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通过加权基因共表达网络分析(WGCNA)鉴定骨肉瘤转移相关基因。

Identifying osteosarcoma metastasis associated genes by weighted gene co-expression network analysis (WGCNA).

作者信息

Tian Honglai, Guan Donghui, Li Jianmin

机构信息

Department of Orthopaedics, Affiliated Hospital of Shandong University of Traditional Chinese Medicine Department of Orthopaedics, Qilu Hospital of Shandong University, Wenhua West Road, Jinan City, Shandong, China.

出版信息

Medicine (Baltimore). 2018 Jun;97(24):e10781. doi: 10.1097/MD.0000000000010781.

DOI:10.1097/MD.0000000000010781
PMID:29901575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6023727/
Abstract

Osteosarcoma (OS), the most common malignant bone tumor, accounts for the heavy healthy threat in the period of children and adolescents. OS occurrence usually correlates with early metastasis and high death rate. This study aimed to better understand the mechanism of OS metastasis.Based on Gene Expression Omnibus (GEO) database, we downloaded 4 expression profile data sets associated with OS metastasis, and selected differential expressed genes. Weighted gene co-expression network analysis (WGCNA) approach allowed us to investigate the most OS metastasis-correlated module. Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to give annotation of selected OS metastasis-associated genes.We select 897 differential expressed genes from OS metastasis and OS non-metastasis groups. Based on these selected genes, WGCNA further explored 142 genes included in the most OS metastasis-correlated module. Gene Ontology functional and KEGG pathway enrichment analyses showed that significantly OS metastasis-associated genes were involved in pathway correlated with insulin-like growth factor binding.Our research figured out several potential molecules participating in metastasis process and factors acting as biomarker. With this study, we could better explore the mechanism of OS metastasis and further discover more therapy targets.

摘要

骨肉瘤(OS)是最常见的恶性骨肿瘤,对儿童和青少年时期的健康构成严重威胁。骨肉瘤的发生通常与早期转移和高死亡率相关。本研究旨在更好地了解骨肉瘤转移的机制。基于基因表达综合数据库(GEO),我们下载了4个与骨肉瘤转移相关的表达谱数据集,并筛选出差异表达基因。加权基因共表达网络分析(WGCNA)方法使我们能够研究与骨肉瘤转移相关性最高的模块。基因本体功能和京都基因与基因组百科全书(KEGG)通路富集分析用于对选定的骨肉瘤转移相关基因进行注释。我们从骨肉瘤转移组和非转移组中筛选出897个差异表达基因。基于这些选定的基因,WGCNA进一步探究了与骨肉瘤转移相关性最高的模块中包含的142个基因。基因本体功能和KEGG通路富集分析表明,与骨肉瘤转移显著相关的基因参与了与胰岛素样生长因子结合相关的通路。我们的研究确定了几个参与转移过程的潜在分子和作为生物标志物的因素。通过这项研究,我们可以更好地探索骨肉瘤转移的机制,并进一步发现更多的治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/675467a9745f/medi-97-e10781-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/c70c021028e4/medi-97-e10781-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/f89a436583be/medi-97-e10781-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/d417181594b5/medi-97-e10781-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/3f8703e25118/medi-97-e10781-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/0c6f58d03e20/medi-97-e10781-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/675467a9745f/medi-97-e10781-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/c70c021028e4/medi-97-e10781-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/9b8a6db51d8c/medi-97-e10781-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/224269b1e4f3/medi-97-e10781-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/2fdc3ae87d44/medi-97-e10781-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/f89a436583be/medi-97-e10781-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/d417181594b5/medi-97-e10781-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/3f8703e25118/medi-97-e10781-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/0c6f58d03e20/medi-97-e10781-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2fb2/6023727/675467a9745f/medi-97-e10781-g012.jpg

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