Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer
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ABSTRACT:
PROVIDER: PRJNA490335 | ENA |
REPOSITORIES: ENA
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Project description:In this study, we used RNA-seq to investigate the transcriptomic (mRNA and miRNA) profiles of pancreatic cancer in 10 paired tumor and normal pancreatic samples from patients
2018-09-12 | GSE119794 | GEO
Project description:In a previous microarray study, we identified a subset of micro RNAS (miRNAs), which expression was distinctly higher in atretic than healthy follicles of cattle. In the present study, we investigated the involvement of those miRNAs in granulosa and theca cells during atresia. Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) confirmed that miR-21-5p/-3p, miR-150, miR-409a, miR-142-5p, miR-378, miR-222, miR-155, and miR-199a-5p were expressed at higher levels in atretic than healthy follicles (9-17 mm, classified based on steroidogenic capacity). All miRNAs except miR-21-3p and miR-378 were expressed at higher levels in theca than granulosa cells. The expression of 13 predicted miRNA targets was determined in follicular cells by RT-qPCR, revealing downregulation of HIF1A, ETS1, JAG1, VEGFA, and MSH2 in either or both cell types during atresia. Based on increases in miRNA levels simultaneous with decreases in target levels in follicular cells, several predicted miRNA target interactions were confirmed that are putatively involved in follicular atresia, namely miR-199a-5p/miR-155-HIF1A in granulosa cells, miR-155/miR-222-ETS1 in theca cells, miR-199a-5p-JAG1 in theca cells, miR-199a-5p/miR-150/miR-378-VEGFA in granulosa and theca cells, and miR-155-MSH2 in theca cells. These results offer novel insight on the involvement of miRNAs in follicle development by identifying a miRNA target network that is putatively involved in follicle atresia.
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Project description:Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in humans is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this article, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values. Our approach calculates network-wide effects of perturbations in the expression level of one or more nodes in the presence or absence of miRNA interaction factors such as seed type, binding energy. We carried out the analysis of large-scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer-associated genes and miRNAs. Our network-based approach takes the sponge effect into account and helps to unveil the crosstalk between nodes in miRNA:target network. The model has potential to reveal unforeseen regulations that are only evident in the network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs, and available as R package ceRNAnetsim: https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html.
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Project description:MicroRNAs (miRNAs) modulate the post-transcriptional regulation of target genes and are related to biology of complex human traits, but genetic landscape of miRNAs remains largely unknown. Given the strikingly tissue-specific miRNA expression profiles, we here expand a previous method to quantitatively evaluate enrichment of genome-wide association study (GWAS) signals on miRNA-target gene networks (MIGWAS) to further estimate tissue-specific enrichment. Our approach integrates tissue-specific expression profiles of miRNAs (?1800 miRNAs in 179 cells) with GWAS to test whether polygenic signals enrich in miRNA-target gene networks and whether they fall within specific tissues. We applied MIGWAS to 49 GWASs (nTotal = 3 520 246), and successfully identified biologically relevant tissues. Further, MIGWAS could point miRNAs as candidate biomarkers of the trait. As an illustrative example, we performed differentially expressed miRNA analysis between rheumatoid arthritis (RA) patients and healthy controls (n = 63). We identified novel biomarker miRNAs (e.g. hsa-miR-762) by integrating differentially expressed miRNAs with MIGWAS results for RA, as well as novel associated loci with significant genetic risk (rs56656810 at MIR762 at 16q11; n = 91 482, P = 3.6 × 10-8). Our result highlighted that miRNA-target gene network contributes to human disease genetics in a cell type-specific manner, which could yield an efficient screening of miRNAs as promising biomarkers.
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Project description:Acute respiratory distress syndrome (ARDS) is a respiratory failure in critically ill patients, and the molecular mechanisms underlying its pathogenesis and severity are poorly understood. We evaluated mRNA and miRNA in patients with ARDS and elucidated the pathogenesis of ARDS, following mRNA and miRNA integration analysis. Thirty-four ARDS patients were compared with 15 healthy donors. 1233 mRNAs and 6 miRNAs were upregulated and 1580 mRNAs and 13 miRNAs were downregulated in ARDS patients compared healthy donors. In both mRNA and miRNA-targeted mRNA, the canonical pathway analysis showed that PD-1 and PD-L1 cancer immunotherapy pathway was most activated and Th2 pathway was most suppressed. miR-149-3p and several miRNAs were identified as upstream regulators. Integrated analysis of mRNAs and miRNAs showed that T cells were dysfunctional in ARDS patients.
2024-03-15 | GSE243067 | GEO
Project description:BackgroundMiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited.ResultsIn this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE).ConclusionWe propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.
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Project description:Virally Encoded Connectivity Transgenic Overlay RNA sequencing (VECTORseq) defines projection neurons involved in sensorimotor integration
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Project description:Acute respiratory distress syndrome (ARDS) is a respiratory failure in critically ill patients, and the molecular mechanisms underlying its pathogenesis and severity are poorly understood. We evaluated mRNA and miRNA in patients with ARDS and elucidated the pathogenesis of ARDS, following mRNA and miRNA integration analysis. Thirty-four ARDS patients were compared with 15 healthy donors. 1233 mRNAs and 6 miRNAs were upregulated and 1580 mRNAs and 13 miRNAs were downregulated in ARDS patients compared healthy donors. In both mRNA and miRNA-targeted mRNA, the canonical pathway analysis showed that PD-1 and PD-L1 cancer immunotherapy pathway was most activated and Th2 pathway was most suppressed. miR-149-3p and several miRNAs were identified as upstream regulators. Integrated analysis of mRNAs and miRNAs showed that T cells were dysfunctional in ARDS patients.
2024-03-15 | GSE243066 | GEO
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.
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