Gastric Adenocarcinoma: A Multimodal Approach.
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ABSTRACT:
SUBMITTER: Quadri HS
PROVIDER: S-EPMC5540948 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:The aim of this study was to identify biomarkers for gastric cancer (GC) by iTRAQ. Using proteins extracted from a panel of 4 pairs of gastric adenocarcinoma samples (stage III-IV, Her-2 negative), we identified 10 up regulated and 9 down regulated proteins in all four pairs of GC samples compared to adjacent normal gastric tissue. The up regulated proteins are mainly involved in cell motility, while the down regulated proteins are mitochondrial enzymes involved in energy metabolism. The expression of three up regulated proteins (ANXA1, NNMT, fibulin-5) and one of the down regulated proteins (UQCRC1) was validated by Western Blot in 97 GC samples. ANXA1 was up regulated in 61.36% of stage I/II GC samples compared to matched adjacent normal gastric tissue, and its expression increased further in stage III/IV samples. Knockdown of ANXA1 by siRNA significantly inhibited GC cell migration and invasion, whereas over expression of ANXA1 promoted migration and invasion. We found decreased expression of UQCRC1 in all stages of GC samples. Our data suggest that increased cell motility and decreased mitochondrial energy metabolism are important hallmarks during the development of GC.
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Project description:Gastric adenocarcinoma (GAC) is a heterogeneous disease and at least two major studies have recently provided a molecular classification for this tumor: The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ARCG). Both classifications quote four molecular subtypes, but these subtypes only partially overlap. In addition, the classifications are based on complex and cost-intensive technologies, which are hardly feasible for everyday practice. Therefore, simplified approaches using immunohistochemistry (IHC), in situ hybridization (ISH) as well as commercially available next generation sequencing (NGS) have been considered for routine use. In the present study, we screened 115 GAC by IHC for p53, MutL Homolog 1 (MLH1) and E-cadherin and performed ISH for Epstein-Barr virus (EBV). In addition, sequencing by NGS for TP53 and tumor associated genes was performed. With this approach, we were able to define five subtypes of GAC: (1) Microsatellite Instable (MSI), (2) EBV-associated, (3) Epithelial Mesenchymal Transition (EMT)-like, (4) p53 aberrant tumors surrogating for chromosomal instability and (5) p53 proficient tumors surrogating for genomics stable cancers. Furthermore, by considering lymph node metastasis in the p53 aberrant GAC, a better prognostic stratification was achieved which finally allowed us to separate the GAC highly significant in a group with poor and good-to-intermediate prognosis, respectively. Our data show that molecular classification of GAC can be achieved by using commercially available assays including IHC, ISH and NGS. Furthermore, we present an integrative workflow, which has the potential to overcome the uncertainty resulting from discrepancies from existing classification schemes.
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Project description:We have created mice by performing gastric epithelium-specific knockout of common tumor suppressor genes.
2014-04-10 | GSE45956 | GEO
Project description:We have created mice by performing gastric epithelium-specific knockout of common tumor suppressor genes. Mice with transgene were monitored for spontaneous gastric cancer. Gene expression profiles were obtained from fresh frozen mouse primary gastric cancer samples.
2014-04-10 | E-GEOD-45956 | biostudies-arrayexpress
Project description:In the present study, gene expression profiles were analyzed to identify the molecular mechanisms underlying gastric cardia adenocarcinoma (GCA) and gastric non-cardia adenocarcinoma (GNCA). A gene expression dataset (accession number GSE29272) was downloaded from Gene Expression Omnibus, and consisted of 62 GCA samples and 62 normal controls, as well as 72 GNCA samples and 72 normal controls. The two groups of differentially-expressed genes (DEGs) were compared to obtain common and unique DEGs. A differential analysis was performed using the Linear Models for Microarray Data package in R. Functional enrichment analysis was conducted for the DEGs using the Database for Annotation, Visualization and Integrated Discovery. Protein-protein interaction (PPI) networks were constructed for the DEGs with information from the Search Tool for the Retrieval of Interacting Genes. Subnetworks were extracted from the whole network with Cytoscape. Compared with the control, 284 and 268 genes were differentially-expressed in GCA and GNCA, respectively, of which 194 DEGs were common between GCA and GNCA. Common DEGs [e.g., claudin (CLDN)7, CLDN4 and CLDN3] were associated with cell adhesion and digestion. GCA-unique DEGs [e.g., MAD1 mitotic arrest deficient like 1, cyclin (CCN)B1, CCNB2 and CCNE1] were associated with the cell cycle and the regulation of cell proliferation, while GNCA-unique DEGs (e.g., GATA binding protein 6 and hyaluronoglucosaminidase 1) were implicated in cell death. A PPI network with 141 nodes and 446 edges were obtained, from which two subnetworks were extracted. Genes [e.g., fibronectin 1, collagen type I ?2 chain (COL1A2) and COL1A1] from the two subnetworks were implicated in extracellular matrix organization. These common DEGs could advance our understanding of the etiology of gastric cancer, while the unique DEGs in GCA and GNCA could better define the properties of specific cancers and provide potential biomarkers for diagnosis, prognosis or therapy.
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Project description:Background and aimGastric/gastroesophageal junction (GEJ) adenocarcinoma is a heterogeneous disease, with various etiologies and with tumors encompassing a spectrum of histologic and molecular subtypes. "Autophagy" includes two related but distinct homeostatic processes that promote cell survival under adverse conditions, namely macro- and chaperone-mediated autophagy. There is increasing evidence of the roles autophagy may play in tumorigenesis.MethodsAutophagic pathways were examined in the context of the heterogeneity intrinsic to gastric/GEJ adenocarcinoma, utilizing immunohistochemistry targeting specific proteins within the pathways (p62, LAMP2A, LC3B). We examined whole sections of normal and dysplastic gastric mucosa, as well as a tissue microarray of adenocarcinomas.ResultsDysplastic gastric epithelium was marked by frequent nuclear p62 and aberrant LAMP2A expression compared to normal. Examining the pattern of LC3B/cytoplasmic p62 immuno-reactivity in gastric adenocarcinoma demonstrated a predominant pattern of LC3BHigh/p62High staining (56/86, 65.1%), which has been previously associated with active, but impaired macroautophagy. There were no statistically significant associations seen between LC3B/cytoplasmic p62 staining patterns with tumor grade, histotype, or approximated TCGA molecular subtype. LAMP2A and nuclear p62 and staining patterns were also heterogeneous across the cohort, but with no statistically significant associations seen. The prognostic significance of the three proteins was limited, however high nuclear p62 levels were associated with worse overall survival (log-rank p-value = 0.0396).ConclusionOur data demonstrate the dynamic nature of autophagic proteins in the gastric epithelium, and we expand the biological heterogeneity observed in gastric/GEJ adenocarcinoma to include autophagy.
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Project description:Gastric cardia adenocarcinoma (GCA) has a high mortality rate worldwide; however, current early diagnostic methods lack efficacy. Therefore, the aim of the present study was to identify potential biomarkers for the early diagnosis of GCA. Global metabolic profiles were obtained from plasma samples collected from 21 patients with GCA and 48 healthy controls using ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry. The orthogonal partial least squares discrimination analysis model was applied to distinguish patients with GCA from healthy controls and to identify potential biomarkers. Metabolic pathway analysis was performed using MetaboAnalyst (version 4.0) and revealed that 'glycerophospholipid metabolism', 'linoleic acid metabolism', 'fatty acid biosynthesis' and 'primary bile acid biosynthesis' were significantly associated with GCA. In addition, an early diagnostic model for GCA was established based on the relative levels of four key biomarkers, including phosphorylcholine, glycocholic acid, L-acetylcarnitine and arachidonic acid. The area under the receiver operating characteristic curve revealed that the diagnostic model had a sensitivity and specificity of 0.977 and 0.952, respectively. The present study demonstrated that metabolomics may aid the identification of the mechanisms underlying the pathogenesis of GCA. In addition, the proposed diagnostic method may serve as a promising approach for the early diagnosis of GCA.
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Project description:BackgroundActivation of the phosphatidylinositol 3-kinase (PI3K) through mutational inactivation of PTEN tumour suppressor gene is common in diverse cancer types, but rarely reported in gastric cancer. Recently, mutations in PIK3CA, which encodes the p110alpha catalytic subunit of PI3K, have been identified in various human cancers, including 3 of 12 gastric cancers. Eighty percent of these reported mutations clustered within 2 regions involving the helical and kinase domains. In vitro study on one of the "hot-spot" mutants has demonstrated it as an activating mutation.MethodsBased on these data, we initiated PIK3CA mutation screening in 94 human gastric cancers by direct sequencing of the gene regions in which 80% of all the known PIK3CA mutations were found. We also examined PIK3CA expression level by extracting data from the previous large-scale gene expression profiling study. Using Significance Analysis of Microarrays (SAM), we further searched for genes that show correlating expression with PIK3CA.ResultsWe have identified PIK3CA mutations in 4 cases (4.3%), all involving the previously reported hotspots. Among these 4 cases, 3 tumours demonstrated microsatellite instability and 2 tumours harboured concurrent KRAS mutation. Data extracted from microarray studies showed an increased expression of PIK3CA in gastric cancers when compared with the non-neoplastic gastric mucosae (p < 0.001). SAM further identified 2910 genes whose expression levels were positively associated with that of PIK3CA.ConclusionOur data suggested that activation of the PI3K signalling pathway in gastric cancer may be achieved through up-regulation or mutation of PIK3CA, in which the latter may be a consequence of mismatch repair deficiency.
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