Identification of novel molecular markers for detection of gastric cancer cells in the peripheral blood circulation using genome-wide microarray analysis

  • Authors:
    • Nobuyuki Matsumura
    • Hitoshi Zembutsu
    • Koji Yamaguchi
    • Kazuaki Sasaki
    • Tetsuhiro Tsuruma
    • Toshihiko Nishidate
    • Ryuichi Denno
    • Koichi Hirata
  • View Affiliations

  • Published online on: April 8, 2011     https://doi.org/10.3892/etm.2011.252
  • Pages: 705-713
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Abstract

Although metastasis or relapse is a leading cause of death for patients with gastric cancer, the hematogenous spread of cancer cells remains undetected at the time of initial therapy. The development of novel diagnostic molecular marker(s) to detect circulating gastric cancer cells is an issue of great clinical importance. We obtained peripheral blood samples from 10 patients with gastric cancer who underwent laparotomy and 4 healthy volunteers. Microarray analysis consisting of 30,000 genes or ESTs was carried out using eight gastric cancer tissues and normal gastric mucosae. We selected 53 genes up-regulated in gastric cancer compared to normal gastric mucosae from our microarray data set, and, among these, identified five candidate marker genes (TSPAN8, EPCAM, MMP12, MMP7 and REG3A) which were not expressed in peripheral blood mononuclear cells (PBMCs) from 4 healthy volunteers. We further carried out semi-quantitative nested reverse transcription-polymerase chain reaction (RT-PCR) for HRH1, EGFR, CK20 and CEA in addition to the five newly identified genes using PBMCs of patients with gastric cancer, and found that expression of one or more genes out of the nine was detected in 80% of the patients with gastric cancer. Moreover, the numbers of genes expressed in PBMCs were ≤2 and ≥2 in all vascular invasion-negative cases and in 5 of 6 positive cases, respectively, showing significant differences between the two groups (P=0.041). Nested RT-PCR analysis for the set of nine marker genes using PBMCs may provide the potential for detection of circulating gastric cancer cells prior to metastasis formation in other organs.

Introduction

Gastric cancer causes approximately 800,000 deaths worldwide per year and is still one of the leading causes of cancer-related death in the world (1). Most gastric cancers at an early stage can be cured by surgical resection; however, patients with advanced gastric cancers have worse prognosis than those with early stage disease (2). Although metastasis or relapse is the main cause of death for patients with gastric cancer (3), the hematogenous spread of malignant cells remains undetected at the time of initial therapy. During the development of cancer, tumor cells may detach from the primary tumor and disseminate into the lymph system and/or blood circulation, and grow in the bone marrow, liver, kidney and other organs, which is called micrometastasis (4). Micrometastasis is barely detected by routine biochemical and histopathological assays or graphical methods, such as X-ray, CT and MRI (3). Detection of circulating tumor cells at the mRNA level [reverse transcription-polymerase chain reaction (RT-PCR)] in blood samples of patients with cancer could serve as a unique and easy diagnostic tool to predict cancer recurrence and to monitor treatment effectiveness (57). However, molecular marker(s) that detect circulating gastric cancer cells for routine clinical use have not yet been identified. Hence, the development of novel diagnostic molecular marker(s) to detect circulating gastric cancer cells is an issue of great clinical importance.

Carcinoembryonic antigen (CEA) is a well-known tumor marker and has been used to detect small amounts of adenocarcinoma cells in the blood, peritoneal wash or other body fluids (812). However, the expression of CEA mRNA is not specific to cancer cells and often produces false-positive results (13). Profiling of gene expression patterns on genome-wide microarrays enables investigators to perform comprehensive characterization of molecular activities in cancer cells (1417). Systematic analysis of expression levels for thousands of genes is also a useful approach for identifying molecular markers to detect small amounts of circulating cancer cells (18). In this study, we identified genes whose expression had been altered during gastric carcinogenesis using genome-wide information obtained from 8 cases on a microarray consisting of 30,000 transcribed elements. Based on the results of the microarray assay, we identified five candidate genes for the specific detection of circulating gastric cancer cells at the mRNA level. We suggest that such information may lead ultimately to improve the prognosis of patients with gastric cancers.

Materials and methods

Blood and tissue samples

Blood samples were obtained from 10 patients with gastric cancer who underwent laparotomy and 4 healthy volunteers after obtaining informed consent. Heparinized blood samples (5 ml) from the 10 patients with gastric cancer were obtained from a peripheral artery through a catheter used for monitoring arterial blood pressure during surgical operation. Peripheral venous blood was obtained from 4 healthy volunteers for control after discarding the initial 10 ml of blood to protect the mixture from epithelial cells. Clinicopathological characteristics of the 10 patients are shown in Table I. Clinical stage of each patient was judged according to the Union for International Cancer Control (UICC) TNM classification. Among the 10 patients with gastric cancer, 8 primary gastric cancer tissues and corresponding non-cancerous gastric mucosae from surgically resected tissues were obtained at Sapporo Medical University and Douto Hospital after each patient had provided informed consent. The samples that had been confirmed histologically as gastric adenocarcinoma were used for microarray study. These samples were immediately frozen and stored at −80°C. All cancer tissues were obtained from the margin of the tumor mass, while non-cancerous tissues were obtained from corresponding normal mucosae of the same stomach. This study was approved by the Ethics Committee of Sapporo Medical University, School of Medicine, Hokkaido, Japan.

Table I.

Characteristics of patients included in the nested RT-PCR analysis of PBMCs.

Table I.

Characteristics of patients included in the nested RT-PCR analysis of PBMCs.

ParametersNo. of patients
Gender (male:female)5:5
Age range (average), in years41–82 (61.9)
Depth of tumor invasion (T1:T2:T3:T4)1:6:2:1
Lymph node metastasis (N0:N1:N2:N3)4:3:2:1
Distant metastasis (M0:M1)10:0
Liver metastasis (H0:H1)10:0
Peritoneal metastasis (P0:P1)9:1
Peritoneal lavage cytology (CY0:CY1)9:1
Stage (I:II:III:IV)4:1:2:3
Lymphatic invasion (ly0:ly1–3)2:8
Vessel invasion (v0:v1–3)4:6
RNA extraction of blood samples

We prepared peripheral blood mononuclear cells (PBMCs) using Ficoll (Amersham Biosciences, Buckinghamshire, UK) and extracted total RNA using TRIzol (Invitrogen, Inc., Carlsbad, CA, USA) according to the manufacturer’s instructions. Before the synthesis of cDNA, deoxyribonuclease I (DNase I) (Nippon Gene, Japan) was added to each sample of total RNA according to the manufacturer’s instructions.

Analysis of microarray

Total RNA was extracted from each gastric tissue using TRIzol according to the manufacturer’s instructions. To guarantee the quality of RNAs, total RNA extracted from the residual tissue of each case was electrophoresed on a denaturing agarose gel, and the quality was confirmed by the presence of rRNA bands. After treatment with DNase I, T7-based RNA amplification was carried out as described previously with a few modifications (19). Using 5 μg of total RNA from each tissue sample as starting material, one round of amplification was performed; the amount of each amplified RNA (aRNA) was measured by a spectrophotometer. A mixture of normal gastric mucosae from 8 patients was prepared as a universal control and was amplified in the same manner; 2.5 μg of aRNAs from each cancerous tissue and from the control was reversely transcribed in the presence of Cy5-dCTP and Cy3-dCTP, respectively (15). AceGene 30K-1 Chip Version (Hitachi Software Engineering Co., Japan) was used for microarray analysis. The procedures for hybridization, washing, photometric quantification of signal intensities of each spot and normalization of data were according to the manufacturer’s instructions. To normalize the amount of mRNA between tumors and controls, the fluorescence intensities of Cy5 (gastric cancer) and Cy3 (control) for each target spot were adjusted so that the mean Cy5/Cy3 ratio of 30,000 genes equaled 1. Genes were categorized into three groups according to the cancer/normal ratio of their mean signal intensity: up-regulated (expression ratio >5.0), down-regulated (expression ratio <0.2) and unchanged expression (expression ratio between 0.2 and 5.0).

Semi-quantitative RT-PCR

To validate the result of the microarray analysis, we examined the expression levels of the genes up-regulated in gastric cancer by means of semi-quantitative RT-PCR analysis. Total RNAs (3 μg) extracted from each cancerous tissue and normal gastric mucosa were reversely transcribed for single-stranded cDNAs using oligo(dT)12–18 primer with Superscript II reverse transcriptase (Life Technologies, Inc.). Each single-stranded cDNA was diluted for subsequent PCR amplification. A housekeeping gene, GAPDH, served as the internal control. The PCR reaction was conducted at 95°C for 5 min, and then for 30 cycles at 95°C for 30 sec, 60°C for 30 sec and 72°C for 1 min followed by 72°C for 10 min, in the Gene Amp PCR System 9700 (Perkin-Elmer Applied Biosystems, Foster City, CA, USA).

Nested RT-PCR using blood samples

We performed nested RT-PCR using total RNAs extracted from PBMCs to accurately examine mRNA levels of the candidate marker genes. Initially, RT-PCR was carried out as described above. In nested RT-PCR, 1 ml of the initial PCR product, 4 ml of 10X PCR buffer, 200 mmol/l dNTP mixture, 0.2 mmol/l primers and 1 unit Taq DNA polymerase (Takara) were added to a 40-ml aliquot of the reaction mixture. The PCR reaction was conducted at 95°C for 5 min, and then 30 cycles at 95°C for 30 sec, 60°C for 30 sec and 72°C for 1 min followed by 72°C for 10 min, in the Gene Amp PCR System 9700. The RT-PCR products were detected using 2% agarose gel electrophoresis. The primer sequences are summarized in Table II.

Table II.

Primer sequences for semi-quantitative nested RT-PCR.

Table II.

Primer sequences for semi-quantitative nested RT-PCR.

GeneForward primerReverse primer
TSPAN8 5′-TCAACTTCTTGTTCTGGCTATGT-3′ 5′-TATAGCTTTGGCATGGTCTCTGC-3′
EPCAM 5′-TGATCCTGACTGCGATGAGAGC-3′ 5′-CAGCTTTCAATCACAAATCAGT-3′
MMP12 5′-AACCAGCTCTCTGTGACCCCA-3′ 5′-TCCAAGGATGTTAGGAAGCAAC-3′
MMP7 5′-TCTCTGGACGGCAGCTATGCG-3′ 5′-AATAAGACACAGTCACACCATAA-3′
REG3A 5′-GTATCTTGGATGCTGCTTTCCTG-3′ 5′-GTATGACAAAATGAAGAGACTGA-3′
HRH1 5′-TACAAGGCCGTACGACAACACT-3′ 5′-TCTGCTGTTCTTCTATGGTGCCT-3′
EGFR 5′-ATGTCCCCACGGTACTTACTCCC-3′ 5′-TCTTAACAATGCTGTAGGGGCTC-3′
CK20 5′-TGGATTTCAGTCGCAGA-3′ 5′-ATGTAGGGTTAGGTCATCAAAG-3′
CEA 5′-TTCTCCTGGTCTCTCAGCTGGG-3′ 5′-AATGCTTTAAGGAAGAAGCAA-3′

Results

Identification of up- or down-regulated genes in the gastric cancers

We extracted RNAs from eight primary gastric cancer tissues and corresponding normal gastric mucosae as control, and carried out gene expression analysis using a microarray consisting of 30,000 genes or ESTs. We then selected genes from our data set according to the criterion that the cancer/ normal ratio of the mean signal intensity of a given gene was >5.0 or <0.2, and 53 genes were identified as up-regulated and 123 genes as down-regulated in the gastric cancer tissues compared to the normal gastric mucosa (Tables III and IV). The up-regulated genes represented a variety of functions, including genes associated with signal-transduction pathways (SFRP4 and TSPAN8), genes encoding transcription factors (TRIM33), genes involved in various metabolic pathways (ADH4, USP33, RNF128, MAN2A1, UBD and GCNT3), apoptosis (SPP1 and RIPK2), chemokines (CCL20), DNA replication and recombination (SNRPA1), cell adhesion and cytoskeleton (LAMB3, EPCAM, MMP7 and COL1A1), cell-cell signaling (CEACAM6 and CXCL9), cell cycle (CDC2, BUB1 and CCNB2), cell proliferation (REG1B and REG3A), or other functions (SPINK4, TMC5, LGALS2, KYNU, DDX58, LY96, UMPS and RNF157).

Table III.

Genes up-regulated in advanced gastric cancer.

Table III.

Genes up-regulated in advanced gastric cancer.

No.Accession no.Gene symbolDescription
1NM_006507REG1BRegenerating islet-derived 1 β (pancreatic stone protein, pancreatic thread protein)
2NM_004577PSPHPhosphoserine phosphatase
3NM_138938REG3ARegenerating islet-derived 3 α
4NM_014471SPINK4Serine peptidase inhibitor, Kazal type 4
5NM_001105249TMC5Transmembrane channel-like 5
6NM_002426MMP12Matrix metallopeptidase 12 (macrophage elastase)
7NM_002423MMP7Matrix metallopeptidase 7 (matrilysin, uterine)
8NM_002483CEACAM6Carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific cross reacting antigen)
9NM_001786CDC2Cell division cycle 2, G1 to S and G2 to M
10NM_000582SPP1Secreted phosphoprotein 1 (osteopontin, bone sialoprotein I, early T-lymphocyte activation 1)
11NM_004751GCNT3Glucosaminyl (N-acetyl) transferase 3, mucin type
12NM_000574CD55CD55 molecule, decay accelerating factor for complement (Cromer blood group)
13NM_002443MSMBMicroseminoprotein, β
14NM_004336BUB1UB1 budding uninhibited by benzimidazoles 1 homolog (yeast)
15NM_015017USP33Ubiquitin-specific peptidase 33
16NM_004591CCL20Chemokine (C-C motif) ligand 20
17NM_017633FAM46AFamily with sequence similarity 46, member A
18NM_000088COL1A1Collagen, type I, α 1
19XR_017717 ADAMTSL3ADAMTS-like 3
20NM_138938REG3ARegenerating islet-derived 3 α
21NM_017934PHIPPleckstrin homology domain interacting protein
22XR_016124Similar to p21-activated kinase 2
23NM_006398UBDUbiquitin D
24NM_002358MAD2L1MAD2 mitotic arrest deficient-like 1 (yeast)
25NM_002483CEACAM6Carcinoembryonic antigen-related cell adhesion molecule 6 (non-specific cross-reacting antigen)
26NM_173164IPO9Importin 9
27NM_003014SFRP4Secreted frizzled-related protein 4
28NM_004616TSPAN8Tetraspanin 8
29NM_002354EPCAMEpithelial cell adhesion molecule
30NM_006498LGALS2Lectin, galactoside-binding, soluble, 2
31NM_002372MAN2A1Mannosidase, α, class 2A, member 1
32NM_003937KYNUKynureninase (L-kynurenine hydrolase)
33NM_003821RIPK2 Receptor-interacting serine-threonine kinase 2
34NM_00108039ITGA7Integrin, α 7
35NM_000670ADH4Alcohol dehydrogenase 4 (class II), π polypeptide
36NM_014314DDX58DEAD (Asp-Glu-Ala-Asp) box polypeptide 58
37NM_006418OLFM4Olfactomedin 4
38NM_198187.3ASTN2Astrotactin 2
39NM_015364LY96Lymphocyte antigen 96
40NM_000574CD55CD55 molecule, decay accelerating factor for complement (Cromer blood group)
41NM_018964SLC37A1Solute carrier family 37 (glycerol-3-phosphate transporter), member 1
42NM_018455CENPNCentromere protein N
43NM_001710CFBComplement factor B
44NM_033020TRIM33Tripartite motif-containing 33
45NM_003090SNRPA1Small nuclear ribonucleoprotein polypeptide A'
46NM_000373UMPSUridine monophosphate synthetase (orotate phosphoribosyl transferase and orotidine-5′-decarboxylase)
47NM_144584C1orf59Chromosome 1 open reading frame 59
48NM_052916.2RNF157Ring finger protein 157
49NM_006332IFI30Interferon, γ-inducible protein 30
50NM_002416CXCL9Chemokine (C-X-C motif) ligand 9
51NM_001017402LAMB3Laminin, β 3
52NM_004701CCNB2Cyclin B2
53NM_194463RNF128Ring finger protein 128

Table IV.

Genes down-regulated in advanced gastric cancer.

Table IV.

Genes down-regulated in advanced gastric cancer.

No.Accession no.Gene symbolDescription
1NM_004190LIPFLipase, gastric
2NM_020143PNO1Partner of NOB1 homolog (S. cerevisiae)
3NM_000257MYH7Myosin, heavy chain 7, cardiac muscle, β
4NM_015173TBC1D1TBC1 (tre-2/USP6, BUB2, cdc16) domain family, member 1
5NM_005408CCL13Chemokine (C-C motif) ligand 13
6NM_174929ZMIZ2Zinc finger, MIZ-type containing 2
7NM_004747DLG5Discs, large homolog 5 (Drosophila)
8NM_024872.2DOK3Docking protein 3
9NM_201653CHIAChitinase, acidic
10NM_003893LDB1LIM domain binding 1
11NM_012455.2PSD4Pleckstrin and Sec7 domain containing 4
12NM_005213CSTACystatin A (stefin A)
13NM_005416SPRR3Small proline-rich protein 3
14NM_014989RIMS1Regulating synaptic membrane exocytosis 1
15NM_001018005TPM1Tropomyosin 1 (α)
16NM_213589RAPH1Ras association (RalGDS/AF-6) and pleckstrin homology domains 1
17NM_004898CLOCKClock homolog (mouse)
18NM_013292Fast skeletal myosin light chain 2
19NM_020321ACCN3Amiloride-sensitive cation channel 3
20NM_002754MAPK13Mitogen-activated protein kinase 13
21NM_013443 ST6GALNAC6ST6 (α-N-acetyl-neuraminyl-2,3-β-galactosyl-1,3)-N-acetylgalactosaminide α-2,6-sialyltransferase 6
22NM_001042453Serine/threonine protein kinase MST4
23NM_032646TTYH2Tweety homolog 2 (Drosophila)
24NM_015089p53-associated parkin-like cytoplasmic protein
25NM_003609HIRIP3HIRA interacting protein 3
26NR_002219BIRC5Baculoviral IAP repeat-containing 5 (survivin)
27NM_000068CACNA1ACalcium channel, voltage-dependent, P/Q type, α 1A subunit
28NM_203377MBMyoglobin
29NM_003768PEA15Phosphoprotein enriched in astrocytes 15
30NM_053013ENO3Enolase 3 (β, muscle)
31XR_018802PI4K2A Phosphatidylinositol 4-kinase type 2 α
32NM_003725HSD17B6Hydroxysteroid (17-β) dehydrogenase 6 homolog (mouse)
33NM_006063KBTBD10Kelch repeat and BTB (POZ) domain containing 10
34NM_012288TRAM2Translocation associated membrane protein 2
35NM_000730CCKARCholecystokinin A receptor
36NM_000290PGAM2Phosphoglycerate mutase 2 (muscle)
37NM_199354PRB1Proline-rich protein BstNI subfamily 1
38XR_019039ACTBActin, β
39NM_006478GAS2L1Growth arrest-specific 2 like 1
40NM_024674LIN28Lin-28 homolog (C. elegans)
41NM_001070TUBG1Tubulin, γ 1
42NM_015654NAT9N-acetyltransferase 9
43NM_003643GCM1Glial cells missing homolog 1 (Drosophila)
44NM_006901.2MYO9AMyosin IXA
45NM_017785CCDC99Coiled-coil domain containing 99
46NM_025135FHOD3Formin homology 2 domain containing 3
47NM_022566MESDC1Mesoderm development candidate 1
48NM_198255TERTTelomerase reverse transcriptase
49NM_018231Amino acid transporter
50NM_002458MUC5BMucin 5B, oligomeric mucus/gel-forming
51NM_001001522TAGLNTransgelin
52NM_002631PGDPhosphogluconate dehydrogenase
53NM_006984CLDN10Claudin 10
54NM_004359CDC34Cell division cycle 34 homolog (S. cerevisiae)
55NM_001824CKMCreatine kinase, muscle
56NM_002274KRT13Keratin 13
57XR_019039ACTBActin, β
58NM_000477ALBAlbumin
59NM_001519.2BRF1BRF1 homolog, subunit of RNA polymerase III transcription initiation factor IIIB (S. cerevisiae)
60NM_006790MYOTMyotilin
61NM_021948BCANBrevican
62NM_001142404.1CD164CD164 molecule, sialomucin
63BC050364.1C7orf13Chromosome 7 open reading frame 13
64NM_005177 ATP6V0A1ATPase, H+ transporting, lysosomal V0 subunit a1
65NM_020393PGLYRP4Peptidoglycan recognition protein 4
66XM_937007FRMPD3FERM and PDZ domain containing 3
67NM_024754PTCD2Pentatricopeptide repeat domain 2
68NM_001098511KIF2AKinesin heavy chain member 2A
69NM_025058TRIM46Tripartite motif-containing 46
70AK126458.1MYO15BMyosin XVB pseudogene
71NM_018659CYTL1Cytokine-like 1
72NM_002965S100A9S100 calcium binding protein A9
73NM_032566SPINK7Serine peptidase inhibitor, Kazal type 7 (putative)
74NM_001669ARSDArylsulfatase D
75NM_206820MYBPC1Myosin binding protein C, slow type
76NM_003200TCF3Transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47)
77NM_031413CECR2Cat eye syndrome chromosome region, candidate 2
78NM_017539DNAH3Dynein, axonemal, heavy chain 3
79NM_017426NUP54Nucleoporin 54 kDa
80NM_002020FLT4Fms-related tyrosine kinase 4
81NM_007320RANBP3RAN binding protein 3
82NM_005286NPBWR2Neuropeptides B/W receptor 2
83NM_006428MRPL28Mitochondrial ribosomal protein L28
84NM_014280.2DNAJC8DnaJ (Hsp40) homolog, subfamily C, member 8
85NM_020679MIF4GDMIF4G domain containing
86NM_001823CKBCreatine kinase, brain
87NM_000477ALBAlbumin
88NM_001927DESDesmin
89NM_005416SPRR3Small proline-rich protein 3
90NM_022468MMP25Matrix metallopeptidase 25
91NM_016599MYOZ2Myozenin 2
92NM_000243MEFVMediterranean fever
93NM_002272KRT4Keratin 4
94NM_003279TNNC2Troponin C type 2 (fast)
95NM_006685SMR3BSubmaxillary gland androgen regulated protein 3 homolog B (mouse)
96NM_014760TATDN2TatD DNase domain containing 2
97NM_006928SILVSilver homolog (mouse)
98NM_016522Neurotrimin
99NM_000760CSF3RColony stimulating factor 3 receptor (granulocyte)
100NM_003167SULT2A1Sulfotransferase family, cytosolic, 2A, dehydroepiandrosterone (DHEA)-preferring, member 1
101NM_183360DTNBDystrobrevin, β
102NM_001711BGNBiglycan
103NM_023077 C1orf163Chromosome 1 open reading frame 163
104NM_015926.4TEX264Testis expressed 264
105NM_006757TNNT3Troponin T type 3 (skeletal, fast)
106NM_002675PMLPromyelocytic leukemia
107XR_018113GAPDH Glyceraldehyde-3-phosphate dehydrogenase
108NM_021245MYOZ1Myozenin 1
109NM_000383AIREAutoimmune regulator
110NM_006846SPINK5Serine peptidase inhibitor, Kazal type 5
111XM_939725AP1S2Adaptor-related protein complex 1, sigma 2 subunit
112NM_024505NOX5NADPH oxidase, EF-hand calcium binding domain 5
113NM_020145SH3GLB2SH3-domain GRB2-like endophilin B2
114NM_016192TMEFF2Transmembrane protein with EGF-like and two follistatin-like domains 2
115NM_006472TXNIPThioredoxin interacting protein
116NM_031866FZD8Frizzled homolog 8 (Drosophila)
117NM_003808TNFSF13Tumor necrosis factor (ligand) superfamily, member 13
118NM_015503SH2B1SH2B adaptor protein 1
119NM_014047 C19orf53Chromosome 19 open reading frame 53
120NM_022754SFXN1Sideroflexin 1
121NM_003061SLIT1Slit homolog 1 (Drosophila)
122NM_003047SLC9A1Solute carrier family 9 (sodium/hydrogen exchanger), member 1 (antiporter, Na+/H+, amiloride sensitive)
123NM_021991JUPJunction plakoglobin

On the other hand, the down-regulated genes included those associated with various metabolic pathways (CKM, ARSD and BGN), small molecule transport (ACCN3, ATP6V0A1 and SFXN1), signal transduction (FLT4, NPBWR2, CSF3R and FZD8), cell cycle regulation (TBC1D1, DLG5, GAS2L1, CDC34 and SH2B1), cell adhesion (RAPH1, CLDN10, BCAN, CD164 and JUP), transcription factors (LDB1, CLOCK, GCM1, BRF1, TCF3, PML and AIRE), cell-cell signaling (PGD, S100A9, CCL13 and RIMS1) or other functions.

Identification of candidate genes as molecular markers for the detection of circulating gastric cancer cells in human peripheral blood

Of the 53 genes that were up-regulated in the gastric cancer compared to the normal gastric tissues, we identified five candidate marker genes [tetraspanin 8 (TSPAN8), epithelial cell adhesion molecule (EPCAM), matrix metallopeptidase 12 (MMP12), matrix metallopeptidase 7 (MMP7) and regenerating islet-derived 3 α (REG3A)] for the detection of circulating gastric cancer cells in peripheral blood in accordance with the following criteria: i) no or weak expression in human normal tissues in the published database (20), ii) no expression in PBMCs from 4 healthy volunteers by nested RT-PCR. In addition to the above five newly identified genes, we analyzed histamine receptor H1 (HRH1) since a previous study reported that this gene was overexpressed in gastric cancer cells, and the expression of this gene satisfied the above criteria (21). Moreover, three candidate marker genes [keratin 20 (CK20), epidermal growth factor receptor (EGFR) and carcinoembryonic antigen (CEA)], which have been reported to be promising markers for the detection of cancer cells, were further analyzed (11,13,22,23).

Association of the expression of the nine marker genes for the detection of circulating gastric cancer cells with clinicopathological parameters by nested RT-PCR

We carried out semi-quantitative nested RT-PCR analysis of the nine candidate marker genes for the detection of circulating cancer cells using PBMCs of patients with gastric cancer. Of the nine candidate genes, the expression of MMP12 and CEA mRNAs was positive in 40% of the patients with gastric cancer. However, the expression of the other seven genes was positive in ≤30% of the patients, respectively (Table V). We then investigated a combined effect of the expression of the nine genes on the detection of circulating cancer cells. Expression of one or more genes out of the nine was detected in 80% of the patients with gastric cancer by nested RT-PCR (Table VI).

Table V.

Positive ratio of the nine marker genes for detection of circulating gastric cancer cells.

Table V.

Positive ratio of the nine marker genes for detection of circulating gastric cancer cells.

Marker genesTSPAN8EPCAMHRH1CK20MMP12MMP7EGFRREG3ACEA
Positive cases (%)203030204010102040

Table VI.

Number of positive genes in 10 cases by nested RT-PCR.

Table VI.

Number of positive genes in 10 cases by nested RT-PCR.

CasesGC-1GC-2GC-3GC-4GC-5GC-6GC-7GC-8GC-9GC-10
No. of positive genes1023561202

We further investigated the association of the expression of the nine candidate marker genes with clinicopathological parameters of the 10 cases. We focused on four parameters: vascular invasion (v factor), lymphatic invasion (ly factor), lymph node metastasis (N factor) and pathological stage I–IV, and investigated the association of these parameters with the total number of positive genes in the PBMCs of each patient (Fig. 1). Of the four parameters, the numbers of genes expressed in the PBMCs were ≤2 in all of the vascular invasion-negative cases (v 0), while the numbers of genes were ≥2 in 5 of 6 positive cases (v 1–3), exhibiting a significant difference between the two groups (P=0.041; Fig. 1A). However, no significant association was observed for the other three parameters (Fig. 1B–D), suggesting that the combined expression analysis of the nine marker genes using PBMCs detected micrometastasis through vascular invasion in the primary gastric cancer tissues.

Discussion

Microarrays, at present, are widely used to analyze expression of thousands of genes simultaneously in cancer tissues. In the present study, we identified five genes (TSPAN8, EPCAM, MMP12, MMP7 and REG3A) as potential markers for the detection of circulating cancer cells in the peripheral blood of patients with gastric cancer through genome-wide gene expression profiling in combination with nested RT-PCR. Some of these genes have previously been reported to be up-regulated in gastric cancer cells; however, they have not previously been designated for the detection of circulating gastric cancer cells by nested RT-PCR. Furthermore, the combined expression analysis of the five genes and four previously reported marker genes, HRH1, EGFR, CK20 and CEA, revealed that one or more mRNAs among the nine genes could be detected in 80% of the patients with gastric cancer by nested RT-PCR, suggesting that a set of nine marker genes is more sensitive than a single marker gene for detection of circulating gastric cancer cells. In this study, we did not investigate the association of distant metastasis with expression of the nine marker genes since no patients had distant metastasis among the 10 studied patients. Although we could not exclude false-positive cases due to non-malignant epithelial cells which may have contaminated the blood samples during collection and which may have expressed the targeted transcripts (18), pathological v factor showed a significant association with the total number of marker genes expressed in the PBMCs of the patients. Hence, the set of nine marker genes may be promising for the detection of minimal amounts of circulating gastric cancer cells prior to the metastatic growth of gastric cancer cells in organ(s).

Among the five marker genes which were newly identified in the microarray analysis, we identified epithelial cell adhesion molecule (EPCAM) which is a member of a family of type I membrane proteins and pan-epithelial differentiation antigen expressed in many types of carcinomas (2428). Magnetic beads or structures coated with EPCAM monoclonal antibodies have been recently used for circulating cancer cell separation (2931). Although we did not compare the accuracy of the detection of gastric cancer cells by these methods to that of nested RT-PCR since we did not conduct the former assays, 30% of patients with gastric cancer exhibited EPCAM-positivity in PBMCs by nested RT-PCR. Further clinical study investigating the relationship between the clinical outcome of patients and EPCAM expression in PBMCs by nested RT-PCR may clarify whether this method could be clinically applied for the detection of circulating gastric cancer cells. Two matrix metalloproteinases, MMP7 and MMP12, were among the five marker genes which were newly identified in this study. MMPs are a family of zinc-dependent proteolytic enzymes capable of cleaving extracellular matrix proteins, and the expression of MMPs in cancer tissue has been reported to be associated with the risk of metastasis (3238). These two MMPs may play important roles in tumor invasion and the formation of metastasis in gastric cancer.

In conclusion, five novel marker genes were designated for the detection of circulating gastric cancer cells. The nested RT-PCR analysis for the set of nine marker genes, TSPAN8, EPCAM, MMP12, MMP7, REG3A, HRH1, EGFR, CK20 and CEA, using PBMCs of patients with gastric cancer may provide the potential for the detection of circulating gastric cancer cells prior to the formation of metastasis in other organs. Our data suggest that early detection and personalized therapy for gastric cancers, by prescribing the appropriate treatment to patients with a high risk of metastasis, may be achievable by utilizing specific sets of marker genes according to the approach shown here.

Acknowledgements

We thank Tomohisa Furuhata, Yasutoshi Kimura, Chikashi Kihara, Kenji Okita and Noriko Nishikawa for the helpful discussions.

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Spandidos Publications style
Matsumura N, Zembutsu H, Yamaguchi K, Sasaki K, Tsuruma T, Nishidate T, Denno R and Hirata K: Identification of novel molecular markers for detection of gastric cancer cells in the peripheral blood circulation using genome-wide microarray analysis. Exp Ther Med 2: 705-713, 2011
APA
Matsumura, N., Zembutsu, H., Yamaguchi, K., Sasaki, K., Tsuruma, T., Nishidate, T. ... Hirata, K. (2011). Identification of novel molecular markers for detection of gastric cancer cells in the peripheral blood circulation using genome-wide microarray analysis. Experimental and Therapeutic Medicine, 2, 705-713. https://doi.org/10.3892/etm.2011.252
MLA
Matsumura, N., Zembutsu, H., Yamaguchi, K., Sasaki, K., Tsuruma, T., Nishidate, T., Denno, R., Hirata, K."Identification of novel molecular markers for detection of gastric cancer cells in the peripheral blood circulation using genome-wide microarray analysis". Experimental and Therapeutic Medicine 2.4 (2011): 705-713.
Chicago
Matsumura, N., Zembutsu, H., Yamaguchi, K., Sasaki, K., Tsuruma, T., Nishidate, T., Denno, R., Hirata, K."Identification of novel molecular markers for detection of gastric cancer cells in the peripheral blood circulation using genome-wide microarray analysis". Experimental and Therapeutic Medicine 2, no. 4 (2011): 705-713. https://doi.org/10.3892/etm.2011.252