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

Singh N, Eberhardt M, Wolkenhauer O, Vera J, Gupta SK (2020)

Publication Type: Journal article

Publication year: 2020

Journal

BMC Bioinformatics BMC

Book Volume: 21

Pages Range: 329-

Journal Issue: 1

DOI: 10.1186/s12859-020-03656-6

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.

Authors with CRIS profile

Martin Eberhardt Lehrstuhl für Biochemie und Molekulare Medizin Julio Vera González Professur für Tumorimmunologie (Schwerpunkt Systembiologie)

Involved external institutions

Universität Rostock

Germany (DE) University of Lucknow

India (IN)

How to cite

APA:

Singh, N., Eberhardt, M., Wolkenhauer, O., Vera, J., & Gupta, S.K. (2020). An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma. BMC Bioinformatics, 21(1), 329-. https://doi.org/10.1186/s12859-020-03656-6

MLA:

Singh, Nivedita, et al. "An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma." BMC Bioinformatics 21.1 (2020): 329-.

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