Savli H., Sunnetci D., Cine N., Gluzman D.F., Zavelevich M.P.*, Sklyarenko L.M., Nadgornaya V.A., Koval S.V.

Summary. Background: Genetic mechanisms that result in the development and progression of B-cell chronic lymphocytic leukemia (B-CLL) are mainly unknown. We have analyzed gene expression patterns in Ukrainian B-CLL patients with the aim of identifying B-CLL involved / associated genes in order to shed light on the biology of this pathological entity. Material and methods: The samples of the peripheral blood and bone marrow of 44 Ukrainian B-CLL patients with no characteristics indicative of unfavorable course of the disease such as CD38 were analyzed morphologically and immunocytochemically according to the new WHO classification. Total RNA was isolated, and gene expression levels were determined by microarray method comparing with the sample from 17 healthy donors. Results: We investigated interactions using the Ingenuity Pathway Analysis (IPA) software and found 1191 network eligible up-regulated genes and 3398 Functions/Pathways eligible up-regulated genes, 1225 network eligible down-regulated genes and 2657 Functions/Pathways eligible down-regulated genes. Conclusion: In B-CLL patients, gene networks around MYC, HNF1A and HNF4A, YWHAG, NF-κB1 and SP1 are identified as up-regulated; CEBPA, YWHAG, SATB1 and RB1 — as down-regulated. G protein coupled receptor signaling, arachidonic acid and linoleic acid metabolisms, calcium signaling, metabolism of xenobiotics by cytochrome P450 are found out as significant up-regulated pathways. EIF2 and Cdc42 signaling, regulation of eIF4 and p70S6k signaling, protein ubiquitination pathway and oxidative phosphorylation are the most significant down-regulated pathways obtained in our study. The involvement of NF-κB gene network and upregulated levels of G protein coupled receptor signaling pathway, which has an important role in transcription of NF-κB, are important and need further examination.

Received: February 2, 2012.
*Correspondence: E-mail:
Abbreviations used: AML — acute myelogenous leukemia; B‑CLL — B-cell chronic lymphocytic leukemia; IPA — Ingenuity Pathway Analysis; IR — ionizing radiation.

Ionizing radiation (IR) is one of the most studied carcinogens in the development of multiple myeloma, primary myelofibrosis, polycythemia vera, non-Hodgkin’s lymphomas, myelodysplastic syndromes and some forms of acute and chronic leukemia, especially in acute myelogenous leukemia (AML) [1, 2]. Until recently, chronic lymphocytic leukemia (CLL) has not been considered as a radiation-associated leukemia. Nevertheless, current understanding of radiation-induced tumorigenesis and the etiology of lymphatic neoplasia show that IR exposure increases CLL risk [3].

After Chernobyl nuclear accident, people living in the contaminated areas of Ukraine are still exposed to low doses of IR. Analysis of the patients with various forms of the malignancies of hematopoietic and lymphoid tissues has not revealed the differences in B-CLL percentage among Chernobyl clean-up wor­kers and Ukrainian population in whole. B-CLL was shown to be a predominant form of hematopoietic malignancies in clean-up workers as well as in general population [4]. Genetic mechanisms that result in the development and progression of CLL are mainly unknown [5]. Gene expression profiling by microarray is useful to understand B-CLL origin and development [6]. The analysis of the molecular genetic features should be advantageous in elucidating the putative association of IR and B-CLL.

Earlier, we have studied gene expressions of seve­ral apoptosis related genes in different types of tumors of hematopoietic and lymphoid tissues in 189 patients including those with B-CLL living in areas of Ukraine contaminated with radionuclides in post-Chernobyl period [7]. In the present study, we have analyzed gene expression patterns in samples from 44 B-CLL Ukrainian patients in post-Chernobyl period with the aim of identifying the genes related to or involved in this pathology in order to shed light on the biology of B-CLL.


The samples of the peripheral blood of B-CLL patients were obtained from R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobio­logy of the National Academy of Sciences of Ukraine. All the patients were referred to Reference Laboratory of Immunocytochemistry and Oncohematology Department of the Institute for verifying the diagnosis. Bone marrow and peripheral blood smears stained by May-Grunwald-Giemsa were studied morphologically. Immunocytochemical techniques (APAAP, LSAB-AP) and a broad panel of monoclonal antibodies against lineage specific, differentiation and activation antigens of leukocytes were employed for immunophenotyping pathological cells in blood and bone marrow. The main forms and cytological variants of hematological malignancies were diagnosed according to new WHO classification [8]. All the samples were immunophenotyped, and only 44 samples from CD38-negative B-CLL patients out of 127 diagnosed patients with B-CLL/B-cell lymphoma from small lymphocytes [7] were included in the study. Control group comprised peripheral blood samples from 17 healthy donors. The ethic committees of both collaborating research institutions approved the design of the study.

Total RNA isolation. Total RNA was isolated from mononuclear cells for each patient using QIAamp RNA Blood Mini Kit (QIAGEN, Valencia, CA, USA) and treated with DNase I according to the manufacturer’s instructions. The quality of the RNA was assessed by loading 300 ng of total RNA onto an RNA Labchip (Agilent Technologies, Waldbronn, Germany), followed by analysis (A2100 Bioanalyzer; Agilent Technologies). An RNA integrity value (RIN) of 7.0 was considered acceptable.

RNAs from 44 B-CLL patients and 17 healthy donors were pooled seperately. Pooling process was performed in the way that 100 ng RNA sample was used from each B-CLL patient/healthy donor. Each RNA pool was prepared as three replicates.

Microarray analysis. Microarray analysis was performed using the Whole Human Genome Oligo Microarray (Agilent Technologies), encompassing more than 44,000 human DNA probes. The full list of cDNAs is available online ( Protocols for sample preparation and hybridization of the mononuclear cells were adaptations of those in the Agilent Technical Manual. In short, first strand cDNA was trans­cribed from 300 ng of total RNA using T7-Oligo(dT) Promoter Primer. Samples were transcribed in vitro and Cy-3-labelled by using a Quick-AMP labeling kit (Agilent Technologies). Following a further clean-up round (Qiagen), cRNA was fragmented into pieces ranging from 35 to 200 bases in size. Fragmented cRNA samples (1.65 mg) were hybridized onto chips by means of 17 h of incubation at 65°C with constant rotation, followed by a two-step microarray wash of 1 min in two washing buffers (Agilent Technologies). Hybridized microarrays were scanned in a Agilent Technologies Scanner (model G2505B) and numerical results were extracted with Feature Extraction version using 014850_D_F_20060807 grid, GE1-v5_95_Feb07 protocol and GE1_QCM_Feb07 QC metric set.

The microarray data were analyzed using GeneSpring software version 9.0 (Agilent Technologies, Santa Clara, CA). The fold changes were analyzed by filtering the dataset using P-value < 0.01 and a signal-to-noise ratio >2 for use in T-test statistical analysis. Additional filtering (minimum 2-fold change) was applied to extract the most these genes, which were analyzed using Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Redwood City, CA). Those genes with known gene symbols (HUGO) and their corresponding expression values were uploaded into the software. Each gene symbol was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. Networks of these genes were algorithmically generated based on their connectivity and assigned a score. The score is a numerical value used to rank Networks according to how relevant they are to the genes in the input dataset but may not be an indication of the quality or significance of the network. The score takes into account the number of focus genes in the network and the size of the network to approximate how relevant this network is to the original list of focus genes. The network identified is then presented as a graph indicating the molecular relationships between genes/gene products. Genes are represented as nodes, and the biological relationship between two nodes is represented as an edge (line). The intensity of the node color indicated the degree of up- or down-regulation. The node shapes are disclosed in corresponding figure legends. Canonical pathway analysis identified the pathways from the IPA library of canonical pathways, which were most significant to the input data set. The significance of the association between the data set and the canonical pathway was determined based on two parameters: (1) A ratio of the number of genes from the data set that map to the pathway divided by the total number of genes that map to the canonical pathway and (2) a P value calculated using Fischer’s exact test determining the probability that the association between the genes in the data set and the canonical pathway is due to chance alone.

Quantitative real-time PCR (Q-RT-PCR). cDNA was synthesized using RevertAid First Strand cDNA Synthesis Kit (Fermentas Inc., Maryland, USA). Q-RT-PCR was performed as we described previously for determination of MYC, BAX, BCL-2 and FAS1 gene expressions [9, 10]. Standard curves were obtained using serial dilutions of the beta-globulin gene (DNA Control Kit, Roche). Gene-specific primers (Table 1) were obtained from Integrated DNA Technologies (Iowa, USA). Obtained gene expression values were normalized using a housekeeping gene of beta2 microglobulin. Gene expression ratios were compared in patient and control groups using REST (Relative Expression Software Tool).

Table 1. List of the primers used for the quantitative RT-PCR
Genes Primer sequences
Beta2 microglobulin (F) 5’ TGA CTT TGT CAC AGC CCA AGA TA 3’


Differentially expressed genes are shown in two separate tables. The 100 most up-regulated genes are shown in Table 2. The 100 most down-regulated genes are shown in Table 3. Both sets of results were obtained based on minimum 2-fold change using GeneSpring software version 9.0 (Agilent Technologies, Santa Clara, CA). In Table 4 the gene expression results of four genes (MYC, BAX, BCL-2 and FAS1) obtained by real-time PCR and microarray methods are compared. Real-time PCR results of MYC, BCL-2 and BAX are in a good agreement with microarray expression rates.

Table 2. The 100 most up-regulated genes in B-CLL
Fold Change Gene
9.971721 CB162722
9.893506 THC2579650
9.856071 IRX5
9.828577 SAPS1
9.718567 THC2671344
9.659609 LRRC2
9.598212 PIGR
9.52783 BX119852
9.385712 FMOD
9.34002 CGB1
9.180631 VPS18
9.170944 RAPH1
9.11223 RNF150
9.11183 RAP1GAP
9.109504 RPA4
9.073619 THC2672701
9.049471 CD86
9.029652 RBM22
8.81397 AA704712
8.759307 AA479896
8.743843 AKAP12
8.691222 CCDC66
8.670482 ABCA4
8.608516 CV575560
8.573189 GRAMD1C
8.567822 EFTUD1
8.518443 LOC389043
8.484631 S71486
8.467656 BTC
8.455834 SMARCA4
8.4216795 MGC39584
8.39463 BF368414
8.346779 C1orf173
8.317559 NDP
8.281372 BI826226
8.207127 RPTN
8.186712 PRRX1
8.142795 BQ286187
8.100048 L5
8.054283 ATXN3L
8.05317 AK098548
8.044337 TEF
8.034349 WDR33
8.031527 CASKIN2
8.008858 FLJ25770
7.9823356 THC2686753
7.9713397 KLHL23
7.9610386 POLR2J2
7.9588156 STARD13
7.950879 MLL
7.9061837 TTC23
7.886104 SFRP1
7.8818917 FLJ32679
7.8160353 MMP14
7.798868 MEGF10
7.7877035 WDR21C
7.775479 BU587941
7.7426653 BCR
7.7220807 THC2676656
7.706189 AI089002
7.6771984 WNT3
7.648338 UCP3
7.647829 NFE2L1
7.6217384 C1orf168
7.6014295 TMPRSS3
7.6004906 WNT2B
7.5972705 TUSC5
7.5422063 TEX12
7.522491 MGC88374
7.4850636 ST6GAL1
7.4668427 LOC645478
7.4543867 KIAA0672
7.4285965 NAV2
7.419999 THC2537502
7.419809 KIAA1946
7.3947935 BX647159
7.3545713 BG190682
7.3339643 RUNDC2B
7.3178434 GBP6
7.2903414 ZNF713
7.2862663 ASB16
7.2639813 THC2530551
7.2611523 PPM1F
7.2371364 MYOC
7.228985 LOC643401
7.2250643 KALRN
7.215619 MYCNOS
7.1989717 CRISPLD2
7.1989717 CRISPLD2
7.192935 ADIPOQ
7.192935 ADIPOQ
7.1847763 SLC44A5
7.1847763 SLC44A5
7.1711025 ZCCHC13
7.135996 SLC27A1
7.1255236 ZNF2
7.122238 MSTP9
7.0874977 PSPH
7.048849 PYY2
7.032443 AD7C-NTP
Table 3. The 100 most down-regulated genes in B-CLL
Fold Change Gene
-9.467819 THC2588392
-8.866756 HBG1
-8.638458 SELENBP1
-8.50988 HBA2
-8.47693 HBG1
-7.8862677 SAT1
-7.8419037 FCGR3A
-7.80179 RGS2
-7.7845144 SLC25A39
-7.7299724 ALAS2
-7.686287 KRT1
-7.6433597 SRGN
-7.6020937 PROK2
-7.5707283 S100P
-7.5505257 TNFRSF10C
-7.475219 MXD1
-7.389961 HBD
-7.376893 CLEC4E
-7.297138 CMTM6
-7.2936077 FTL
-7.292986 PAIP2
-7.2235703 ALAS2
-7.182671 HBD
-7.1385164 LGALS3
-7.1038146 IFIT2
-7.0883236 ANXA1
-7.055394 AQP9
-7.054615 LOC552891
-6.935093 C6orf32
-6.9139557 PDZK1IP1
-6.892113 FBXL5
-6.8429413 CMTM2
-6.823658 HBQ1
-6.8207946 BNIP3L
-6.7675858 CLC
-6.7639685 AP1S2
-6.7029543 ALOX5AP
-6.678584 ACTG1
-6.6524496 GIMAP7
-6.643402 GCA
-6.632475 CSTA
-6.6212616 PBEF1
-6.5431356 LIMK2
-6.537367 SOD2
-6.535038 TP53INP1
-6.5181375 IFIT1
-6.475131 BID
-6.470724 HIST1H2AC
-6.470461 DUSP1
-6.461632 MNDA
-6.4505854 BCL2A1
-6.4489126 TTRAP
-6.3537326 TNFAIP2
-6.341367 IL1R2
-6.3040967 FYB
-6.26194 S100A12
-6.2470803 TLR2
-6.2420635 SNCA
-6.2413063 PBEF1
-6.2392893 THC2586959
-6.231715 CAMP
-6.2299414 S100A8
-6.2271876 KRT23
-6.193751 DYNLT1
-6.171741 SLC31A2
-6.153518 RGS18
-6.139215 SIPA1L1
-6.125804 CCR1
-6.0938168 ADD3
-6.021562 NFE2
-6.0161657 QPCT
-5.994034 ITM2B
-5.9857407 YPEL5
-5.9691944 IFNGR1
-5.955679 IL8RB
-5.950643 C20orf24
-5.9466496 GLUL
-5.9364004 NINJ1
-5.9354315 C5orf32
-5.9249115 VPS4B
-5.9206657 FLJ10357
-5.9169197 HSD17B11
-5.904073 UBB
-5.895618 FTL
-5.894103 SAT1
-5.8842864 CKLF
-5.8623157 MYL4
-5.8620443 FBXO7
-5.8529325 LCP1
-5.8372726 SNN
-5.8210387 BNIP3L
-5.8020077 MTPN
-5.7948103 COPS5
-5.7918744 NGFRAP1
-5.782423 MFSD1
-5.7802 MPP1
-5.7671204 HIPK1
-5.7636905 PBEF1
-5.746536 PAG1
-5.7328815 APOBEC3A
Table 4. Summarized real-time PCR confirmation results of the four genes
Genes Ratios obtained by RT-PCR Ratios obtained by arrays
BAX 5.0265 (Up-regulated) 2.592 (Up-regulated)
BCL-2 16.696 (Up-regulated) 1.747 (Up-regulated)
MYC 4.15 (Up-regulated) 2.794 (Up-regulated)
FAS1 4.536 (Up-regulated) 2.460 (Down-regulated)

We investigated interactions using IPA software and found 1191 network eligible up-regulated genes and 3398 Functions/Pathways eligible up-regulated genes. Fig. 1 shows the most significant four gene networks of over-expressed genes in B-CLL samples. Top functions of these genes are related to hematopoiesis, lipid metabolism, small molecule biochemistry, cancer, infectious diseases, cell cycle, cardiovascular system deve­lopment and function, gene expression, embryonic development, tissue morphology, inflammatory response. Up-regulated gene networks are identified around MYC, HNF1A and HNF4A, YWHAG, NF-κB1 and SP1.

We also found 1225 network eligible down-regulated genes, and 2657 Functions/Pathways eligible down-regu­lated genes. Fig. 2 shows four gene networks of down-regulated genes in B-CLL. The main functions of the genes are related to cellular functions and maintenance, protein synthesis, dermatological diseases and conditions, cell death, gene expression, inflammatory disease, cellular growth and proliferation, post-translational modification, cancer, infectious diseases, cell morphology, and deve­lopment. Down-regulated gene networks are identified around CEBPA, YWHAG, SATB1 and RB1.


B-CLL is a heterogeneous disease and a predominant form of hematopoietic malignancies. Despite new molecular methods identifying important prognostic and diagnostic genetic markers, genetic mechanisms involved in B-CLL origin are mainly unknown. A number of novel prognostic markers such as Bcl-2, MAP-kinase, NF-κB, ZAP-70 were identified applying gene expression profiling before [11, 12].

We have analyzed gene expression patterns in samples from 44 B-CLL Ukrainian patients in post-Chernobyl period to identify genes associated with this form of lymphoproliferative malignancy. Our study has demonstrated new genetic networks and biological pathways in both up- and down-regulated gene expression levels.

Analysis using IPA software revealed 1191 network eligible up-regulated genes and 3398 Functions/Pathways eligible up-regulated genes. The individual genes are found in multiple categories of functions related to hematopoiesis, lipid metabolism, small molecule biochemistry, cancer, infectious diseases, cell cycle, development and function of cardiovascular system, gene expression, embryonic development, tissue morphology, inflammatory response.

One important gene network is identified around the up-regulated MYC and SP1 genes (Fig. 1, a). MYC, a strong proto-oncogene, plays very important roles in cellproliferation (by upregulating cyclins, downregulating p21), controlling cell growth (by upregulating ribosomal RNA and proteins), apoptosis (by downregulating BCL-2), differentiation and stem cell self-renewal. Mutations, overexpression, rearrangement and translocation of MYC have been associated with a variety of hematopoietic tumors, leukemias and lymphomas, including Burkitt lymphoma [13]. High expression level of MYC has been reported in more aggressive and apoptosis resistant forms of B-CLL and might be used as molecular marker specific of resistant B-CLL subsets [14, 15]. SP1, a zinc finger transcription factor, is involved in cell differentiation, cell growth, apoptosis, immune response, response to DNA damage, and chromatin remodeling. SP1 and MYC are involved cooperatively in telomerase activation, which is a critical step in cellular immortalization and carcinogenesis. Kyo et al. have suggested that the level of SP1 expression might be a critical determinant of telomerase activity both in cancer and normal cells [16].

Another network is identified around NF-κB1 gene (Fig. 1, b). NF-κB regulates several genes that mediate tumorigenesis and metastasis and also plays an important role in pathogenesis of B-cell neoplasms. Carcinogens, tumor promoters, inflammatory cytokines, and chemotherapeutic agents activate NF-κB and this activation can suppress apoptosis, thus promoting chemoresistance and tumorigenesis. Bharti et al. suggested that NF-κB might be an ideal target for chemoprevention and chemosensitization [17, 18]. In addition, we have found NF-κB gene centered around two up- and down-regulated networks in our previous study on prostate cancer [19].

Canonical pathway analysis revealed that G-protein coupled receptor (GPCR) signaling is an important pathway modulated by the up-regulated genes in B-CLL. It is known that GPCRs regulate proliferation, differentiation, chemotaxis and also they play an important role in inflammatory diseases and cancer [20]. GPCRs are involved in control of transcription factors such as STAT3, NF-κB and CREB by G protein subfamilies [21]. Enhanced viability of CLL cells by the STAT3 phosphorylation and interaction between hepatocyte growth factor and its receptor (c-MET), which was found up-regulated in our study, was reported before [22]. CREB (cAMP response element binding protein) had been found overexpressed in bone marrow samples from patients with acute lymphoid and myeloid leukemia and associated with a poor outcome in AML patients according to previous studies [23].

A network is also identified around HNF1A and HNF4A (Fig. 1, c). HNF1A is a transcription factor required for the expression of several liver-specific genes and the expression of this gene is controlled by HNF4A, which may play role in development of the liver, kidney and intestines.

Another significant signaling pathway is calcium signaling involved in many processes such as cell survival/apoptosis, cell cycle progression, differentiation, cross-talk between intracellular compartments (ER, mitochondria), general metabolism and telomerase activity. The calcineurin/NFAT signaling pathway is important in lymphoma/leukemogenesis [24]. Deregulation of this signaling and/or abnormal expression of its components has been reported in solid tumors of epithelial origin, lymphoma and lymphoid leukemia. Mouse models of human T-ALL/lymphoma showed the pro-oncogenic effect of active calcineurin/NFAT signaling in vivo [25]. NFAT transcription factors form four calcium signaling responsive members: NFATc1, NFATc2, NFATc3 and NFATc4. Among these members NFATc1 and NFATc2, which are found up-regulated in our study, were reported to be involved in the development, differentiation and functioning of multiple T-and B-cell subsets in previous studies. NFATc1 was found to be expressed in a majority of aggressive B-cell lymphomas. On the other hand, NFATc2 activation was shown to be responsible in B-CLL, in cooperation with STAT6, for the high expression of CD23 [24].

Metabolism of xenobiotics by cytochrome P450 pathway has been shown as highly significant in our study. The enhanced expression of several P450s like CYP1A, CYP2C and CYP3A, that are up-regulated in our study, was reported in tumor cells elsewhere [26].

Arachidonic acid and linoleic acid metabolisms are the other significant pathways modulated by the up-regulated genes in our study.

Analysis using IPA software revealed 1225 network eligible down-regulated genes, and 2657 Functions/Pathways eligible down-regulated genes. These individual genes are related to cell functions and maintenance, protein synthesis, dermatological diseases and conditions, cell death, gene expression, inflammatory disease, cell growth and proliferation, post-translational modification, cancer, infectious diseases, cell morphology, and development.

One important down-regulated network is identified around RB1 gene (Fig. 2, a). The role of RB1 in B-CLL has been reported based on cytogenetic data [27]. RB1 deletions involved in 13q14 abnormalities have been reported in B-CLL before [28].

Another down-regulated network is identified around SATB1 gene (Fig. 2, b). SATB1 is a new type of gene regulator expressing in various human cancers and thought to be related to the malignant potential. Overexpression of this gene has been reported as a predictor of poor prognosis in lung and gastric cancers [29, 30].

An important network is identified around CEBPA gene (Fig. 2, c). CEBPA is a critical transcriptional factor and regulates the balance between cell proliferation and differentiation during early hematopoietic development and myeloid differentiation [31]. CEBPA has a tumor-suppressor function in leukemogenesis and both loss of function and gain of function have leukemogenic potential. It was reported that overexpression of CEBPA could contribute to B-ALL and loss of function could contribute to AML [32]. On the other hand, down-regulated CEBPA was found in acute promyelocytic leukemia stem cells in animal models [33].

Canonical pathway analysis revealed that oxidative phosphorylation is an important pathway modulated by the down-regulated genes in B-CLL. In fact, previous studies suggested that the oxidative phosphorylation (OXPHOS) system is severely compromised in various cancers [34].

EIF2 signaling is another significant pathway. Suppression of head and neck, colorectal carcinoma and multiple myeloma tumor growth and/or survival by phosphorylation of eIF2α was reported before [35].

IPA reveals regulation of eIF4 and p70s6K signa­ling pathway. eIF4E down-regulated in our study plays an important role in tumor initiation and progression when its overexpression cooperates with oncogenes to accelerate transformation in cell lines and animal models [36]. p70s6K is a serine/threonine kinase and its target substrate is S6 ribosomal protein [37]. Inhibition of p70s6K was related to cell cycle arrest at G0/G1 phase in human cancer cells before [38].

Protein ubiquitination is another pathway found significant in our study. Ubiquitination of key signaling molecules by E3 ubiquitin ligases forms an important regulatory mechanism for NF-κB signaling. Deubiquitinases (DUBs) counteract E3 ligases and play a substantial role in down-regulation of NF-κB signaling and homeostasis [39].

Cdc42 signaling is a highly significant pathway. Cdc42 promotes or inhibits tumor progression depending on the cellular context and contributes to cancer development through its different roles in intracellular trafficking, cell cycle regulation and survival, polarity, migration and transcriptional control [40]. Cdc42 is also important in the development and progression of lymphoma. Genetic knockdown or pharmacological inhibition of Cdc42 resulting in a cell cycle arrest and apoptosis of anaplastic large cell lymphoma cells has been reported [41].

An important network is identified around both down-regulated and up-regulated YWHAG gene in our study (Fig. 1 and Fig. 2). This gene encoding for 14–3-3 protein gamma was found highly expressed in skeletal and heart muscles. It has been suggested that this protein has an important role in muscle tissue [42, 43]. 14–3-3 proteins play critical regulatory roles in signaling pathways in cell division and apoptosis [44]. Further investigations are required to establish the function of YWHAG gene in B-CLL.

Fig. 1. Significant up-regulated gene networks identified around MYC and SP1 genes (a), NF-κB1 gene (b), HNF1A and HNF4A genes (c), YWHAG gene (d) in B-CLL samples. The node shapes denote enzymes (), phosphatases (), kinases (), peptidases
(), G-protein coupled receptor (), transmembrane receptor (), cytokines (), growth factor (), ion channel (), transporter (), translation factor (), nuclear receptor (), transcription factor () and other ().The intensity of the node color-(red) indicated the degree of up-regulation
Fig. 2. A significant down-regulated gene network identified around RB1 gene (a), SATB1 gene (b) in B-CLL samples, CEBPA gene (c), YWHAG gene (d) in B-CLL samples. The keys to the node shapes are the same as in Fig. 1. The intensity of the node color-(green) indicates the degree of down-regulation

Real-time PCR confirmation results of four genes (MYC, FAS1, BAX and BCL-2) show that only MYC, BAX and BCL-2 expressions are in agreement with microarray results. Up-regulation of MYC is compatible with our expectations.

Previous studies indicate that high ratio of Bcl-2 to Bax proteins confers a poor prognosis with decreased rates of complete remission and overall survival [45]. In our study, BCL-2 upregulation level is superior to that of BAX in real-time PCR results but not in microarrays being analyzed.

FAS1 expression was found up-regulated in real-time PCR but down-regulated in microarrays in our study. It has been reported that Fas expression is not very high in B-CLL [46] that coincides with our fin­dings of relatively small up-regulation by real-time PCR. It should be noted that Fas was mentioned as apoptosis regulator [47] in CLL cells exposed to IR.

NF-κB gene network was conspicuous in terms of being determined also in our previous studies of gene expression in prostate cancer. In addition, upregulated levels of G protein coupled receptor signaling pathway, which has an important role in transcription of NF-κB, need advanced examinations. In this sense, NF-κB gene which is important in both cell cycle regulation and cancer progression deserves further study.

Our study has presented the gene expression profiling in B-CLL patients of Ukrainian population as whole. We believe that the contribution of IR as the putative factor in the origin of B-CLL should be further evaluated using such molecular genetic approach.


The study was financed within the framework of the joint research project M/32–2008 “Cytomorphological, immunocytochemical and molecular biological features of leukemias in persons exposed to ionizing radiation” according to the Agreement between the Ministry of Education and Science of Ukraine and the Scientific and Technical Research Council of Turkey (TUBITAK).


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