Open Access

Transcriptional patterns, biomarkers and pathways characterizing nasopharyngeal carcinoma of Southern China

  • Weiyi Fang1,
  • Xin Li1, 2,
  • Qingping Jiang1,
  • Zhen Liu1,
  • Huiling Yang4,
  • Shuang Wang1,
  • Siming Xie1,
  • Qiuzhen Liu1,
  • Tengfei Liu1,
  • Jing Huang1,
  • Weibing Xie1,
  • Zuguo Li1,
  • Yingdong Zhao3,
  • Ena Wang2,
  • Francesco M Marincola2Email author and
  • Kaitai Yao1Email author
Contributed equally
Journal of Translational Medicine20086:32

DOI: 10.1186/1479-5876-6-32

Received: 25 March 2008

Accepted: 20 June 2008

Published: 20 June 2008

Abstract

Background

The pathogenesis of nasopharyngeal carcinoma (NPC) is a complicated process involving genetic predisposition, Epstein-Bar Virus infection, and genetic alterations. Although some oncogenes and tumor suppressor genes have been previously reported in NPC, a complete understanding of the pathogenesis of NPC in the context of global gene expression, transcriptional pathways and biomarker assessment remains to be elucidated.

Methods

Total RNA from 32 pathologically-confirmed cases of poorly-differentiated NPC was divided into pools inclusive of four consecutive specimens and each pool (T1 to T8) was co-hybridized with pooled RNA from 24 normal non-cancerous nasopharyngeal tissues (NP) to a human 8K cDNA array platform. The reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and immunohistochemistry.

Results

Stringent statistical filtering parameters identified 435 genes to be up-regulated and 257 genes to be down-regulated in NPC compared to NP. Seven up-regulated genes including CYC1, MIF, LAMB3, TUBB2, UBE2C and TRAP1 had been previously proposed as candidate common cancer biomarkers based on a previous extensive comparison among various cancers and normal tissues which did not, however, include NPC or NP. In addition, nine known oncogenes and tumor suppressor genes, MIF, BIRC5, PTTG1, ATM, FOXO1A, TGFBR2, PRKAR1A, KLF5 and PDCD4 were identified through the microarray literature-based annotation search engine MILANO, suggesting these genes may be specifically involved in the promotion of the malignant conversion of nasopharyngeal epithelium. Finally, we found that these differentially expressed genes were involved in apoptosis, MAPK, VEGF and B cell receptor signaling pathways and other functions associated with cell growth, signal transduction and immune system activation.

Conclusion

This study identified potential candidate biomarkers, oncogenes/tumor suppressor genes involved in several pathways relevant to the oncogenesis of NPC. This information may facilitate the determination of diagnostic and therapeutic targets for NPC as well as provide insights about the molecular pathogenesis of NPC.

Background

The synergetic effect of virus infection, genetic aberrations and environmental factors may lead to sequential alterations of gene expression involved in several biological pathways at different stages of nasopharyngeal carcinoma (NPC) oncogenesis. Contemporary advances in cancer genomic analysis including microarray, array-based high throughput comparative genomic hybridization (aCGH), detection of promoter hypermethylation, and analysis of gene mutation have greatly accelerated our understanding of NPC-associated genes. With the increased application of microarray technology to investigate genes differentially expressed in NPC[1, 2], many functional associations with NPC pathogenesis have been gradually discovered[3, 4]. Accumulation of CGH data indicated that genetic imbalances occur consistently in particular chromosomal regions in which a high frequency of oncogenes and tumor suppressor genes are gathered [38]. However, in spite of these important insights the pathogenesis of NPC remains elusive as a complete identification of genes associated with its development is not available. Highly frequent mutations of p53 gene, a classical tumor suppressor gene, associated with most of human malignancies, do not link to the pathogenesis of sporadic NPC consistently, strongly suggesting NPC has its specific pattern of gene expression and other genes may play more significant roles in its oncogenesis and tumor progression [9]. Therefore, in the present study, we utilized 8K cDNA microarray and several bioinformatics tools (KEGG database, online MILANO, BRB arraytool's gene set comparison) to profile differential gene expression between NPC and NP samples from Southern China, the region with highest NPC prevalence in the world. Several oncogenes and tumor suppressor genes were identified as candidate biomarkers associated with important pathways relevant to NPC oncogenesis, this may facilitate the development of important diagnostic and therapeutic targets for NPC as well as provide further insights about the molecular pathogenesis of NPC.

Methods

Samples collection and screening

One-hundred-and-two primary tumor biopsies diagnosed as poorly differentiated squamous cell carcinoma were obtained from primary NPC patients. In addition, 24 non-cancer nasopharyngeal (NP) tissues were obtained from patients with or without NPC. Biopsy samples containing more than 70% of tumor cells [10] were selected for further analysis. All participants (with/without NPC) gave their informed consents before the biopsies at Jiangmen Center Hospital, Guangdong Province and Tumor Hospital of Hunan Province. In addition, three well-characterized NPC cell lines, 5–8F with highly tumorigenic and metastatic potential and 6–10B and CNE2 with tumorigenic potential but disability to metastasize were collected and analyzed.

Hybridization to arrays

All experiments were performed in Shenzhen Chipscreen Biosciences Limited of China [11]. A pooling strategy was applied; every four NPC biopsies were pooled and three cell lines (5–8F, 6–10B and CNE2) were pooled (1:1:1). In addition, all normal NPs were pooled together to be used as the normal reference. Total RNA samples extracted by Trizol reagent were further purified using Qiagen RNeasy mini kit (Qiagen, Inc.). 20 μg of total RNA samples isolated respectively from each NPC pool and corresponding normal reference were labeled by Cy5-dCTP and Cy3-dCTP respectively in the presence of 2 μg oligo(dT)18 primer in a reverse transcription reaction. The resulting labeling reactions were treated with 2 μl of 0.5 M NaOH hydrolyzing RNA for 15 mins at room temperature, and then neutralized with 2 μl of 0.5 M HCl. The labeled first-strand products were purified by QIAquick PCR purification kit and dried by speedvac. Finally, the balanced mixture of Cy5- and Cy3-labeled targets was co-hybridized against the 8K Human cDNA microarrays (CSC-GE) from Shenzhen Chipscreen Biosciences Limited, in a humidified chamber in 30 μl of hybridization solution (7.5 μl of 4 × hybridization buffer solution, 15 μl of 50% formamide and 7.5 μl of purified water) at 42°C overnight after denaturized at 95°C for 5 minutes. Slides were washed twice for 20 min each time in 0.1% SSC at 55°C., dried, and scanned with a Generation III array scanner (Amersham Pharmacia). The scanned images were converted to digital data by Arrayvision 6.0.

Statistical analysis and bioinformatics analysis

Statistical identification of candidate NPC biomarkers was based on BRB Array tool version 3.6[12]; the raw data were first filtered to exclude uninformative spots using the following filtering parameters: 1) minimum intensity was set 200 in both fluorescence channels; 2) the lowess smother correction method with median over entire array was used for normalization; 3) More than 60% of expression data should have at least a 2-fold change in either direction from gene's median value; 4) Percent of data missing or filtered out could not exceed 50%. Class comparison between groups of arrays was performed by paired samples (NPC and NP from same array) with a univariate significance threshold set at a p2-value < 0.005.

To computationally analyze KEGG pathways based on differential gene expression data, Principal Component Analysis was performed by using BRB array tool. Gene set expression comparisons were performed at a nominal 0.005 level of the Hotelling T-square test. P-values of the univariate test were calculated, followed by 1000 multivariate permutations test. Significant pathways that have more genes differentially expressed between NPC and NP classes than expected by chance were selected [13].

Genes differentially expressed by NPC compared to NP samples were evaluated for functional annotations according to the Microarray literature-based annotation(MILANO)[14, 15]. This program performed automatic searches in Pubmed collection for articles containing co-occurrences of search terms. A list of genes was used by pasting differentially expressed genes in the "Primary Search Term" field; "oncogene, proto-oncogene, tumor suppressor gene and nasopharyngeal carcinoma" search terms where targeted within the "Secondary Search Term" field. The output was a table containing the number of hits within the Pubmed literatures for each pair of search terms.

Semiquantitative RT-PCR and Immunohistochemistry

Purified total RNA was treated with RNase-free DNase I (TaKaRa). After removal of the DNase I, cDNA was reversely transcribed from 1 μg of total RNA using oligo(dT)18 in 20 μl reaction volume. Five randomly selected genes (PDGFRA, BIRC5, CTGF, EBI2, and TGFBR2) differentially expressed in NPC samples compared with NP samples according to our microarray data and an invariant housekeeping gene control, ACTG1 (actin, gamma 1), were amplified from 5% of synthesized cDNA according to the designed primers. The primer pair designed for each gene spanned at least an intron to distinguish possibly amplified cDNA products from genomic DNA. Subsequently, 5 μl of each PCR reaction product was analyzed on 1.5% agarose gel from which the intensity of each band was quantitated by the Vilber gel documentation system (Vilber Limited). The RT-PCR signal from each gene was normalized by the ACTG1 gene.

Paraffin sections (4 μm) available from NPC and NP samples were deparaffinized in 100% xylene and re-hydrated in descending ethanol series according to standard protocols. Microwave-induced epitope retrieval was performed in 10 mM citrate buffer at 95°C for 15 min. Endogenous peroxidase activity and non-specific antigen were blocked with peroxidase blocking reagent containing 3% hydrogen peroxide and serum followed by incubation with rabbit anti- BIRC5, CTGF and TGFBR2 protein antibody (1:100, Boshide Company, China) at 4°C overnight. After washing, the sections incubated using biotin-goat anti-mouse/rabbit at room temperature for 10 minutes, were then conjugated with horseradish peroxidase (Maixin Company, China). The peroxidase reaction was developed with 3, 3-diaminobenzidine chromogen solution in DAB buffer substrate. Sections were counterstained with hematoxylin, mounted in neutral gum and analyzed using a bright field microscope. The results were finally analyzed by Mann-Whitney test of SPSS11.5 software.

Results

NPC samples screening

One-hundred-and-two primary NPC samples were screened to ascertain the validity of the diagnosis based on frozen sections. Among them, 32 samples containing more than 70% of cancer cells [10] were qualified for further analysis. All samples included were diagnosed as poorly differentiated NPC.

Differentially expressed genes by NPC compare with the normal tissue

All 32 qualified NPC samples were pooled into 8 pools (n = 32/4) ranging from T1 to T8 which were then co-hybridized with NP (pooled normal reference). In parallel, three NPC cell lines were pooled as T9 and co-hybridized with the NP reference as well. A Total of 692 genes were identified to be differentially expressed. Among them, 435 genes were shown to be up-regulated and 257 genes down-regulated in NPC (Table 1 and Figure 1). The top 70 up-regulated and down-regulated genes are shown in Table 2 and Table 3 respectively.
https://static-content.springer.com/image/art%3A10.1186%2F1479-5876-6-32/MediaObjects/12967_2008_Article_262_Fig1_HTML.jpg
Figure 1

Class comparison and hierarchical clustering analysis. T1-T8: Pooled NPCs compared to pooled NPs; T9: Pooled NPC cells compared to pooled NPs; Green region: down-regulated genes, Red region: up-regulated genes.

Table 1

Differentially expressed genes between NPC and NP

Differential expression

Number of genes

Fold difference (NPC/NP)

Up-regulated

435

2.8–70.3

Down-regulated

257

0.01–0.46

Total

692

 

Number of genes significant at 0.005 level of the univariate test: 692

Global test: probability of getting at least 692 genes significant by chance (at the 0.005 level) if there are no real differences between the classes: 0.00391

Table 2

Top 70 up-regulated genes

No.

Gene symbol

False discovery rate

Fold difference

Description

1

SCAM-1

< 1e-07

22.68

vinexin beta (SH3-containing adaptor molecule-1)

2

SNTB1

< 1e-07

22.04

syntrophin, beta 1 (dystrophin-associated protein A1, 59 kDa, basic component 1)

3

DMWD

< 1e-07

21.75

dystrophia myotonica-containing WD repeat motif

4

NPFF

< 1e-07

20.72

neuropeptide FF-amide peptide precursor

5

FXYD3

< 1e-07

6.97

FXYD domain containing ion transport regulator 3

6

ATP6V0B

< 1e-07

5.77

ATPase, H+ transporting, lysosomal 21 kDa, V0 subunit c"

7

PPIB

< 1e-07

4.64

peptidylprolyl isomerase B (cyclophilin B)

8

HIST1H4C

< 1e-07

3.81

histone 1, H4c

9

PHB

9.40e-06

7.07

prohibitin

10

NME1

9.40e-06

5.00

non-metastatic cells 1, protein (NM23A) expressed in

11

NDUFA9

9.40e-06

3.34

NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 9, 39 kDa

12

BUB1B

1.20e-05

2.77

BUB1 budding uninhibited by benzimidazoles 1 homolog beta (yeast)

13

GRK5

1.34e-05

21.03

G protein-coupled receptor kinase 5

14

TPI1

1.34e-05

10.82

triosephosphate isomerase 1

15

CYC1 *

1.34e-05

10.36

cytochrome c-1

16

GPC5

1.55e-05

19.28

glypican 5

17

BYSL

1.67e-05

41.59

bystin-like

18

COX5B

1.67e-05

12.52

cytochrome c oxidase subunit Vb

19

KATNB1

1.84e-05

18.37

katanin p80 (WD repeat containing) subunit B 1

20

PRKAB2

2.03e-05

14.24

protein kinase, AMP-activated, beta 2 non-catalytic subunit

21

PSMB3

2.03e-05

7.43

proteasome (prosome, macropain) subunit, beta type, 3

22

PET112L

2.16e-05

21.46

PET112-like (yeast)

23

PSMD8

2.16e-05

4.88

proteasome (prosome, macropain) 26S subunit, non-ATPase, 8

24

CSF1

2.25e-05

20.35

colony stimulating factor 1 (macrophage)

25

SFTPA2

2.25e-05

17.66

surfactant, pulmonary-associated protein A2

26

PTCD1

2.25e-05

7.36

pentatricopeptide repeat domain 1

27

AARS

2.25e-05

6.65

alanyl-tRNA synthetase

28

GUK1

2.58e-05

5.61

guanylate kinase 1

29

RPS5

2.58e-05

4.66

ribosomal protein S5

30

MTL5

3.3e-05

14.60

metallothionein-like 5, testis-specific (tesmin)

31

MIF *

3.8e-05

10.72

macrophage migration inhibitory factor (glycosylation-inhibiting factor)

32

POLR2H

3.94e-05

4.68

polymerase (RNA) II (DNA directed) polypeptide H

33

IFITM2

4.38e-05

4.17

interferon induced transmembrane protein 2 (1–8D)

34

MUTYH

4.41e-05

19.61

mutY homolog (E. coli)

35

ITPKA

4.41e-05

15.19

inositol 1,4,5-trisphosphate 3-kinase A

36

FDPS

5.36e-05

3.45

farnesyl diphosphate synthase

37

HIST1H2BG

6.08e-05

10.23

histone 1, H2bg

38

INPP5D

6.15e-05

17.02

inositol polyphosphate-5-phosphatase, 145 kDa

39

IDH3G

6.47e-05

47.05

isocitrate dehydrogenase 3 (NAD+) gamma

40

ERF

6.49e-05

15.57

Ets2 repressor factor

41

PTPRJ

6.54e-05

13.86

protein tyrosine phosphatase, receptor type, J

42

ATP5G1

7.90e-05

4.82

ATP synthase, H+ transporting, mitochondrial F0 complex, subunit c, isoform 1

43

CDC45L

8.49e-05

22.94

CDC45 cell division cycle 45-like (S. cerevisiae)

44

ENO1

9.67e-05

4.75

enolase 1, (alpha)

45

IDH3B

0.0001064

3.81

isocitrate dehydrogenase 3 (NAD+) beta

46

ST3GAL4

0.0001140

28.99

ST3 beta-galactoside alpha-2,3-sialyltransferase 4

47

DDR2

0.0001152

15.87

discoidin domain receptor family, member 2

48

IFI30

0.0001188

3.88

interferon, gamma-inducible protein 30

49

EIF4G1

0.0001282

3.31

eukaryotic translation initiation factor 4 gamma, 1

50

CXorf27

0.0001372

11.62

chromosome X open reading frame 27

51

EPB41

0.0001423

4.05

erythrocyte membrane protein band 4.1 (elliptocytosis 1, RH-linked)

52

RPS15

0.0001427

6.91

ribosomal protein S15

53

ABCA3

0.0001432

30.18

ATP-binding cassette, sub-family A (ABC1), member 3

54

CGA

0.0001508

12.31

glycoprotein hormones, alpha polypeptide

55

SLC37A4

0.0001527

23.08

solute carrier family 37 (glycerol-6-phosphate transporter), member 4

56

NDUFB8

0.0001527

3.56

NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 8, 19 kDa

57

CSPG4

0.0001583

60.14

chondroitin sulfate proteoglycan 4 (melanoma-associated)

58

MTHFD1

0.0001583

21.57

methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 1,

59

CYP3A5

0.0001586

17.90

cytochrome P450, family 3, subfamily A, polypeptide 5

60

FKBP4

0.0001611

6.51

FK506 binding protein 4, 59 kDa

61

SCO2

0.0001760

14.31

SCO cytochrome oxidase deficient homolog 2 (yeast)

62

DPM2

0.0001846

12.99

dolichyl-phosphate mannosyltransferase polypeptide 2, regulatory subunit

63

NRGN

0.0001945

16.26

neurogranin (protein kinase C substrate, RC3)

64

TUBA2

0.0001988

8.87

tubulin, alpha 2

65

GAPD

0.0001990

4.99

glyceraldehyde-3-phosphate dehydrogenase

66

CDK4

0.0002205

6.73

cyclin-dependent kinase 4

67

BIRC5

0.0002380

6.62

baculoviral IAP repeat-containing 5 (survivin)

68

LAMB3 *

0.0002380

5.33

laminin, beta 3

69

SSR4

0.0002380

3.81

signal sequence receptor, delta (translocon-associated protein delta)

70

PIK4CA

0.0002398

12.01

phosphatidylinositol 4-kinase, catalytic, alpha polypeptide

* Genes appeared in table 4

Table 3

Top 70 down-regulated genes

No.

Gene symbol

False discovery rate

Fold difference

Description

1

HERC3

< 1e-07

0.10

hect domain and RLD 3

2

PDCD4

< 1e-07

0.10

programmed cell death 4 (neoplastic transformation inhibitor)

3

SFRS5

< 1e-07

0.11

splicing factor, arginine/serine-rich 5

4

ST6GAL1

< 1e-07

0.13

ST6 beta-galactosamide alpha-2,6-sialyltranferase 1

5

DXYS155E

< 1e-07

0.16

DNA segment on chromosome X and Y (unique) 155 expressed sequence

6

KIAA1641

< 1e-07

0.17

KIAA1641

7

LEPROTL1

< 1e-07

0.17

leptin receptor overlapping transcript-like 1

8

ELL2

< 1e-07

0.23

elongation factor, RNA polymerase II, 2

9

FUSIP1

< 1e-07

0.24

FUS interacting protein (serine-arginine rich) 1

10

OGT

< 1e-07

0.24

O-linked N-acetylglucosamine (GlcNAc) transferase

11

MYST4

< 1e-07

0.26

MYST histone acetyltransferase (monocytic leukemia) 4

12

RUVBL2

0.0000063

0.13

RuvB-like 2 (E. coli)

13

ARF6

0.0000063

0.13

ADP-ribosylation factor 6

14

ACTR2

0.0000063

0.14

ARP2 actin-related protein 2 homolog (yeast)

15

TRIM23

0.0000063

0.15

tripartite motif-containing 23

16

HDAC3

0.0000063

0.15

histone deacetylase 3

17

PRKAR1A

0.0000063

0.17

protein kinase, cAMP-dependent, regulatory, type I, alpha

18

DR1

0.0000063

0.18

down-regulator of transcription 1, TBP-binding (negative cofactor 2)

19

MAP4K3

0.0000063

0.21

mitogen-activated protein kinase kinase kinase kinase 3

20

PPP1CB

0.0000063

0.21

protein phosphatase 1, catalytic subunit, beta isoform

21

SEL1L

0.0000063

0.24

sel-1 suppressor of lin-12-like (C. elegans)

22

RAD17

0.0000063

0.24

RAD17 homolog (S. pombe)

23

ABI1

0.0000063

0.27

abl-interactor 1

24

ATM

0.0000063

0.27

ataxia telangiectasia mutated (includes complementation groups A, C and D)

25

STK17A

0.0000063

0.28

serine/threonine kinase 17a (apoptosis-inducing)

26

PRR4

0.0000094

0.02

proline rich 4 (lacrimal)

27

CHL1

0.0000094

0.06

cell adhesion molecule with homology to L1CAM (close homolog of L1)

28

PRKACB

0.0000094

0.10

protein kinase, cAMP-dependent, catalytic, beta

29

ELF1

0.0000094

0.13

E74-like factor 1 (ets domain transcription factor)

30

EPS15

0.0000094

0.16

epidermal growth factor receptor pathway substrate 15

31

NKTR

0.0000094

0.20

natural killer-tumor recognition sequence

32

DNAJC10

0.0000094

0.21

DnaJ (Hsp40) homolog, subfamily C, member 10

33

RARB

0.0000094

0.25

retinoic acid receptor, beta

34

FBXO9

0.0000094

0.29

F-box protein 9

35

MSMBROCK1

0.0000120

0.13

microseminoprotein, beta-

36

TADA3L

0.0000120

0.14

Rho-associated, coiled-coil containing protein kinase 1

37

TNFAIP8

0.0000120

0.16

transcriptional adaptor 3 (NGG1 homolog, yeast)-like

38

TGFBR2

0.0000120

0.16

tumor necrosis factor, alpha-induced protein 8

39

SLC23A1

0.0000120

0.19

transforming growth factor, beta receptor II (70/80 kDa)

40

FOXO1A

0.0000120

0.20

solute carrier family 23 (nucleobase transporters), member 1

41

USP12

0.0000120

0.20

forkhead box O1A (rhabdomyosarcoma)

42

CD2AP

0.0000134

0.18

ubiquitin specific protease 12

43

UBE4A

0.0000134

0.18

CD2-associated protein

44

SCP2

0.0000134

0.21

ubiquitination factor E4A (UFD2 homolog, yeast)

45

CAPN7

0.0000134

0.23

sterol carrier protein 2

46

PTPRS

0.0000134

0.24

calpain 7

47

BCL6

0.0000134

0.25

protein tyrosine phosphatase, receptor type, S

48

AGA

0.0000134

0.32

B-cell CLL/lymphoma 6 (zinc finger protein 51)

49

PROS1

0.0000134

0.33

aspartylglucosaminidase

50

UBL3

0.0000155

0.06

protein S (alpha)

51

BRF1

0.0000155

0.12

ubiquitin-like 3

52

ANP32B

0.0000155

0.15

BRF1 homolog, subunit of RNA polymerase III transcription initiation factor IIIB

53

AMY2B

0.0000155

0.25

acidic (leucine-rich) nuclear phosphoprotein 32 family, member B

54

FUBP3

0.0000167

0.07

amylase, alpha 2B; pancreatic

55

MAN2A1

0.0000167

0.15

far upstream element (FUSE) binding protein 3

56

TERF2IP

0.0000167

0.21

mannosidase, alpha, class 2A, member 1

57

PRKRIR

0.0000167

0.24

telomeric repeat binding factor 2, interacting protein

58

HRB2

0.0000167

0.27

protein-kinase, interferon-inducible double stranded RNA dependent inhibitor, repressor of (P58 repressor)

59

MAP4K5

0.0000167

0.28

HIV-1 rev binding protein 2

60

PPM1B

0.0000184

0.16

mitogen-activated protein kinase kinase kinase kinase 5

61

PSIP1

0.0000184

0.16

protein phosphatase 1B (formerly 2C), magnesium-dependent, beta isoform

62

TMEM1

0.0000184

0.18

PC4 and SFRS1 interacting protein 1

63

CDK5R1

0.0000184

0.31

transmembrane protein 1

64

ZA20D2

0.0000203

0.27

cyclin-dependent kinase 5, regulatory subunit 1 (p35)

65

AIM2

0.0000216

0.14

zinc finger, A20 domain containing 2

66

NBR1

0.0000216

0.19

absent in melanoma 2

67

TCN1

0.0000216

0.26

neighbor of BRCA1 gene 1

68

STXBP3

0.0000225

0.20

transcobalamin I (vitamin B12 binding protein, R binder family)

69

ZNF265

0.0000225

0.20

syntaxin binding protein 3

70

HERC3

0.0000240

0.24

zinc finger protein 265

Proposed biomarkers and known oncogenes/tumor suppression genes for NPC

In 2006, we proposed common cancer biomarkers expressed in cancers of various histology (melanoma, colon, ovarian, and esophageal carcinoma) but not in normal tissues. The analysis included training and a prediction set and only genes highly significantly specific for the cancerous tissues were proposed as biomarker candidates. 16 genes were proposed as common cancer biomarkers based on 20 cDNA clones (cutoff P < 1.7 × 10-16), showing a prediction accuracy of approximately 90% [16]. To verify whether the previous results were predictive also for NPC, a comparison was made between the two studies; 6 genes (CYC1, MIF, LAMB3, TSTA3, TUBB2, and UBE2C) of the 16 genes proposed by the previous study were consistently highly expressed in NPC as well. The seventh, TRAP1, was also included among the first 50 genes in the previous study (Table 4). In addition, CyC1, MIF, and LAMB3 appeared among the top 70 up-regulated genes in NPC (Table 2).
Table 4

Proposed NPC biomarkers according to our previous study (Basil et al [14])

Gene symbol

Fold difference (NPC/NP)

Parametric p-value

False discovery rate

Description

CYC1 *

10.36

4.00E-07

1.34E-05

Cytochrome c-1

MIF *

10.72

2.10E-06

3.83E-05

Macrophage migration inhibitory factor

LAMB3 *

5.33

2.43E-05

0.000238

Laminin, beta 3

TSTA3

27.22

9.77E-05

0.0007454

Tissue specific transplantation antigen P35B

TUBB2

43.51

0.0001256

0.0009051

Tubulin, beta 2

UBE2C

20.29

0.0009964

0.0045488

Ubiquitin-conjugating enzyme E2C

TRAP1

10.94

0.0026581

0.0097038

TNF receptor-associated protein 1

* The genes were in top 70 up-regulated genes of table 2

MILANO analysis utilizes archived literature as a database linking gene name with reported biological functions. To detect whether these 692 differentially expressed genes were known to be associated with NPC oncogenesis and tumor progression, the following key words: oncogene, proto-oncogene, tumor suppressor and nasopharyngeal carcinoma were applied as input in the MILANO searcher engine. In this way, we could screen for known and candidate oncogenes or tumor suppressor genes reported in other human malignancies or in NPC. Table 5 presents 10 genes (4 up-regulated and 6 down-regulated in NPC) with known oncogenic or tumor suppressor function in various human malignancies. Some genes, such as MIF and CCDN2, bore predominant pro-tumor progression functions, other genes, such as KLF5 and TGFBR2, contribute to tumor suppression functions. Notably, the results from MILANO analysis were consistent with our microarray data; the differential expression of the genes proposed as oncogene or Proto-oncogene by MILANO consisted of genes with >5-fold difference (our microarray data), ranging from 6.62 to 20.15. Conversely, the fold difference of genes associated with tumor suppressor function by MILANO was less than 0.5, ranging from 0.14 to 0.27.
Table 5

MILANO analysis for oncogenes and tumor suppressor genes

Gene symbol

Role1

Fold difference2 (NPC/NP)

Description

Map Location

MIF

Candidate oncogene

10.72

macrophage migration inhibitory factor (glycosylation-inhibiting factor)

22q11.23

BIRC5

Oncogene

6.62

baculoviral IAP repeat-containing 5 (survivin)

17q25

PTTG1

Proto-oncogene

7.35

pituitary tumor-transforming 1

5q35.1

CCND2

Proto-oncogene

20.15

cyclin D2

12p13

ATM

Tumor suppressor

0.27

ataxia telangiectasia mutated (includes complementation groups A, C and D)

11q22-q23

FOXO1A

Tumor suppressor

0.20

forkhead box O1A (rhabdomyosarcoma)

13q14.1

TGFBR2

Tumor suppressor

0.19

transforming growth factor, beta receptor II

3p22

PRKAR1A

Tumor suppressor

0.17

protein kinase, cAMP-dependent, regulatory, type I, alpha

17q23-q24

KLF5

Tumor suppressor

0.17

Kruppel-like factor 5 (intestinal)

13q22.1

PDCD4

Tumor suppressor

0.10

programmed cell death 4 (neoplastic transformation inhibitor)

10q24

1 Role: functions identified by MILANO

2 Fold difference: extracted from our microarray data.

Computational pathway analysis

The pathway analysis was done using the gene set expression comparison kit [11] implemented in BRB-ArrayTools. The human pathway lists determined by KEGG Pathways Database was selected. Significance threshold of Hotelling's T-square test was set at 0.005 using the first three principal components. Significant pathways were listed in Table 6. Among them, several pathways were involved in cell growth and death, important signal transduction and immune system. Interestingly, several pathways associated with immune function are involved (Table 6 and 7) with the overexpression of interleukin-1β and interleukin-6.
Table 6

Significant pathways at the nominal 0.005 level of the Hotelling T-square test *

Related Function

Kegg Pathway

Pathway description

Number of genes

p-value

Cell Growth & Death

hsa04210

Apoptosis

9

1.20E-06

 

hsa04110

Cell cycle

10

2.10E-06

Signal Transduction

hsa04020

Calcium signaling pathway

13

1.80E-06

 

hsa04010

MAPK signaling pathway

19

1.11E-05

 

hsa04310

Wnt signaling pathway

10

1.24E-05

 

hsa04630

Jak-STAT signaling pathway

7

3.64E-05

 

hsa04070

Phosphatidylinositol signaling system

7

8.03E-05

 

hsa04370

VEGF signaling pathway

5

0.000333

Immune System

hsa04650

Natural killer cell mediated cytotoxicity

7

1.67E-05

 

hsa04640

Hematopoietic cell lineage

6

2.63E-05

 

hsa04662

B cell receptor signaling pathway

9

5.01E-05

 

hsa04660

T cell receptor signaling pathway

6

5.06E-05

 

hsa04664

Fc epsilon RI signaling pathway

7

9.76E-05

 

hsa04610

Complement and coagulation cascades

5

0.0001297

 

hsa04620

Toll-like receptor signaling pathway

5

0.003536

Signaling Molecules and Interaction

hsa04080

Neuroactive ligand-receptor interaction

12

1.00E-07

 

hsa04060

Cytokine-cytokine receptor interaction

15

3.00E-07

 

hsa04514

Cell adhesion molecules (CAMs)

9

1.50E-05

 

hsa04512

ECM-receptor interaction

8

0.0004294

Endocrine System

hsa04910

Insulin signaling pathway

8

6.00E-06

 

hsa04920

Adipocytokine signaling pathway

5

6.90E-06

 

hsa03320

PPAR signaling pathway

6

3.42E-05

 

hsa04912

GnRH signaling pathway

8

3.66E-05

Cell Communication

hsa04510

Focal adhesion

17

9.20E-06

 

hsa04520

Adherens junction

5

2.95E-05

 

hsa04540

Gap junction

10

3.89E-05

 

hsa04530

Tight junction

10

4.48E-05

Others

hsa04810

Regulation of actin cytoskeleton

11

3.90E-06

 

hsa00260

Glycine, serine and threonine metabolism

6

1.54E-05

 

hsa00190

Oxidative phosphorylation

17

3.14E-05

 

hsa00230

Purine metabolism

18

3.22E-05

 

hsa00500

Starch and sucrose metabolism

9

7.68E-05

 

hsa00980

Metabolism of xenobiotics by cytochrome P450

7

8.06E-05

 

hsa00240

Pyrimidine metabolism

12

0.0001352

 

hsa04730

Long-term depression

6

0.0002265

 

hsa04720

Long-term potentiation

7

0.0003216

 

hsa04360

Axon guidance

11

0.0007991

* Significance threshold of permutation tests: 0.005

Significance threshold of Hotelling's T-square tests: 0.005

Number of principal components for Hotelling's T-square tests: 3

Square tests: 3

Table 7

List of genes involving in Immune function

Pathway

Description

Gene symbol

Parametric p-value

Fold Difference (NPC/NP)

 

protein kinase C, beta 1

PRKCB1

2.20E-05

0.14

 

integrin, beta 2 (antigen CD18 (p95), lymphocyte function-associated antigen 1; macrophage antigen 1 (mac-1) beta subunit)

ITGB2

0.0001733

0.18

Natural killer cell mediated

nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1

NFATC1

0.0004205

0.23

cytotoxicity

phosphoinositide-3-kinase, catalytic, gamma polypeptide

PIK3CG

0.0002216

0.24

 

vav 2 oncogene

VAV2

0.0024579

7.77

 

TYRO protein tyrosine kinase binding protein

TYROBP

0.0005303

14.39

 

tumor necrosis factor receptor superfamily, member 10b

TNFRSF10B

0.0006507

14.91

 

complement component (3d/Epstein Barr virus) receptor 2

CR2

0.0001842

0.02

 

interleukin 1, beta

IL1B

0.0043926

8.27

Hematopoietic cell lineage

interleukin 6 (interferon, beta 2)

IL6

0.0024281

8.29

 

integrin, beta 3 (platelet glycoprotein IIIa, antigen CD61)

ITGB3

0.0008031

10.39

 

CD14 antigen

CD14

4.15E-05

16.88

 

colony stimulating factor 1 (macrophage)

CSF1

1.00E-06

20.35

 

complement component (3d/Epstein Barr virus) receptor 2

CR2

0.0001842

0.02

 

protein kinase C, beta 1

PRKCB1

2.20E-05

0.14

B cell receptor

Bruton agammaglobulinemia tyrosine kinase

BTK

5.39E-05

0.17

signaling pathway

nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1

NFATC1

0.0004205

0.23

 

phosphoinositide-3-kinase, catalytic, gamma polypeptide

PIK3CG

0.0002216

0.24

 

nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

NFKB2

0.0019236

7.57

 

vav 2 oncogene

VAV2

0.0024579

7.77

 

glycogen synthase kinase 3 beta

GSK3B

0.0021047

13.20

 

inositol polyphosphate-5-phosphatase, 145kDa

INPP5D

4.10E-06

17.02

 

nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1

NFATC1

0.0004205

0.23

 

phosphoinositide-3-kinase, catalytic, gamma polypeptide

PIK3CG

0.0002216

0.24

T cell receptor

cyclin-dependent kinase 4

CDK4

2.20E-05

6.73

signaling pathway

nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

NFKB2

0.0019236

7.57

 

vav 2 oncogene

VAV2

0.0024579

7.77

 

mitogen-activated protein kinase kinase kinase 14

MAP3K14

0.0008295

11.17

 

membrane-spanning 4-domains, subfamily A, member 2 (Fc fragment of IgE, high affinity I, receptor for; beta polypeptide)

MS4A2

6.30E-06

0.04

Fc epsilon RI signaling pathway

protein kinase C, beta 1

PRKCB1

2.20E-05

0.14

 

Bruton agammaglobulinemia tyrosine kinase

BTK

5.39E-05

0.17

 

phosphoinositide-3-kinase, catalytic, gamma polypeptide

PIK3CG

0.0002216

0.24

 

phospholipase A2, group V

PLA2G5

0.0017931

6.86

 

vav 2 oncogene

VAV2

0.0024579

7.77

 

inositol polyphosphate-5-phosphatase, 145kDa

INPP5D

4.10E-06

17.02

 

complement component 3

C3

2.25E-05

0.01

Complement and coagulation

complement component (3d/Epstein Barr virus) receptor 2

CR2

0.0001842

0.02

cascades

protein S (alpha)

PROS1

5.00E-07

0.06

 

coagulation factor XIII, A1 polypeptide

F13A1

0.0002893

0.22

 

complement factor H

CFH

0.0018395

0.26

 

phosphoinositide-3-kinase, catalytic, gamma polypeptide

PIK3CG

0.0002216

0.24

Toll-like receptor signaling pathway

nuclear factor of kappa light polypeptide gene enhancer in B-cells 2

NFKB2

0.0019236

7.57

 

interleukin 1, beta

IL1B

0.0043926

8.27

 

interleukin 6 (interferon, beta 2)

IL6

0.0024281

8.29

 

CD14 antigen

CD14

4.15E-05

16.88

Validation of differential regulated genes by Semi-quantitative RT-PCR and Immunohistochemistry

To validate the microarray data, differential gene expression was confirmed by Semi-quantitative RT-PCR in 5 genes randomly selected from the 692 differentially expressed genes. These genes displayed similar expression patterns (Figure 2) to the microarray data; ratio of expression in NPC/NP was 0.32 in CTGF, 0.24 in TGFBR2, 0.36 in PDGFRA, 0.46 in EBI2, 2.67 in BIRC5, which was concordant with array data.
https://static-content.springer.com/image/art%3A10.1186%2F1479-5876-6-32/MediaObjects/12967_2008_Article_262_Fig2_HTML.jpg
Figure 2

Histogram identification of differentially expressed genes using semi-quantitative RT-PCR (average ratio).

To further evaluate the reliability of the microarray data, three genes, selected as representatives were analyzed by Immunohistochemistry. According to our array analysis, BIRC5 was up-regulated and both of TGFBR2 and CTGF(Figure 3) were down-regulated in NPC. The protein expression of these three genes was observed in totally 55 NPC samples and 46 NP samples. The Mann-Whitney test of positive rates in NPC and NP samples indicated that BIRC5 protein expression was significantly higher in NPC (P < 0.004) compared to NP samples, while TGFBR2 and CTGF were significantly lower in NPC (P < 0.002 and 0.001 respectively) compared to NP samples, supporting the reliability of the array data.
https://static-content.springer.com/image/art%3A10.1186%2F1479-5876-6-32/MediaObjects/12967_2008_Article_262_Fig3_HTML.jpg
Figure 3

Immunohistochemistry detection of three proteins in NPC and NP (×200). A: BIRC5 in NPC (strong positive); B: BIRC5 in NP (weak positive); C: CTGF in NP (strong positive); D: CTGF in NPC (weak positive); E: TGFBR2 in NP (strong positive); F: TGFBR2 in NPC (weak positive).

Discussion

Nasopharyngeal carcinoma is a special type of squamous cell carcinoma of head and neck associated with EBV infection, environmental factors and genetic aberrance. Clinically, poorly differentiated squamous cell carcinoma may account for 98% NPC patients while well differentiated squamous cell carcinoma and adenocarcinoma are rarely encountered in Southern China where this disease is particularly prevalent. The present study focused on alteration of gene expression in poorly differentiated squamous cell carcinoma of the nasopharynx. Owing to anatomical limitations, NPC and NP specimens collected from the clinic are usually very scarce and, because in most cases biopsies are semi-blindly performed through a forceps rather than a scalpel, it, is hard to accurately exclude normal NP tissue contamination from cancerous ones. Even in NPC biopsies without normal NP cells, the content of NPC cell appears to vary greatly. Therefore, screening the specimens obtained from the clinic for presence of a sufficient number of cancer cells was the critical step in order to assure the uniformity and validity of the microarray results. We found that tumor cell content varied from 5% to 95% in 102 pathologically diagnosed primary NPC samples among which only 32 samples contained more than 70% of cancer cells; the latter were qualified for further analysis.

In the present study, we applied the cDNA microarray technology to study differential gene expression between NPC and NP samples from Southern China. A pooling strategy was used to improve the statistical power and reduce individual heterogeneity. Class comparison analysis revealed 692 deferentially expressed genes with 2-fold or greater difference at a significance p-value of < 0.005. Of these genes, 435 were up-regulated and 257 were down-regulated. Subsequently, all the differentially expressed genes were further screened by MILANO analysis. After excluding the genes without association with limited oncogene and tumor suppressor terms when searching in MILANO, 10 genes were found to be reported consistently as known oncogenes and tumor suppressor genes in various human malignancies in previous studies. 4 genes were up-regulated and 6 genes down-regulated. Interestedly, of these 10 genes, three genes (MIF, BIRC5 and ATM) have been documented in NPC associated articles, and the rest genes (PTTG1, FOXO1A, TGFBR2, PRKAR1A, CCND2, KLF5, and PDCD4) have never reported or poorly understood in NPC before.

It is well known that oncogenes tend to be up-regulated and tumor suppressor genes are down-regulated in malignant condition. Based on CGH, chromosomal abnormalities associated with primary and metastatic NPC have been identified in all human chromosomes except chromosome Y [3, 4, 7, 1720]. BIRC5 up-regulated in NPC (oncogenes) was located on 17q21.3-25. As down-regulated tumor suppressors in NPC, FOXO1A and KLF5 existed on 13q14.1-34, ATM on 11 q13-23, and TGFBR2 on 3p21.3-24. The expression level of these genes was consistent with corresponding chromosomal gains or losses. However, the gene expression of the rest of the genes did not correspond with their chromosomal status. MIF and PTTG1 up-regulated in NPC (oncogenes) were on 19p13.3 and 5q35.1 respectively and PRKAR1A and PDCD4 down-regulated in NPC (tumor suppressors) were mapped to chromosome 17q22-q24 and 10q24 respectively, but no corresponding high frequency of chromosomal alterations in those region have been reported in association with NPC. Down-regulation of some genes might be involved in other mechanisms including promoter hypermethylation or additional modification, promoter mutation and gene mutation.

Several studies have shown that several differentially expressed genes identified in this study have functions associated with cell proliferation, cell apoptosis, cell cycle, angiogenesis and signal transducer activity, creating an active proliferation and progression status at tumor site. As an up-regulated oncogene, MIF, a macrophage migration inhibitory factor, may function as an autocrine mediator of both growth factor- and integrin-dependent sustained ERK MAPK activation, cyclin D1 expression, and cell cycle progression. Swant[19]http://linus.nci.nih.gov/BRB-ArrayTools.html found that MIF could stimulate cyclin D1 by recruiting Rho GTPase and its downstream signaling for MAPK activation. EBV oncogene LMP1 in NPC, PTGS2 in endometrial carcinoma[21] and MYC in breast cancer[22] may induce the BIRC5 expression by different pathways. As a tumor suppressor, KLF5, a transcription factor associated with cellular signaling involved in cell proliferation and oncogenesis, inhibits the expression of BIRC5 by binding p53 in acute lymphoblastic leukemia[23], suggesting that anti-apoptotic factor BIRC5 may play a key role in gene-regulated network of NPC. Pituitary tumor-transforming gene-1 (PTTG1) is overexpressed in a variety of endocrine-related tumors and nonendocrine-related cancers involving the central nervous, pulmonary, and gastrointestinal systems. It is a potent oncogene because of its ability to combine with p53 and thus to prevent p53 from binding to DNA and inducing cell death[24]. DNA damage and the consequent activation of the DNA damage response (DDR) pathway is one of the occasions for the tumor initiation and progression. Among tumor suppressors, ATM, a major regulator of the cellular response to DNA double-strand breaks, may be a key factor in the DDR pathway. Knocking down ATM with a short hairpin RNA (shRNA) could block p53 induction in response to aberrant STAT5A activation and bypass the senescence response to this oncogene when the Rb pathway was also inactivated. In addition, knocking down ATM could inhibit E2F1-induced senescence and, in combination with Rb inactivation, suppress RasV12-induced senescence[25]. TGF-β exerted its tumor-suppressor effects on many tumors by binding to the transmembrane TGF-β type II receptor (TGFBR2, a tumor suppressor), which caused the recruitment of the TGF-β type I receptor (TGFBR1) with subsequent activation of the receptor complex. Decreased expression of TGFBR2 found in NPC may block this important pathway of tumor-growth inhibition and promote the NPC malignant transformation[26]. Programmed cell death-4 (PDCD4) is a new discovered tumor suppressor protein that inhibits protein synthesis by suppression of translation initiation. A recent study showed that PDCD4 suppressed tumor progression in human colon carcinoma cells by the novel mechanism of down-regulating MAP4K1 transcription, with consequent inhibition of c-Jun activation and AP-1-dependent transcription[27]. Down-regulated expression of PDCD4 in NPC tissue and cells suggested a negatively regulation role in NPC pathogenesis. Tumor suppressor FOXO1A, a FOXO transcription factor, has been implicated in several human cancers. However, it has not been reported in NPC to date. Shore[28] found that Expression of the EBV genes for latent membrane protein 1 and latent membrane protein 2A could decrease Foxo1A expression by phosphatidylinositol 3-kinase-mediated nuclear export. Oncoprotein P3k and Akt can suppress the FoxO1 expression by a common denominator of their pathway[29]. The tumor suppressor gene, PRKAR1A, coding for the Type 1alpha regulatory subunit of protein kinase A, a critical cellular component of a number of cyclic nucleotide-dependent signaling pathways, is mutated in Carney complex, a familial neoplasia syndrome that is associated with thyroid tumors[30]. Its decreased expression in NPC may be caused by similar gene mutation, which is remained to be further elucidated.

Interestedly, several studies really supported the dysregulation of several pathways, such as Wnt signaling, MARK signaling [31, 32], NF-kB-Apoptosis resistance, integrin signaling[32] in NPC although most of pathways involving cell growth and death, cell communication, and immune system, etc. have not been well documented. In our present study, our computational pathway analysis of 692 differentially expressed genes strongly supported that multiple biological pathways were indeed involved in NPC oncogenesis. Particularly, we found that most of immune system associated pathways, such as Natural killer cell mediated cytotoxicity, Hematopoietic cell lineage, B cell receptor signaling, T cell receptor signaling, Fc epsilon RI signaling, Complement and coagulation cascades, and Toll-like receptor signaling, were involved, suggesting that they may play important roles in NPC.

Recently, there has been increasing interest in identify diagnostic tools that could complement standard histopathologic evaluation to determine the presence of cancer cells in tissues[33], a previous study from our group applied a high throughput microarray technology to investigate a broad range of cancer types and identified 16 proposed universal cancer biomarkers with high prediction accuracy. These biomarkers could be used broadly to improve the sensitivity and specificity of cancer staging and early detection of loco-regional or systemic recurrence [16]. Among the 16 common cancer biomarkers identified as highly predictive by the previous study which did not include NPC samples, 6 genes (CYC1, MIF, LAMB3, TSTA3, TUBB2, and UBE2C) were found to be highly expressed in NPC by the study. The seventh gene, TRAP1, was also included in the first 50 genes. Therefore, CYC1, MIF, LAMB3, TSTA3, TUBB2, UBE2C, and TRAP1 may possibly represent NPC candidate biomarkers adding a molecular dimension to the histo-pathological diagnosis of the disease and these biomarkers may be added to the pathologist's repertoire for the uncovering of NPC pathogenesis when comprehensive histologic evaluation is not sufficient. Obviously further validation will be needed in the future. Moreover, NPC could share the expression of various biomarkers with other cancers of disparate histology suggesting the existence of common pathways of oncogenesis that may be most relevant as therapeutic targets.

Conclusion

Using microarray technology and bioinformatics analysis, we identified 692 differentially expressed genes. Among these genes, 10 known oncogenes and tumor suppressors were picked out as key genes which mainly promoted NPC tumor genesis. Seven genes were also proposed as NPC candidate cancer biomarkers as they were observed universal cancer biomarkers in a previous study analyzing tumors of different histology. Computational pathway analysis suggested the likelihood that the multi-pathways are involved in the oncogenesis of NPC including a chronic inflammatory process mediated through classic cytokine pathways.

Notes

Declarations

Acknowledgements

This work was supported by grants from National key Natural Science Foundation of China (No. 90612020), United Grant of Guangdong Province(No.u07320062) and Key Foundations of Guangzhou Science and Technology Committee (No.2004z2-E0111 and No.2005z1-E4024).

Authors’ Affiliations

(1)
Key Lab for Transcriptomics and Proteomics of Human Fatal Diseases Supported by Ministry of Education and Guangdong Province, Cancer Research Institute of Southern Medical University
(2)
Infectious Disease and Immunogenetics Section, Department of Transfusion Medicine, Warren G. Magnuson Clinical Center
(3)
Biometrics Research Branch, National Cancer Institute
(4)
Medical Clinical Research Institute, The first affiliated hospital of Nanhua University

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© Fang et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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