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利用分子相互作用网络扩展途径和过程,以分析癌症基因组数据。

Extending pathways and processes using molecular interaction networks to analyse cancer genome data.

机构信息

Nottingham University, UK.

出版信息

BMC Bioinformatics. 2010 Dec 13;11:597. doi: 10.1186/1471-2105-11-597.

DOI:10.1186/1471-2105-11-597
PMID:21144022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3017081/
Abstract

BACKGROUND

Cellular processes and pathways, whose deregulation may contribute to the development of cancers, are often represented as cascades of proteins transmitting a signal from the cell surface to the nucleus. However, recent functional genomic experiments have identified thousands of interactions for the signalling canonical proteins, challenging the traditional view of pathways as independent functional entities. Combining information from pathway databases and interaction networks obtained from functional genomic experiments is therefore a promising strategy to obtain more robust pathway and process representations, facilitating the study of cancer-related pathways.

RESULTS

We present a methodology for extending pre-defined protein sets representing cellular pathways and processes by mapping them onto a protein-protein interaction network, and extending them to include densely interconnected interaction partners. The added proteins display distinctive network topological features and molecular function annotations, and can be proposed as putative new components, and/or as regulators of the communication between the different cellular processes. Finally, these extended pathways and processes are used to analyse their enrichment in pancreatic mutated genes. Significant associations between mutated genes and certain processes are identified, enabling an analysis of the influence of previously non-annotated cancer mutated genes.

CONCLUSIONS

The proposed method for extending cellular pathways helps to explain the functions of cancer mutated genes by exploiting the synergies of canonical knowledge and large-scale interaction data.

摘要

背景

细胞过程和途径,其失调可能导致癌症的发展,通常表示为从细胞表面到细胞核传递信号的蛋白质级联。然而,最近的功能基因组实验已经鉴定了数千种信号经典蛋白的相互作用,这对途径作为独立功能实体的传统观点提出了挑战。因此,结合来自途径数据库的信息和从功能基因组实验获得的相互作用网络是获得更稳健的途径和过程表示的有前途的策略,有利于研究与癌症相关的途径。

结果

我们提出了一种方法,通过将它们映射到蛋白质-蛋白质相互作用网络上来扩展代表细胞途径和过程的预定义蛋白质集,并将它们扩展到包含密集相互作用的伙伴。添加的蛋白质显示出独特的网络拓扑特征和分子功能注释,可以作为假定的新成分,以及/或作为不同细胞过程之间通信的调节剂进行提议。最后,这些扩展的途径和过程用于分析它们在胰腺突变基因中的富集。鉴定了突变基因与某些过程之间的显著关联,从而可以分析以前未注释的癌症突变基因的影响。

结论

通过利用规范知识和大规模相互作用数据的协同作用,扩展细胞途径的方法有助于通过利用规范知识和大规模相互作用数据的协同作用来解释癌症突变基因的功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/207dd1268f5b/1471-2105-11-597-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/00acfc7c5386/1471-2105-11-597-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/3f555f3d972d/1471-2105-11-597-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/976470108238/1471-2105-11-597-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/207dd1268f5b/1471-2105-11-597-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/00acfc7c5386/1471-2105-11-597-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/3f555f3d972d/1471-2105-11-597-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/976470108238/1471-2105-11-597-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a1f/3017081/207dd1268f5b/1471-2105-11-597-4.jpg

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