MiRNA expression, mRNA expression upon miRNA reconstitution, and direct mRNA target identification in prostate cancer cell lines
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE17362 | GEO | 2012/03/07
SECONDARY ACCESSION(S): PRJNA119083
REPOSITORIES: GEO
Similar Datasets
Project description:This SuperSeries is composed of the following subset Series: GSE17315: mRNA expression upon reconstitution of miR-130a, miR-203 and miR-205 in prostate cancer cell line LNCaP GSE17317: miRNA expression in LNCaP, PC3, Du-145 and RWPE-1 cell lines GSE22979: Profiling of direct mRNA targets of miR-130a, miR-203 and miR-205 in prostate cancer cell line LNCaP Refer to individual Series
2012-03-07 | E-GEOD-17362 | biostudies-arrayexpress
Project description:miRNA expression, mRNA expression upon miRNA reconstitution, and direct mRNA target identification in prostate cancer cell lines
| PRJNA119083 | ENA
Project description:MicroRNAs (miRNAs) are small non-coding RNAs that mediate gene expression at the post-transcriptional and translational levels by an imperfect binding to target mRNA 3'UTR regions. While the ab-initio computational prediction of miRNA-mRNA interactions still poses significant challenges, it is possible to overcome some of its limitations by carefully integrating into the analysis the paired expression profiles of miRNAs and mRNAs. In this work, we show how the choice of a proper probe annotation for microarray platforms is an essential requirement to achieve good sensitivity in the identification of miRNA-mRNA interactions. We compare the results obtained from the analysis of the same expression profiles using both gene and transcript based custom CDFs that we have developed for a number of different annotations (ENSEMBL, RefSeq, AceView). In all cases, transcript-based annotations clearly improve the effectiveness of data integration and thus provide a more reliable confirmation of computationally predicted miRNA-mRNA interactions.
| S-EPMC2853140 | biostudies-literature
Project description:BackgroundAortic dissection (AD) is one of the most lethal cardiovascular diseases. The aim of this study was to identify core genes and pathways revealing pathogenesis in AD.MethodsWe screened differentially expressed mRNAs and miRNAs using mRNA and miRNA expression profile data of AD from Gene Expression Omnibus. Then functional and pathway enrichment analyses of differential expression genes (DEGs) was performed utilizing the database for annotation, visualization, and integrated discovery (DAVID). Target genes with differential expression miRNAs (DEMIs) were predicted using the miRWalk database, and the intersection between these predictions and DEGs was selected as differentially expressed miRNA-target genes. In addition, a protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed.ResultsIn total, 130 DEGs and 47 DEMIs were identified from mRNA and miRNA microarray, respectively, and 45 DEGs were DEMI-target genes. The PPI and miRNA-mRNA network included 79 node genes and 74 node genes, respectively, while 23 hub genes and 2 hub miRNAs were identified. The DEGs, PPI and modules differential expression miRNA-target genes were all mainly enriched in cell cycle, cell proliferation and cell apoptosis signaling pathways.ConclusionTaken above, the study reveals some candidate genes and pathways potentially involving molecular mechanisms of AD. These findings provide a new insight for research and treatment of AD.
| S-EPMC6587623 | biostudies-literature
Project description:Prostate cancer (PC) is one of the major male cancers. Differential diagnosis of PC is indispensable for the individual therapy, i.e., Gleason score (GS) that describes the grade of cancer can be used to choose the appropriate therapy. However, the current techniques for PC diagnosis and prognosis are not always effective. To identify potential markers that could be used for differential diagnosis of PC, we analyzed miRNA-mRNA interactions and we build specific networks for PC onset and progression. Key differentially expressed miRNAs for each GS were selected by calculating three parameters of network topology measures: the number of their single regulated mRNAs (NSR), the number of target genes (NTG) and NSR/NTG. miRNAs that obtained a high statistically significant value of these three parameters were chosen as potential biomarkers for computational validation and pathway analysis. 20 miRNAs were identified as key candidates for PC. 8 out of 20 miRNAs (miR-25-3p, miR-93-3p, miR-122-5p, miR-183-5p, miR-615-3p, miR-7-5p, miR-375, and miR-92a-3p) were differentially expressed in all GS and proposed as biomarkers for PC onset. In addition, "Extracellular-receptor interaction", "Focal adhesion", and "microRNAs in cancer" were significantly enriched by the differentially expressed target genes of the identified miRNAs. miR-10a-5p was found to be differentially expressed in GS 6, 7, and 8 in PC samples. 3 miRNAs were identified as PC GS-specific differentially expressed miRNAs: miR-155-5p was identified in PC samples with GS 6, and miR-142-3p and miR-296-3p in PC samples with GS 9. The efficacy of 20 miRNAs as potential biomarkers was revealed with a Random Forest classification using an independent dataset. The results demonstrated our 20 miRNAs achieved a better performance (AUC: 0.73) than miRNAs selected with Boruta algorithm (AUC: 0.55), a method for the automated feature extraction. Studying miRNA-mRNA associations, key miRNAs were identified with a computational approach for PC onset and progression. Further experimental validations are needed for future translational development.
| S-EPMC8844601 | biostudies-literature
Project description:We introduce mirTarRnaSeq, an R/Bioconductor package for quantitative assessment of miRNA-mRNA relationships within sample cohorts. mirTarRnaSeq is a statistical package to explore predicted or pre-hypothesized miRNA-mRNA relationships following target prediction.We present two use cases applying mirTarRnaSeq. First, to identify miRNA targets, we examined EBV miRNAs for interaction with human and virus transcriptomes of stomach adenocarcinoma. This revealed enrichment of mRNA targets highly expressed in CD105+ endothelial cells, monocytes, CD4+ T cells, NK cells, CD19+ B cells, and CD34 cells. Next, to investigate miRNA-mRNA relationships in SARS-CoV-2 (COVID-19) infection across time, we used paired miRNA and RNA sequenced datasets of SARS-CoV-2 infected lung epithelial cells across three time points (4, 12, and 24 hours post-infection). mirTarRnaSeq identified evidence for human miRNAs targeting cytokine signaling and neutrophil regulation immune pathways from 4 to 24 hours after SARS-CoV-2 infection. Confirming the clinical relevance of these predictions, three of the immune specific mRNA-miRNA relationships identified in human lung epithelial cells after SARS-CoV-2 infection were also observed to be differentially expressed in blood from patients with COVID-19. Overall, mirTarRnaSeq is a robust tool that can address a wide-range of biological questions providing improved prediction of miRNA-mRNA interactions.
| S-EPMC9191533 | biostudies-literature
Project description:Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.
| S-EPMC4385567 | biostudies-literature
Project description:Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the 'CancerSubtypes' package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs.
| S-EPMC7785043 | biostudies-literature
Project description:miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.
| S-EPMC5376335 | biostudies-literature
Project description:RT-qPCR offers high sensitivity, for accurate interpretations of qPCR results however, normalisation using suitable reference genes is fundamental. Androgens can regulate transcriptional expression including reference gene expression in prostate cancer. In this study, we evaluated ten mRNA and six non-protein coding RNA reference genes in five prostate cell lines under varied dihydrotestosterone (DHT) treatments. We validated the effects of DHT-treatments using media containing charcoal-stripped serum prior to DHT stimulation on the test samples by Western blot experiments. Reference gene expression stability was analysed using three programs (geNorm, NormFinder and BestKeeper), and the recommended comprehensive ranking is provided. Our results reveal that ACTB and GAPDH, and miR-16 and miR-1228-3p are the most suitable mRNA and miRNA reference genes across all cell lines, respectively. Considering prostate cancer cell types, ACTB/GAPDH and ACTB/HPRT1 are the most suitable reference gene combinations for mRNA analysis, and miR-16/miR-1228-3p and RNU6-2/RNU43 for miRNA analysis in AR+, and AR- and normal cell lines, respectively. Comparison of relative target gene (PCA3 and miR-141) expression reveals different patterns depending on reference genes used for normalisation. To our knowledge, this is the first report on validation of reference genes under different DHT treatments in prostate cancer cells. This study provides insights for discovery of reliable DHT-regulated genes in prostate cells.
| S-EPMC5792445 | biostudies-literature