Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis

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) betwee...

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Veröffentlicht in:Medical oncology (Northwood, London, England) London, England), 2015-05, Vol.32 (5), p.153-153, Article 153
Hauptverfasser: Sun, Baoyong, Wang, Fangxin, Li, Min, Yang, Mingshan
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creator Sun, Baoyong
Wang, Fangxin
Li, Min
Yang, Mingshan
description 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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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. 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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.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>25832865</pmid><doi>10.1007/s12032-015-0604-0</doi><tpages>1</tpages></addata></record>
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subjects Bioinformatics
Bone Neoplasms - genetics
Bone Neoplasms - pathology
Computational Biology - methods
Down-Regulation - genetics
Gene Expression Profiling - methods
Gene Expression Regulation, Neoplastic - genetics
Hematology
Humans
Internal Medicine
Medicine
Medicine & Public Health
Neoplasm Metastasis - genetics
Oncology
Original Paper
Osteosarcoma - genetics
Osteosarcoma - pathology
Pathology
Transcriptome - genetics
Up-Regulation - genetics
title Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis
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