An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma.
BMC Bioinformatics
; 21(1): 329, 2020 Jul 23.
Article
em En
| MEDLINE
| ID: mdl-32703153
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.Palavras-chave
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Regulação Neoplásica da Expressão Gênica
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Redes Reguladoras de Genes
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RNA Longo não Codificante
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Melanoma
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Limite:
Humans
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Middle aged
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article