dc.contributor.author
Yang, Jinpo
dc.contributor.author
Zhang, Anran
dc.contributor.author
Luo, Huan
dc.contributor.author
Ma, Chao
dc.date.accessioned
2024-03-01T14:11:04Z
dc.date.available
2024-03-01T14:11:04Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/42604
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-42328
dc.description.abstract
Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan-Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature's predictive ability was further examined by the Kaplan-Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells' roles in the gene signature's prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature's prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Osteosarcoma (OS)
en
dc.subject
Gene signatur
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1279
dcterms.bibliographicCitation.doi
10.1038/s41598-022-05341-5
dcterms.bibliographicCitation.journaltitle
Scientific Reports
dcterms.bibliographicCitation.number
1
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
12
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
35075228
dcterms.isPartOf.eissn
2045-2322