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多网络和通路的整合分析鉴定了泛癌分析中的癌症驱动基因。

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.

机构信息

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090, Milan, Segrate-Milan, Italy.

Interuniversity Institute of Bioinformatics in Brussels (IB)2, 1050, Brussels, Belgium.

出版信息

BMC Genomics. 2018 Jan 6;19(1):25. doi: 10.1186/s12864-017-4423-x.

DOI:10.1186/s12864-017-4423-x
PMID:29304754
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5756345/
Abstract

BACKGROUND

Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.

RESULTS

We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.

CONCLUSIONS

Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.

摘要

背景

现代高通量基因组技术代表了癌症研究中分子变化的全面特征。尽管已经揭示了不同的癌症基因特征，但肿瘤发生的机制尚未完全理解。通路和网络是解释基因在功能基因组研究中作用的重要工具。然而，很少有方法考虑通路中基因的功能非均等作用以及网络中复杂的基因-基因相互作用。

结果

我们提出了一种新的泛癌症分析方法，通过整合通路和网络数据来识别具有功能作用的失调基因。对来自 16 种癌症类型的 7158 个肿瘤/正常样本进行的泛癌症分析确定了 895 个在通路中起核心作用且在癌症中失调的基因。将我们的方法与 15 种当前识别癌症驱动基因的工具进行比较，我们发现我们方法识别的 895 个基因中有 35.6%已被发现为具有至少 2/15 种工具的癌症驱动基因。最后，我们在 16 个独立的 GEO 癌症数据集上应用机器学习算法来验证癌症驱动基因对每种癌症的诊断作用。我们获得了每个癌症中排名前十的癌症驱动基因列表。

结论

我们的分析 1）证实了不同类型癌症中存在几种已知的癌症驱动基因，2）强调了癌症驱动基因能够调节关键通路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/ff459027c085/12864_2017_4423_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/58e5ebe54382/12864_2017_4423_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/27b4aa50bb1c/12864_2017_4423_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/d66c40483e5e/12864_2017_4423_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/7c8098f6b03d/12864_2017_4423_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/a864ee4e69b2/12864_2017_4423_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/23f1552203dd/12864_2017_4423_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/3fa7b004a2a0/12864_2017_4423_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/756ae009c9f3/12864_2017_4423_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/ff459027c085/12864_2017_4423_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/58e5ebe54382/12864_2017_4423_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/27b4aa50bb1c/12864_2017_4423_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/d66c40483e5e/12864_2017_4423_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/7c8098f6b03d/12864_2017_4423_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/a864ee4e69b2/12864_2017_4423_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/23f1552203dd/12864_2017_4423_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/3fa7b004a2a0/12864_2017_4423_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/756ae009c9f3/12864_2017_4423_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0c8/5756345/ff459027c085/12864_2017_4423_Fig9_HTML.jpg

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