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基因优先级排序、共性分析、网络和代谢综合途径，以更好地理解乳腺癌发病机制。

Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis.

机构信息

Centro de Investigación Genética y Genómica, Facultad de Ciencias de la Salud Eugenio Espejo, Universidad UTE, Mariscal Sucre Avenue, 170129, Quito, Ecuador.

RNASA-IMEDIR, Computer Sciences Faculty, University of Coruna, 15071, Coruna, Spain.

出版信息

Sci Rep. 2018 Nov 12;8(1):16679. doi: 10.1038/s41598-018-35149-1.

DOI:10.1038/s41598-018-35149-1
PMID:30420728
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6232116/
Abstract

Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities directly involved in breast cancer (BC) pathogenesis. We evaluated the consensus between 8 prioritization strategies for the early recognition of pathogenic genes. A communality analysis in the protein-protein interaction (PPi) network of previously selected genes was enriched with gene ontology, metabolic pathways, as well as oncogenomics validation with the OncoPPi and DRIVE projects. The consensus genes were rationally filtered to 1842 genes. The communality analysis showed an enrichment of 14 communities specially connected with ERBB, PI3K-AKT, mTOR, FOXO, p53, HIF-1, VEGF, MAPK and prolactin signaling pathways. Genes with highest ranking were TP53, ESR1, BRCA2, BRCA1 and ERBB2. Genes with highest connectivity degree were TP53, AKT1, SRC, CREBBP and EP300. The connectivity degree allowed to establish a significant correlation between the OncoPPi network and our BC integrated network conformed by 51 genes and 62 PPi. In addition, CCND1, RAD51, CDC42, YAP1 and RPA1 were functional genes with significant sensitivity score in BC cell lines. In conclusion, the consensus strategy identifies both well-known pathogenic genes and prioritized genes that need to be further explored.

摘要

共识策略已被证明在识别基因-疾病关联方面非常有效。因此，本研究的主要目的是应用理论方法直接探索与乳腺癌（BC）发病机制直接相关的基因和社区。我们评估了 8 种优先识别致病基因的策略之间的共识。对先前选择的基因的蛋白质-蛋白质相互作用（PPi）网络进行了同质性分析，并利用 OncoPPi 和 DRIVE 项目进行了基因本体、代谢途径以及oncogenomics 验证的富集。对共识基因进行了合理的过滤，得到了 1842 个基因。同质性分析显示，有 14 个社区特别与 ERBB、PI3K-AKT、mTOR、FOXO、p53、HIF-1、VEGF、MAPK 和催乳素信号通路相关。排名最高的基因是 TP53、ESR1、BRCA2、BRCA1 和 ERBB2。连接度最高的基因是 TP53、AKT1、SRC、CREBBP 和 EP300。连接度允许在 OncoPPi 网络和我们由 51 个基因和 62 个 PPi 组成的综合 BC 网络之间建立显著的相关性。此外，CCND1、RAD51、CDC42、YAP1 和 RPA1 是 BC 细胞系中具有显著敏感性评分的功能基因。总之，共识策略可以识别出已知的致病基因和需要进一步探索的优先基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/553106fd0280/41598_2018_35149_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/87c93b909760/41598_2018_35149_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/86d0f9ef4626/41598_2018_35149_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/3a3c4d617d4e/41598_2018_35149_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/760e3b58148d/41598_2018_35149_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/553106fd0280/41598_2018_35149_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/87c93b909760/41598_2018_35149_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/86d0f9ef4626/41598_2018_35149_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/3a3c4d617d4e/41598_2018_35149_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/760e3b58148d/41598_2018_35149_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d6e/6232116/553106fd0280/41598_2018_35149_Fig5_HTML.jpg

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