Identification and Development of a Novel 4-Gene Immune-Related Signature to Predict Osteosarcoma Prognosis
| Autor: | Xusheng Huang, Hong Chang, Hualiang Xu, Hua Wang, Yuchen Wang, Mingde Cao, Yancheng Song, Zhujian Lin, Junhui Zhang, Xiang Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Oncology
medicine.medical_specialty bioinformatics analysis medicine.medical_treatment Biochemistry Genetics and Molecular Biology (miscellaneous) Biochemistry gene signature Internal medicine osteosarcoma Gene expression Medicine Molecular Biosciences Molecular Biology Gene lcsh:QH301-705.5 Original Research Framingham Risk Score business.industry Immunotherapy Gene signature medicine.disease Regression lcsh:Biology (General) Cohort Osteosarcoma prognosis immunotherapy business |
| Zdroj: | Frontiers in Molecular Biosciences, Vol 7 (2020) Frontiers in Molecular Biosciences |
| DOI: | 10.3389/fmolb.2020.608368/full |
| Popis: | Osteosarcoma (OS) is a malignant disease that develops rapidly and is associated with poor prognosis. Immunotherapy may provide new insights into clinical treatment strategies for OS. The purpose of this study was to identify immune-related genes that could predict OS prognosis. The gene expression profiles and clinical data of 84 OS patients were obtained from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. According to non-negative matrix factorization, two molecular subtypes of immune-related genes, C1 and C2, were acquired, and 597 differentially expressed genes between C1 and C2 were identified. Univariate Cox analysis was performed to get 14 genes associated with survival, and 4 genes (GJA5, APBB1IP, NPC2, andFKBP11) obtained through least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct a 4-gene signature as a prognostic risk model. The results showed that highFKBP11expression was correlated with high risk (a risk factor), and that highGJA5, APBB1IP, orNPC2expression was associated with low risk (protective factors). The testing cohort and entire TARGET cohort were used for internal verification, and the independent GSE21257 cohort was used for external validation. The study suggested that the model we constructed was reliable and performed well in predicting OS risk. The functional enrichment of the signature was studied through gene set enrichment analysis, and it was found that the risk score was related to the immune pathway. In summary, our comprehensive study found that the 4-gene signature could be used to predict OS prognosis, and new biomarkers of great significance for understanding the therapeutic targets of OS were identified. |
| Databáze: | OpenAIRE |
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