| 基于生物信息学分析骨肉瘤肺转移的关键基因和功能鉴定 |
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投稿时间：2023-07-18
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| 作者 | Author | 单位 | Address | E-Mail |
| 王鑫 |
WANG Xin |
重庆医科大学附属璧山医院骨科, 重庆 402760 |
Department of Orthopaedics, Bishan Hospital of Chongqing Medical University, Chongqing 402760, China |
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| 彭李华 |
PENG Li-hua |
重庆医科大学附属璧山医院骨科, 重庆 402760 |
Department of Orthopaedics, Bishan Hospital of Chongqing Medical University, Chongqing 402760, China |
363670397@qq.com |
| 陈兴旺 |
CHEN Xing-wang |
重庆医科大学附属璧山医院骨科, 重庆 402760 |
Department of Orthopaedics, Bishan Hospital of Chongqing Medical University, Chongqing 402760, China |
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| 期刊信息：《中国骨伤》2024年，第37卷，第7期，第718-724页 |
| DOI：10.12200/j.issn.1003-0034.20221326 |
| 基金项目: |
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中文摘要:
目的：采用生物信息学的方法筛选骨肉瘤肺转移的差异表达基因,并探讨其功能及调控网络。
方法：从GEO 数据库 (http://www.ncbi.nlm.nih.gov/gds) 中筛选数据集 GSE14359, 使用 GEO2R 在线工具筛选差异表达基因(differentially expressed gene,DEG);在线 HMMD 数据库(http://www.cuilab.cn/hmdd)下载骨肉瘤疾病相关的 miRNA,FunRich 软件预测靶基因,与 DEG 取交集,获得目标基因;根据靶向关系形成 miRNA-mRNA 关系对,数据导入 Cy-toscape 可视化;DAVID 对目标基因行 GO 和 KEGG 通路富集分析;STRING 构建 PPI 网络,Cytoscape 可视化,Cyto-Hubba 插件筛选中枢基因,在线网站进行表达和生存分析。
结果：共鉴定出 704 个 DEG,由 477 个上调基因和 227 个下调基因组成。FunRich 预测出 mRNA 7 888 个,两者交集,获得目标基因 343 个。KEGG 富集分析显示:目标基因主要参与焦点粘连、细胞外基质(extracellularmatrix,ECM)受体相互作用、肿瘤坏死因子(trmor necrosis factor,TNF)信号通路、PI3K-Akt 信号通路、白细胞介素-17(interleukin 17,IL-17)信号通路、MAPK 信号通路。获得 10 个中枢基因(CC-NB1、CHEK1、AURKA、DTL、RRM2、MELK、CEP55、FEN1、KPNA2、TYMS),CCNB1、DTL、MELK 和预后不良高度相关。
结论：该研究确定的关键基因和功能通路可能有助于了解骨肉瘤肺转移癌发生和进展的分子机制,并提供潜在的治疗靶点。 |
| 【关键词】骨肉瘤 肺转移 生物信息学 基因 |
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| Identification of key genes and functions in lung metastasis of osteosarcoma based on bioinformatics |
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ABSTRACT
Objective To screen the differentially expressed genes of lung metastasis of osteosarcoma by bioinformatics, and explore their functions and regulatory networks.
Methods The data set of GSE14359 was screened from GEO database (http://www.ncbi.nlm.nih.gov/gds) and the differentially expressed gene (DEG) was identified using GEO2R online tool. Download osteosarcoma disease related miRNAs from the online HMMD database (http://www.cuilab.cn/hmdd) and then FunRich software was used to predict the target gene,intersects with DEG to obtains the target gene. The miRNA-mRNA relationship pairs were formed according to the targeted joints,then the data was imported into Cytoscape for visualization,DAVID was used to performe GO and KEGG analysis on target genes,STRING was used to construct PPI network,Cytoscape visualization,CytoHubba plug in screening central genes and online website for expression and survival analysis.
Results Total 704 DEGs were identified,consisting of 477 up regulated genes and 227 down regulated genes. FunRich predicted 7 888 mRNAs and 343 target genes were obtained through intersection of the two. KEGG analysis showed that it was mainly involved in focal adhesion,ECM receptor interaction,TNF signal pathway,PI3K-Akt signal pathway,IL-17 signal pathway and MAPK signal pathway. Ten central genes (CCNB1,CHEK1,AURKA,DTL,RRM2,MELK,CEP55,FEN1,KPNA2,TYMS) were identified as potential key genes. Among them,CCNB1,DTL,MELK were highly correlated with poor prognosis.
Conclusion The key genes and functional pathways identified in this study may be helpful to understand the molecular mechanism of the occurrence and progression of lung metastases from osteosarcoma,and provide potential therapeutic targets. |
| KEY WORDS Osteosarcoma Lung metastasis Bioinformatics Gene |
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| 引用本文，请按以下格式著录参考文献： |
| 中文格式: | 王鑫,彭李华,陈兴旺.基于生物信息学分析骨肉瘤肺转移的关键基因和功能鉴定[J].中国骨伤,2024,37(7):718~724 |
| 英文格式: | WANG Xin,PENG Li-hua,CHEN Xing-wang.Identification of key genes and functions in lung metastasis of osteosarcoma based on bioinformatics[J].zhongguo gu shang / China J Orthop Trauma ,2024,37(7):718~724 |
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