Genes regulated by TAZ in lung cancer cell lines
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Noguchi S
PROVIDER: S-ECPF-GEOD-51270 | biostudies-other | 2014 Sep
REPOSITORIES: biostudies-other
Similar Datasets
Project description:To investigate the roles of TAZ in lung cancer cell proliferation, we compared the expression profiles of A549 and H441 lung adenocarcinoma cell lines transfected with control siRNA and siTAZ. We collected RNA from A549 and H441 cell lines transfected with control siRNA and siTAZ.
2014-06-25 | E-GEOD-51270 | biostudies-arrayexpress
Project description:To investigate the roles of TAZ in lung cancer cell proliferation, we compared the expression profiles of A549 and H441 lung adenocarcinoma cell lines transfected with control siRNA and siTAZ.
2014-06-25 | GSE51270 | GEO
Project description:BackgroundNon-small cell lung cancer (NSCLC) is the most common type of lung cancer and has a poor prognosis. Identifying biomarkers based on molecular mechanisms is critical for early diagnosis, timely treatment, and improved prognosis of lung cancer. MALAT1 has been reported to have overexpressed and tumor-promoting functions in NSCLC. It has been proposed as a potential biomarker for the diagnosis and prognosis of cancer. Therefore, this study was conducted to profile the changes in gene expression according to the regulation of expression of MALAT1 in NSCLC cell lines and to investigate the correlation through bioinformatic analysis of differentially expressed genes (DEGs).MethodsMALAT1 expression levels were measured using RT-qPCR. The biological functions of MALAT1 in NSCLC were analyzed by cell counting, colony forming, wound-healing, and Transwell invasion assays. In addition, gene expression profiling in response to the knockdown of MALAT1 was analyzed by transcriptome sequencing, and differentially expressed genes regulated by MALAT1 were performed by GO and KEGG pathway enrichment analyses. Bioinformatic databases were used for gene expression analysis and overall survival analysis.ResultsComparative analysis versus MALAT1 expression in MRC5 cells (a normal lung cell line) and the three NSCLC cell lines showed that MALAT1 expression was significantly higher in the NSCLC cells. MALAT1 knockdown decreased cell survival, proliferation, migration, and invasion in all three NSCLC cell lines. RNA-seq analysis of DEGs in NSCLC cells showed 198 DEGs were upregulated and 266 DEGs downregulated by MALAT1 knockdown in all three NSCLC cell lines. Survival analysis on these common DEGs performed using the OncoLnc database resulted in the selection of five DEGs, phosphoglycerate mutase 1 (PGAM1), phosphoglycerate mutase 4 (PGAM4), nucleolar protein 6 (NOL6), nucleosome assembly protein 1 like 5 (NAP1L5), and sestrin1 (SESN1). The gene expression levels of these selected DEGs were proved to gene expression analysis using the TNMplot database.ConclusionMALAT1 might function as an oncogene that enhances NSCLC cell survival, proliferation, colony formation, and invasion. RNA-seq and bioinformatic analyses resulted in the selection of five DEGs, PGAM1, PGAM4, NOL6, NAP1L5, and SESN1, which were found to be closely related to patient survival and tumorigenesis. We believe that further investigation of these five DEGs will provide valuable information on the oncogenic role of MALAT1 in NSCLC.
| S-EPMC10476395 | biostudies-literature
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.
| S-EPMC2903569 | biostudies-literature
Project description:To investigate the roles of TAZ in lung fibroblasts, we compared the expression profiles of a lung fibroblast cell line, HFL-1, transfected with control siRNA and siTAZ.
2017-02-17 | GSE73555 | GEO
Project description:Diindolylmethane (DIM), a biologically active congener of indole-3-carbinol (I3C) derived from cruciferous vegetables, is a promising agent for the prevention of estrogen-sensitive cancers. Both DIM and estrogen affect transcription of genes by binding receptors, such as aryl hydrocarbon receptor (AhR) or estrogen receptors (ER). Gene regulation by DIM and estradiol (E2) can be very complex. While DIM typically binds the AhR, this complex can directly associate with the ER, recruit co-activators that bind to estrogen-responsive promoters, and activate transcription. Alternately, DIM can bind the ER directly. In this study, we have analyzed gene expression using microarray profiling and quantitative real time-polymerase chain reaction in MCF7 breast cancer cells treated with E2 (1 nM) or DIM (25 microM) alone or in combination for 16 h. The interplay of E2 and DIM was reflected in the expression of a subset of genes (<90) in which the combination of E2 and DIM acted either additively or antagonistically to alter gene expression.
| S-EPMC1869626 | biostudies-literature
Project description:A549 and H1299 cell lines were transfected with hMOF specific siRNA or control siRNA, and Affymetrix oligonucleotide microarray was conducted to systematically determine downstream targets regulated by hMOF. Two NSCLC cell lines (four samples) transfected with hMOF specific siRNA or control siRNA
| S-ECPF-GEOD-40424 | biostudies-other
Project description: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.
| S-ECPF-GEOD-12513 | biostudies-other
Project description:To investigate genes possibly regulated by TTF-1 in small cell lung cancer cell lines, we compared gene expression profiles of NCI-H209 and Lu139 cell lines electroporated with control and TTF-1 siRNAs.
2018-07-11 | GSE106091 | GEO
Project description:siIGF2BP3 regulated genes in lung cancer cell
| PRJNA632900 | ENA