Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer

Tang, Yifei; Yan, Wenying; Chen, Jiajia; Luo, Cheng; Kaipia, Antti; Shen, Bairong

doi:10.1186/1752-0509-7-s3-s6

Cited by 36 publications

(24 citation statements)

References 36 publications

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“…Hence, we can conclude that the over-expression of miR-182 and the down-regulation of ESR1 could be associated with PCa progression and potentially useful as prognostic biomarker. Moreover, Tang and colleagues [ 22 ] have recently applied a computational approach identifying two putative novel miRNA regulatory pathways in PCa: 1) ligand-independent activation of ESR1 and ESR2 and 2) membrane-bound ESR1 (interaction with growth factors signalling), showing a new possible role of miRNAs in the regulation of ESR1 and the consistence of our results. It is interesting to note that ESR1 and the other genes in our top 20 list interact with each other.…”

Section: Resultssupporting

confidence: 62%

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A New Method for Discovering Disease-Specific MiRNA-Target Regulatory Networks

et al. 2015

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Genes and their expression regulation are among the key factors in the comprehension of the genesis and development of complex diseases. In this context, microRNAs (miRNAs) are post-transcriptional regulators that play an important role in gene expression since they are frequently deregulated in pathologies like cardiovascular disease and cancer. In vitro validation of miRNA - targets regulation is often too expensive and time consuming to be carried out for every possible alternative. As a result, a tool able to provide some criteria to prioritize trials is becoming a pressing need. Moreover, before planning in vitro experiments, the scientist needs to evaluate the miRNA-target genes interaction network. In this paper we describe the miRable method whose purpose is to identify new potentially relevant genes and their interaction networks associate to a specific pathology. To achieve this goal miRable follows a system biology approach integrating together general-purpose medical knowledge (literature, Protein-Protein Interaction networks, prediction tools) and pathology specific data (gene expression data). A case study on Prostate Cancer has shown that miRable is able to: 1) find new potential miRNA-targets pairs, 2) highlight novel genes potentially involved in a disease but never or little studied before, 3) reconstruct all possible regulatory subnetworks starting from the literature to expand the knowledge on the regulation of miRNA regulatory mechanisms.

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Section: Resultssupporting

confidence: 62%

“… Estrogen Receptor 1 (ESR1) . The Estrogen Receptor 1 is an inhibitor of cell migration and its repression enhances cell migration and accelerates tumor formation and metastasis [ 22 ]. This gene is known to be involved in breast cancer.…”

Section: Resultsmentioning

confidence: 99%

A New Method for Discovering Disease-Specific MiRNA-Target Regulatory Networks

et al. 2015

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“…We further explored the roles of uniquely regulated genes of the identified miRNAs in AMI and UA by functional enrichment analysis using the MetaCore software [39–44]. In pathway analysis, we found 35 significantly enriched pathways in AMI and 18 in UA ( p value < 0.05 and FDR < 0.05; see Figures 3(a) and 3(b)).…”

Section: Resultsmentioning

confidence: 99%

Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach

Zhu

Lin

Yan

et al. 2016

BioMed Research International

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Acute coronary syndrome (ACS) is a life-threatening disease that affects more than half a million people in United States. We currently lack molecular biomarkers to distinguish the unstable angina (UA) and acute myocardial infarction (AMI), which are the two subtypes of ACS. MicroRNAs play significant roles in biological processes and serve as good candidates for biomarkers. In this work, we collected microRNA datasets from the Gene Expression Omnibus database and identified specific microRNAs in different subtypes and universal microRNAs in all subtypes based on our novel network-based bioinformatics approach. These microRNAs were studied for ACS association by pathway enrichment analysis of their target genes. AMI and UA were associated with 27 and 26 microRNAs, respectively, nine of them were detected for both AMI and UA, and five from each subtype had been reported previously. The remaining 22 and 21 microRNAs are novel microRNA biomarkers for AMI and UA, respectively. The findings are then supported by pathway enrichment analysis of the targets of these microRNAs. These novel microRNAs deserve further validation and will be helpful for personalized ACS diagnosis.

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“…Thus, similar to prostate cancer11, ASD seems to be a complex disease where the dysregulation of miRNAs is generally heterogeneous due to different genetic and non-genetic risk factors. Many mechanisms can lead to ASD and diverse miRNA expression profiles are used as factors to identify associations with ASD.…”

mentioning

confidence: 99%

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers

Shen

Lin

Sun

et al. 2016

Sci Rep

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Autism spectrum disorder (ASD) is a severe neurodevelopmental disease with a high incidence and effective biomarkers are urgently needed for its diagnosis. A few previous studies have reported the detection of miRNA biomarkers for autism diagnosis, especially those based on bioinformatics approaches. In this study, we developed a knowledge-guided bioinformatics model for identifying autism miRNA biomarkers. We downloaded gene expression microarray data from the GEO Database and extracted genes with expression levels that differed in ASD and the controls. We then constructed an autism-specific miRNA–mRNA network and inferred candidate autism biomarker miRNAs based on their regulatory modes and functions. We defined a novel parameter called the autism gene percentage as autism-specific knowledge to further facilitate the identification of autism-specific biomarker miRNAs. Finally, 11 miRNAs were screened as putative autism biomarkers, where eight miRNAs (72.7%) were significantly dysregulated in ASD samples according to previous reports. Functional enrichment results indicated that the targets of the identified miRNAs were enriched in autism-associated pathways, such as Wnt signaling (in KEGG and IPA), cell cycle (in KEGG), and glioblastoma multiforme signaling (in IPA), thereby supporting the predictive power of our model.

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Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer

Cited by 36 publications

References 36 publications

A New Method for Discovering Disease-Specific MiRNA-Target Regulatory Networks

A New Method for Discovering Disease-Specific MiRNA-Target Regulatory Networks

Novel Biomarker MicroRNAs for Subtyping of Acute Coronary Syndrome: A Bioinformatics Approach

Knowledge-Guided Bioinformatics Model for Identifying Autism Spectrum Disorder Diagnostic MicroRNA Biomarkers

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