Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy
Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy
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DOI：
10.3389/fonc.2020.01628
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发表时间：
2020-08-21
影响因子：
4.7
通讯作者：
Liang, Xin
中科院分区：
文献类型：
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作者：
Niu, Jianfang;Yan, Taiqiang;Liang, Xin
骨肉瘤是世界上最具侵袭性的恶性骨肿瘤之一。虽然新辅助化疗的出现使其治疗取得了很大进展，但肺转移的问题是改善生存结局的主要障碍。因此，本研究的目的是筛选新的和关键的生物标志物，这可能是潜在的预后标志物和骨肉瘤的治疗靶点。我们利用鲁棒秩聚合（RRA）方法整合从基因表达Omnibus（GEO）数据库下载的三个骨肉瘤微阵列数据集，并确定了原发性和转移性骨肉瘤组织之间的鲁棒差异表达基因（DEG）。基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析进行了探索功能强大的DEG。富集分析结果表明，强DEG与骨肉瘤的发生发展密切相关。采用CIBERSORT算法对免疫细胞浸润情况进行分析，发现巨噬细胞是骨肉瘤中最主要的浸润免疫细胞，尤其是巨噬细胞M0和M2。然后，利用Cytoscape构建蛋白质相互作用网络和关键模块，并利用插件cytoHubba从整个网络中筛选出10个hub基因。还基于产生有效治疗的治疗适用性研究（TARGET）数据库进行了枢纽基因的存活分析。整合生物信息学分析为骨肉瘤的发展和转移提供了新的见解，并确定了EGFR 1，CXCL 10，MYC和CXCR 4作为骨肉瘤预后的潜在生物标志物。
Osteosarcoma is one of the most aggressive malignant bone tumors worldwide. Although great advancements have been made in its treatment owing to the advent of neoadjuvant chemotherapy, the problem of lung metastasis is a major obstacle in the improvement of survival outcomes. Thus, the aim of the present study is to screen novel and key biomarkers, which may act as potential prognostic markers and therapeutic targets in osteosarcoma. We utilized the robust rank aggregation (RRA) method to integrate three osteosarcoma microarray datasets downloaded from the Gene Expression Omnibus (GEO) database, and we identified the robust differentially expressed genes (DEGs) between primary and metastatic osteosarcoma tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of robust DEGs. The results of enrichment analysis showed that the robust DEGs were closely associated with osteosarcoma development and progression. Immune cell infiltration analysis was also conducted by CIBERSORT algorithm, and we found that macrophages are the most principal infiltrating immune cells in osteosarcoma, especially macrophages M0 and M2. Then, the protein-protein interaction network and key modules were constructed by Cytoscape, and 10 hub genes were selected by plugin cytoHubba from the whole network. The survival analysis of hub genes was also carried out based on the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The integrated bioinformatics analysis was utilized to provide new insight into osteosarcoma development and metastasis and identifiedEGR1,CXCL10,MYC, andCXCR4as potential biomarkers for prognosis of osteosarcoma.