Journal of Cancer Research and Clinical Oncology

, Volume 130, Issue 8, pp 480–486 | Cite as

Differential expression of S100 gene family in human esophageal squamous cell carcinoma

  • Junfang Ji
  • Liqun Zhao
  • Xiuqin Wang
  • Chuannong Zhou
  • Fang Ding
  • Lei Su
  • Chunlin Zhang
  • Xuezheng Mao
  • Min Wu
  • Zhihua Liu
Original Paper

Abstract

Purpose

To study the differential expression of the S100 gene family at the RNA level in human esophageal squamous cell carcinoma (ESCC), and to find the relationship of the S100 gene family with ESCC.

Methods

Firstly, the specific primers were designed for the different S100 genes with Software Primer 3, which required that both primer sequences of each S100 gene were from two different exons respectively. Then, the differential expression of 16 S100 genes was examined by semiquantitative reverse transcription-polymerase chain reaction (RT-PCR) in 62 cases of ESCC versus the corresponding normal esophageal mucosa. All RT-PCR products were analyzed by 1.5% agarose gel. With Fluor-S MultiImager and Multi-Analyst software, the electrophoresis images were evaluated with statistics analysis using SAS 8.1 software.

Results

Eleven out of 16 S100 genes were significantly downregulated (p<0.05) in ESCC versus the normal counterparts such as S100A1, S100A2, S100A4, S100A8, S100A9, S100A10, S100A11, S100A12, S100A14, S100B, and S100P genes. Only the S100A7 gene in the S100 family was markedly upregulated (p<0.05). Moreover, the S100B gene was significantly correlated with histological differentiation of ESCC (p=0.0247), and the deregulation of some S100 genes was closely correlated (p<0.05), such as S100A10/S100A11, S100A2/S100A8, S100A2/S100A14, S100A8/S100A14, and S100A2/S100P etc.

Conclusions

The S100 gene family is closely associated with ESCC.

Keywords

S100 gene family Human esophageal squamous cell carcinoma Histological differentiation 

Introduction

Esophageal squamous cell carcinoma (ESCC) is one of the most common human cancers in the world, especially in developing countries. Despite the recent advances in surgical techniques, chemotherapy, radiotherapy, and palliative measures, the 5-year survival rate is only approximately 5% (Ribeiro et al. 1996). In developed countries, the incidence rates of ESCC are about 1.5–5.0 per 100,000, whereas in some regions of China, the relative risk is as high as 300–500 fold (Togawa et al. 1994).

The first S100 calcium-binding protein was isolated from bovine brain in 1965 by Moore (1965). Subsequent studies identified many members of this family based on their homology of amino acid sequences and their feature of calcium-binding properties. The S100 family became a major interest because of its deregulated expression in human diseases, especially in cancer; the cluster organization of S100 genes on human chromosome 1q21, which is a region frequently rearranged in tumors (Watson et al. 1998); and the widespread application of S100 protein antibodies for tumor diagnosis by immunohistochemistry. Up to now, 21 genes encoding S100 calcium-binding proteins have been identified, and a cluster of 16 S100 genes is mapped to the human chromosome 1q21 region (Gendler et al. 1990; Schafer et al. 1995; Weterman et al. 1996; Pietas et al. 2002). Early studies have shown that S100A2, S100A4, S100A12, and S100P proteins were deregulated in esophageal cancer tissues and cell lines and could influence cell differentiation and cell cycle (Kyriazanos et al. 2002; Ninomiya et al. 2001; Hitomi et al. 1998; Sato et al. 2002). Moreover, it has been reported that many S100 genes were expressed at high levels in stratified epithelia of the upper digestive tract. All these factors indicate that the S100 gene family may be correlated with ESCC. However no study about their relationship has been reported yet. In this study, we focused on the differential expression of 16 S100 genes in 62 pairs of ESCC samples (cancer tissues and their corresponding normal counterparts).

Materials and Methods

Patients and samples

Esophageal cancer and matched adjacent normal mucosa were taken from surgical specimens of 62 patients who had not been treated with radiotherapy or chemotherapy before surgery. Cancer sample was taken from the tumor tissue where there was no hemorrhage or putrescence, whereas the matched normal mucosa was taken from the surgical cutting edge, which was approximately 3–5 cm away from the cancerous lesion. Fresh samples were dissected by hand to remove mixed connective tissues and stored in liquid nitrogen immediately. The clinical diagnosis of all 62 patients was approved by histological diagnosis after surgery. Among them, 45 patients were diagnosed and underwent surgery at the Xinxiang Central Hospital in Henan Province and 17 at Peking Union Hospital.

RNA extraction and reverse transcription

Total RNA was extracted with TRIzol Reagent (GIBCOBRL, New York, NY, USA) according to the manufacturer’s instructions. Five micrograms total RNA of each sample was used to synthesize the first strand cDNA in a 20 μl volume with SuperScript First-Strand Synthesis System for RT-PCR Kit (GIBCOBRL). The single-strand cDNA synthesized was used as the template for PCR.

Primer design

It was very important to choose the most specific primers for each S100 gene because of their high homologies to each other. The sequences of the PCR primer pairs of 16 S100 members listed in Table 1 were designed as follows: (1) Selection and design of primer by Software Primer 3 according to standard criteria (http://www-genome.wi.mit.edu/cgi-bin/primer/primer3_www.cgi); (2) test of the primer specificity through nucleotide BLAST comparison analysis with all known human sequences; and (3) test of whether both primer sequences of each paired primer were from two different exons in order to distinguish the cDNA and residual genomic DNA in total RNA.
Table 1

Primers used for RT-PCR and conditions of polymerase chain reaction (PCR). F forward; R reverse; Dir direction; Ref accession number from GenBank; Anne annealing temperature

S100 genes

Dir*

Primer sequences (5’-3’)

Start point

Product size

Ref*

Anne (°C)*

PCR cycles

S100A1

F

AGGAGCTGAAAGAGCTGCTG

208

279

NM_006271

65.5

29

R

AGGGATAAGTGGGGTGAGGT

486

S100A2

F

CACTACCTTCCACAAGTACT

79

229

NM_005978

56

27

R

GAAGTCATTGCACATGAC

307

S100A3

F

GCAGGCGGTAGCTGCCATC

101

255

NM_002960

59

29

R

TTGAAGTACTCGTGGCAGTAG

355

S100A4

F

CAAGTACTCGGGCAAAGAGG

120

250

NM_002961

58

27

R

CTTCCTGGGCTGCTTATCTG

369

S100A6

F

GGACCGCTATAAGGCCAGTC

2

285

NM_014624

60

27

R

GGTCCAAGTCTTCCATCAGC

286

S100A7

F

TGAGCAACACTCAAGCTGAG

73

336

NM_002963

63

27

R

GGGTCTCTGGAGGCCCATTG

408

S100A8

F

TGTCAGCCGTCTTTCAGAAG

9

286

BC005928

58

27

R

ACGCCCATCTTTATCACCAG

294

S100A9

F

GGGAATTCAAAGAGCTGGTG

149

267

NM_002965

58

26

R

CACTGTGATCTTGGCCACTG

415

S100A10

F

CATCTCAAATGGAACACGCC

116

321

NM_002966

56

27

R

TATCAGGGAGGAGCGAACT

436

S100A11

F

ATCGAGTCCCTGATTGCTGT

160

331

NM_005620

59

27

R

AGAAAGGCTGGAAGGAAAGG

490

S100A12

F

TTGAAGAGCATCTGGAGGG

79

269

NM_005621

56

27

R

CTACTCTTTGTGGGTGTGG

347

S100A13

F

ACCACCTTCTTCACCTTTGC

125

254

NM_005979

60

28

R

AGGCGGCTTTACTTCTTCCT

378

S100A14

F

CATGAGCCATCAGCTCCTCT

23

276

NM_020672

59

27

R

TTCTCTTCCAGGCCACAGTT

298

S100B

F

ATGTCTGAGCTGGAGAAGG

73

338

NM_006272

58

28

R

CTGTCTGCTTTCTTGCATG

410

S100P

F

AGGTGCTGATGGAGAAGGAG

122

223

NM_005980

60

28

R

ATGGCTCTGCAGGAATCTGT

344

S100Z

F

TGGAGTGGTCAGTTCTGCTG

307

230

NM_130772

62

32

R

CGTTGTCCTTATTGGCATCC

536

Semiquantitative PCR and image analysis

Every PCR reaction was performed in 25 μl final volume containing 1×PCR buffer, 1.5 mM MgCl2, 0.2 mM each dNTPs, 1.25 U Taq DNA polymerase (TakaRa, Dalian, China), 1 μl RT product, 0.3–0.5 μM of each primer. The housekeeping gene glyceraladehyde-3-phosphate dehydrogenase (GAPDH) was used as an internal control. The conditions of PCR reaction were as follows: initial denaturalization for 4 min at 94°C, amplification for 26–32 cycles (30 s at 94°C, 30 s at 55–65°C, 30 s at 72°C), which were varying in different genes and are listed in Table 1, and final extension for 4 min at 72°C. The RT-PCR reaction products were analyzed by electrophoresis on 1.5% agarose gel. Then, the electrophoresis images were scanned with Fluor-S MultiImager (Bio-Rad, California, USA), and the original intensities of specific bands were quantified by Multi-Analyst software (Bio-Rad). The final data were obtained after normalization by the intensity of GAPDH, which included the expression amount of every gene in each case of ESCC (RC), the corresponding normal esophageal mucosa (RN), and the ratio of RC to RN (RC/N).

Statistical analysis

All data were analyzed with SAS 8.1 software for Windows 98. With the Wilcoxon paired signed-rank test (nonparametric variance for pair comparisons), the difference between RN and RC or the differential expression of the 16 S100 genes in ESCC versus normal tissues was examined. After every RC/N was obtained, the correlation of each S100 gene with the histological differentiation (including well, moderate, and poor differentiation) of ESCC was analyzed with the Kruskal-Wallis test. When the RC/N of two different S100 genes in the same case were used as variables, the possible correlations between every two S100 genes were carried out with the Pearson test. A p value of <0.05 was considered as significant.

Results

Primer design

With Software Primer 3 and BLAST analysis, 16 specific primer pairs were obtained (Table 1) that could discriminate the different members of the S100 gene family. At the same time, both primer sequences of each S100 gene were from two different exons respectively so that each primer pair also was able to discriminate the cDNA and genomic DNA. Thus, residual genomic DNA in total RNA could not influence the RT-PCR results.

Semiquantitative RT-PCR

With semiquantitative RT-PCR, the differential expression of 16 S100 genes was examined in 62 cases of ESCC. The results evaluated with the Wilcoxon paired sign-rank test showed that 12 S100 genes were significantly deregulated in ESCC versus the normal counterparts, which were S100A1 (p=0.0007), S100A2 (p<0.0001), S100A4 (p=0.0003), S100A7 (p=0.0200), S100A8 (p<0.0001), S100A9 (p<0.0001), S100A10 (p<0.0001), S100A11 (p<0.0001), S100A12 (p<0.0001), S100A14 (p<0.0001), S100B (p=0.0006), and S100P (p<0.0001). However, the expression of other genes such as S100A3, S100A6, S100A13, and S100Z was not significantly different in ESCC and the counterparts (Table 2). Partial electrophoresis images of semiquantitative RT-PCR were shown in Fig. 1. At the same time, the criteria of a gene overexpression or underexpression in ESCC were decided by three independent researchers. The criteria were that a gene was considered as upregulated in one case of ESCC when RC/N was higher than 4/3, as downregulated when RC/N was lower than 3/4, and the alteration of the gene was not considered as significant when RC/N was between 3/4 and 4/3 (including 3/4 and 4/3). With this criteria, the number of underexpression cases was found in significant downregulated genes such as S100A8 (51 cases, 82.3%), S100A14 (49 cases, 79.0%), S100A9 (48 cases, 77.4%), S100A12 (39 cases, 62.9%), S100A10 (37 cases, 59.7%), S100P (36 cases, 58.1%), S100A2 (34 cases, 54.8%), S100A4 (33 cases, 53.2%), S100A1 (31 cases, 50.0%), S100A11 (31 cases, 50.0%), and S100B (31 cases, 50.0%). That of overexpression cases was found in significant upregulated gene, the S100A7 gene (35 cases, 56.5%) in ESCC (Table 2, Fig. 2).
Table 2

The differential expression of S100 genes

S100 genes

Differential expression

Up cases*

NS cases*

Down cases*

S

p value

S100A1

8(12.90%)

23(37.10%)

31(50.00%)

467.5

0.0007

S100A2

9(14.52%)

19(30.65%)

34(54.84%)

529.5

<0.0001

S100A3

17(27.42%)

26(41.94%)

19(30.65%)

−23.5

NS*

S100A4

8(12.90%)

21(33.87%)

33(53.23%)

498.5

0.0003

S100A6

18(29.03%)

31(50.00%)

13(20.97%)

−193.0

NS

S100A7

35(54.45%)

7(11.29%)

20(32.26%)

−328.5

0.0200

S100A8

6(9.68%)

5(8.06%)

51(82.26%)

830.5

<0.0001

S100A9

4(6.45%)

10(16.13%)

48(77.42%)

871.5

<0.0001

S100A10

5(8.06%)

20(32.26%)

37(59.68%)

768.5

<0.0001

S100A11

7(11.29%)

24(38.71%)

31(50.00%)

610.5

<0.0001

S100A12

10(16.13%)

13(20.97%)

39(62.90%)

544.5

<0.0001

S100A13

15(24.19%)

29(46.77%)

18(29.03%)

−24.5

NS

S100A14

5(8.06%)

8(12.90%)

49(79.03%)

864.5

<0.0001

S100B

8(12.90%)

23(37.10%)

31(50.00%)

472

0.0006

S100P

8(12.90%)

18(29.03%)

36(58.06%)

660.5

<0.0001

S100Z

14(22.58%)

24(38.71%)

24(38.71%)

230.5

NS

Fig. 1

Partial electrophoresis images of semiquantitative RT-PCR results. (M: marker; N: normal; C: cancer) The expression of GAPDH was used as internal control, and the size of the amplified fragment was 452 bp. The pUC Mix Marker 8 (MBI, Lithuania) was used as molecular-weight marker

Fig. 2

Differential expression of 16 S100 genes in 62 cases of esophageal squamous cell carcinoma (ESCC) examined by RT-PCR (up: up-regulation, white bars; NS: not significant, grey bars; down: downregulation, black bars). Every column expressed the number of upregulated or downregulated or insignificantly deregulated cases

Correlation of the S100 gene expression with the histological differentiation

In 62 cases of ESCC, there were 39 cases with well-known histological differentiation. The correlation analyses showed that only the S100B gene was significantly correlated with the histological differentiation (p=0.0247), and its mean rank score was the lowest in moderately (15.82) and the highest in poorly differentiated ESCC (28.00). At the same time, the expression of S100A3, S100A4, S100A7, S100A12, and S100P genes showed increasing tendency and the S100A13 gene showed decreasing tendency from poorly to well via moderately differentiated ESCC, but the difference was not significant (data not shown).

Correlation analysis of the deregulation of 16 S100 genes

Fig. 3 showed that the deregulation of many S100 genes in ESCC was significantly correlated (p<0.05). Among S100A2, S100A8, S100A14, and S100P genes, the deregulation of each other was markedly correlated (p<0.0001), and their correlation coefficients (r) were 0.6829 (S100A2/S100A8), 0.6818 (S100A2/S100A14), 0.6549 (S100A2/S100P), 0.6744 (S100A8/S100A14), 0.6422 (S100A8/S100P), and 0.5801 (S100A14/S100P) respectively. In addition, S100A10/S100A11 (r=0.7559), S100A9/S100A10 (r=0.5928), S100A7/S100A10 (r=0.5900), S100A9/S100A14 (r=0.5427), and S100A8/S100A9 (r=0.5374) etc., deregulation of these was also significantly correlated (p<0.0001) (Fig 3).
Fig. 3

Correlation analysis of differential expression of 16 S100 genes. r: correlation coefficient; p: p value; NS: not significant. The figure at the top right corner was the scanning spot of the correlation of S100A10 and S100A11 (r=0.7559, p<0.0001). In this figure, each spot expressed the RC/N of S100A10 and S100A11 genes in each case of esophageal squamous cell carcinoma (ESCC)

Discussion

The S100 gene family is a family encoding calcium-binding proteins, which contains one canonical EF-hand in the C-terminal half and a specific motif in the N-terminal half. Early studies have shown that some S100 members are associated with ESCC, such as S100A2, S100A4, S100A12, and S100P proteins. In this work, the differential expression of 16 S100 genes was examined in ESCC at mRNA level by RT-PCR. The results showed that 11 genes were significantly downregulated (p<0.05), and only the S100A7 gene was markedly upregulated (p<0.05) in ESCC. This indicates that the S100 gene family is closely correlated with ESCC and that some deregulated S100 genes may be important ESCC-associated genes.

Chromosome 1q21 is a region of the structural and numerical aberration in many tumors (Lestou et al. 2003; Chen et al. 2003; Wong et al. 2003; Watson et al. 1998). It also may be closely associated with the development of ESCC due to the following reasons: (1) The chromosome 1q21 has been known to be involved in chromosome rearrangement in various human tumors (Watson et al. 1998); (2) the chromosome region 1q21–1q22 contains the high-density CpG island (Wright et al. 2001); (3) ten genes were significantly deregulated out of the 13 S100 genes that were clustered in 1q21 among the total 16 S100 genes in our work; (4) other genes in 1q21, such as SPRR3 and TGM-3, were also deregulated in ESCC (Chen et al. 2000; Chen et al. 2002).

The S100A2 protein was proposed as a valuable prognostic marker in tumors including ESCC (Kyriazanos et al. 2002). Due to the underexpression in some tumors and the stable expression in normal epithelia of the S100A2 gene, it was considered as a candidate tumor-suppressor gene (Nagy et al. 2001; Liu et al. 2000; Hitomi et al. 1996). Our results have shown that the S100A2 gene was significantly downregulated (54.8%, p<0.0001) in ESCC as compared with normal mucosa so that it also might be a candidate tumor-suppressor gene in ESCC. It has been shown that the S100A4 protein was upregulated in ESCC versus normal counterparts, which was contrary with our result that the S100A4 gene was downregulated (Ninomiya et al. 2001). With regard to the S100A4 primer specificity, plentiful cases, and proper statistical analysis, our result was convincing.

The reason for inconsistent expression of S100A4 at the protein and gene level has not yet been reported. The inconsistency might indicate that the modulation of translation from mRNA to protein was important and complex in the process of tumorigenesis. S100P and S100A12 have been found to be downregulated in ESCC at the protein level and produced by esophageal epithelial cells in the process of cell differentiation. They were considered to be associated with the differentiation of ESCC (Hitomi et al. 1998; Sato et al. 2002). However, our results only indicated that their expression at the RNA level were downregulated (p<0.05), and their mean rank score of RC/N took on an increasing tendency from poorly to well via moderately differentiated ESCC. The S100P protein-positive cells were located mainly in the second to fourth layers just above the basal cells (Sato et al. 2002), and the S100A12 protein was located in the superbasal squamous epithelial cells undergoing differentiation but not in the cells in the proliferating basal layer (Hitomi et al. 1996). All these factors suggest that the deregulation of S100P and S100A12 proteins may be an earlier event in the terminal differentiation of human ESCC. However, most cases we collected were a later stage of ESCC; therefore, the correlation of S100P and S100A12 genes with histological differentiation could not be detected. In addition, the limited cases of well- and poorly differentiated ESCC (ten and six cases respectively) and subjectivity of histological differentiation diagnosis might have an effect on the results.

The S100B gene was first detected significantly downregulated (50.0%, p=0.0006) in ESCC. Its underexpression may be accordant with its possible tumor-suppressor activity that S100B could protect p53 from thermal denaturation and aggregation and cooperate with p53 to cause cell growth arrest and apoptosis (Baudier et al. 1992; Scotto et al. 1998). Moreover, the S100B gene was significantly correlated with the histological differentiation (p=0.0247). Its expression was the lowest in moderately differentiated ESCC, next to well-differentiated ESCC, and highest in poorly differentiated ESCC. This just confirmed the hypothesis of S100B possible tumor-promoting activity that S100B could actually block p53 based on structural and functional analysis of the S100B-p53 interaction (Rustandi et al. 2000; Lin et al. 2001). According to these authors, the decrease of S100B gene expression in ESCC would result in the increase of p53 function and lead to a decreased cell proliferation rate. It was believed that the proliferation rate of poorly differentiated cells was higher than that of well- and moderately differentiated cells. So, in poorly differentiated ESCC, S100B gene expression should be higher. All these factors suggest that S100B may play a different role in different differentiation stages of ESCC.

S100A1, S100A7, S100A8, S100A9, S100A10, S100A11, and S100A14 genes were also first detected significantly deregulated in ESCC. The precise mechanism of their deregulated expression in ESCC was still unknown; the deregulation may influence tumorigenesis by affecting cell cycle or proliferation, cell differentiation, and apoptosis. It has been reported that nuclear S100A11 inhibited cell growth. In normal cells, S100A11 protein was phosphorylated and moved to nuclei from cytoplasm to inhibit DNA synthesis. Whereas in immortalized cells, S100A11 was neither phosphorylated nor imported to the nuclei but remained in the cytoplasm so that it could not inhibit DNA synthesis (Sakaguchi et al. 2000). In B16 melanoma cells, the S100A4 protein has been shown to sequester p53 to form a complex of S100A4 with p53. The complex could abrogate cell G1-S checkpoint control and result in the increase of the size of the S-phase fraction (Parker et al. 1994). Moreover, the cells induced by the S100A4 protein to enter S-phase could successfully negotiate the G2-M checkpoint control to transit into mitosis (Cajone et al. 1999). All these factors suggest that in the initiation and development of ESCC, it may be an important mechanism that the S100 family influences on the cell cycle. None of these genes were significantly correlated with the histological differentiation, but some genes might be similar with S100P and S100A12 genes and might be associated with the earlier differentiation of ESCC cells.

Early studies have revealed that S100A8 and S100A9 proteins can form heterodimer, which was preferred within cells (Strupat et al. 2000; Propper et al. 1999). This suggested that the deregulation of theS100A8 gene would be consistent with that of S100A9, which has been confirmed by our data. Our data also showed that there was significant correlation in many genes such as S100A2/S100A8, S100A2/S100A14, and S100A2/S100P genes, etc. The correlation coefficients of some genes were more than 0.5349, which was the correlation coefficient of S100A8 and S100A9 genes (Fig. 3). This meant that the interaction of these genes might be closer than that of S100A8 and S100A9 genes. All these correlations indicate that some S100 genes may be modulated by some common factors or modulate each other in ESCC. Although the precise modes of interaction remained unknown, the correlation of some S100 genes provided an important clue for illustrating the function of S100 members and the roles they played in the mechanisms of ESCC initiation and progression. Considering the close association of the chromosome 1q21 region and the S100 gene family with ESCC and the possible interaction of S100 members, it would be worthwhile to further analyze the S100 genes and proteins in ESCC.

Notes

Acknowledgements

This work was cosupported by the China Key Program on Basic Research (G1998051021), the Chinese Hi-tech R&D program (2001AA231041), and the National Science Foundation of China (30170519).

References

  1. Baudier J, Delphin C, Grunwald D, Khochbin S, Lawrence JJ (1992) Characterization of the tumor suppressor protein p53 as a protein kinase C substrate and a S100b-binding protein. Proc Natl Acad Sci USA 89:11627–11631PubMedGoogle Scholar
  2. Cajone F, Sherbet GV (1999) Stathmin is involved in S100A4-mediated regulation of cell cycle progression. Clin Exp Metastasis 17:865–871CrossRefPubMedGoogle Scholar
  3. Chen B, Wang M, Cai Y, Xu X, Xu Z, Han Y, Wu M (2000) Transglutaminase-3, an esophageal cancer-related gene. Int J Cancer 88:862–865CrossRefPubMedGoogle Scholar
  4. Chen B, Wang M, Cai Y, Xu X, Xu Z, Han Y, Wu M (2002) Decreased expression of SPRR3 in Chinese human oesophageal cancer. Carcinogenesis 21:2147–2150CrossRefGoogle Scholar
  5. Chen YJ, Vortmeyer A, Zhuang Z, Huang S, Jensen RT (2003) Loss of heterozygosity of chromosome 1q in gastrinomas: occurrence and prognostic significance. Cancer Res 63:817–823PubMedGoogle Scholar
  6. Gendler SJ, Cohen EP, Craston A, Duhig T, Johnstone G, Barnes D (1990) The locus of the polymorphic epithelial mucin (PEM) tumour antigen on chromosome 1q21 shows a high frequency of alteration in primary human breast tumours. Int J Cancer 45:431-435PubMedGoogle Scholar
  7. Hitomi J, Yamaguchi K, Kikuchi Y, Kimura T, Maruyama K, Nagasaki K (1996) A novel calcium-binding protein in amniotic fluid, CAAF1: its molecular cloning and tissue distribution. J Cell Sci 109:805-815Google Scholar
  8. Hitomi J, Kimura T, Kusumi E, Nakagawa S, Kuwabara S, Hatakeyama K, Yamaguchi K (1998) Novel S100 proteins in human esophageal epithelial cells: CAAF1 expression is associated with cell growth arrest. Arch Histol Cytol 61:163-178PubMedGoogle Scholar
  9. Kyriazanos ID, Tachibana M, Dhar DK, Schibakita M, Ono T, Kohno H, Nagasue N (2002) Expression and prognostic significance of S100A2 protein in squamous cell carcinoma of the esophagus. Oncol Rep 9:503–510PubMedGoogle Scholar
  10. Lestou VS, Ludkovski O, Connors JM, Gascoyne RD, Lam WL, Horsman DE (2003) Characterization of the recurrent translocation t(1;1)(p36.3;q21.1–2) in non-Hodgkin lymphoma by multicolor banding and fluorescence in situ hybridization analysis. Genes Chromosomes Cancer 36:375–381CrossRefPubMedGoogle Scholar
  11. Lin J, Blake M, Tang C, Zimmer D, Rustandi RR, Weber DJ, Carrier F (2001) Inhibition of p53 transcriptional activity by the S100B calcium-binding protein. J Biol Chem 276:35037–35041CrossRefPubMedGoogle Scholar
  12. Liu D, Rudland PS, Sibson DR, Platt-Higgins A, Barraclough R (2000) Expression of calcium-binding protein S100A2 in breast lesions. Br J Cancer 83:1473–1479CrossRefPubMedGoogle Scholar
  13. Moore BW (1965) A soluble protein characteristic of the nervous system. Biochem Biophys Res Commun 19:739–744PubMedGoogle Scholar
  14. Nagy N, Brenner C, Markadieu N, Chaboteaux C, Camby I, Schafer BW, Pochet R, Heizmann CW, Salmon I, Kiss R, Decaestecker C (2001) S100A2, a putative tumor suppressor gene, regulates in vitro squamous cell carcinoma migration. Lab Invest 81:599–612PubMedGoogle Scholar
  15. Ninomiya I, Ohta T, Fushida S, Endo Y, Hashimoto T, Yagi M, Fujimura T, Nishimura G, Tani T, Shimizu K, Yonemura Y, Heizmann CW, Schafer BW, Sasaki T, Miwa K (2001) Increased expression of S100A4 and its prognostic significance in esophageal squamous cell carcinoma. Int J Oncol 18:715–720PubMedGoogle Scholar
  16. Parker C, Lakshmi MS, Piura B, Sherbet GV (1994) Metastasis-associated mts1 gene expression correlates with increased p53 detection in the B16 murine melanoma. DNA Cell Biol 13:343–351PubMedGoogle Scholar
  17. Parker C, Whittaker PA, Usmani BA, Lakshmi MS, Sherbet GV (1994) Induction of 18A2/mts1 gene expression and its effects on metastasis and cell cycle control. DNA Cell Biol 13:1021–1028PubMedGoogle Scholar
  18. Pietas A, Schluns K, Marenholz I, Schafer BW, Heizmann CW, Petersen I (2002) Molecular cloning and characterization of the human S100A14 gene encoding a novel member of the S100 family. Genomics 79:513–522CrossRefPubMedGoogle Scholar
  19. Propper C, Huang X, Roth J, Sorg C, Nacken W (1999) Analysis of the MRP8-MRP14 protein-protein interaction by the two-hybrid system suggests a prominent role of the C-terminal domain of S100 proteins in dimer formation. J Biol Chem 274:183–188CrossRefPubMedGoogle Scholar
  20. Ribeiro U Jr, Posner MC, Safatle-Ribeiro AV, Reynolds JC (1996) Risk factors for squamous cell carcinoma of the oesophagus. Br J Surg 83:1174–1185CrossRefPubMedGoogle Scholar
  21. Rustandi RR, Baldisseri DM, Weber DJ (2000) Structure of the negative regulatory domain of p53 bound to S100B(betabeta). Nat Struct Biol 7:570–574CrossRefPubMedGoogle Scholar
  22. Sakaguchi M, Miyazaki M, Inoue Y, Tsuji T, Kouchi H, Tanaka T, Yamada H, Namba M (2000) Relationship between contact inhibition and intranuclear S100C of normal human fibroblasts. J Cell Biol 149:1193-1206Google Scholar
  23. Sato N, Hitomi, J (2002) S100P expression in human esophageal epithelial cells: Human esophageal epithelial cells sequentially produce different S100 proteins in the process of differentiation. Anat Rec 267:60–69CrossRefPubMedGoogle Scholar
  24. Schafer BW, Wicki R, Engelkamp D, Mattei MG, Heizman CW (1995) Isolation of a YAC clone covering a cluster of nine S100 genes on human chromosome 1q21: rationale for a new nomenclature of the S100 calcium-binding protein family. Genomics 25:638–643CrossRefPubMedGoogle Scholar
  25. Scotto C, Deloulme JC, Rousseau D, Chambaz E, Baudier J (1998) Calcium and S100B regulation of p53-dependent cell growth arrest and apoptosis. Mol Cell Biol 18:4272–4281PubMedGoogle Scholar
  26. Strupat K, Rogniaux H, van Dorsselaer A, Roth J, Vogl T (2000) Calcium-induced noncovalently linked tetramers of MRP8 and MRP14 are confirmed by electrospray ionization-mass analysis. J Am Soc Mass Spectrom 11:780–788CrossRefPubMedGoogle Scholar
  27. Togawa K, Jaskiewize K, Takahashi H, Meltzer SJ, Rustgi AK (1994) Human papillomavirus DNA sequences in esophagus squamous cell carcinoma. Gastroenterology 107:128–136PubMedGoogle Scholar
  28. Watson PH, Leygue ER, Murphy LC (1998) Psoriasin (S100A7). Int J Biochem Cell Biol 30:567–571CrossRefPubMedGoogle Scholar
  29. Weterman MA, Wilbrink M, Dijkhuizen T, van den Berg E, Geurts van Kessel A (1996) Fine mapping of the 1q21 breakpoint of the papillary renal cell carcinoma-associated (X;1) translocation. Hum Genet 98:16–21CrossRefPubMedGoogle Scholar
  30. Wong N, Chan A, Lee SW, Lam E, To KF, Lai PB, Li XN, Liew CT, Johnson PJ (2003) Positional mapping for amplified DNA sequences on 1q21-q22 in hepatocellular carcinoma indicates candidate genes over-expression. J Hepatol 38:298–306CrossRefPubMedGoogle Scholar
  31. Wright FA, Lemon WJ, Zhao WD, Sears R, Zhuo D, Wang JP, Yang HY, Bser T, Stredney D, Spitzner J, Stutz A, Krahe R, Yuan B (2001) A draft annotation and overview of the human genome. Genome Biol 2:0025.1–0025.18Google Scholar

Copyright information

© Springer-Verlag 2004

Authors and Affiliations

  • Junfang Ji
    • 1
    • 2
  • Liqun Zhao
    • 2
  • Xiuqin Wang
    • 1
  • Chuannong Zhou
    • 1
  • Fang Ding
    • 1
  • Lei Su
    • 1
  • Chunlin Zhang
    • 3
  • Xuezheng Mao
    • 3
  • Min Wu
    • 1
  • Zhihua Liu
    • 1
  1. 1.National Lab of Molecular Oncology, Cancer InstituteChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingP.R. China
  2. 2.Henan Institute of Medical ScienceZhengzhouP.R. China
  3. 3.Cancer Institute of Yangquan CityShanxiP.R. China

Personalised recommendations