ArticleMedical oncology (Northwood, London, England)2015
Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis.
Article in Medical oncology (Northwood, London, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- KRAS regulates IL-17 signal activity by affect the metastasis of osteosarcoma via an IL-17A-dependent manner.JBMR plus · 2025Article
- Article
- miR‑193b exhibits mutual interaction with MYC, and suppresses growth and metastasis of osteosarcoma.Oncology reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To investigate the differences in gene expression level between metastatic and non-metastatic osteosarcoma (OS) samples and the potential mechanism. Gene expression profile data GSE9508 were downloaded from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between metastatic, non-metastatic OS samples, and normal control samples via SAM method. Function expression matrix of the DEGs was constructed by calculating the functional node scores based on the genes sets collected from the pathways recorded in MsigDB database. Next, t test was applied to screen the differentially expressed functional nodes between each two kinds of samples. Finally, we compared the significant genes between selected DEGs and genes in differentially expressed functional nodes. There were 79 up-regulated DEGs between non-metastatic OS and normal samples, 380 up-regulated and 134 down-regulated DEGs between the metastatic OS and normal samples, and 761 up-regulated plus 186 down-regulated DEGs between metastatic and non-metastatic OS samples. A total of 3846 functional gene sets were collected to form the function expression profile matrix. The numbers of differentially expressed functional nodes between non-metastatic OS and normal samples, metastatic OS and normal samples, and metastatic and non-metastatic OS samples were 8, 39, and 5, respectively. The gene level difference between metastatic and non-metastatic OS samples can be distinguished using bioinformatics analysis. TGFB1, LFT3, KDM1A, and KRAS genes have the potential to be used as biomarkers for OS; however, further analysis is needed to verify the current results.
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Registered trials
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