Effects of β-catenin on differentially expressed genes in multiple myeloma

Abstract

This study aimed to identify the differentially expressed genes after silencing of β-catenin in multiple myeloma transduced with β-catenin shRNA. The DNA microarray dataset GSE17385 was downloaded from Gene Expression Omnibus, including 3 samples of MM1.S (human multiple myeloma cell lines) cells transduced with control shRNA and 3 samples of MM1.S cells transduced with β-catenin shRNA. Then the differentially expressed genes (DEGs) were screened by using Limma. Their underlying functions were analyzed by employing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Moreover, DEGs annotation was conducted based on the databases of tumor associated genes, tumor suppressed genes and the transcriptional regulation from patterns to profiles. Furthermore, the protein-protein interaction (PPI) relationship was obtained from STRING and the protein-protein interaction network and the functional modules were visualized by Cytoscape. Then, the pathway enrichment for the DEGs in the functional module was performed. A total of 301 DEGs, including 124 up-regulated and 117 down-regulated DEGs, were screened. Functional enrichment showed that CCNB1 and CDK1 were significantly related to the function of cell proliferation. FOS and JUN were related to innate immune response-activating signal transduction. Pathway enrichment analysis indicated that CCNB1 and CDK1 were most significantly enriched in the pathway of cell cycle. Besides, FOS and JUN were significantly enriched in the Toll-like receptor signaling pathway. FOXM1 was identified as a transcription factor. Moreover, there existed interactions among CCNB1, FOXM1 and CDK1 in PPI network. The expression of FOS, JUN, CCNB1, FOXM1 and CDK1 may be affected by β-catenin in multiple myeloma.

Keywords

multiple myeloma / β-catenin / differentially gene expression / functional enrichment

References

[1] HideshimaT, MitsiadesC, TononG, et al. . Understanding multiple myeloma pathogenesis in the bone marrow to identify new therapeutic targets. Nat Rev Cancer, 2007, 7(8): 585-59 PMID: 17646864

[2] HemdonTM, DeisserothA, KaminskasE, et al. . U.S. Food and Drug Administration approval: carfilzomib for the treatment of multiple myeloma. Clin Cancer Res, 2013, 19(17): 4559-4563

[3] SiegelR, NaishadhamD, JemalA. Cancer statistics for Hispanics/Latinos, 2012. CA Cancer J Clin, 2012, 62(5): 283-298 PMID: 22987332

[4] SukhdeoK, ManiM, ZhangY, et al. . Targeting the beta-catenin/TCF transcriptional complex in the treatment of multiple myeloma. Proc Natl Acad Sci USA, 2007, 104(18): 7516-7521 PMCID: 1863489 PMID: 17452641

[5] Dutta-SimmonsJ, ZhangY, GorgunG, et al. . Aurora kinase A is a target of Wnt/beta-catenin involved in multiple myeloma disease progression. Blood, 2009, 114(13): 2699-2708 PMID: 19652203

[6] LiangW, YangC, QianY, et al. . Effects of short-hairpin RNA-inhibited β-catenin expression on the growth of human multiple myeloma cells in vitro and in vivo. Biochem Biophys Res Commun, 2012, 422(4): 681-686 PMID: 22609776

[7] ZiFM, HeJS, LiY, et al. . Fibroblast activation protein protects bortezomib-induced apoptosis in multiple myeloma cells through β-catenin signaling pathway. Cancer Biol Ther, 2014, 15(10): 1413-1422 PMID: 25046247

[8] NarasipuraSD, HendersonLJ, FuSW, et al. . Role of β-catenin and TCF/LEF family members in transcriptional activity of HIV in astrocytes. J Virol, 2012, 86(4): 1911-1921 PMCID: 3302377 PMID: 22156527

[9] AfsariB, GemanD, FertigEJ. Learning dysregulated pathways in cancers from differential variability analysis. Cancer Inform, 2014, 13: 61-67 PMCID: 4218688 PMID: 25392694

[10] IrizarryRA, BolstadBM, ColinF, et al. . Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 2003, 31(4): 15

[11] GentlemanR. Reproducible research: a bioinformatics case study. Stat Appl Genet Mol Biol, 2005, 4: 2

[12] BenjaminiY, HochbergY. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B (Methodological), 1995, 57(1): 289-300

[13] AshburnerM, BallCA, BlakeJA, et al. . Gene Ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25(1): 25-29 PMID: 10802651

[14] OgataH, GotoS, SatoK, et al. . KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 1999, 27(1): 29-34 PMCID: 148090 PMID: 9847135

[15] MatysV, FrickeE, GeffersR, et al. . TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res, 2003, 31(1): 374-378 PMCID: 165555 PMID: 12520026

[16] ZhaoM, SunJ, ZhaoZ. TSGene: a web resource for tumor suppressor genes. Nucleic Acids Res, 2013, 41(1): 970-976

[17] ChenJS, HungWS, ChanHH, et al. . In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma. Bioinformatics, 2013, 29(4): 420-427 PMID: 23267173

[18] FranceschiniA, SzklarczykD, FrankidS, et al. . STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res, 2013, 41(1): 808-815

[19] SaitoR, SmootME, OnoK, et al. . A travel guide to Cytoscape plugins. Nat Methods, 2012, 9(11): 1069-1076 PMCID: 3649846 PMID: 23132118

[20] BeisserD, KlauGW, DandekarT, et al. . BioNet: an R-Package for the functional analysis of biological networks. Bioinformatics, 2010, 26(8): 1129-1130 PMID: 20189939

[21] MunshiNC, LiC, MinvielleS. Alternate splicing is a frequent event and impacts clinical outcome in myeloma: A High-density exon array analysis of uniformly treated newly-diagnosed myeloma patients. Blood, 2008, 112(11): 189-189

[22] MunshiNC, Avet-LoiseauH. Genomics in multiple myeloma. Clin Cancer Res, 2011, 17(6): 1234-1242 PMCID: 3783001 PMID: 21411439

[23] SongWH, ZhangCL, HuYG, et al. . Bone marrow microenvironment affects the pathogenesis of multiple myeloma through downregulation of alternative splicing factor FOX2 in myeloma cells. Blood, 2013, 122(21): 3085

[24] LiY, ChenYL, XieYT, et al. . Association study of germline variants in CCNB1 and CDK1 with breast cancer susceptibility, progression, and survival among Chinese Han women. PloS One, 2013, 8(12): 84489

[25] DaiH, YeM, PengM, et al. . Aptamer TY04 inhibits the growth of multiple myeloma cells via cell cycle arrest. Tumor Biol, 2014, 35(8): 7561-7568

[26] FuZ, MalureanuL, HuangJ, et al. . Plk1-dependent phosphorylation of FoxM1 regulates a transcriptional programme required for mitotic progression. Nat cell Biol, 2008, 10(9): 1076-1082 PMCID: 2882053 PMID: 19160488

[27] OrenM. Decision making by p53: life, death and cancer. Cell Death Differ, 2003, 10(4): 431-442 PMID: 12719720

[28] SahaMN, JiangH, YangY, et al. . Targeting p53 via JNK pathway: a novel role of RITA for apoptotic signaling in multiple myeloma. PloS One, 2012, 7(1): 30215

[29] LiX, JiangS, TappingRI. Toll-like receptor signaling in cell proliferation and survival. Cytokine, 2010, 49(1): 1-9 PMCID: 2808458 PMID: 19775907

[30] BagartuniT, KastritisE, LiacosC, et al. . Toll-Like receptor activation promotes multiple myeloma cell growth and survival by suppression of endoplasmic reticulum stress factor CHOP. Blood, 2013, 122(21): 3112-3112

[31] BaoH, LuP, LiY, et al. . Triggering of toll-like receptor-4 in human multiple myeloma cells promotes proliferation and alters cell responses to immune and chemotherapy drug attack. Cancer Biol Ther, 2011, 11(1): 58-67 PMID: 21248470

[32] CholujovaD, JakubikovaJ, CzakoB, et al. . MGN-3 arabinoxylan rice bran modulates innate immunity in multiple myeloma patients. Cancer Immunol Immunother, 2013, 62(3): 437-445 PMID: 22941038

[33] DhodapkarMV, GellerMD, ChangDH, et al. . A reversible defect in natural killer T cell function characterizes the progression of premalignant to malignant multiple myeloma. J Exp Med, 2003, 197(12): 1667-1676 PMCID: 2193955 PMID: 12796469