骨肉瘤潜在关键基因及相关通路的鉴定△

Identification of potential core genes and related pathways in osteosarcoma

摘要:

［目的］ 基于生物信息学分析骨肉瘤差异表达谱中的关键基因及相关通路。［方法］ 本研究利用 GEO （gene expres- sion omnibus, GEO） 数据库中的两个芯片数据集 （GSE11414, GSE14359） 筛选人骨肉瘤样本与人成骨细胞之间的差异表达基因，并通过基因本体论 （Gene Ontology, GO） 和京都基因与基因组百科全书 （Kyoto Encyclopedia of Genes and Genomes, KEGG） 富集分析挖掘其潜在的生物学功能。通过 STRING（Search Tool for the Retrieval of Interacting Genes, STRING）数据库和 Cytoscape 软件构建蛋白互作网络，并进一步筛选关键基因。最后利用 LOGpc（Long-term Outcome and Gene Expression Profiling database of pan-cancers, LOGpc）数据库评估关键基因在骨肉瘤中的预后价值。［结果］GSE11414 和 GSE14359 中共有 111 个共表达差异表达基因，包括 28 个上调和 83 个下调基因，功能富集分析结果显示其主要富集于细胞外基质组织、整合素结合、糖胺聚糖结合、包含胶原的细胞外基质、p53 信号通路及 TGF-β 信号通路。通过蛋白互作网络分析筛选得到 10 个关键基因。无复发生存分析证实，THBS1 和 IGFBP3 的高表达与骨肉瘤患者的不良预后有关。［结论］ 本研究通过生物信息学分析构建骨肉瘤差异基因的蛋白互作网络，挖掘与骨肉瘤发病机制密切相关的枢纽基因，可能为骨肉瘤提供新的预后标志物和治疗靶点。

Abstract:

[Objective] To analyze the core genes and functional pathways in the differential expression profile of osteosarcoma based on bioinformatics analysis. [Methods] In this study, two microarray datasets, including GSE11414 and GSE14359, from the gene expres- sion omnibus (GEO) database were used to screen differentially expressed genes (DEGs) between human osteosarcoma and osteoblasts sam- ples. The potential biological functions of DEGs were explored through the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses. The Search Tool for the Retrieval of Interacting Genes (STRING) database and Cy- toscape software were used to further construct a protein-protein interaction network and screen core genes. Finally, the prognostic value of core genes in osteosarcoma was assessed using the Long-term Outcome and Gene Expression Profiling database of pan-cancers (LOGpc) database. [Results] There were 111 DEGs in GSE11414 and GSE14359, including 28 up-regulated and 83 down-regulated DEGs. Func- tional enrichment analysis showed that DEGs were mainly enriched in the extracellular matrix organization, integrin binding, glycosamino- glycan binding, collagen-containing extracellular matrix, p53 signaling pathway, and TGF-β signaling pathway. Ten core genes were ob- tained by protein-protein interaction network analysis. Recurrence-free survival analysis confirmed that high expression of THBS1 and IGFBP3 was associated with poor prognosis in patients with osteosarcoma. [Conclusion] In this study, the protein-protein interaction net- work of differential genes in osteosarcoma was constructed by bioinformatics analysis. The core genes closely related to the pathogenesis of osteosarcoma were explored, which may provide new prognostic markers and therapeutic targets for osteosarcoma.