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 |
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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. |
doi_str_mv | 10.1007/s12032-015-0604-0 |
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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.</description><identifier>ISSN: 1357-0560</identifier><identifier>EISSN: 1559-131X</identifier><identifier>DOI: 10.1007/s12032-015-0604-0</identifier><identifier>PMID: 25832865</identifier><identifier>CODEN: MONCEZ</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>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</subject><ispartof>Medical oncology (Northwood, London, England), 2015-05, Vol.32 (5), p.153-153, Article 153</ispartof><rights>Springer Science+Business Media New York 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c512t-6712012e3a2df7a66decc66a08f2597a4532c6f1a3d64df5f75f9697575adb193</citedby><cites>FETCH-LOGICAL-c512t-6712012e3a2df7a66decc66a08f2597a4532c6f1a3d64df5f75f9697575adb193</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12032-015-0604-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12032-015-0604-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25832865$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Baoyong</creatorcontrib><creatorcontrib>Wang, Fangxin</creatorcontrib><creatorcontrib>Li, Min</creatorcontrib><creatorcontrib>Yang, Mingshan</creatorcontrib><title>Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis</title><title>Medical oncology (Northwood, London, England)</title><addtitle>Med Oncol</addtitle><addtitle>Med Oncol</addtitle><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.</description><subject>Bioinformatics</subject><subject>Bone Neoplasms - genetics</subject><subject>Bone Neoplasms - pathology</subject><subject>Computational Biology - methods</subject><subject>Down-Regulation - genetics</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation, Neoplastic - genetics</subject><subject>Hematology</subject><subject>Humans</subject><subject>Internal Medicine</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neoplasm Metastasis - genetics</subject><subject>Oncology</subject><subject>Original Paper</subject><subject>Osteosarcoma - genetics</subject><subject>Osteosarcoma - pathology</subject><subject>Pathology</subject><subject>Transcriptome - genetics</subject><subject>Up-Regulation - genetics</subject><issn>1357-0560</issn><issn>1559-131X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp1kU1rFTEUhoNY7If-ADcScOMmmo-bZGYppdpCwY2Cu5CbnJSUmeSaMxfptr_cTG8tRXCVkPO8bxIeQt4K_lFwbj-hkFxJxoVm3PAN4y_IidB6ZEKJny_7XmnLuDb8mJwi3nIuhZbjK3Is9aDkYPQJub-KUJaccvBLrgVpTfQGCiw50JhTggYlANItLL8BCp1h8bj4dexLpKUW9uyo4gIVfQt19hT9vJvWqEeItBa6zTWXVNu8stjzfrrDjK_JUfITwpvH9Yz8-HLx_fySXX_7enX--ZoFLeTCjO2_FRKUlzFZb0yEEIzxfEhSj9ZvtJLBJOFVNJuYdLI6jWa02moft2JUZ-TDoXfX6q894OLmjAGmyReoe3TCmHFQG8VtR9__g97WfevvfaAGZYZRyk6JAxVaRWyQ3K7l2bc7J7hbBbmDINcFuVWQ4z3z7rF5v50hPiX-GumAPADYR-UG2rOr_9v6B9S3naE</recordid><startdate>20150501</startdate><enddate>20150501</enddate><creator>Sun, Baoyong</creator><creator>Wang, Fangxin</creator><creator>Li, Min</creator><creator>Yang, Mingshan</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20150501</creationdate><title>Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis</title><author>Sun, Baoyong ; Wang, Fangxin ; Li, Min ; Yang, Mingshan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c512t-6712012e3a2df7a66decc66a08f2597a4532c6f1a3d64df5f75f9697575adb193</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Bioinformatics</topic><topic>Bone Neoplasms - genetics</topic><topic>Bone Neoplasms - pathology</topic><topic>Computational Biology - methods</topic><topic>Down-Regulation - genetics</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Regulation, Neoplastic - genetics</topic><topic>Hematology</topic><topic>Humans</topic><topic>Internal Medicine</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neoplasm Metastasis - genetics</topic><topic>Oncology</topic><topic>Original Paper</topic><topic>Osteosarcoma - genetics</topic><topic>Osteosarcoma - pathology</topic><topic>Pathology</topic><topic>Transcriptome - genetics</topic><topic>Up-Regulation - genetics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Baoyong</creatorcontrib><creatorcontrib>Wang, Fangxin</creatorcontrib><creatorcontrib>Li, Min</creatorcontrib><creatorcontrib>Yang, Mingshan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Medical oncology (Northwood, London, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Baoyong</au><au>Wang, Fangxin</au><au>Li, Min</au><au>Yang, Mingshan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Identifications of genetic differences between metastatic and non-metastatic osteosarcoma samples based on bioinformatics analysis</atitle><jtitle>Medical oncology (Northwood, London, England)</jtitle><stitle>Med Oncol</stitle><addtitle>Med Oncol</addtitle><date>2015-05-01</date><risdate>2015</risdate><volume>32</volume><issue>5</issue><spage>153</spage><epage>153</epage><pages>153-153</pages><artnum>153</artnum><issn>1357-0560</issn><eissn>1559-131X</eissn><coden>MONCEZ</coden><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.</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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