摘要
目的 采用微阵列技术筛选横纹肌肉瘤(rhabdomyosarcoma, RMS)组织与正常骨骼肌组织的核心差异表达基因(differentially expressed gene, DEG),分析DEG对其生存时间的影响。方法 GSE28511数据集在基因表达数据库(Gene Expression Omnibus, GEO)中下载获得,包含18例RMS组织样本(10例腺泡状横纹肌肉瘤组织,8例胚胎性横纹肌肉瘤组织)和6例正常骨骼肌样本。使用GEO2R检测RMS和正常组织之间的DEG。进行京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes, KEGG)和基因本体论(gene ontology, GO)通路富集分析,构建蛋白-蛋白相互作用(protein-protein interaction, PPI)网络,识别重要模块和核心基因,并对核心基因进行生存分析和表达分析。结果RMS组织中有181个DEG。GO分析显示,变异主要富集于肌肉收缩、横纹肌收缩、肌节组织、肌原纤维组装、心脏收缩力调节、肌肉收缩调节、横纹肌收缩调节、Z盘、肌原纤维、肌球蛋白、胞液、肌节、肌肉结构成分和肌动蛋白结合等。KEGG分析显示,DEG在心肌收缩通道、心肌细胞的肾上腺素能信号、糖酵解和糖异生、钙信号通路、氨基酸的生物合成和细胞周期中大量富集。PPI网络共有134个节点和986条相互作用关系。共鉴定了4个核心基因(TTN、TNNI2、TNNT3、NEB),其中NEB基因在RMS高表达时,患者生存时间较短(P<0.05)。结论 生物信息学技术可用于探讨RMS的发病机制。RMS与正常组织之间存在差异,NEB基因在RMS组织中高表达,NEB基因可能作为RMS早期诊断和特异性治疗的生物标志物。
Objective To screen the differentially expressed gene(DEG) of rhabdomyosarcoma(RMS) tissue and normal skeletal muscle tissue by microarray technology, and to analyze the effect of core DEG on survival time. Methods The GSE28511 dataset was downloaded from the Gene Expression Omnibus(GEO) database, including 18 RMS tissue samples(10 alveolar rhabdomyosarcoma tissues and eight embryonal rhabdomyosarcoma tissues) and six normal skeletal muscle samples. The DEG between RMS and normal tissue samples were detected with GEO2R. Enrichment analysis of Kyoto Encyclopedia of Genes and Genomes(KEGG) and gene ontology(GO) pathways were performed, protein-protein interaction(PPI) network was constructed, important modules and core genes were identified, and survival analysis and expression analysis of core genes were performed. Results There were 181 DEG in RMS tissue. GO analysis showed that the variation was mainly concentrated in muscle contraction, striated muscle contraction, sarcomere organization, myofibril assembly, cardiac contractile force regulation, muscle contraction regulation, regulation of rhabdomytic contraction, Z-disc,myofibril, myosin, cytosol, sarcomere, muscle structural components, actin binding, etc. KEGG analysis showed that DEG was enriched in myocardial contraction channels, adrenergic signaling of cardiomyocytes, glycolysis and gluconeogenesis,calcium signaling pathways, amino acid biosynthesis and cell cycle. There were 134 nodes and 986 interactions in the PPI network. Four core genes(TTN, TNNI2, TNNT3 and NEB) were identified, and the survival time of patients was shorter when NEB gene was expressed in RMS(P<0.05). Conclusions Bioinformatics techniques can be used to investigate the pathogenesis of RMS. There are differences between RMS and normal tissue, NEB gene is highly expressed in RMS tissues.NEB gene may be a biomarker for early diagnosis and specific treatment of RMS.
作者
袁征
赵昌松
高峥嵘
张强
Yuan Zheng;Zhao Changsong;Gao Zhengrong;Zhang Qiang(Department of Orthopedic,Beijing Ditan Hospital,Capital Medical University,Beijing 100015,China)
出处
《北京医学》
CAS
2022年第7期603-608,共6页
Beijing Medical Journal
关键词
横纹肌肉瘤
差异表达基因
蛋白-蛋白相互作用网络
生物信息学
核心基因
rhabdomyosarcoma(RMS)
differentially expressed gene(DEG)
protein-protein interaction(PPI)network
bioinformatics
core genes
