An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma
Date
2020-07-23
Journal Title
Journal ISSN
Volume Title
Publisher
BMC (part of Springer Nature)
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/hsamiR-
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.
Description
CITATION: Singh, N., et al. 2020. An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma. BMC Bioinformatics, 21:329, doi:10.1186/s12859-020-03656-6.
The original publication is available at https://bmcbioinformatics.biomedcentral.com
The original publication is available at https://bmcbioinformatics.biomedcentral.com
Keywords
Melanoma, Metastasis, Gene regulatory networks
Citation
Singh, N., et al. 2020. An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma. BMC Bioinformatics, 21:329, doi:10.1186/s12859-020-03656-6