Overexpression of the S100A2 protein as a prognostic marker for patients with stage II and III colorectal cancer
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- Published online on: January 11, 2016 https://doi.org/10.3892/ijo.2016.3329
- Pages: 975-982
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Copyright: © Masuda et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Colorectal cancer (CRC) is one of the most prevalent cancers in the world (1). It is the second leading cause of cancer-related mortality worldwide. In Japan, the incidence of CRC has doubled over the past 20 years such that CRC is now the second most deadly neoplastic disease (2,3).
Surgery is still the most effective treatment for CRC. Among the patients that undergo curative surgery, some develop local recurrence or distant metastases that lead to shorter survival times (4). Distant metastasis has a critical influence on the prognosis of CRC. Clinicopathological indicators such as the TNM classification proposed by the International Union Against Cancer (UICC) remain the indicator of prognosis and provide the basis for therapeutic decision making. However, the current TNM classification system is limited in that it cannot predict prognosis for individual patients (5). In order to develop personalized therapeutic regimens, it is therefore critical that novel genes involved in distant metastasis are identified that can serve as prognostic biomarkers (6). Microarray is a particularly powerful tool for identifying potential biomarker genes for use in cancer prognosis (7–9). Using microarray analysis it is now possible to investigate several thousand cancer-related or cancer-specific genes at once.
Chromosomal structural alterations play an important role in cancer development. In CRC, copy number aberrations (CNAs), including gains on chromosomes 7, 8, 13 and 20, and losses on chromosomes 1p, 8p, 17p and 18, are frequently observed (10–13). Some of these CNAs are related to metastasis of CRC and can thus be used in prognosis. The single nucleotide polymorphism microarray (SNP array) analysis has become a useful tool for examining CNAs, permitting highly accurate exploration of thousands of genetic markers in a single study (14).
Studies of the relationship between chromosomal aberrations and gene expression in cancers, including CRC, have shown that CNAs directly influence gene expression (15–19). Several groups have therefore suggested that integrating gene expression analysis with genomic profiling represents an efficient approach for the discovery of cancer-related genes (20–22). Genes that show a strong positive correlation between expression and copy number may play an important role in cancer progression. In this study, we therefore integrated gene expression and copy number analyses to identify novel genes associated with the distant metastasis of CRC. We focused on the genes that are overexpressed and have an amplified copy number in cases of distant metastasis because such characteristics indicate that these genes have the potential to serve as useful therapeutic targets or clinical biomarkers.
Using the aforementioned comprehensive analysis, we identified S100 calcium-binding protein A2 (S100A2) as a gene involved in the distant metastasis of stage II and III CRC. It has been suggested that S100A2 plays an important role in cell cycle progression, and overexpression of S100A2 has been reported in several cancers (23–28). This is the first study to demonstrate the prognostic significance of S100A2 expression in CRC, using integrated copy number and gene expression analyses of clinical tissue samples.
Patients and methods
Patients
Primary tumors from 278 patients who underwent curative surgery for stage II and III CRC between 2002 and 2009 at the Tokyo Medical and Dental University Hospital (Tokyo, Japan) were studied. Written informed consent was obtained from all the patients, and the study was approved by the ethics committee of Tokyo Medical and Dental University, and all the following procedures were performed strictly in accordance with the ethical standards established by this committee. Clinical data were obtained from the medical records of each patient, and histopathological evaluations were assessed by reference to the criteria of the TNM-system of the UICC, 7th edition. A total of 125 patients were assigned to the comprehensive analyses for extraction of candidate genes. All of the patients were assigned to the gene expression and the CNA study, including 66 patients with stage II and 59 patients with stage III disease. The median follow-up time for these patients was 62 months (range, 1–76 months). Quantitative reverse transcription polymerase chain reaction (RT-PCR) assays were performed for validation using samples from 50 stage II and III CRC patients, including 24 patients with stage II and 26 patients with stage III disease. The median follow-up time for these patients was 61 months (range, 7–96 months). Furthermore, 161 patients, including patients subjected to RT-PCR validation, were analyzed using immunohistochemistry (IHC). The IHC study included 80 patients with stage II and 81 patients with stage III disease. The median follow-up time for these patients was 86 months (range, 1–96 months). The patients enrolled in the comprehensive analyses were excluded from these validation studies.
DNA extraction
After resection, cancer tissues were immediately embedded in Tissue-Tek OCT compound medium (Sakura Finetek Japan, Tokyo, Japan). Serial frozen sections of 9-mm in thickness were mounted onto a 90 FOIL-SL25 foil-coated glass slide (Leica Microsystems, Wetzlar, Germany). Laser capture microdissection (LCM) was performed using an Application Solutions LCM System (Leica Microsystems). Tumor DNA was extracted and purified using a QIAamp DNA micro kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Non-neoplastic tissues were homogenized in microtubes, and DNA was extracted and purified from these tissues using a QIAamp DNA mini kit (Qiagen) according to the manufacturer's instructions.
CNA analysis
Copy number analysis was performed using a GeneChip® Human Mapping 250K Sty array (Affymetrix, Santa Clara, CA, USA) in strict adherence to the assay manual. Genomic DNA was digested using the enzyme StyI, and a Sty1 adaptor was used prior to the PCR reaction. Amplicons were fragmented after purification and then labeled. After hybridization, the microarrays were transferred to a totally automated GeneChip® Fluidics Station 450 (Affymetrix) for the washing and staining steps. After fluorescence staining, microarray images were scanned using a GeneChip® Scanner 3000 7G (Affymetrix). The microarray data from the scanner were used for copy number analysis with the Chromosome Copy Number Analysis Tool (Affymetrix). Data were analyzed using R statistical software (version 2.12.1; http://www.r-project.org/).
RNA extraction
Cancer cells were microdissected using LCM. Total RNA was extracted from cancer cells and purified using an RNeasy micro kit (Qiagen) with on-column DNase digestion, according to the manufacturer's instructions. Total RNA collected from bulk samples of cancer tissues and adjacent non-neoplastic tissues was extracted and purified using an RNeasy mini kit (Qiagen) with on-column DNase digestion, according to the manufacturer's instructions. The integrity of the total RNA was assessed using an Agilent 2100 BioAnalyzer (Agilent Technologies, Palo Alto, CA, USA). Samples with an RNA integrity number >5.0 were used for the rest of the experiments.
Gene expression analysis
Complementary RNA was prepared from total RNA using two-cycle target labeling and a control reagents kit (Affymetrix). The experiment was performed using the GeneChip® Human Genome U133 Plus 2.0 Array (Affymetrix), according to the manufacturer's instructions. Statistical analyses of microarray data were normalized using the robust multi-array average method with R statistical software (version 2.12.1; http://www.r-project.org/) together with the BioConductor package (http://www.bioconductor.org/).
Extraction of candidate genes
We defined patients with metastatic recurrence of stage II and III CRC as the recurrence group, and patients without any recurrence as the non-recurrence group. Local recurrence and lymph node recurrence were excluded from the recurrence group. The CNA data and the gene expression data were analyzed and compared between the 2 groups to identify genes involved in the metastatic recurrence of stage II and III CRC.
Data regarding genes which showed a copy number gain in the recurrence group were extracted using Fisher's exact test (P<0.01). Data regarding genes that were significantly upregulated in the recurrence group were extracted using the Wilcoxon exact rank sum test (P<0.01). Among the genes that were common to both groups, those that were overexpressed (fold change >1.5) in the recurrence group were selected as candidates for further analysis.
The gene expression data and DNA copy number data were deposited in the Gene Expression Omnibus (GEO) under accession IDs GSE64256, GSE64257 and GSE64258.
Quantitative RT-PCR
Total RNA collected from bulk samples of cancer tissues and adjacent non-neoplastic tissues was reverse-transcribed into cDNA using a High Capacity cDNA Reverse Transcription kit (Applied Biosytems, Foster City, CA, USA) according to the manufacturer's instructions. A TaqMan® gene expression assay (Applied Biosystems; S100A2; Hs00195582_m1, β-actin; Hs99999903_m1) was used to investigate the expression of S100A2, and β-actin was used as an internal control. The PCR reaction was carried out using TaqMan® Universal PCR Master Mix (Applied Biosystems). The thermal cycling conditions were as follows: 50ºC for 2 min, 95ºC for 10 min, and 40 cycles of denaturation at 95ºC for 15 sec and annealing at 60ºC for 1 min. All calculated concentrations of target genes were normalized by the amount of the endogenous reference using the comparative Ct method for relative quantification with Relative Quantification Study Software (7300 Sequence Detection System version 1.2.1, Applied Biosystems).
Immunohistochemistry
IHC analyses of S100A2 were conducted on formalin-fixed paraffin-embedded tissue blocks from each patient. For S100A2 staining, antigen retrieval by autoclave treatment was carried out for 15 min in 1X TE (1X Tris-EDTA, pH 8.0) at 121ºC after deparaffinization in xylene and rehydration through a series of incubations in graded concentrations of ethanol. The slides were then incubated in a solution of 3% hydrogen peroxide in 100% methanol for 15 min at room temperature in order to quench endogenous peroxidase activity. Subsequently, the slides were incubated with mouse monoclonal antibody against S100A2 (Sigma-Aldrich), at a 1:50 dilution, for 30 min at room temperature. The slides were then incubated with peroxidase-labeled antibody [Histofine Simple Stain Max PO (MULTI; Nichirei Bioscience)] for 30 min at room temperature. Peroxidase activity was detected with DAB Solution (Histofine Simple Stain DAB Solution; Nichirei BioScience). Finally, the slides were counterstained with 1% Mayer's hematoxylin.
All the sections were divided into four stages (negative, weak, moderate, or strong) by staining intensity. Expression was graded by two independent observers who were blinded to the patient information.
Statistical analysis of S100A2 expression
Statistical analyses of S100A2 expression were carried out using SPSS (version 17.0, SPSS Inc, Chicago, IL, USA) software for Windows. To estimate the significance of differences between groups, Wilcoxon signed-rank, Mann-Whitney U, and C2 tests were used where appropriate. Survival curves were estimated using the Kaplan-Meier method, and curves were compared using the log-rank test. Survival times were determined from the date of surgery. Prognostic factors were examined with univariate and multivariate analyses using the Cox proportional hazards model. A P-value <0.05 was considered statistically significant.
Results
Gene expression and copy number analyses
In the copy number and gene expression analyses, 9 genes (S100A2, PROX1, TCN1, PROM1, CHRM3, ZNF678, CREB5, PPARGC1A, and ATF6) were identified that fulfilled the specified criteria. Of the 9 genes with both elevated copy number and expression, the expression was upregulated with a fold change >1.5 (Fig. 1). Only S100A2 and TCN1 of these genes have been shown to be associated with cancer. Overexpression of S100A2 has been reported to occur in several cancers, although it has not been reported in relation to prognosis in CRC (23–29). We therefore focused on S100A2 in the subsequent analyses.
S100A2 mRNA and protein expression
Quantitative RT-PCR analysis of stage II and III CRC tissue from 50 patients showed that expression of S100A2 mRNA is significantly higher in cancerous tissue than in neighboring non-neoplastic tissue (data not shown). The cellular localization of the S100A2 protein was investigated using cancer tissue from the same 50 CRC patients that were analyzed using RT-PCR. IHC indicated that the S100A2 protein is localized in the cytoplasm of CRC cells, whereas staining for the S100A2 protein in normal epithelial cells adjacent to the cancer cells was negative or weak. There was no difference in staining intensity of the invasive tumor front and the marginal tissue of the tumor. As a result of these data, we then estimated the extent and intensity of S100A2 staining in sections containing the area of infiltration in stage II and III CRC tissue samples from 161 patients. As shown in Fig. 2, in the cytoplasm of cancer cells, staining was observed in three patterns, strong (Fig. 2A), moderate (Fig. 2B) and weak (Fig. 2C).
Relationship between the expression of S100A2 and patient characteristics
For statistical evaluation purposes, the 161 samples that underwent IHC were divided into 2 groups: a high-expression group (strong staining spread of >10% or moderate staining spread of >70%, n=91) and a low-expression group (the others, n=70).
The correlation between S100A2 expression and various clinicopathological factors in 161 patients with stage II or III CRC is shown in Table I. Location (rectum; P=0.001), venous invasion (positive; P=0.011), and the presence or absence of recurrence (recurrence group; P<0.001) were significantly associated with overexpression of S100A2.
Table IRelationship between clinicopathologic variables and S100A2 expression in patients with stage II–III colorectal cancer. |
S100A2 expression and prognosis of stage II and III patients
The relapse-free survival (RFS) rate was significantly lower (P<0.001) in the S100A2 high-expression group than in the low-expression group (Fig. 3A). Univariate analysis indicated that gender (P=0.024), histology (P=0.022), location (P=0.003), lymphatic invasion (P=0.024), lymph node metastasis (P=0.007), CEA level (5.0 ng/ml or higher; P=0.003), and S100A2 expression (P<0.001) were significantly associated with RFS. Multivariate analysis indicated that overexpression of S100A2 is a significant prognostic factor of RFS for Stage II or III CRC patients (P=0.007; relative risk (RR) = 2.726; 95% confidence interval, 1318–5.638) (Table II).
Table IIUnivariate and multivariate analysis of clinicopathologic factors affecting relapse-free survival in patients with stage II–III colorectal cancer. |
Likewise, the overall survival (OS) rate was significantly lower (P<0.001) in the S100A2 high-expression group than in the low-expression group (Fig. 3B). Univariate analysis indicated that gender (P=0.007), histology (P=0.005), location (P=0.009), tumor depth (P=0.007), lymphatic invasion (P=0.013), lymph node metastasis (P=0.024), CEA level (P=0.004), and S100A2 expression (P<0.001) were significantly associated with OS. Multivariate analysis indicated that S100A2 overexpression is an independent and significant prognostic factor of OS for patients with stage II or III CRC (P=0.008; RR=3.941; 95% confidence interval, 1.434–10.830) (Table III).
Table IIIUnivariate and multivariate analysis of clinicopathologic factors affecting overall survival in patients with stage II–III colorectal cancer. |
Fig. 3C shows RFS curves stratified by TNM-7th stage and S100A2 expression level group. RFS curves were significantly separated by S100A2 expression level group in both stage II and III patients. RFS was significantly worse in the S100A2 high expression group than in the S100A2 low expression group in stage II (P=0.041) as well as in stage III (P<0.001) patients.
Discussion
This study is the first to demonstrate the prognostic significance of intratumoral S100A2 expression in clinical tissue samples of CRC. S100A2 was identified as a recurrence-related gene in the combined analysis of gene expression and copy number, and high S100A2 expression was an independent and significant prognostic factor of distant recurrence after curative surgery for stage II or III CRC.
The S100A2 gene is located on chromosome 1q.21. The human S100A2 is a member of the S100 family. It has been suggested that this family promotes tumor progression and metastasis by regulating the cell cycle, motility, and invasion in many human neoplasms (30–34). S100A2 is an EF-hand calcium-binding protein that regulates protein phosphorylation, cytoskeletal components, and calcium homeostasis both inside and outside of cells (30,35). The S100A2 protein, which is found in the cytoplasm and nucleus of epithelial cells including those in the esophagus and colon, is involved in TGF-β signaling (36,37). It has also been suggested to play a role in the cell cycle regulation of p53. S100A2 protein overexpression has been reported in lung cancer, brain cancer, and several types of gastroenterological cancers such as pancreatic and esophageal cancers (35). The S100A2 protein is reported to be involved in the chemotactic activity of tumor cells in colon cancer (37,38). In addition, S100A2 knockdown has been reported to reduce TGF-β-induced cellular chemotaxis (37). In the present study, high expression of S100A2 was significantly related with recurrence with distant metastasis. Our results support the idea that S100A2 might play an important role in distant metastasis of CRC. Further studies are warranted to investigate the roles and functions of S100A2 in CRC.
At present, recurrence risk and prognosis are predicted largely based on pathological tumor staging (TNM classification). The usefulness of postoperative adjuvant chemotherapy for stage II CRC has not been established yet, and it is recommended that determination of whether or not to use adjuvant chemotherapy should be based on the recurrence risk predicted for each patient. Major western guidelines, such as the National Comprehensive Cancer Network of Clinical Practice Guidelines in Oncology, recommend adjuvant chemotherapy when patients have risk factors including T4 lesions, less than 12 lymph nodes examined, perforation, poorly differentiated histology, and lymphovascular involvement. Our results suggested that stage II CRC patients with high S100A2 expression might be candidates for adjuvant chemotherapy as high-risk patients of distant recurrence. For stage III CRC, postoperative chemotherapy is recommended without exception. However, when stage III is further divided into IIIA, IIIB, and IIIC according to the TNM-7th classification and each sub-stage is considered separately, it has been reported that an additive effect of oxaliplatin cannot be anticipated in stage IIIA patients, and that stratification of the recurrence risk is important for stage III colon cancer as is the case for stage II CRC (39,40). Our study suggests that stage III CRC patients with high S100A2 expression may require strong adjuvant chemotherapy. Validation of the usefulness of risk-guided treatment is important in postoperative chemotherapy for stage II and III CRC and further clinical studies need to be conducted.
In conclusion, this study demonstrated that S100A was expressed at a significantly increased level in the CRC recurrence group. In our screen, we focused on highly expressed genes, because we intended to use the identified gene, S100A2, as a blood biomarker in actual clinical practice. The results of our study suggest the potential of the S100A protein as a recurrence-predicting factor and a target for molecular-targeted drugs for CRC.
Acknowledgements
The authors thank Y. Takagi and M. Itoda for excellent technical assistance.
References
Ricchi P, Zarrilli R, Di Palma A and Acquaviva AM: Nonsteroidal anti-inflammatory drugs in colorectal cancer: From prevention to therapy. Br J Cancer. 88:803–807. 2003. View Article : Google Scholar : PubMed/NCBI | |
Tsukuma H, Ajiki W and Oshima A: Cancer incidence in Japan. Gan To Kagaku Ryoho. 31:840–846. 2004.In Japanese. PubMed/NCBI | |
Matsuda T, Marugame T, Kamo K, Katanoda K, Ajiki W and Sobue T; Japan Cancer Surveillance Research Group. Cancer incidence and incidence rates in Japan in 2004: Based on data from 14 population-based cancer registries in the Monitoring of Cancer Incidence in Japan (MCIJ) Project. Jpn J Clin Oncol. 40:1192–1200. 2010. View Article : Google Scholar : PubMed/NCBI | |
Kobayashi H, Mochizuki H, Sugihara K, Morita T, Kotake K, Teramoto T, Kameoka S, Saito Y, Takahashi K, Hase K, et al: Characteristics of recurrence and surveillance tools after curative resection for colorectal cancer: A multicenter study. Surgery. 141:67–75. 2007. View Article : Google Scholar | |
Weitz J, Koch M, Debus J, Höhler T, Galle PR and Büchler MW: Colorectal cancer. Lancet. 365:153–165. 2005. View Article : Google Scholar : PubMed/NCBI | |
Ross JS, Torres-Mora J, Wagle N, Jennings TA and Jones DM: Biomarker-based prediction of response to therapy for colorectal cancer: Current perspective. Am J Clin Pathol. 134:478–490. 2010. View Article : Google Scholar : PubMed/NCBI | |
Wang Y, Jatkoe T, Zhang Y, Mutch MG, Talantov D, Jiang J, McLeod HL and Atkins D: Gene expression profiles and molecular markers to predict recurrence of Dukes' B colon cancer. J Clin Oncol. 22:1564–1571. 2004. View Article : Google Scholar : PubMed/NCBI | |
Shih W, Chetty R and Tsao MS: Expression profiling by microarrays in colorectal cancer (Review). Oncol Rep. 13:517–524. 2005.PubMed/NCBI | |
Nannini M, Pantaleo MA, Maleddu A, Astolfi A, Formica S and Biasco G: Gene expression profiling in colorectal cancer using microarray technologies: Results and perspectives. Cancer Treat Rev. 35:201–209. 2009. View Article : Google Scholar | |
Aragane H, Sakakura C, Nakanishi M, Yasuoka R, Fujita Y, Taniguchi H, Hagiwara A, Yamaguchi T, Abe T, Inazawa J, et al: Chromosomal aberrations in colorectal cancers and liver metastases analyzed by comparative genomic hybridization. Int J Cancer. 94:623–629. 2001. View Article : Google Scholar : PubMed/NCBI | |
Kurashina K, Yamashita Y, Ueno T, Koinuma K, Ohashi J, Horie H, Miyakura Y, Hamada T, Haruta H, Hatanaka H, et al: Chromosome copy number analysis in screening for prognosis-related genomic regions in colorectal carcinoma. Cancer Sci. 99:1835–1840. 2008. View Article : Google Scholar : PubMed/NCBI | |
Nakao M, Kawauchi S, Furuya T, Uchiyama T, Adachi J, Okada T, Ikemoto K, Oga A and Sasaki K: Identification of DNA copy number aberrations associated with metastases of colorectal cancer using array CGH profiles. Cancer Genet Cytogenet. 188:70–76. 2009. View Article : Google Scholar | |
Yamamoto S, Midorikawa Y, Morikawa T, Nishimura Y, Sakamoto H, Ishikawa S, Akagi K and Aburatani H: Identification of chromosomal aberrations of metastatic potential in colorectal carcinoma. Genes Chromosomes Cancer. 49:487–496. 2010.PubMed/NCBI | |
Yau C and Holmes CC: CNV discovery using SNP genotyping arrays. Cytogenet Genome Res. 123:307–312. 2008. View Article : Google Scholar : PubMed/NCBI | |
Phillips JL, Hayward SW, Wang Y, Vasselli J, Pavlovich C, Padilla-Nash H, Pezullo JR, Ghadimi BM, Grossfeld GD, Rivera A, et al: The consequences of chromosomal aneuploidy on gene expression profiles in a cell line model for prostate carcinogenesis. Cancer Res. 61:8143–8149. 2001.PubMed/NCBI | |
Hyman E, Kauraniemi P, Hautaniemi S, Wolf M, Mousses S, Rozenblum E, Ringnér M, Sauter G, Monni O, Elkahloun A, et al: Impact of DNA amplification on gene expression patterns in breast cancer. Cancer Res. 62:6240–6245. 2002.PubMed/NCBI | |
Pollack JR, Sørlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Børresen-Dale AL and Brown PO: Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 99:12963–12968. 2002. View Article : Google Scholar : PubMed/NCBI | |
Tsafrir D, Bacolod M, Selvanayagam Z, Tsafrir I, Shia J, Zeng Z, Liu H, Krier C, Stengel RF, Barany F, et al: Relationship of gene expression and chromosomal abnormalities in colorectal cancer. Cancer Res. 66:2129–2137. 2006. View Article : Google Scholar : PubMed/NCBI | |
Ramakrishna M, Williams LH, Boyle SE, Bearfoot JL, Sridhar A, Speed TP, Gorringe KL and Campbell IG: Identification of candidate growth promoting genes in ovarian cancer through integrated copy number and expression analysis. PLoS One. 5:e99832010. View Article : Google Scholar : PubMed/NCBI | |
Nigro JM, Misra A, Zhang L, Smirnov I, Colman H, Griffin C, Ozburn N, Chen M, Pan E, Koul D, et al: Integrated array-comparative genomic hybridization and expression array profiles identify clinically relevant molecular subtypes of glioblastoma. Cancer Res. 65:1678–1686. 2005. View Article : Google Scholar : PubMed/NCBI | |
Cardoso J, Boer J, Morreau H and Fodde R: Expression and genomic profiling of colorectal cancer. Biochim Biophys Acta. 1775:103–137. 2007. | |
Yoshida T, Kobayashi T, Itoda M, Muto T, Miyaguchi K, Mogushi K, Shoji S, Shimokawa K, Iida S, Uetake H, et al: Clinical omics analysis of colorectal cancer incorporating copy number aberrations and gene expression data. Cancer Inform. 9:147–161. 2010.PubMed/NCBI | |
Pedrocchi M, Schäfer BW, Mueller H, Eppenberger U and Heizmann CW: Expression of Ca(2+)-binding proteins of the S100 family in malignant human breast-cancer cell lines and biopsy samples. Int J Cancer. 57:684–690. 1994. View Article : Google Scholar : PubMed/NCBI | |
Böni R, Heizmann CW, Doguoglu A, Ilg EC, Schäfer BW, Dummer R and Burg G: Ca(2+)-binding proteins S100A6 and S100B in primary cutaneous melanoma. J Cutan Pathol. 24:76–80. 1997. View Article : Google Scholar : PubMed/NCBI | |
Lauriola L, Michetti F, Maggiano N, Galli J, Cadoni G, Schäfer BW, Heizmann CW and Ranelletti FO: Prognostic significance of the Ca(2+) binding protein S100A2 in laryngeal squamous-cell carcinoma. Int J Cancer. 89:345–349. 2000. View Article : Google Scholar : PubMed/NCBI | |
Feng G, Xu X, Youssef EM and Lotan R: Diminished expression of S100A2, a putative tumor suppressor, at early stage of human lung carcinogenesis. Cancer Res. 61:7999–8004. 2001.PubMed/NCBI | |
Gupta S, Hussain T, MacLennan GT, Fu P, Patel J and Mukhtar H: Differential expression of S100A2 and S100A4 during progression of human prostate adenocarcinoma. J Clin Oncol. 21:106–112. 2003. View Article : Google Scholar | |
Tsai ST, Jin YT, Tsai WC, Wang ST, Lin YC, Chang MT and Wu LW: S100A2, a potential marker for early recurrence in early-stage oral cancer. Oral Oncol. 41:349–357. 2005. View Article : Google Scholar : PubMed/NCBI | |
Simonsen K, Rode A, Nicoll A, Villadsen G, Espelund U, Lim L, Angus P, Arachchi N, Vilstrup H, Nexo E, et al: Vitamin B12 and its binding proteins in hepatocellular carcinoma and chronic liver diseases. Scand J Gastroenterol. 49:1096–1102. 2014. View Article : Google Scholar : PubMed/NCBI | |
Salama I, Malone PS, Mihaimeed F and Jones JL: A review of the S100 proteins in cancer. Eur J Surg Oncol. 34:357–364. 2008. View Article : Google Scholar | |
Haase-Kohn C, Wolf S, Lenk J and Pietzsch J: Copper-mediated cross-linking of S100A4, but not of S100A2, results in proinflammatory effects in melanoma cells. Biochem Biophys Res Commun. 413:494–498. 2011. View Article : Google Scholar : PubMed/NCBI | |
Jamieson NB, Carter CR, McKay CJ and Oien KA: Tissue biomarkers for prognosis in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. Clin Cancer Res. 17:3316–3331. 2011. View Article : Google Scholar : PubMed/NCBI | |
McKiernan E, McDermott EW, Evoy D, Crown J and Duffy MJ: The role of S100 genes in breast cancer progression. Tumour Biol. 32:441–450. 2011. View Article : Google Scholar | |
Jin L, Shen Q, Ding S, Jiang W, Jiang L and Zhu X: Immunohistochemical expression of Annexin A2 and S100A proteins in patients with bulky stage IB-IIA cervical cancer treated with neoadjuvant chemotherapy. Gynecol Oncol. 126:140–146. 2012. View Article : Google Scholar : PubMed/NCBI | |
Wolf S, Haase-Kohn C and Pietzsch J: S100A2 in cancero-genesis: A friend or a foe? Amino Acids. 41:849–861. 2011. View Article : Google Scholar | |
Ranganathan P, Agrawal A, Bhushan R, Chavalmane AK, Kalathur RK, Takahashi T and Kondaiah P: Expression profiling of genes regulated by TGF-beta: Differential regulation in normal and tumour cells. BMC Genomics. 8:982007. View Article : Google Scholar : PubMed/NCBI | |
Naz S, Ranganathan P, Bodapati P, Shastry AH, Mishra LN and Kondaiah P: Regulation of S100A2 expression by TGF-β-induced MEK/ERK signalling and its role in cell migration/invasion. Biochem J. 447:81–91. 2012. View Article : Google Scholar : PubMed/NCBI | |
Giráldez MD, Lozano JJ, Cuatrecasas M, Alonso-Espinaco V, Maurel J, Mármol M, Hörndler C, Ortego J, Alonso V, Escudero P, et al: Gene-expression signature of tumor recurrence in patients with stage II and III colon cancer treated with 5-fluoruracil-based adjuvant chemotherapy. Int J Cancer. 132:1090–1097. 2013. View Article : Google Scholar | |
Haller DG, Tabernero J, Maroun J, de Braud F, Price T, Van Cutsem E, Hill M, Gilberg F, Rittweger K and Schmoll HJ: Capecitabine plus oxaliplatin compared with fluorouracil and folinic acid as adjuvant therapy for stage III colon cancer. J Clin Oncol. 29:1465–1471. 2011. View Article : Google Scholar : PubMed/NCBI | |
Gao P, Song YX, Wang ZN, Xu YY, Tong LL, Sun JX, Yu M and Xu HM: Is the prediction of prognosis not improved by the seventh edition of the TNM classification for colorectal cancer? Analysis of the surveillance, epidemiology, and end results (SEER) database. BMC Cancer. 13:1232013. View Article : Google Scholar : PubMed/NCBI |