<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>11(7)</volume><submitter>Li G</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 a</pubmed_abstract><journal>Translational cancer research</journal><pagination>2374-2387</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9372256</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Development of novel gene signatures for the risk stratification of prognosis and diagnostic prediction of osteosarcoma patients using bioinformatics analysis.</pubmed_title><pmcid>PMC9372256</pmcid><pubmed_authors>Li G</pubmed_authors><pubmed_authors>Zhang H</pubmed_authors><pubmed_authors>Huang B</pubmed_authors><pubmed_authors>Wu H</pubmed_authors></additional><is_claimable>false</is_claimable><name>Development of novel gene signatures for the risk stratification of prognosis and diagnostic prediction of osteosarcoma patients using bioinformatics analysis.</name><description>&lt;h4>Background&lt;/h4>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.&lt;h4>Methods&lt;/h4>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 a</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 Jul</publication><modification>2026-05-09T14:11:55.588Z</modification><creation>2024-11-13T11:01:39.831Z</creation></dates><accession>S-EPMC9372256</accession><cross_references><pubmed>35966307</pubmed><doi>10.21037/tcr-22-1706</doi></cross_references></HashMap>