Liver Cancer
Copyright ©2005 Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 14, 2005; 11(42): 6613-6619
Published online Nov 14, 2005. doi: 10.3748/wjg.v11.i42.6613
Modulation of gene expression in MHCC97 cells by interferon alpha
Wei-Zhong Wu, Hui-Chuan Sun, Lu Wang, Jie Chen, Kang-Da Liu, Zhao-You Tang
Wei-Zhong Wu, Hui-Chuan Sun, Lu Wang, Jie Chen, Kang-Da Liu, Zhao-You Tang, Liver Cancer Institute and Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, China
Author contributions: All authors contributed equally to the work.
Supported by the Key Projects for the Clinical Medicine from the Ministry of Public Health of China (2002–2005)
Correspondence to: Zhao-You Tang, Liver Cancer Institute and Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, China.
Telephone: +86-21-6403-7181 Fax: +86-21-6403-7181
Received: March 17, 2005
Revised: April 26, 2005
Accepted: April 30, 2005
Published online: November 14, 2005


AIM: To elucidate the molecular mechanisms of the inhibitory effects of IFN-α on tumor growth and metastasis in MHCC97 xenografts.

METHODS: Three thousand international units per milliliter of IFN-α-treated and -untreated MHCC97 cells were enrolled for gene expression analysis using cDNA microarray. The mRNA levels of several differentially expressed genes in cDNA microarray were further identified by Northern blot and RT-PCR.

RESULTS: A total of 190 differentially expressed genes including 151 IFN-α-repressed and 39 -stimulated genes or expressed sequence tags from 8 464 known human genes were found to be regulated by IFN-α in MHCC97. With a few exceptions, mRNA levels of the selected genes in RT-PCR and Northern blot were in good agreement with those in cDNA microarray.

CONCLUSION: IFN-α might exert its complicated anti-tumor effects on MHCC97 xenografts by regulating the expression of functional genes involved in cell metabolism, proliferation, morphogenesis, angiogenesis, and signaling.

Key Words: Interferon α, cDNA microarray, Gene expression profile, HCC


Human hepatocellular carcinoma (HCC) is one of the most prevalent malignancies in China. Patients with HCC often die of tumor metastasis and recurrence even after curative resection. Recently, a metastatic human HCC model in nude mice (LCI-D20) and a series of HCC cell lines (MHCC97, MHCC97-H, MHCC97-L) with different metastatic potentials derived from LCI-D20 have been established in our institute[1,2]. Using this model, IFN-α significantly inhibits tumor growth and metastasis of MHCC97 xenografts has been found[3-5]. However, the underlying molecular mechanisms are still unclear.

IFN-α is a multifunctional cytokine capable of inter-fering with viral infection, inhibiting cell proliferation, regulating cell differentiation, as well as modulating immune response[6-9]. It is well known that these pleiotropic effects of IFN-α are mediated primarily through the tran-scriptional regulation of many different functional genes. Thanks to the rapid progress in human genetic projects; many functional human genes and expressed sequence tags (ESTs) are identified and released, which make us possible to use cDNA microarray to survey IFN-α-modulated genes in MHCC97 cells. In this study, we identified 190 differentially expressed genes from 8 464 known human genes, which might mediate various biological functions of IFN-α. These data provide us useful clues for further studying the anti-tumor mechanisms of IFN-α and finding the IFN-α mimics for HCC therapy.

Cell culture

MHCC97, a metastatic HCC cell line derived from LCI-D20 xenografts, was cultured in high glucose Dulbecco’s modified Eagle’s medium (Gibco-BRL, NY, USA) supplemented with 10% fetal calf serum (Hyclone, UT, USA), 100 U/mL penicillin and 100 μg/mL streptomycin in 20-cm2 tissue culture flasks. Cells were grown at 37 °C in a humidified atmosphere of 50 mL/L CO2 and passaged every 3 d.

cDNA microarray analysis

A total of 8 464 cDNAs of known human genes (United Gene Holding, Ltd, Shanghai) were amplified by polymerase chain reaction (PCR) using universal primers and spotted onto silylated slides (CEL Associates, Houston, TX, USA) using a Cartesian PixSys 7500 motion control robot (Cartesian Tech, Irvine, CA, USA) fitted with ChipMaker micro-spotting technology (TeleChem, Sunnyvale, CA, USA). After being hydrated, dried, cross linked and washed, the microarray was ready for use. Total RNA was isolated from IFN-α-treated and untreated (3 000 IU/mL, 16 h) cells using TRIzol (Gibco-BRL). cDNA probes were prepared by reverse transcription and purified according to the methods described by Schena et al[10]. Then equal amount of cDNA from IFN-α-untreated and treated MHCC97 cells was labeled with Cy3-dUTP and Cy5-dUTP, respectively. The mixed Cy3/Cy5 probes were purified and dissolved in 20 μL of hybridization solution (0.75 mol/L NaCl, 0.075 mol/L sodium citrate, 0.4% SDS, 50% formamide, 0.1% Ficoll, 0.1% polyvinylpyrrolidone and 0.1% BSA). Microarrays were pre-hybridized with 0.5 mg/mL salmon sperm DNA at 42 °C for 6 h. After being extensively washed, the denatured (95 °C, 5 min) fluorescent-labeled probe mixture was applied onto the pre-hybridized chips and further hybridized at 42 °C for 15-17 h under a cover glass. Subsequently, chips were sequentially washed for 10 min at 60 °C with 2×SSC+0.2% SDS, 0.1×SSC+0.2% SDS and 0.1×SSC solutions and dried at room temperature (1×SSC: 150 mmol/L NaCl, 15 mmol/L sodium citrate). Both Cy3 and Cy5 fluorescent signals of hybridized chips were scanned by ScanArray 4000 (GSI Lumonics, MA, USA) and analyzed using Genepix Pro 3.0 software (BioDiscovery Inc., CA, USA). To minimize artifacts arising from low expression, only genes whose Cy3 and Cy5 fluorescent intensities were both over 200 counts, or genes whose Cy3 or Cy5 fluorescent intensity was over 800 were selected for calculating the normalization cofactor (ln(Cy5/Cy3)). Genes were identified as differentially expressed, if the ratio of Cy5/(Cy3×normalization cofactor) (Cy5/Cy3*) was more than 2 or less than 0.5.

Reverse transcription and polymerase chain reaction

MHCC97 cells (106) cultured in 20-cm2 flasks were treated with 3 000 IU/mL IFN-α (Roche, Shanghai) for 0 or 16 h, and total RNA was extracted (RNeasy Mini Kit, QIAGEN Inc., CA, USA). One microgram RNA was used to set-up reverse transcription reactions (Gibco-BRL, NY, USA). Nine differentially expressed genes identified by cDNA microarray were selected for analysis by semi-quantitative PCR. Appropriate primers were designed using Primer3 software ( γ-Actin was used as an internal standard. PCR reaction conditions and primer sequences are summarized in Table 1.

Table 1 Primer sequence and condition for PCR analysis of selected genes.
CategoryGeneSense and antisense primersAnnealing(°C)CyclesSize(bp)
Cytoskeletal geneNeutral calponin5’-TGGCACCAGCTAGAAAACCT-3’; 5’-CAGGGACATGGAGGAGTTGT-3’5626498
Angiogenic geneVEGF165 receptor5’-GAAGCACCGAGAGAACAAGG-3; 5’-CACCTGTGAGCTGGAAGTCA-3’5630359
MAPK pathway-related genesERK activator kinase (MEK2)5’-CGAAAGGATCTCAGAGCTGG-3’; 5’-GTGCTTCTCTCGGAGGTACG-3’5626349
cAMP/PI3 pathway-related geneAdenylyl cyclase5’-CCAGGAGCCTGAAGAATGAG-3’; 5’-GGCTTCTGAGCTCCAATCAC-3’5335439
Housekeeping geneγ-Actin5’-ATGGAAGAAGAAATCGCCGC-3’; 5’-ACACGCAGCTCGTTGTAGAA-3’5525287
Northern blot analysis

Total RNA of 3 000 IU/mL IFN-α-treated or untreated MHCC97 cells was isolated as described above. Thirty microgram was separated by 1% agarose formaldehyde gel electrophoresis and transferred to a nylon membrane (Millipore, MA, USA) in 10×SSC by capillary blotting. The membrane was hybridized with the appropriate cDNA probe prepared from the human library of cDNA clones (Biostar Genechip Inc., Shanghai) and labeled with [α-32P]dCTP (Yahui Biomedical, Beijing) using random primer (Ambion Inc., Austin, TX, USA).

Gene expression profile identified by cDNA microarray

It is well known that the gene expression pattern of cells often varies with time and differentiation status and that cells derived from different individuals often have different genetic expression profiles. As a result, it is often difficult to extract useful information on the possible causes of phenotypic differences by comparing the genetic expression profiles of different cell lines. To minimize such complicated factors, we compared the gene expression profiles in 3 000 IU/mL IFN-α-treated and untreated (0 IU/mL) MHCC97 cells in two independent cDNA microarray analyses. We reasoned that such an internally consistent comparison might provide useful information on explaining the anti-tumor molecular mechanism of IFN-α in MHCC97 xenografts.

In 8 464 tested genes and ESTs, 190 genes were ide-ntified to be modulated by 3 000 IU/mL IFN-α treatment in MHCC97 cells. Among them the ex-pression of 151 genes was downregulated by IFN-α and the expression of 39 genes was upregulated by IFN-α. All differentially expressed genes are listed in Table 2 and the gene expression profiles obtained by cDNA microarray analysis are shown in Figure 1.

Figure 1
Figure 1 Representative hybrid result (A) and scatter plots (B) of cDNA microarray analysis in IFN-α treated MHCC97.
Table 2 Gene expression profile of MHCC97 cells induced by IFN-α.
CategoryGenBank IDGene descriptionCy5/Cy3* (average)
2.1 Metabolism related genesHUMCRTRCreatine transporter0.251
HUM2OGDH2-Oxoglutarate dehydrogenase0.298
AF034544Delta7-sterol reductase0.318
HUMTKThymidine kinase0.333
HSU12778Acyl-CoA dehydrogenase0.341
HUMTHBPThyroid hormone-binding protein(p55)0.349
AF0671277-Dehydrocholesterol reductase (DHCR)0.356
HSPRCOXPristanoyl-CoA oxidase0.364
AF035429Cytochrome oxidase subunit 10.372
AF070544Glucose transporter glycoprotein (SGLT)0.379
HSPFKLALiver-type1-phosphofructokianse (PFKL)0.392
HUMSHMTSerine hydroxymethyltransferase 2 (SHMT2)0.407
HUMMGPHBBrain glycogen phosphorylase0.413
HUMTCBACytosolic thyroid hormone-binding protein (p58)0.415
D88152Acetyl-coenzyme A transporter0.451
HUMPKM2LM2-type pyruvate kinase0.456
HSLDHBRLactate dehydrogenase B2.156
AF108211Inorganic pyrophosphatase2.25
HSCOXVIICytochrome C oxidase VII2.279
HUMCYCPSKCytochrome C (HS7)2.574
HUMDBIDiazepam binding inhibitor2.628
2.2 Proliferation, apoptosis and damaged DNA repairing related genesHSATPBRNa/K ATPase beta subunit0.208
HUMP53TMutant p53 protein0.233
HSMITGMitochondrial DNA0.309
HSNUMAMRNuclear mitotic apparatus protein0.325
G28520STS HSGC-31478 (homolog to Rad23a)0.341
AF096870Estrogen-responsive B box protein0.352
AF001609EXT like protein 30.367
AF015283Selenoprotein W0.369
AF011905Putative checkpoint control protein hRad10.398
HUMHMAM2Minichromosome maintenance 20.408
HUMRNAPIIRNA polymerase II 23 ku subunit0.408
AF007790Inversely correlated with estrogen receptor Expression (ICERE-1)0.413
AF004162Nickel-specific induction protein (Cap43)0.434
HSU3298UV-damaged DNA binding factor0.437
HSU72649B cell translocation gene 20.444
AF031523bcl-xL/bcl-2 associated death promoter (BAD)0.481
AF132973CGI-39 (homolog to GRIM-19)2.079
2.3 Morphogenesis, adhesion, and cytoskeletonD38735Neutral calponin0.141
AF006082Actin-related protein Arp20.197
U01244Fibulin 1D0.212
remodeling related genesAF070593Beta tublin0.236
HSU35622EWS-E1A-F chimeric protein0.255
AF049259Keratin 130.335
HSPRO4HYProlyl 4-hydoxylase beta0.337
HUMCN4GELCollagenase type IV0.36
AF005654Actin-binding double zinc-finger protein0.378
HUMEPSURANSurface antigen0.389
AF004841CAM-related/down-regulated by oncogenes0.398
HUMCA1XIAAlpha-1 type XI collagen0.423
HUMMCPGVMacrophage capping protein0.461
HSTUMPTranslationally controlled tumor protein2.022
2.4 Signal transmitting related genesHUMEPHT2RProtein tyrosine kinase (NET PTK)0.248
HUMMEK2NFERK activator kinase (MEK2)0.271
HUMHRGAArab GDI alpha0.285
AF053535ras-GAP/RNA binding protein G3BP20.296
HSU45973Pt Ins (4,5) P(2) 5-phosphatase0.324
HSU07139Voltage-gated calcium channel beta0.329
HUMFTPBFarnesyl-protein transferase beta0.345
HSU33053Lipid-activated protein kinase (PRK1)0.352
HUMHK1ACalcium-ATPase (HK1)0.386
HSU66406EPH-related PTK receptor ligand LERK-80.386
HSPP15Placental protein 150.387
HSADCYCLAdenylyl cyclase0.409
HUMCHEDcdc2-related protein kinase (CHED)0.412
AF093265Homer 30.415
HSU40282Integrin-linked kinase0.416
HUMGKASStimulatory G protein0.416
HSU43939Nuclear transport factor 20.429
HUMCAKTyrosine protein kinase (CAK)0.439
HUMGNOS48Endothelial nitric oxide synthase0.443
HUMCDPKIVCalmodulin-dependent protein kinase IV0.449
HSPKX1MRProtein kinase, PKX10.469
D83760Mother against dpp (Mad) related protein0.472
HUMEGFGRBAEGF receptor binding protein GRB20.481
HSU51004Protein kinase C inhibitor (PKCI-1)2.223
2.5 Tumor angiogenesis related genesHUMRNAMBPEGolli-mbp0.236
AF016050VEGF 165 receptor/neuropilin0.25
AF001307Aryl hydrocarbon receptor nuclear translocator0.27
HSU64791Golgi membrane sialoglycoprotein MG 1600.355
HUMPTPRZProtein tyrosine phosphatase Zeta-polypeptide0.363
HSU28811Cysteine-rich FGFR (CFR1)0.414
HUMTR107DNA-binding protein, TAXREB1072.24
2.6 Transcriptional activity related genesS66431Retinoblastoma binding protein 20.182
HUMANT61KMedium antigen-associated 61 ku protein0.183
HSU58197Interleukin enhancer binging factor 20.226
HSUBPUpstream binding factor0.266
4758315ets-related molecule, ETV50.267
AF099013Glucocorticoid modulatory element binding protein 10.309
HSU72621Lost on transformation 1(LOT1)0.313
HUMFOSOncogene protein, c-fos0.361
AB019524Nuclear receptor co-repressor0.369
HS14AGGREConserved gene telomeric to alpha globin cluster0.398
HSU74667tat interactive protein (tip60)0.404
AF114816KRAB-zinc finger protein SZF1-10.406
HSU80456Drosophila single-minded, SIM20.409
AF117756TRAP 1500.41
HSU15306Cysteine rich DNA binding protein NFX10.417
S57153Retinoblastoma binding protein 10.469
HUMTR107DNA binding protein. TAXREB 1072.24
HUMMSS1Mammalian suppressor of sgv 1, MSS 12.313
2.7 mRNA and protein processing, secretory, proteolysis related genesHSU39412Alpha SNAP0.141
HSU47927Isopeptidase T (ISOT)0.229
HSU72355hsp27 ERE-TATA bind protein, HET0.231
AF077039TIM17 homolog0.238
HUMHRH1RNA helicase, HRH10.251
AF206402U5 SnRNP 100 ku protein0.255
D85429Heat shock protein 400.344
HSU85946hSec 10p0.378
HSY10806Arginine methyltransferase0.412
AB002135Glycophosphatidylinositol anchor attachment 10.428
AB007510PRP8 protein0.436
HSU24105Coatomer protein (COPA)0.455
HSCANPXCalpain-like protease (CANPX)0.456
HSRBPRL7ARibosomal protein L72.067
D89678A+U-rich element RNA-binding protein2.069
HSU14966Ribosomal protein L52.113
HSRPL31Ribosomal protein L312.142
HUMPSC9Proteasome subunit HC92.179
HSU26312Heterochromatin protein HP1 HS-gamma2.182
HUMRPS7ARibosomal protein S72.289
HSUCEH3Ubiquitin-conjugated enzyme UbCH22.323
HUMRPS7ARibosomal protein S72.289
HSUCEH3Ubiquitin-conjugated enzyme UbCH22.323
HUMRPS25Ribosomal protein S252.326
HUMRPSA3ARibosomal protein S3a2.328
HSRNASMGSm protein G2.334
HUMRPS18Ribosomal protein S182.341
HUMRP4SXRibosomal protein S4 isoform2.346
HUMPSC3Proteasome subunit HC32.368
HUMTCP20Chaperonin protein, TCP202.572
4504522Chaperonin protein, hsp102.686
2.8 Tumor antigen processing, anti-viral infection related genesHUMSAPC1Cerebroside sulfate activator protein0.211
AF077011Interleukin 160.23
AF055008Epithelin 1 and 20.363
HSU58766FX protein0.393
HSU46194RAGE 40.43
HSU18121136 ku double-stranded RNA binding protein0.469
AF021315Reverse transcriptase0.483
S74095Preproenkephalin A2.115
HUM927AInterferon inducible protein 9-272.356
HSIFI56RInterferon inducible protein 56 ku3.829
HUMHCAMAP1Interferon inducible protein 44 ku4.03
2.9 Genes with unknown biological functionsD50928KIAA01380.23
HSU10362GB36b glycoprotein0.335
4579277A homolog of proteasome regulatory S20.352
4505130A homolog of MCM30.371
HSNIPSNA1NIPSNAP1 protein0.391
HSU90907Regulatory subunit of P55 PIK0.407
Nine differentially expressed genes evaluated by RT-PCR and Northern blot

To validate the results of cDNA microarray, we selected nine genes whose expressions were clearly altered by IFN-α and evaluated their expressions by PCR and Northern blot. We enrolled IFN-α-regulated genes and found that the results were consistent with the previous reports[11,12].

For PCR analysis, we synthesized primers as indicated in Table 1 and performed semi-quantitative RT-PCR as outlined under “Materials and methods” after treatment of MHCC97 cells with 3 000 IU/mL IFN-α for 0 or 16 h. The transcription patterns of the same genes were also analyzed by Northern blot. Among the nine selected genes, seven downregulated genes were proved by cDNA microarray, six by RT-PCR and five by Northern blot analysis. Two stimulated genes, ISG-56 ku and 9-27 were proved by cDNA microarray, RT-PCR and Northern blot analysis. ERK activator kinase (MEK2), one repressed gene in cDNA microarray, was not changed in RT-PCR or Northern blot analysis. Thus, with a few exceptions, the results of RT-PCR and Northern blot were in good agreement with those of cDNA microarray analysis (Figure 2).

Figure 2
Figure 2 Confirmation of gene expression profiles in cDNA microarray analysis with RT-PCR and Northern blot.

cDNA microarray is a useful technique for rapid screening of gene expressions in cells, although the results need to be further confirmed by other molecular methods. Using this method, we found 211 hybrid dots, whose Cy5/Cy3* ratio was either more than 2 or less than 0.5 in IFN-α-treated MHCC97. Blasting the cDNA sequences in public database showed that these dots represented 190 different human genes or ESTs due to the redundant hybrids. Based on the results of RT-PCR and Northern blot, we believe that our cDNA microarray data are reliable. These differentially expressed genes might mediate the multiple biological functions of IFN-α directly or indirectly in MHCC97. We have artificially categorized these genes into nine functional clusters (Table 2).

IFN-α might interfere with cellular metabolisms by downregulating metabolic gene expression. In detail, IFN-α can inhibit glycolysis, glycogen degradation, gluconeogenesis as well as creatine or glucose tran-sportation by repressing the expressions of liver-type phosphofructokinase (hPFKL), M2-type pyruvate kinase, brain glycogen phosphorylase, 2-oxoglutarate de-hydrogenase, glucose transporter glycoprotein (SGLT) and cytosolic thyroid hormone-binding protein[13]. IFN-α can also inhibit lipolysis by reducing the expression of delta7-sterol reductase and pristanoyl-CoA oxidase, two key enzymes in lipid metabolism[14,15]. In addition, IFN-α reduces purine and pyridine biosynthesis by repressing the expression of GARs-AIRs-GART and serine hydro-xymethyltransferase 2 (SHMT2). All these indicate that IFN-α-treated MHCC97 can result in lower ATP production and DNA synthesis, and slow down cell proliferation.

Many proliferation-, apoptosis- and cell cycle-regulating genes are modulated by IFN-α in MHCC97. Downregulating the expression of mutant p53, mito-chondrial DNA, nuclear mitotic apparatus protein (NuMA), and RNA polymerase II 23 ku subunit (polR2) might cause cell cycle arrest[16,17]. Downregulating the expression of DNA ligase III, hRad1, minichromosome maintenance 2 (hMCM2) as well as UV-damaged DNA binding factor might hinder damaged DNA repairing[18,19]. Stimulating retinoid-IFN-induced mortality 19 (GRIM-19) expression might promote IFN-α-induced apoptosis[20].

Several genes functionally related to cell morphogenesis, adhesion, and cytoskeleton remodeling are also modulated by IFN-α in MHCC97. For example, decreasing the expression of calponin, actin-related protein 2 (Arp2), fibulin 1D, beta-tublin and epidermal surface antigen (ESA), etc., might damage mitotic spindle formation and might interfere with actin-based cell motility, migration, adhesion and morphogenesis[21-24]. Reducing the expression of prolyl 4-hydroxylase beta, a key enzyme in collagen biosynthesis and type IV collagenase, a tumor-derived extracellular matrix metalloproteases might block tumor invasion and metastasis. Although most genes in this category were first identified as IFN-α regulating genes, their roles in mediating IFN-α functions need to be further studied.

In this study, we found that many genes functionally related to signal transmitting were affected by IFN-α in MHCC97. By repressing the expressions of discoidin domain receptor, integrin-linked kinase, EPH-related tyrosine kinase (EPT2) and MEK2, etc., IFN-α might block cellular signaling initiated by tyrosine-kinase receptors[25,26]. By modulating the expressions of Rab GDI, Ras-related GTP-binding proteins and farnesyl-protein transferase and nuclear transport factor (NTF2) and G3BP2, a Ras-GAP/RNA binding protein, IFN-α might interfere with GTP/GDP exchange and nuclear import, thus influencing the recycles and activities of ras and its homologs[27-29]. By attenuating the expressions of adenylyl cyclase (AC) and phosphatidylinositol 4,5-bisphosphate 5-phosphatase (PtdIns (4,5)P(2)5- phospharase), a catalyzer of pho-sphatidylinositol 4,5-bisphosphate and PRK1, IFN-α might decrease inositol polyphosphate levels in cytosol and might inhibit the serine/threonine-kinase activities through cAMP/ PI3P signal pathway[30,31]. All these changes might exert inhibitory effects of IFN-α on MAPK and PI3K signaling. In addition, other signaling pathways such as Ca(2+), NO and TGFβ/hMAD-dependent signaling pathways are suppressed by IFN-α as well[32,33]. Plausibly Jak/STATs pathway, the most important IFN-α signaling pathway, is confirmed not to be regulated in IFN-α-treated MHCC97. The deficient expression of p48 (ISGF3γ) in this cell line may be the possible mechanism for the non-response of IFN-α priming via Jak/STATs pathway (data not shown).

In this study, we found that many angiogenic-related genes were regulated by IFN-α. By attenuating the expressions of Golli-MBP[34], VEGF 165 receptor and aryl hydrocarbon receptor nuclear translocator (ARNT)[35] as well as Golgi membrane sialoglycoprotein MG 160, a bFGF binding protein and cysteine-rich FGF receptor (CFR-1)[36], IFN-α may destroy the balance between pro- and anti-angiogenic factors and exert its inhibitory effects on tumor angiogenesis.

It is well known that cells usually respond to various stimuli by rapidly shifting the functions of transcriptional factors. Using this strategy, IFN-α might impose its anti-proliferative functions and hormone response by fluctuating the expression of several transcriptional factors or their cofactors such as retinoblastoma binding protein2 (RBP2), interleukin enhancer binding factor 2, lost on transformation 1 (LOT1) and KRAB-zinc finger protein (SZF1)[37-40].

In addition, IFN-α might hinder with mRNA/rRNA spicing and maturation by downregulating RNA helicase (HRH1), U5 snRNP[41] and affect protein transportation, secretion and proteolysis by downregulating alpha SNAP, GPAA1, hSec10p, hsp40 and isopeptidase T, a putative molecular in ubiquitin–proteasome pathway[42-44]. Meanwhile IFN-α might evoke anti-viral or tumor immune response by upregulating 9-27, 56 ku protein and p44 expressions.

Except for functionally definite genes, many ESTs with unknown functions were identified as IFN-α-regulated genes in our study (Table 2). In conclusion, cDNA microarray is a useful, rapid method for screening transcriptome of cells and potentially paves a way for elucidating IFN-α effects on tumor growth and metastasis.


We thank Shanghai Biostar Genechip Inc. for cDNA microarray service.


Science Editor Wang XL and Guo SY Language Editor Elsevier HK

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