ArticleTranslational cancer research2022
Development of novel gene signatures for the risk stratification of prognosis and diagnostic prediction of osteosarcoma patients using bioinformatics analysis.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Review
- Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods.Translational cancer research · 2023Article
- Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis.Annals of translational medicine · 2023Article
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Authors and funding
4 authors.
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Abstract
Background: Osteosarcoma (OS) is a common malignant bone cancer in children and teenagers that originates from osteoblast cells. Although many biomarkers have been reported in OS, they have not improved the prognosis of this disease. This study sought to identify effective biomarkers for the early diagnosis and prognosis of OS using a comprehensive bioinformatics analysis. Methods: OS-associated microRNAs (miRNAs) were screened in the Human microRNA Disease Database (HMDD). The differentially expressed genes (DEGs) related to OS were screened using 3 data sets (GSE16088, GSE36001, and GSE56001) from the Gene Expression Omnibus (GEO) database. By comparing the targets of these miRNAs with DEGs in response to OS, we identified OS-associated candidate genes. The gene expression and clinical data of 96 OS samples with complete clinical information was downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Comprehensive bioinformatics analyses, including univariate, multivariate Cox, and Kaplan-Meier (KM) analyses were conducted based on these data to identify the prognostic genes and construct prognostic signature for OS survival and recurrence. Logistic regression analysis was performed based on the GSE42352 data set (including 103 OS and 15 normal samples) to develop a diagnostic model for OS. Results: By comparing the DEGs and predicted targets of the 28 OS survival-associated miRNAs, we identified 267 OS-associated candidate genes. Additionally, 14 genes were found to be significantly associated with the survival of OS patients. Finally, 3 genes [i.e., Conclusions: We constructed a 3-gene prognostic signature and a 2-gene diagnostic signature that have the potential to assist in prognosis predicting and diagnosis of OS in clinic.
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