Gene expression profiles of primary colorectal carcinomas, liver metastases, and carcinomatoses
© Kleivi et al; licensee BioMed Central Ltd. 2007
- Received: 07 September 2006
- Accepted: 03 January 2007
- Published: 03 January 2007
Despite the fact that metastases are the leading cause of colorectal cancer deaths, little is known about the underlying molecular changes in these advanced disease stages. Few have studied the overall gene expression levels in metastases from colorectal carcinomas, and so far, none has investigated the peritoneal carcinomatoses by use of DNA microarrays. Therefore, the aim of the present study is to investigate and compare the gene expression patterns of primary carcinomas (n = 18), liver metastases (n = 4), and carcinomatoses (n = 4), relative to normal samples from the large bowel.
Transcriptome profiles of colorectal cancer metastases independent of tumor site, as well as separate profiles associated with primary carcinomas, liver metastases, or peritoneal carcinomatoses, were assessed by use of Bayesian statistics. Gains of chromosome arm 5p are common in peritoneal carcinomatoses and several candidate genes (including PTGER4, SKP2, and ZNF622) mapping to this region were overexpressed in the tumors. Expression signatures stratified on TP53 mutation status were identified across all tumors regardless of stage. Furthermore, the gene expression levels for the in vivo tumors were compared with an in vitro model consisting of cell lines representing all three tumor stages established from one patient.
By statistical analysis of gene expression data from primary colorectal carcinomas, liver metastases, and carcinomatoses, we are able to identify genetic patterns associated with the different stages of tumorigenesis.
- Liver Metastasis
- Hierarchical Cluster Analysis
- Peritoneal Carcinomatosis
- Peritoneal Metastasis
- TP53 Mutation
Colorectal cancer (CRC) is the second most common cause of cancer related deaths in developed countries, including Norway [1, 2]. Despite the fact that metastases are the leading cause of colorectal cancer deaths, the majority of genetic studies of colorectal carcinogenesis have focused on changes found in primary carcinomas, and the knowledge about the underlying molecular changes in more advanced disease stages remain limited. To obtain insights to this process, identification of molecular key events that distinguish primary from metastatic tumors is important. DNA microarray technology has become powerful for whole-genome investigations . Recently, several reports have shown that results obtained by this technology can distinguish among subgroups of the same cancer tissue [4–7] as well as among different cancer types . Additionally, genetic profiles have been identified that predict patients' clinical outcome in cancers of the breast, lung, central nervous system, digestive system, and prostate [9–15]. Several studies has investigated the expression profile of primary colorectal carcinomas . However, only a few have investigated the gene profiles of lymph node and liver metastases derived from colorectal carcinomas [17–24], and so far none have studied metastasis to the peritoneal cavity by DNA microarrays. Whereas previous reports have focused only on the comparisons between normal mucosa and primary carcinomas, or primary carcinomas and metastases, we aimed to investigate the relationship between the primary carcinomas and metastases regardless of site, as well as the genetic patterns that might distinguish the different metastatic sites from each other. Therefore, we have analyzed the gene expression profiles of normal colon, primary carcinomas, liver metastases and peritoneal metastases, as well as an in vitro model of CRC progression by oligo microarrays, to compare the genetic patterns from the different stages of the colorectal tumorigenesis.
Gene expression pattern in metastases versus those of primary tumors
Dukes' stage a
TP53 mutation status b
ex8, c273, CGT→CAT, Arg→His
ex6, c190, CCT→CTT, Pro→Leu
ex6, 5 bp insertion; c216–217: GTG GTG to GTGgtggtGTG
ex7, c241–242, TCCTGC→TTCCGC, Ser-Cys→Phe-Arg
ex8, c272, CTG→ATG, Val→Met
ex8, c282, CGG→TGG, Arg→Trp
ex7, c244, GGC→GTC, Gly→Val
ex5, c132, AAG→AGG, Lys→Arg
ex7, c241, TCC→TC, 1 bp deletion
ex5, c175, CGC→CAC, Arg→His
Genes (n = 29) associated with colorectal carcinomatoses as compared to primary tumors and liver metastases.
Fold change liver
Fold change carcinomatoses
Fold change primary
Relative difference, carcinomatosis vs. primary
chromosome 1 open reading frame 41
solute carrier family 35, member F2
collagen, type XII, alpha 1
PCTAIRE protein kinase 1
ankyrin repeat and SOCS box-containing 12
brother of CDO
transmembrane protein 16A
metalloprotease related protein 1
thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-erb-a) oncogene homolog, avian)
hemoglobin, theta 1
connective tissue growth factor
vav 3 oncogene
EBNA1 binding protein 2
neuropilin (NRP) and tolloid (TLL)-like 2
myosin head domain containing 1
acyl-CoA synthetase long-chain family member 5
REC8-like 1 (yeast)
Zic family member 1 (odd-paired homolog, Drosophila)
chromosome condensation 1
DEP domain containing 7
malic enzyme 2, NAD(+)-dependent, mitochondrial
suppression of tumorigenicity 7 like
Genes (n = 20), located to chromosome arm 5p that are upregulated in carcinomatoses.
Fold change carcinomatoses
Fold change liver
Folde change primary
Fold change carcinomatoses as compared to liver and primary
Prostaglandin E receptor 4 (subtype EP4)
Hypothetical protein FLJ14054
Ribosomal protein L37
Brain abundant, membrane attached signal protein 1
Retinoic acid induced 14
Hyperpolarization activated cyclic nucleotide-gated potassium channel 1
Hypothetical protein FLJ11127
Zinc finger protein 622
F-box and leucine-rich repeat protein 7
Hypothetical protein FLJ21657
Solute carrier family 9 (sodium/hydrogen exchanger), isoform 3
Succinate dehydrogenase complex, subunit A, flavoprotein (Fp)
PDZ domain containing 3
Nipped-B homolog (Drosophila)
Membrane-associated RING-CH protein VI
Activated RNA polymerase II transcription cofactor 4
Hypothetical protein MGC5309
Nuclear RNase III Drosha
Hypothetical protein MGC26610
S-phase kinase-associated protein 2 (p45)
Expression profile stratified by TP53 mutation status
Altogether, ten of 26 tumors harbor TP53 mutation in exons 5–8 (seven of 18 primary carcinomas, two of four liver metastases, and one of four carcinomatoses; Table 1). In order to investigate the influence of the TP53 mutation status on the gene expression signatures, BAMarray analysis was performed on all tumors dependent on TP53 mutation status. A posterior variance between 0.90 and 1.13 were used, and the hundred most differentially expressed genes (with statistical significance) both in the tumors with TP53 mutation (absolute Z-cut ranging from 3.49 to 2.41) and from those with wild type TP53 were chosen (absolute Z-cut 3.64 to 2.24). Among these two hundred genes, 75 were expressed more than two-fold differently between the groups (27 genes with expression level above 3.0). Of these 33 genes were associated with tumors harboring TP53 mutation, and 42 genes with those without [see Additional file 2]. PCA and HCA were performed on the 75 genes chosen from BAM analysis, and both analyses show a clear tendency to discriminate the tumors with TP53 mutation from those without, independently of stage [see Additional file 3]. In the same manner, the mutant TP53 primary tumors (n = 7) have been analyzed versus the wild type TP53 primary tumors (n = 11), and the gene lists associated with either group is overlapping with the ones found for all tumors stratified by TP53 mutation status.
Cell line model
Comparisons of in vivo tumors with in vitro model
To address whether the cell lines derived from the different stages are representative models of in vivo tumors, we performed hierarchical cluster analysis on the primary carcinomas (n = 18), liver metastases (n = 4), and carcinomatoses (n = 4), based on the most dysregulated genes found associated with each cell line [see Additional file 4]. Three of the four liver metastases cluster close to each other, whereas the carcinomatoses are spread among the primary tumors.
Genes in common among in vivo tumors and in vitro cell lines.
Fold change IS1
Fold change IS2
Fold change IS3
connective tissue growth factor
vav 3 oncogene
neuropilin (NRP) and tolloid (TLL)-like 2
acyl-CoA synthetase long-chain family member 5
novel 58.3 KDA protein
malic enzyme 2, NAD(+)-dependent, mitochondrial
nitric oxide synthase 1 (neuronal)
lung type-I cell membrane-associated glycoprotein
cytochrome P450 4Z2 pseudogene
carcinoembryonic antigen-related cell adhesion molecule 7
leucine-rich repeat LGI family, member 4
taste receptor, type 2, member 13
collagen, type VI, alpha 1
nuclear receptor subfamily 4, group A, member 1
DnaJ (Hsp40) homolog, subfamily C, member 5 gamma
hypothetical protein MGC24103
protein inhibitor of activated STAT, 2
amnionless homolog (mouse)
zinc finger protein 213
solute carrier family 39 (zinc transporter), member 8
a disintegrin-like and metalloprotease (reprolysin type) with thrombospondin type 1 motif, 9
fibroblast growth factor 7 (keratinocyte growth factor)
CD36 antigen (collagen type I receptor, thrombospondin receptor)
hypothetical gene BC008967
vesicle-associated membrane protein 8 (endobrevin)
chromosome 2 open reading frame 23
proteolipid protein 1 (Pelizaeus-Merzbacher disease, spastic paraplegia 2, uncomplicated)
collectin sub-family member 12
ATP-binding cassette, sub-family A (ABC1), member 8
UDP-glucose ceramide glucosyltransferase-like 1
SH3 multiple domains 2
internexin neuronal intermediate filament protein, alpha
solute carrier family 37 (glycerol-3-phosphate transporter), member 2
receptor (calcitonin) activity modifying protein 1
hypothetical protein FLJ11383
When evaluating the genes associated with carcinomatosis from in vivo and in vitro (IS3) models, we found that 20 of the 29 genes defined from the in vivo data had the same type of alteration also in the cell line model (six of 29 genes were most dysregulated in IS3 compared to IS1 and IS2; Table 2). Among the upregulated genes on 5p in carcinomatoses (in vivo model), four genes showed the same type of alteration in the carcinomatosis cell line IS3 as compared to IS1 and IS2 (Table 3).
Several studies have investigated the expression profiles of human tumors taking advantage of the microarray technology, including some studies of primary colorectal carcinomas . Despite the fact that metastases are the leading cause of CRC deaths, few have investigated the expression profiles of metastases, and the reports published have focused on lymph nodes and liver metastases from CRC [19–24, 28, 29]. Using 22k oligo microarrays we have nearly doubled the number of DNA sequences studied compared to most previous publications investigating gene expression levels of CRC metastases [18–21, 24]. By comparing the genetic profile from different tumor stages of CRC, including primary tumors and two metastatic sites, liver and peritoneum, we were able to find potential genes associated with metastasis, which might play an important role in the metastatic process. By using Bayesian ANOVA for microarray , we were able to identify differentially expressed genes associated with the groups included. This method has its strengths when comparing more than two groups. Further statistical tools, such as HCA and PCA, visualize the differences in the gene expression between the different stages of CRC, as well as between the two metastatic sites, liver and the peritoneum (Figures 1 and 2). Tumors from the two metastatic sites reveal gene expression profiles more closely related to each other than to the primary carcinomas. We selected the primary samples in order to obtain a similar representation from the different topographical sites in colon and rectum, from patients from the intermediate clinical groups (Dukes' B and C). Thus, it seems reasonable to expect that the expression profiles of these are representative, supporting the findings of distinct profiles of the metastases.
A general gene expression pattern for metastases
HCA and PCA were used to visualize the different transcript levels of 89 genes in primary tumors and metastases. Forty genes in this expression profile were specific for the metastasis group [see Additional file 1], including several genes previously reported in relation to cancer metastasis. Interestingly, most of the genes have not previously been described in colorectal metastases, and the genes of particular interest are involved in processes like apoptosis and cell growth. Among the downregulated genes are CASP1, ELAC1, INCENP, ME2, and PLA2G2A. CASP1 has been shown to induce apoptosis, and disruption of apoptotic pathways is in general an important factor in tumor development, and downregulation of this gene has also previously been reported in primary CRCs . ELAC1, encoding an RNA processing enzyme, is located on the chromosome band 18q21, which chromosomal loss has previously been linked to poor prognosis in colorectal cancer . The ELAC1 locus was targeted in a 300 kb homozygous deletion in lung cancer, which also involved the ME2 gene . INCENP is required for correct chromosome segregation and cytokinesis during mitosis and complexes with Aurora B kinases . Inhibition of INCENP is associated with chromosome aneuploidy, and downregulation of this gene might be important in metastases. Mice lacking expression of PLA2G2A have revealed increased colonic polyposis, and although gene mutations is not reported, lack of expression and sequence losses from this locus (chromosome band 1p36) are found in human colorectal carcinomas . Interestingly, TM4SF1, a member of the transmembrane 4 superfamily, was upregulated in the metastases group. This antigen is known to be highly expressed in several cancer types, including CRC [22, 35], and increased level of TM4SF1 has been associated with development of metastases and poor clinical outcome in patients with lung cancer .
Genes differentially expressed between primary CRCs and normal tissue have been reported by several studies , but only few have shown the differences in expression profiles between primary tumor and lymph node- and liver metastases. By statistical analyses we found 49 genes associated with primary carcinomas as compared with both liver metastases and carcinomatoses [see Additional file 1]. Among the genes with increased expression were CDCA7, CXCL1, CXLC2, CXCL3, and LCN2. Cell division cycle associated 7, CDCA7, upregulated among the primary carcinomas, is suggested to be involved in neoplastic transformation as it acts as a direct Myc target gene . The chemokines CXCL1, CXCL2, and CXCL3 also called GRO oncogenes, are involved in angiogenesis, development, and homeostasis. Upregulation of CXCL1 [16, 21, 38–41] and CXCL3  has previously been observed in CRCs and other cancer types . LCN2 binds and transports small lipophilic molecules, and is involved in cell regulation . Additionally, LCN2 acts as a subunit of the MMP-9 that has been observed in increased levels in tumor cells in the transition from colonic adenomas to carcinomas . Among the downregulated genes in primary carcinomas were AKR1B10, CD36, and LMNB1. The expression of aldo-keto reductase (AKR1B10) and collagen receptor CD36 is highly reduced in the primary group, and is previously reported downregulated in CRCs . LMNB1 belongs to the lamin family, where the proteins are involved in nuclear stability, chromatin structure and gene expression. Reduced expression have been seen in several cancer types, including CRC .
Genes associated with liver metastases
By using BAMarray on expression profiles of liver metastases, in comparison with primary carcinomas and carcinomatoses, we identified the most statistically significant genes associated with liver metastases (Figure 2B). These genes might play a significant role in the metastasis to the liver. Several interesting genes were found downregulated, such as ADAMTS9 and COL6A1 in the liver metastasis group. ADAMTS9, a thrombospondin metalloproteinase, is a member of the ADAM-TS family, which controls organ shape during development, inhibit angiogenesis, and are implicated in cancer [48, 49]. Recently, we have found another gene in the same family, ADAMTS1, to be a novel candidate for epigenetic inactivation by promoter hypermethylation in colorectal carcinomas . COL6A1 belongs to a collagen family, and are previously reported upregulated in metastases from medulloblastoma and cancers of the breast and prostate . Carcinoembryonic antigen-related cell adhesion molecule 7 (CEACAM7) is expressed in normal colon, but reported downregulated in adenomas and colorectal carcinomas [42, 51]. Controversially, we found CEACAM7 upregulated in the liver metastases, suggesting another function in the metastatic tumors. Another gene with increased expression in liver metastases of particular interest was PIAS2. Protein inhibitor of activated STAT2 (PIAS2) is a transcription factor controlling cell cycle arrest after DNA damage through various cellular pathways , such as STAT-, MYC- and TP53 pathways, as transcriptional coregulators [53, 54]. The conflicting RT-PCR and microarray data for PIAS2 may be due to their targeting of different mRNA splice variants. The PIAS2 microarray probe targets the exon-exon junction 12–13, whereas the RT-PCR primers target the exon-exon junction 5–6 of the transcript.
Genes associated with peritoneal carcinomatoses
To our knowledge, only one molecular genetic study has previously been performed on carcinomatoses from colorectal cancer , and for the first time, carcinomatoses are investigated at the gene expression level. By using Bayesian ANOVA statistics we identified a gene pattern associated with carcinomatoses (Table 2, Figure 2). Of the 29 genes expressed above two-fold in the carcinomatosis group compared to primary carcinomas and liver metastases, several of the genes found were of interest in relation to cancer biology, such as the upregulation of DKFZp564I1922 (alias adlican), and CTGF, and the reduced expression of CCNE1, CHC1, and MYOHD1. The gene encoding the hypothetical protein adlican is previously seen highly expressed in colorectal cancer compared to normal tissue . Expression studies of primary CRCs have observed dysregulation of several collagens [16, 40, 55–57]. CTGF is a connective tissue growth factor that promotes proliferation, and seems to play an important role in the metastatic process, as this gene has been associated with tumor progression in several types of cancer [58–61]. However, the expression of CTGF seems to play a varying role in several cancer metastases, as expression of this gene is also reported as a factor for better prognosis by suppression of tumor growth . CCNE1 is an important component in the cell cycle regulation, and as a target in the carcinogenesis, overexpression over cyclin E has been observed in several tumor types [63–65]. However, decrease of CCNE1 from primary colorectal carcinomas to liver metastases is seen, and reduction of cyclin E in primary carcinomas is associated with poor prognosis and metastasis to the peritoneum . This is in line with our observation, as CCNE1 showed a reduced expression level in peritoneal carcinomatoses compared to primary tumors. CHC1 is located at chromosome band 1p36 that is commonly deleted in CRC . It binds to chromatin and is involved in the regulation of onset of chromosome condensation , thus reduced expression of this gene might lead to failure in the chromosome segregation. Several myosin genes are previously associated with metastasis , and interestingly, myosin head domain (MYOHD1) is found dysregulated in carcinomatoses and liver metastases in the present dataset.
By using genomic profiling techniques on different stages of the CRC progression, we have previously identified gain of 5p by DNA copy number alterations to be specific for the metastatic process to peritoneal cavity [26, 27]. In this chromosomal region we found 20 genes upregulated in carcinomatoses as compared to the other stages (more than two-fold; Table 3), including FBXL7, PTGER4, SKP2, and ZNF622.
TP53 gene profile
By using BAMarray, we distinguished the expression pattern of the tumors according to their TP53 mutation status. Mutations in TP53 are one of the most frequently encountered genetic alterations in human solid tumors. More than half of all primary CRCs carry a mutation within this gene, and inactivation of TP53 is believed to play a central role in the genetic tumor progression model . Interestingly, there seem to be differences in the genetic pattern in tumors revealing mutation from those with wild type TP53 across the tumor stages [see Additional files 2 and 3], supporting the importance of TP53 mutation independent of CRC stage. Additionally, the same pattern is observed in the primary colorectal carcinomas. A similar pattern has been observed in breast carcinomas as tumors with TP53 mutation show a different gene expression profile than those without . Taken together, these observations suggested that inactivation of TP53, indirectly or directly, leads to altered expression of the downstream genes.
Comparison of in vitro models with in vivo tumors
The gene expression variations in the cell line model representing three different tumor stages: primary carcinomas, liver metastasis, and peritoneal metastasis from the same patient, provide clues to the understanding of the cancer progression process (Figure 4) . We arranged the solid tumors by hierarchical clustering based on genes derived from the cell line model [see Additional file 4]. The in vivo tumors are on the dendrogram partly positioned into correct stages, but not as successfully as by using the genes derived from the in vi vo tumors themselves (Figure 2). Comparisons of the genetic patterns derived from analyses of the in vivo tumors with corresponding expression patterns from the cell line model reveal analogous expression changes of many genes, and thus strengthen our findings in the solid tumors (Tables 2, 3, and 4). However, the relationship between cell lines and in vivo tumors based on gene expression should be handled with caution. Comparisons of gene expression patterns in cell lines compared to their corresponding tumor tissue reveal similarities, and cell lines are thought to reflect the molecular signatures of the tissue from which the cell lines originated. Nevertheless, it has been shown that clustering algorithms separate cell lines from the in vivo tumors of the same cancer disease [71, 72].
By studying the gene expression of primary colorectal carcinomas, liver metastases and carcinomatoses, we were able to identify genetic patterns associated with each of the different stages. We emphasize the importance of the genetic profiles, where the combination of several genes is the key feature that is associated with the different stages of CRC. Several interesting candidate genes representing potentially therapeutic targets are found in the present data set. Validation of gene expression signatures in larger series needs to be performed to improve the understanding of the metastatic process of CRC further.
Altogether, 29 tissue samples were included in this study; three of these were from normal colon, eighteen primary colorectal carcinomas (14 Dukes' B and four Dukes' C; 8 from the right side of colon, 5 from the left side, and 5 from rectum), four liver metastases, and four peritoneal metastases (carcinomatoses). In addition, as an in vitro model for cancer progression, three cell lines derived from tumor samples of the same patient were included (Table 1). These were Isreco1 (IS1) from a primary carcinoma, Isreco2 (IS2) from a liver metastasis, and Isreco3 (IS3) from a peritoneal metastasis [27, 73]. The cell lines were kindly provided by Richard Hamelin, INSERM, Paris, France. The normal colon samples from three patients with colorectal cancer were taken in a distance from the tumor sites. Microscopic evaluation of tissue sections stained by haematoxylin and eosin confirmed that the normal samples did not contain any tumor cells. For the primary carcinomas the median age at diagnosis was 75.5 years (range 58 – 88 years), and the median survival time for these patients was 116 months (range 13 – 147 months). The median age for patients with liver metastases was 71 years (range 55 – 75) with a median survival of 27 months (range 11 – 93). The median age for patients with carcinomatoses was 64.5 years (range 40 – 72) with a median survival at 28 months (range 19 – 65). The series consisted of 8 females and 18 males. Frozen sections were taken from all samples prior to RNA extraction, haematoxylin and eosin stained, and examined by a pathologist. All tumors were confirmed carcinomas and visually estimated to contain at least 40% tumor cells; for primaries the median was 70% (range: 40–90%) for liver metastases the median was 55% (range: 50–60%), and for the carcinomatoses 80% (range: 60–80%). The samples are taken from a research bio-bank registered at the National Health Institute and the project is approved by The Norwegian Data Inspectorate according to the national legislation.
TP53 mutation status
DNA was extracted from tumor tissue pieces neighboring the ones used for RNA extraction (se below). All tumor samples were previously analyzed for TP53 mutations within exons 5–8 by screening for aberrantly migrating PCR fragments in constant denaturing gradient gel electrophoresis followed by identification of the specific mutations by direct sequencing (primary tumors, ; metastases, unpublished data).
Total RNA extraction
The tissues were ground in liquid nitrogen and homogenized with a pellet pestle motor in 1ml of Trizol (Invitrogen, Carlsbad, CA). 0.2 ml of chloroform was added and the samples were vigorously shaken for 20s, and then incubated at RT for 5 min. After centrifugation at 12,000 × g for 15 min, the aqueous phase was mixed with 0.5 ml isopropanol. The RNA was allowed to precipitate for 10 min and collected after centrifugation at 12,000 × g for 10 min at 4°C. The RNA pellet was washed with 75% ethanol, collected after a brief centrifugation, air dried, and re-suspended in H2O at 55°C in 10 min. The purified RNA was quantified by spectrophotometer (NanoDrop 1000, NanoDrop Technologies, Boston, MA), and the quality was evaluated by capillary electrophoresis (Agilent 2100 Bioanalyzer, Agilent Technologies, Palo Alto, CA).
For each of the test and reference samples, 20 μg total RNA was reversely transcribed using the Agilent direct-label cDNA synthesis kit (Agilent Technologies) according to the manufacturer's directions. As a common reference for all samples, we used the "Universal Human Reference RNA", containing mRNA from ten cancer cell lines (Stratagene, La Jolla, CA). cDNA was labeled with cyanine 5-dCTP for test samples and cyanine 3-dCTP for the common reference (PerkinElmer Life Science, Boston, MA), and was purified using QIAquick PCR Purification columns (Qiagen, Valencia, CA). The cDNA was suspended in hybridization buffer and hybridized to Agilent Human 1A v2 22 k oligo microarrays (Agilent Technologies) for 17 h at 60°C according to the Agilent protocol. The slides were scanned by a laser confocal scanner (Agilent Technologies).
Microarray data analyses
The image processing was performed with Agilent Feature Extraction 7.5 (Agilent Technologies). Local background subtraction and linear/LOWESS normalization were performed. Semi-processed values were imported into BASE (BioArray Software Environment;  customized for Agilent microarrays by the Norwegian Microarray Consortium), where spots with inadequate measurements were flagged and ratios calculated. Oligonucleotide probes with inadequate measurements in more than five of the 29 tumor samples were excluded from the analyses. For further analyses, we used data corresponding to 18 264 unique gene bank accession numbers, represented by 16 553 unique gene symbols .
BAMarray 2.0 (Bayesian ANOVA Analyses of Variation of Microarrays)  was used with default settings for detecting differentially expressed genes between two or more groups. BAMarray uses shrinkage estimation combined with model averaging. This provides a good balance between false rejection (the total number of genes falsely identified as being differentially expressed) and false non-rejections (the total number of genes falsely identified as being non-differentially expressed). By combing Z-cut and posterior variances from Bayesian ANOVA for microarray, we are likely to identify the differentially expressed target genes. Missing values were estimated in J-Express Pro 2.6  with k-nearest neighbor imputation (k = 10). The most statistically significant genes associated with each group were reported with normal colon mucosa as the "baseline group".
Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were performed in J-Express Pro 2.6 . PCA reduces the dimensionality and detects structure in the relationships among variables (classify variables) . HCA by use of average-linkage and Euclidean distance similarity measure was used to arrange variables according to groups based on their similarity. Afterwards, the results were visualized in a dendrogram. For each gene, expression values in tumor samples were centered over the median expression of the normal colon epithelial tissues before clustering.
Quantitative real-time gene expression analyses
The mRNA expression of five potential target genes, CCNE1, ELAC1, INCENP, PIAS2, and TM4SF1, was measured by quantitative real-time fluorescence detection using TaqMan 7900 HT (Applied Biosystems, Foster City, CA). For each sample, cDNA was generated from five μg total RNA using a high capacity cDNA archive kit (Applied Biosystems) following the manufacturers' protocol. Ten ng cDNA was amplified for each gene using pre-designed assays (Hs00233356_m1, Hs00218846_m1, Hs00220336_m1, Hs00190699_m1, and Hs00371997_m1, respectively; Applied Biosystems). All samples were amplified in triplicates and the quantitative expression levels were measured against a standard curve generated from dilutions of cDNA from the human universal reference RNA (containing a mixture of RNA from ten different cell lines; Stratagene, CA). The median expression value of each sample was normalized against the average of the median of two endogenous controls, ACTB (4352935E; Applied Biosystems) and GUSB (4333767F; Applied Biosystems).
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