Identification of key genes and miRNA-mRNA regulatory networks associated with bone marrow immune microenvironment regulations in multiple myeloma by integrative bioinformatics analysis

References

• Contributors MM, Firth J. Haematology: Multiple myeloma. Clin. Med. 2019;19:58–60. doi:10.7861/clinmedicine.19-1-58.
   PubMedGoogle Scholar
• Pawlyn C, Morgan GJ. Evolutionary Biology of high-risk Multiple myeloma. Nat Rev Cancer. 2017;17:543–556. doi:10.1038/nrc.2017.63.
   PubMed Web of Science ®Google Scholar
• Hou J, Wei R, Qian J, et al. The impact of the bone marrow microenvironment on Multiple Myeloma (review). Oncol. Rep. 2019;42:1272–1282. doi:10.3892/or.2019.7261.
   PubMed Web of Science ®Google Scholar
• Manier S, Sacco A, Leleu X, et al. Bone marrow microenvironment in Multiple Myeloma progression. J. Biomed. Biotechnol. 2012;2012:e157496), doi:10.1155/2012/157496.
   PubMed Web of Science ®Google Scholar
• Ribatti D, Nico B, Vacca A. Importance of the bone marrow microenvironment in inducing the Angiogenic response in Multiple myeloma. Oncogene. 2006;25:4257–4266. doi:10.1038/sj.onc.1209456.
   PubMed Web of Science ®Google Scholar
• Kawano Y, Moschetta M, Manier S, et al. Targeting the bone marrow microenvironment in Multiple myeloma. Immunol. Rev. 2015;263:160–172. doi:10.1111/imr.12233.
   PubMed Web of Science ®Google Scholar
• Raimondi L, De Luca A, Morelli E, et al. MicroRNAs: novel crossroads between Myeloma Cells and the bone marrow microenvironment. BioMed Res. Int. 2016;2016:e6504593), doi:10.1155/2016/6504593.
   PubMed Web of Science ®Google Scholar
• Soley L, Falank C, Reagan MR. MicroRNA transfer between bone marrow adipose and Multiple Myeloma cells. Curr. Osteoporos. Rep. 2017;15:162–170. doi:10.1007/s11914-017-0360-5.
   PubMed Web of Science ®Google Scholar
• Wang W, Corrigan-Cummins M, Barber EA, et al. Aberrant levels of MiRNAs in bone marrow microenvironment and peripheral Blood of Myeloma patients and disease progression. J. Mol. Diagn. JMD. 2015;17:669–678. doi:10.1016/j.jmoldx.2015.06.006.
   PubMed Web of Science ®Google Scholar
• Fulciniti M, Amodio N, Bandi RL, et al. MiR-23b/SP1/c-Myc forms a feed-forward Loop Supporting Multiple Myeloma cell growth. Blood Cancer J. 2016;6:e380), doi:10.1038/bcj.2015.106.
   PubMed Web of Science ®Google Scholar
• Bong IPN, Ng CC, Baharuddin P, et al. MicroRNA Expression Patterns and target prediction in Multiple Myeloma Development and malignancy. Genes Genomics. 2017;39:533–540. doi:10.1007/s13258-017-0518-7.
   PubMed Web of Science ®Google Scholar
• Löffler D, Brocke-Heidrich K, Pfeifer G, et al. Interleukin-6 dependent survival of Multiple Myeloma cells involves the Stat3-mediated induction of MicroRNA-21 through a highly conserved enhancer. Blood. 2007;110:1330–1333. doi:10.1182/blood-2007-03-081133.
   PubMed Web of Science ®Google Scholar
• Farswan A, Gupta A, Gupta R, et al. Imputation of gene expression data in Blood Cancer and Its Significance in inferring biological pathways. Front. Oncol. 2020;9:1–14. doi:10.3389/fonc.2019.01442.
   Web of Science ®Google Scholar
• López-Corral L, Corchete LA, Sarasquete ME, et al. Transcriptome analysis Reveals molecular Profiles associated with evolving steps of monoclonal gammopathies. Haematologica. 2014;99:1365–1372. doi:10.3324/haematol.2013.087809.
   PubMed Web of Science ®Google Scholar
• Hanamura I, Huang Y, Zhan F, et al. Prognostic Value of cyclin D2 MRNA expression in Newly Diagnosed Multiple Myeloma treated with high-dose chemotherapy and tandem autologous stem cell transplantations. Leukemia. 2006;20:1288–1290. doi:10.1038/sj.leu.2404253.
   PubMed Web of Science ®Google Scholar
• Danziger SA, McConnell M, Gockley J, et al. Bone marrow microenvironments that contribute to patient outcomes in Newly Diagnosed Multiple Myeloma: A cohort study of patients in the total therapy clinical trials. PLoS Med. 2020;17:e1003323), doi:10.1371/journal.pmed.1003323.
   PubMed Web of Science ®Google Scholar
• Gutiérrez NC, Sarasquete ME, Misiewicz-Krzeminska I, et al. Deregulation of MicroRNA expression in the different genetic subtypes of Multiple Myeloma and correlation with gene expression profiling. Leukemia. 2010;24:629–637. doi:10.1038/leu.2009.274.
   PubMed Web of Science ®Google Scholar
• Agnelli L, Mosca L, Fabris S, et al. A SNP Microarray and FISH-based procedure to detect allelic imbalances in Multiple myeloma: An Integrated Genomics Approach Reveals a Wide gene dosage effect. Genes. Chromosomes Cancer. 2009;48:603–614. doi:10.1002/gcc.20668.
   PubMed Web of Science ®Google Scholar
• Fan Y, Siklenka K, Arora SK, et al. MiRNet - dissecting MiRNA-target interactions and Functional associations through Network-Based Visual analysis. Nucleic Acids Res. 2016;44:W135–W141. doi:10.1093/nar/gkw288.
   PubMed Web of Science ®Google Scholar
• Kanehisa M, Furumichi M, Tanabe M, et al. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–D361. doi:10.1093/nar/gkw1092.
   PubMed Web of Science ®Google Scholar
• Vlachos IS, Zagganas K, Paraskevopoulou MD, et al. DIANA-MiRPath v3.0: deciphering MicroRNA function with Experimental support. Nucleic Acids Res. 2015;43:W460–W466. doi:10.1093/nar/gkv403.
   PubMed Web of Science ®Google Scholar
• Paraskevopoulou MD, Georgakilas G, Kostoulas N, et al. DIANA-MicroT Web server v5.0: service integration into MiRNA Functional analysis workflows. Nucleic Acids Res. 2013;41:W169–W173. doi:10.1093/nar/gkt393.
   PubMed Web of Science ®Google Scholar
• Vlachos IS, Paraskevopoulou MD, Karagkouni D, et al. DIANA-TarBase v7.0: indexing more than half a million experimentally supported MiRNA:MRNA interactions. Nucleic Acids Res. 2015;43:D153–D159. doi:10.1093/nar/gku1215.
   PubMed Web of Science ®Google Scholar
• Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein–protein Association networks with increased coverage, Supporting Functional discovery in Genome-Wide Experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi:10.1093/nar/gky1131.
   PubMed Web of Science ®Google Scholar
• Chin C-H, Chen S-H, Wu H-H, et al. Cytohubba: identifying Hub objects and Sub-networks from Complex interactome. BMC Syst. Biol. 2014;8:S11), doi:10.1186/1752-0509-8-S4-S11.
   PubMed Web of Science ®Google Scholar
• Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for Integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–2504. doi:10.1101/gr.1239303.
   PubMed Web of Science ®Google Scholar
• Subramanian A, Tamayo P, Mootha VK, et al. Gene Set enrichment analysis: A knowledge-based Approach for interpreting Genome-Wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 2005;102:15545–15550. doi:10.1073/pnas.0506580102.
   PubMed Web of Science ®Google Scholar
• Mizuno H, Kitada K, Nakai K, et al. Prognoscan: A New database for meta-analysis of the Prognostic Value of genes. BMC Med. Genomics. 2009;2:18), doi:10.1186/1755-8794-2-18.
   PubMed Web of Science ®Google Scholar
• Aran D, Hu Z, Butte AJ. XCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18:220. doi:10.1186/s13059-017-1349-1.
   PubMed Web of Science ®Google Scholar
• Hänzelmann S, Castelo R, Guinney J. GSVA: Gene Set variation analysis for Microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7), doi:10.1186/1471-2105-14-7.
   PubMed Web of Science ®Google Scholar
• Xia J, Gill EE, Hancock REW. NetworkAnalyst for statistical, Visual and Network-Based meta-analysis of gene expression data. Nat. Protoc. 2015;10:823–844. doi:10.1038/nprot.2015.052.
   PubMed Web of Science ®Google Scholar
• Yin C, Zheng X, Xiang H, et al. Differential expression Profile analysis of cisplatin-regulated MiRNAs in a Human gastric cancer cell line. Mol. Med. Rep. 2019;20:1966–1976. doi:10.3892/mmr.2019.10430.
   PubMed Web of Science ®Google Scholar
• Fateh A, Feizi MAH, Safaralizadeh R, et al. Importance of MiR-299-5p in Colorectal cancer. Ann. Gastroenterol. 2017;30:322–326. doi:10.20524/aog.2017.0139.
   PubMed Web of Science ®Google Scholar
• Ganesan S, Palani HK, Lakshmanan V, et al. Stromal cells downregulate MiR-23a-5p to activate protective autophagy in Acute myeloid leukemia. Cell Death Dis. 2019;10:736. doi:10.1038/s41419-019-1964-8.
   PubMed Web of Science ®Google Scholar
• Krist B, Florczyk U, Pietraszek-Gremplewicz K, et al. The Role of MiR-378a in metabolism, angiogenesis, and muscle biology. Int. J. Endocrinol. 2015;2015:e281756), doi:10.1155/2015/281756.
   PubMed Web of Science ®Google Scholar
• Purushothaman A, Toole BP. Serglycin Proteoglycan Is required for Multiple Myeloma cell adhesion, in vivo growth, and vascularization. J. Biol. Chem. 2014;289:5499–5509. doi:10.1074/jbc.M113.532143.
   PubMed Web of Science ®Google Scholar
• Kyriazoglou A, Ntanasis-Stathopoulos I, Terpos E, et al. Emerging insights into the Role of the Hippo pathway in Multiple Myeloma and associated bone disease. Clin. Lymphoma Myeloma Leuk. 2020;20:57–62. doi:10.1016/j.clml.2019.09.620.
   PubMed Web of Science ®Google Scholar
• Dong M, Blobe GC. Role of transforming Growth factor-β in hematologic malignancies. Blood. 2006;107:4589–4596. doi:10.1182/blood-2005-10-4169.
   PubMed Web of Science ®Google Scholar
• Xiong T, Luo Z. The expression of actin-related protein 2/3 Complex subunit 5 (ARPC5) expression in Multiple Myeloma and Its prognostic significance. Med. Sci. Monit. Int. Med. J. Exp. Clin. Res. 2018;24:6340–6348. doi:10.12659/MSM.908944.
   Google Scholar
• Sprynski AC, Hose D, Caillot L, et al. The Role of IGF-1 as a major Growth factor for Myeloma cell lines and the prognostic relevance of the expression of Its receptor. Blood. 2009;113:4614–4626. doi:10.1182/blood-2008-07-170464.
   PubMed Web of Science ®Google Scholar
• Viziteu E, Klein B, Basbous J, et al. RECQ1 helicase Is involved in replication stress survival and Drug resistance in Multiple myeloma. Leukemia. 2017;31:2104–2113. doi:10.1038/leu.2017.54.
   PubMed Web of Science ®Google Scholar
• Chen S, Xiao Z, Zhou J, et al. ZNF382: a Transcription inhibitor down-regulated in Multiple tumors Due to promoter methylation. Clin. Chim. Acta Int. J. Clin. Chem. 2020;500:220–225. doi:10.1016/j.cca.2019.10.021.
   PubMed Web of Science ®Google Scholar
• Yan H, Zheng G, Qu J, et al. Identification of Key Candidate genes and pathways in Multiple Myeloma by Integrated bioinformatics analysis. J. Cell. Physiol. 2019;234:23785–23797. doi:10.1002/jcp.28947.
   PubMed Web of Science ®Google Scholar
• Pichiorri F, Suh S-S, Rocci A, et al. Downregulation of P53-inducible MicroRNAs 192, 194, and 215 impairs the P53/MDM2 autoregulatory Loop in Multiple Myeloma development. Cancer Cell. 2010;18:367–381. doi:10.1016/j.ccr.2010.09.005.
   PubMed Web of Science ®Google Scholar
• Ohtani H. Focus on TILs: Prognostic Significance of tumor infiltrating lymphocytes in Human Colorectal cancer. Cancer Immun. 2007;7:4.
   PubMedGoogle Scholar
• Joshua DE, Brown RD, Ho PJ, et al. Regulatory T cells and Multiple myeloma. Clin Lymphoma Myeloma. 2008;8:283–286. doi:10.3816/CLM.2008.n.039.
   PubMedGoogle Scholar
• Chung DJ, Pronschinske KB, Shyer JA, et al. T cell exhaustion in Multiple Myeloma relapse after autotransplant: optimal timing of immunotherapy. Cancer Immunol. Res. 2016;4:61–71. doi:10.1158/2326-6066.CIR-15-0055.
   PubMed Web of Science ®Google Scholar
• Kim H-J, Cantor H. The path to reactivation of antitumor immunity and checkpoint immunotherapy. Cancer Immunol. Res. 2014;2:926–936. doi:10.1158/2326-6066.CIR-14-0153.
   PubMed Web of Science ®Google Scholar
• Cossío I, Lucas D, Hidalgo A. Neutrophils as regulators of the hematopoietic niche. Blood. 2019;133:2140–2148. doi:10.1182/blood-2018-10-844571.
   PubMed Web of Science ®Google Scholar
• Tamura H, Ishibashi M, Sunakawa-Kii M, et al. PD-L1–PD-1 pathway in the pathophysiology of Multiple myeloma. Cancers (Basel). 2020;12:1–14. doi:10.3390/cancers12040924.
   Web of Science ®Google Scholar
• Braga WMT, da Silva BR, de Carvalho AC, et al. FOXP3 and CTLA4 overexpression in Multiple Myeloma bone marrow as a sign of accumulation of CD4(+) T regulatory cells. Cancer Immunol. Immunother. CII. 2014;63:1189–1197. doi:10.1007/s00262-014-1589-9.
   PubMed Web of Science ®Google Scholar
• Zhao R, Chinai JM, Buhl S, et al. HHLA2 is a member of the B7 family and inhibits Human CD4 and CD8 T-cell function. Proc. Natl. Acad. Sci. U. S. A. 2013;110:9879–9884. doi:10.1073/pnas.1303524110.
   PubMed Web of Science ®Google Scholar
• Kryukov F, Dementyeva E, Kubiczkova L, et al. Cell cycle genes Co-expression in Multiple Myeloma and plasma cell leukemia. Genomics. 2013;102:243–249. doi:10.1016/j.ygeno.2013.06.007.
   PubMed Web of Science ®Google Scholar
• Wu SY, Rupaimoole R, Shen F, et al. A MiR-192-EGR1-HOXB9 regulatory network controls the Angiogenic switch in cancer. Nat. Commun. 2016;7:11169), doi:10.1038/ncomms11169.
   PubMed Web of Science ®Google Scholar
• Sheedy P, Medarova Z. The fundamental Role of MiR-10b in metastatic cancer. Am. J. Cancer Res. 2018;8:1674–1688.
   PubMed Web of Science ®Google Scholar
• Wang S, Liu N, Tang Q, et al. MicroRNA-24 in cancer: A double side medal With opposite properties. Front. Oncol. 2020;10:1–9. doi:10.3389/fonc.2020.553714.
   PubMed Web of Science ®Google Scholar
• Gao H, Wang H, Yang W. Identification of Key genes and Construction of MicroRNA-MRNA regulatory networks in Multiple Myeloma by Integrated Multiple GEO datasets using bioinformatics analysis. Int. J. Hematol. 2017;106:99–107. doi:10.1007/s12185-017-2216-2.
   PubMed Web of Science ®Google Scholar
• Yang Y, Ding R, Wang R. Identification of Candidate targets and mechanisms involved in MiRNA regulation in Multiple myeloma. World J. Surg. Oncol. 2022;20:23), doi:10.1186/s12957-021-02482-1.
   PubMed Web of Science ®Google Scholar
• Lionetti M, Biasiolo M, Agnelli L, et al. Identification of MicroRNA Expression Patterns and definition of a MicroRNA/MRNA regulatory network in distinct molecular groups of Multiple myeloma. Blood. 2009;114:e20–e26. doi:10.1182/blood-2009-08-237495.
   PubMed Web of Science ®Google Scholar