Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE119794 | GEO | 2018/09/12
REPOSITORIES: GEO
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Project description:Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer
| PRJNA490335 | ENA
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.
| S-EPMC5145806 | biostudies-other
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.
| S-EPMC7983860 | biostudies-literature
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.
| S-EPMC6294505 | biostudies-literature
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy. 42 human PDAC tumors and 7 non-malignant pancreas samples snap-frozen at the time of surgery were chosen. Representative H&E sections of each were evaluated by a practicing gastrointestinal pathologist to confirm diagnosis and determine relative percentage of malignant cells. Samples with tumor cell content >30% were chosen for final multi-platform analysis (N=25). Genomic miRNAa of human pancreatic samples were profiled on miRCURY LNA™ microRNA Array kit v.11.0 - human, mouse & rat arrays per manufacturer's instructions.
2011-10-07 | E-GEOD-32678 | biostudies-arrayexpress
Project description:The miRNA expression between normal and cancerous pancreatic tissues and identified preferentially expressed miRNAs. Two-condition experiment, pancreatic cancer vs. paired normal control. Biological replicates: 3 paired patients tissues.
2011-04-26 | E-GEOD-28862 | biostudies-arrayexpress
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.
| S-EPMC6740005 | biostudies-literature
Project description:To perform an integrative profile of human pancreatic cancer (PDAC) to identify prognosis-significant genes and their related pathways. A concordant survival-based whole genome in silico array analysis of DNA copy number, and mRNA & micro RNA (miRNA) expression in 25 early stage PDAC was performed. A novel composite score simultaneously integrated gene expression with regulatory mechanisms to identify the signature genes with the most levels of prognosis-significant evidence. The predominant signaling pathways were determined via a pathway-based approach. Independent patient cohorts (n= 150 and 42) were then used as in vitro validation of the array findings. We find that EGFR, SRC signaling, and PI3K/AKT pathway activation are strongly linked to clinical disease progression. Furthermore, we identify two discrete subsets of pancreatic tumors characterized by either SRC or PI3K/AKT signaling that may dictate variable responses to targeted therapy.
2011-10-08 | GSE32678 | GEO
Project description:Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms. We used quantitative global proteomic analysis on 40 PNENs to compliment paired transcriptome data.
2021-11-30 | PXD024175 | Pride
Project description:This study aims to identify and characterise circulating pancreatic stellate cells in an orthotopic mouse model of pancreatic cancer using single cell RNA sequencing.
2021-06-07 | GSE175837 | GEO