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Meta-analysis of differentially expressed genes in osteosarcoma based on gene expression data

机译:基于基因表达数据的骨肉瘤差异表达基因的Meta分析

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Background To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. Methods We used publicly available GEO datasets of OS to perform a meta-analysis. We performed Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Protein-Protein interaction (PPI) networks analysis. Results Eight GEO datasets, including 240 samples of OS and 35 samples of controls, were available for the meta-analysis. We identified 979 DEGs across the studies between OS and NC tissues (472 up-regulated and 507 down-regulated). We found GO terms for molecular functions significantly enriched in protein binding (GO: 0005515, P = 3.83E-60) and calcium ion binding (GO: 0005509, P?=?3.79E-13), while for biological processes, the enriched GO terms were cell adhesion (GO:0007155, P?=?2.26E-19) and negative regulation of apoptotic process (GO: 0043066, P?=?3.24E-15), and for cellular component, the enriched GO terms were cytoplasm (GO: 0005737, P?=?9.18E-63) and extracellular region (GO: 0005576, P?=?2.28E-47). The most significant pathway in our KEGG analysis was Focal adhesion (P?=?5.70E-15). Furthermore, ECM-receptor interaction (P?=?1.27E-13) and Cell cycle (P?=?4.53E-11) are found to be highly enriched. PPI network analysis indicated that the significant hub proteins containing PTBP2 (Degree?=?33), RGS4 (Degree?=?15) and FXYD6 (Degree?=?13). Conclusions Our meta-analysis detected DEGs and biological functions associated with gene expression changes between OS and NC tissues, guiding further identification and treatment for OS.
机译:背景为了揭示涉及骨肉瘤(OS)发育的基因,我们进行了OS基因芯片数据的荟萃分析,以鉴定差异表达基因(DEG)和与OS和正常对照(NC)组织之间基因表达变化相关的生物学功能。方法我们使用OS的公开可用的GEO数据集进行荟萃分析。我们进行了基因本体论(GO)富集分析,京都市基因与基因组百科全书(KEGG)途径分析和蛋白质-蛋白质相互作用(PPI)网络分析。结果有8个GEO数据集,包括240个OS样本和35个对照样本,可用于荟萃分析。在整个OS和NC组织之间的研究中,我们确定了979个DEG(472个上调和507个下调)。我们发现分子功能的GO术语显着丰富了蛋白质结合(GO:0005515,P = 3.83E-60)和钙离子结合(GO:0005509,Pα=?3.79E-13),而对于生物学过程, GO术语是细胞粘附(GO:0007155,P?=?2.26E-19)和凋亡过程的负调控(GO:0043066,P?=?3.24E-15),对于细胞成分,富集的GO术语是细胞质(GO:0005737,Pα=≥9.18E-63)和细胞外区域(GO:0005576,Pα=β2.28E-47)。在我们的KEGG分析中,最重要的途径是粘着斑(P?=?5.70E-15)。此外,发现ECM-受体相互作用(P1 =α1.27E-13)和细胞周期(P2 =α4.53E-11)是高度富集的。 PPI网络分析表明,重要的毂蛋白含有PTBP2(度数= 33),RGS4(度数= 15)和FXYD6(度数= 13)。结论我们的荟萃分析检测了OS和NC组织之间与基因表达变化相关的DEG和生物学功能,指导OS的进一步鉴定和治疗。

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