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Genes regulated by AGR2 in pancreatic cancer cell lines FA6 and MiaPaCa2

ABSTRACT: We and others have shown that AGR2 is frequently upregulated during the development of pancreatic cancer. We used microarray to look at the target genes regulated by AGR2 in pancreatic cancer cell lines FA6 and MiaPaCa2. Keywords: gene knock-down, overexpression We transiently down-regulated AGR2 expression in FA6 pancreatic cancer cells using INTERFERin transfection reagent and either AGR2 siRNA or non-targeting control siRNA for 48 hours. RNA was extracted and hybridized on Affymetrix microarrays. We looked for new target genes regulated by AGR2. We generated stable cell lines by introducing control vector pCEP4 or AGR2 overexpressing vector pCEP4-AGR2 into the pancreatic cancer cell line MiaPaCa2, single cell clones were then isolated. RNA was extracted and hybridized on Affymetrix microarrays.We looked for new target genes regulated by AGR2.

ORGANISM(S): Homo sapiens

SUBMITTER: Whiteman Hannah

PROVIDER: S-ECPF-GEOD-12513 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundMethylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis.ResultsOur two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes.ConclusionsOur results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

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Genes regulated by AGR2 in pancreatic cancer cell lines FA6 and MiaPaCa2

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