An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma
dc.contributor.author | Singh, Nivedita | en_ZA |
dc.contributor.author | Eberhardt, Martin | en_ZA |
dc.contributor.author | Wolkenhauer, Olaf | en_ZA |
dc.contributor.author | Vera, Julio | en_ZA |
dc.contributor.author | Gupta, Shailendra K. | en_ZA |
dc.date.accessioned | 2020-07-27T07:04:36Z | |
dc.date.available | 2020-07-27T07:04:36Z | |
dc.date.issued | 2020-07-23 | |
dc.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. | |
dc.description | The original publication is available at https://bmcbioinformatics.biomedcentral.com | |
dc.description.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. | |
dc.description.uri | https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03656-6 | |
dc.description.version | Publisher's version | |
dc.format.extent | 17 pages | |
dc.identifier.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 | |
dc.identifier.issn | 1471-2105 (online) | |
dc.identifier.other | doi:10.1186/s12859-020-03656-6 | |
dc.identifier.uri | http://hdl.handle.net/10019.1/108725 | |
dc.publisher | BMC (part of Springer Nature) | |
dc.rights.holder | Authors retain copyright | |
dc.subject | Melanoma | en_ZA |
dc.subject | Metastasis | en_ZA |
dc.subject | Gene regulatory networks | en_ZA |
dc.title | An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma | en_ZA |
dc.type | Article |