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Identification of differentially expressed genes using an annealing control primer system in stage III serous ovarian carcinoma

  • Yun-Sook Kim1,
  • Jin Hwan Do2,
  • Sumi Bae2,
  • Dong-Han Bae1 and
  • Woong Shick Ahn3Email author
BMC Cancer201010:576

DOI: 10.1186/1471-2407-10-576

Received: 4 May 2010

Accepted: 22 October 2010

Published: 22 October 2010

Abstract

Background

Most patients with ovarian cancer are diagnosed with advanced stage disease (i.e., stage III-IV), which is associated with a poor prognosis. Differentially expressed genes (DEGs) in stage III serous ovarian carcinoma compared to normal tissue were screened by a new differential display method, the annealing control primer (ACP) system. The potential targets for markers that could be used for diagnosis and prognosis, for stage III serous ovarian cancer, were found by cluster and survival analysis.

Methods

The ACP-based reverse transcriptase polymerase chain reaction (RT PCR) technique was used to identify DEGs in patients with stage III serous ovarian carcinoma. The DEGs identified by the ACP system were confirmed by quantitative real-time PCR. Cluster analysis was performed on the basis of the expression profile produced by quantitative real-time PCR and survival analysis was carried out by the Kaplan-Meier method and Cox proportional hazards multivariate model; the results of gene expression were compared between chemo-resistant and chemo-sensitive groups.

Results

A total of 114 DEGs were identified by the ACP-based RT PCR technique among patients with stage III serous ovarian carcinoma. The DEGs associated with an apoptosis inhibitory process tended to be up-regulated clones while the DEGs associated with immune response tended to be down-regulated clones. Cluster analysis of the gene expression profile obtained by quantitative real-time PCR revealed two contrasting groups of DEGs. That is, a group of genes including: SSBP1, IFI6 DDT, IFI27, C11orf92, NFKBIA, TNXB, NEAT1 and TFG were up-regulated while another group of genes consisting of: LAMB2, XRCC6, MEF2C, RBM5, FOXP1, NUDCP2, LGALS3, TMEM185A, and C1S were down-regulated in most patients. Survival analysis revealed that the up-regulated genes such as DDAH2, RNase K and TCEAL2 might be associated with a poor prognosis. Furthermore, the prognosis of patients with chemo-resistance was predicted to be very poor when genes such as RNase K, FOXP1, LAMB2 and MRVI1 were up-regulated.

Conclusion

The DEGs in patients with stage III serous ovarian cancer were successfully and reliably identified by the ACP-based RT PCR technique. The DEGs identified in this study might help predict the prognosis of patients with stage III serous ovarian cancer as well as suggest targets for the development of new treatment regimens.

Background

Ovarian cancer is a complex disease, characterized by successive accumulation of multiple molecular alterations in both the cells undergoing neoplastic transformation and host cells [1]. These anomalies disturb the expression of genes that control critical cell processes, leading to the initiation of tumorigenesis and development. At the time of diagnosis most patients with ovarian cancer have advanced stage disease (i.e., stage III-IV) where surgery and chemotherapy results in an approximately 25% overall 5-year survival rate. Consequently, ovarian cancer is the leading cause of death from a gynecological malignancy. Epithelial ovarian cancer (EOC) accounts for 90% of all ovarian cancers; there is significant heterogeneity within the EOC group. For example, histologically defined subtypes such as serous, endometrioid, mucinous, and low- and high-grade malignancies all have variable clinical manifestations and underlying molecular signatures [2].

Gene expression has been extensively applied to screening for the prognostic factors associated with ovarian cancer; identification of such factors would help to determine patient prognosis. Studies have focused on differential gene expression between tumor and normal tissues [3], distinguishing between histological subtypes [4] and identifying differences between invasive tumors and those with low malignant potential [5]. However, to date, the use of differentially expressed genes (DEG) have not been implemented in ovarian cancer therapies; this is mainly because their reliability and validity have not yet been well established. Microarray technology permits large scale analysis of expression surveys to identify the genes that have altered expression as a result of disease. However, microarray data is notorious for its unreliable reproducibility of DEGs across platforms and laboratories, as well as validation problems associated with prognostic signatures [6]. In addition, identification of a gene responsible for a specialized function during a certain biological stage can be difficult to determine because the gene might be expressed at low levels, whereas the bulk of mRNA transcripts within a cell are abundant [7].

To screen DEGs in low concentrations, while minimizing false positive results, the polymerase chain reaction (PCR) based technique has been used. One screening method, differential display, requires PCR using short arbitrary primers. This method is simple, rapid and only requires small amounts of total RNA. However, many investigators have reported significantly high false-positive rates [8] and poor reproducibility of the results [9] because of nonspecific annealing by the short arbitrary primers. Recently, the annealing control primer (ACP) system has been developed; this technique provides a primer with annealing specificity to the template and allows only genuine products to be amplified [10]. The structure of the ACP includes (i) a 3" end region with a target core nucleotide sequence that substantially complements the template nucleic acid of hybridization; (ii) a 5" end region with a non-target universal nucleotide sequence; and (iii) a polydeoxyinosine [poly(dI)] linker bridging the 3" and 5" end sequences. Because of the high annealing specificity during PCR using the ACP system, the application of the ACP to DEG identification generates reproducible, accurate, and long (100 bp to 2 kb) PCR products that are detectable on agarose gels.

In this study, the ACP-based PCR method was used to identify the DEGs of patients with stage III serous ovarian cancer, the findings were compared to normal ovarian tissue. A total of 60 arbitrary ACPs were used and 114 DEGs were identified by sequencing differentially expressed bands. For the confirmation of differential expression of the DEGs, quantitative real-time PCR was performed on 38 selected DEGs; the results showed good agreement with the ACP findings. These results could be used as preliminary data for further study of the molecular mechanism underlying stage III serous ovarian cancer.

Methods

Patient information

After obtaining written informed consent from all patients included in the study, samples of primary epithelial ovarian cancer were snap frozen in liquid nitrogen and stored at -80°C. Analysis of tissues from patients was approved by the Institutional Review Board of The Catholic University of Korea (Seoul, Korea). The histopathological diagnoses were determined using the WHO criteria, and the tumor histotype was serous adenocarcinoma in all patients. Classification of cancer stage and grade was performed according to the International Federation of Gynecology and Obstetrics (FIGO). A total of 16 patients with serous ovarian carcinoma were enrolled in this study, all patients were diagnosed as stage IIIC with high-grade cancer (grade 3).

ACP-based GeneFishing™ reverse transcription polymerase chain reaction

Total RNAs from the ovarian tissues of the serous carcinoma were isolated by gentle homogenization using Trizol®. The normal human ovary total RNA was purchased from Stratagene (Total RNA Human Ovary, #540071). The RNA was used for the synthesis of first-strand cDNAs by reverse transcriptase. Reverse transcription was performed 1.5 hours at 42°C in a final reaction volume of 20 μl containing 3 μg of the purified total RNA, 4 μl of 5" reaction buffer (Promega, Madison, WI, USA), 5 μl of dNTPs (2 mmol each), 2 μl of 10 μM dT-ACP1 (5"-CTGTGAATGCTGCGACTACGATIIIII(T)18)-3", where "I" represents deoxyinosine), 0.5 μL of RNasin® RNase Inhibitor (40 U/μl, Promega), and 1 μl of Moloney murine leukemia virus reverse transcriptase (200 U/μl, Promega). First-strand cDNAs were diluted by the addition of 80 μL of RNase-free water for the GeneFishing PCR and stored -20°C until use.

DEGs were screened by ACP-based PCR method using the GeneFishing™ DEG kits (Seegene, Seoul, South Korea). Briefly, second-strand cDNA synthesis was conducted at 50°C (low stringency) during one cycle of first-stage PCR in a final reaction volume of 49.5 μl containing 3-5 μl (about 50 ng) of diluted first-strand DNA cDNA, 5 μl of 10x PCR buffer plus Mg (Roche Applied Science, Mannheim, Germany), 5 μl of dNTP (each 2 mM), 1 μl of 10 μM dT-ACP2 (5'-CTGTGAATGCTGCGACTACGATIIIII(T)15-3"), and 1 μl of 10 μM arbitrary ACP. The tube containing the reaction mixture was kept at 94°C while 0.5 μl of Taq DNA polymerase was added to the reaction mixture (5 U/μl, Roche Applied Science). Sixty PCR reactions for each sample were carried out with 60 arbitrary ACPs, respectively. The PCR protocol for second-strand synthesis was one cycle at 94°C for 1 min, followed by 50°C for 3 min, and 72°C for 1 min. After completion of second-strand DNA synthesis, 40 cycles were performed. Each cycle involved denaturation at 94°C for 40 sec, annealing at 65°C for 40 sec, extension at 72°C for 40 sec, and a final extension at 72°C to complete the reaction. The amplified PCR products were separated in 2% agarose gel and stained with ethidium bromide. The overall scheme of the experiment is shown in Figure 1.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig1_HTML.jpg
Figure 1

A schematic diagram of the experimental procedure.

Cloning and sequencing

The differentially expressed bands were extracted from the gel using the GENCLEAN® II Kit (Q-BIO gene, Carlsbad, CA.,USA), and directly cloned into a TOPO TA® cloning vector (Invitrogen, Karlsruhe, Germany) according to the manufacturer's instructions. The cloned plasmids were sequenced with an ABI PRISM® 3100 Genetic Analyzer (Applied Biosystems, Foster City, CA., USA). Complete sequences were analyzed by searching for similarities using the Basic Local Alignment Search Tool (BLAST) search program at the Genbank database of the National Center for Biotechnology Information (NCBI).

Quantitative real-time polymerase chain reaction and statistical analysis

For the confirmation of the differential expression of DEGs, quantitative real-time PCR was carried out for 38 DEGs selected from 114 DEGs. The concentrations of the reagents were adjusted to reach a final volume of 20 μL containing 5 ng of cDNA template, 10 μl of SYBR® Premis Ex Taq™ II (Takara Bio,Otsu, Japan), 0.4 μl of ROX™ reference Dye II, 0.4 μl of 10 μM forward and reverse primers of DEGs with β-actin as an internal control (Table 1). The cDNA templates were constructed with the total RNA extracted from 16 ovarian cancer tissues and normal human ovary total RNA. The PCR amplification protocol was 50°C for 2 min and 95°C for 10 min followed by 40 cycles of 95°C for 30 sec, 60°C for 30 sec, and 72°C for 30 sec. The real-time PCR analysis was performed on an Applied Biosystems Prism 7900 Sequence Detection System (Applied Biosystems). Relative quantification with the data obtained was performed according to the user's manual. The fold change for gene expression, between the cancer and normal samples, was calculated by using the threshold cycle (CT): fold change = 2-ΔΔ C T , ΔΔC T = [(CT of gene of interest - CT of β-actin)cancer sample- (CT gene of interest - CT of β-actin)normal sample)]. The fold change was log2 transformed for the cluster and survival analysis. The R packages mclust and survival http://www.r-project.org were used for the cluster and survival analysis, respectively.
Table 1

Primer sequences of 38 DEGs and β-actin used for the quantitative real-time PCR.

DEG

Forward

Reverse

Amplicon size

NM_003143.1

AAAGATCCCTGAATCGTGTGC

TCGCCACATCTCATTAGTTGC

119

AY871274

GTCCACTGCACAGTTCGAGG

GGCCTCCTCTTTGCTGATTC

279

NM_022873.2

CAGAAGGCGGTATCGCTTTTC

CCTGCATCCTTACCCGCATT

89

NM_001355.3

AGCGCCCACTTCTTTGAGTTT

TCCCTATCTTGCCAATCTGCC

106

BC000523

GTGCCTAAGACAGAAATTCGGG

TGCAAGTCTATGTTTGGGTTCAT

174

NM_005532

GCAGCCTTGTGGCTACTCTG

TAGAACCTCGCAATGACAGCC

112

NM_207429.2

GGAGCTCCTTGGAAGTCAGG

GCCAGCAACAGCACTGAGAT

129

BC012823

CGCTCTCTTTTCTCCCGTTT

TCGCAGCATGCTCAACATTA

236

NM_020529

CTCCGAGACTTTCGAGGAAATAC

GCCATTGTAGTTGGTAGCCTTCA

135

NM_001101654

GTGCAATCGCCATTACTGCT

GAATGCAGGGTGTAAGGGGT

248

NM_001867

AAAGGTCTTGGTGAGGTGCC

ACGGACCACAGAGGTTGTGA

119

BC013003

TCAGCACCTTGGAACCTTTGA

AAGACACTCTCTCGGTAGTCATT

100

NM_000978

TCCTCTGGTGCGAAATTCCG

CGTCCCTTGATCCCCTTCAC

119

NM_032470

TTGTCCAGATAGCGGCAAAC

AGCGAGCTCTGGAAGAGGAG

149

NM_013974.1

GGTCGATGGAGTCCGCAAAG

GGTGAAGAGAACGTCAGTGC

100

XM_002345433

CGAGTTCGTGGACCTGTACG

GCCATTAAACCTGCCTGTGA

127

NM_001034996

CATGCCGGAAAATTGGTCGC

CACTGTGCGGAAACTTGAGGA

145

NM_001037637

CATCTCTGGCAGCGAACACTT

AGTCAGACTATCCGCACCAAG

107

BC107854

AGGGGTAAGCTCATCGCAGT

CCGGAAAGTGTCTTCGATCTCA

150

NM_001020

TCGGACGCAAGAAGACAGC

AGCAGCTTGTACTGTAGCGTG

118

NR_003225

GAAACCCAAACCCTCAAGGA

GCACTTGGCTGTCCAGAAGA

247

NM_001004333.3

ATTCCTTGCGCTTATTGAGCC

GCCCCCAGAACATATACAACCT

123

NM_080390

TGCAGGGAGGATCAAAGACA

GGCTCTCCCTCACTCTCTGG

103

NM_013318

AAGCCCTCTGGATCAGCAGT

TCAGTAGGGAGAGGCGAGGT

227

EF177379

GTTTCCAGGCCTTGCTCA

ATTCATGGGCTCTGGAACAA

158

AK026649

GGTTCTCGCTCTTGTCGTGTC

ATATCCTTCGCGTACTGACGG

101

AK302766

TGGCTTTGTAACAAGTGCTGC

CGGAGCTATGTTCCGAAGAATG

168

NM_032682

TCCCGTGTCAGTGGCTATGAT

CTCTTTAGGCTGTTTTCCAGCAT

226

NG_001229

TCATGAGGCCCAGATCAAGA

ACCACGTCCTTCCCTTTCAG

219

AK025219

AGGACCAGAACTGCAAGCTG

GCGCTCTTCCAAGTCAGTGA

155

BC001120

GCAGACAATTTTTCGCTCCA

GCACTTGGCTGTCCAGAAGA

287

NM_032508.1

ATGAACCTGAGGGGCCTCTT

TGATGCCATCCAAACGAAGGG

106

NM_002292.3

ATGCTGGTGGAACGCTCAG

CTCGCCTTCAGTGGATGGC

171

AK293439

GGTCCAGAAGGCTCTCAAGC

GGGCCTCAGGTAATGGTGTT

265

NM_001131005

AGACATCGTGGAGGCATTGA

GTGGCAATAGGTTGGGGTTT

263

NM_201442

CAAGTCCCATACAACAAACTCCA

CAGGAGCAGAAGTAACCACCA

176

BC023599

TCCCTTGTCCGGAGGATATT

TAATGGATTCAATCATCTTTATTAACC

164

NM_001100167

TCTGAAGAGTCCCCCAAATG

AATCCAGCACTTCCTCTCCA

209

β-actin

GGCTGTATTCCCCTCCATCG

CCAGTTGGTAACAATGCCATGT

154

Results

Differentially expressed genes (DEGs) in stage III serous ovarian cancers

The patients included in this study ranged in age between 38 and 69 (mean age 53.3 ± 7.5 years). All 16 patients had a diagnosis of FIGO stage III papillary serous ovarian carcinoma. To identify genes that showed a predominant change of expression in patients with stage III serous ovarian cancer, the total RNAs from 16 serous ovarian tissues of stage III and the normal human ovary total RNA (Stratagene, #540071) were individually subjected to ACP-based RT PCR analysis using a combination of 60 arbitrary primers and two anchored oligo (dT) primers (dT-ACP1 and dT-ACP2). All PCR amplicons were compared on agarose gels (Figure 2). When the bands generated by the normal sample showed a clear difference compared to the bands generated on the cancer sample, the band was defined as a differentially expressed band. After all poor appearing bands were excluded, the differentially expressed bands were extracted, amplified using the TOPO TA Cloning Kit (Invitrogen, Cat. #K4500-1) and sequenced. The sequences of 114 DEGs were obtained. The DNA sequence of each DEG was analyzed by searching for similarities using the BLASTX program at the Genbank database (NIH, MD, USA). Table 2 shows the 114 DEGs assessed by Genbank and the best homologues.
Table 2

Annotation of the 114 DEGs by the BLAST search.

Clone name

Annotation

GenBank accession no.

E-value

U1

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

BC013003

4.00E-112

U2

Homo sapiens adaptor-related protein complex 3, delta 1 subunit (AP3D1), transcript variant 2

NM_003938

6.00E-10

U3

Homo sapiens adenine phosphoribosyltransferase (APRT), transcript variant 2

NM_001030018

7.00E-08

U4

Homo sapiens genomic DNA, chromosome 11q, clone:CMB9-1B14, complete sequences

AP000659

4.00E-73

U5

Homo sapiens chromosome 19 open reading frame 53 (C19orf53)

NM_014047

1.00E-19

U6

Homo sapiens chromosome 1 open reading frame 115 (C1orf115)

NM_024709

5.00E-24

U7

Homo sapiens coatomer protein complex, subunit alpha (COPA), transcript variant 1

NM_001098398

4.00E-22

U8

Homo sapiens dimethylarginine dimethylaminohydrolase 2 (DDAH2)

NM_013974.1

2.00E-123

U9

Homo sapiens dimethylarginine dimethylaminohydrolase 2

BC001435

9.00E-168

U10

Homo sapiens dynein, light chain, LC8-type 1 (DYNLL1), transcript variant 3

NM_003746

0.00E+00

U11

Homo sapiens ferritin, light polypeptide

BC004245

0.00E+00

U12

Homo sapiens glutamic-oxaloacetic transaminase 2, mitochondrial (aspartate aminotransferase 2) (GOT2), nuclear gene encoding mitochondrial protein

NM_002080

7.00E-21

U13

Homo sapiens sarcoma antigen NY-SAR-48

BC040564

3.00E-95

U14

Homo sapiens heat shock protein 90 kDa alpha (cytosolic), class B member 1 (HSP90AB1)

NM_007355.2

0.00E+00

U15

Homo sapiens interferon, alpha-inducible protein 27 (IFI27), transcript variant 2

NM_005532

6.00E-99

U16

Homo sapiens interferon, alpha-inducible protein 6 (IFI6), transcript variant 3

NM_022873.2

2.00E-35

U17

Homo sapiens iron-responsive element binding protein 2 (IREB2)

NM_004136

5.00E-66

U18

Homo sapiens keratinocyte associated protein 2

BC029806

5.00E-71

U19

PREDICTED: Homo sapiens similar to ribosomal protein S21, transcript variant 2 (LOC100291837)

XM_002345433

1.00E-100

U20

Homo sapiens mesothelin (MSLN), transcript variant 1

NM_005823

0.00E+00

U21

Homo sapiens nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (NFKBIA)

NM_020529

1.00E-95

U22

Homo sapiens nuclear distribution gene C homolog (A. nidulans) pseudogene 2 (NUDCP2) on chromosome 2

NG_001229

1.00E-101

U23

Homo sapiens PRP8 pre-mRNA processing factor 8 homolog (S. cerevisiae) (PRPF8)

NM_006445.3

4.00E-110

U24

Homo sapiens RAB5B, member RAS oncogene family (RAB5B)

NM_002868

2.00E-123

U25

Homo sapiens cDNA FLJ76524 complete cds

AK289930

2.00E-79

U26

Homo sapiens cell growth-inhibiting protein 34 mRNA, complete cds

AY871274

0.00E+00

U27

Homo sapiens ribosomal protein L23 (RPL23)

NM_000978

1.00E-87

U28

Homo sapiens ribosomal protein L6 pseudogene 27 (RPL6P27) on chromosome 18

NG_009652

2.00E-111

U29

Homo sapiens ribosomal protein S8 (RPS8)

NM_001012

2.00E-67

U30

Homo sapiens TRK-fused gene

BC023599

4.00E-79

U31

Homo sapiens ribosomal protein S8

BC070875

1.00E-60

U32

Homo sapiens mRNA similar to eukaryotic translation initiation factor 3, subunit 7 (zeta, 66/67 kD)

BC011740

8.00E-142

U33

Homo sapiens ribosomal protein S24

BC000523

4.00E-45

U34

Homo sapiens cDNA, FLJ18539

AK311497

4.00E-26

U35

Homo sapiens cDNA clone IMAGE:2822193

BC005845

8.00E-155

U36

Homo sapiens ATPase, H+ transporting, lysosomal 14 kDa, V1 subunit F

BC107854

3.00E-147

U37

Homo sapiens cytochrome c oxidase subunit VIIc (COX7C), nuclear gene encoding mitochondrial protein

NM_001867

5.00E-145

U38

Homo sapiens tenascin XB (TNXB), transcript variant XB-S

NM_032470

0.00E+00

U39

Homo sapiens septin 9 (SEPT9) on chromosome 17

NG_011683

5.00E-45

U40

Homo sapiens chromosome 11 open reading frame 92 (C11orf92)

NM_207429.2

3.00E-45

U41

Homo sapiens D-dopachrome tautomerase (DDT), transcript variant 1

NM_001355.3

5.00E-94

U42

Homo sapiens single-stranded DNA binding protein 1 (SSBP1)

NM_003143.1

0.00E+00

D1

Homo sapiens actin, beta (ACTB),

NM_001101.2

0.00E+00

D2

Homo sapiens acidic (leucine-rich) nuclear phosphoprotein 32 family, member B (ANP32B)

NM_006401.2

4.00E-115

D3

Homo sapiens Rho guanine nucleotide exchange factor (GEF) 17 (ARHGEF17)

NM_014786

7.00E-37

D4

Homo sapiens ATPase, H+ transporting, lysosomal 13kDa, V1 subunit G1

BC003564

0.00E+00

D5

Homo sapiens UDP-Gal:betaGal beta 1,3-galactosyltransferase polypeptide 6 (B3GALT6)

NM_080605

8.00E-29

D6

Homo sapiens HLA-B associated transcript 2-like 1 (BAT2L1)

NM_013318

4.00E-121

D7

Homo sapiens cDNA FLJ77629 complete cds, highly similar to Homo sapiens bone marrow stromal cell antigen 2 (BST2)

AK291099

0.00E+00

D8

Homo sapiens basic transcription factor 3 (BTF3), transcript variant 1

NM_001037637

0.00E+00

D9

Homo sapiens complement component 1, s subcomponent (C1S), transcript variant 1

NM_201442

0.00E+00

D10

Homo sapiens CD74 molecule, major histocompatibility complex, class II invariant chain (CD74), transcript variant 1

NM_001025159.1

8.00E-179

D11

Homo sapiens chloride intracellular channel 1

BC064527

2.00E-143

D12

Homo sapiens CXXC finger 1 (PHD domain) (CXXC1), transcript variant 1

NM_001101654

1.00E-180

D13

Homo sapiens early growth response 1 (EGR1)

NM_001964.2

0.00E+00

D14

Homo sapiens Finkel-Biskis-Reilly murine sarcoma virus (FBR-MuSV) ubiquitously expressed (FAU)

NM_001997

5.00E-99

D15

Homo sapiens F-box protein Fbx7 (FBX7) mRNA, complete cds

AF129537

2.00E-105

D16

Homo sapiens Fc fragment of IgG, receptor, transporter, alpha

BC008734

1.00E-79

D17

Homo sapiens forkhead box P1 (FOXP1), transcript variant 1

NM_032682

2.00E-130

D18

Homo sapiens guanine nucleotide binding protein (G protein), beta polypeptide 1

BC004186

1.00E-55

D19

Homo sapiens H19, imprinted maternally expressed transcript (non-protein coding) (H19), non-coding RNA

NR_002196

2.00E-34

D20

Homo sapiens high density lipoprotein binding protein (HDLBP), transcript variant 2

NM_203346.2

0.00E+00

D21

Homo sapiens cDNA FLJ52975 complete cds, highly similar to Heterogeneous nuclear ribonucleoproteins C

AK299923

0.00E+00

D22

Homo sapiens cDNA clone IMAGE:3898245

BC010864

1.00E-138

D23

Homo sapiens cDNA fis, A-KAT03057, highly similar to Homo sapiens mitochondrion, ATP synthase 6

AK026530

0.00E+00

D24

Homo sapiens cDNA: FLJ21566 fis, clone COL06467

AK025219

0.00E+00

D25

Homo sapiens cDNA: FLJ22996 fis, clone KAT11938

AK026649

4.00E-132

D26

Homo sapiens coiled-coil-helix-coiled-coil-helix domain containing 1 (CHCHD1)

NM_203298.1

0.00E+00

D27

Homo sapiens mRNA similar to guanine nucleotide binding protein-like 1

BC048213

6.00E-29

D28

Homo sapiens mRNA similar to ribosomal protein L30

BC012823

2.00E-131

D29

Homo sapiens ribosomal protein, large, P1

BC053844

5.00E-152

D30

Homo sapiens sarcoma antigen NY-SAR-71 mRNA, partial cds

AY211920

1.00E-138

D31

Homo sapiens tumor necrosis factor, alpha-induced protein 1 (endothelial)

BC006208

8.00E-116

D32

Homo sapiens zinc finger, FYVE domain containing 20

BC021246

1.00E-138

D33

Homo sapiens heat shock factor binding protein 1 (HSBP1)

NM_001537

1.00E-121

D34

Homo sapiens HtrA serine peptidase 1 (HTRA1) on chromosome 10

NG_011554

0.00E+00

D35

Human mitochondrial specific single stranded DNA binding protein mRNA, complete cds

M94556

1.00E-146

D36

Homo sapiens immunoglobulin kappa constant

BC095490

0.00E+00

D37

Homo sapiens immunoglobulin lambda locus, mRNA (cDNA clone MGC:88803 IMAGE:4765294), complete cds

BC073786

3.00E-101

D38

Homo sapiens jun B proto-oncogene (JUNB) gene, complete cds

AY751746

2.00E-20

D39

Homo sapiens L antigen family, member 3, mRNA (cDNA clone MGC:23038 IMAGE:4899044), complete cds

BC015744

6.00E-29

D40

Homo sapiens laminin, beta 2 (laminin S) (LAMB2)

NM_002292.3

0.00E+00

D41

Homo sapiens lectin, galactoside-binding, soluble, 1, mRNA (cDNA clone MGC:1818 IMAGE:2967299), complete cds

BC001693

5.00E-52

D42

Homo sapiens lectin, galactoside-binding, soluble, 3, mRNA (cDNA clone MGC:2058 IMAGE:3050135), complete cds

BC001120

0.00E+00

D43

Homo sapiens lectin, galactoside-binding, soluble, 3 (LGALS3), transcript variant 2, non-coding RNA

NR_003225

0.00E+00

D44

Homo sapiens mRNA for LGALS3 protein variant protein

AB209391

0.00E+00

D45

PREDICTED: Homo sapiens similar to ribosomal protein L13a (LOC100293761)

XM_002344734

8.00E-51

D46

Homo sapiens mediator complex subunit 1 (MED1)

NM_004774

1.00E-62

D47

Homo sapiens mediator complex subunit 14 (MED14)

NM_004229

3.00E-49

D48

Homo sapiens myocyte enhancer factor 2C (MEF2C), transcript variant 2

NM_001131005

1.00E-99

D49

Homo sapiens murine retrovirus integration site 1 homolog (MRVI1), transcript variant 4

NM_001100167

0.00E+00

D50

Homo sapiens myosin light chain kinase (MYLK), transcript variant 3A

NM_053027

2.00E-97

D51

Homo sapiens cDNA FLJ53659 complete cds, highly similar to Myosin light chain kinase, smooth muscle

AK300610

0.00E+00

D52

Homo sapiens nuclear receptor co-repressor 2 (NCOR2), transcript variant 1

NM_006312.3

0.00E+00

D53

Homo sapiens nuclear enriched abundant transcript 1 (NEAT1) mRNA, complete sequence

EF177379

1.00E-80

D54

Homo sapiens trophoblast MHC class II suppressor mRNA, complete sequence

AF508303

1.00E-67

D55

Homo sapiens cDNA FLJ53682 complete cds, highly similar to RNA-binding protein 5

AK302766

2.00E-153

D56

Homo sapiens RNA binding motif protein 5

BC002957

2.00E-153

D57

Homo sapiens RNA binding motif protein 8A (RBM8A)

NM_005105.2

1.00E-135

D58

Homo sapiens ribonuclease, RNase K (RNASEK)

NM_001004333.3

6.00E-99

D59

Homo sapiens ribosomal protein L14 (RPL14), transcript variant 1

NM_001034996

0.00E+00

D60

Homo sapiens ribosomal protein L27a (RPL27A)

NM_000990

4.00E-171

D61

Homo sapiens ribosomal protein L29 (RPL29)

NM_000992.2

6.00E-154

D62

Homo sapiens ribosomal protein S16 (RPS16)

NM_001020

7.00E-136

D63

Homo sapiens ribosomal protein S20 (RPS20), transcript variant 2

NM_001023

0.00E+00

D64

Homo sapiens ribosomal protein S21 (RPS21)

NM_001024

8.00E-122

D65

Homo sapiens single-stranded DNA binding protein 1

BC000895

1.00E-146

D66

Homo sapiens transcription elongation factor A (SII)-like 2 (TCEAL2)

NM_080390

0.00E+00

D67

Homo sapiens tubulointerstitial nephritis antigen-like 1 (TINAGL1)

NM_022164

2.00E-153

D68

Homo sapiens transmembrane protein 185A (TMEM185A)

NM_032508.1

0.00E+00

D69

Homo sapiens cDNA FLJ57738 complete cds, highly similar to Translationally-controlled tumor protein

AK296587

2.00E-104

D70

Homo sapiens X-ray repair complementing defective repair in Chinese hamster cells 6 (XRCC6)

NM_001469.3

0.00E+00

D71

Homo sapiens cDNA FLJ53970 complete cds, highly similar to ATP-dependent DNA helicase 2 subunit 1

AK293439

0.00E+00

D72

Homo sapiens phosphodiesterase 7B (PDE7B) on chromosome 6

NG_011994

3.00E-41

The prefix U and D in the clone name represent the clone from the up- and down-regulated bands in the agarose gel. The expression of DEGs marked in bold was further confirmed by quantitative real-time PCR.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig2_HTML.jpg
Figure 2

An example of GeneFishing™ using an arbitrary ACP in combination with an oligo (dT) ACP as indicated in the Methods section. M and N represents a 100-bp size marker generated by Forever 100-bp Ladder Personalizer (Seegene, Seoul, South Korea) and a normal sample, respectively. The lanes 1-16 include each of the cancer samples from the 16 patients. The bands showing a clear difference between the normal and cancer samples, here marked with a red arrow, were excised from the gel for further cloning and sequencing.

The DEGs identified from the clones that were up-regulated included: AP000659 (U4, ARHGEF12), NM_013974.1 (U8, DDAH2), NM_022873.2 (U16, IFI6), NM_020529 (U21, NFKBIA), BC023599 (U30, TFG), and NG_011683 (U39, SEPT9). The up-regulation of SEPT9 mRNA was reported in a bank of ovarian tumors, which included benign, borderline and malignant tumors [11]. The genes including DDAH2, IFI6 and NFKBIA are known to be involved in the apoptosis inhibitory process while ARHGEF12 and TGF have been implicated in signaling pathways. The DEGs identified from down-regulated clones included: AK291099 (D7, BST2), NM_001025159.1 (D10, CD74), BC004186 (D18, GNB1), NG_011554 (D34, HTRA1), BC095490 (D36, IGKC), and BC001693 (D41, LGALS1). The down-regulation of HTRA1 was associated with ovarian cancer metastasis [12]. The genes including BST2, CD74 and IGKC were associated with the immune system; while GNB1 and LGALS1 were related to the modulation of cell-cell interaction and the G protein coupled receptor protein signaling pathway, respectively.

Confirmation of ACP observation by quantitative real-time PCR and cluster analysis

To confirm the efficacy of the ACP system, confirmation of the differential expression of DEGs was performed with quantitative real-time PCR for 38 DEGs selected from the total 114 DEGs using a specific primer pair for each gene (Table 1 and Additional file 1). The expression ratio of the cancer to normal sample was calculated by using CT and then was log2 transformed (see Methods section for detail). The DEGs were considered differentially expressed if the log2 ratio was > 1.0 or < -1.0. Differential expression was clearly observed in all 38 DEGs, which indicates a high reliability of the ACP system (Figure 3).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig3_HTML.jpg
Figure 3

Clustering of the log 2 expression ratios of the cancer to normal samples measured by the quantitative real-time PCR for the 38 DEGs from the 16 patients and its representation as a heat map. Patients are ordered along the X-axis and genes along the Y-axis.

For the detection of more conserved expression patterns in patients with stage III serous ovarian cancer, cluster analysis was performed using the R package mclust http://www.r-project.org. Thirty eight DEGs were divided into four groups according to their expression profiles with assignment of each gene to a group (Figure 3). A clear contrast in expression patterns was noted between groups 1 and 4. That is, the overall up- and down-regulation in group 1 was 86.8% and 5.6%, respectively; the overall up- and down-regulation in group 4 was 9.3% and 60.3%, respectively, when up-regulation corresponds to log2 ratio > 1 and down-regulation to log2 ratio < -1 (Table 3). This regulation pattern in each group was well maintained at various thresholds used for definition of differential expression. These findings suggest that the genes in groups 1 and 4 might be used as potential markers for prognosis in patients with stage III serous ovarian cancer. The group 1 consisted of: NM_003143.1 (SSBP1), NM_022873.2 (IFI6), NM_001355.3 (DDT), NM_005532 (IFI27), NM_207429.2 (C11orf92), NM_020529 (NFKBIA), NM_032470 (TNXB), EF177379 (NEAT1) and BC02359 (TFG); group 4 was composed of: NM_002292.3 (LAMB2), AK025219, AK293439 (XRCC6), NM_001131005 (MEF2C), AK302766 (RBM5), NM_032682 (FOXP1), NG_001229 (NUDCP2), BC001120 (LGALS3), NM_032508.1 (TMEM185A), and NM_201442 (C1S) (Table 3). All genes except for NEAT1 in group 1 were identified from up-regulated clones while all genes except for NUDCP2 in group 4 were identified from down-regulated clones (Tables 2 and 3). These results were in good agreement with the ACP findings.
Table 3

Clustering of 38 DEGs according to the expression profiles of 16 patients with serous ovarian cancer; Up-regulation corresponds to log2 ratio > threshold while down-regulation to log2 ratio < minus value of threshold.

Threshold

Group 1

Group 2

Group 3

Group 4

 

up-regulated

down-regulated

up-regulated

down-regulated

up-regulated

down-regulated

up-regulated

down-regulated

0.5

88.9%

6.9%

68.8%

15.6%

63.9%

22.2%

19.4%

68.8%

1.0

86.8%

5.6%

58.8%

12.5%

55.6%

18.1%

9.3%

60.3%

1.5

84.0%

4.2%

45.6%

7.5%

48.6%

13.2%

6.3%

46.3%

2.0

77.1%

3.5%

31.9%

6.9%

41.0%

11.8%

3.1%

33.1%

Accession no. (gene symbol)

NM_003143.1 (SSBP1)

AY871274 (RPL11)

NM_001867 (COX7C) BC013003 (ANP32B)

AK302766 (RBM5) NM_032682 (FOXP1)

 

NM_022873.2 (IFI6)

BC000523 (RPS24)

BC013003 (ANP32B)

NM_032682 (FOXP1)

 

NM_001355.3 (DDT)

BC012823

NM_000978 (RPL23)

NG_001229 (NUDCP2)

 

NM_005532(IFI27)

NM_001101654(CXXC1)

NM_013974.1 (DDAH2)

AK025219

 

NM_207429.2 (C11orf92)

NM_001034996(RPL14)

XM_002345433

BC001120 (LGALS3) NM_032508.1 (TMEM185A)

 

NM_020529(NFKBIA)

NM_001037637(BTF3)

BC107854 (ATP6V1F)

 
 

NM_032470(TNXB)

NM_001020(RPS16)

NM_001004333.3 (RNASEK)

NM_002292.3 (LAMB2)

 

EF177379 (NEAT1)

NR_003225(LGALS3)

NM_080390(TCEAL2)

AK293439 (XRCC6)

 

BC02359 (TFG)

NM_013318(BAT2L1) AK026649

NM_001100167(MRVI1)

NM_001131005(MEF2C) NM_201442 (C1S)

The percentage of up- and down-regulation was calculated for each group.

Survival analysis

The Kaplan-Meier method was performed using the 38 DEGs that were up- and down-regulated. The up and down regulated genes were considered according to the log2 expression ratio > 0 and < 0, respectively. A significant difference, in the overall survival (p values < 0.05) between the up- and down-regulated group, was observed for three DEGs including NM_013974.1 (dimethylarginine dimethylaminohydrolase 2, DDAH2), NM_001004333.3 (ribonuclease K, RNase K) and NM_080390 (transcription elongation factor A (SII)-like 2, TCEAL2) (Figure 4). The overall survival decreased with up-regulation of these genes. DDAH2 predominates in the vascular endothelium, which is the site of endothelial nitric oxide synthase (eNOS) expression [13, 14]. TCEAL2 is a nuclear phosphoprotein that modulates transcription in a promoter context-dependent manner and has been recognized as an important nuclear target for intracellular signal transduction. The function of RNase K is unknown but might be related to an enhanced degradation of the tumor suppressor gene mRNAs, which leads to the development of cancer. The up-regulation of these three genes was observed in more than 60% of the total number of patients.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig4_HTML.jpg
Figure 4

Kaplan-Meier estimates of overall survival stratified by up- and down-regulation for three genes including DDAH2 , RNase K and TCEAL2.

The survival analysis also was performed for chemo-resistance. Following debulking surgery, all patients received platinum-based chemotherapy, considered the standard of care for patients with advanced ovarian cancer. The patients that had either progression during chemotherapy or relapse within six months of treatment were considered chemo-resistant. Among 16 patients, eight were classified as chemo-resistant and the others were categorized as chemo-sensitive. The difference in overall survival between these two groups was significant (p value < 0.05, Figure 5). The shorter survival time of patients with chemo-resistance is consistent with a prior report [15]. To consider chemo-resistance and gene expression simultaneously, in the prediction of overall survival, the Cox multivariate analysis was carried out with the expression information of 38 DEGs and chemo-resistance information from 16 patients. Multivariate analysis demonstrated a significant difference in overall survival between the chemo-resistant and sensitive groups for four DEGs including: NM_001004333.3 (RNase K), NM_032682 (forkhead box transcription factor family, FOXP1), NM_002292.3 (a family of extracellular matrix glycoproteins, LAMB2), and NM_001100167 (murine retrovirus integration site 1 homolog, MRVI1) (p values < 0.05, Figure 6). The RNase K showed significance in both univariate (gene expression) and multivariate (gene expression and chemo-resistance) analysis while DDAH2 and TCEAL2 showed significance only in univariate analysis. This was mainly due to the similar proportion of up- and down-regulation of DDAH2 and TCEAL2 between the chemo-resistant and chemo-sensitive groups. The overall survival of patients with chemo-resistance was significantly decreased with up-regulation of these four genes; while the chemo-sensitive patients had down-regulation of these genes and a good prognosis. FOXP1 has a diverse repertoire of functions ranging from the regulation of B-cell development and monocyte differentiation to the facilitation of cardiac valve and lung development [16, 17]. LAMB2 might be involved in the cell adhesion or motility of prostate cancer cells [18]. The down-regulation of FOXP1 and LAMB2 was observed in more than 60% of all patients while MRVI was up-regulated among 63%.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig5_HTML.jpg
Figure 5

Kaplan-Meier estimates of overall survival stratified by chemo-resistance.

https://static-content.springer.com/image/art%3A10.1186%2F1471-2407-10-576/MediaObjects/12885_2010_Article_2375_Fig6_HTML.jpg
Figure 6

The overall survival estimated by the Cox proportional hazards multivariate model including gene expression and chemo-resistance. The up- and down-regulation is represented by red and blue, respectively.

Discussion

Ovarian cancer is the most common cause of death among all gynecological malignancies. The five-year survival rates in patients with ovarian cancer are about 80-90% for stage Ia-Ic, 70-80% for stage IIa-IIc, 30-50% for stages IIIa-IIIc and 13% for stage IV [19]. The high rate of death is due to the fact that most of patients (>60%) present with advanced stage disease (FIGO stages III/IV). Despite an initial response rate of 65%-80% to first-line chemotherapy, most ovarian carcinomas relapse. Acquired resistance to further chemotherapy is generally responsible for treatment failure. Several studies have sought to identify gene expression signatures that correlate with clinical outcome to identify those genes that are associated with survival and relapse and to use as predictive biomarkers for response to chemotherapy [2022].

There are many types of ovarian cancer. EOC accounts for 85%-90%; half of such cases are serous EOC. As with many cancers thought to be of epithelial origin, it is important to establish an appropriate control for evaluating differential gene expression between "normal" and cancer. Expression profiling studies of ovarian cancer have relied on a variety of sources of normal cells or comparison with tumors, including whole ovary samples (WO), ovarian surface epithelium (OSE), exposed to short-term culture, and immortalized OSE cell lines (IOSE) [23]. Direct comparison of the gene expression profiles generated from OSE brushings, WO samples, short-term cultures of normal OSE (NOSE), and telomerase-immortalized OSE (TIOSE) cell lines revealed that these "normal" samples formed robust, but very distinct groups in hierarchical clustering [24]. These indicate that the selection of normal control to compare epithelial ovarian samples in microarray studies can strongly influence the genes that are identified as differentially expressed. In this study, the total RNA from WO (Stratagene, http://www.stratagene.com) was used as control. WO samples have potential to obscure epithelial pattern due to large amounts of stroma, but they offer the advantages of avoidance of exposure to culture conditions and identification of differential gene expression pattern between tumor and normal tissue [25].

A total of 114 DEGs from patients with serous ovarian cancer stage III were identified using the ACP-based GeneFishing™ PCR system, which uses primers that anneal specifically to the template and allows only genuine products to be amplified. As the GeneFishing™ system is based on PCR, it can overcome the difficulty in identifying the genes responsible for a specialized function during a certain biological stage; this is because the gene is expressed at low levels, whereas most mRNA transcripts within a cell are abundantly expressed. Among the 114 DEGs, 42 were identified as up-regulated clones while 72 were down-regulated clones. These DEGs were involved in a variety of biological processes including apoptosis, signal transduction and the immune response. Apoptosis inhibitory processes were associated with genes such as NFKBIA, DDAH2 and IFI6 identified from up-regulated clones; while the immune system associated genes such as IGKC, CD74 and BST2 were found in down-regulated clones. The differential expression of DEGs identified by the Genefishing™ system showed good agreement with the results of the quantitative real-time PCR.

Cluster analysis based on gene expression profiles identified two groups showing a contrast in the expression pattern. That is, one group including: SSBP1, IFI6 DDT, IFI27, C11orf92, NFKBIA, TNXB, NEAT1 and TFG, was up-regulated in most patients with stage III serous ovarian cancer (Figure 3 and Table 3). These genes might be utilized as potential targets in patients with stage III serous ovarian cancer. IFI6 is involved in apoptosis inhibitory activity while TGF is implicated in up regulation of the I-κ B kinase/NF-κ B cascade. TNXB encodes TNX, a protein of unknown function that is mainly expressed in the peripheral nervous system and muscles. The promotion of tumor invasion and metastasis has been reported in mice deficient in TNX through the activation of the matrix metalloproteinase 2 (MMP2) and MMP9 genes [26]. This indicates that the up-regulation of TNXB, in patients with advanced stage of ovarian cancer, might induce low expression of MMP2 and MMP9. The decrease of MMP2 has been reported in liver metastases in advanced colorectal cancers [27]. Another group consists of LAMB2, XRCC6, MEF2C, RBM5, FOXP1, NUDCP2, LGALS3, TMEM185A, and C1S, which was down-regulated in most patients with stage III serous ovarian cancer (Figure 4 and Table 3). The down-regulation of LAMB2 and MEF2C might be involved with cell adhesion or motility in invasive prostate cancer cells [18] and apoptosis via BCL2 transformation [28], respectively. FOXP1 is a potential therapeutic target in cancer and can be considered either an oncogene and/or a tumor suppressor gene [29]. That is, its over-expression confers a poor prognosis in a number of types of lymphomas while the loss of its expression in breast cancer is associated with a poor outcome. The function of FOXP1 in serous ovarian cancer remains unclear. LGALS3 encoding galectin-3 has been implicated in advanced stage disease [30]. The distribution of galectin has been associated with stage III-V with cellular changes such as dysplasia, cancer cells' nest formation, breakage of the basement membrane, and infiltration of cells into non-native tissue. However, 13 out of the total 16 patients with stage III serous ovarian cancer had down-regulation of LGALS3. This is consistent with the report by van den Brule et al. [31] that showed that galectin-3 expression was decreased in 67% of cases compared to the normal epithelial cells. However, it conflicts with the observations of Lurisci et al. [32] that galectin-3 serum levels in patients with ovarian cancer were significantly elevated. The expression of LGALS3 might be affected by the stage of ovarian cancer.

The overall survival estimated by the Kaplan-Meier method was significantly different between the up- and down-regulated patient cohort with regard to three genes including DDAH2, RNase K and TCEAL2. The up-regulation of these genes was associated with a shorter overall survival (Figure 4). In rapidly growing cells like tumor cells, the activity of RNases is decreased [33], as dictated by the requirement of significant amounts of RNA for protein synthesis. However, high RNase activity has been reported in chronic myeloid leukemia [34] and pancreatic carcinoma [35]. In this study, 70% of the patients with stage III serous ovarian cancer showed up-regulation of RNase K. The function of RNase K in advanced ovarian cancer remains to be clarified. In addition, the overall survival of patients with chemo-resistance was significantly decreased with up-regulation of the genes including: RNase K, FOXP1, LAMB2 and MRVI1 (Figure 6). This might implicate these genes in chemoresistance.

Conclusion

One hundred and fourteen DEGs were identified from 16 patients with stage III serous ovarian carcinoma using the ACP-based RT-PCR technique. Fifteen percent of the total DEGs were associated with apoptosis, the immune response, cell adhesion, and signal pathways. The genes related to apoptosis inhibitory processes tended to be up-regulated while the genes associated with the immune response tended to be down-regulated. The up- and down-regulated genes were identified in most of the patients and might be used as predictive markers in stage III serous ovarian cancer.

Declarations

Acknowledgements

This study was supported by a grant from the National R&D Program for Cancer Control, Ministry for Health, Welfare and Family affairs, Republic of Korea (0820330).

Authors’ Affiliations

(1)
Department of Obstetrics and Gynecology, Soonchunhyang University Chunan Hospital
(2)
Cancer Research Institute of Medical Science, The Catholic University of Korea
(3)
Department of Obstetrics and Gynecology, The Catholic University of Korea

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  36. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/10/576/prepub

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