AUTHOR=Niu Jianfang , Yan Taiqiang , Guo Wei , Wang Wei , Zhao Zhiqing , Ren Tingting , Huang Yi , Zhang Hongliang , Yu Yiyang , Liang Xin TITLE=Identification of Potential Therapeutic Targets and Immune Cell Infiltration Characteristics in Osteosarcoma Using Bioinformatics Strategy JOURNAL=Frontiers in Oncology VOLUME=10 YEAR=2020 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.01628 DOI=10.3389/fonc.2020.01628 ISSN=2234-943X ABSTRACT=

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 identified EGR1, CXCL10, MYC, and CXCR4 as potential biomarkers for prognosis of osteosarcoma.