Exploring the role of the Rab network in epithelial-to-mesenchymal transition.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Bioinformatics advances Pub Date : 2024-12-14 eCollection Date: 2025-01-01 DOI:10.1093/bioadv/vbae200
Unmani Jaygude, Graham M Hughes, Jeremy C Simpson
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引用次数: 0

Abstract

Motivation: Rab GTPases (Rabs) are crucial for membrane trafficking within mammalian cells, and their dysfunction is implicated in many diseases. This gene family plays a role in several crucial cellular processes. Network analyses can uncover the complete repertoire of interaction patterns across the Rab network, informing disease research, opening new opportunities for therapeutic interventions.

Results: We examined Rabs and their interactors in the context of epithelial-to-mesenchymal transition (EMT), an indicator of cancer metastasizing to distant organs. A Rab network was first established from analysis of literature and was gradually expanded. Our Python module, resnet, assessed its network resilience and selected an optimally sized, resilient Rab network for further analyses. Pathway enrichment confirmed its role in EMT. We then identified 73 candidate genes showing a strong up-/down-regulation, across 10 cancer types, in patients with metastasized tumours compared to only primary-site tumours. We suggest that their encoded proteins might play a critical role in EMT, and further in vitro studies are needed to confirm their role as predictive markers of cancer metastasis. The use of resnet within the systematic analysis approach described here can be easily applied to assess other gene families and their role in biological events of interest.

Availability and implementation: Source code for resnet is freely available at https://github.com/Unmani199/resnet.

探索 Rab 网络在上皮细胞向间质转化过程中的作用。
目的:rabb GTPases (Rabs)在哺乳动物细胞内的膜运输中起着至关重要的作用,其功能障碍与许多疾病有关。这个基因家族在几个关键的细胞过程中起作用。网络分析可以揭示Rab网络中相互作用模式的完整曲目,为疾病研究提供信息,为治疗干预开辟新的机会。结果:我们在上皮-间质转化(EMT)的背景下研究了Rabs及其相互作用物,EMT是癌症转移到远处器官的一个指标。拉布网络首先从文献分析中建立起来,并逐渐扩大。我们的Python模块resnet评估了其网络弹性,并选择了一个最佳大小的弹性Rab网络进行进一步分析。通路富集证实了其在EMT中的作用。然后,我们确定了73个候选基因,在10种癌症类型中,与仅原发部位肿瘤相比,在转移性肿瘤患者中表现出强烈的上调/下调。我们认为它们编码的蛋白可能在EMT中发挥关键作用,需要进一步的体外研究来证实它们作为癌症转移的预测标志物的作用。在这里描述的系统分析方法中使用resnet可以很容易地应用于评估其他基因家族及其在感兴趣的生物学事件中的作用。可用性和实现:resnet的源代码可在https://github.com/Unmani199/resnet免费获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
1.60
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