Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis.
Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis.
复制标题
作者：
Jia Y;Liu Y;Han Z;Tian R
骨肉瘤（OS）是儿童和青少年最常见的恶性骨癌，死亡率高。这项工作的目的是筛选新的潜在的基因签名与OS的基因表达综合数据库（GEO）的集成微阵列分析。从GEO数据库中检索并下载OS微阵列数据集，以确定OS和正常样本之间的差异表达基因（DEG）。随后，通过功能富集分析、蛋白质-蛋白质相互作用（PPI）网络分析和转录因子（TF）-靶基因调控网络分析，揭示了DEGs的生物学功能。最后，从GEO数据库中获得两个已发表的OS数据集（GSE 39262和GSE 126209），用于评估关键基因的表达水平和诊断价值。 在OS和正常样品之间总共筛选了1，059个DEG（569个上调的DEG和490个下调的DEG）。功能分析表明，这些DEG明显富集在214个GO术语和54个KEGG通路中，例如癌症中的通路。5个基因（CAMP、胃窦L7 A、TCN 1、LTF和CXCL 12）在PPI网络中充当枢纽基因。此外，胃L7 A、CYP 4F 3、TCN 1、LTF和NETO 2是TF-基因网络中的关键基因。此外，Pax-6还调节4个关键基因（TCN 1、CYP 4F 3、NETO 2和CXCL 12）。GSE 39262中的四个基因（胃L7 A、TCN 1、CXCL 12和NETO 2）的表达水平与我们的整合分析一致。GSE 126209组中两个基因（CXCL 12和NETO 2）的表达水平与我们的整合分析一致。GSE 39262集的ROC分析显示，CYP 4F 3、CXCL 12、胃L7 A、TCN 1和NETO 2对OS患者有较好的诊断价值。GSE 126209集的ROC分析显示，CXCL 12、胃L7 A、TCN 1和NETO 2对OS患者具有良好的诊断价值。
Osteosarcoma (OS) is the most primary malignant bone cancer in children and adolescents with a high mortality rate. This work aims to screen novel potential gene signatures associated with OS by integrated microarray analysis of the Gene Expression Omnibus (GEO) database. The OS microarray datasets were searched and downloaded from GEO database to identify differentially expressed genes (DEGs) between OS and normal samples. Afterwards, the functional enrichment analysis, protein–protein interaction (PPI) network analysis and transcription factor (TF)-target gene regulatory network were applied to uncover the biological function of DEGs. Finally, two published OS datasets (GSE39262 and GSE126209) were obtained from GEO database for evaluating the expression level and diagnostic values of key genes. In total 1,059 DEGs (569 up-regulated DEGs and 490 down-regulated DEGs) between OS and normal samples were screened. Functional analysis showed that these DEGs were markedly enriched in 214 GO terms and 54 KEGG pathways such as pathways in cancer. Five genes (CAMP, METTL7A, TCN1, LTF and CXCL12) acted as hub genes in PPI network. Besides, METTL7A, CYP4F3, TCN1, LTF and NETO2 were key genes in TF-gene network. Moreover, Pax-6 regulated four key genes (TCN1, CYP4F3, NETO2 and CXCL12). The expression levels of four genes (METTL7A, TCN1, CXCL12 and NETO2) in GSE39262 set were consistent with our integration analysis. The expression levels of two genes (CXCL12 and NETO2) in GSE126209 set were consistent with our integration analysis. ROC analysis of GSE39262 set revealed that CYP4F3, CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients. ROC analysis of GSE126209 set revealed that CXCL12, METTL7A, TCN1 and NETO2 had good diagnostic values for OS patients.
登录
查看更多内容
影响因子：
2.8
作者：
Shyr, Chih-Rong;Tsai, Men-Yin;Yeh, Shuyuan;Kang, Hong-Yo;Chang, Yun-Chao;Wong, Pei-Ling;Huang, Chao-Cheng;Huang, Ko-En;Chang, Chawnshang
通讯作者：
Chang, Chawnshang
影响因子：
24.5
作者：
Sainz, Bruno, Jr.;Alcala, Sonia;Heeschen, Christopher
通讯作者：
Heeschen, Christopher
影响因子：
5.3
作者：
von Haussen, Judith;Koczulla, Rembert;Bals, Robert
通讯作者：
Bals, Robert
影响因子：
4.7
作者：
Nagasawa, Takashi
通讯作者：
Nagasawa, Takashi
影响因子：
3.4
作者：
Pollino, Serena;Palmerini, Emanuela;Pazzaglia, Laura
通讯作者：
Pazzaglia, Laura