Gene expression-based dentification of microRNA-9-3 target genes in breast epithelial cells
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Hsu Pei-Yin
PROVIDER: S-ECPF-GEOD-15749 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:In current study, DES could effectively suppress the expression of miR-9-3 through the ERa-dependent epigenetic machinery in MDECs and MCF-7 cells. We want to figure out the biological significance and the potential targets of miR-9-3 and the related signaling cascades.
2011-12-16 | GSE15749 | GEO
Project description:In current study, DES could effectively suppress the expression of miR-9-3 through the ERa-dependent epigenetic machinery in MDECs and MCF-7 cells. We want to figure out the biological significance and the potential targets of miR-9-3 and the related signaling cascades. MCF-7 cells were transfected with two pre-designed maturated mimics of miR-9-3, miR-9 and miR-9*. After 48hr of transfection, total RNA was collected for gene expression analysis to figure out the potential targets of miR-9-3.
2011-12-16 | E-GEOD-15749 | biostudies-arrayexpress
Project description:MicroRNAs (miRNAs) and transcription factors control eukaryotic cell proliferation, differentiation, and metabolism through their specific gene regulatory networks. However, differently from transcription factors, our understanding of the processes regulated by miRNAs is currently limited. Here, we introduce gene network analysis as a new means for gaining insight into miRNA biology. A systematic analysis of all human miRNAs based on Co-expression Meta-analysis of miRNA Targets (CoMeTa) assigns high-resolution biological functions to miRNAs and provides a comprehensive, genome-scale analysis of human miRNA regulatory networks. Moreover, gene cotargeting analyses show that miRNAs synergistically regulate cohorts of genes that participate in similar processes. We experimentally validate the CoMeTa procedure through focusing on three poorly characterized miRNAs, miR-519d/190/340, which CoMeTa predicts to be associated with the TGF? pathway. Using lung adenocarcinoma A549 cells as a model system, we show that miR-519d and miR-190 inhibit, while miR-340 enhances TGF? signaling and its effects on cell proliferation, morphology, and scattering. Based on these findings, we formalize and propose co-expression analysis as a general paradigm for second-generation procedures to recognize bona fide targets and infer biological roles and network communities of miRNAs.
| S-EPMC3371699 | biostudies-literature
Project description:BackgroundAccumulating evidence suggests that microRNA-target genes are closely related to tumorigenesis and progression. This study aims to screen the intersection of differentially expressed mRNAs (DEmRNAs) and the target genes of differentially expressed microRNAs (DEmiRNAs), and to construct a prognostic gene model of esophageal cancer (EC).MethodsGene expression, microRNA expression, somatic mutation, and clinical information data of EC from The Cancer Genome Atlas (TCGA) database were used. The intersection of DEmRNAs and the target genes of DEmiRNAs predicted by the Targetscan database and microRNA Data Integration Portal (mirDIP) database were screened. The screened genes were used to construct a prognostic model of EC. Then, the molecular and immune signatures of these genes were explored. Finally, the GSE53625 dataset from the Gene Expression Omnibus (GEO) database was further used as a validation cohort to confirm the prognostic value of the genes.ResultsSix genes on the grounds of the intersection of DEmiRNAs target genes and DEmRNAs were identified as prognostic genes, including ARHGAP11A, H1.4, HMGB3, LRIG1, PRR11, and COL4A1. Based on the median risk score calculated for these genes, EC patients were divided into a high-risk group (n=72) and a low-risk group (n=72). Survival analysis showed that the high-risk group had a significantly shorter survival time than the low-risk group (TCGA and GEO, P<0.001). The nomogram evaluation showed high reliability in predicting the 1-year, 2-year, and 3-year survival probability of EC patients. Compared to low-risk group, higher expression level of M2 macrophages was found in high-risk group of EC patient (P<0.05), while STAT3 checkpoints showed attenuated expression level in high-risk group.ConclusionsA panel of differential genes was identified as potential EC prognostic biomarkers and showed great clinical significance in EC prognosis.
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Project description:ObjectivesEribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B. Eribulin is a mechanistically unique inhibitor of microtubule dynamics. In this study, we investigated whether selective signal pathways were associated with eribulin activity compared to paclitaxel, which stabilizes microtubules, based on gene expression profiling of cell line panels of breast, endometrial, and ovarian cancer in vitro.ResultsWe determined the sets of genes that were differentially altered between eribulin and paclitaxel treatment in breast, endometrial, and ovarian cancer cell line panels. Our unsupervised clustering analyses revealed that expression profiles of gene sets altered with treatments were correlated with the in vitro antiproliferative activities of the drugs. Several tubulin isotypes had significantly lower expression in cell lines treated with eribulin compared to paclitaxel. Pathway enrichment analyses of gene sets revealed that the common pathways altered between treatments in the 3 cancer panels were related to cytoskeleton remodeling and cell cycle regulation. The epithelial-mesenchymal transition (EMT) pathway was enriched in genes with significantly altered expression between the two drugs for breast and endometrial cancers, but not for ovarian cancer. Expression of genes from the EMT pathway correlated with eribulin sensitivity in breast cancer and with paclitaxel sensitivity in endometrial cancer. Alteration of expression profiles of EMT genes between sensitive and resistant cell lines allowed us to predict drug sensitivity for breast and endometrial cancers.ConclusionGene expression analysis showed that gene sets that were altered between eribulin and paclitaxel correlated with drug in vitro antiproliferative activities in breast and endometrial cancer cell line panels. Among the panels, breast cancer provided the strongest differentiation between eribulin and paclitaxel sensitivities based on gene expression. In addition, EMT genes were predictive of eribulin sensitivity in the breast and endometrial cancer panels.
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Project description:BackgroundThis study aimed to determine the miRNA profile in breast cancer stem cells (BCSCs) and to explore the functions of characteristic BCSC miRNAs.MethodsWe isolated ESA+CD44+CD24-/low BCSCs from MCF-7 cells using fluorescence-activated cell sorting (FACS). A human breast cancer xenograft assay was performed to validate the stem cell properties of the isolated cells, and microarray analysis was performed to screen for BCSC-related miRNAs. These BCSC-related miRNAs were selected for bioinformatic analysis and target prediction using online software programs.ResultsThe ESA+CD44+CD24-/low cells had up to 100- to 1000-fold greater tumor-initiating capability than the MCF-7 cells. Tumors initiated from the ESA+CD44+CD24-/low cells were included of luminal epithelial and myoepithelial cells, indicating stem cell properties. We also obtained miRNA profiles of ESA+CD44+CD24-/low BCSCs. Most of the possible targets of potential tumorigenesis-related miRNAs were oncogenes, anti-oncogenes or regulatory genes.ConclusionsWe identified a subset of miRNAs that were differentially expressed in BCSCs, providing a starting point to explore the functions of these miRNAs. Evaluating characteristic BCSC miRNAs represents a new method for studying breast cancer-initiating cells and developing therapeutic strategies aimed at eradicating the tumorigenic subpopulation of cells in breast cancer.
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Project description:MicroRNAs (miRNAs) are small noncoding RNAs that control gene expression by inducing RNA cleavage or translational inhibition. Most human miRNAs are intragenic and are transcribed as part of their hosting transcription units. We hypothesized that the expression profiles of miRNA host genes and of their targets are inversely correlated and devised a novel procedure, HOCTAR (host gene oppositely correlated targets), which ranks predicted miRNA target genes based on their anti-correlated expression behavior relative to their respective miRNA host genes. HOCTAR is the first tool for systematic miRNA target prediction that utilizes the same set of microarray experiments to monitor the expression of both miRNAs (through their host genes) and candidate targets. We applied the procedure to 178 human intragenic miRNAs and found that it performs better than currently available prediction softwares in pinpointing previously validated miRNA targets. The high-scoring HOCTAR predicted targets were enriched in Gene Ontology categories, which were consistent with previously published data, as in the case of miR-106b and miR-93. By means of overexpression and loss-of-function assays, we also demonstrated that HOCTAR is efficient in predicting novel miRNA targets and we identified, by microarray and qRT-PCR procedures, 34 and 28 novel targets for miR-26b and miR-98, respectively. Overall, we believe that the use of HOCTAR significantly reduces the number of candidate miRNA targets to be tested compared to the procedures based solely on target sequence recognition. Finally, our data further confirm that miRNAs have a significant impact on the mRNA levels of most of their targets.
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Project description:Recent evidence supports a role for microRNAs (miRNAs) in regulating the life span of model organisms. However, little is known about how these small RNAs contribute to human aging. Here, we profiled the expression of over 800 miRNAs in peripheral blood mononuclear cells from young and old individuals by real-time RT-PCR analysis. This genome-wide assessment of miRNA expression revealed that the majority of miRNAs studied decreased in abundance with age. We identified nine miRNAs (miR-103, miR-107, miR-128, miR-130a, miR-155, miR-24, miR-221, miR-496, miR-1538) that were significantly lower in older individuals. Among them, five have been implicated in cancer pathogenesis. Predicted targets of several of these miRNAs, including PI3 kinase (PI3K), c-Kit and H2AX, were found to be elevated with advancing age, supporting a possible role for them in the aging process. Furthermore, we found that decreasing the levels of miR-221 was sufficient to cause a corresponding increase in the expression of the predicted target, PI3K. Taken together, these findings demonstrate that changes in miRNA expression occur with human aging and suggest that miRNAs and their predicted targets have the potential to be diagnostic indicators of age or age-related diseases.
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Project description:MicroRNAs (miRNAs) play crucial roles in a variety of biological processes via regulating expression of their target genes at the mRNA level. A number of computational approaches regarding miRNAs have been proposed, but most of them focus on miRNA gene finding or target predictions. Little computational work has been done to investigate the effective regulation of miRNAs.We propose a method to infer the effective regulatory activities of miRNAs by integrating microarray expression data with miRNA target predictions. The method is based on the idea that regulatory activity changes of miRNAs could be reflected by the expression changes of their target transcripts measured by microarray. To validate this method, we apply it to the microarray data sets that measure gene expression changes in cell lines after transfection or inhibition of several specific miRNAs. The results indicate that our method can detect activity enhancement of the transfected miRNAs as well as activity reduction of the inhibited miRNAs with high sensitivity and specificity. Furthermore, we show that our inference is robust with respect to false positives of target prediction.A huge amount of gene expression data sets are available in the literature, but miRNA regulation underlying these data sets is largely unknown. The method is easy to be implemented and can be used to investigate the miRNA effective regulation underlying the expression change profiles obtained from microarray experiments.
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