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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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>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.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The results of this study showed that network motif UCA1\/AKT1\/hsa-miR-125b-1 has the highest prediction accuracy (ACC\u2009=\u20090.88) 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.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-020-03656-6","type":"journal-article","created":{"date-parts":[[2020,7,23]],"date-time":"2020-07-23T11:04:10Z","timestamp":1595502250000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An integrative network-driven pipeline for systematic identification of lncRNA-associated regulatory network motifs in metastatic melanoma"],"prefix":"10.1186","volume":"21","author":[{"given":"Nivedita","family":"Singh","sequence":"first","affiliation":[]},{"given":"Martin","family":"Eberhardt","sequence":"additional","affiliation":[]},{"given":"Olaf","family":"Wolkenhauer","sequence":"additional","affiliation":[]},{"given":"Julio","family":"Vera","sequence":"additional","affiliation":[]},{"given":"Shailendra K.","family":"Gupta","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,23]]},"reference":[{"issue":"56","key":"3656_CR1","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1098\/rsif.2010.0285","volume":"8","author":"P Ciarletta","year":"2010","unstructured":"Ciarletta P, Foret L, Ben Amar M. 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