Dataset Information

Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles.

ABSTRACT: The production rate of gene expression data is nothing less than astounding. However, with the benefit of hindsight we can assert that, since we completely ignored the non-coding part of the transcriptome, we spent the last decade to study cell mechanisms having few data in our hands. In this scenario, microRNAs, which are key post-trascriptional regulators, deserve special attention. Given the state of knowledge about their biogenesis, mechanisms of action and the numerous experimentally validated target genes, miRNAs are also gradually appearing in the formal pathway representations such as KEGG and Reactome maps. However, the number of miRNAs annotated in pathway maps are very few and pathway analyses exploiting this new regulatory layer are still lacking. To fill these gaps, we present 'micrographite' a new pipeline to perform topological pathway analysis integrating gene and miRNA expression profiles. Here, micrographite is used to study and dissect the epithelial ovarian cancer gene and miRNA transcriptome defining and validating a new regulatory circuit related to ovarian cancer histotype specificity.

SUBMITTER: Calura E

PROVIDER: S-EPMC4066781 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles.

Calura Enrica E Martini Paolo P Sales Gabriele G Beltrame Luca L Chiorino Giovanna G D'Incalci Maurizio M Marchini Sergio S Romualdi Chiara C

Nucleic acids research 20140506 11

The production rate of gene expression data is nothing less than astounding. However, with the benefit of hindsight we can assert that, since we completely ignored the non-coding part of the transcriptome, we spent the last decade to study cell mechanisms having few data in our hands. In this scenario, microRNAs, which are key post-trascriptional regulators, deserve special attention. Given the state of knowledge about their biogenesis, mechanisms of action and the numerous experimentally valida ...[more]

PMID: 24803669

Similar Datasets

Project description:BackgroundOsteoarthritis (OA) is a chronic degenerative joint disease and the most frequent type of arthritis. This study aimed to identify the key miRNAs and genes associated with OA progression.MethodsThe GSE105027 (microRNA [miRNA/miR] expression profile; 12 OA samples and 12 normal samples) and GSE48556 (messenger RNA [mRNA] expression profile; 106 OA samples and 33 normal samples) datasets were selected from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and miRNAs (DEMs) were analyzed using the limma and ROCR packages in R, respectively. The target genes that negatively correlated with the DEMs were predicted, followed by functional enrichment analysis and construction of the miRNA-gene and miRNA-transcription factor (TF)-gene regulatory networks. Additionally, key miRNAs and genes were screened, and their expression levels were verified by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR).ResultsA total of 1696 DEGs (739 upregulated and 957 downregulated) and 108 DEMs (56 upregulated and 52 downregulated) were identified in the OA samples. Furthermore, 56 target genes that negatively correlated with the DEMs were predicted and found to be enriched in three functional terms (e.g., positive regulation of intracellular protein transport) and three pathways (e.g., human cytomegalovirus infection). In addition, three key miRNAs (miR-98-5p, miR-7-5p, and miR-182-5p) and six key genes (murine double minute 2, MDM2; glycogen synthase kinase 3-beta, GSK3B; transmembrane P24-trafficking protein 10, TMED10; DDB1 and CUL4-associated factor 12, DCAF12; caspase 3, CASP3; and ring finger protein 44, RNF44) were screened, among which the miR-7-5p ? TMED10/DCAF12, miR-98-5p ? CASP3/RNF44, and miR-182-5p ? GSK3B pairs were observed in the regulatory network. Moreover, the expression levels of TMED10, miR-7-5p, CASP3, miR-98-5p, GSK3B, and miR-182-5p showed a negative correlation with qRT-PCR verification.ConclusionMiR-98-5p, miR-7-5p, miR-182-5p, MDM2, GSK3B, TMED10, DCAF12, CASP3, and RNF44 may play critical roles in OA progression.

Project description:Several studies have investigated miRNA and mRNA co-expression to identify regulatory networks at the transcriptional level. A typical finding of these studies is the presence of both negative and positive miRNA-mRNA correlations. Negative correlations are consistent with the expected, faster degradation of target mRNAs, whereas positive correlations denote the existence of feed-forward regulations mediated by transcription factors. Both mechanisms have been characterized at the molecular level, although comprehensive methods to represent miRNA-mRNA correlations are lacking. At present, genome-wide studies are able to assess the expression of more than 1000 mature miRNAs and more than 35,000 well-characterized human genes. Even if studies are generally restricted to a small subset of genes differentially expressed in specific diseases or experimental conditions, the number of potential correlations remains very high, and needs robust multivariate methods to be conveniently summarized by a small set of data.Nonparametric Kendall correlations were calculated between miRNAs and mRNAs differentially expressed in livers of patients with acute liver failure (ALF) using normal livers as controls. Spurious correlations due to the histopathological composition of samples were removed by partial correlations. Correlations were then transformed into distances and processed by multidimensional scaling (MDS) to map the miRNA and mRNA relationships. These showed: (a) a prominent displacement of miRNA and mRNA clusters in ALF livers, as compared to control livers, indicative of gene expression dysregulation; (b) a clustering of mRNAs consistent with their functional annotations [CYP450, transcription factors, complement, proliferation, HLA class II, monocytes/macrophages, T cells, T-NK cells and B cells], as well as a clustering of miRNAs with the same seed sequence; and (c) a tendency of miRNAs and mRNAs to populate distinct regions of the MDS plot. MDS also allowed to visualize the network of miRNA-mRNA target pairs.Different features of miRNA and mRNA relationships can be represented as thematic maps within the framework of MDS obtained from pairwise correlations. The symmetric distribution of positive and negative correlations between miRNA and mRNA expression suggests that miRNAs are involved in a complex bidirectional molecular network, including, but not limited to, the inhibitory regulation of miRNA targets.

Project description:Background Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer. Methods One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software. Results A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494. Conclusions The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

Dataset Information

Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles.

Publications

Wiring miRNAs to pathways: a topological approach to integrate miRNA and mRNA expression profiles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure