Purpose: This paper aims to explore the genetic correlation between osteosarcoma (OS) and Ewing’s sarcoma (EWS) by bioinformatics, and to find the common differentially expressed genes between the two in order to pro...Purpose: This paper aims to explore the genetic correlation between osteosarcoma (OS) and Ewing’s sarcoma (EWS) by bioinformatics, and to find the common differentially expressed genes between the two in order to provide reference for early clinical diagnosis. Method: The GEO gene chip public database in NCBI was used for data retrieval, and the chip data GSE17674 and GSE16088 were selected as the analysis objects. DEmRNAs were screened by R language limma kit, and the data were standardized. The common differentially expressed genes were screened by Venn diagram. The R language clusterProfiler package was used to perform GO function and KEGG pathway enrichment analysis on the common differentially expressed genes. The String database was used for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Result: In this study, 1482 differentially expressed genes were screened from GSE17674 and 933 differentially expressed genes were screened from GSE16088. The Wayne diagram analyzed 335 common differentially expressed genes. GO/KEGG analysis suggested that the above common differentially expressed genes were mainly involved in cell cycle, ECM receptor interaction, sister chromatid separation, ossification, etc. Five core genes NCAPG, MAD2L1, CDK1, RRM2 and RFC4 were screened from the PPI network. The five genes were highly expressed in sarcoma. Conclusion: The five core common differentially expressed genes and related signaling pathways screened by bioinformatics analysis are helpful to understand the molecular mechanism of OS and ES pathogenesis, and are related to the prognosis of patients. They may become potential biomarkers for future research on OS comorbid ES, provide a basis for early diagnosis of OS combined with ES, and provide new ideas for clinical drug treatment research.展开更多
文摘Purpose: This paper aims to explore the genetic correlation between osteosarcoma (OS) and Ewing’s sarcoma (EWS) by bioinformatics, and to find the common differentially expressed genes between the two in order to provide reference for early clinical diagnosis. Method: The GEO gene chip public database in NCBI was used for data retrieval, and the chip data GSE17674 and GSE16088 were selected as the analysis objects. DEmRNAs were screened by R language limma kit, and the data were standardized. The common differentially expressed genes were screened by Venn diagram. The R language clusterProfiler package was used to perform GO function and KEGG pathway enrichment analysis on the common differentially expressed genes. The String database was used for PPI analysis, and the results were imported into Cytoscape software to obtain PPI interaction map, core module and Hub gene. Result: In this study, 1482 differentially expressed genes were screened from GSE17674 and 933 differentially expressed genes were screened from GSE16088. The Wayne diagram analyzed 335 common differentially expressed genes. GO/KEGG analysis suggested that the above common differentially expressed genes were mainly involved in cell cycle, ECM receptor interaction, sister chromatid separation, ossification, etc. Five core genes NCAPG, MAD2L1, CDK1, RRM2 and RFC4 were screened from the PPI network. The five genes were highly expressed in sarcoma. Conclusion: The five core common differentially expressed genes and related signaling pathways screened by bioinformatics analysis are helpful to understand the molecular mechanism of OS and ES pathogenesis, and are related to the prognosis of patients. They may become potential biomarkers for future research on OS comorbid ES, provide a basis for early diagnosis of OS combined with ES, and provide new ideas for clinical drug treatment research.
