Introduction

Molecular Medicine Reports

1791-2997 1791-3004

D.A. Spandidos

10.3892/mmr.2022.12707

MMR-25-05-12707

Articles

Copy number alterations and epithelial-mesenchymal transition genes in diffuse and intestinal gastric cancers in Mexican patients

Larios-Serrato

Violeta

1 * Martínez-Ezquerro

José-Darío

2 3 * Valdez-Salazar

Hilda-Alicia

4 * Torres

Javier

4 Camorlinga-Ponce

Margarita

4 Piña-Sánchez

Patricia

5 Ruiz-Tachiquín

Martha-Eugenia

1Laboratory of Biotechnology and Genomic Bioinformatics, National School of Biological Sciences (ENCB), National Polytechnic Institute (IPN), Lázaro Cárdenas Professional Unit, Mexico City 11340, Mexico 2Epidemiological and Health Services Research Unit, Aging Area (UIESSAE), XXI Century National Medical Center, Mexican Social Security Institute (IMSS), Mexico City 06720, Mexico 3Complexity Sciences Center (C3), National Autonomous University of Mexico (UNAM), Mexico City 04510, Mexico 4Infectious and Parasitic Diseases Medical Research Unit (UIMEIP), High Specialty Medical Unit (UMAE)-Pediatrics Hospital ‘Dr. Silvestre Frenk Freund’, XXI Century National Medical Center, IMSS, Mexico City 06720, Mexico 5Oncological Diseases Medical Research Unit (UIMEO), UMAE-Oncology Hospital, XXI Century National Medical Center, Mexican Social Security Institute (IMSS), Mexico City 06720, Mexico

Correspondence to: Dr Martha-Eugenia Ruiz-Tachiquín, Oncological Diseases Medical Research Unit (UIMEO), UMAE-Oncology Hospital, XXI Century National Medical Center, Mexican Social Security Institute (IMSS), Av. Cuauhtémoc 330, Colonia Doctores, Alcaldía Cuauhtémoc, Mexico City 06720, Mexico, E-mail: mertachiquin@gmail.com *

Contributed equally

05 2022

31 03 2022

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2022

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Gastric cancer (GC) is a common malignancy with the highest mortality rate among diseases of the digestive system, worldwide. The present study of GC alterations is crucial to the understanding of tumor biology and the establishment of important aspects of cancer prognosis and treatment response. In the present study, DNA from Mexican patients with diffuse GC (DGC), intestinal GC (IGC) or non-atrophic gastritis (NAG; control) was purified and whole-genome analysis was performed with high-density arrays. Shared and unique copy number alterations (CNA) were identified between the different tissues involving key genes and signaling pathways associated with cancer. This led to the molecular distinction and identification of the most relevant molecular functions to be identified. A more detailed bioinformatics analysis of epithelial-mesenchymal transition (EMT) genes revealed that the altered network associated with chromosomal alterations included 11 genes that were shared between DGC, IGC and NAG, as well as 19 DGC- and 7 IGC-exclusive genes. Furthermore, the main molecular functions included adhesion, angiogenesis, migration, metastasis, morphogenesis, proliferation and survival. The present study provided the first whole-genome high-density array analysis in Mexican patients with GC and revealed shared and exclusive CNA-associated genes in DGC and IGC. In addition, a bioinformatics-predicted network was generated, focusing on CNA-altered genes associated with EMT and the hallmarks of cancer, as well as precancerous alterations that may lead to GC. Molecular signatures of diffuse and intestinal GC, predicted bioinformatically, involve common and distinct CNA-EMT genes related to the hallmarks of cancer that are potential candidates for screening biomarkers of GC, including early stages.

copy number alterations CNA gastric cancer high-density arrays epithelial-mesenchymal transition EMT

Fondo de Investigación en Salud-Instituto Mexicano del Seguro Social

FIS/IMSS/PROT/G16/1573

FIS/IMSS/PROT/PRIO/13/027

The present study was supported by the Fondo de Investigación en Salud-Instituto Mexicano del Seguro Social (grant nos. FIS/IMSS/PROT/G16/1573 and FIS/IMSS/PROT/PRIO/13/027).

Introduction

According to the Global Cancer Observatory statistics, cancer is the leading cause of death in the world, with 9.9 million deaths in 2020; the incidence rate of cancer was 20% in the Caribbean and South America, with high mortality rates (14%). Worldwide, gastric cancer (GC) is estimated to be the fifth most common cancer type in both sexes, ranking sixth for new cases, with over one million cases per year and third in mortality (1).

In Mexico, according to statistics from the National Institute of Statistics and Geography, three out of 10 cancer-associated deaths among patients aged 30–59 years were due to cancer of the digestive system. From 2011 to 2016, four out of 10 and three out of 10 cancer-associated deaths respectively occurred in females and males aged >60 years and resulted from tumors in digestive organs (2).

GC refers to any malignancy originating in the region between the gastroesophageal junction and the pylorus. The World Health Organization and the Lauren classification system (3) have classified GC into two types: Intestinal GC (IGC) and diffuse GC (DGC). Intestinal or differentiated gastric cancer is characterized by localized and expansive growth, while DGC has an infiltrating growth pattern, is an undifferentiated adenocarcinoma and features dispersed cells with individual or group invasive capacity (4). The development of IGC is preceded by a precancerous process of several years and stages: Active chronic gastritis, multifocal atrophic gastritis, complete intestinal metaplasia, incomplete intestinal metaplasia, dysplasia and adenocarcinoma (5). GC has a multifactorial origin: Diet, lifestyle, genetics and socioeconomic factors, and it has been observed that 80% of cases of IGC are associated with previous Helicobacter pylori (H. pylori) infection (6,7). GC is characterized by a complex etiology with a set of factors, including genetic alterations and external factors. However, it has been reported that <3% of GC is due to heredity and includes hereditary DGC, proximal polyposis of the stomach and hereditary colorectal cancer not associated with polyposis (6). With respect to molecular pathogenesis, chromosomal instability (aneuploidy, chromosomal translocation, amplification, deletions and loss of heterozygosity), gene fusion and microsatellite instability (hypermethylation of gene repair promoters) are involved (7).

Copy number alterations (CNA) represent a class of genetic variation that involve cumulative somatic variations. CNA are defined as non-inherited genetic alterations that occur in somatic cells (8). These unbalanced structural variants usually contain gains or losses. Their interpretation and the CNA report continue to be a topic of interest in health and have an important role in GC (9,10).

The majority of gastric adenocarcinomas, similar to numerous other types of solid tumor, exhibit defects in the maintenance of genome stability, resulting in DNA CNA that may be analyzed using comparative genomic hybridization (CGH) (11). This is a widespread and common phenomenon among humans and several studies have focused on understanding these genomic alterations that are responsible for cancer and may be used for its diagnosis and prognosis (12).

At present, there are few published studies involving genotyping of GC samples using high-density microarrays (13–15); however, in those altered chromosomes, gains and losses have a phenotypic impact and different signaling pathways are involved. The presence of CNA changes the genetic dose and would modify several molecular mechanisms, such as epithelial-mesenchymal transition (EMT), which is the transformation of epithelial cells to mesenchymal cells and is a critical stage for the transition to metastasis (16). There are currently >1,184 genes in the EMT Gene Database (dbEMT 2.0), which are involved in other cancer-related processes, such as proliferative signaling, evasion of growth suppressors, avoidance of immune destruction, inactivation of replicative immortality, tumor-promoting inflammation, induction of angiogenesis, genomic instability, mutation, resting cell death, deregulation of cellular energetic activity, invasion and cell plasticity (17). EMT includes activation of transcription factors, expression of specific cell-surface proteins, reorganization and expression of cytoskeletal proteins and the production of extracellular matrix-degrading enzymes (18). EMT has been associated with the progression of cancer and increased stemness of tumors (18,19), and was observed to be involved in the formation, invasion and metastasis of GC (20,21). In addition, an association has been established between the presence of CNA and its effect on the expression level of EMT-associated genes in different cancer cell lines (22). Studies on CNA events involving Latin American populations are limited (23,24). In fact, at present, only a small number of studies have performed GC genotyping using whole-genome high-density microarrays in Mexican patients with GC (14,15). Therefore, the present study aimed to determine CNA in DGC and IGC to identify, through bioinformatics analyses, the main genes and signaling pathways involving EMT-associated genes.

Materials and methods <sec> <title>Samples

Institutional Review Board approval was obtained for the present study (approval no. 2008-785-001). The samples were collected at the Regional General Hospital No. 1 ‘Dr. Carlos MacGregor Sánchez Navarro’, Specialty Hospital ‘Dr. Bernardo Sepúlveda’ and Oncology Hospital from IMSS in Mexico City (Mexico). Clinical data and patient samples were processed following obtainment of written informed consent. All of the samples were collected over three years (April 2010 to May 2013) following standardized endoscopy preservation protocols (25). Histological assessment of the biopsies was performed by two trained pathologists independently. They assigned the phenotypic diagnosis of diffuse or intestinal tumors and non-atrophic gastritis (NAG) samples. Only samples with the same diagnosis (‘identical results’) by two independent expert pathologists were included in the analysis.

A total of 21 patients (5 females and 16 males) with tissue samples that met the criteria for DGC (n=7) and IGC (n=7) diagnoses, as well as subjects with NAG (n=7) as controls, were included. In the absence of an established measurement (gold standard), a value was arbitrarily determined to provide guidance to investigate relevant alterations. To identify the most relevant alterations for GC, the present analysis focused on alterations present in at least three patients (cut-off, ≥3 patients; ≥40% samples).

DNA extraction

DNA extraction was performed using a commercial kit (QIAamp^® DNA Micro Kit; Qiagen GmbH) according to the manufacturer's protocol. The extraction was modified to include an initial incubation at 95°C for 15 min, followed by a 5-min incubation at room temperature, prior to digestion with proteinase K (Qiagen GmbH) for three days at 56°C in a water bath, and fresh enzyme was added at 24 h intervals, as described previously (26).

DNA quality assessment and preparation

The extracted DNA was quantified using spectrophotometry (Nanodrop 2000; Thermo Fisher Scientific, Inc.). Multiplex PCR was performed to assess the quality of DNA (Multiplex PCR kit; Qiagen GmbH) with a set of primers to amplify various regions of the GAPDH gene (27). Products were visualized using 1% agarose gel electrophoresis (RedGel^® Nucleic acid gel stain; Biotium) and documented under an ultraviolet light transilluminator system (Syngene).

High-density whole-genome microarray analysis

The samples were analyzed using the Affymetrix^® CytoScan™ microarray (Affymetrix; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol and with 250 ng DNA, except for the addition of five PCR cycles to increase the DNA sample. The PCR products (90 µg) were fragmented and labeled using additional PCR (https://assets.thermofisher.com/TFS-Assets/LSG/manuals/703038_cytoscan_assay_UG.pdf).

Copy number processing

The raw intensity files (.CEL), retrieved from the commercial platform, were analyzed using their proprietary software, Chromosome Analysis Suite (ChAS) v3.2 and NetAff 33 Libraries, based on the construction of the hg19 genome (February 2009) as a reference model.

Data processing was based on the segmentation algorithm, where the Log₂ ratio for each marker was calculated relative to the reference signal profile. To calculate the copy number variation (CNV), the data were normalized to baseline reference intensities using the reference model (provided by ChAS), including 270 HapMap samples and 96 healthy individuals. The Hidden Markov Model, available in ChAS, was used to determine the CN state and their breakpoints. The customized high-resolution condition was used as a filter for the determination of CNV: CN gains with a 50-marker count and 400 Kb, and CN losses with a 50-marker count and 100 Kb. The median absolute pairwise difference (MAPD) and the single nucleotide polymorphism quality control (SNP QC) score were used as the quality control parameters. Only samples with values of MAPD >0.25 and SNP QC <15 were included in the further analysis.

Bioinformatics analysis

A Perl script was developed to load the CNV segment data files generated by ChAS for each sample to compare the files to generate a list of genes that contained event types (gains or losses), frequencies of altered regions, including chromosomes and cytogenetic bands and Online Mendelian Inheritance in Man information, and to incorporate additional information from different databases (haploinsufficiency information from the DECIPHER database of genomic variation, genes reported at dbEMT 2.0 and genes affected in gastric adenocarcinoma from Harmonized Cancer Datasets; Table SI).

The genes altered in at least three patients (cut-off, ≥3) with DGC, IGC or NAG were included for analysis and visualizations were performed using R v4.0.2 and Bioconductor v3.12 packages (Table SII). The karyotype was created with KaryoploteR and the Bioconductor software annotation package (BSgenome.Hsapiens.UCSC.hg19 v1.4.0). The comparison among samples was performed by generating Venn diagrams with the jvenn server and a heatmap with gplots. Gene Ontology (GO) analyses were performed with the ClusterProfiler v3.16.1 packages (org.Hs.eg.db v3.11.4, enrich plot v1.8.1 and GOplot v1.0.2), with the support of functional enrichment analysis using the database for annotation, visualization and integrated discovery (DAVID) v6.8 resource (Table SII). The profile of altered molecular function (MF) terms in GC was summarized according to the proportion of CNA-associated genes and the MF GO terms from the DAVID database, adjusted by the false discovery rate. Dot plots, heatmaps and chord plots were utilized to visualize the GC CNA profiles for DGC, IGC and NAG.

To identify the main genes and signaling pathways involving CNA EMT-associated genes, GC CNA-associated genes (cut-off, ≥3) were analyzed and compared according to those previously reported in the dbEMT 2.0, accessed on 12th October, 2020.

Finally, to establish the profile-associated hallmarks of cancer involving DGC, IGC and NAG EMT-associated genes, an interaction network was generated using CNA type (gains and losses) based on genetic and physical interactions and biological pathways. Furthermore, associations were determined using the GeneMANIA prediction server and Cytoscape v.3.8.2, including the manual annotation of their corresponding cancer hallmarks [adhesion, angiogenesis, inflammation, migration, metastasis, morphogenesis, proliferation and survival (28)], with punctual scrutiny and assistance from databases, such as The Human Protein Atlas. Table SII provides information on the databases, protocols, software and specific packages used.

Results <sec> <title>Sample characteristics

Samples from 21 patients with GC from Mexico (third-generation Mexicans) between 35 and 91 years of age (mean ± SD, 59.61 ± 15.94 years), without any previous cancer treatment (naïve) were included in the present study. The samples included seven cases who had DGC, seven who had IGC and seven who had NAG (control samples). The raw data were deposited in the NCBI Gene Expression Omnibus database (ID no. GSE117093). There are seven adjacent tissue files (.CEL); however, these files were not included in the data analysis, as certain adjacent tissues were contaminated with cancer cells or these were not of the quality required for subsequent analyses.

Table I presents the ID and the percentage of neoplastic cells for tumor tissues ranging between 50 and 70%. Blood agar culture indicated that one patient with IGC and three patients with NAG were positive for H. pylori (data obtained from our biobank database). The patient data are also presented in Table I (29–31).

Genomic detection of CNA

The total number of CNAs was obtained and they were classified as either gains or losses for each chromosome in the GC and NAG samples. From the total CNAs, DGC had more CNAs compared to IGC (3,505 and 2,781, respectively), while there were 828 events in the NAG samples. With respect to the tissue, more gains than losses were observed in both cancer types, DGC (2,310 and 1,195, respectively) and IGC (1,550 and 1,231, respectively), but the opposite was observed in NAG (375 and 453, respectively) (Table SIII).

To identify the most relevant CNA in GC and NAG, alterations occurring in at least three patients (cut-off, ≥3) were analyzed. This comparison indicated a similar pattern for total CNA, with more events in DGC (n=710) than in IGC (n=590) or in NAG (n=332). In addition, more gains than losses were observed in DGC (516 and 194, respectively), IGC (314 and 276, respectively) and even in NAG (196 and 136, respectively), which was different when all of the patients were included. Furthermore, DGC had the highest number of gains and IGC had the highest number of losses (Table SIII). Table II lists chromosomes and sizes with gain and loss numbers, representative and summarized.

To visualize the distribution of DGC and IGC chromosome gains and losses, the identified CNA present in a karyogram (cut-off, ≥3) was plotted, which displayed alterations according to the coordinates of the Human genome hg19 (Fig. 1). The top five altered cytobands are provided in Tables III and SIV.

Of note, in DGC and IGC, the most frequent CNA lengths were between 100 and 200 Kb, while lengths of 1–50 Kb were more common in NAG, with respect to gains and losses (Table SV).

GC genes associated with CNA

Overall, 2,441 CNA-associated genes were identified in DGC, IGC and NAG. GC had 2,420 affected genes (99%), while only 108 genes (4%) were affected in NAG; of these alterations, certain candidates were shared between GC and NAG. There were 1,317 unique CNA-associated genes in DGC, 596 in IGC and 21 in NAG. Furthermore, both cancer types shared 420 genes, while 60 genes were shared between GC and NAG. In addition, 19 genes in NAG were shared with DGC and eight genes with IGC (Fig. 2A; Table SVI).

To identify the possible emerging patterns among the samples, hierarchical clustering heatmaps were generated (Fig. 2B). The results provided the molecular signature and hierarchical clustering of samples according to the 2,441 genes. The emerging pattern of altered genes affected by CNA distinguishes DGC and IGC from NAG.

GO analysis of GC

The functional profile for GC was generated through enrichment and GO analysis of the 2,420 GC-altered genes; 1,317 genes were only altered in DGC and 596 only altered in IGC. To identify the principal MFs altered in GC, these CNA-associated genes were categorized, independently of the GC type, into two groups: Gains and losses (Fig. 3A and B). The top 10 MFs associated with CNA gains or losses revealed that transcription activator, tyrosine kinase activity, growth factors and hormone binding, as well as intracellular signal transduction genes were enriched in GC. Gene losses mainly involved transcription coactivator and serine/threonine kinase activity, as well as several receptors binding to hormone, steroid hormone, nuclear receptor, β-catenin, intermediate filament and mitogen-activated protein kinase binding genes. In addition, the principal CNA-associated genes affecting the MF by GC type (DGC and IGC) were identified (Figs. 3C and D and S1).

CNA-EMT genes in DGC and IGC

To identify the main genes and signaling pathways involving the CNA-EMT genes in GC and NAG, GC CNA-associated genes were compared against a comprehensive and annotated database of EMT genes (dbEMT 2.0). A total of 551 CNA-EMT genes were found in DGC, 619 in IGC and 28 in NAG. Using the cut-off ≥3, 112 genes in DGC, 66 in IGC and 5 in NAG were obtained. The complete data of EMT-associated genes for DGC, IGC and NAG, with chromosome and cytoband locations, CNA type (gain or loss), and the P-values are provided in Table SI.

GO analysis of the EMT-associated genes

GO enrichment analysis was performed to determine the MF of the main CNA-EMT genes affected in DGC, IGC and NAG (Fig. 4). The results indicated that gains in the CNA-EMT genes in DGC were associated with transmembrane receptor tyrosine kinase, DNA and RNA binding and receptor binding for insulin, growth factors, Toll-like receptors, hormone, as well as SMAD, p53, chromatin, calcium ion binding and microtubule binding (Fig. 4A). Losses in CNA-EMT genes included associations with DNA and chromatin binding, nuclear hormone receptor binding, β-catenin, steroid hormone, mitogen-activated protein binding, intermediate filament binding, p53 binding and RNA polymerase II-specific DNA binding (Fig. 4B). Furthermore, gains in CNA-EMT genes in IGC were associated with insulin receptor substrate and phosphatase binding, kinase regulation, neurotrophin receptor binding, 1-phosphatidylinositol-3-kinase activity, transmembrane receptor protein tyrosine kinase adaptor activity and VEGF receptor binding, while losses in CNA-EMT genes in IGC were only associated with coenzyme binding and transcription coactivator activity (Fig. 4C). On the other hand, the main MF for gains in the CNA-EMT genes in NAG included transcription regulatory region DNA, transcriptional activator activity RNA, armadillo repeat and C2H2 zinc finger domain binding, γ- and β-catenin binding, as well as cysteine-type endopeptidase inhibitor activity involved in apoptotic process and estrogen receptor, as well as steroid hormone receptor activity, while losses in CNA-EMT genes in NAG were associated with damaged DNA, WW domain, p53 binding and DNA-binding transcription activator activity (Fig. 4D).

CNA-EMT genes associated with the hallmarks of cancer

Based on the main molecular profile of altered CNA-EMT genes in GC and NAG, the functional network between 39 previously selected unique CNA-EMT genes (19 genes for DGC, 7 for IGC, 11 common to GC and two for NAG; cut-off, ≥3 patients) was generated. Gained genes, with the highest degree and at least four interactions per gene, were EGFR, MICAL2, MYC, NDRG and PIK3R1, while lost genes included GLI2, EP300 and PTPN11. The principal functions associated with these CNA-EMT genes have been previously associated with several hallmarks of cancer: Adhesion, angiogenesis, inflammation, migration, metastasis, morphogenesis, proliferation and survival (Fig. 5).

Discussion

To the best of our knowledge, the present study was the first whole-genome high-density array study on GC in Mexican patients with DGC and IGC, as well as NAG as non-cancerous controls. Using this experimental strategy, it was possible to generate a karyogram and obtain molecular signatures for DGC and IGC, and their association with CNA-EMT genes, independent of age, sex, percentage of cancer cells, presence/absence of H. pylori infection, TNM and treatment (naïve samples in the present study). In addition, the genomic analysis was focused on the molecular profile of GC, particularly involving alterations of EMT-associated genes, given their role in cancer progression, as epithelial cell transformation to mesenchymal cells is fundamental to metastasis (32) and chemoresistance (33,34). The results of the present study are consistent with those previously reported in the literature (detailed above), which provides validity and robustness to the results and enables the reporting of novel data or data not yet investigated to identify potential diagnostic, prognostic and treatment response markers.

Globally, the alteration profile in GC was dominated by gains. This phenomenon, where gains are more abundant than losses, has been previously reported in different tumor cell lines, including gastric cancer cell lines (35). Chromosomal gains in cancer may result in increased gene functions, providing cancer cells with a competitive advantage for the development of metastasis (36), while chromosomal losses may involve the downregulation of tumor suppressor genes (37), disrupting homeostasis and accelerating cancer progression. The most affected CNA chromosomes for DGC were 1, 4 and 5; for IGC 1, 8 and X, and for NAG 6, 7, 14, 17 and X. The altered cytobands associated with GC observed in the present study are in agreement with previous studies. For instance, 8q24 has been associated with the development of different types of tumor (38). The highest frequencies of gains in advanced GC were found at 8q24.21 (65%) and 8q24.3 (60%), and the pattern of CNA in advanced GC was different from that in early GC. This increase in CNA numbers is associated with disease progression from early to advanced GC (39). The 8q24 cytoband has also been reported in Latin American countries, such as Brazil (40) and Venezuela (41), as well as in Asian countries, including Korea (42).

Of note, the most frequent CNA length in GC was 100–200 Kb, in both DGC and IGC compared with 1–50 Kb in NAG. The biological implications of this difference in length in GC compared with non-cancerous tissues, such as NAG, is yet to be determined. Furthermore, it is important to highlight that a resolution of 100–200 Kb versus Mb is an advantage of molecular resolution approaches over classical cytogenetics (CGH and fluorescence in situ hybridization) to discover ‘small’ potentially important alterations in cancer samples.

The cumulative length averages (Megabases, Mb) of these alterations were 183.44 Mb for DGC, 113.56 Mb for IGC and 1.19 Mb for NAG. These lengths, whether gained or lost, describe the magnitude of global alterations per tissue; however, their relevance lies in the MFs, biological processes and interaction networks in which they participate.

In the present study, a molecular profile that distinguishes GC from NAG was identified based on 2,441 genes affected by CNA. They are associated with GC, as well as the differences and similarities among histological subtypes (undifferentiated DGC and well-differentiated IGC) compared with that in non-cancerous tissue, such as NAG (43). Of note, 60 affected genes shared between GC and NAG were identified; 19 genes were shared exclusively with DGC, while only eight were shared with IGC. This emerging pattern of shared altered genes between cancerous and non-cancerous tissues should be further studied to identify possible CNA-dependent oncogenic pathways and progression trajectories from NAG to either GC subtype, particularly in conjunction with environmental factors, such as H. pylori infection, diet and lifestyle, that may be associated with the spread patterns affecting patient survival (43).

In the heatmap, a separation between NAG and GC was observed, exhibiting clusters based on the molecular profiles of CNA-associated genes. There is a greater heterogeneity among the IGC samples in clusters but there were more genes affected in DGC. The front-line tool for IGC distinction has been based on different criteria, such as the Lauren histopathological classification system. However, due to challenges, including disagreements in the correct assignment, diagnosis and treatment, new criteria have been proposed, such as molecular characterization according to The Cancer Genome Atlas (TCGA) Research Network, which divides GC into four subtypes (44). The results of the present study agree with the requirement for new proposals for the classification of GC, which includes defined subgroups, with the integration of several genomic and genetic parameters where CNA are present.

In the present study, the MF profile of GC CNA-associated genes was analyzed and determined. With respect to gains, there were increased alterations involving transcription, signaling, tyrosine kinases, growth factors, hormones and insulin, while with respect to losses, molecules involved in transcription, serine/threonine and MAP kinases, steroid hormones, β-catenin binding and filament binding were decreased. These gene sets are important in GC biology. There were 13 CNA genes in IGC (including CDH1, LAST1, ROCK1 and WWOX) and 49 CNA genes in DGC (including CRIM1, EGFR, MIR9-1, MUC1, MYC, NDRG1, SCRIB, SNAI2, VEGF and ZEB2); therefore, these genes were further analyzed with the intention of comparing the results of the present study with those of others and organizing the data in a biologically coherent context. For instance, CDH1 codes for E-cadherin and, from a simplified viewpoint, E-cadherin maintains the epithelial phenotype; if CDH1 is lost, this promotes the mesenchymal phenotype, i.e., it favors loss of adhesion and metastasis (32).

In the present study, an enrichment analysis of unique CNA-associated genes for all tissues was performed and several shared MFs, such as protein binding, were obtained. Several gained-genes that encode for RNA-binding proteins (45) have diverse targets and participate in tumor progression by regulating homeostasis and changing expression patterns. Chromatin binding is another altered function in GC that participates in regulating eukaryotic gene expression, methylation profile modulation, and genome stability maintenance (46). EMT is a process that involves changes in histone modification, DNA methylation and chromatin accessibility. These changes may be promoted through transcription, allowing the cell to have an identity or to have a mesenchymal-epithelial transition-EMT conversion (47). The kinase function in DGC and IGC gains have recently been considered key regulators in the development of cancer (48). Numerous kinases were associated with the initiation and progression of carcinogenesis and are one of the main therapeutic targets for the development of inhibitors in the clinical field. Kinases are able to promote EMT and enhance invasion, migration and evasion of apoptosis (49). In the present study, PIK3R1 and PIK3CA were associated with IGC. The PI3K pathway is a key regulatory hub for cell growth, survival and metabolism (50). Activation of PI3K is a frequent hallmark of cancer, highlighted by the prevalence of somatic mutations in genes encoding key components of this pathway (51). These enzymes are responsible for transferring a phosphate group; however, the reverse process is performed by phosphatases, which are also affected in IGC. PIK3R1 is a gene frequently affected by mutations or copy numbers in various types of cancer, according to the TCGA project. These genes converge with the PI3K/AKT/mTOR pathway, which is involved in the regulation of several processes (51).

To date, the differences between DGC and IGC have been insufficiently investigated and understood; differences in etiology, location, incidence and genetic profiles have been observed (52). In the present study, a CNA-EMT network for GC was generated with relevant genes according to different criteria: Frequency among patients, genetic connections, reported pathways and experimental associations with several databases [dbEMT 2.0, The Human Protein Atlas, COSMIC (53)] and Cancer Hallmark Genes (54). Shared and exclusively altered genes were observed for each tissue type. The common CNA-EMT genes between DGC and IGC include GLI2, which has been associated with proliferation (55); EP300, with multiple functions as an inhibitor of antitumor immune response via metabolic modulation (56); PTPN11, associated with GC progression; and NDRG1, associated with metastasis and poor prognosis in GC (57). Another relevant gene in DGC is EGFR, which, due to its association with CNV GC, is now the target for the development of anti-GC therapies (58). IGC-associated EMT genes include MICAL2, MYC and PIK3R1. MICAL2, a destabilizing F-actin in cytoskeletal dynamics, has been associated with poor prognosis in GC (59). MYC gains have also been reported in several GC studies, as expected for a common oncogenic gene (60) associated with proliferation, differentiation and apoptosis (61). PIK3R1 participates in the PI3K/AKT signaling pathway, with roles in apoptosis and cell survival, as well as chemotherapy resistance in GC (62).

A large amount of data remains to be analyzed, including loss of heterozygosity, mosaicism and other gene sets that participate in different hallmarks of cancer. Another limitation of the present study was the absence of a transcriptomic analysis to validate the GC EMT signature, particularly for DGC and IGC. Yet, the concordance of CNA with expression alterations in EMT-associated genes is plausible, as previously observed for multiple types of cancer from TCGA (35). In addition, further inclusion of precancerous stages would allow further analysis of the ‘profile’ of IGC progression. The results of the present genomic approach coincide with those already reported in the literature, which provides validity and solidity to the results. After all, this strategy allowed us to report novel or thus far scarce data, or those not previously investigated, to identify differential GC CNA, identify associations with relevant MFs associated with the hallmarks of cancer and predict the EMT signature for DGC and IGC. It may be hypothesized that these networks will potentially provide treatment targets, as well as diagnostic and prognostic markers. In addition, the use of NAG as a non-malignant control allowed for investigation of the molecular and cellular events of GC and the identification of potential biomarkers for the ‘early’ stages of GC.

Supplementary Material

Supporting Data

Acknowledgements

The authors would like to thank Ms. Irma P. Ramos-Vega, Infectious and Parasitic Diseases Medical Research Unit (UIMEIP), High Specialty Medical Unit (UMAE)-Pediatrics Hospital ‘Dr. Silvestre Frenk Freund’, XXI Century National Medical Center, IMSS; Mr. Brian-Alexander Cruz-Ramírez and Ms. Alejandra García-Bejarano, Oncological Diseases Medical Research Unit (UIMEO), UMAE-Oncology Hospital, XXI Century National Medical Center, IMSS for their technical assistance.

Availability of data and materials

All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data have been deposited in the Gene Expression Omnibus database (GEO; http://www.ncbi.nlm.nih.gov/geo/) under accession no. GSE117093. Pre-print: doi: https://doi.org/10.1101/2021.11.22.469612.

Authors' contributions

VLS, HAVS and JDME performed the molecular experiments, participated in data analysis to provide the results and prepared, wrote and discussed the manuscript. JT, MCP and PPS were responsible for clinical aspects and recruited the patients, discussed the data and revised the manuscript. MERT contributed to the design of the study, supervised the study and critically reviewed, revised and wrote the manuscript. JT and MERT were responsible for acquiring financial support. VLS and MERT confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Institutional Review Board approval was obtained for the study (approval no. 2008-785-001). Clinical data and patient samples were processed following written informed consent.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References 1

Sung

Ferlay

Siegel

Laversanne

Soerjomataram

Jemal

Bray

Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

CA Cancer J Clin712092492021

10.3322/caac.21660

33538338

INEGI-Social Communication

Statistics on World Cancer Day (February 4)-National Data

INEGI

2018

Lauren

The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma: An attempt at a histo-clinical classification

Acta Pathol Microbiol Scand6431491965

10.1111/apm.1965.64.1.31

14320675

Espejo Romero

Navarrete Siancas

Classification of stomach adenocarcinomas

Rev Gastroenterol Peru231992122003(In Spanish)

14532921

Piazuelo

Epplein

Correa

Gastric cancer: An infectious disease

Infect Dis Clin North Am24853869VII2010

10.1016/j.idc.2010.07.010

20937454

Nagini

Carcinoma of the stomach: A review of epidemiology, pathogenesis, molecular genetics and chemoprevention

World J Gastrointest Oncol41561692012

10.4251/wjgo.v4.i7.156

22844547

McLean

El-Omar

Genetics of gastric cancer

Nat Rev Gastroenterol Hepatol116646742014

10.1038/nrgastro.2014.143

25134511

Mikhail

Biegel

Cooley

Dubuc

Hirsch

Horner

Newman

Shao

Wolff

Raca

Technical laboratory standards for interpretation and reporting of acquired copy-number abnormalities and copy-neutral loss of heterozygosity in neoplastic disorders: A joint consensus recommendation from the American College of Medical Genetics and Genomics (ACMG) and the Cancer Genomics Consortium (CGC)

Genet Med21190319162019

10.1038/s41436-019-0545-7

31138931

Wallander

Eisfeldt

Lindblad

Nilsson

Billiau

Foroughi

Nordenskjöld

Liedén

Tham

Cell-free tumour DNA analysis detects copy number alterations in gastro-oesophageal cancer patients

PLoS One16e02454882021

10.1371/journal.pone.0245488

33539436

Han

Ren

Mao

Song

Yang

Zhang

Gao

Tumor copy number alteration (CNA) burden as a prognostic factor for overall survival in Chinese gastric cancers

J Clin Oncol37(Suppl 15)e155552019

10.1200/JCO.2019.37.15_suppl.e15555

Milne

Early-onset gastric cancer: Learning lessons from the young

World J Gastrointest Oncol259642010

10.4251/wjgo.v2.i2.59

21160922

Iafrate

Feuk

Rivera

Listewnik

Donahoe

Scherer

Lee

Detection of large-scale variation in the human genome

Nat Genet369499512004

10.1038/ng1416

15286789

Seabra

Araújo

Mello Junior

Di Felipe Ávila Alcântara

De Barros

De Assumpção

Montenegro

Guimarães

Demachki

Burbano

Khayat

High-density array comparative genomic hybridization detects novel copy number alterations in gastric adenocarcinoma

Anticancer Res34640564152014

25368240

Morales-Guerrero

Rivas-Ortiz

Ponce de León-Rosales

Gamboa-Domínguez

Rangel-Escareño

Uscanga-Domínguez

Aguilar-Gutiérrez

Kershenobich-Stalnikowitz

Castillo-Rojas

López-Vidal

Translation of gastric disease progression at gene level expression

J Cancer115205322020

10.7150/jca.29038

31897247

Nadauld

Garcia

Natsoulis

Bell

Miotke

Hopmans

Pai

Palm

Regan

Metastatic tumor evolution and organoid modeling implicate TGFBR2 as a cancer driver in diffuse gastric cancer

Genome Biol154282014

10.1186/s13059-014-0428-9

25315765

Vasaikar

Deshmukh

den Hollander

Addanki

Kuburich

Kudaravalli

Joseph

Chang

Soundararajan

Mani

EMTome: A resource for pan-cancer analysis of epithelial-mesenchymal transition genes and signatures

Br J Cancer1242592692021

10.1038/s41416-020-01178-9

33299129

Varga

Greten

Cell plasticity in epithelial homeostasis and tumorigenesis

Nat Cell Biol19113311412017

10.1038/ncb3611

28945230

Kalluri

Weinberg

The basics of epithelial-mesenchymal transition

J Clin Invest119142014282009

10.1172/JCI39104

19487818

Christofori

New signals from the invasive front

Nature4414444502006

10.1038/nature04872

16724056

Murai

Yamada

Fuchs

Fujii

Nakayama

Sugimoto

Koike

Fujiwara

Tanabe

Kodera

Epithelial-to-mesenchymal transition predicts prognosis in clinical gastric cancer: EMT in clinical gastric cancer

J Surg Oncol1096846892014

10.1002/jso.23564

24453058

Xia

Epithelial-mesenchymal transition and gastric cancer stem cell

Tumour Biol3910104283176983732017

10.1177/1010428317698373

28459211

Wang

Anastassiou

Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

Sci Rep10171992020

10.1038/s41598-020-74276-6

33057153

Owen

Pinto

Retamal

Fernádez

Cisternas

Mondaca

Sanchez

Galindo

Nervi

Ibañez

Chilean gastric cancer task force: A study protocol to obtain a clinical and molecular classification of a cohort of gastric cancer patients

Medicine (Baltimore)97e04192018

10.1097/MD.0000000000010419

29668600

Araújo

Seabra

Lima

Assumpção

Montenegro

Demachki

Burbano

Khayat

Recurrent amplification of RTEL1 and ABCA13 and its synergistic effect associated with clinicopathological data of gastric adenocarcinoma

Mol Cytogenet9522016

10.1186/s13039-016-0260-x

27366209

Eltoum

Fredenburgh

Myers

Grizzle

Introduction to the theory and practice of fixation of tissues

J Histotechnol241731902001

10.1179/his.2001.24.3.173

Fischer

Cunliffe

Bollo

Weiner

Devinsky

Ruiz-Tachiquin

Venuto

Pearlman

Chiriboga

Schneider

Glioma-like proliferation within tissues excised as tubers in patients with tuberous sclerosis complex

Acta Neuropathol11667772008

10.1007/s00401-008-0391-1

18581125

Utrera-Barillas

Valdez-Salazar

Gómez-Rangel

Alvarado-Cabrero

Aguilera

Gómez-Delgado

Ruiz-Tachiquin

Is human cytomegalovirus associated with breast cancer progression?

Infect Agent Cancer8122013

10.1186/1750-9378-8-12

23557440

Hanahan

Weinberg

Hallmarks of cancer: The next generation

Cell1446466742011

10.1016/j.cell.2011.02.013

21376230

Jeon

Cheong

Clinical implementation of precision medicine in gastric cancer

J Gastric Cancer192352532019

10.5230/jgc.2019.19.e25

31598369

Zubarayev

Min

Son

Clinical and molecular prognostic markers of survival after surgery for gastric cancer: Tumor-node-metastasis staging system and beyond

Transl Gastroenterol Hepatol4592019

10.21037/tgh.2019.08.05

31559340

Brierley

Gospodarowicz

Wittekind

TNM Classification of Malignant Tumours: Edition 8

John Wiley & Sons

New Jersey

1162016

27527344

Bure

Nemtsova

Zaletaev

Roles of E-cadherin and noncoding RNAs in the epithelial-mesenchymal transition and progression in gastric cancer

Int J Mol Sci2028702019

10.3390/ijms20122870

31212809

Marin

JJG

Perez-Silva

Macias

RIR

Asensio

Peleteiro-Vigil

Sanchez-Martin

Cives-Losada

Sanchon-Sanchez

Sanchez De Blas

Herraez

Molecular bases of mechanisms accounting for drug resistance in gastric adenocarcinoma

Cancers (Basel)1221162020

10.3390/cancers12061663

32751679

De Las Rivas

Brozovic

Izraely

Casas-Pais

Witz

Figueroa

Cancer drug resistance induced by EMT: Novel therapeutic strategies

Arch Toxicol95227922972021

10.1007/s00204-021-03063-7

34003341

Zhao

Liu

Expression of epithelial-mesenchymal transition-related genes increases with copy number in multiple cancer types

Oncotarget724688246992016

10.18632/oncotarget.8371

27029057

Wee

Wang

Liu

Zhao

A pan-cancer study of copy number gain and up-regulation in human oncogenes

Life Sci2112062142018

10.1016/j.lfs.2018.09.032

30243646

Zhao

Concordance of copy number loss and down-regulation of tumor suppressor genes: A pan-cancer study

BMC Genomics17Suppl 7(Suppl 7)5322016

10.1186/s12864-016-2904-y

27556634

Tong

Tang

Zhao

Ying

Niu

Cumulative evidence of relationships between multiple variants in 8q24 region and cancer incidence

Medicine (Baltimore)99e207162020

10.1097/MD.0000000000020716

32590746

Arakawa

Sugai

Habano

Eizuka

Sugimoto

Akasaka

Toya

Yamamoto

Koeda

Sasaki

Genome-wide analysis of DNA copy number alterations in early and advanced gastric cancers

Mol Carcinog565275372017

10.1002/mc.22514

27312513

Anauate

Leal

Wisnieski

Santos

Gigek

Chen

Calcagno

Assumpção

Demachki

Arasaki

Analysis of 8q24.21 miRNA cluster expression and copy number variation in gastric cancer

Future Med Chem119479582019

10.4155/fmc-2018-0477

31141411

Labrador

Torres

Camargo

Santiago

Valderrama

Chiurillo

Association of common variants on chromosome 8q24 with gastric cancer in Venezuelan patients

Gene5661201242015

10.1016/j.gene.2015.04.081

25939847

Jin

Park

Lee

Kim

Park

Copy number gains at 8q24 and 20q11-q13 in gastric cancer are more common in intestinal-type than diffuse-type

PLoS One10e01376572015

10.1371/journal.pone.0137657

26360582

Korivi

Faria

Aly

Sun

Patnana

Jensen

Wagner-Bartak

Bhosale

Intestinal and diffuse gastric cancer: A retrospective study comparing primary sites

Clin Imaging5633402019

10.1016/j.clinimag.2019.03.002

30870726

Cancer Genome Atlas Research Network

Comprehensive molecular characterization of gastric adenocarcinoma

Nature5132022092014

10.1038/nature13480

25079317

Qin

Liu

Yuan

Zheng

RNA-binding proteins in tumor progression

J Hematol Oncol13902020

10.1186/s13045-020-00927-w

32653017

Morgan

Shilatifard

Chromatin signatures of cancer

Genes Dev292382492015

10.1101/gad.255182.114

25644600

Brabletz

Kalluri

Nieto

Weinberg

EMT in cancer

Nat Rev Cancer181281342018

10.1038/nrc.2017.118

29326430

Bhullar

Lagarón

McGowan

Parmar

Jha

Hubbard

Rupasinghe

HPV

Kinase-targeted cancer therapies: Progress, challenges and future directions

Mol Cancer17482018

10.1186/s12943-018-0804-2

29455673

Olea-Flores

Zuñiga-Eulogio

Mendoza-Catalán

Rodríguez-Ruiz

Castañeda-Saucedo

Ortuño-Pineda

Padilla-Benavides

Navarro-Tito

Extracellular-signal regulated kinase: A central molecule driving epithelial-mesenchymal transition in cancer

Int J Mol Sci2028852019

10.3390/ijms20122885

31200510

Castel

Toska

Engelman

Scaltriti

The present and future of PI3K inhibitors for cancer therapy

Nat Cancer25875972021

10.1038/s43018-021-00218-4

35118422

Zhang

Kwok-Shing Ng

Kucherlapati

Chen

Liu

Tsang

de Velasco

Jeong

Akbani

Hadjipanayis

A pan-cancer proteogenomic atlas of PI3K/AKT/mTOR pathway alterations

Cancer Cell31820832.e32017

10.1016/j.ccell.2017.04.013

28528867

Assumpção

Barra

Ishak

Coelho

LGV

Coimbra

FJF

Freitas

Dias-Neto

Camargo

Szklo

The diffuse-type gastric cancer epidemiology enigma

BMC Gastroenterol202232020

10.1186/s12876-020-01354-4

32660428

Sondka

Bamford

Cole

Ward

Dunham

Forbes

The COSMIC cancer gene census: Describing genetic dysfunction across all human cancers

Nat Rev Cancer186967052018

10.1038/s41568-018-0060-1

30293088

Zhang

Huo

Xie

Zhang

Liu

Jin

Chen

CHG: A systematically integrated database of cancer Hallmark genes

Front Genet11292020

10.3389/fgene.2020.00029

32117445

Wan

Zhou

Zhao

Wang

Wei

Gao

Wang

Cui

Sonic hedgehog pathway contributes to gastric cancer cell growth and proliferation

Biores Open Access353592014

10.1089/biores.2014.0001

24804165

Krupar

Watermann

Idel

Ribbat-Idel

Offermann

Pasternack

Kirfel

Sikora

Perner

In silico analysis reveals EP300 as a panCancer inhibitor of anti-tumor immune response via metabolic modulation

Sci Rep1093892020

10.1038/s41598-020-66329-7

32523042

Dong

Hong

Sun

Chen

Zhao

Sun

NDRG1 suppresses vasculogenic mimicry and tumor aggressiveness in gastric carcinoma

Oncol Lett18300330162019

31452779

Liang

Fang

Gastric cancer and gene copy number variation: Emerging cancer drivers for targeted therapy

Oncogene35147514822016

10.1038/onc.2015.209

26073079

Mariotti

Barravecchia

Vindigni

Pucci

Balsamo

Libro

Senchenko

Dmitriev

Jacchetti

Cecchini

MICAL2 is a novel human cancer gene controlling mesenchymal to epithelial transition involved in cancer growth and invasion

Oncotarget7180818252016

10.18632/oncotarget.6577

26689989

Herold

Herkert

Eilers

Facilitating replication under stress: An oncogenic function of MYC?

Nat Rev Cancer94414442009

10.1038/nrc2640

19461668

Zhang

Hou

Ashktorab

Gao

Zhai

Zhang

The impact of C-MYC gene expression on gastric cancer cell

Mol Cell Biochem3441251352010

10.1007/s11010-010-0536-0

20737197

Huang

Zhang

Wang

Zeng

Zhang

Circular RNA AKT3 upregulates PIK3R1 to enhance cisplatin resistance in gastric cancer via miR-198 suppression

Mol Cancer18712019

10.1186/s12943-019-0969-3

30927924

Figure 1.

Karyogram with CNA distribution in DGC and IGC. CNA events (gains or losses) were present from chromosomes 1 to 22, as well as X and Y. Gains (blue and dark blue) and losses (orange and red) are plotted for ≥3 samples from patients with DGC or IGC. Cytobands (gray, black or white bars) and centromeres (green bars) are presented. CNA, copy number alteration; DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; Chr, chromosome.

Figure 2.

Profile of CNA-associated genes from ≥3 patients with GC. (A) Venn diagram presenting the frequencies of specific and shared genes with CNA among the DGC, IGC and NAG subjects. (B) Heatmap displaying hierarchical clustering of gains (blue), losses (red) and no alterations (light blue and light red). DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; NAG, non-atrophic gastritis; CNA, copy number alteration.

Figure 3.

MF profiles of GC CNA-associated genes. Enrichment analysis of genes affected by CNA-associated genes in ≥3 patients with GC. Data are summarized by CNA type, with dot plots for (A) gains and (B) losses, according to the proportion of CNA-associated genes and MF GO terms, adjusted by the false discovery rate. The CNA-associated genes from the MF networks reveal the possible association between CNA (C) gains and (D) losses within GC. The gene ratio (M/N) is the proportion between CNA genes in ≥3 patients with GC (M) and the collection of genes from the GO term database function (N). Counts (circle sizes) represent the number of CNA genes associated with MF. GC, gastric cancer; MF, molecular function; GO, Gene Ontology.

Figure 4.

Principal MF associated with CNA-EMT genes in GC and NAG. The main MFs associated with CNA-EMT are displayed as chord plots and dot graphics for (A) DGC gains, (B) DGC losses, (C) IGC gains or losses and (D) NAG gains or losses. Chord plots (left panels) present associations between genes and MFs, indicating their CNA type by color (blue for gains and red for and losses). Dot plots (right panels) provide an enrichment analysis of MF and loss or gain genes counts in the samples. CNA, copy number alteration; EMT, epithelial mesenchymal transition; DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; NAG, non-atrophic gastritis; MF, molecular function.

Figure 5.

GC and NAG CNA/EMT-associated gene network and associated hallmarks of cancer. The functional interactions among CNA/EMT genes identified in DGC (rectangles), IGC (ovals) and shared genes (hexagons) were identified by their CNA type (blue represents gains and red represents losses) and associated hallmarks of cancer (colored dots). DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; NAG, non-atrophic gastritis; EMT, epithelial-mesenchymal transition; CNA, copy number alterations.

Table I.

Characteristics of GC and NAG cases analyzed in the present study (n=21).

ID	Age, years	Sex	Cancer type	% CC	H. pylori	TNM	Treatment
3CG-008	72	M	Intestinal	70	Positive	IB T1 N1 M0	Naïve
3CG-126	80	M	Intestinal	60	Negative	IIA T4 N0 M0	Naïve
3CG-128	91	M	Intestinal	70	Negative	IIA T3 N2 M0	Naïve
3CG-046	52	F	Intestinal	60	Negative	IV T4 N2 M0	Naïve
3CG-099	59	M	Intestinal	50	Negative	II T3 N0 M0	Naïve
3CG-146	71	M	Intestinal	60	Negative	IIB T3 N2 M0	Naïve
3CG-104	69	M	Intestinal	60	Negative	III A T4 N0 M0	Naïve
3CG-047	58	M	Diffuse	70	Negative	IV T4 N3 M0	Naïve
3CG-173	76	M	Diffuse	70	Negative	III A T2 N3 M0	Naïve
8CG-004	76	M	Diffuse	70	Negative	II T1 N0 M0	Naïve
1CG-001	45	M	Diffuse	60	Negative	IV T4N2M1	Naïve
3CG-035	55	M	Diffuse	60	Negative	IV T4N2M0	Naïve
3CG-042	64	M	Diffuse	50	Negative	IV T4, N2 M0	Naïve
3CG-064	38	M	Diffuse	50	Negative	IV T4 N2 M0	Naïve
4GB-001	64	M	NAG	0	Negative	NA	NA
4GB-031	62	M	NAG	0	Negative	NA	NA
4GB-015	35	F	NAG	0	Negative	NA	NA
4GB-025	39	M	NAG	0	Positive	NA	NA
4GB-033	76	F	NAG	0	Positive	NA	NA
4GB-036	38	F	NAG	0	Positive	NA	NA
4GB-042	77	F	NAG	0	Negative	NA	NA

ID, identification code; CC, cancer cells; M, male; F, female; NA, not applicable; TNM-T, extent of the primary tumor; TNM-N, absence or presence and extent of regional lymph node involvement; TNM-M, absence or presence of distant metastasis; NAG, non-atrophic gastritis.

Table II.

Principal affected chromosomes by CNA cumulative length in DGC, IGC and NAG.

Type/chromosome	Gains	Losses	Length, Mb-cl
DGC
1	327	-	117.9
4	-	155	40.8
5	-	148	74.23
IGC
1	-	148	33.78
8	365	-	139.8
X	-	66	167.1
NAG
6	-	28	0.40
7	21	-	0.20
14	10	-	3.02
17	-	15	1.86
X	-	87	0.47
X	207	-	1.20

The locations of copy number alterations are presented in Table SIV. DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; NAG, non-atrophic gastritis; Mb-cl, Megabases cumulative length.

Table III.

Top five altered cytobands in DGC, IGC and NAG.

Type/cytoband	Gains	Length, Mb-cl	Number of patients
DGC
Xq28	37	11.5	6
8q24.22	25	15.8	5
1q32.1	26	12.16	5
8q24.3	25	17.46	6
1q23.3	22	15.26	6
IGC
8q24.3	24	12.662	4
13q34	18	4.9018	4
8q24.21	18	6.2619	4
8q24.22	16	6.4856	4
8q12.1	16	5.8523	4
NAG
Xq26.2	24	34.026	6
Xq21.2	22	33.586	7
Xp22.33	19	167.504	7
Xq23	16	63.79	6
Xq26.3	13	217.774	7

DGC, diffuse gastric cancer; IGC, intestinal gastric cancer; NAG, non-atrophic gastritis; Mb-cl, Megabases cumulative length.