D. Sohn, Apoptotic effect of fluoxetine through the endoplasmic reticulum stress pathway in the human gastric cancer cell line AGS, <i>Naunyn Schmiedebergs Arch. Pharmacol.<\/i>, <b>393<\/b> (2020), 537-549. https:\/\/doi.org\/10.1007\/s00210-019-01739-7","DOI":"10.1007\/s00210-019-01739-7"},{"key":"key-10.3934\/mbe.2022112-86","doi-asserted-by":"crossref","unstructured":"M. Marcinkute, S. Afshinjavid, A. A. Fatokun, F. A. Javid, Fluoxetine selectively induces p53-independent apoptosis in human colorectal cancer cells, <i>Eur. J. Pharmacol.<\/i>, <b>857<\/b> (2019), 172441. <a href=\"https:\/\/doi.org\/10.1016\/j.ejphar.2019.172441\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.ejphar.2019.172441<\/a>","DOI":"10.1016\/j.ejphar.2019.172441"},{"key":"key-10.3934\/mbe.2022112-87","doi-asserted-by":"publisher","unstructured":"V. Kannen, S. B. Garcia, W. A. Silva, M. Gasser, R. Monch, E. J. Alho, et al., Oncostatic effects of fluoxetine in experimental colon cancer models, <i>Cell Signal<\/i>, <b>27<\/b> (2015), 1781-1788. https:\/\/doi.org\/10.1016\/j.cellsig.2015.05.008","DOI":"10.1016\/j.cellsig.2015.05.008"},{"key":"key-10.3934\/mbe.2022112-88","doi-asserted-by":"crossref","unstructured":"V. Kannen, H. Hintzsche, D. L. Zanette, W. A. Silva, S. B. Garcia, A. M. Waaga-Gasser, et al., Antiproliferative effects of fluoxetine on colon cancer cells and in a colonic carcinogen mouse model, <i>PLoS One<\/i>, <b>7<\/b> (2012), e50043. <a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0050043\" target=\"_blank\">https:\/\/doi.org\/10.1371\/journal.pone.0050043<\/a>","DOI":"10.1371\/journal.pone.0050043"},{"key":"key-10.3934\/mbe.2022112-89","doi-asserted-by":"publisher","unstructured":"H. Stopper, S. B. Garcia, A. M. Waaga-Gasser, V. Kannen, Antidepressant fluoxetine and its potential against colon tumors, <i>World J. Gastrointest. Oncol.<\/i>, <b>6<\/b> (2014), 11-21. https:\/\/doi.org\/10.4251\/wjgo.v6.i1.11","DOI":"10.4251\/wjgo.v6.i1.11"},{"key":"key-10.3934\/mbe.2022112-90","doi-asserted-by":"crossref","unstructured":"S. J. Koh, J. M. Kim, I. K. Kim, N. Kim, H. C. Jung, I. S. Song, et al., Fluoxetine inhibits NF-kappaB signaling in intestinal epithelial cells and ameliorates experimental colitis and colitis-associated colon cancer in mice, <i>Am. J. Physiol. Gastrointest. Liver Phys.<\/i>, <b>301<\/b> (2011), G9-19. <a href=\"https:\/\/doi.org\/10.1152\/ajpgi.00267.2010\" target=\"_blank\">https:\/\/doi.org\/10.1152\/ajpgi.00267.2010<\/a>","DOI":"10.1152\/ajpgi.00267.2010"},{"key":"key-10.3934\/mbe.2022112-91","doi-asserted-by":"publisher","unstructured":"K. H. Liu, S. T. Yang, Y. K. Lin, J. W. Lin, Y. H. Lee, J. Y. Wang, et al., Fluoxetine, an antidepressant, suppresses glioblastoma by evoking AMPAR-mediated calcium-dependent apoptosis, <i>Oncotarget<\/i>, <b>6<\/b> (2015), 5088-5101. https:\/\/doi.org\/10.18632\/oncotarget.3243","DOI":"10.18632\/oncotarget.3243"},{"key":"key-10.3934\/mbe.2022112-92","doi-asserted-by":"publisher","unstructured":"J. Ma, Y. R. Yang, W. Chen, M. H. Chen, H. Wang, X. D. Wang, et al., Fluoxetine synergizes with temozolomide to induce the CHOP-dependent endoplasmic reticulum stress-related apoptosis pathway in glioma cells, <i>Oncol. Rep.<\/i>, <b>36<\/b> (2016), 676-684. https:\/\/doi.org\/10.3892\/or.2016.4860","DOI":"10.3892\/or.2016.4860"}],"container-title":["Mathematical Biosciences and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2022112?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,8]],"date-time":"2022-01-08T06:13:34Z","timestamp":1641622414000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2022112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":92,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.3934\/mbe.2022112","relation":{},"ISSN":["1551-0018"],"issn-type":[{"value":"1551-0018","type":"print"}],"subject":[],"published":{"date-parts":[[2022]]}}}