Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods

Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a C...

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Main Authors: Haofuzi Zhang, Yutao Huang, Erwan Yang, Xiangyu Gao, Peng Zou, Jidong Sun, Zhicheng Tian, Mingdong Bao, Dan Liao, Junmiao Ge, Qiuzi Yang, Xin Li, Zhuoyuan Zhang, Peng Luo, Xiaofan Jiang
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Biomolecules
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Online Access:https://www.mdpi.com/2218-273X/12/11/1598
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author Haofuzi Zhang
Yutao Huang
Erwan Yang
Xiangyu Gao
Peng Zou
Jidong Sun
Zhicheng Tian
Mingdong Bao
Dan Liao
Junmiao Ge
Qiuzi Yang
Xin Li
Zhuoyuan Zhang
Peng Luo
Xiaofan Jiang
author_facet Haofuzi Zhang
Yutao Huang
Erwan Yang
Xiangyu Gao
Peng Zou
Jidong Sun
Zhicheng Tian
Mingdong Bao
Dan Liao
Junmiao Ge
Qiuzi Yang
Xin Li
Zhuoyuan Zhang
Peng Luo
Xiaofan Jiang
author_sort Haofuzi Zhang
collection DOAJ
description Background: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.
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spelling doaj.art-52f623d1d60a4d159af29f255f1b89eb2023-11-24T03:53:13ZengMDPI AGBiomolecules2218-273X2022-10-011211159810.3390/biom12111598Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics MethodsHaofuzi Zhang0Yutao Huang1Erwan Yang2Xiangyu Gao3Peng Zou4Jidong Sun5Zhicheng Tian6Mingdong Bao7Dan Liao8Junmiao Ge9Qiuzi Yang10Xin Li11Zhuoyuan Zhang12Peng Luo13Xiaofan Jiang14Department of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Anesthesiology, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaDepartment of Neurosurgery, Xijing Hospital, Fourth Military Medical University, Xi’an 710032, ChinaBackground: Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model. Methods: A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the “limma” R package. The “clusterProfiler” R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan–Meier (K–M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibroblast-related high- and low-risk subgroups using gene set variation analysis (GSEA). The immune infiltration cell and the stromal cell were calculated using the microenvironment cell populations-counter (MCP-counter) method, and the immunotherapy response was assessed with the SubMap algorithm. The chemotherapy sensitivity was estimated using the “pRRophetic” R package. Results: A total of 93 differentially expressed fibroblast-related genes (DEFRGs) were uncovered in glioma. Seven prognostic genes were filtered out to create a fibroblast-related gene signature in the TCGA-glioma cohort training set. We then affirmed the fibroblast-related risk model via TCGA-glioma cohort and CGGA-glioma cohort testing sets. The Cox regression analysis proved that the fibroblast-related risk score was an independent prognostic predictor in prediction of the overall survival of glioma patients. The fibroblast-related gene signature revealed by the GSEA was applicable to the immune-relevant pathways. The MCP-counter algorithm results pointed to significant distinctions in the tumor microenvironment between fibroblast-related high- and low-risk subgroups. The SubMap analysis proved that the fibroblast-related risk score could predict the clinical sensitivity of immunotherapy. The chemotherapy sensitivity analysis indicated that low-risk patients were more sensitive to multiple chemotherapeutic drugs. Conclusion: Our study identified prognostic fibroblast-related genes and generated a novel risk signature that could evaluate the prognosis of glioma and offer a theoretical basis for clinical glioma therapy.https://www.mdpi.com/2218-273X/12/11/1598gliomafibroblast-related genesrisk scoreGSEAprognosistherapy
spellingShingle Haofuzi Zhang
Yutao Huang
Erwan Yang
Xiangyu Gao
Peng Zou
Jidong Sun
Zhicheng Tian
Mingdong Bao
Dan Liao
Junmiao Ge
Qiuzi Yang
Xin Li
Zhuoyuan Zhang
Peng Luo
Xiaofan Jiang
Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
Biomolecules
glioma
fibroblast-related genes
risk score
GSEA
prognosis
therapy
title Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
title_full Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
title_fullStr Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
title_full_unstemmed Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
title_short Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods
title_sort identification of a fibroblast related prognostic model in glioma based on bioinformatics methods
topic glioma
fibroblast-related genes
risk score
GSEA
prognosis
therapy
url https://www.mdpi.com/2218-273X/12/11/1598
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