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通过生物信息学分析鉴定头颈部鳞状细胞癌的潜在生物标志物并分析其预后价值

Identification of potential biomarkers and analysis of prognostic values in head and neck squamous cell carcinoma by bioinformatics analysis.

作者信息

Yang Bo, Chen Zhifeng, Huang Yu, Han Guoxu, Li Weizhong

机构信息

Department of Oral and Maxillofacial Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.

出版信息

Onco Targets Ther. 2017 Apr 26;10:2315-2321. doi: 10.2147/OTT.S135514. eCollection 2017.

Abstract

The purpose of this study was to find disease-associated genes and potential mechanisms in head and neck squamous cell carcinoma (HNSCC) with deoxyribonucleic acid microarrays. The gene expression profiles of GSE6791 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained with packages in R language and STRING constructed protein-protein interaction (PPI) network of the DEGs with combined score >0.8. Subsequently, module analysis of the PPI network was performed by Molecular Complex Detection plugin and functions and pathways of the hub gene in subnetwork were studied. Finally, overall survival analysis of hub genes was verified in TCGA HNSCC cohort. A total of 811 DEGs were obtained, which were mainly enriched in the terms related to extracellular matrix (ECM)-receptor interaction, ECM structural constituent, and ECM organization. A PPI network was constructed, consisting of 401 nodes and 1,254 edges and 15 hub genes with high degrees in the network. High expression of 4 genes of the 15 genes was associated with poor OS of patients in HNSCC, including , , , and . Two significant modules were detected from the PPI network, and the enriched functions and pathways included proteasome, ECM organization, and ECM-receptor interaction. In conclusion, we propose that , , , and may be further explored as potential biomarkers to aid HNSCC diagnosis and treatment.

摘要

本研究的目的是利用脱氧核糖核酸微阵列寻找头颈部鳞状细胞癌(HNSCC)中的疾病相关基因和潜在机制。从基因表达综合数据库下载GSE6791的基因表达谱。使用R语言中的软件包获得差异表达基因(DEG),STRING构建了综合得分>0.8的DEG的蛋白质-蛋白质相互作用(PPI)网络。随后,通过分子复合物检测插件对PPI网络进行模块分析,并研究子网络中枢纽基因的功能和通路。最后,在TCGA HNSCC队列中验证枢纽基因的总生存分析。共获得811个DEG,主要富集在与细胞外基质(ECM)-受体相互作用、ECM结构成分和ECM组织相关的术语中。构建了一个PPI网络,由401个节点和1254条边组成,网络中有15个高度连接的枢纽基因。15个基因中的4个基因高表达与HNSCC患者的不良总生存期相关,包括 、 、 和 。从PPI网络中检测到两个显著模块,富集的功能和通路包括蛋白酶体、ECM组织和ECM-受体相互作用。总之,我们建议 、 、 和 可作为潜在生物标志物进一步探索,以辅助HNSCC的诊断和治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6c88/5414612/9aa6a7f8061f/ott-10-2315Fig1.jpg

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