A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes
Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening o...
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| description | Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model. Results. 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage. Conclusion. The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM. |
| doi_str_mv | 10.1155/2021/6680066 |
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| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8225435</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A683583993</galeid><sourcerecordid>A683583993</sourcerecordid><originalsourceid>FETCH-LOGICAL-c504t-ec32df8fe47431d264993c8fef70cbee446e5883824a57a2fb4a4263789a86523</originalsourceid><addsrcrecordid>eNp9kc1PHCEYh0ljU41667kh8WKiq3wvczFZjW6b2NS0eiYs886KmQWFGc3-9zLudvtxkAsQHp73hR9Cnyk5oVTKU0YYPVVKE6LUB7TDOBUjRQXd2qw530b7OT-QMjRVpFKf0DYXjDGhyQ76OcE3Kc5DzJ13-HusocVNTPg8WR_wtPVxYfGN7TyELuNzm6HGMeAK__LzYLs-QYGWLrbL4f4UAuQ99LGxbYb99byL7q4uby--jq5_TL9dTK5HThLRjcBxVje6ATEWnNZMiariruybMXEzACEUSK25ZsLKsWXNTFjBFB_rymolGd9FZyvvYz9bQO1Kh8m25jH5hU1LE603_54Ef2_m8dloxqTgsggO14IUn3rInVn47KBtbYDYZ1MoLauqdFbQg__Qh9inUJ43UMVGC_iHmtsWjA9NLHXdIDUTVSmtFX8r-w6ludS8qAp1vKJcijknaDYPo8QM2Zshe7POvuBf_v6MDfw76QIcrYB7H2r74t_XvQKyMrI2</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2545431599</pqid></control><display><type>article</type><title>A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes</title><source>MEDLINE</source><source>Wiley Online Library Open Access</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><source>PubMed Central Open Access</source><creator>Bingxiang, Xiao ; Panxing, Wu ; Lu, Feng ; Xiuyou, Yan ; Chao, Ding</creator><contributor>B D, Parameshachari ; Parameshachari B D</contributor><creatorcontrib>Bingxiang, Xiao ; Panxing, Wu ; Lu, Feng ; Xiuyou, Yan ; Chao, Ding ; B D, Parameshachari ; Parameshachari B D</creatorcontrib><description>Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model. Results. 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage. Conclusion. The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.</description><identifier>ISSN: 2314-6133</identifier><identifier>EISSN: 2314-6141</identifier><identifier>DOI: 10.1155/2021/6680066</identifier><identifier>PMID: 34222480</identifier><language>eng</language><publisher>United States: Hindawi</publisher><subject>Accuracy ; Aged ; Biomarkers, Tumor - genetics ; Biomedical research ; Brain cancer ; Brain Neoplasms - diagnosis ; Brain Neoplasms - metabolism ; Cancer ; Cancer therapies ; Care and treatment ; Chemotherapy ; Colon ; Databases, Genetic ; Diagnosis ; Evaluation ; Female ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genes ; Genetic aspects ; Glioma ; Glioma - diagnosis ; Glioma - metabolism ; Gliomas ; Glucose ; Glycolysis ; Health aspects ; Humans ; Kaplan-Meier Estimate ; Male ; Medical prognosis ; Middle Aged ; Model accuracy ; Multivariate Analysis ; Mutation ; Mutation rates ; Physiological aspects ; Prognosis ; Proportional Hazards Models ; Regression Analysis ; Regression models ; Risk factors ; RNA, Messenger - genetics ; Software ; Survival analysis ; Tumors</subject><ispartof>BioMed research international, 2021, Vol.2021 (1), p.6680066-6680066</ispartof><rights>Copyright © 2021 Xiao Bingxiang et al.</rights><rights>COPYRIGHT 2021 John Wiley & Sons, Inc.</rights><rights>Copyright © 2021 Xiao Bingxiang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><rights>Copyright © 2021 Xiao Bingxiang et al. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c504t-ec32df8fe47431d264993c8fef70cbee446e5883824a57a2fb4a4263789a86523</citedby><cites>FETCH-LOGICAL-c504t-ec32df8fe47431d264993c8fef70cbee446e5883824a57a2fb4a4263789a86523</cites><orcidid>0000-0001-5003-3933</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225435/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8225435/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,4010,27902,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34222480$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>B D, Parameshachari</contributor><contributor>Parameshachari B D</contributor><creatorcontrib>Bingxiang, Xiao</creatorcontrib><creatorcontrib>Panxing, Wu</creatorcontrib><creatorcontrib>Lu, Feng</creatorcontrib><creatorcontrib>Xiuyou, Yan</creatorcontrib><creatorcontrib>Chao, Ding</creatorcontrib><title>A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes</title><title>BioMed research international</title><addtitle>Biomed Res Int</addtitle><description>Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model. Results. 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage. Conclusion. The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.</description><subject>Accuracy</subject><subject>Aged</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Biomedical research</subject><subject>Brain cancer</subject><subject>Brain Neoplasms - diagnosis</subject><subject>Brain Neoplasms - metabolism</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Care and treatment</subject><subject>Chemotherapy</subject><subject>Colon</subject><subject>Databases, Genetic</subject><subject>Diagnosis</subject><subject>Evaluation</subject><subject>Female</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Glioma</subject><subject>Glioma - diagnosis</subject><subject>Glioma - metabolism</subject><subject>Gliomas</subject><subject>Glucose</subject><subject>Glycolysis</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Kaplan-Meier Estimate</subject><subject>Male</subject><subject>Medical prognosis</subject><subject>Middle Aged</subject><subject>Model accuracy</subject><subject>Multivariate Analysis</subject><subject>Mutation</subject><subject>Mutation rates</subject><subject>Physiological aspects</subject><subject>Prognosis</subject><subject>Proportional Hazards Models</subject><subject>Regression Analysis</subject><subject>Regression models</subject><subject>Risk factors</subject><subject>RNA, Messenger - genetics</subject><subject>Software</subject><subject>Survival analysis</subject><subject>Tumors</subject><issn>2314-6133</issn><issn>2314-6141</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kc1PHCEYh0ljU41667kh8WKiq3wvczFZjW6b2NS0eiYs886KmQWFGc3-9zLudvtxkAsQHp73hR9Cnyk5oVTKU0YYPVVKE6LUB7TDOBUjRQXd2qw530b7OT-QMjRVpFKf0DYXjDGhyQ76OcE3Kc5DzJ13-HusocVNTPg8WR_wtPVxYfGN7TyELuNzm6HGMeAK__LzYLs-QYGWLrbL4f4UAuQ99LGxbYb99byL7q4uby--jq5_TL9dTK5HThLRjcBxVje6ATEWnNZMiariruybMXEzACEUSK25ZsLKsWXNTFjBFB_rymolGd9FZyvvYz9bQO1Kh8m25jH5hU1LE603_54Ef2_m8dloxqTgsggO14IUn3rInVn47KBtbYDYZ1MoLauqdFbQg__Qh9inUJ43UMVGC_iHmtsWjA9NLHXdIDUTVSmtFX8r-w6ludS8qAp1vKJcijknaDYPo8QM2Zshe7POvuBf_v6MDfw76QIcrYB7H2r74t_XvQKyMrI2</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Bingxiang, Xiao</creator><creator>Panxing, Wu</creator><creator>Lu, Feng</creator><creator>Xiuyou, Yan</creator><creator>Chao, Ding</creator><general>Hindawi</general><general>John Wiley & Sons, Inc</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><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>7QL</scope><scope>7QO</scope><scope>7T7</scope><scope>7TK</scope><scope>7U7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-5003-3933</orcidid></search><sort><creationdate>2021</creationdate><title>A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes</title><author>Bingxiang, Xiao ; Panxing, Wu ; Lu, Feng ; Xiuyou, Yan ; Chao, Ding</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c504t-ec32df8fe47431d264993c8fef70cbee446e5883824a57a2fb4a4263789a86523</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Aged</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Biomedical research</topic><topic>Brain cancer</topic><topic>Brain Neoplasms - diagnosis</topic><topic>Brain Neoplasms - metabolism</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Care and treatment</topic><topic>Chemotherapy</topic><topic>Colon</topic><topic>Databases, Genetic</topic><topic>Diagnosis</topic><topic>Evaluation</topic><topic>Female</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Glioma</topic><topic>Glioma - diagnosis</topic><topic>Glioma - metabolism</topic><topic>Gliomas</topic><topic>Glucose</topic><topic>Glycolysis</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Kaplan-Meier Estimate</topic><topic>Male</topic><topic>Medical prognosis</topic><topic>Middle Aged</topic><topic>Model accuracy</topic><topic>Multivariate Analysis</topic><topic>Mutation</topic><topic>Mutation rates</topic><topic>Physiological aspects</topic><topic>Prognosis</topic><topic>Proportional Hazards Models</topic><topic>Regression Analysis</topic><topic>Regression models</topic><topic>Risk factors</topic><topic>RNA, Messenger - genetics</topic><topic>Software</topic><topic>Survival analysis</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bingxiang, Xiao</creatorcontrib><creatorcontrib>Panxing, Wu</creatorcontrib><creatorcontrib>Lu, Feng</creatorcontrib><creatorcontrib>Xiuyou, Yan</creatorcontrib><creatorcontrib>Chao, Ding</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><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>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science 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>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content 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><collection>PubMed Central (Full Participant titles)</collection><jtitle>BioMed research international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bingxiang, Xiao</au><au>Panxing, Wu</au><au>Lu, Feng</au><au>Xiuyou, Yan</au><au>Chao, Ding</au><au>B D, Parameshachari</au><au>Parameshachari B D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes</atitle><jtitle>BioMed research international</jtitle><addtitle>Biomed Res Int</addtitle><date>2021</date><risdate>2021</risdate><volume>2021</volume><issue>1</issue><spage>6680066</spage><epage>6680066</epage><pages>6680066-6680066</pages><issn>2314-6133</issn><eissn>2314-6141</eissn><abstract>Objective. To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model. Methods. The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model. Results. 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage. Conclusion. The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.</abstract><cop>United States</cop><pub>Hindawi</pub><pmid>34222480</pmid><doi>10.1155/2021/6680066</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-5003-3933</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Aged Biomarkers, Tumor - genetics Biomedical research Brain cancer Brain Neoplasms - diagnosis Brain Neoplasms - metabolism Cancer Cancer therapies Care and treatment Chemotherapy Colon Databases, Genetic Diagnosis Evaluation Female Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Genes Genetic aspects Glioma Glioma - diagnosis Glioma - metabolism Gliomas Glucose Glycolysis Health aspects Humans Kaplan-Meier Estimate Male Medical prognosis Middle Aged Model accuracy Multivariate Analysis Mutation Mutation rates Physiological aspects Prognosis Proportional Hazards Models Regression Analysis Regression models Risk factors RNA, Messenger - genetics Software Survival analysis Tumors |
| title | A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes |
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