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Keywords:

  • microRNA;
  • gastrointestinal stromal tumors;
  • KIT;
  • loss of 14q

Abstract

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

MicroRNAs are known to regulate gene expression. Although unique microRNA expression profiles have been reported in several tumors, little is known about microRNA expression profiles in GISTs. To evaluate the relationship between microRNA expression and clinicopathologic findings of GISTs, we analyzed the microRNA expression profiles of GISTs. We used fresh frozen tissues from 20 GISTs and analyzed KIT and PDGFRA mutations and chromosomal loss status. MicroRNA expression was analyzed using a microRNA chip containing 470 microRNAs. Using unsupervised hierarchical clustering analysis, we found four distinct microRNA expression patterns in our 20 GISTs. Six GISTs that did not have 14q loss formed a separate cluster. In the 14 GISTs with 14q loss, 5 small bowel GISTs formed a separate cluster and the remaining 9 GISTs could be divided into two groups according to frequent chromosomal losses and tumor risk. We found 73 microRNAs that were significantly down-regulated in the GISTs with 14q loss; 38 of these microRNAs are encoded on 14q. We also found many microRNAs that were down-regulated in small bowel and high-risk group GISTs. Most of the microRNAs down-regulated in the high-risk group and small bowel GISTs are known to be involved in tumor progression, specifically by stimulating mitogen-activated protein kinase (MAPK) and the cell cycle. The microRNA expression patterns of GISTs are closely related to the status of 14q loss, anatomic site, and tumor risk. These findings suggest that microRNA expression patterns can differentiate several subsets of GISTs.

The molecular features of gastrointestinal stromal tumors (GISTs) are among the best characterized of all human tumors.1–4 Activating mutations of the v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT), a member of the receptor tyrosine kinase III family, are the most common genetic events in GISTs. KIT mutations are known to be present in ∼80% of GISTs5–7 and result in the autophosphorylation of KIT, resulting in activation of downstream signaling pathways.8–10 Gain-of-function mutations of platelet-derived growth factor receptor α (PDGFRA), another member of the receptor tyrosine kinase III family, are present in ∼35% of GISTs that lack KIT mutations.11 Activating mutations of KIT and PDGFRA are mutually exclusive, and mutations in PDGFRA are regarded as an alternative oncogenic mechanism in GISTs.11, 12

The characteristic fragile genomic sites of GISTs are well-known. The most common and characteristic genomic change is the loss of the long arm of chromosome 14 (14q).13, 14 The other well-known chromosomal alterations are deletions of chromosome 1p and 22q.15–19 Loss of 14q is known to be present in ∼70% of GISTs, has no relationship to tumor risk, and can be found in any type of GIST.16, 20 The other chromosomal changes are relatively infrequent, but occur more frequently in high-risk GISTs.15, 16, 21

Although certain molecular changes are characteristic of GISTs, few of these molecular characteristics explain the biologic behavior of the tumor or are useful for molecular classification. In previous studies, we demonstrated that the gene expression profiles of GISTs are relatively homogeneous and have some relationship to the absence or presence of 14q and KIT mutations.12, 22 We could not, however, find any other relationship between the gene expression profile and biological behavior of GISTs. The microRNA expression profiles of GISTs have been compared to other types of sarcomas.23 However, the microRNA expression characteristics of subsets of GISTs and their relationship to genetic and clinicopathologic factors are unknown. It has been reported that microRNA expression is related to tumorigenesis and the phenotypic expression of many tumors.24–26 Therefore, a microRNA expression study of GISTs might contribute to their accurate molecular characterization and classification. In this study, we evaluated the microRNA expression patterns of GISTs and analyzed the relationship between these patterns and the molecular and clinicopathologic characteristics of GISTs.

Material and Methods

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Patients and tissue samples

Twenty GISTs were included in this study. All cases were identified in the Department of Pathology at Yonsei University Medical Center between August 1997 and June 2006 for molecular marker studies. Authorization for the use of these tissues for research purposes was obtained from the Institutional Review Board of Yonsei Medical Center. Some of the fresh specimens were obtained from the Liver Cancer Specimen Bank of the National Research Resource Bank Program of the Korea Science and Engineering Foundation of the Ministry of Science and Technology.

Among 20 GISTs, 10 samples had previously been used for chromosomal and proteome analysis.12, 22, 27 Information on clinical features and tumor sites were obtained from hospital charts and clinicians. The subjects included 11 females and 9 males ranging in age from 26 to 76 years (Table 1). All of the patients had been operated on directly without neoadjuvant therapy. Conventional pathologic parameters (anatomic site, risk, and tumor size) were examined prospectively without prior knowledge of the molecular data. GISTs were divided into four groups based on tumor risk according to the criteria of Fletcher et al.28

Table 1. Clinicopathologic and genetic status in 20 GISTs
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Analysis of mutations in KIT and PDGFRA

We selected tumor tissues comprising more than 80% tumor cells by evaluating a validation block or frozen tissues. Somatic mutations in exons 9, 11, 13, and 17 of KIT, and mutations in exons 12 and 18 of PDGFRA were analyzed in our 20 GISTs using PCR-based assays as described previously.5, 6, 8, 11, 29 The PCR products were sequenced using an ABI Prism 310 Genetic Analyzer (Applied Biosystems, Foster City, CA).

Loss of heterozygosity analysis

Loci of 1p, 14q, and 22q were PCR-amplified from DNA extracted from GISTs and matched normal tissues to evaluate the frequency and extent of deletions. PCR reactions were carried out in a volume of 20 μL containing 1.5 mmol/L MgCl2, 20 pmol primer, 0.2 mmol/L of dATP, dGTP, and dTTP, 5 μmol/L dCTP, 1 μCi of [α-32P] dCTP (3000 Ci/mmol; NEN DuPont, Boston, MA), 50 ng of sample DNA, 1× PCR buffer, and 1.25 U Taq polymerase (Gibco-BRL, Grand Island, NY). After denaturation at 95°C for 5 min, DNA amplification was performed for 30 cycles of denaturation at 95°C for 30 seconds, primer annealing at 55–60°C for 30 seconds, and elongation at 72°C for 15 sec. The primer sequences for LOH analysis are listed in Supporting Information Table 1. PCR products were separated on 6% polyacrylamide gels containing 5.6 mol/L urea, followed by autoradiography. Allelic deletion was scored when the band intensity of one marker was significantly decreased (>70% reduction) in the tumor DNA compared to the DNA from non-tumor tissue. To examine in detail the deletion status of chromosomes 1p, 14q, and 22q, we used 5, 14, and 7 microsatellite markers, respectively. We used DNA extracted from matched normal tissues, namely grossly normal-looking mucosa or smooth muscle. The matched normal tissue was not available in one case (Case 16), and an array CGH was used in two cases (Cases 6 and 16). CGH was performed at LNC Bio Inc. (Seoul, Korea) using an Agilent Oligonucleotide Array-Based CGH for genomic DNA analysis (Version 2.0) (Agilent Technologies, Santa Clara, CA).

RNA preparation and microarray analysis

Total RNA was extracted from frozen tissues using TRIZOL as per the manufacturer's instructions (Life Technology, Rockville, MD). One hundred micrograms of total RNA was used as input for small RNA isolation. To collect pure microRNA, we extracted the microRNA from total RNA using Microcon YM-3 and YM-100 columns (Millipore. Billerica, MA). The collected microRNA was labeled using a MessageAmp™ II-biotin Enhanced Kit (Ambion, Austin, USA) according to the manufacturer's instructions. After microRNA labeling, the RNAs were hybridized to the Homo Sapiens CombiMatrix chip (4 × 2K) (CombiMatrix Corp. Mukilteo, WA), which contains probe designs based on unique microRNA species from the current release of the Sanger database (Version 9.0 is at http://microrna.sanger.ac.uk/sequences). Hybridized microarrays were scanned using a pixel size of 5 and a focus position of 130 using a GenePix 4000B microarray scanner (Axon Instruments, Union City).

Data normalization was performed for all filtered microRNA probes with the exception of nonmicroRNA controls (such as the positive, negative, and degradation controls). This was followed by within-global scale factor normalization to ensure that all sample backgrounds had the same value. Average log2 ratios were calculated from the normalized data based on two measurements of each microRNA. RNA hybridization and scanning were performed by Macrogen Inc. (Seoul, Korea).

MicroRNA RT-PCR

Total RNA from 8 cases was extracted from fresh frozen tissues using TRIZOL (Life Technology, Rockville, MD). One hundred micrograms of total RNA was used as input for small RNA isolation. cDNAs were generated using M-MLV reverse transcriptase (Invitrogen, California, USA) according to the manufacturer's instructions. PCR reactions were carried out in a 20 μL mixture containing 1.5 mmol/L MgCl2, 20 pmol primer, 2.5 mmol/L each dATP, dGTP, dTTP, and dCTP, 4 μg of cDNA, 10× PCR gold buffer, and AmpliTaq Gold® (Applied Biosystems). After denaturation at 95°C for 10 min, DNA amplification was performed for 30 cycles of denaturation at 95°C for 30 sec, primer annealing at 55–60°C for 30 sec, and elongation at 72°C for 30 sec. The relative intensity of mRNA expression of each sample was then normalized against 5S RNA as a surrogate for total mRNA. miRNA primer sequences are listed in Supporting Information Table 3. Each of the PCR products (20 μL volume) was directly loaded onto a 3.5% agarose gel stained with ethidium bromide, and visualized directly under ultraviolet illumination.

Northern blot analysis of mRNA

Total RNAs (30 μg) from snap-frozen tissues were fractionated on a denaturing 15% TBE-UREA gel. The gel was then transferred to Hybond-N+ membranes (Amersham Biosciences, NY, USA) and fixed by ultraviolet cross-linking at 1200 μJ. Membranes were then hybridized overnight at 37°C in ULTRAhyb-oligo (Ambion, TX, USA), together with a locked nucleic acid modified oligonucleotide probe complementary to the mature microRNAs that was labeled with T4 polynucleotide kinase (NEB) and γ-P32 ATP (Bio-Medical Science, Seoul, Korea). The sequences of probes are as follows: miR-377: 5′-ACAAAAG TTGCCTTTGTGTGAT-3′, miR-154: 5′-AATAGGTCAACCG TGTATGATT-3′. The relative intensity of RNA expression of each sample was then normalized against 5S RNA. Subsequently, the blots were washed three times at 37°C for 5 min each in 2× SSC/0.1% SDS. The blots were then incubated with the BAS cassette 2040 (Fuji photo film, Tokyo, Japan) and exposed to Fuji Medical X-ray film (Fuji photo film).

Western blot analysis of KIT

Whole lysates from tumor specimens were prepared using lysis buffer (50 mM Tris (pH 7.4), 1% Triton X-100, 5 mM EDTA, 1 mM KCl, 140 mM NaCl, 2 mM MgCl2, 1 mM phenylmethylsulfonyl fluoride, 1 mM sodium fluoride, 1% aprotinin, 1 M leupeptin, and 1 mM sodium ortho-vanadate). Total protein lysates (20 μg) were loaded into each lane, size-fractionated by SDS–PAGE, and transferred to a polyvinylidene difluoride membrane that was blocked with Tris-buffered saline–Tween 20 containing 5% skim milk. Primary antibodies against KIT (1: 4000. Santa Cruz Biotech, Santa Cruz, CA) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 1:200,000. Trevigen, Gaitherburg, MD) were incubated with the membranes for 1 hr at room temperature. After washing, membranes were incubated with a secondary goat anti-rabbit IgG-HRP conjugated antibody (Santa Cruz Biotech) for anti-KIT and with a secondary rabbit anti-mouse IgG-HRP conjugated antibody (Santa Cruz Biotech) for anti-GAPDH, washed, and then developed using ECL-Plus reagents (Amersham Pharmacia Biotech, Uppsala, Sweden).

Immunohistochemical staining

Immunohistochemistry was performed using an avidin-biotin peroxidase complex system with diaminobenizidine (DAB) as the chromogen. Primary antibodies included c-kit (1:50, DAKO, Copenhagen, Denmark) and CD34 (1:50, DAKO, Copenhagen, Denmark).

Immunohistochemical results were evaluated semi-quantitatively as follows. Strong positivity: more than 10% of tumor cells moderately or strongly positive; weak positivity: less than 10% of tumor cells moderately or strongly positive, or more than 10% of the tumor cells, weakly positive; negative: less than 10% of tumor cells weakly positive for the stain or no positive cells.

Agglomerative hierarchical clustering

Unsupervised hierarchical clustering analysis was used to classify the 20 GISTs according to their gene expression patterns. We used a data set of genes that satisfied the following filtering criteria: genes with log-transformed ratio values of more than 80% (across all arrays) were taken and genes with log-transformed ratio of less than 0.2 standard deviations were discarded. The selected gene data set was then analyzed using complete-linkage hierarchical clustering using the uncentered correlation similarity metric method in Cluster version 2.11. The expression map results were visualized with Treeview version 1.60 software (http://rana.lbl.gov/EisenSoftware.htm).

Identification of differentially expressed genes according to anatomic site, tumor risk, and loss of 14q

To detect differentially expressed microRNAs according to the anatomic site of the tumor, tumor risk, and loss of 14q status, 20 GISTs were classified and analyzed. We ranked the microRNAs using the Mann-Whitney rank sum test. Outlier genes responsible for anatomic site or chromosomal alteration status were selected by p < 0.05. In addition, significant outlier subset genes were further narrowed down by filtering genes showing greater than ±2-fold expression changes.

Results

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Clinicopathologic characteristics and mutation status of KIT and PDGFRA in GISTs

Among the 20 GISTs, 4 cases were categorized as low-risk, 6 cases as intermediate-risk, and 10 cases as high-risk according to NCI consensus criteria.28 Fifteen cases were stomach GISTs and the remaining five cases were small bowel GISTs. Immunoreactivity for KIT and CD34 were present in 19 and 16 cases, respectively. Deletion of 14q was detected in 14 cases. KIT mutations were detected in 13 of 20 cases. In the 13 GISTs with KIT mutations, 9 mutations were present in exon 11 and 4 mutations were present in exon 9. Insertion mutations were most common, and were found in 7 cases, while point mutations were found in one case and deletions were found in 5 cases. Among the remaining 7 cases lacking KIT mutations, 4 cases had a PDGFRA mutation, and all mutations were point mutations in exon 18 (Table 1). Among 13 KIT-mutated GISTs, 10 had a spindle cell type and 3 had a mixed cell type; all 4 PDGFRA-mutated GISTs had a mixed cell type.

Unsupervised hierarchical clustering analysis distinguishes four subtypes of GISTs

We initially performed a molecular pattern analysis to identify different subsets of GISTs according to microRNA expression profiles. All array data for the samples described in this study can be accessed on our web page (http://www.molpathol.org). Using a relevant set of 363 pre-filtered genes (see “Materials and Methods”), we conducted a complete-linkage hierarchical clustering analysis of 20 arrays. Our microRNA expression study classified GISTs into four subgroups; a two-way hierarchical clustering analysis completely distinguished GISTs into two clusters and these two clusters were further divided into two additional clusters each (Branches A, B, C, and D; Fig. 1). Cases in Branch A and B were high-risk GISTs and had more frequent chromosomal losses than the cases in branches C and D. GISTs in branches A and B were separated by anatomic site (small bowel versus stomach). All 5 GISTs in branch A developed in the small bowel, while 4 GISTs in branch B were stomach GISTs. Furthermore, all the GISTs in branches C and D were stomach GISTs and were separated according to 14q loss. All 6 stomach GISTs without 14q loss formed a separate cluster in branch D. The mutation status of KIT or PDGFRA, clinicopathologic factors such as histological type, age, sex, and the immunohistochemical expression status of KIT or CD34 were not related to the microRNA expression pattern.

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Figure 1. Unsupervised hierarchical clustering analysis of microRNA expression in 20 GISTs. A 2-way hierarchical clustering analysis revealed 2 GIST clusters that were each divided into another 2 clusters. Branch A is composed of high-risk GISTs in the small bowel while branch B contains high-risk stomach GISTs with frequent chromosomal changes. Branch C is composed of intermediate-risk stomach GISTs with 14q loss. All six stomach GISTs lacking 14q loss formed a separate cluster in branch D. The mutation status of KIT and PDGFRA was not related to the microRNA expression pattern. (1) H, high-risk group; I, intermediate-risk group; L, low-risk group. (2) Sb, small bowel; St, stomach. (3) Black box, chromosomal loss; White box, no chromosomal loss. (4) M, mutation positive; W, wild-type.

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Differentially expressed microRNAs according to anatomic site and tumor risk

Because all 5 small bowel GISTs formed a separate cluster, we attempted to identify a robust set of anatomic site-related genes by a supervised rank-sum analysis using the Mann-Whitney rank sum test. To minimize the microRNA expression differences due to different clinical features and chromosomal changes, we selected 9 GISTs in branches A and B (5 small bowel GISTs and 4 stomach GISTs), and used a stringent selection criterion (See “Materials and Methods”). Forty-five microRNAs were found to be either up-regulated (5 genes) or down-regulated (40 genes) in the 5 small bowel GISTs compared to the 4 stomach GISTs. Examples of genes differentially expressed (p < 0.05, fold change > 2.5) in the small bowel and stomach GISTs are listed in Supporting Information Table 2. We then analyzed which specific microRNAs were differentially expressed according to tumor risk. We compared 10 high-risk GISTs (6 gastric and 4 small bowel GISTs) to 4 low-risk GISTs (4 gastric GISTs), and found that 28 microRNAs (p < 0.05, fold change > 2.0; Table 2) were down-regulated in the high-risk GISTs. When we compared six high-risk gastric GISTs and four low-risk gastric GISTs, we found 32 microRNAs that were down-regulated in high-risk gastric GISTs (p < 0.1, fold change > 2.0). Among the 28 microRNAs that were down-regulated in high-risk GISTs, we found that 16 (16/28, 57.1%) microRNAs were also down-regulated in high-risk gastric GISTs. The remaining 12 microRNA were also down-regulated in high-risk gastric GISTs, although the values did not fulfill the statistical significance (Supporting Information Table 4). We next compared the relationship between mRNA expression and down-regulated microRNA expression in GISTs by using our previous data. Our previous mRNA expression study of GISTs was performed using gastric GISTs. Our previous mRNA expression study contained the same 4 high-risk and 2 low-risk gastric GIST cases. We therefore compared the 32 microRNAs that were down-regulated in high-risk gastric GISTs (p < 0.1, fold change > 2.0). These 32 microRNAs are known to affect the expression of 37 genes, and our DNA chip contained 30 of these 37 genes. When we evaluated the mRNA expression of these genes, we found that 20 of the 30 genes were up-regulated while 10 genes were down-regulated or had the same expression level in the high-risk gastric GISTs.

Table 2. Differentially expressed microRNAs between 10 high-risk and 4 low-risk GISTs
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Differentially expressed microRNAs according to 14q loss

Our study found that 6 GISTs that did not have 14q loss formed a separate cluster (Fig. 1, branch D). When we analyzed the chromosomal loss status of 14q by deletion mapping, 13 GISTs showed total loss while 1 GIST showed partial loss (Supporting Information Fig. 1). The GIST with the partial 14q loss (Case 16) was a small bowel GIST with an intact proximal chromosome fragment of 14q (Supporting Information Fig. 2). We then analyzed the differently expressed microRNAs according to 14q loss status. In total, a subset of 73 microRNAs were at a significant level differentially expressed between GISTs with 14q loss and GISTs without 14q loss. Among the 73 microRNAs, 38 microRNAs were encoded by 14q. When we analyzed the expression profiles of the 73 microRNAs using supervised hierarchical clusters analysis, we found that there was a trend of GISTs grouping according to anatomic site (Fig. 2a).

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Figure 2. Identification of differentially expressed microRNAs according to 14q chromosome loss. We ranked the microRNAs using the Mann-Whitney rank sum test. Outlier genes responsible for anatomic site or chromosomal alteration status were selected by p < 0.05. In addition, significant outlier subset genes were further narrowed down by filtering genes showing greater than ±2-fold expression changes. (a) Supervised hierarchical clustering analysis of GISTs using 73 differentially expressed genes. GISTs were separated according to anatomic site and 14q loss status. (b) 30 differentially expressed microRNAs from two clusters on 14q32.33. Six GISTs lacking 14q loss showed strong microRNA expression while severe down- regulation was noted in nine stomach GISTs with 14q loss. In five small bowel GISTs with 14q loss, mild down-regulation was noted. (c) Validation of microRNA expression by RT-PCR. (d) Validation of microRNA expression by Northern blotting. P and N indicate the positive and negative control, respectively.

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We further analyzed the 38 down-regulated microRNAs located at chromosome 14q. In chromosome 14q, two loci of microRNA clusters are present near the distal end, 14q 32.33. Among these two clusters, the proximal locus has 10 genes and the distal locus has 41 genes. Our microRNA chip contains 5 of the 10 genes at the proximal locus and 25 of the 41 genes at the distal locus. These 30 genes were significantly down-regulated in GISTs with 14q loss. Therefore, among the 38 down-regulated microRNAs from 14q, 30 were encoded by these two microRNA clusters, and microRNAs from these two loci showed different patterns according to anatomic site. In the 5 small bowel GISTs with 14q loss, relatively similar or slight down-regulation of the 30 microRNAs was observed compared to the 9 stomach GISTs with 14q loss (Fig. 2b). To examine the reliability of the array data, we selected 6 microRNAs (miR-495, miR-376, miR-134, miR-377, miR-539 and miR-154) encoded on 14q and analyzed their expression pattern by RT-PCR (five microRNAs) and Northern blotting (two microRNAs). We found that the expression levels of these microRNAs as analyzed by microarray, RT-PCR, and Northern blotting, were similar (Figs. 2c and 2d).

Relationship between dysregulated microRNAs in GISTs and KIT overexpression

The characteristic molecular changes of GISTs are KIT gain-of-function mutations and KIT overexpression.9, 10 Although KIT mutations are directly related to KIT activation, the mechanism of KIT overexpression is not well-known. In our 20 cases, the amount of KIT expression was directly related to KIT mutations, with the exception of three cases (cases 15, 13, and 3). The association between KIT activation and down-regulation of mir-221 and mir-222 has been reported previously.48, 49 We hypothesized that microRNAs play an important role in KIT overexpression. First, we used http://www.targetscan.org to search for candidate microRNAs that can bind the 3′UTR region of KIT in a site-specific manner and found 111 microRNA candidates. Among these candidates, our microRNA chip contained 62 microRNAs and 16 of these were located on 14q. Expression of these 16 microRNAs was homogeneously down-regulated in 14 GISTs with 14q loss (Fig. 3). We evaluated the relationship between the expression of 62 microRNAs and KIT overexpression. We found that the expression of five microRNAs (mir-510, mir-142-5p, mir-9*, mir-370, and mir-494) was significantly related to KIT expression (Fig. 3). We also found that miR-221 and miR-222 were down-regulated; these two microRNAs have previously been reported to be associated with KIT overexpression. In our GISTs, KIT overexpression was found in 14 of 20 cases, and down-regulation of miR-221 and miR-222 was found in 11 of these 14 cases, but this association was not statistically significant.

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Figure 3. The expression of five microRNAs (two microRNAs from 14q and three microRNAs from other chromosomes) correlated to KIT overexpression in GISTs. Expression of KIT was analyzed by Western blotting and compared to the expression of 62 microRNAs possibly targeting KIT. KIT expression was related to KIT mutations, and microRNA expression was related to the status of chromosome 14 loss. There was a significant relationship between KIT and microRNA expression in 5 of 62 microRNAs (p < 0.05, t-test). Among the five microRNAs, two microRNAs (miR-370 and miR-494) were located on 14q.

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Discussion

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

In this study, we analyzed the microRNA expression patterns of 20 GISTs and investigated the relationship between microRNA expression profiles and genetic changes and clinicopathologic features of GISTs. We found four distinct microRNA expression patterns in our GISTs; these patterns were correlated with 14q chromosomal loss status, anatomic site, and tumor risk. These findings suggest that the microRNA expression patterns of GISTs are related to genomic changes and the biological behavior of the tumor.

GISTs are well-known mesenchymal tumors with unique morphological and genetic characteristics.50, 51 A previous report described unique genomic alterations and homogeneous mRNA and microRNA expression profiles in GISTs in contrast to other types of sarcomas.23 These differences might be related to the unique genomic structure of GISTs. Loss of 14q is a unique genomic change in GISTs and has been reported in more than 70% of GISTs.16, 20 A strong association between microRNA expression and genomic changes has been reported in many tumors.24–26, 52 This unique chromosomal change in GISTs might contribute to the distinct microRNA expression profiles of GISTs compared to other sarcomas.

In this study, we demonstrated that the microRNA expression profile of GISTs is dependent upon the type of chromosomal change, especially 14q loss status and anatomic site. Chromosome 14q contains two large microRNA clusters that have been identified to date. Our microRNA expression study demonstrated that the microRNAs encoded by these two clusters on 14q were severely down-regulated in the 9 stomach GISTs with 14q loss. We also found that the down-regulation of microRNAs in 14q was different between stomach and small bowel GISTs. The 5 small bowel GISTs with 14q loss also showed down-regulation of microRNAs from the two clusters of 14q; however, the intensity of down-regulation was much lower than that observed in the nine stomach GISTs with 14q loss. When we compared the microRNA expression between 4 stomach and 5 small bowel GISTs in the high-risk group containing the 14q loss, two microRNAs (mir-136 and mir-409-3p) encoded by 14q were significantly up-regulated and two microRNAs (mir-638 and mir-625) were significantly down-regulated in the small bowel GISTs, although all 9 stomach and small bowel GISTs had 14q loss (Supporting Information Table 2).

GISTs are homogenous mesenchymal tumors of the gastrointestinal tract. However, many pathological and biological differences between stomach and small bowel GISTs have been reported. Histologically, small bowel GISTs occasionally display skeinoid fibers and the organoid feature, which is absent in stomach GISTs. In addition, PDGFRA-mutated GISTs, which are known to be of relatively low malignant potential, arise exclusively in the stomach. Small bowel GISTs are high-risk, have higher proliferation rates and a shorter disease-free survival.13 The overall tumor-related mortality of small bowel GISTs and their recurrence rate are higher than those of stomach GISTs. Small bowel GISTs, even when of similar size and mitotic count to stomach GISTS, have a worse prognosis.53 It has been reported that a small bowel location is a predictable factor for recurrence after complete excision of primary GISTs.54 In addition to these pathological and biological differences, Antonescu et al.55 reported that there was a notable difference in gene expression between stomach and small bowel GISTs. Hierarchical cluster analysis of GISTs according to location showed two distinct genomic clusters: stomach and small bowel GISTs. This was confirmed using four familial tumors from one patient, two of which were in the stomach and two that were intestinal.55 Additionally, another study on the gene expression profile of GISTs confirmed that the most determinant factor separating GISTs from the stomach and small bowel in an unsupervised hierarchical clustering was tumor location.44 The different types of KIT mutation that characterize small bowel and gastric GISTs might contribute to their different gene and microRNA expression patterns. For example, mutation of exon 9 of KIT is often found in small bowel GISTs.56 When we analyzed the microRNA expression profile of our 13 GISTs with KIT mutations using unsupervised hierarchical clustering analysis, we found that cases with exon 9 mutations formed a separate cluster. The other cases with exon 11 mutations were subdivided according to 14q loss (Supporting Information Fig. 3). Here, we found that small bowel GISTs had unique microRNA expression patterns. Most of the down-regulated microRNAs in small bowel GISTs are known to play a role in the activation of mitogen-activated protein kinase (MAPK) and the cell cycle, suggesting that these microRNA expression differences may be related to the aggressive biologic behavior of small bowel GISTs and may contribute to the differences in biologic behavior between stomach and small bowel GISTs.

Our study demonstrated that several microRNAs are differentially expressed according to GIST tumor risk. When we compared 10 high-risk GISTs to 4 low-risk GISTs, we found that 28 microRNAs were significantly down-regulated in the high-risk GISTs. Most of these microRNAs are related to MAPK and the cell cycle, and therefore it is expected that the down-regulation of these microRNAs will be positively correlated to cell proliferation and tumor progression. Among these microRNAs, miR-125b and miR-21 have been reported to be down-regulated in breast cancer33 and to be related to tumor stage, proliferation index and vascular invasion. miR-342 had been reported to be specifically down-regulated in colon cancer according to the adenoma-carcinoma sequence.57 The down-regulation of miR-342 is due to promoter methylation; in normal mucosa, only 12% of cases were methylated whereas in adenoma, 67% of cases were methylated and the methylation increased to 86% in carcinoma.57 The methylation of miR-342 results in increased cell proliferation, because silencing of miR-342 inhibits apoptosis. All of these findings indicate that differences in microRNA expression between high-risk and low-risk GISTs can contribute to rapid tumor progression in GISTs in the high-risk group.

Furthermore, we demonstrated that the microRNA expression signatures of our GISTs might be related to KIT overexpression. It is well-known that microRNAs regulate many genes and tumors can develop with the loss of microRNA regulation. We initially hypothesized that differently expressed microRNAs are important for GIST development. Using in silico analysis, we found 111 microRNAs that can bind to KIT and our microRNA chip contained 62 out of these 111 microRNA candidates. These findings raised the possibility that the down-regulation of these microRNAs may be related to KIT overexpression in GISTs. When we compared the expression profiles of the 62 microRNAs with KIT overexpression, we found that KIT overexpression was significantly correlated to the expression of 5 microRNAs and KIT mutations. The role of KIT mutations in GIST tumorigenesis is well known. KIT is frequently overexpressed and is generally used as a diagnostic marker of GISTs. Although the relationship between KIT mutations and KIT overexpression has been demonstrated,8 some exceptional cases have also been reported.30 Here, we showed that expression changes in some microRNAs are related to KIT overexpression. These results suggest a possible role of microRNAs in KIT overexpression in GISTs.

In conclusion, we found that microRNA expression patterns can differentiate several subsets of GISTs and are closely related to the presence or absence of 14q, tumor risk and anatomic site. Our findings suggest that microRNA expression profiles can be used for the molecular classifications of GISTs.

References

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Material and Methods
  4. Results
  5. Discussion
  6. References
  7. Supporting Information

Additional Supporting Information may be found in the online version of this article.

FilenameFormatSizeDescription
IJC_24897_sm_suppfig1.tif5555KSupplementary Figure 1. LOSS OF HETEROZYGOSITY (LOH) MAPPING OF CHROMOSOME 14Q. Chromosomal loss is indicated as black line. Thirteen out of 14 cases showed total loss of 14q and one case (case 16) showed partial loss.
IJC_24897_sm_suppfig2.tif1209KSupplementary Figure 2. ARRAY CGH ANALYSIS OF CHROMOSOME 14Q OF CASE 6 AND CASE 16. Array CGH demonstrated total loss of 14q in case 6, and partial loss in case 16.
IJC_24897_sm_suppfig3.tif559KSupplementary Figure 3. MICRORNA EXPRESSION PATTERNS OF THE 13 GISTS WITH KIT MUTATION. MicroRNA expression profile of the 13 KIT-mutated GISTs was analyzed by unsupervised hierarchical cluster analysis. From GISTs with KIT mutation in exon 9 showed a separate cluster.
IJC_24897_sm_supptable1.doc47KSupporting Information.
IJC_24897_sm_supptable2.doc79KSupporting Information.
IJC_24897_sm_supptable3.doc29KSupporting Information.
IJC_24897_sm_supptable4.doc86KSupporting Information.

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