ArticleJournal of bioenergetics and biomembranes2020
Four genes predict the survival of osteosarcoma patients based on TARGET database.
Article in Journal of bioenergetics and biomembranes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.Scientific reports · 2024Article
- Article
- Comprehensive Analysis Reveals Prognostic and Therapeutic Immunity-Related Biomarkers for Pediatric Metastatic Osteosarcoma.Medicina (Kaunas, Lithuania) · 2024Article
- A telomere-related signature for predicting prognosis and assessing immune microenvironment in osteosarcoma.Frontiers in pharmacology · 2024Article
- Clinical significance and immune landscape of a novel ferroptosis-related prognosis signature in osteosarcoma.BMC cancer · 2023Article
- A five metastasis-related long noncoding RNA risk signature for osteosarcoma survival prediction.BMC medical genomics · 2021Article
- An Integrative Pharmacology-Based Strategy to Uncover the Mechanism of Xiong-Pi-Fang in Treating Coronary Heart Disease with Depression.Frontiers in pharmacology · 2021Article
- Article
- Transcriptome Analysis Identifies Novel Prognostic Genes in Osteosarcoma.Computational and mathematical methods in medicine · 2020Article
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3 authors.
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Abstract
Osteosarcoma represents one of the most aggressive tumors of bone among adolescents and young adults. Despite improvements in treatment, osteosarcoma has a grave prognosis. The identification of prognostic factors is still in its infancy. Weighted gene correlation network analysis (WGCNA) was conducted on mRNA-sequencing and clinical information (gender, survival and metastasis) of osteosarcoma patients from the TARGET database to obtain genes in modules associated with metastasis of osteosarcoma. The Cox regression analysis was then performed on the gene expression profile from TARGET to screen genes associated with patients' survival. Known genes related to osteosarcoma were obtained by intersecting osteosarcoma-related genes from DisGeNET and DiGSeE, followed by the construction of PPI network of osteosarcoma-related genes and survival-related genes in modules. The screened key genes were subject to multi-factor Cox proportional hazards model, and osteosarcoma patients were classified into high- and low- risk groups according to the risk score to evaluate the potential of key genes to predict the survival of osteosarcoma patients. The WGCNA showed that 4 genes in tan and 19 genes in pink modules were related to the survival of osteosarcoma patients. Osteosarcoma-related known genes (9) were obtained in intersection of DisGeNET and DiGSeE. PPI network identified 4 key genes (KRT5, HIPK2, MAP3K5 and CD5) closely associated with survival of osteosarcoma patients. HIPK2, MAP3K5 and CD5 expression was inversely correlated with survival risk, while KRT5 expression was positively correlated with survival risk. These results show KRT5, HIPK2, MAP3K5 and CD5 serve as prognostic factors of osteosarcoma patients.
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