Department of Biochemistry, Babu Banarasi Das University, Faizabad Road, Lucknow, Uttar Pradesh, 226028, India.
Laboratory of Systems Tumor Immunology, Department of Dermatology, Universitätsklinikum Erlangen and Faculty of Medicine, Friedrich-Alexander University of Erlangen-Nürnberg, Hartmannstr.14, 91052, Erlangen, Germany.
BMC Bioinformatics. 2020 Jul 23;21(1):329. doi: 10.1186/s12859-020-03656-6.
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.
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.
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.
黑色素瘤表型及其进展的动态是由不同类型的调节分子之间的复杂相互作用决定的。特别是转录因子（TFs）、microRNAs（miRNAs）和长链非编码 RNA（lncRNAs）在层与层之间相互作用，融合成大型分子相互作用网络。我们的目标是研究与各种网络层之间的串扰相关的分子及其对肿瘤进展的影响。
为了阐明它们对疾病的贡献，我们开发了一种整合的计算分析流程，构建并分析了一个以 lncRNA、其 miRNA 和蛋白质靶标、miRNA 靶基因以及调节 miRNA 的 TF 为重点的黑色素瘤网络。在网络中，我们确定了每个都由 lncRNA、miRNA 和 TF 组成的三节点调控环。为了根据它们在黑色素瘤进展中的作用对这些基序进行优先级排序，我们整合了来自 TCGA（SKCM）黑色素瘤队列的患者衍生的 RNAseq 数据集，使用加权多目标函数。我们研究了排名靠前的基序的表达谱，并使用它们将患者分为转移性和非转移性表型。
这项研究的结果表明，网络基序 UCA1/AKT1/hsa-miR-125b-1 对区分转移性和非转移性黑色素瘤表型具有最高的预测准确性（ACC=0.88）。这一观察结果也通过无进展生存分析得到了证实，其中具有该基序转移性表达谱的患者组的生存显著降低。这一发现表明网络基序对黑色素瘤的分类和治疗具有预后价值。
