RESUMEN
The dismal outcome of gastric cancer patients highlights the need for diagnostic biomarkers and effective therapeutic targets, such as microRNAs. We sought to discover microRNAs involved in gastric cancer, and to elucidate their downstream target mechanisms. Both cultured gastric epithelial cells (HFE145 and NCI-N87) and primary human gastric tissues (31 non-neoplastic stomach (NS) and 25 gastric carcinomas (GC)) were studied. MicroRNA microarrays and quantitative RT-PCR were applied to discover and verify differentially expressed microRNAs. in vitro cell migration and invasion, cell proliferation, cell cycle and apoptosis assays were executed to elucidate biological effects of microRNA-192 and -215. Western blotting and luciferase assays were performed to confirm direct messenger RNA targeting by microRNA-192 and -215. MicroRNA microarray analyses revealed that 25 and 20 microRNAs were upregulated and downregulated in GC vs NS, respectively. Expression levels of both microRNA-192 and -215 were significantly higher in GC than in NS (P<0.05). Luciferase assays suggested that microRNA-215 inhibits activated leukocyte cell adhesion molecule (ALCAM) expression at the posttranscriptional level. In addition, expression levels of ALCAM were significantly lower in GC than in NS. Mimics and inhibitors, respectively, of microRNA-192 or -215 exerted no effect on cell cycle or apoptosis in the immortalized normal gastric cell line HFE145 or the gastric cancer cell line NCI-N87. However, mimics of microRNA-192 or -215 significantly increased growth rates in HFE145 cells, whereas inhibitors of microRNA-192 or -215 caused significant decreases in growth rates in NCI-N87 cells. ALCAM knockdown by an ALCAM-specific siRNA significantly increased cell growth in HFE145 cells. Both transfection of mimics of microRNA-192 or -215 and ALCAM knockdown by an ALCAM-specific siRNA significantly increased the migration of HFE145 cells. In conclusion, in gastric cancer, both microRNA-192 and -215 are overexpressed in vivo and exert cell growth and migration-promoting effects in vitro, thus representing potential microRNAs with a role in cancer in the human stomach.
Asunto(s)
Antígenos CD/fisiología , Moléculas de Adhesión Celular Neuronal/fisiología , Proteínas Fetales/fisiología , Regulación Neoplásica de la Expresión Génica , MicroARNs/fisiología , Neoplasias Gástricas/genética , Antígenos CD/análisis , Antígenos CD/genética , Apoptosis , Moléculas de Adhesión Celular Neuronal/análisis , Moléculas de Adhesión Celular Neuronal/genética , Ciclo Celular , Línea Celular Tumoral , Proliferación Celular , Proteínas Fetales/análisis , Proteínas Fetales/genética , Humanos , MicroARNs/análisis , Análisis de Secuencia por Matrices de Oligonucleótidos , Reacción en Cadena de la Polimerasa de Transcriptasa Inversa , Neoplasias Gástricas/patología , Regulación hacia ArribaRESUMEN
In order to identify and contrast global gene expression profiles defining the premalignant syndrome, Barrett's esophagus, as well as frank esophageal cancer, we utilized cDNA microarray technology in conjunction with bioinformatics tools. We hybridized microarrays, each containing 8000 cDNA clones, to RNAs extracted from 13 esophageal surgical or endoscopic biopsy specimens (seven Barrett's metaplasias and six esophageal carcinomas). Hierarchical cluster analysis was performed on these results and displayed using a color-coded graphic representation (Treeview). The esophageal samples clustered naturally into two principal groups, each possessing unique global gene expression profiles. After retrieving histologic reports for these tissues, we found that one main cluster contained all seven Barrett's samples, while the remaining principal cluster comprised the six esophageal cancers. The cancers also clustered according to histopathological subtype. Thus, squamous cell carcinomas (SCCAs) constituted one group, adenocarcinomas (ADCAs) clustered separately, and one signet-ring carcinoma was in its own cluster, distinct from the ADCA cluster. We conclude that cDNA microarrays and bioinformatics show promise in the classification of esophageal malignant and premalignant diseases, and that these methods can be applied to small biopsy samples.