International Journal of General Medicine (May 2021)
Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma
Abstract
Yuanguo Luo,1,* Bo Lv,2,3,* Shaokang He,4,* Kai Zou,1 Kezhi Hu2,3 1Department of Orthopedics, The 923rd Hospital of the Joint Logistics Support Force of the People’s Liberation Army, Nanning, People’s Republic of China; 2Department of Orthopedics, People’s Hospital of Guilin, Guilin, Guangxi, 541001, People’s Republic of China; 3Department of Orthopedics, Fifth Clinical Medical College, Guilin Medical University, Guilin, Guangxi, 541001, People’s Republic of China; 4Department of Orthopedics, The Tenth People’s Hospital of Nanning, Nanning, Guangxi, 530105, People’s Republic of China*These authors contributed equally to this workCorrespondence: Bo LvPeople’s Hospital of Guilin, 12 Wenming Road, Guilin, Guangxi, 541001, People’s Republic of ChinaTel +867738997962Email [email protected]: Osteosarcoma is the most common malignant bone cancer affecting adolescents and young adults. This study aimed to screen potential diagnostic and therapeutic markers for osteosarcoma.Methods: Differential expression analysis between osteosarcoma and control was performed in GSE99671, the differentially expressed genes (DEGs) were subjected to co-expression analysis. Enrichment analysis was employed to identify the biological functions and KEGG signaling pathways of module genes. In addition, a differential analysis was also performed between recurrent and non-recurrent osteosarcoma samples in GSE39055, and enrichment analysis was performed for DEGs. Further, Kaplan–Meier curve analysis was performed on the module genes, and receiver operating characteristic (ROC) curve was drawn. Comparison of the module with the highest correlation to osteosarcoma identified key genes. Cox regression model was utilized to identify the predictive ability of key genes for the prognosis of osteosarcoma.Results: A total of 13 co-expression modules were identified from 4871 DEGs of GSE99671, module 1 had the highest positive correlation with osteosarcoma. Module genes were mainly enriched in autophagy and macrophage migration functions. A total of 1126 DEGs were obtained from GSE39055, significantly involved in neutrophil mediated immunity. Screening of genes with area under the ROC curve (AUC) values greater than 0.73 in both GSE99671 and GSE39055 identified 5 key genes when compared with genes from module 1. The nomogram results showed that ATF5, CHCHD8, ENOPH1, and LOC286367 might predict 5-year or 8-year survival time of osteosarcoma patients. The Cox model results confirmed that the signals of ATF5, CHCHD8, and LOC286367 were robust, and it may be used in the diagnosis, treatment, and prognosis of osteosarcoma.Conclusion: We found that ATF5, CHCHD8, and LOC286367 can effectively identify osteosarcoma tumorigenesis and even recurrence status. This is helpful for early diagnosis and treatment, improving the clinical treatment of patients with osteosarcoma.Keywords: osteosarcoma, predictive biomarkers, recurrence, weighted co-expressed network analysis
