Laser Capture Microdissection with Genome-Wide

Cancer Investigation, Early Online:1–9, 2011 ISSN: 0735-7907 print / 1532-4192 online C Informa Healthcare USA, Inc. Copyright DOI: 10.3109/07357907.2011.633290

ORIGINAL ARTICLE

Laser Capture Microdissection with Genome-Wide Expression Profiling Displayed Gene Expression Signatures in Endometrioid Endometrial Cancer

Cancer Invest Downloaded from informahealthcare.com by Universiti Kebangsaan Malaysia on 11/29/11 For personal use only.

Norfilza Mohd. Mokhtar,1,2 Nurul Hanis Ramzi,1 Wong Yin-Ling,4 Isa Mohamed Rose,2 Ahmad Zailani Hatta Mohd Dali,3 and Rahman Jamal1 UKM Medical Molecular Biology Institute,1 Departments of Pathology, Faculty of Medicine,2 Obstetrics and Gynecology, Faculty of Medicine,3 Physiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia4 total hysterectomy with bilateral salpingo-oophorectomy, with or without retroperitoneal lymphadenectomy. Despite the well-established treatment, the recurrence is still high (4). Much of the ongoing research still focuses on identifying biomarkers for early detection, understanding the pathophysiology, and monitoring the course of treatments. There was a published report on endometrial carcinogenesis from a normal surface epithelium of the endometrium to nontypical hyperplasia involving germline mutation of MLH-2 and MLH-6 (5) genes. Among genetic alterations occurring in the early stage of EEC were silencing of phosphate and tensin homolog (PTEN), microsatellite instability, especially in the DNA repair genes, and mutation of K-ras and β-catenin (6–9). An animal model of endometrial cancer induced by diethylstilbestrol displayed significantly downregulation of PTEN in cancer cells compared with that in the noncancerous cells (10). Using gene expression microarrays, several studies have identified diverse signaling pathways in different histological subtypes of endometrial cancer (8, 11, 12). Research reports from our laboratory showed genes that were grouped in 10 different gene sets. These included ALK pathway, myogenesis pathway, integrin pathway, endothelial cell differentiation, histone deacetylase pathway, pyrimidine degradation pathway, B-cell differentiation, myogenin pathway, adipocyte differentiation, and neurological aging process (12). For this study, we have taken a step forward to combine the microarray with laser capture microdissection (LCM) to compare the gene expression between the tumor cells and the neighboring normal cells in the case of endometrial cancer. Among the challenges that scientists faced in gene expression studies were the scientific interpretation and reproducibility of their results (13). One of the obvious reasons for these was the use of nondissected complex tissues that may contain non-neoplastic cells, inflammatory cells, fibroblasts, and endothelial cells. This may mask the gene expression changes that are specific to cancer (14). Using an

This research determined genes contributing to the pathogenesis of endometrioid endometrial cancer (EEC). Eight pairs of microdissected EEC samples matched with normal glandular epithelium were analyzed using microarray. Unsupervised analysis identified 162 transcripts (58 up- and 104 down-regulated) that were differentially expressed (p < .01, fold change ≥ 1.5) between both groups. Quantitative real-time polymerase chain reaction (qPCR) validated the genes of interest: SLC7A5, SATB1, H19, and ZAK (p < .05). Pathway analysis revealed genes involved in acid amino transport, translation, and chromatin remodeling (p < .05). Laser capture microdissection (LCM) followed by microarray enabled precise assessment of homogeneous cell population and identified putative genes for endometrial carcinogenesis. Keywords: Endometrioid endometrial cancer; Laser capture microdissection; Microarray technology; Gene expression; Genomics

INTRODUCTION The incidence of the endometrial cancer cases, which has increased over the years, is attributable to the high usage of estrogen, high prevalence of obesity, smoking, nulliparity, and hypertension (1). In Malaysia, the age-specific incidence has been reported to be 5.3 per 100,000 individuals from 2003 till 2005. The number of cases showed increases above the age of 30, with the peak among 60–69 years old (2). This is different from the data from the National Cancer Institute, Bethesda, MD, where the age-adjusted incidence rate was 23.5 per 100,000 population and the cases were more common after 45 years of age (3). The commonest type, which is the endometrioid endometrial cancer (EEC) or type I, has an underlying endometrial hyperplasia, and low-grade and minimal myometrial invasion. EEC exhibits a stable clinical course and the established treatment of

Correspondence to: Norfilza Mohd. Mokhtar, PhD, UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, JalanYaacob Latif, Cheras 56000, Kuala Lumpur, Malaysia. email: [email protected]

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infrared laser, pure population of cells can be isolated from clinical samples. There have been a few reports describing LCM that combines with microarray application (11, 15–17). However, these studies did not compare the gene expression pattern of cancer cells with that of noncancerous cells obtained operatively from the same adjacent normal endometrial specimens. Thus, the main aim of this study is to provide an insight into significant gene profiling with the use of LCM technique using paired samples of EEC and noncancerous adjacent cells.


MATERIALS AND METHODS Clinical sample preparation Fresh frozen tissue specimens were obtained through informed consent at the time of total hysterectomy from confirmed diagnosis with EEC at the Universiti Kebangsaan Malaysia Medical Centre and Hospital, Kuala Lumpur, Malaysia. Based on the microarray sample size calculation with 95% confidence interval and 80% power of study, approximately n = 8 samples per group (EEC and normal cells) were used for the microarray study (http://bioinformatics. mdanderson.org/MicroarraySampleSize/). The cancer tissues with their respective neighboring noncancerous tissues were excised and stored in liquid nitrogen till further analysis. Meanwhile, for the microarray validation step, we used 18 EECs and eight normal endometrial tissues. All procedures followed the approved protocol from the Medical Research Ethics Committee (NMRR-07–598-870). Tissue sections were stained with H&E for confirmation of tissue quality. The postoperative diagnosis was based on the International Federation of Gynecology and Obstetrics (FIGO) standards and the World Health Organization (WHO). The age of patients with EEC and their corresponding patients with normal endometrial tissues was between 36 and 73 years (mean [SD]: 55.8 [±9.6] years). There was no statistical difference in age between tumor and control groups. In this set of endometrial cancers, 13 were in stage I and five in stage II. Out of the 18 endometrioid adenocarcinomas, 15 were of grade 1 and three of grade 2.

Laser capture microdissection (LCM) Each specimen was cut into 10–20 serial frozen sections with a thickness of 7 µm and mounted on a polyethylene (PEN) membrane glass slide (Arcturus Bioscience, Mountain View, CA) and stored at −80◦ C. The section was fixed in 70% ethanol for 2 min at −20◦ C and then stained with 25 µL HistoGene using a frozen section staining kit (Figure 1A–F). Then, it was rinsed with ice-cold RNA nuclease-free water at −20◦ C, incubated in xylene for 2 min at −20◦ C and airdried for 2 min. The targeted cells were microdissected using UV cutting and laser capture procedure by the Veritas LCM system (Arcturus Engineering, Mountain View, CA), as previously described (Virginia et. al, 2006). EEC cells (Figure 1A–C) and normal epithelial cells (Figure 1D–F) were cut and captured on CapSure Macro LCM cap (Arcturus Bioscience). The caps containing the captured cells were placed on the top of a microfuge tube (Applied Biosystem, Foster City, CA) that contained 100 µL of RLT lysis buffer (Qiagen, Valencia, CA) and 1% of β-mercaptoethanol (BioBasic, CA). The tube was then inverted, vortexed for 30 s and placed on ice at intervals. For the microarray experiment, 10 caps were used to collect for each tissue from 10–20 serial sections. All the microdissected tissues on the caps were pooled in 100 µL of RLT lysis buffer. RNA extraction and quality Total RNA was extracted according to the manufacturer’s protocol from the RNeasy Plus Micro kit (Qiagen) by adding 250 µL of lysis buffer. To remove genomic DNA, the provided gDNA eliminator column was used. The extracted RNA sample was then analyzed using the RNA 6000 Pico Assay employing RNA Pico LabChips (Agilent Technologies, Palo Alto, CA) to determine RNA integrity. The concentration and purity of the total RNA were determined using the NanoDrop ND-1000 Spectrophotometer (NanoDrop Technologies, Wilmington, DE). The RNA concentration obtained was between 0.8 ng and 12 ng, and thus required further amplification procedures. Samples with RNA integrity number (RIN) of more than 7 were considered suitable for the microarray analysis.

Figure 1. Separation of the endometrial cancer cells and adjacent normal endometrial cells by LCM. Sections were stained by HistoGene solution. A, B, and C are frozen sections from endometrioid endometrial cancer stage 1A (malignant epithelial cells before LCM, after LCM,and on LCM cap) respectively. D, E, and F are frozen sections of normal endometrium from the same patient (normal epithelial cells before LCM, after LCM, and on LCM cap). Cancer Investigation

Gene Signature in Endometrioid Endometrial Cancer 


Linear amplification and ST-cDNA preparation The total RNA was amplified using the WT-OvationTM Pico RNA amplification system (NuGEN Technologies Inc., San Carlos, CA) following the protocol provided. The synthesized cDNA was purified with the DNA Clean and ConcentratorTM -25 kit (Zymo Research, Orange, CA). Three micrograms of the synthesized probe was used to produce sense transcript–cDNA using the WT-OvationTM Exon Module and purified with the Zymo II columns (Zymo Research, Orange, CA). The purified cDNA probe was stored at –20◦ C prior to fragmentation and labeling. Fragmentation, labeling, and microarray hybridization Five-microgram cDNA probe was then labeled with the FL-OvationTM Biotin Module V2 (NuGEN Technologies). Targets for hybridization to the Affymetrix Gene 1.0 ST GeneChip (Affymetrix Inc., Santa Clara, CA) were made using the Affymetrix hybridization, wash, and stain kit. The reaction contained 25 µL of probe, 1.8 µL control oligonucleotide B2, 5.5 µL 20X eukaryotic hybridization controls (bioB, bioC, bioD, cre), 55µL 2X hybridization buffer, 11 µL 100% DMSO, and water to a final volume of 110 µL. The chip was hybridized with 80 µL of hybridization cocktail target at 45◦ C for 18 hours with rotation 60 psi. An automated washing and staining were done using the Affymetrix Fluidics Station 400 followed the protocol provided. The arrays were scanned with a confocal laser GeneChip scanner. The data were extracted using the Affymetrix GeneChip Operating Software (GCOS). Data analysis and comparison expression The intensity values of different probe sets generated by GCOS were imported into the GeneSpring GX 10.0.2 software (Agilent Technologies). The analysis of 16 samples of microdissected tumor cells versus normal endometrial epithelial cells was performed using paired t-test, fold change of ≥ 1.5 and p < .01 as filter criteria. An unsupervised analysis of these samples distinguished two major groups, cancer and noncancerous groups. Pathway studio software was used to correlate differences in the gene expression between EEC and noncancerous endometrium to functional pathways. Validation of the microarray data Quantitative real-time polymerase chain reaction (qPCR) was conducted in a duplex Taqman assay on a Rotor-Gene RG-6000 Real-Time Thermal Cycler (Corbett Research, Sydney, Australia). Total RNA was obtained from similar specimens for microarray as well as an independent set of EEC (n = 18) and noncancerous tissues (n = 8). The reaction consisted of 0.6 µl primers, 0.4 µM probe, 7.5 µl of SensiMix one step (Quantace Ltd, London, UK), 0.3 µl of RNAse inhibitor (Quantace Ltd, London, UK), 1 µg RNA template and Mili-Q water to a final volume of 15 µl. Thermal cycling conditions were reverse transcription step at 42◦ C for 30 min, enzyme activation step at 95◦ C for 10 min, followed by 45 cycles at 95◦ C for 15 s and 60◦ C for 1 min. A negative control without the RNA template was run in parallel to assess the overall specificity of the reaction. Primers and C 2011 Informa Healthcare USA, Inc. Copyright

probes were designed using the Beacon DesignerTM (Premier Biosoft International, Palo Alto, CA). Relative quantification of gene expression was performed using the comparative CT (cycle threshold) method. The number of target gene copies (SLC7A5, SATB1, ZAK, and H19) was normalized to the endogenous reference gene – β-actin. All assays were done in triplicate. The relative fold changes in gene expression between both samples were measured using the CT method. Student’s t-test was used to assess the statistical difference in the gene expression between the EEC and noncancerous groups. RESULTS Microdissection and expression arrays Gene expression profiles were obtained from eight sample pairs of microdissected EEC and noncancerous epithelial cells. Approximately 1.5 µm2 of the cell nests were cut and captured per section from 20 serial sections of noncancerous endometrial and EEC tissue samples. The endometrial tissues were stained with H&E stain to identify the areas for microdissection (Figure 1A and D) before LCM. Despite the laborious microdissection technique, we managed to obtain a total amount of RNA ranging between 0.8 ng and 12 ng from each sample with RNA integrity number of ≥ 7.0. Using the GeneSpring GX 10.0.2 software (Agilent Technologies), the Box–Whisker plot of the raw log ratios of all transcripts on all the 16 arrays displayed that the means of all distributions were equal to zero (data not shown). Data filtering identified 22,760 out of 28,869 probe sets possessing informative signal intensity values across all samples. To characterize the relationships between noncancerous endometrial cancer and EEC, unsupervised hierarchical clustering and principal component analysis were applied to all 22,760 probe sets. A dendrogram possessing two distinct arms separating EEC cells from normal endometrium was identified (Figure 2A). The principle component analysis clearly displayed that the EEC tissues clustered separately from their normal counterparts and showed significant differences in their gene expression (Figure 2B). A pair of samples, noncancerous 4 (N4) and tumor 4 (T4), was removed from further analysis because of its low-quality control measurement during preprocess data analysis. A paired t-test was performed to produce gene signatures specific for EEC. A list of 162 transcripts was differentially regulated based on fold changes of ≥ 1.5 fold and p value of < .01 in EEC cells versus normal endometrium. Among these genes, 55 were down-regulated, while 107 were up-regulated in EEC versus normal samples. The gene set enrichment analysis (GSEA) using a molecular signature database was used to assign pathways and biological process to the annotated transcripts. The gene sets were downloaded from the Broad Institute (http://www. broadinstitute.org/gsea/index.jsp). The q value < .4 showed three significant gene sets with 13 overlapped genes. All the gene set names were based on the original articles that published the involved genes (Table 1). The most significant gene set (p value = .00, q value = .28) was Chiang liver cancer subclass CTNNB1 up, which contained


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Figure 2. (A) Relative expression levels of genes in the endometrial samples represent in a hierarchical clustering or heatmap. Each horizontal line represents a single gene, and each column represents a single sample. (B) 3D principle component analysis (PCA) plot of the differentially expressed genes at p value < .01 with ≥1.5-fold change. Red circles represent EEC, while blue circles represent normal endometrium. The heat map was color-coded using red for up-regulation from EEC and green for down-regulation.

eight overlapped genes that were closely related to the Wnt-β ( wingless-type MMTV integration site family-β) catenin pathway. The second significant gene set was the Ehler aneuploidy up (p value = .00, q value = .22) and had a single gene that contributed to the chromosomal abnormality. The final significant gene set was Delys thyroid cancer down (p value = .016, q value = .39) and had four overlapped genes with different molecular pathways and biological processess. The q value is the adjusted p value for multiple hypothesis testing (false discovery rate) in the GeneSpring GX 10.0.2 analysis. Pathway analysis To identify signaling pathways that are associated with endometrial tumorigenesis, the filtered, informative 22,760

probe sets from our raw data set were subjected to the Pathway Studio program analysis (Ariadne Genomics, Rockville, MD). The program allowed us to perform statistical analysis as well as GSEA to view significant pathways or gene ontology (GO) involved in our data set. GSEA in Ariadne pathways revealed 44 pathways, which were significant at corrected p value < .05 (Benjamini false discovery rate) (Table 2). GO analysis required differential expression of > 1.5 fold relative to normal endometrium (p < .01). The top five discriminating pathways were insulin action (p = 1.32582e-006), cell cycle regulation (p = 1.34133e-006), Notch pathway (p = 6.34963e-006), B-cell activation (p = .000320984) and CCR1 → STAT signaling (p = .000342322). Four genes were randomly chosen for the validation experiment based on the analysis data using the Partek software and

Table 1. Gene Set Enrichment Analysis with Corrected p Value < .05 (Benjamini False Discovery Rate) Using the GeneSpring and Pathway Studio 6.2 Software No 1

2 3

Gene set Chiang liver cancer subclass CTNNB1 up

Ehlers aneuploidy up Delys thyroid cancer down

Gene symbol

Transcript cluster ID

SNAI2

8150698

KIAA0888 PDK1 AXIN2 TBX3 DNAJC12 SLC16A1

8112649 8046408 8017718 7966690 7933933 7918622

GSTM4—GSTM2

7903742

GLTSCR2 IGF2 SLC38A1 WASF3 OGN

7986323 7937772 7962516 7968212 8162373

Gene description

Fold change −2.1

Snail homolog 2 (Drosophila)

Pyruvate dehydrogenase kinase, isozyme 1 AXIN 2 (conductin, axil) T-box 3 (ulnar mammary syndrome) DnaJ (Hsp40) homolog, subfamily C, member 12 Solute carrier family 16, member 1 (monocarboxylic acid transporter 1) Glutathione S-transferase M4 — glutathione S-transferase M2 (muscle) Glioma tumor suppressor candidate region gene 2 Insulin-like growth factor 2 (somatomedin A) Solute carrier family 38, member 1 WAS protein family, member 3 Osteoglycin

−1.8 −1.6 −1.7 −1.8 −1.5 −1.6 −1.5 −1.6 −6.0 3.2 −1.8 −5.6

Cancer Investigation

Gene Signature in Endometrioid Endometrial Cancer  Table 2. Ariadne Signaling Pathways Involved in Endometrioid Endometrial Cancer Versus Normal Group at Corrected p Value < .05 (Benjamini False Discovery Rate)


Name Insulin action Cell cycle regulation Notch pathway B-cell activation CCR1 → STAT signaling Focal adhesion regulation Actin cytoskeleton regulation IGF1R → STAT signaling TGFBR → SMAD1/5/9 signaling IL4R → ELK-SRF/HMGY signaling EphrinR → actin signaling IL31R → STAT signaling GFR → AP-1/CREB/CREBBP/ELK-SRF/MYC signaling KIT → STAT signaling UrokinaseR → STAT signaling IL8R → CREB/EGR signaling GFR → NCOR2 signaling CXCR4 → STAT signaling GFR → FOXO3A signaling Adherens junction regulation IL7R → STAT signaling IL5R → STAT signaling HGFR → STAT signaling UrokinaseR → ELK-SRF signaling EDG2 → ELK-SRF signaling IL2R → STAT signaling PDGFR → STAT signaling T-cell receptor → ATF/CREB signaling pathway IGF1R → ELK-SRF/HIF1A/MYC/SREBF signaling Tight junction regulation OncostatinR → STAT3 signaling ALK → STAT signaling IL6R → STAT signaling IL2R → ELK-SRF/MYC signaling InsulinR → ELK-SRF/SREBF signaling ProlactinR → STAT signaling CD19 → AP-1/ELK-SRF signaling CNTFR → STAT3 signaling IFNGR → STAT signaling Axon guidance T-cell receptor → AP-1 signaling ProstaglandinFR → ATF1/ELK-SRF/CREB signaling

No. of entities 50 135 40 41 16 41 51 9 7 27 15 8 50 6 7 33 27 9 7 41 9 9 8 30 33 10 9 49 23 5 6 6 8 24 25 10 29 9 8 58 44 28

our recent study using bulk tissue (12). Figure 3 contains four differentially regulated genes encoding proteins (SLC7A5, SATB1, ZAK, and H19) that were linked to amino acid transport, translation, and chromatin remodeling in EEC. qPCR validation assay The results of the microarray profiling study were validated using an independent set of EEC and noncancerous specimens. qPCR was chosen as the technique to confirm the relative expression of genes between EEC and normal tissues. The validation technique was done on four different target genes: SLC7A5, SATB1, ZAK, and H19. These genes were randomly selected from the significant gene list that were found to be differentially expressed between the EEC cells and the normal endometrial cells. Of the 162 transcripts that were specific for microdissected EEC, the SLC7A5 gene was found to be overexpressed and the SATB1, ZAK, and C 2011 Informa Healthcare USA, Inc. Copyright

Expanded no. of entities 909 2,168 1,494 1,040 16 309 541 9 7 59 217 8 157 6 7 33 131 9 95 693 9 9 8 58 78 10 9 71 46 69 6 6 8 52 53 10 57 9 8 1,056 62 47

No. of measured entities 788 1,908 1,223 895 11 271 472 7 6 55 193 5 141 5 5 21 115 8 84 548 6 6 7 51 68 8 9 58 43 57 6 6 7 48 50 7 49 8 6 880 51 41

p value 1.32582e-006 1.34133e-006 6.34963e-006 0.000320984 0.000342322 0.000454382 0.000732278 0.00147498 0.00211923 0.00310409 0.00313112 0.00622701 0.00772019 0.00774892 0.00874811 0.0103705 0.0136817 0.0151998 0.0162535 0.0168243 0.0190438 0.0218149 0.0245894 0.0253725 0.0258002 0.0259856 0.026913 0.0332217 0.0353005 0.0365155 0.0392074 0.0405068 0.040901 0.0416184 0.042911 0.043315 0.0453526 0.0454374 0.0458234 0.0477644 0.0496132 0.0497258

H19 were underexpressed. From the array, SLC7A5 was significantly up-regulated (p < .01) with 2.49-fold change in EEC cells when compared with noncancerous endometrium. The qPCR confirmed overexpression of SLC7A5 (3.27-fold change) in EEC samples relative to the corresponding noncancerous endometrial samples (p < .01) (Figure 4A). SATB1 (special AT-rich sequence binding protein 1) was noted to be significantly down-regulated by 3.95-fold change (p < .05) in the EEC cells from the microarray data. The qPCR confirmed this finding in which the transcript level of SATB1 was lower by 7.69 fold in the EEC samples compared with noncancerous endometrial samples (p < .05) (Figure 4B). The third and fourth validated genes, ZAK and H19, showed 2.38-fold and 5.69-fold change, respectively, in the EEC cells compared with noncancerous cells from the microarray data. The qPCR showed both genes were downregulated in the same pattern as in the microarray data.


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Figure 3. Pathway analysis of differentially expressed genes identified in endometrial endometrioid cancer compared with normal endometrium. Pathway diagram was generated using the Pathway Studio software overlaid with gene expression data. Genes included in the analysis were required to have a 1.5-fold change relative to normal endometrium (p < .01).

DISCUSSION This study represented an assessment of differential gene expression in endometrioid endometrial tumors, involving the whole-genome Affymetrix Human Gene 1.0 ST microarray. The strength of the study was in the use of LCM followed by microarray analysis. We believe that the resulting high-purity of cancer tissues and corresponding tissues captured by LCM allowed a more accurate comparison of gene expression between the diseased and the control group. This fact is clearly shown in the analysis of microarray data, in which hierarchical clustering separated the data from the microdissected 16 samples into two distinct groups, EEC and normal tissues. A gene signature consisting of 162 transcripts was able to discriminate between these groups at fold change ≥ 1.5 fold and p value < .01 in EEC cells versus normal endometrium. Out of these 162 genes, 55 were noted to be down-regulated, while 107 were up-regulated in EEC versus normal samples. The results of the present study revealed less number of genes than our previous data using whole tissues (12). The key difference in the present study was the use of LCM to obtain the pure population of cells before the microarray analysis. Our main aim was to determine genes that were involved in EEC in the microdissected EEC cells in comparison with the microdissected normal endometrial cells with the application of gene expression profiling. Some of these genes differentially expressed included oncogenes and tumor suppressor genes that may be related to the establishment of EEC.

For example, insulin-like growth factor-2 (IGF2) was found to be down-regulated. The polymorphism in the IGF2 gene has been postulated to influence the endometrial cancer risk (18). Another example is osteoglycin (OGN),which is a protein that is related to transforming growth factor-β (19). This gene could act as tumor suppressor gene as it was expressed at a lower level in the microdissected tumor cells than in the normal cells (Table 1). In relation to other cancers, protein level of OGN was also underexpressed in colorectal adenoma as well as in colorectal cancer (p < .01) (20). To provide some perspectives of potential biological process and molecular pathways of significant gene list, we performed GSEA with the Gene Spring GX 10.0.2 and Pathway Studio software. The present results can be validated using previous published data on endometrial cancer. For instance, genes related to the gene set name, Chiang liver cancer subclass CTNNB1 up, was closely linked to the canonical Wnt signaling pathway. All the genes found under this gene set were downregulated, including PDK1, TBX3, KIAA0888, DNAJC12, SNAI2, GSTM4—GSTM2, and AXIN2. One of these genes, which is AXIN2, was reported to be involved in the regulation of β-catenin by forming β-catenin destruction complex in the Wnt signaling pathway (21). AXIN2 seems to act as a tumor suppressor gene in EEC as it was found to be downregulated in this study. Mutations in the AXIN2 gene have also been reported in colon cancer (22) and ovarian carcinoma (23).



Gene Signature in Endometrioid Endometrial Cancer 

Figure 4. Histogram showing qPCR and microarray expression analysis of SLC7A5, SATB1, ZAK, and H19 in EEC and normal endometrium (p < .05). (A) SLC7A5 was significantly up-regulated (p < .01) with a 2.49-fold change in EEC compared with normal endometrium, as observed in microarray analysis. The qPCR result confirmed that SLC7A5 with a 3.27-fold change was up-regulated in EEC when compared with normal endometrium samples (p < .01). (B) SATB1 was significantly down-regulated (p < .05) with a –3.95-fold change in EEC compared with normal endometrium, as observed in microarray analysis. The qPCR result confirmed that SATB1 with a –7.69-fold change was down-regulated in EEC when compared with normal endometrial samples (p < .05). (C) ZAK was significantly down-regulated (p < .05) with a –2.38-fold change in EEC compared with normal endometrial samples, as observed in microarray analysis. The qPCR confirmed overexpression of SLC7A5 (3.27-fold change) in EEC relative to their corresponding noncancerous endometrial samples (p < .01). (D) H19 was significantly down-regulated (p < .01) with a –5.69-fold change in EEC compared with normal endometrial samples, as observed in microarray analysis. The qPCR result confirmed the down-regulation of H19 by 4.35 fold in 18 cancers compared with eight normal counterparts (p < .05).

We also validated four differentially regulated genes, namely SLC7A5, SATB1, ZAK, and H19 (Figure 3), which encode for proteins linked to acid amino transport, translation, and chromatin remodeling in EEC. SATB1 is a nuclear C 2011 Informa Healthcare USA, Inc. Copyright

protein that is associated with cell aggressiveness, based on the in-vivo nude mice model and in-vitro breast cancer cell culture (24). The mRNA level of this gene was significantly associated with poorer prognosis in breast cancer (25). There


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is currently no published data describing the association between SATB1 and endometrial cancer. Our microarray data and qPCR showed SATB1 mRNA was significantly downregulated by 3.95 fold and 7.69 fold (p < .05), respectively, in the EEC tissues compared with the noncancerous tissues. This can be explained with the role of microRNA in the regulation of this gene, as seen in the breast cancer cells (26). The second validated gene SLC7A5, also known as L-type amino acid transporter 1 (LAT1), has also been shown to be highly expressed in the established tumor cell lines (27) and primary human neoplasms (28). This gene is linked to transport large amino acids and plays an important role in cellular infiltration (29). In our study, SLC7A5 was significantly upregulated in the isolated cancer cells as compared with the noncancerous cells and this was validated with qPCR. Additional approaches on the functional aspect of this gene are essential in order to clarify its role in EEC. Besides the gene set analysis, our gene signatures allowed the determination of significant pathways involved in the EEC. The four significant pathways are the insulin action, cell cycle, notch pathway, and B-cell activation. Prolonged exposure to estrogen is one of the major risk factors for the uncontrolled proliferation of EEC. The action of estrogen is mediated through IGF receptor signaling (30). One of the genes involved in this pathway is IGF2 (somatomedin A), which was identified in the significant gene list of our study. A previous study showed the low expression of IGF2 to be correlated with an early stage of EEC (31). Our study concurred with this as we found that IGF2 was down-regulated by six fold from our microarray data and the fact that the majority of our clinical samples were obtained from the early stage of EEC. CONCLUSION We have identified 162 differentially expressed transcripts in EEC when compared with the adjacent normal cells in this study using the LCM combined with microarray analysis. Among the key gene pathways were Wnt-β catenin, chromosomal abnormality, and IGF signaling. These are closely related to EEC and thus might represent therapeutic targets to combat this cancer. The validated genes, which were SLC7A5, ZAK, SATB1, and H19, represented novel findings in this cancer and require further clarification in their biological functions. ACKNOWLEDGMENT This work was supported by a science fund grant from the Ministry of Science, Technology and Innovation (grant no. 02–01-02-SF0296). DECLARATION OF INTEREST The authors declare that they have no competing interests. The authors alone are responsible for the content and writing of the paper.

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