Dataset Information

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

ABSTRACT:

Background

Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression.

Results

To elucidate their contribution to disease, we developed an integrative computational pipeline to construct and analyze a melanoma network focusing on lncRNAs, their miRNA and protein targets, miRNA target genes, and TFs regulating miRNAs. In the network, we identified three-node regulatory loops each composed of lncRNA, miRNA, and TF. To prioritize these motifs for their role in melanoma progression, we integrated patient-derived RNAseq dataset from TCGA (SKCM) melanoma cohort, using a weighted multi-objective function. We investigated the expression profile of the top-ranked motifs and used them to classify patients into metastatic and non-metastatic phenotypes.

Conclusions

The results of this study showed that network motif UCA1/AKT1/hsa-miR-125b-1 has the highest prediction accuracy (ACC?=?0.88) for discriminating metastatic and non-metastatic melanoma phenotypes. The observation is also confirmed by the progression-free survival analysis where the patient group characterized by the metastatic-type expression profile of the motif suffers a significant reduction in survival. The finding suggests a prognostic value of network motifs for the classification and treatment of melanoma.

SUBMITTER: Singh N

PROVIDER: S-EPMC7376740 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

Singh Nivedita N Eberhardt Martin M Wolkenhauer Olaf O Vera Julio J Gupta Shailendra K SK

BMC bioinformatics 20200723 1

<h4>Background</h4>Melanoma phenotype and the dynamics underlying its progression are determined by a complex interplay between different types of regulatory molecules. In particular, transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) interact in layers that coalesce into large molecular interaction networks. Our goal here is to study molecules associated with the cross-talk between various network layers, and their impact on tumor progression.<h4>Results</h4>To ...[more]

PMID: 32703153

Similar Datasets

Project description:Rationale: Most current cardiac regeneration approaches result in very limited cell division and little new cardiomyocyte (CM) mass. Positive feedback loops are vital for cell division, but their role in CM regeneration remains unclear. We aimed to determine whether the lncRNA small nucleolar RNA host gene 1 Snhg1 (Snhg1) could form a positive feedback loop with c-Myc to induce cardiac regeneration. Methods: Quantitative PCR and in situ hybridization experiments were performed to determine the Snhg1 expression patterns in fetal and myocardial infarction (MI) hearts. Gain- and Loss-of-function assays were conducted to explore the effect of Snhg1 on cardiomyocyte (CM) proliferation and cardiac repair following MI. We further constructed CM-specific Snhg1 knockout mice to confirm the proliferative effect exerted by Snhg1 using CRISPR/Cas9 technology. RNA sequencing and RNA pulldown were performed to explore how Snhg1 mediated cardiac regeneration. Chromatin immunoprecipitation and luciferase reporter assays were used to demonstrate the positive feedback loop between Snhg1 and c-Myc. Results: Snhg1 expression was increased in human and mouse fetal and myocardial infarction (MI) hearts, particularly in CMs. Overexpression of Snhg1 promoted CM proliferation, angiogenesis, and inhibited CM apoptosis after myocardial infarction, which further improved post-MI cardiac function. Antagonism of Snhg1 in early postnatal mice inhibited CM proliferation and impaired cardiac repair after MI. Mechanistically, Snhg1 directly bound to phosphatase and tensin homolog (PTEN) and induced PTEN degradation, activating the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathway to promote CM proliferation. The c-Myc protein, one of downstream targets of PI3K/AKT signaling, functioned as a transcription factor by binding to the promoter regions of Snhg1. Perturbation of the positive feedback between Snhg1 and c-Myc by mutation of the binding sequence significantly affected Snhg1-induced CM proliferation. Conclusions: Snhg1 effectively elicited CM proliferation and improved cardiac function post-MI by forming a positive feedback loop with c-Myc to sustain PI3K/Akt signaling activation, and thus may be a promising cardiac regeneration strategy in treating heart failure post-MI.

Project description:Background:Hepatocellular carcinoma (HCC) is an aggressive cancer with a poor prognosis and a high incidence. The molecular changes and novel biomarkers of HCC need to be identified to improve the diagnosis and prognosis of this disease. We investigated the current research concentrations of HCC and identified the transcriptomics-related biomarkers of HCC from The Cancer Genome Atlas (TGCA) database. Methods:We investigated the current research concentrations of HCC using literature metrology analysis for studies conducted from 2008 to 2018. We identified long noncoding RNAs (lncRNAs) that correlated with the clinical features and survival prognoses of HCC from The Cancer Genome Atlas (TGCA) database. Differentially expressed genes (lncRNAs, miRNAs, and mRNAs) were also identified by TCGA datasets in HCC tumor tissues. A lncRNA competitive endogenous RNA (ceRNA) network was constructed from lncRNAs based on intersected lncRNAs. Survival times and the association between the expression levels of the key lncRNAs of the ceRNA network and the clinicopathological characteristics of HCC patients were analyzed using TCGA. Real-time polymerase chain reaction (qRT-PCR) was used to validate the reliability of the results in tissue samples from 20 newly-diagnosed HCC patients. Results:Analysis of the literature pertaining to HCC research revealed that current research is focused on lncRNA functions in tumorigenesis and tumor development. A total of 128 HCC dysregulated lncRNAs were identified; 66 were included in the co-expressed ceRNA network. We analyzed survival times and the associations between the expression of 66 key lncRNAs and the clinicopathological features of the HCC patients identified from TCGA. Twenty-six lncRNAs were associated with clinical features of HCC (P < 0.05) and six key lncRNAs were associated with survival time (log-rank test P < 0.05). Six key lncRNAs were selected for the validation of their expression levels in 20 patients with newly diagnosed HCC using qRT-PCR. Consistent fold changes in the trends of up and down regulation between qRT-PCR validation and TCGA proved the reliability of our bioinformatics analysis. Conclusions:We used integrative bioinformatics analysis of the TCGA datasets to improve our understanding of the regulatory mechanisms involved with the functional features of lncRNAs in HCC. The results revealed that lncRNAs are potential diagnostic and prognostic biomarkers of HCC.

Dataset Information

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

Background

Results

Conclusions

Publications

An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure