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基于生物信息学分析鉴定化疗耐药性骨肉瘤患者的关键基因和 miRNAs。

Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis.

机构信息

Department of Medical Oncology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Department of Sports Medicine, School of Medicine, Zhejiang University, Zhejiang, China.

出版信息

Biomed Res Int. 2018 Apr 22;2018:4761064. doi: 10.1155/2018/4761064. eCollection 2018.

DOI:10.1155/2018/4761064
PMID:29850522
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5937522/
Abstract

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

摘要

化疗耐药性是骨肉瘤患者预后不良的一个重要因素。本研究旨在从基因表达综合数据库（GEO）中鉴定与化疗耐药相关的基因特征和 microRNAs（miRNAs）。基因本体论（GO）和京都基因与基因组百科全书（KEGG）的结果包括转录的正调控、DNA 模板、色氨酸代谢等。然后将差异表达基因（DEGs）上传至搜索工具检索基因相互作用（STRING）以构建蛋白质-蛋白质相互作用（PPI）网络，并筛选出 9 个关键基因，如岩藻糖基转移酶 3（Lewis 血型）（FUT3），其在化疗耐药样本中的表达水平较高，但骨肉瘤患者的预后较好。此外，还探讨了 DEGs 与差异表达 miRNAs（DEMs）之间的关系。利用 GEO2R 筛选出 DEGs 和 DEMs。从 GSE7437 和 GSE30934 中提取了 668 个 DEGs 和 5 个 DEMs，这些样本用于区分化疗反应良好和较差的患者。使用数据库注释、可视化和综合发现（DAVID）进行 GO 和 KEGG 通路富集分析，以鉴定与骨肉瘤化疗耐药相关的潜在通路和功能注释。本研究可能为骨肉瘤化疗耐药的调控基因提供更深入的了解，并为骨肉瘤的潜在治疗靶点提供依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/0426cc1eb204/BMRI2018-4761064.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/b6ae813c3aa1/BMRI2018-4761064.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/0426cc1eb204/BMRI2018-4761064.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/b6ae813c3aa1/BMRI2018-4761064.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/b606757d39e4/BMRI2018-4761064.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/705dae14746b/BMRI2018-4761064.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/a4b72b999430/BMRI2018-4761064.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8f27/5937522/a2a012823f68/BMRI2018-4761064.005.jpg
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