Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis
Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2020-09-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.01631/full |
| _version_ | 1828554045473685504 |
|---|---|
| author | Zheng He Zheng He Zheng He Chengcheng Wang Hao Xue Hao Xue Hao Xue Rongrong Zhao Rongrong Zhao Rongrong Zhao Gang Li Gang Li Gang Li |
| author_facet | Zheng He Zheng He Zheng He Chengcheng Wang Hao Xue Hao Xue Hao Xue Rongrong Zhao Rongrong Zhao Rongrong Zhao Gang Li Gang Li Gang Li |
| author_sort | Zheng He |
| collection | DOAJ |
| description | Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high- and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high- and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM. |
| first_indexed | 2024-12-12T05:29:13Z |
| format | Article |
| id | doaj.art-d9696f5b8b2440a6846d25e4b0707332 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-12T05:29:13Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-d9696f5b8b2440a6846d25e4b07073322022-12-22T00:36:22ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-09-011010.3389/fonc.2020.01631565793Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic AnalysisZheng He0Zheng He1Zheng He2Chengcheng Wang3Hao Xue4Hao Xue5Hao Xue6Rongrong Zhao7Rongrong Zhao8Rongrong Zhao9Gang Li10Gang Li11Gang Li12Department of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaShandong Key Laboratory of Brain Function Remodeling, Jinan, ChinaInstitute of Brain and Brain-Inspired Science, Shandong University, Jinan, ChinaDepartment of Pharmacy, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, ChinaDepartment of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaShandong Key Laboratory of Brain Function Remodeling, Jinan, ChinaInstitute of Brain and Brain-Inspired Science, Shandong University, Jinan, ChinaDepartment of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaShandong Key Laboratory of Brain Function Remodeling, Jinan, ChinaInstitute of Brain and Brain-Inspired Science, Shandong University, Jinan, ChinaDepartment of Neurosurgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaShandong Key Laboratory of Brain Function Remodeling, Jinan, ChinaInstitute of Brain and Brain-Inspired Science, Shandong University, Jinan, ChinaAltered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high- and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high- and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.https://www.frontiersin.org/article/10.3389/fonc.2020.01631/fullglioblastomametabolismprognosissignatureheterogeneity |
| spellingShingle | Zheng He Zheng He Zheng He Chengcheng Wang Hao Xue Hao Xue Hao Xue Rongrong Zhao Rongrong Zhao Rongrong Zhao Gang Li Gang Li Gang Li Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis Frontiers in Oncology glioblastoma metabolism prognosis signature heterogeneity |
| title | Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis |
| title_full | Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis |
| title_fullStr | Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis |
| title_full_unstemmed | Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis |
| title_short | Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis |
| title_sort | identification of a metabolism related risk signature associated with clinical prognosis in glioblastoma using integrated bioinformatic analysis |
| topic | glioblastoma metabolism prognosis signature heterogeneity |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.01631/full |
| work_keys_str_mv | AT zhenghe identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT zhenghe identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT zhenghe identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT chengchengwang identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT haoxue identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT haoxue identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT haoxue identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT rongrongzhao identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT rongrongzhao identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT rongrongzhao identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT gangli identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT gangli identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis AT gangli identificationofametabolismrelatedrisksignatureassociatedwithclinicalprognosisinglioblastomausingintegratedbioinformaticanalysis |