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Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma
PURPOSE: 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 d...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113014/ https://www.ncbi.nlm.nih.gov/pubmed/33994806 http://dx.doi.org/10.2147/IJGM.S312277 |
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author | Luo, Yuanguo Lv, Bo He, Shaokang Zou, Kai Hu, Kezhi |
author_facet | Luo, Yuanguo Lv, Bo He, Shaokang Zou, Kai Hu, Kezhi |
author_sort | Luo, Yuanguo |
collection | PubMed |
description | PURPOSE: 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. |
format | Online Article Text |
id | pubmed-8113014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-81130142021-05-13 Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma Luo, Yuanguo Lv, Bo He, Shaokang Zou, Kai Hu, Kezhi Int J Gen Med Original Research PURPOSE: 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. Dove 2021-05-07 /pmc/articles/PMC8113014/ /pubmed/33994806 http://dx.doi.org/10.2147/IJGM.S312277 Text en © 2021 Luo et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Luo, Yuanguo Lv, Bo He, Shaokang Zou, Kai Hu, Kezhi Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title | Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title_full | Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title_fullStr | Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title_full_unstemmed | Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title_short | Identification of Gene as Predictive Biomarkers for the Occurrence and Recurrence of Osteosarcoma |
title_sort | identification of gene as predictive biomarkers for the occurrence and recurrence of osteosarcoma |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113014/ https://www.ncbi.nlm.nih.gov/pubmed/33994806 http://dx.doi.org/10.2147/IJGM.S312277 |
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