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Comparison of Global Gene Expression of Gastric Cardia and Noncardia Cancers from a High-Risk Population in China

  • Gangshi Wang ,

    Contributed equally to this work with: Gangshi Wang, Nan Hu

    Current address: Department of Geriatric Gastroenterology, China PLA General Hospital, Beijing, PR China

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Nan Hu ,

    Contributed equally to this work with: Gangshi Wang, Nan Hu

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Howard H. Yang,

    Affiliation Office of the Director, Center for Cancer Research, NCI, Bethesda, Maryland, United States of America

  • Lemin Wang,

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Hua Su,

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Chaoyu Wang,

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Robert Clifford,

    Affiliation Multidrug Resistant Organism Repository and Surveillance Network, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America

  • Erica M. Dawsey,

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Jian-Min Li,

    Affiliation Shanxi Cancer Hospital, Taiyuan, Shanxi, PR China

  • Ti Ding,

    Affiliation Shanxi Cancer Hospital, Taiyuan, Shanxi, PR China

  • Xiao-You Han,

    Affiliation Shanxi Cancer Hospital, Taiyuan, Shanxi, PR China

  • Carol Giffen,

    Affiliation Information Management Services, Inc., Silver Spring, Maryland, United States of America

  • Alisa M. Goldstein,

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Philip R. Taylor ,

    ptaylor@mail.nih.gov (PRT); leemax@mail.nih.gov (MPL)

    Affiliation Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland, United States of America

  • Maxwell P. Lee

    ptaylor@mail.nih.gov (PRT); leemax@mail.nih.gov (MPL)

    Affiliation Office of the Director, Center for Cancer Research, NCI, Bethesda, Maryland, United States of America

Comparison of Global Gene Expression of Gastric Cardia and Noncardia Cancers from a High-Risk Population in China

  • Gangshi Wang, 
  • Nan Hu, 
  • Howard H. Yang, 
  • Lemin Wang, 
  • Hua Su, 
  • Chaoyu Wang, 
  • Robert Clifford, 
  • Erica M. Dawsey, 
  • Jian-Min Li, 
  • Ti Ding
PLOS
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Abstract

Objective

To profile RNA expression in gastric cancer by anatomic subsites as an initial step in identifying molecular subtypes and providing targets for early detection and therapy.

Methods

We performed transcriptome analysis using the Affymetrix GeneChip U133A in gastric cardia adenocarcinomas (n = 62) and gastric noncardia adenocarcinomas (n = 72) and their matched normal tissues from patients in Shanxi Province, and validated selected dysregulated genes with additional RNA studies. Expression of dysregulated genes was also related to survival of cases.

Results

Principal Component Analysis showed that samples clustered by tumor vs. normal, anatomic location, and histopathologic features. Paired t-tests of tumor/normal tissues identified 511 genes whose expression was dysregulated (P<4.7E-07 and at least two-fold difference in magnitude) in cardia or noncardia gastric cancers, including nearly one-half (n = 239, 47%) dysregulated in both cardia and noncardia, one-fourth dysregulated in cardia only (n = 128, 25%), and about one-fourth in noncardia only (n = 144, 28%). Additional RNA studies confirmed profiling results. Expression was associated with case survival for 20 genes in cardia and 36 genes in noncardia gastric cancers.

Conclusions

The dysregulated genes identified here represent a comprehensive starting point for future efforts to understand etiologic heterogeneity, develop diagnostic biomarkers for early detection, and test molecularly-targeted therapies for gastric cancer.

Introduction

Gastric cancer is the fourth most common cancer and the second most frequent cause of cancer death worldwide [1]. As a result of its large population and high rates, China accounts for 42% of all gastric cancer deaths in the world each year [1]. Shanxi Province is one of the regions with the highest incidence rate of gastric cancer in China [2], [3]. In fact, gastric cancer remains the leading cause of death from cancer in both men (36%) and women (28%) in this region [4], despite the decline in incidence for this cancer in northern China.

Gastric cancer rates in China are highest in the north and risk factors for both cardia and noncardia gastric cancers have been previously studied there. Increased age, male gender, a family history of upper gastrointestinal tract cancer, tobacco exposure, and Helicobacter pylori infection have all been consistent risk factors for both gastric cardia adenocarcinoma (GCA) and gastric noncardia adenocardinoma (GNCA) [5][13]; additionally, emerging evidence supports increased risk from thermal damage from hot food [6]. Diet, particularly micronutrients, appear to play an important protective role, as evidenced by results from a large, randomized controlled trial conducted in Linxian which showed reduced GCA and GNCA mortality from the antioxidant combination of selenium, vitamin E, and beta-carotene [14], [15]; other questionnaire-based nutritional studies also support the role of nutrition in gastric cancer etiology [6].

Gastric cancers are histopathologically classified into diffuse and intestinal types [16] for both cardia and noncardia. Anatomically, the cardia lies between the end of the esophagus and the body of the stomach, and is a small macroscopically indistinct zone immediately distal to the gastro-esophageal junction. It merges distally into the fundus and is distinguishable only by its histological pattern.

In addition to being anatomically adjacent, GCA and esophageal squamous cell carcinoma (ESCC) both occur at epidemic rates in this population, share some etiologic risk factors, and before the widespread use of endoscopy and biopsy, were diagnosed as a single disease referred to as “esophageal cancer” or “hard swallowing disease” [17]. The ability to diagnose GCA and accurately distinguish it from ESCC has led to an increase in the incidence in gastric cancer in this region [18]. The reason for the high rates of GCA and ESCC in this geographic area and their relation to each other remains unclear, but there are almost certainly common etiologically important environmental exposures, and a recent genome-wide association study of germline DNA found a common gene (PLCE1) associated with risk for both GCA and ESCC [19].

Gastric adenocarcinomas are a heterogeneous group of tumors. GCAs show biologic, epidemiologic, and clinicopathologic features that more closely resemble esophageal adenocarcinomas (EACs) than GNCAs, suggesting that tumors arising in the stomach may have distinct etiologies. For example, several studies detected substantially higher TP53 mutation rates in cases with GCA than GNCA, while the TP53 mutation spectrum in GCA more closely resembled EAC [20]. A number of other genetic alterations have been reported in gastric cancer, including CDH1 [21], β-catenin [22], TFF1 [23], and Met [24], but no study compared these alterations by anatomic subsite. Further, although several gastric cancer gene expression profiling studies have previously been reported [25][31], none has directly compared GCA and GNCA cases from a high-risk geographic region using a common protocol.

The objective of this study was to identify genomic differences between gastric cancer by anatomic subtypes to aid our understanding of the etiologies of these two distinct cancers and facilitate the development of appropriate targeted strategies for early detection, prognosis, and therapy.

Materials and Methods

1. Patient Selection and Follow-up

This study was approved by the Institutional Review Boards of the Shanxi Cancer Hospital in China and the National Cancer Institute (NCI) in the USA. Patients admitted to the Shanxi Cancer Hospital between 1998 and 2001 with a diagnosis of GCA or GNCA and considered candidates for curative surgical resection were identified and recruited to participate in the study. None of the patients had prior therapy, and Shanxi was the ancestral home for all. After obtaining informed consent, patients were interviewed to obtain information on demographic and lifestyle cancer risk factors and clinical data and samples were obtained.

Gastric cancer here was defined by histology (only adenocarcinomas were included) and anatomic sites were defined as gastric cardia adenocarcinoma (GCA) and gastric noncardia adenocarcinoma (GNCA). Cardia cancers were gastric cancers located in the proximal three centimeters of the stomach (C16.0), while noncardia cancers were those in the remainder of the stomach (fundus, body, antrum, pylorus (C16.1-8), and unspecified location (C16.9) based on site codes from the International Classification of Malignant Tumours (Seventh edition)) [32].

Between 2005 and 2007, all patients (or their families) from this study were re-contacted to ascertain vital status. For those who had died, date and cause of death were determined.

2. Sample Collection

Tumor and matched normal tissues obtained during surgery were snap-frozen in liquid nitrogen and stored at −130 degrees C until used. Cases were chosen for this study based on three criteria: (i) histological diagnosis of GCA or GNCA confirmed by pathologists at both the Shanxi Cancer Hospital and the NCI; (ii) tumor samples that were at least 50% tumor; and (iii) tissue RNA quality/quantity adequate for testing.

3. Sample Preparation and Chip Hybridization

Total RNA was extracted from frozen tumor and matched normal tissues using TRIzol reagent (Invitrogen, Carlsbad, CA) in accordance with the manufacturer’s instructions. RNA purification was performed according to the manufacturer’s instructions for the RNeasy Mini Kit (Qiagen Inc, Valencia, CA) and RNase-Free DNase Set digestion (Qiagen Inc, Valencia, CA). RNA quality and quantity were determined using the RNA 6000 Labchip/Agilent 2100 Bioanalyzer (Agilent Technologies, Germantown, MD).

Microarray experiments were performed using 8 µg total RNA; details of reverse transcription, labeling, and hybridization were according to the manufacturer’s protocol (http://www.affymetrix.com/support/technical/manual/expression_manual.affx; accessed 2013 Apr 14). Briefly, the procedures included first strand cDNA synthesis, second strand cDNA synthesis, double-stranded cDNA clean up, in vitro transcription, cRNA purification, and fragmentation. Twenty µg biotinylated cRNA were used in each array hybridization. Samples were hybridized onto Affymetrix GeneChip Human Genome U133A chips (Affymetrix, Santa Clara, CA). After hybridization at 45°C overnight, arrays were subsequently developed with phycoerythrin-conjugated streptavidin by fluidics station (GeneChip Fluidics Station 450, Santa Clara, CA) and were scanned (GeneChip Scanner 3000, Santa Clara, CA) to obtain quantitative gene expression levels. Paired tumor and normal tissue specimens from each patient were processed simultaneously throughout the experimental process. The average present call for the 124 chips from the 62 GCA patients was 50.0%; for the 144 chips from the 72 GNCA patients it was 51.5%.

4. Statistical Analysis

There are 22,283 probe sets on the Affymetrix GeneChip Human Genome U133A (HG_U133A). The Robust Multiarray Average (RMA) algorithm [33], [34] implemented in Bioconductor in R (http://www.bioconductor.org; accessed 2013 Apr 14) was used for background correction and normalization across all samples. All statistical methods were developed in R. The GEO accession number for these array data is GSE29272.

Principal Components Analysis (PCA) was used for clustering analysis of all gastric cancer samples analyzed here. For the PCA only, we also included data from a recently published expression array study of ESCC cases for comparison [35].

We applied paired t-tests to each of the 22,283 probesets to identify genes that were differentially expressed between tumors and their matched normal samples, but we present results only for the 21,130 probesets that mapped to 13,003 genes. To account for multiple comparisons, we selected genes that showed significant differences with P-values less than 4.73E-07 (equal to 0.01 divided by 21,130, ie, a conservative Bonferroni adjustment). In addition to the P-value cutoff, differentially-expressed genes had to show at least a two-fold difference in gene expression magnitude between tumor and normal tissues (ie, fold change either ≥2 or ≤0.50).

To identify dysregulated genes whose expression was associated with personal (gender and family history of upper gastrointestinal or UGI cancer) and clinical (tumor stage, grade, lymph node metastasis) characteristics, we performed unpaired t-tests for gene expression differences between samples using the ratio of tumor gene expression divided by the matched normal gene expression. A P-value threshold (P<0.005) was used for significance for these analyses; no fold change criteria were applied.

To assess the relation of gene expression to survival, Kaplan-Meier (KM) plots were used to visualize survival differences by high (above median) vs low (below median) gene expression status and log-rank tests were used to test for differences using the tumor probeset signal for each differentially-expressed gene identified in the tumor/normal paired t-test analysis described above. Genes whose expression was significantly related to survival in log-rank tests were further evaluated in Cox proportional hazard models for high vs low expression with adjustment for demographic and clinical characteristics of tumors (ie, age, sex, stage, grade, metastasis). For all survival analyses, we used a two-sided P-value <0.05 as our threshold for statistical significance.

5. Validation of Differentially-Expressed Genes Using Quantitative Real-Time RT-PCR

A total of 21 differentially-expressed genes (12 for GCA and 9 for GNCA) were selected for validation using quantitative Real-Time RT-PCR (qRT-PCR). For technical validation, qRT-PCR assays were performed using the same tumor and normal RNAs analyzed in the microarray experiment for a subset of GCAs (n = 41 of 62) and GNCAs (n = 50 of 72). For replication validation, tumor and matched normal RNAs from a new set of GNCAs (n = 44) were tested.

First strand cDNA was synthesized using 3 µg total RNA with Oligo (dT)12−18 (500 µg/ml) in a 20 µl reaction with Superscript II reverse transcriptase system (Invitrogen, Carlsbad, CA). The cDNA products were then diluted at 1∶100. Real-time PCR reactions were performed using an ABI Prism 7000 Sequence Detection System (Perkin-Elmer Applied Biosystems, Foster City, CA). All primers and probes of seven target genes and an internal control gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were purchased from Applied Biosystems. qRT-PCR reactions were carried out according to the manufacturer’s protocol, as described previously [36]. The thermal cycling conditions included an initial denaturing step at 95°C for 10 min, 40 cycles at 95°C each for 15 sec, 60°C for one min, and 72°C for one min. Gene expression was analyzed using 2–ΔΔCT algorithm.

Results

1. Patient Characteristics

A total of 62 GCA and 72 GNCA patients were analyzed using the Affymetrix U133A array. Personal and clinical data for patients studied are summarized in Table 1 (Clinical characteristics for individual cases studied here are shown in Table S1). The average age at diagnosis was mid-to-late 50s, males predominated, and about one-fourth had a family history of upper gastrointestinal (UGI) cancer (ie, esophageal or gastric cancer in first, second, or third degree relatives). For both GCA and GNCA, most cases studied were late stage, high grade, and intestinal cell type tumors with lymph node metastasis. Median survival for GCA cases was 20.3 months, and for GNCA cases was 27.8 months, based on a total of 50 and 52 deaths, respectively.

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Table 1. Summary of personal and clinical characteristics of gastric cardia adenocarcinoma (GCA) and gastric noncardia adenocarcinoma (GNCA) cases.

https://doi.org/10.1371/journal.pone.0063826.t001

2. Principal Component Analysis of Gene Expression Microarray Data

We used Principal Component Analysis (PCA) to gain an understanding of global gene expression of these samples. In this analysis, we evaluated 124 samples from 62 GCA patients (each with tumor and normal pair) and 144 samples from 72 GNCA patients (each with tumor and normal pair) as well as 106 samples from ESCC patients (each with tumor and normal pair) from a previous study [35]. PCA revealed two major clusters of samples separating all gastric cancers combined (GCA in red, GNCA in blue) from ESCC (green) in the PC1 axis (Figure 1). The two clusters were further divided into normal and tumor tissues by PC2 (tumor and normal are denoted by t and n, respectively). The difference between GCA (red) and GNCA (blue) was also noticeable, especially for normal tissues. We then concentrated on the analyses of gastric cancer. PCA of GCA (Figure 2) and GNCA (Figure 3) showed again the separation of samples into tumor (t) and normal (n) clusters.

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Figure 1. Principle component analyses of RNA expression for gastric cardia, gastric noncardia, and esophageal cancers.

PCA revealed two major clusters of samples separating gastric cancer (CC [GCA] in red, N = 62; BC [GNCA] in blue, N = 72) from EC [ESCC] (green, N = 53) in the PC1 axis. PC2 further divided clusters into tumor (t) and normal (n) samples. (Note regarding comparability of expression results: Cases of ESCC, GCA, and GNCA were enrolled concurrently from a single hospital using a common protocol; samples were collected, processed, stored, and transported in an identical manner; and laboratory analyses were performed during the same time period, in the same lab, by the same technical staff, using the same platform, and the same technical approach.).

https://doi.org/10.1371/journal.pone.0063826.g001

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Figure 2. PCA analysis of GCA patients (124 chips from 62 patients).

Red “t “ represents tumor and green “n” represents matched normal tissue.

https://doi.org/10.1371/journal.pone.0063826.g002

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Figure 3. PCA analysis of GNCA patients (144 chips from 72 patients).

Red “t “ represents tumor and green “n” represents matched normal tissue.

https://doi.org/10.1371/journal.pone.0063826.g003

3. Identification of Genes Up- or Down-Regulated in GCA and GNCA

For GCA, a total of 367 genes were differentially expressed between tumors and their matched normal samples. Of these genes, 199 genes were up-regulated and 168 were down-regulated (Figure 4). For GNCA, a total of 383 genes were differentially expressed between tumors and matched non-tumor samples, including 192 genes up-regulated and 191 genes down- regulated (Figure 4).

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Figure 4. Diagram showing the number of significantly dysregulated genes (GCA only, GNCA only, and common to both GCA and GNCA).

https://doi.org/10.1371/journal.pone.0063826.g004

4. Comparison of Gene Expression in GCA and GNCA

We compared the two sets of genes that showed significant differences in gene expression for GCA and GNCA and identified 239 genes that were dysregulated in both GCA and GNCA, among which 113 were up- and 126 down-regulated (Figure 4 and Table S2). In addition, we found that 128 genes were dysregulated only in GCA (86 up- and 42 down-regulated) (Figure 4 and Table S3), and 144 genes were dysregulated only in GNCA (79 up- and 65 down-regulated) (Figure 4 and Table S4).

Among the 113 genes up-regulated in both GCA and GNCA were genes associated with cell cycle checkpoint (eg, CDC2, TOP2A), Wnt signaling (eg, SULF1, SFRP4, LEF1, LAMB1), adhesion (eg, FN1), and the TGF-β pathway (eg, COL1A1, COL1A2, COL3A1). In contrast, the 126 down-regulated genes common to both GCA and GNCA were enriched in processes involved in metabolism (eg, AADAC, CA2, CA9), digestion (eg, ATP4A, ATP4B), and the development of gastrointestinal tissue (eg, GIF, MUC5A, MUC6, TFF1, TFF2) (Table S2). These results suggest that GCA and GNCA share many common etiologic pathways.

Genes up-regulated only in GCA were involved in cell cycle checkpoint regulation (eg, CCNB1, CCNB2, CCNE1, CDC25B, SFN), CXC chemokine (eg, CXCL1, CXCL10), and extracellular matrix (eg, MMP9, MMP11); while GCA-only down-regulated genes were associated with detoxification (eg, GPX3) and oncogenes (eg, FOS, JUN) (Table S3).

Among genes altered significantly in GNCA only, up-regulated genes included those related to epithelial-to-mesenchymal transition (eg, CALD1, IGFBP4) and actin filaments (eg, DES, FLNA). By contrast, down-regulated genes functioned in detoxification (eg, CYP2C9) and epithelial surfaces (eg, MUC1) (Table S4).

5. Relation Between Gene Expression and Patient Personal/Clinical Characteristics

In GCA, differentially-expressed genes were found to be related to family history of UGI cancer (Table S5a) and lymph node metastasis (Table S5b), but not other characteristics (ie, gender, tumor stage, tumor grade; data not shown). Sixty-seven genes were significantly dysregulated (47 up- and 20 down-regulated) in patients with a family history of UGI cancer (n = 16 cases) compared to patients without such history (n = 46 cases), but fold changes were generally small: the largest fold change among up-regulated genes was 1.39 (ie, JDP1), while four down-regulated genes (LMO4, ABHD2, LAMA3, MAP17) were reduced by one-third or more (Table S5a). For clinical characteristics, we identified 57 genes that were significantly dysregulated (9 up- and 48 down-regulated) in GCA patients with positive lymph nodes (n = 50 cases) compared to lymph node negative patients (n = 12 cases); 11 of these genes (six down- and five up-regulated) reached 1.5-fold change (Table S5b).

For GNCA, 37 genes had significantly different expression levels in family history positive (n = 16) versus negative (n = 56) cases (Table S6a), but fold changes were all less than 1.5. Significant differentially-expressed genes were also identified for several tumor clinical characteristics, including late (III/IV) versus early (I/II) stage (n = 90 genes), and high (3) versus low (1/2) grade (n = 89 genes) (Tables S6b and S6c). Lymph node metastasis, the strongest clinical characteristic predictive of survival, was also associated with expression levels in 57 genes (Table S6d).

6. Gene Expression and Survival

We evaluated the relation of RNA expression to survival for each of the genes/probesets on the microarray. For GCA, 20 genes were significantly associated with survival (nominal P-value <0.05) by log rank tests (Table 2). An illustrative example of the survival curve for one of these genes (MMP9) is shown in Figure 5. Eleven genes remained significant after further adjustment for covariates in Cox models. Similar analyses for GNCA showed that 36 genes were significantly associated with survival in log rank tests, including 27 that remained significant after covariate adjustment (Table 3); a survival curve for one of the 36 (ESRRG) is shown in Figure 6.

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Figure 5. Survival curve for MMP9 in GCA by Kaplan-Meier analyses.

Dotted red lines indicate high (above the median) expression and broken blue lines indicate low (below the median) expression.

https://doi.org/10.1371/journal.pone.0063826.g005

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Figure 6. Survival curve for ESRRG in GNCA by Kaplan-Meier analyses.

Dotted red lines indicate high (above the median) expression and broken blue lines indicate low (below the median) expression.

https://doi.org/10.1371/journal.pone.0063826.g006

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Table 2. Genes whose expression was significantly associated with survival in gastric cardia adenocarcinoma cancers (log-rank p-value <0.05; n = 62).

https://doi.org/10.1371/journal.pone.0063826.t002

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Table 3. Genes whose expression was significantly associated with survival in gastric noncardia adenocarcinoma cancers (log-rank p-value <0.05; n = 72).

https://doi.org/10.1371/journal.pone.0063826.t003

7. Validation of Differentially-Expressed Genes Using Quantitative Real-Time RT-PCR

We performed technical validation experiments for 12 genes in 41 of the 62 GCA cases whose samples still had sufficient RNA quantity after completion of the array study. Four up-regulated (SULFI, CDC2, TOP2A, BUB1B) and eight down-regulated (CA9, CCKBR, PIK3C2G, FOS, JUN, KLF4, KLK11, NUCB2) genes were assayed using quantitative real-time RT-PCR (qRT-PCR). Our results showed that gene expression patterns were very similar to RNA array experiment results (Table 4 and Table S7).

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Table 4. Gastric cardia adenocarcinoma tumor/normal RNA expression fold change for genes validated by RT-PCR and comparison with microarray results (n = 41 cases, 12 genes).

https://doi.org/10.1371/journal.pone.0063826.t004

For GNCA, nine differentially-expressed genes (five up- and four down-regulated) were selected for validation by qRT-PCR. Sample pairs for 50 of 72 cases examined on the RNA chip were evaluated for technical replication and showed results similar to those from the array (Table 5 and Table S8a). In addition, matched tumor/normal samples from a new set of 44 GNCA cases were tested for replication validation. Results were consistent with those from the expression array data (Table 5 and Table S8b).

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Table 5. Gastric noncardia adenocarcinoma tumor/normal RNA expression fold change for genes validated by RT-PCR and comparison with microarray results (n = 94 cases, 9 genes).

https://doi.org/10.1371/journal.pone.0063826.t005

Discussion

GCA is one of the few malignancies that has increased sharply in developed countries in recent years for reasons that are as yet unexplained, and the molecular events surrounding this gastric cancer remain largely unknown [37], [38]. To better understand the molecular events in gastric cancer and its anatomic subtypes, we profiled gene expression in GCA and GNCA patients from a high-risk population in China using high density RNA expression microarrays. We identified 511 genes whose expression was dysregulated in gastric cancer overall, including nearly one-half (n = 239, 47%) dysregulated in both GCA and GNCA, one-fourth dysregulated in GCA only (n = 128, 25%), and about one-fourth in GNCA only (n = 144, 28%). Associations with family history of UGI cancer hint at genetic susceptibility in etiology, while associations with clinical characteristics and survival suggest potential therapeutic targets for further evaluation.

The common up-regulated genes identified are involved in many pathways related to the development of cancer, including the cell cycle, cellular growth and proliferation, cell cycle checkpoint, extracellular matrix remodeling, and angiogenesis (eg, Wnt signaling and cell cycle checkpoint pathways, such as SULF1, SFRP4, LEF1, TOP2A, and CDC2 [26], [26], [27], [39][41], and the integrin signaling pathway (ARPC1B, COL1A1, COL4A1, FN1, and LAMB1)). Some genes are also related to adaptive immune responses (eg, CD14) and tumor metastasis (eg, CD9). The common down-regulated genes found in GCA and GNCA are consistent with other studies on gastric cancer using microarrays, such as AKR1B10, ALDH3A1, ATP4B, CA2, IGFBP2, KLF4, MUC5AC, MUC6, TFF1, and TFF2 [25], [29], [39]. The down-regulated genes in our study are mainly involved in metabolic pathways, digestive system development, or mucosal integrity. Several genes are thought to have specific functions in gastric epithelium, such as PGC and GIF, implying that dedifferentiation is a common feature of carcinogenesis [26]. BUB1B is a spindle-assembly checkpoint gene. A recent report of a case with multiple gastrointestinal neoplasias, including gastric adenocarcinomas, identified a germline homozygous intronic mutation in BUB1B, with low levels of BUB1B mRNA and protein in lymphocytes and fibroblasts, suggesting that BUB1B is a susceptibility gene for this tumor [40]. In our study BUB1B was up-regulated in both GCA (2.56 fold) and GNCA (2.11 fold), which is opposite to the case report cited, suggesting that it would be useful to investigate BUB1B mutation status in our GCA and GNCA patients.

For genes dysregulated significantly in GCA only, we note a couple of interesting examples here. SOX9 showed a 2.13-fold change in GCA patients but no significant increase in GNCA cases. SOX9 (located on 17q24.3-q25.1) is thought to play an essential role in sex determination and marks the precursor cell population during physiological cell replacement including the regenerative process after injury [41], [42]. The expression of SOX9 has previously been reported in several organs such as pancreas and intestine [41], but not stomach. A recently published study found that “Sox9 marks a putative adult stem cell population that contributes to the self-renewal and repair of the liver, exocrine pancreas and intestine, three organs of endodermal origin” [41]. COL2A is a candidate regulatory target of SOX9 [42]. In our GCA cases, COL2A1 was down-regulated (fold change 0.27), which may be a result of SOX9 up-regulation.

Some of the differentially-expressed genes reported in GCA were also dysregulated in a similar pattern as esophageal squamous cell carcinomas (ESCC) examined from this same high-risk population, such as CDC25B and COL1A2 [43]. This similarity suggests that despite their differences in cell type, GCA and ESCC from this high-risk population of China likely share common genetic and/or environmental factors in their etiology. Evidence for a common genetic influence is evident by results from a recent genome-wide association study which found a shared susceptibility locus in PLCE1 for both GCA and ESCC [19].

Among the genes significantly dysregulated in GNCA only, DES is the only one that showed a different expression directionality in GNCA (3.24 fold change) than GCA (0.85 fold change). DES (Desmin on 2q35) encodes desmin, a muscle-specific cytoskeletal protein found in smooth, cardiac, and heart muscles. We identified several genes associated with actin filaments, such as FLNA, ACTN1, SVIL and TPM1. Several genes dysregulated only in GNCA were also related with extracellular matrix, such as EMILIN1 and TNC. Studies on TNC indicated that up-regulation of TNC disrupted cell substrate adhesion [44].

Another purpose of this study was to investigate how gene expression profiles in the tumors differed among patients with different clinical phenotypes. Although we identified a large number of associations at our designated P-value threshold of 0.005, only three remained significant after Bonferroni correction for multiple comparisons (ie, P<2.36E-06). The most significant associations were for COL11A1 and ITGAX with tumor stage in GNCA, and UNG with metastasis, also in GNCA. COL11A1 [45] and ITGAX [46] have both been previously related to tumor stage for other cancers (eg, melanoma, non-small cell lung cancer), but there are no reports for UNG and metastasis.

We also sought to evaluate survival by gene expression for genes that were significantly dysregulated. Among the 20 genes related to GCA survival and the 36 genes related to GNCA survival were just three genes that overlapped – CTSB, LEPR, and LIPF. The most significant statistical associations observed with survival (P<0.01 in log-rank tests) were for COL11A1, CTSB, and MMP9 for GCA, and ADA, ESRRG, and LHFP for GNCA. Although no studies have yet reported on COL10A1, ADA, or LHFP and cancer survival, CTSB [47] and MMP9 [48] have both been previously associated with survival in gastric cancer, while ESRRG expression has been associated with survival in prostate cancer [49].

Conclusion

This is the first report focused on global gene expression in GCA and GNCA in a high-risk population from Shanxi China. Our study identified hundreds of genes that are changed between tumor and normal tissues as well as genes that distinguish between clinical phenotypes and predict survival. Results described here represent a comprehensive starting point for future efforts to understand etiologic heterogeneity, develop diagnostic biomarkers for early detection, and test molecularly-targeted therapies for gastric cancer.

Supporting Information

Table S1.

Clinical characteristics of cases (Table S1a: Clinical characteristics of GCA cases (N = 62); Table S1b: Clinical characteristics of GNCA cases (N = 72).

https://doi.org/10.1371/journal.pone.0063826.s001

(XLS)

Table S2.

Summary of 239 genes that are significantly differentially expressed in both GCA and GNCA.

https://doi.org/10.1371/journal.pone.0063826.s002

(XLS)

Table S3.

Summary of 128 genes that are significantly differentially expressed in GCA only.

https://doi.org/10.1371/journal.pone.0063826.s003

(XLS)

Table S4.

Summary of 144 genes that are significantly differentially expressed in GNCA only.

https://doi.org/10.1371/journal.pone.0063826.s004

(XLS)

Table S5.

Genes significantly associated with patient’s personal/clinical characteristics for GCA (Table S5a: Genes related to family history of UGI cancer in GCA cases; Table S5b: Genes related to lymph node metastasis in GCA cases).

https://doi.org/10.1371/journal.pone.0063826.s005

(XLS)

Table S6.

Genes significantly associated with patient’s personal/clinical characteristics for GNCA (Table S6a: Genes related to family history of UGI cancer in GNCA cases; Table S6b: Genes related to tumor stage in GNCA cases; Table 6Sc: Genes related to tumor grade in GNCA cases; Table S6d: Genes related to metastasis in GNCA cases).

https://doi.org/10.1371/journal.pone.0063826.s006

(XLS)

Table S7.

Comparison of gene expression for 12 genes in 41 GCA patients studied using both microarray and qRT-PCR.

https://doi.org/10.1371/journal.pone.0063826.s007

(XLS)

Table S8.

Comparison of gene expression for 9 genes in 50 GNCA patients studied using both microarray and qRT-PCR (Table S8a). Results of gene expression by qRT-PCR for 9 genes in 44 new GNCA cases (Table S8b).

https://doi.org/10.1371/journal.pone.0063826.s008

(XLS)

Acknowledgments

The authors thank the subjects who participated in this study.

Author Contributions

Conceived and designed the experiments: GW NH JML TD XYH CG AMG PRT MPL. Performed the experiments: GW NH LW HS CW EMD. Analyzed the data: HHW RC CG MPL. Wrote the paper: GW NH AMG PRT MPL. Oversaw field and clinical work to recruit subjects, collect data, and oversaw biologic sample collection and processing: JML TD XYH NH AMG PRT.

References

  1. 1. Parkin DM, Bray F, Ferlay J, Pisani P (2005) Global cancer statistics, 2002. CA Cancer J Clin 55: 74–108.
  2. 2. Li JY (1982) Epidemiology of esophageal cancer in China. Natl Cancer Inst Monogr 62: 113–120.
  3. 3. Sun X, Mu R, Zhou Y, Dai X, Qiao Y, et al. (2002) [1990–1992 mortality of stomach cancer in China]. Zhonghua Zhong Liu Za Zhi 24: 4–8.
  4. 4. Cancer Institute CAoMS (2011) China Cancer Database. http://cancernet.cicams.ac.cn. (accessed 2013 Apr 14)
  5. 5. Anderson WF, Camargo MC, Fraumeni JF Jr, Correa P, Rosenberg PS, et al. (2010) Age-specific trends in incidence of noncardia gastric cancer in US adults. JAMA 303: 1723–1728 303/17/1723 [pii];10.1001/jama.2010.496 [doi].
  6. 6. Gao Y, Hu N, Han XY, Ding T, Giffen C, et al. (2011) Risk factors for esophagel and gastric cancers in Shanxi Province, China: A case-control study. Cancer Epidemiol 35: e91–e99.
  7. 7. Li JY, Taylor PR, Li B, Dawsey S, Wang GQ, et al. (1993) Nutrition intervention trials in Linxian, China: multiple vitamin/mineral supplementation, cancer incidence, and disease-specific mortality among adults with esophageal dysplasia. J Natl Cancer Inst 85: 1492–1498.
  8. 8. Mark SD, Qiao YL, Dawsey SM, Wu YP, Katki H, et al. (2000) Prospective study of serum selenium levels and incident esophageal and gastric cancers. J Natl Cancer Inst 92: 1753–1763.
  9. 9. Wei W, Abnet CC, Qiao YL, Dawsey SM, Dong ZW, et al. (2004) Prospective study of serum selenium concentration and esophageal and gastric cardia cancer, heart disease, stroke, and total death. Am J Clin Nutr 79: 80–85.
  10. 10. Gao Y, Hu N, Han X, Giffen C, Ding T, et al. (2009) Family history of cancer and risk for esophageal and gastric cancer in Shanxi, China. BMC Cancer 9: 269.
  11. 11. Sipponen P (1995) Helicobacter pylori: a cohort phenomenon. Am J Surg Pathol 19 Suppl 1S30–S36.
  12. 12. Tran GD, Sun XD, Abnet CC, Fan JH, Dawsey SM, et al. (2005) Prospective study of risk factors for esophageal and gastric cancers in the Linxian general population trial cohort in China. Int J Cancer 113: 456–463.
  13. 13. Kamangar F, Qiao YL, Blaser MJ, Sun XD, Katki H, et al. (2007) Helicobacter pylori and oesophageal and gastric cancers in a prospective study in China. Br J Cancer 96: 172–176.
  14. 14. Blot WJ, Li JY, Taylor PR, Guo W, Dawsey S, et al. (1993) Nutrition intervention trials in Linxian, China: supplementation with specific vitamin/mineral combinations, cancer incidence, and disease-specific mortality in the general population. J Natl Cancer Inst 85: 1483–1492.
  15. 15. Qiao YL, Dawsey SM, Kamangar F, Abnet CC, Johnson LL, et al. (2009) Total and cancer mortality following supplementation with vitamins and minerals: Post-intervention follow-up of the General Population Nutrition Intervention Trial in Linxian China. J Natl Cancer Inst 101: 507–518.
  16. 16. Lauren P (1965) The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma. An attempt at histo-clinical classification. Acta Pathol Microbiol Scand 64: 31–49.
  17. 17. Liu SF, Shen Q, Dawsey SM, Wang GQ, Nieberg RK, et al. (1994) Esophageal balloon cytology and subsequent risk of esophageal and gastric-cardia cancer in a high-risk Chinese population. Int J Cancer 57: 775–780.
  18. 18. Corley DA, Kubo A (2004) Influence of site classification on cancer incidence rates: an analysis of gastric cardia carcinomas. J Natl Cancer Inst 96: 1383–1387 10.1093/jnci/djh265 [doi];96/18/1383 [pii].
  19. 19. Abnet CC, Freedman ND, Hu N, Wang Z, Yu K, et al. (2010) A shared susceptibility locus in PLCE1 at 10q23 for gastric adenocarcinoma and esophageal squamous cell carcinoma. Nat Genet 42: 764–767 ng.649 [pii];10.1038/ng.649 [doi].
  20. 20. Gleeson CM, Sloan JM, McManus DT, Maxwell P, Arthur K, et al. (1998) Comparison of p53 and DNA content abnormalities in adenocarcinoma of the oesophagus and gastric cardia. Br J Cancer 77: 277–286.
  21. 21. Berx G, Becker KF, Hofler H, van RF (1998) Mutations of the human E-cadherin (CDH1) gene. Hum Mutat 12: : 226–237. 10.1002/(SICI)1098-1004(1998)12:4<226::AID-HUMU2>3.0.CO;2-D [pii];10.1002/(SICI)1098-1004(1998)12:4<226::AID-HUMU2>3.0.CO;2-D [doi].
  22. 22. Park WS, Oh RR, Park JY, Lee SH, Shin MS, et al. (1999) Frequent somatic mutations of the beta-catenin gene in intestinal-type gastric cancer. Cancer Res 59: 4257–4260.
  23. 23. Park WS, Oh RR, Park JY, Lee JH, Shin MS, et al.. (2000) Somatic mutations of the trefoil factor family 1 gene in gastric cancer. Gastroenterology 119: 691–698. S0016508500762664 [pii].
  24. 24. Lee JH, Han SU, Cho H, Jennings B, Gerrard B, et al. (2000) A novel germ line juxtamembrane Met mutation in human gastric cancer. Oncogene 19: 4947–4953 10.1038/sj.onc.1203874 [doi].
  25. 25. Hasegawa S, Furukawa Y, Li M, Satoh S, Kato T, et al. (2002) Genome-wide analysis of gene expression in intestinal-type gastric cancers using a complementary DNA microarray representing 23,040 genes. Cancer Res 62: 7012–7017.
  26. 26. Jinawath N, Furukawa Y, Hasegawa S, Li M, Tsunoda T, et al. (2004) Comparison of gene-expression profiles between diffuse- and intestinal-type gastric cancers using a genome-wide cDNA microarray. Oncogene 23: 6830–6844 10.1038/sj.onc.1207886 [doi];1207886 [pii].
  27. 27. Boussioutas A, Li H, Liu J, Waring P, Lade S, et al. (2003) Distinctive patterns of gene expression in premalignant gastric mucosa and gastric cancer. Cancer Res 63: 2569–2577.
  28. 28. Weiss MM, Kuipers EJ, Postma C, Snijders AM, Siccama I, et al. (2003) Genomic profiling of gastric cancer predicts lymph node status and survival. Oncogene 22: 1872–1879 10.1038/sj.onc.1206350 [doi];1206350 [pii].
  29. 29. Kim B, Bang S, Lee S, Kim S, Jung Y, et al. (2003) Expression profiling and subtype-specific expression of stomach cancer. Cancer Res 63: 8248–8255.
  30. 30. Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, et al. (2002) Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res 62: 233–240.
  31. 31. Shah MA, Khanin R, Tang L, Janjigian YY, Klimstra DS, et al. (2011) Molecular classification of gastric cancer: a new paradigm. Clin Cancer Res 17: 2693–2701 1078-0432.CCR-10-2203 [pii];10.1158/1078-0432.CCR-10–2203 [doi].
  32. 32. (2009) TNM classification of malignant tumours. Chichester, West Sussex, UK: A John Wiley & Sons, Ltd.
  33. 33. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, et al. (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4: 249–264.
  34. 34. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19: 185–193.
  35. 35. Su H, Hu N, Yang HH, Wang C, Takikita M, et al. (2011) Global gene expression profiling and validation in esophageal squamous cell carcinoma and its association with clinical phenotypes. Clin Cancer Res 17: 2955–2966 1078-0432.CCR-10–2724 [pii];10.1158/1078-0432.CCR-10-2724 [doi].
  36. 36. Hu N, Qian L, Hu Y, Shou JZ, Wang C, et al. (2006) Quantitative real-time RT-PCR validation of differential mRNA expression of SPARC, FADD, Fascin, COL7A1, CK4, TGM3, ECM1, PPL, and EVPL in esophageal squamous cell carcinoma. BMC Cancer 6: 33.
  37. 37. Tytgat GN, Bartelink H, Bernards R, Giaccone G, van Lanschot JJ, et al. (2004) Cancer of the esophagus and gastric cardia: recent advances. Dis Esophagus 17: 10–26 10.1111/j.1442-2050.2004.00371.x [doi];DES371 [pii].
  38. 38. Tajima Y, Nakanishi Y, Yoshino T, Kokawa A, Kusano M, et al.. (2001) Clinicopathological study of early adenocarcinoma of the gastric cardia: comparison with early adenocarcinoma of the distal stomach and esophagus. Oncology 61: 1–9. 55345 [pii].
  39. 39. Chen X, Leung SY, Yuen ST, Chu KM, Ji J, et al. (2003) Variation in gene expression patterns in human gastric cancers. Mol Biol Cell 14: 3208–3215 10.1091/mbc.E02-12-0833 [doi];E02-12-0833 [pii].
  40. 40. Rio FT, Lavoie J, Hamel N, Geyer FC, Kushner YB, et al. (2010) Homozygous BUB1B mutation and susceptibility to gastrointestinal neoplasia. N Engl J Med 363: 2628–2637 10.1056/NEJMoa1006565 [doi].
  41. 41. Furuyama K, Kawaguchi Y, Akiyama H, Horiguchi M, Kodama S, et al. (2011) Continuous cell supply from a Sox9-expressing progenitor zone in adult liver, exocrine pancreas and intestine. Nat Genet 43: 34–41 ng.722 [pii];10.1038/ng.722 [doi].
  42. 42. Huch M, Clevers H (2011) Sox9 marks adult organ progenitors. Nat Genet 43: 9–10 ng0111-9 [pii];10.1038/ng0111-9 [doi].
  43. 43. Su H, Hu N, Shih J, Hu Y, Wang QH, et al. (2003) Gene expression analysis of esophageal squamous cell carcinoma reveals consistent molecular profiles related to a family history of upper gastrointestinal cancer. Cancer Res 63: 3872–3876.
  44. 44. Olson EN, Srivastava D (1996) Molecular pathways controlling heart development. Science 272: 671–676.
  45. 45. Chong IW, Chang MY, Chang HC, Yu YP, Sheu CC, et al. (2006) Great potential of a panel of multiple hMTH1, SPD, ITGA11 and COL11A1 markers for diagnosis of patients with non-small cell lung cancer. Oncol Rep 16: 981–988.
  46. 46. Baumgartner JM, Gonzalez R, Lewis KD, Robinson WA, Richter DA, et al. (2009) Increased survival from stage IV melanoma associated with fewer regulatory T Cells. J Surg Res 154: 13–20 S0022-4804(08)00344-2 [pii];10.1016/j.jss.2008.04.043 [doi].
  47. 47. van DH, Tilanus HW, Hop WC, Dinjens WN, Wink JC, et al. (2009) Array comparative genomic hybridization, expression array, and protein analysis of critical regions on chromosome arms 1q, 7q, and 8p in adenocarcinomas of the gastroesophageal junction. Cancer Genet Cytogenet 189: 37–42 S0165-4608(08)00536-0 [pii];10.1016/j.cancergencyto.2008.08.018 [doi].
  48. 48. Peng CW, Liu XL, Liu X, Li Y (2010) Co-evolution of cancer microenvironment reveals distinctive patterns of gastric cancer invasion: laboratory evidence and clinical significance. J Transl Med 8: 101 1479-5876-8-101 [pii];10.1186/1479-5876-8-101 [doi].
  49. 49. Fujimura T, Takahashi S, Urano T, Ijichi N, Ikeda K, et al. (2010) Differential expression of estrogen-related receptors beta and gamma (ERRbeta and ERRgamma) and their clinical significance in human prostate cancer. Cancer Sci 101: 646–651 CAS1451 [pii];10.1111/j.1349-7006.2009.01451.x [doi].