RESEARCH ARTICLE

Gene set enrichment analysis of the NF-κB/Snail/YY1/RKIPcircuitry in multiple myeloma

Apostolos Zaravinos & Peggy Kanellou &

George Ι. Lambrou & Demetrios A. Spandidos

Received: 20 November 2013 /Accepted: 14 January 2014# International Society of Oncology and BioMarkers (ISOBM) 2014

Abstract The presence of a dysregulated NF-κB/Snail/YY1/RKIP loop was recently established in metastatic prostatecancer cells and non-Hodgkin’s lymphoma; however, its in-volvement in multiple myeloma (MM) has yet to be investi-gated. Aim of the study was to investigate the role of theNF-κB/Snail/YY1/RKIP circuitry in MM and how each geneis correlated with the remaining genes of the loop. Using geneset enrichment analysis and gene neighbours analysis in datareceived from four datasets included in the Multiple MyelomaGenomics Portal of the Multiple Myeloma ResearchConsortium, we identified various enriched gene sets associ-ated with each member of the NF-κB/Snail/YY1/RKIP cir-cuitry. In each dataset, the 20 most co-expressed genes withthe circuitry genes were isolated subjected to Gene Ontologyand Kyoto Encyclopedia of Genes and Genomes enrichment.Among many, we highlighted on FNDC3B, TPD52, BBX,MBNL1 and MFAP2. Many co-expressed genes participatedin the regulation of metabolic processes and nucleic acidbinding, or were transcription factor binding genes and genes

with metallopeptidase activity. The transcription factorsFOXO4, GATA binding factor, Sp1 and AP4 most likelyaffect the expression of the NF-κB/Snail/YY1/RKIP cir-cuitry genes. Computational analysis of various GEOdatasets revealed elevated YY1 and RKIP levels in MMvs. the normal plasma cells, as well as elevated RKIPlevels in MM vs. normal B lymphocytes. The present studyhighlights the relationships of the NF-κB/Snail/YY1/RKIPcircuitry genes with specific cancer-related gene sets inmultiple myeloma.

Keywords NF-κB/Snail/YY1/RKIP circuitry . Multiplemyeloma . Epithelial-to-mesenchymal transition . Gene setenrichment analysis . Gene neighbours analysis . Geneco-expression . Gene sets

AbbreviationsAML Acute myeloid leukemiaBBX Bobby sox homolog (Drosophila)DLBCL Diffuse large B-cell lymphomaEMT Epithelial-to-mesenchymal transitionES Enrichment scoreFNDC3B Fibronectin type III domain containing 3BGEO Gene expression omnibusGO Gene ontologyGSEA Gene set enrichment analysisKEGG Kyoto encyclopedia of genes and genomesMBNL1 Muscleblind-like splicing regulator 1MFAP2 Microfibrillar-associated protein 2MGUS Monoclonal gammopathy of undetermined

significanceMM Multiple myelomaNES Normalised enrichment scoreNF-κB Nuclear factor kappa-light-chain-enhancer of

activated B cellsNHL Non-Hodgkin’s lymphoma

Electronic supplementary material The online version of this article(doi:10.1007/s13277-014-1659-9) contains supplementary material,which is available to authorized users.

A. Zaravinos (*) :D. A. SpandidosLaboratory of Virology, Medical School, University of Crete,71110 Heraklion, Greecee-mail: azaravinos@gmail.com

A. ZaravinosMolecular Medicine Research Center (MMRC), Department ofBiological Sciences, University of Cyprus, 1678 Nicosia, Cyprus

P. KanellouDepartment of Hematology, University Hospital of Heraklion, Crete,Greece

G. Ι. Lambrou1st Department of Pediatrics, Choremeio Research Laboratory,University of Athens, 11527 Athens, Greece

Tumor Biol.DOI 10.1007/s13277-014-1659-9

PTEN Phosphatase and tensin homologueRKIP Raf-1 kinase inhibitor proteinSnail Snail family zinc finger 1TFBMs Transcription factor binding motifsTPD52 Tumour protein D52YY1 Yin Yang 1

Introduction

Multiple myeloma (MM) is characterised by the clonal pro-liferation of plasma cells in the bone marrow, usually withelevated serum and urine monoclonal paraproteins and asso-ciated end organ damage. MM is the second most frequenthaematological cancer in the USA after non-Hodgkin’s lym-phoma and accounts for 10 % of all haematological malig-nancies. The majority of patients with MM evolve from anasymptomatic premalignant stage termed monoclonalgammopathy of undetermined significance (MGUS) [1].MGUS is present in over 3 % of the population above theage of 50 and over 5 % above the age of 70 and has beenfound to progress to myeloma at a rate of 1 % per year [2].Novel agents including immunomodulatory drugs, protea-some inhibitors, monoclonal antibodies and drugs affectingan interaction with the tumour microenvironment can achieveresponses in patients with relapsed and refractory MM; how-ever, the median survival remains at 6 years, with only 10% ofpatients surviving at 10 years [3–5].

Epithelial-to-mesenchymal transition (EMT) describes areversible series of events, such as loss or redistribution ofepithelial-specific junctional proteins, including E-cadherin,and activation of mesenchymal markers, including Vimentinand N-cadherin. During these events an epithelial cell losescell–cell contacts and acquires mesenchymal characteristics.Both invasion and metastasis may be dependent on the acqui-sition of EMT features by primary cancer cells [6–8].

The Bonavida group has recently established the presenceof a dysregulated nuclear factor kappa-light-chain-enhancer ofactivated B cells/snail family zinc finger 1/Yin Yang 1/Raf-1kinase inhibitor protein (NF-κB/Snail/YY1/RKIP) loop intumour cells and investigated its role in the regulation oftumour cell resistance to chemo- and immune-therapies[9–13]. This loop was found to be pivotal in the regulationof tumour cell sensitivity to cytotoxic apoptotic stimuli.Furthermore, it is involved in the regulation of the EMTphenotype and metastasis [10] (Fig. 1). However, not muchis known regarding the genes that are correlated with thiscircuitry.

Gene set enrichment analysis (GSEA) is a method used tointegrate information from manually accumulated databaseswith large-scale expression data. It provides the potential todetect whole sets of functionally related groups of genes by

comparing expression data with selected gene sets from avail-able gene set databases [14].

In the present study, we investigated the role of the NF-κB/Snail/YY1/RKIP circuitry in multiple myeloma and how eachgene is correlated with the remaining genes of the loop. Weidentified gene sets correlated with the expression profile ofYY1, NFKB1, SNAI1 and RKIP in multiple myeloma, and weisolated the co-expressed genes from four independentdatasets. Furthermore, computational analysis was performedin various myeloma-related Gene Expression Omnibus(GEO) datasets, in order to investigate the expression patternof YY1, NFKB1, SNAI1 and RKIP in the disease.

Materials and methods

Data mining

Microarray data mining permits not only the investigation ofexpression levels and correlations between molecules but alsoof determining which genes have similar expression patterns.In the present study, we used GSEA and gene neighboursanalysis in data received from four independent multiplemyeloma-specific datasets included in the MultipleMyeloma Genomics Portal of the Multiple MyelomaResearch Consortium (http://www.broadinstitute.org/mmgp/home): (1) the Arkansas dataset [15]; (2) the Carrasco dataset[16]; (3) the Mayo Clinic dataset [17] and (4) the MultipleMyeloma Research Consortium (MMRC) dataset. The last

Fig. 1 The NF-κB/Snail/YY1/RKIP circuitry

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one is a comprehensive collection of expression data frommultiple myeloma patients.

GSEA by gene

GSEA finds pathway gene sets that are correlated with theexpression profile of a gene of interest. We performed analysisfor pathway gene sets correlated with the expression profile ofthe genes forming the NF-κB/Snail/YY1/RKIP circuitry. Thegene sets are derived from the canonical pathways collectionof the Molecular Signatures Database (MSigDB) [14]. Eachgene’s microarray probe ID that was used to generate theexpression data set was as follows: for NFKB1, 239876_at(Arkansas dataset) and 23986_at (Carrasco, Mayo Clinic and

MMRC datasets); for SNAI1, 219480_at (Arkansas, Carrascoand Mayo Clinic datasets); for YY1, 224718_at (Arkansas,Carrasco and MMRC datasets) and 200047_s_at (MayoClinic dataset); and for PEBP1 (RKIP) 211941_at (Arkansas,Carrasco, Mao Clinic and MMRC datasets). All gene annota-tions were derived from the HG-U133 Plus 2.0, HG-U133Aand HG-U133B arrays (Affymetrix, Santa Clara, CA, USA).The final database was composed of 238 cancer-related genesets (Table S1).

GSEA statistics

Further statistical processing was performed with GSEA [14],using the Molecular Signatures Database, MSigDB

Fig. 2 Indicative snapshots of theenrichment results for the NF-κB/Snail/YY1/RKIP circuitry genesin the Arkansas dataset. Theenrichment plots provide agraphical view of the enrichmentscore for each gene set. The redportion in the left half of eachgraph shows the positivecorrelation with multiplemyeloma. The height above thedotted line shows the strength ofcorrelation. Panels show theenrichment plots of ribosomalproteins (NF-κB and YY1),active ligand receptor interaction(Snail), and Krebs/TCA Cycle(RKIP), respectively, asmentioned in the Broad Institute’sMolecular Signature Database

Table 1 Genesets positively enriched with YY1 in each dataset. Genesets with a NOM (p value) and FDR (q value)≤0.05 are highlighted

Genesets positively enriched with YY1

Carrasco Arkansas Mayo Clinic MMRCNo genes /data set NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val)

GPCRDB_OTHER 57/57/48/57 1.37 0.01 0.0266 1.02 0.43 1.000 0.87 0.740 1.000 1.11 0.270 0.935GHPATHWAY 27/27/27/27 1.33 0.05 0.0422 0.57 0.98 1.000 0.98 0.510 1.000 0.94 0.590 1.000G1PATHWAY 25/25/25/25 1.16 0.21 0.5908 -1.16 <0.01 0.104 0.83 0.840 1.000 0.82 0.747 1.000HSA03010_RIBOSOME 62/62/61/62 0.45 0.87 0.9942 1.31 0.25 1.000 -1.95 <0.01 0.000 0.46 1.000 1.000RIBOSOMAL_PROTEINS 95/95/91/95 0.65 0.65 0.9888 1.54 0.17 0.978 -1.57 <0.01 0.000 0.75 0.780 1.000DNA_REPLICATION_REACTOME 44/44/44/44 0.7 0.8 1.000 0.65 0.93 1.000 0.84 0.700 1.000 -1.29 <0.01 0.000

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(http://www.broad.mit.edu/gsea/msigdb/index.jsp). Formultiple myeloma phenotypes, a positive enrichment score(ES) indicated correlation with multiple myeloma and anegative enrichment score indicated correlation with thenormal phenotype. The normalised enrichment scores(NES) were used to compare analysis results across thevarious gene sets, using the formula: NES = actual ES/mean (ESs against all permutations of the dataset). Generanking was based on a Pearson metric and the weighted

enrichment statistic was selected. Phenotype labels werepermuted 1,000 times and the normalised p ≤ 0.05 and falsediscovery rate (FDR) ≤ 0.25 were selected as statisticallysignificant enrichments.

Gene neighbours

The gene neighbour marker analysis algorithm calculatesthe nearest neighbours for a particular gene (or other

Table 2 Genesets positively enriched with NFκB in each dataset. Genesets with a NOM (p value) and/or FDR (q value)≤0.25 are highlighted

Genesets positively enriched with NFKB1

Carrasco Arkansas Mayo Clinic MMRC

No genes/dataset NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val)

HSA00190_OXIDATIVE_PHOSPHORYLATION 113/113/100/113 1.12 0.320 1.000 1.74 <0.01 0.145 0.78 0.670 1.000 0.85 0.77 1.000TRANSLATION_FACTORS 46/46/44/46 0.95 0.556 1.000 1.62 0.010 0.160 0.76 0.818 1.000 1.15 0.27 0.586OXIDATIVE_PHOSPHORYLATION 57/57/54/113 1.00 0.450 1.000 1.53 0.020 0.198 0.84 0.673 1.000 0.85 0.77 1.000KREBS_TCA_CYCLE 29/29/28/29 1.03 0.460 1.000 1.46 0.050 0.242 0.78 0.816 1.000 0.88 0.65 1.000PURINE_METABOLISM 114/114/111/142 1.10 0.260 1.000 1.33 0.040 0.038 0.76 0.960 1.000 0.99 0.54 0.964ST_TUMOR_NECROSIS_FACTOR_PATHWAY 29/29/29/29 0.69 0.929 1.000 1.08 0.340 1.000 1.82 <0.01 0.218 1.65 0.010 0.082ST_B_CELL_ANTIGEN_RECEPTOR 39/39/39/39 0.80 0.850 1.000 0.97 0.570 1.000 1.62 <0.01 0.341 1.690 <0.01 0.148APOPTOSIS_KEGG 48/48/48/48 0.78 0.930 1.000 0.99 0.490 1.000 1.28 0.050 0.677 1.670 <0.01 0.093GSK3PATHWAY 26/26/25/26 N/A N/A 1.000 0.54 1.000 1.000 1.18 0.210 0.855 1.630 0.010 0.074HSA04120_UBIQUITIN_MEDIATED_PROTEOLYSIS 36/36/32/36 0.99 0.510 1.000 1.09 0.350 1.000 1.00 0.440 1.000 1.620 <0.01 0.067ST_ERK1_ERK2_MAPK_PATHWAY 30/30/29/30 0.68 0.970 1.000 0.95 0.600 1.000 1.36 0.050 0.525 1.610 <0.01 0.057TOLLPATHWAY 33/33/30/33 0.53 1.000 1.000 0.60 0.970 1.000 1.24 0.162 0.676 1.540 <0.01 0.143SIG_IL4RECEPTOR_IN_B_LYPHOCYTES 27/27/27/27 0.74 0.910 1.000 0.72 0.920 1.000 1.15 0.220 0.94 1.530 <0.01 0.137SIG_CD40PATHWAYMAP 33/33/33/33 0.78 0.890 1.000 0.95 0.590 1.000 1.44 0.020 0.507 1.480 0.010 0.222ST_P38_MAPK_PATHWAY 36/36/35/36 0.87 0.740 1.000 0.94 0.670 1.000 1.29 0.090 0.666 1.460 0.020 0.245ST_GA13_PATHWAY 35/35/35/35 0.85 0.740 1.000 0.97 0.560 1.000 1.60 <0.01 0.288 1.440 0.020 0.238ST_FAS_SIGNALING_PATHWAY 61/61/59/61 0.87 0.730 1.000 1.18 0.140 1.000 1.16 0.170 0.888 1.430 <0.01 0.241KERATINOCYTEPATHWAY 43/43/43/43 0.58 0.990 1.000 1.02 0.450 1.000 1.41 0.030 0.496 1.430 0.020 0.242GLUTATHIONE_METABOLISM 30/37/32/30 0.72 0.940 1.000 1.04 0.460 1.000 0.68 0.960 1.000 -1.310 <0.01 <0.01HSA00565_ETHER_LIPID_METABOLISM 31/31/27/31 1.35 0.050 1.000 1.28 0.120 1.000 0.68 0.930 1.000 1.100 0.330 0.310HSA00790_FOLATE_BIOSYNTHESIS 29/39/36/39 1.32 0.030 1.000 0.89 0.740 1.000 1.03 0.354 1.000 0.990 0.540 0.531

Table 3 Genesets positively enriched with SNAI1 in each dataset. Genesets with a NOM (p value) and FDR (q value)≤0.25 are highlighted

Genesets positively enriched with SNAI1

Carrasco Arkansas Mayo Clinic MMRC

No genes/dataset NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val)

DNA_REPLICATION_REACTOME 44/44/44/44 0.61 0.857 1.000 -1.26 <0.01 0.003 N/A N/A 1.000 0.64 0.84 1.000HSA00030_PENTOSE_PHOSPHATE_PATHWAY 26/26/25/26 0.76 0.850 1.000 -1.88 <0.01 0.000 0.71 0.90 1.000 0.72 0.89 1.000TNFR1PATHWAY 28/28/28/28 0.68 0.929 1.000 -1.13 <0.01 0.004 0.59 0.98 1.000 0.42 1.00 1.000EDG1PATHWAY 25/25/25/25 1.05 0.410 1.000 0.69 0.900 1.000 1.16 0.232 0.19 0.61 0.95 1.000TYROSINE_METABOLISM 57/20/27/30 1.04 0.380 1.000 1.30 0.120 0.049 1.38 0.031 0.028 1.18 0.20 0.167

HSA03010_RIBOSOME 62/62/61/62 1.37 0.221 1.000 0.75 0.625 1.000-

2.33 <0.01 <0.01 N/A N/A 1.000RIBOSOMAL_PROTEINS 95/95/91/95 1.62 0.185 0.257 0.83 0.548 1.000 -1.8 <0.01 <0.01 N/A N/A 1.000HSA01430_CELL_COMMUNICATION 123/123/109/123 1.27 0.010 1.000 1.44 <0.01 0.551 1.27 0.050 1.000 1.390 0.000 0.032HSA04080_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION 250/250/228/250 1.27 0.020 1.000 1.53 <0.01 0.948 1.27 0.040 1.000 1.350 0.000 0.047HSA02010_ABC_TRANSPORTERS_GENERAL 44/44/36/44 1.00 0.525 1.000 1.13 0.220 1.000 0.83 0.840 1.000 1.200 0.160 0.172HSA04340_HEDGEHOG_SIGNALING_PATHWAY 54/54/45/54 1.24 0.080 1.000 0.95 0.630 1.000 1.04 0.450 1.000 1.190 0.110 0.192HSA00980_METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450 60/60/51/60 1.11 0.280 1.000 1.02 0.410 1.000 0.68 0.950 1.000 1.180 0.140 0.213HSA04020_CALCIUM_SIGNALING_PATHWAY 173/173/162/173 1.26 0.020 1.000 1.24 0.010 1.000 1.32 0.030 1.000 1.250 0.010 0.109INFLAMPATHWAY 29/29/29/29 1.68 <0.01 0.287 1.03 0.430 1.000 0.94 0.590 1.000 1.430 0.010 0.017TYROSINE_METABOLISM 30/57/48/30 1.27 0.090 1.000 1.11 0.200 1.000 0.93 0.680 1.000 1.180 0.200 0.167SIG_IL4RECEPTOR_IN_B_LYPHOCYTES 27/27/27/27 0.68 0.940 1.000 -0.92 1.000 1.000 0.7 0.920 1.000 -1.270 0.000 0.250HSA00510_N_GLYCAN_BIOSYNTHESIS 39/39/32/39 0.62 0.980 1.000 0.56 0.990 1.000 0.55 1.000 1.000 -1.150 0.000 0.125FASPATHWAY 27/27/27/27 0.60 0.960 1.000 N/A N/A 1.000 0.32 1.000 1.000 -1.070 0.000 0.150

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continuous vector pseudo-gene) by identifying othergenes whose expression values follow similar trends tothe samples. Gene neighbours analysis was performedfor the NF-κB/Snail/YY1/RKIP circuitry genes in eachone of the four datasets. For each dataset, the 20 mostcommonly expressed genes as well as the gene underinvestigation were isolated. The Pearson correlation test

was used as a distance metric. Each gene could haveseveral probe sets associated with it. The cosine dis-tance metric was used. The cosine distance is given by:

dc ¼X

i

xi*yi

. X

i

x2i *X

i

y2i

!1=2

Table 4 Genesets positively enriched with RKIP in each dataset. Genesets with a NOM (p value)≤0.05 and FDR (q value)<0.25 are highlighted

Genesets positively enriched with RKIP

Carrasco Arkansas Mayo Clinic MMRC

No genes /data set NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val) NES

NOM (p-val)

FDR (q-val)

ST_PHOSPHOINOSITIDE_3_KINASE_PATHWAY 35/35/33/35 1.33 0.040 0.397 1.12 0.260 0.630 1.40 0.020 0.028 1.17 0.200 1.000

ST_GAQ_PATHWAY 27/27/27/27 0.65 0.960 0.972 1.35 0.060 0.381 N/A N/A 1.000 0.97 0.525 1.000

HSA00220_UREA_CYCLE_AND_METABOLISM_OF_AMINO_GROUPS 30/30/27/30 1.11 0.290 0.673 1.21 0.140 0.469 0.86 0.700 1.000 1.82 0.000 0.030

KREBS_TCA_CYCLE 29/29/28/29 0.98 0.560 0.793 1.65 <0.01 0.320 1.41 0.071 1.000 1.26 0.110 1.000

TNFR1PATHWAY 28/28/28/28 1.21 0.212 0.573 1.52 <0.01 0.311 0.76 0.900 1.000 1.23 0.133 1.000

HSA05221_ACUTE_MYELOID_LEUKEMIA 53/53/53/53 0.97 0.570 0.790 1.47 <0.01 0.371 0.71 0.940 1.000 0.98 0.570 1.000

PDGFPATHWAY 27/27/27/27 0.95 0.600 0.797 1.47 <0.01 0.329 0.94 0.600 1.000 1.16 0.220 1.000

G1_TO_S_CELL_CYCLE_REACTOME 67/67/63/67 1.29 0.140 0.438 1.45 <0.01 0.337 0.76 0.880 1.000 0.83 0.770 1.000

HSA05211_RENAL_CELL_CARCINOMA 68/68/67/68 1.15 0.200 0.634 1.42 <0.01 0.320 0.94 0.660 1.000 1.10 0.210 1.000

HSA05212_PANCREATIC_CANCER 72/72/71/72 1.21 0.110 0.572 1.36 <0.01 0.367 1.00 0.450 1.000 1.06 0.310 1.000

Table 5 The 20 most co-expressed to NFKB1genes, for each dataset: FNDC3B (3q26.31), TPD52 (8q21) and BBX (3q13.1) were common between theCarrasco and the Arkansas datasets; MBNL1 (3q25) was common between the Arkansas and the Mayo Clinic patients datasets

Carrasco dataset Arkansas dataset Mayo Clinic dataset MMRC dataset

Probe Gene Probe Gene Probe Gene Probe Gene

239876_at NFKB1 239876_at NFKB1 209239_at NFKB1 239876_at –

228793_at JMJD1C 232210_at BCL2 210843_s_at MFAP3L 232865_at AFF4

243109_at MCTP2 228271_at SND1 210493_s_at MFAP3L 237456_at –

241867_at PARP6 239264_at EXOC4 220594_at OGT 232511_at –

220221_at VPS13D 239285_at JMJD2C 207480_s_at MEIS2 242362_at –

242742_at MYO9B 1557240_a_at BBX 202932_at YES1 1555436_a_at AFF4

1569490_at FNDC3B 232472_at FNDC3B 212628_at PKN2 233369_at –

236645_at HBP1 227576_at chrX:73341703-73342466

212289_at ANKRD12 1566079_at RPS16P5

1564656_at – 232369_at MBNL2 222376_at chr4:103558781-103590192

235959_at –

220553_s_at TAF13 229483_at UBE2H 205442_at MFAP3L 232882_at –

239757_at ZA20D3 239862_at TPD52 220018_at CBLL1 1566959_at –

225239_at chr11:64963458-64964911

232614_at BCL2 202933_s_at YES1 232551_at SLC26A6

238812_at ZA20D3 222371_at PIAS1 201151_s_at MBNL1 244010_at –

220586_at CHD9 221617_at TAF9B 220740_s_at SLC12A6 214814_at YTHDC1

242457_at PAM 228105_at SAPS3 207764_s_at HIPK3 224098_at –

1557240_a_at BBX 241681_at MBNL1 208989_s_at FBXL11 226970_at FBXO33

215209_at SEC24D 232916_at UBE2E2 204527_at MYO5A 239655_at –

243450_at AKAP13 240600_at AP3B1 209961_s_at HGF 236755_at TBC1D23

244156_at chr15:85085271-85085616

232882_at FOXO1A 218161_s_at CLN6 232864_s_at AFF4

239862_at TPD52 230332_at ZCCHC7 213070_at PIK3C2A 1563455_at KIAA0999

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where i is the sample number, xi is the named reference gene’sexpression value for sample i, and yi is the expression value ofthe gene under investigation [18].

Gene Ontology, Kyoto Encyclopedia of Genes and Genomesand transcription factor target enrichment

Gene Ontology (GO) is a database with curated annotationsfor known genes, i.e., gene biological processes, molecularfunctions and cellular components [19–21]. GO analysis forthe most co-expressed genes, as revealed by the gene neigh-bours analysis, was performed using the WebGestalt web-tool (http://bioinfo.vanderbilt.edu/webgestalt) [22]. All thegene definitions and functions were based on the NationalInstitute of Health databases (http://www.ncbi.nlm.nih.gov/sites/entrez/). The hypergeometric test, with Benjamini–Hochberg (BH) correction was used for enrichment evalua-tion analysis. The R function adjP was used in order toadjust simultaneously the nominal p values of the largenumber of categories. The significance level for the adjustedp value was set at 0.01 and the number of minimum genesfor a category was set at 2. A Kyoto Encyclopedia of Genesand Genomes (KEGG) pathway analysis was conducted for

most of the co-expressed genes, as revealed by the geneneighbours analysis, as previously mentioned [23, 24].Transcription Factor Binding Motifs (TFBMs) analysis wasalso performed as previously described in detail [25], inorder to identify transcription factors that might affect theexpression of the NF-κB/Snail/YY1/RKIP circuitry.

Computational analysis of the NF-κB/Snail/YY1/RKIPcircuitry components

Computational analysis was performed to understandwhether differences in YY1, NFKB1, SNAI1 or RKIPtranscript levels occur in multiple myeloma when com-pared with the relative normal tissue, benign tumour orits metastatic counterpart. Four publicly available GEOdatasets were analysed: GDS2643 [26], GSE24870 [3],GSE27838 (unpublished work), GSE2113 [27] andGSE24990 [28]. Gene expression patterns of YY1,NFKB1, SNAI1 or RKIP were extracted from the nor-malised datasets. The results were expressed as meanlevels of the log2 intensity and were statistically com-pared by t test.

Table 6 The 20 most co-expressed to PEBP1 genes, for each dataset: MFAP2 (1p36.1-p35), LOC400451 (15q26.1) and chr12:120109740–120110217were common between the Carrasco and the Arkansas datasets

Carrasco dataset Arkansas dataset Mayo Clinic dataset MMRC dataset

Probe Gene Probe Gene Probe Gene Probe Gene

211941_s_at PEBP1 211941_s_at PEBP1 211941_s_at PEBP1 211941_s_at PEBP1

205353_s_at PEBP1 205353_s_at PEBP1 205353_s_at PEBP1 205353_s_at PEBP1

203417_at MFAP2 210825_s_at PEBP1 210825_s_at PEBP1 210825_s_at PEBP1

227410_at FAM43A 213138_at ARID5A 221648_s_at C1orf121 226177_at GLTP

210825_s_at PEBP1 51158_at LOC400451 206363_at MAF 200804_at TMBIM6

228737_at C20orf100 205718_at ITGB7 209347_s_at MAF 223532_at ANKRD39

232160_s_at TNIP2 221648_s_at C1orf121 219696_at FLJ20054 221972_s_at SDF4

209524_at HDGFRP3 228123_s_at ABHD12 209348_s_at MAF 208858_s_at FAM62A

203423_at RBP1 201212_at LGMN 203119_at CCDC86 217821_s_at WBP11

51158_at LOC400451 200952_s_at CCND2 204070_at RARRES3 226589_at TMEM192

214020_x_at ITGB5 221880_s_at LOC400451 200762_at DPYSL2 202139_at AKR7A2

217678_at SLC7A11 207091_at P2RX7 219377_at FAM59A 224665_at C10orf104

216033_s_at FYN 200965_s_at ABLIM1 208116_s_at MAN1A1 221648_s_at –

209510_at RNF139 224583_at COTL1 204088_at P2RX4 212871_at MAPKAPK5

214692_s_at JRK 47553_at DFNB31 204849_at TCFL5 228123_s_at ABHD12

1556308_at PRRT3 203417_at MFAP2 200660_at S100A11 210716_s_at CLIP1

226973_at C20orf102 205286_at TFAP2C 200952_s_at CCND2 225803_at FBXO32

230741_at chr12:120109740-120110217

200951_s_at CCND2 218858_at DEPDC6 202296_s_at RER1

221539_at EIF4EBP1 230741_at chr12:120109740-120110217

203515_s_at PMVK 208675_s_at DDOST

212259_s_at PBXIP1 201481_s_at PYGB 221060_s_at TLR4 226155_at FAM160B1

Tumor Biol.

Results

GSEA by gene

GSEA first ranks the probes in the expression dataset based onhow closely their expression profiles correlate with the gene ofinterest. It then calculates an enrichment score (ES) for eachgene set, which reflects the degree to which the genes in thegene set are over-represented at the top or bottom of theranked list of probes. Indicative snapshots of the enrichmentresults for the NF-κB/Snail/YY1/RKIP circuitry genes in theArkansas dataset are provided in Fig. 2. The analysis resultsare presented in the Supplementary data file. GSEAwas usedto identify relationships of YY1, NFKB1, SNAI1 and RKIP tospecific cancer-related gene sets, and the results indicated theenrichment of 6, 21, 18 and 10 gene sets associated with YY1,NFKB1, SNAI1and RKIPexpression, respectively, for the fourdatasets for a total of 238 gene sets (Tables 1, 2, 3, and 4).

Gene neighbours

For each dataset, we identified the 20 most co-expressed geneswith those of the NF-κB/Snail/YY1/RKIP circuitry

(Table 5, 6, 7, and 8). The common pattern of expression amonggenes may be attributed either to close gene proximity, or simplyto chance. We also examined whether the 20 most co-expressedgenes and those of the NF-κB/Snail/YY1/RKIP circuitry hadany genes in common. The findings demonstrated that withregard to the 20 most co-expressed genes to YY1 (14q), theywere different in each dataset. Only BRD4 (19p13.1) was com-mon between the Arkansas and MMRC datasets. Of note,FNDC3B (3q26.31), TPD52 (8q21) and BBX (3q13.1) werecommon between the Carrasco and Arkansas datasets, whereasMBNL1 (3q25) was common between the Arkansas and MayoClinic patient datasets. Regarding the genes clustered withRKIP1 (12q24.23) across the four datasets, MFAP2 (1p36.1-p35), LOC400451 (15q26.1) and chr12:120109740–120110217were common between the Carrasco and Arkansas datasets.

GO annotation and KEGG analysis for the most co-expressedgenes with the NF-κB/Snail/YY1/RKIP circuitry

We used GO enrichment to obtain an overview of the mainclasses of biological functions of the most co-expressed geneswith those of the NF-κB/Snail/YY1/RKIP circuitry. Each genecan belong to more than one category (Biological Process,

Table 7 The 20 most co-expressed to SNAI1 genes, for each dataset

Carrasco dataset Arkansas dataset Mayo Clinic dataset MMRC dataset

Probe Gene Probe Gene Probe Gene Probe Gene

219480_at SNAI1 219480_at SNAI1 219480_at SNAI1 219480_at SNAI1

216339_s_at TNXA 229611_at chr3:199249895-199254986

205847_at PRSS22 208009_s_at ARHGEF16

1570127_at chr19:23029466-23035369

228807_at chr17:3352581-3353449

207650_x_at PTGER1 244438_at –

1563590_at chr10:129654277-129657919

214406_s_at SLC7A4 211112_at SLC12A4 243883_at MMP15

1566485_at FHIT 228586_at ENG 206329_at EXTL1 223617_x_at ATAD3B

213978_at LOC92154 1552449_a_at SCGB1C1 214207_s_at CARD10 214387_x_at SFTPC

222539_at CLN6 1564074_at chr11:44599571-44601836 37020_at CRP 210226_at NR4A1

241369_at ADAMTS9 237267_at chr11:32014100-32019405 211431_s_at TYRO3 216340_s_at CYP2A7P1

1560464_at NPLOC4 209844_at HOXB13 208472_at IKZF4 233287_at SLC6A17

210179_at KCNJ13 221180_at YSK4 213381_at C10orf72 229452_at TMEM88

235818_at VSTM1 232855_at THSD4 208300_at PTPRH 211748_x_at PTGDS

205563_at KISS1 232697_at LRFN2 217115_at chr20:44525938-44527200

206725_x_at BMP1

1569422_at BCNP1 1553629_a_at FAM71B 206634_at SIX3 208260_at AVPR1B

229386_at ID4 221456_at TAS2R3 215487_x_at RP13-401 N8.2 1553359_at FBXL18

220505_at C9orf53 216130_at PDZRN3 210961_s_at ADRA1D 205986_at AATK

1556700_a_at chr8:30539983-30543393

219422_at chr1:6443797-6448842

206523_at PSCD3 224079_at IL17C

1559865_at chr6:93154156-93155188

237583_at chr11:130240213-130240557

208455_at PVRL1 230470_at DSCR9

205795_at NRXN3 237285_at SORBS2 210770_s_at CACNA1A 1560154_a_at –

1568920_at SOX5 237360_at ACTRT2 211314_at CACNA1G 206484_s_at XPNPEP2

1560260_at LOC285593 228508_at MAML3 207927_at HTR7 237079_at –

Tumor Biol.

Cellular Component and Molecular Function), as previouslyreported [19]. The results of the functional annotation are pre-sented in Fig. 3. From the large number of functions of the mostco-expressed genes with NFKB1 genes, three categories wereoutlined: regulation of metabolic process genes (CHD9,MEIS2,UBE2E2, JMJD1C, UBE2H, SND1, MBNL1, TAF9B, PIAS1,HBP1, AFF4, NFKB1, BBX, BCL2, HGF, SAPS3, CLN6 andHIPK3); transcription factor binding genes (BCL2, PIAS1,MEIS2, SND1, JMJD1Cand TAF9B) and ligase activity, formingcarbon-nitrogen bonds (UBE2H, CBLL1, PIAS1 andUBE2E2).

As regards the functions of the most co-expressed geneswith SNAI1 genes, we outlined the metallopeptidase activitygenes (MMP15, XPNPEP2, ADAMTS9, THSD4 and BMP1).

Five categories were outlined for the functions of the mostco-expressed genes with YY1 genes: interphase genes (CUL5,FOXN3, KHDRBS1, GAS1 and CUL3); macromolecule meta-bolic process (TNRC6B, BRD4, SF1, PUM1, FOXN3, SFRS8,KHDRBS1, CUL4B, CUL3, TOP2B, DPP8, PAPOLA, TRIM33,ZNF232, SNW1, SMAD2, CUL5, PSMC6, ZHX1,MTA1, DARS,PTPN11, VCPIP1, CHD4, YY1, RNF139, PURA, SAP30L,

TNRC6A and SFPQ); nucleic acid binding (SRP72, TNRC6B,BRD4, SF1, PUM1, FOXN3, SFRS8, KHDRBS1, NSUN6,OP2B, MATR3, PAPOLA, TRIM33, ZNF232, SMAD2, TSNAX,ZHX1, MTA1, ARS, CHD4, YY1, PURA, TNRC6A and SFPQ);and protein binding (WASF2, BTBD7, KHDRBS1, CUL4B,TOP2B, RAPGEF6, GAS1, SNW1, CUL5, MTA1, PTCD3,PTPN11, CHD4, SFPQ, KPNA6, FNBP4, BRD4, SF1, TTYH1,SFRS8, FOXN3, CUL3,MATR3, TRIM33, PHACTR4, SMAD2,PSMC6, ZHX1, DARS, YY1, MYO9A, MYCBPAP, PURA andRNF139) genes; and transcription repressor activity (SNW1,YY1, SF1, FOXN3 and KHDRBS1) genes.

Finally, we outlined seven categories for the functions ofthe most co-expressed to RKIP genes: response to organicsubstance genes (P2RX4, FYN, DDOST, EIF4EBP1, PEBP1,P2RX7, CCND2, TLR4 and SDF4); positive regulation oforganic acid transport genes (P2RX4 and P2RX7); sensoryperception of pain genes (P2RX4, P2RX7 and FYN); prosta-glandin transport genes (P2RX4 and P2RX7); lipopolysaccha-ride binding genes (P2RX7 and TLR4); identical protein bind-ing (P2RX4, P2RX7, CLIP1, ITGB7, FYN, S100A11 and

Table 8 The 20 most co-expressed to YY1 genes, for each dataset: the gene BRD4 was common between the Arkansas and the MMRC datasets

Carrasco dataset Arkansas dataset Mayo Clinic dataset MMRC dataset

Probe Gene Probe Gene Probe Gene Probe Gene

224718_at YY1 224718_at YY1 200047_s_at YY1 224718_at YY1

228827_at chr8:93037703-93039730

202723_s_at FOXO1A 221749_at YTHDF3 224711_at YY1

236074_at ZNF232 201623_s_at DARS 212867_at chr8:71184376-71186906

224562_at WASF2

237524_at chr19:47131993-47132444

219112_at RAPGEF6 208766_s_at HNRPR 201901_s_at YY1

204457_s_at GAS1 1555247_a_at RAPGEF6 201586_s_at SFPQ 202102_s_at BRD4

232121_at DNMT2 204021_s_at PURA 212718_at PAPOLA 229673_at –

1562791_at MYCBPAP 238105_x_at chr22:44694913-44695331

200040_at KHDRBS1 229297_at –

203983_at TSNAX 1557347_at MCPH1 201575_at SNW1 228512_at PTCD3

241721_at – 212103_at chr1:32346243-32414058

209510_at RNF139 205022_s_at FOXN3

231345_s_at FLJ13639 242472_x_at FNBP4 212163_at KIDINS220 1556000_s_at BTBD7

1569354_at NSUN6 226764_at LOC152485 203531_at CUL5 235008_at –

228734_at chr8:49138851-49139819

222831_at SAP30L 201371_s_at CUL3 202774_s_at SFRS8

230991_at chr9:89871172-89871958

208095_s_at SRP72 203075_at SMAD2 212356_at KIAA0323

1552774_a_at SLC25A27 226250_at chr3:115533603-115536130

200073_s_at HNRPD 234734_s_at TNRC6A

201699_at PSMC6 219235_s_at PHACTR4 201166_s_at PUM1 229036_at TNRC6B

244567_at chr5:156386258-156387084

223213_s_at ZHX1 208738_x_at SUMO2 208313_s_at SF1

234439_at chr10:30002393-30390464

212101_at KPNA6 214363_s_at MATR3 210266_s_at TRIM33

234988_at VCPIP1 219027_s_at MYO9A 212610_at PTPN11 201184_s_at CHD4

219415_at TTYH1 202102_s_at BRD4 211987_at TOP2B 228070_at –

232235_at C18orf4 220939_s_at DPP8 202214_s_at CUL4B 211783_s_at MTA1

Tumor Biol.

SDF4); and ATP-gated cation channel activity genes (P2RX4and P2RX7).

Results of the KEGG analysis revealed the most importantpathways in which the most co-expressed to NFKB1, SNAI1,YY1 or RKIP genes, participate (Table 9). Interestingly, amongthe top “NFKB1 neighbours”BCL2, PIAS1, HGF, UBE2H andUBE2E2 participate in ubiquitin-mediated proteolysis, variouspathways in cancer and apoptosis. Among the top “SNAI1neighbours”, AVPR1B, HTR7, CACNA1G, ADRA1D,CACNA1A and PTGER1 play a role in the calcium signallingpathway; whereas among the “YY1 neighbours” the CULgenes are involved in ubiquitin-mediated proteolysis andWASF2 and SMAD2 play a role in adherens junction. Finally,among the top “RKIP neighbours”, ITGB7, FYN, CCND2 andITGB5 are implicated in focal adhesion.

TFBMs analysis

We determined the TFBMs in the promoter regions of theNF-κB/Snail/YY1/RKIP circuitry genes. Results of the

analysis revealed which transcription factors might affect theexpression of the four genes of interest (Table 10). FOXO4(forkhead box O4) was the most significant binding motif forNFKB1 (adjP = 3.39E−07); whereas for SNAI1, it was themotif hsa_V$LMO2COM_02 (adjP = 3.68E−05). The tran-scription factor Sp1 was most probable to affect YY1 expres-sion (adjP = 4.45E−10) and AP4 zinc finger protein was themost significant binding motif for RKIP (adjP = 4.36E−05).

Computational analysis

A computational analysis in the GEO datasets with accessionnumbers GDS2643, GSE27838, GSE24870 and GSE24990was performed to investigate the expression pattern of theNF-κB/Snail/YY1/RKIP circuitry genes in MM compared tonormal plasma cells, normal B lymphocytes or MGUS, re-spectively. In the GDS2643 dataset, YY1 and RKIP weresignificantly over-expressed in multiple myeloma vs. normalplasma cells (p < 0.0001 for both genes). RKIP also exhibitedhigher levels in MM vs. normal B lymphocytes (p < 0.0001)

Fig. 3 Gene Ontology (GO) enrichment was performed to obtain anoverview of the main classes of biological functions of the most co-expressed genes with the genes of the NF-κB/Snail/YY1/RKIP circuitry.Dendrograms (DAG trees) of GO analysis of the NFKB1 (a), RKIP (b),

YY1 (c) or SNAI1 (d) most co-expressed genes were performed. Theresults include those combinations that have manifested significant func-tion annotations at the p<0.05 level

Tumor Biol.

(Fig. 4). In the GSE27838 dataset, NFKB1 was higher inexpanded natural killer (ENK) cells from myeloma patientsvs. non-expanded natural killer (NK) cells from both healthydonors and NK myeloma patients (p < 0.0001). It was alsohigher in ENK vs. NK donors (p < 0.0001). By contrast,SNAI1 showed significantly lower levels in ENK myelomapatients vs. NK ones (p = 0.0034), while RKIPwas higher inENK myeloma patients vs. NK myeloma patients, as well asvs. NK donors (p < 0.001). ENK donors also showed higherRKIP levels vs. NK donors (p < 0.0001), but also vs. ENKmyeloma patients (p = 0.040) (Fig. 5). In dataset GSE24870,YY1 was lower in multiple myeloma vs. the controls

(p = 0.0002). By contrast, RKIPwas higher in multiple mye-loma vs. the controls (p = 0.0005) (Fig. 6). In datasetGSE24990, the log2 fold expression of RKIP was correlatedagainst that of the rest of the genes in the NF-κB/Snail/YY1/RKIP circuitry (Fig. 7).

Discussion

Multiple myeloma remains an incurable disease, at least forthe big majority of patients, in spite of the great progress withnew drugs in the last years [29]. In the present study, we

Table 9 KEGG analysis for the most co-expressed genes with the components of the NF-κB/Snail/YY1/RKIP circuitry

Number of genes Gene names adjP

KEGG pathway analysis for NFKB1 co-expressed genes

Small cell lung cancer 3 BCL2, PIAS1, NFKB1 0.0007

Ubiquitin mediated proteolysis 3 UBE2H, PIAS1, UBE2E2 0.0012

Pathways in cancer 4 BCL2, HGF, PIAS1, NFKB1 0.0012

Apoptosis 2 BCL2, NFKB1 0.0065

Prostate cancer 2 BCL2, NFKB1 0.0065

Tight junction 2 YES1, EXOC4 0.0102

Neurotrophin signalling pathway 2 BCL2, NFKB1 0.0102

Focal adhesion 2 BCL2, HGF 0.0192

KEGG pathway analysis for SNAI1 co-expressed genes

Calcium signalling pathway 6 AVPR1B, HTR7, CACNA1G,ADRA1D, CACNA1A, PTGER1

1.04E−06

Neuroactive ligand-receptor interaction 4 AVPR1B, HTR7, ADRA1D, PTGER1 0.0014

Type II diabetes mellitus 2 CACNA1G, CACNA1A 0.0045

Taste transduction 2 TAS2R3, CACNA1A 0.0045

Adherens junction 2 SNAI1, PVRL1 0.0075

MAPK signalling pathway 3 CACNA1G, NR4A1, CACNA1A 0.0075

Vascular smooth muscle contraction 2 AVPR1B, ADRA1D 0.0123

Cell adhesion molecules (CAMs) 2 NRXN3, PVRL1 0.0145

KEGG pathway analysis for YY1 co-expressed genes

Ubiquitin mediated proteolysis 3 CUL5, CUL4B, CUL3 0.0015

Adherens junction 2 WASF2, SMAD2 0.0048

Neurotrophin signalling pathway 2 KIDINS220, PTPN11 0.0083

KEGG pathway analysis for RKIP co-expressed genes

Focal adhesion 4 ITGB7, FYN, CCND2, ITGB5 0.001

Axon guidance 3 ABLIM1, FYN, DPYSL2 0.0026

N-Glycan biosynthesis 2 DDOST, MAN1A1 0.0052

Pathogenic Escherichia coli infection 2 FYN, TLR4 0.0065

Hypertrophic cardiomyopathy (HCM) 2 ITGB7, ITGB5 0.0078

ECM-receptor interaction 2 ITGB7, ITGB5 0.0078

Arrhythmogenic right ventricular cardiomyopathy (ARVC) 2 ITGB7, ITGB5 0.0078

Dilated cardiomyopathy 2 ITGB7, ITGB5 0.008

Insulin signalling pathway 2 EIF4EBP1, PYGB 0.0152

Calcium signalling pathway 2 P2RX7, P2RX4 0.0224

Tumor Biol.

focussed on the NF-κB/Snail/YY1/RKIP circuitry genes inmultiple myeloma and performed GSEA to identify relation-ships of these genes with specific cancer-related gene setsamong four publicly available datasets. We isolated the 20top co-expressed genes to those of the NF-κB/Snail/YY1/RKIP circuitry and identified their functional categories. Thegene neighbours approach that we utilised depends on theobservation that genes with related functions tend to be moreclosely linked genetically than genes without related func-tions. The close proximity of related genes allows the sharingof promoter elements for coordinated gene expression.

Although it seems that the deregulation of different path-ways is pathogenically important in the development of MM,the molecular mechanisms present in relapsed refractory MMare different from those acting earlier in the disease process.Relatively few factors of the metastatic process have beensuccessfully developed as chemotherapeutic targets. New in-sights into the initial steps of the metastatic process wererevealed in developmental genetics by a set of regulators thatinduce EMT. Among the survival pathways playing a criticalrole in the regulation of metastasis is the NF-κB signallingpathway.

NF-κB has emerged as a crucial player in the patho-genesis of MM, particularly through the regulation oftarget genes involved in cell proliferation and survival[30–33]. Constitutive NF-κB activity is present in humanMM cell lines and patient MM cells and associated withproteasome inhibitor sensitivity [17, 30, 32, 34, 35]. In thecontext of the bone marrow microenvironment, the adhe-sion of MM cells induces NF-κB-dependent cytokine se-cretion, further enhancing NF-κB activity [30, 34].Therefore, NF-κB has emerged as a promising therapeutictarget in MM. Both canonical and non-canonical pathwayscontribute to total NF-κB activity, and recent studies haveshown a critical role for the non-canonical pathway. Adual inhibition of both pathways was recently shown todemonstrate significant antitumour activities in multiplemyeloma [36]. Keats et al. [17] reported mutations inten genes causing the inactivation of TRAF2, TRAF3,CYLD and cIAP1/cIAP2 and activation of NFKB1,NFKB2, CD40, LTBR, TACI and NIK that result primar-ily in constitutive activation of the non-canonical NF-κBpathway, highlighting the critical importance of this path-way in the pathogenesis of multiple myeloma. Demchenkoet al. [32] described how multiple myeloma tumours canachieve increased autonomy from the bone marrow micro-environment by mutations that activate either the classicaland/or the alternative NF-κB pathway. Recently, a broaderthan anticipated role of the NF-κB signalling was indicat-ed by mutations in 11 members of the NF-κB pathway,through massively parallel sequencing [10].

Table 10 Transcription Factor Binding Motifs (TFBMs) analysis identi-fied the transcription factors that might affect the expression of theNF-κB/Snail/YY1/RKIP circuitry

Number ofgenes

adjP

TFBMs for NFKB1

hsa_TTGTTT_V$FOXO4_01 13 3.39E−07hsa_V$FOXO3_01 6 2.76E−06hsa_RTAAACA_V$FREAC2_01 8 2.91E−05hsa_V$FREAC2_01 5 7.87E−05hsa_CACGTG_V$MYC_Q2 7 0.0003

hsa_ACTAYRNNNCCCR_UNKNOWN 5 0.0003

hsa_V$AP2REP_01 4 0.0003

hsa_SCGGAAGY_V$ELK1_02 7 0.0003

hsa_V$FOXO4_01 4 0.0005

hsa_CTGCAGY_UNKNOWN 6 0.0005

TFBMs for SNAI1

hsa_V$LMO2COM_02 6 3.68E-05

hsa_RNGTGGGC_UNKNOWN 7 0.0003

hsa_CAGGTA_V$AREB6_01 7 0.0003

hsa_V$RSRFC4_01 5 0.0003

hsa_GGGTGGRR_V$PAX4_03 9 0.0003

hsa_V$MMEF2_Q6 5 0.0003

hsa_TGGAAA_V$NFAT_Q4_01 10 0.0003

hsa_AACTTT_UNKNOWN 10 0.0003

hsa_TAAWWATAG_V$RSRFC4_Q2 4 0.0004

hsa_YNGTTNNNATT_UNKNOWN 5 0.0005

TFBMs for YY1

hsa_GGGCGGR_V$SP1_Q6 18 4.45E−10hsa_ATGGYGGA_UNKNOWN 5 7.43E−07hsa_ACAWNRNSRCGG_UNKNOWN 3 0.0009

hsa_V$PEA3_Q6 4 0.0014

hsa_V$AP2_Q3 4 0.0015

hsa_GGCNKCCATNK_UNKNOWN 3 0.0016

hsa_AACTTT_UNKNOWN 8 0.0016

hsa_AAGWWRNYGGC_UNKNOWN 3 0.0016

hsa_V$E2F_Q2 3 0.0042

hsa_GGGAGGRR_V$MAZ_Q6 8 0.0049

TFBMs for RKIP

hsa_CAGCTG_V$AP4_Q5 10 4.36E−05hsa_V$ELF1_Q6 4 0.0009

hsa_V$PU1_Q6 4 0.0009

hsa_CTTTGT_V$LEF1_Q2 9 0.0009

hsa_CAGGTG_V$E12_Q6 10 0.0009

hsa_GCANCTGNY_V$MYOD_Q6 6 0.0016

hsa_GTTRYCATRR_UNKNOWN 3 0.0054

hsa_CTGCAGY_UNKNOWN 5 0.0059

hsa_TCANNTGAY_V$SREBP1_01 4 0.0073

hsa_RGAGGAARY_V$PU1_Q6 4 0.0075

Tumor Biol.

Snail is transcriptionally regulated by itself [37], by NF-κB[38] and by YY1 [39]. It functions as a transcriptional repres-sor not only of E-cadherin but also of RKIP, whereas inprimary and metastatic prostate tumours Snail expression isinversely correlated with RKIP and E-cadherin levels [31].These observations suggested that Snail might be an appro-priate therapeutic target to inhibit the EMT process and, inturn, to block tumour invasion [40]. Snail over-expressioncontributes directly to EMT, accelerating tumour survival,migration and poor prognosis, which have been demonstratedin various cancer cell lines and tumour biopsies includingbreast cancer [40], gastric cancer [41], hepatocellular carcino-mas [42], ovarian carcinoma [43], oral squamous cell carci-noma [44] and head and neck cancer [45]. In addition, thepost-transcriptional regulation of Snail involves phosphoryla-tion in the serine-rich domain of amino acids 104–107 byGSK3-β before Snail localisation to the nucleus.Phosphorylation on Ser 96 and 100 by the same protein kinaseinduces Snail binding toβ-TrCP1 andmediates ubiquitinationand proteasomal degradation in the cytoplasm, preventing itfor its transcriptional regulation in the nucleus [46]. Treatment

of B-non-Hodgkin’s lymphoma (B-NHL) cell lines with ri-tuximab (chimeric anti-CD20 mAb) or galiximab (anti-CD80mAb) resulted in the reversal of resistance and chemo-immunosensitisation to apoptosis [47, 48]. These antibodiesinhibited NF-κB and downstream Snail. Treatment with smallinterfering RNA Snail sensitised the B-NHL cells to cisplatinand TNF-related apoptosis-inducing ligand and, thus, mim-icking the antibody-mediated sensitisation [49].

YY1 is a multi-functional DNA-binding protein that canactivate, repress, or initiate transcription depending on thecontext in which it binds (directly or indirectly throughcomplexing with DNA-binding proteins) [50]. ElevatedYY1 levels have already been reported in non-solid tumours,such as acute myeloid leukemia (AML), Hodgkin’s lympho-ma [16], non-Hodgkin’s lymphoma (NHL), follicular lym-phoma [41] and in diffuse large B-cell lymphoma [51–57].YY1 upregulation was also recently reported in MM byPotluri et al. [58]. The authors reported a novel YY1-RelAcomplex formation, which is essential to transcriptionallyrepress the proapoptotic gene Bim and propose the RelA-YY1 transcriptional repression complex as an attractive drug

Fig. 4 Expression levels of theNF-κB/Snail/YY1/RKIP circuitrygenes in multiple myelomacompared to normal plasma cellsand normal B lymphocytes,according to dataset GDS2643.YY1 and RKIP were significantlyover-expressed in multiplemyeloma vs. normal plasma cells(p<0.0001 for both genes). RKIPalso exhibited higher levels inMM vs. normal B lymphocytes(p<0.0001)

Tumor Biol.

target in MM. Davies et al. showed that most gene expressionchanges occur during the transformation of normal plasmacells to malignant MGUS plasma cells, and they highlighted anumber of genes important in this progression includingmembers of theWingless (WNT) and Sonic pathways. In theirprofiling analysis, YY1 was noted to be downregulated inMGUS compared to normal cells [59]. Bonavida andBaritaki [60] recently provided evidence that YY1 participatesin the EMT in the metastatic cascade. The authors describedhow YY1 is an intricate factor in the regulation of EMT viathe dysregulated NF-κB/Snail/YY1/RKIP/PTEN circuit thathas been shown to regulate both EMTand drug resistance, andthey suggested the potential role of YY1 as a target fortherapeutic interventions [60]. Demidem et al. [61] reportedthat rituximab sensitised B-NHL lines to drug-induced apo-ptosis when used in combination with chemotherapeutic drugsin a synergistic fashion. Investigation of the molecular mech-anisms underlying the sensitisation revealed an associationwith the downregulation of the anti-apoptotic gene products,Bcl2 and BclxL, as a consequence of rituximab-mediatedinhibition upstream of the constitutively activated survival/

anti-apoptotic pathways, such as p38 MAPK, NF-κB andERK1/2 [11]. The findings of Siednienko et al. [62]established a mechanism of rituximab-mediated upregulationof Fas in the sensitisation to FasL apoptosis via inhibition ofthe transcription repressor YY1 [13]. Furthermore, the role ofYY1 in the resistance to chemotherapeutic drugs was recentlystudied [63].

The structural and functional homologue of YY1, Yin Yang2 (YY2), has been limitedly studied. YY2 is a transcriptionfactor that has arisen by retrotransposition of YY1 [64] andincludes several Kruppel-like zinc fingers in its C-terminalregion. It possesses both activation and repression domains,thus having both positive and negative effects on the transcrip-tion of target genes. Klar recently summarised all the criticalaspects that are relevant for a better understanding of YY2-mediated cellular control mechanisms [65]. Furthermore, it wasrevealed that YY2 is not redundant to YY1, and it is a signif-icant regulator of genes previously identified as uniquelyresponding to YY1 [32].

RKIP is a member of the phosphatidylethanolamine-binding protein (PEBP) family that functions as a potent

Fig. 5 Expression levels of theNF-κB/Snail/YY1/RKIP circuitrygenes in non-expanded (EK) andexpanded natural killer (ENK)myeloma patients compared tohealthy donors and non-expandednatural killer (NK) cells fromhealthy donors, according todataset GSE27838. NFKB1 washigher in ENK cells frommyeloma patients vs. healthydonors and vs. non-expandednatural killer (NK) cells fromhealthy donors and myelomapatients (p < 0.0001). It was alsohigher in ENK vs. NK donors(p < 0.0001). SNAI1 showedsignificantly lower levels in ENKmyeloma patients vs. the NK ones(p = 0.0034). RKIP was higher inENK myeloma patients vs. theNK myeloma patients, as well vs.the ENK donors (p= 0.040). ENKdonors also showed higher RKIPlevels vs. the NK donors(p < 0.0001)

Tumor Biol.

inhibitor of different kinases in the Raf/MAPK and NF-κBactivation pathways, thereby antagonising both cell survivaland the expression of antiapoptotic gene products [66]. RKIPexpression was shown to be regulated at the transcription levelby Snail [31], while its activity was found to be under thecontrol of a post-translational modification, which involves aprotein kinase C-mediated phosphorylation at Ser 153(p-Ser153 RKIP) [67]. This phosphorylation was observedin response to growth factor stimuli and resulted in the dere-pression of the MAPK pathway. Baritaki et al. [10] identifiedRKIP over-expression in MM cell lines and MM tissues,which was consisted, in large part, of p-Ser153 RKIP. Incontrast to RKIP, p-Ser153 RKIP was shown to lack its abilityto inhibit the MAPK signalling pathway [10]. The authorssuggested that since RKIP expression regulates both theNF-κB and MAPK survival pathways, the over-expressionof “inactive” p-Ser153 RKIP in MM might contribute posi-tively to the overall cell survival/antiapoptotic phenotype anddrug resistance of MM through the constitutive activation ofsurvival pathways and downstream the transcription of anti-apoptotic gene products [10]. The same authors recently sug-gested that the relationship between the levels of the two

forms may be of prognostic significance in multiple myeloma[10]. RKIP expression is significantly downregulated in sev-eral malignancies and almost absent in metastatic tumours[19, 68–73]. Loss of RKIP has also been reported in AML[74, 75] and chronic myelogenous leukaemia [76]. RKIPinhibits the proliferation and transformation of myeloid cellsand decreases the transformation induced by mutant RAS[75]. In patients with AML, its loss was suggested to be afavourable prognostic parameter [75]. RKIP over-expressionin in vitro and in vivo tumour models has shown significantanti-metastatic effects, thus supporting its role as a “metasta-sis-suppressor” gene product [77–79]. RKIP over-expressionwas further demonstrated to directly correlate with the inhibi-tion of EMT and reversal of tumour resistance to apoptoticstimuli, through a common mechanism, which involves inhi-bition of the expressions and activities of NF-κB, Snail andYY1 [10, 80, 81].

Among the most co-expressed to NFKB1 genes we fo-cussed on fibronectin type III domain containing 3B(FNDC3B), tumour protein D52 (TPD52), Bobby sox homo-log (Drosophila) (BBX) and muscleblind-like splicing regu-lator 1 (MBNL1).

Fig. 6 Expression levels of theNF-κB/Snail/YY1/RKIP circuitrygenes in multiple myelomacompared to control patients indataset GSE24870. YY1 waslower in multiple myeloma vs. thecontrols (p = 0.0002). On thecontrary, RKIP was higher inmultiple myeloma vs. the controls(p = 0.0005)

Tumor Biol.

FNDC3B is a member of the fibronectin gene family,located at 3q26, and it has been identified as an importantoncogenic driver being amplified in over 20 % of cancers,usually as part of a broad amplified region encompassing theentire 3q arm [32, 68, 82]. The over-expression of FNDC3Bwas recently found to induce EMTand activate several cancerpathways, including PI3-kinase/Akt, Rb1 and transforminggrowth factor beta signalling [83]. Cai et al. [84] also latelyshowed that FNDC3B over-expression is capable of malig-nantly transforming mammary and kidney epithelial cells inaddition to hepatocytes. Furthermore, its suppression bymiR-143 was found to promote prostate cancer metastasis[85], in line with previous reports where FNDC3B wasdown-regulated in tumour cells with high metastatic potential[86].

The TPD52 gene located in chromosome 8q21 is a coiled-coil motif bearing hydrophilic polypeptide known to be over-expressed in cancers of diverse cellular origins. The TPD52family is comprised of four members, which function as

adaptor proteins, and interact with D52-like proteins, andseveral other intracellular partner proteins [87]. TD52 is fre-quently over-expressed in several carcinomas and has beenidentified as a B cell differentiation marker [88–91]. Its over-expression was recently found to be a favourable independentprognostic marker of potential value in the management ofovarian carcinoma patients [90]. D52-like genes are alsodifferentially expressed in leukaemic cells [92], but not inleukaemic bone marrow in children [85]. Chen et al. recentlyreported that TPD52 might represent a novel negative regula-tor of ATM protein levels [32]. Furthermore, its phosphoryla-tion at the single major phospho-acceptor site serine 136 wasshown to play an important role in the vesicle traffickingevents that direct cell abscission [88].

BBX is a high-mobility-group box transcription factorlocated in chromosome 3q13.1, which along with severalzinc-finger proteins and the DNA-binding protein Helioswas shown to play a role in fine-tuning the recruitment ofHistone deacetylase 1 (HDAC1) complexes to chromatin [93].

Fig. 7 Log2 fold expressionlevels of the NF-κB/Snail/YY1/RKIP circuitry genes in activemultiple myeloma compared toMGUS in dataset GSE24990.RKIP fold-expression wascorrelated against that of the restof the genes in the NF-κB/Snail/YY1/RKIP circuitry

Tumor Biol.

These proteins may serve an essential, though as yet un-known, role within HDAC1/2 complexes to coordinate meth-ylation and deacetylation states [93]. It was recentlyrecognised as a substrate of hNedd4-2 [94], a regulator ofthe epithelial Na± channel, ENaC among other ion channels[95].

MBNL1 (chromosome 3q25) belongs to a conserved fam-ily of RNA-binding proteins characterised by 2 pairs of C3H-type zinc finger-related motifs, which function as target-specific regulators of pre-messenger RNA splicing [1]. Hanet al. [68] recently identified MBNL1 and MBNL2 as con-served and direct negative regulators of a large program ofcassette exon alternative splicing events that are differentiallyregulated between embryonic stem cells and other cell types.Knockdown of MBNL proteins in differentiated cells causedswitching to an ES cell-like alternative splicing pattern forapproximately half of these events, whereas over-expressionof MBNL proteins in ES cells promoted differentiated cell-like alternative splicing patterns.

Microfibrillar-associated protein 2 (MFAP2), in chromo-some 1p36.13, was commonly co-expressed to RKIP betweentwo of the datasets. It was previously found to be upregulatedin head and neck squamous cell carcinomas [96], as well asassociated with pulmonary function [97]. However, no con-nection has been identified to the best of our knowledge inmultiple myeloma or any other type of malignancy, so far.Combs et al. [98] recently showed that loss of microfibril-associated glycoprotein (MAGP2) expression in vivo haspleiotropic effects potentially related to its ability to regulategrowth factors or participate in cell signalling. The locusLOC400451 (recently updated to FAM174B) in chr15q26.1is a relatively new gene with as yet unknown functions and nopreviously reported implication with the disease.

No gene was co-expressed with SNAI1 in two or moredatasets at the same time. The bromodomain-containing pro-tein 4 (BRD4) was co-expressed with YY1 in two of theinvestigated datasets. It was recently identified as a therapeutictarget in acute myeloid leukemia, multiple myeloma, Burkitt’slymphoma, NUT midline carcinoma, colon cancer, and in-flammatory disease; its loss is a prognostic signature formetastatic breast cancer [99–104]. Devaiah et al. [105] recent-ly reported that BRD4 is an atypical kinase that binds to thecarboxyl-terminal domain (CTD) of RNA polymerase II anddirectly phosphorylates its serine 2 (Ser2) sites both in vitroand in vivo under conditions where other CTD kinases areinactive. Their findings implicate BRD4 as a regulator ofeukaryotic transcription.

Our TFBM analysis revealed FOXO4 as the most signifi-cant binding motif for NFKB1. In line with this, Fernandezet al. [106] showed that Foxo along with CnA are required forthe activation of NF-κB. Li et al. showed that Sp1 and YY1bind the same promoter region and that functional interactionbetween the two transcription factors is a crucial step in the

initiation of expression of the mu opioid receptor in lympho-cytes [31]. Sp1 and YY1 were previously shown totransactivate the human ATP2C1 promoter [107]. The bindingmotif of AP4was among the most significant motifs for RKIP.The AP4 protein is a basic helix–loop–helix leucine-zipper(bHLH-LZ) transcription factor, which exclusively formshomodimers that bind to the E-box motif CAGCTG [108].Of interest, Jackstadt et al. [109] recently showed that AP4directly regulates EMT-associated factors and stemnessmarkers, as well as that down-regulation of AP4 resulted inmesenchymal–epithelial transition and inhibited migrationand invasion.

In our study, we further validated the dysregulated NF-κB/Snail/YY1/RKIP circuitry genes in multiple myeloma bycomputational analysis of various GEO datasets. Our analysisrevealed that RKIP is significantly over-expressed in MM vs.normal plasma cells and normal B lymphocytes. However,YY1 dysregulation was conflicting between two datasets,GSE24870 (YY1 dowregulation) and GDS2643 (YY1upregulation).

Overall, the present study highlights the relationships of theNF-κB/Snail/YY1/RKIP circuitry genes with specific cancer-related gene sets among four multiple myeloma datasets.

Conflict of interest None.

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Tumor Biol.