학술논문
Identification of Hypoxia Prognostic Signature in Glioblastoma Multiforme Based on Bulk and Single-Cell RNA-Seq
Document Type
Academic Journal
Author
Source
Cancers. February 2024, Vol. 16 Issue 3
Subject
Language
English
ISSN
2072-6694
Abstract
Author(s): Yaman B. Ahmed [1,2,†]; Obada E. Ababneh [2,†]; Anas A. Al-Khalili [2]; Abdullah Serhan [2]; Zaid Hatamleh [2]; Owais Ghammaz [2]; Mohammad Alkhaldi [2]; Safwan Alomari (corresponding author) [3,*] [...]
This study developed a prognostic signature using hypoxia-related differentially expressed genes (DEGs) in Glioblastoma Multiforme (GBM) and identified three optimal gene signatures (CP, IGFBP2, and LOX) using multi-omics analysis. This was done using bulk and single-cell RNA sequencing to identify DEGs and integrated machine learning particularly LASSO regression to construct a prognostic model. Gene ontology and pathway analysis were used to study the biological processes affected by these genes. Additionally, gene enrichment analysis was incorporated to study the tumor microenvironment and drug sensitivity. An in-depth understanding of the complex biological pathways in GBM using this multi-omics approach is necessary to examine GBM’s behavior and prognosis presenting insights for potential therapeutic targets and survival outcomes of GBM patients. Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.
This study developed a prognostic signature using hypoxia-related differentially expressed genes (DEGs) in Glioblastoma Multiforme (GBM) and identified three optimal gene signatures (CP, IGFBP2, and LOX) using multi-omics analysis. This was done using bulk and single-cell RNA sequencing to identify DEGs and integrated machine learning particularly LASSO regression to construct a prognostic model. Gene ontology and pathway analysis were used to study the biological processes affected by these genes. Additionally, gene enrichment analysis was incorporated to study the tumor microenvironment and drug sensitivity. An in-depth understanding of the complex biological pathways in GBM using this multi-omics approach is necessary to examine GBM’s behavior and prognosis presenting insights for potential therapeutic targets and survival outcomes of GBM patients. Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Hypoxia, a common feature in GBM, has been linked to tumor progression and therapy resistance. In this study, we aimed to identify hypoxia-related differentially expressed genes (DEGs) and construct a prognostic signature for GBM patients using multi-omics analysis. Patient cohorts were collected from publicly available databases, including the Gene Expression Omnibus (GEO), the Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas—Glioblastoma Multiforme (TCGA-GBM), to facilitate a comprehensive analysis. Hypoxia-related genes (HRGs) were obtained from the Molecular Signatures Database (MSigDB). Differential expression analysis revealed 41 hypoxia-related DEGs in GBM patients. A consensus clustering approach, utilizing these DEGs’ expression patterns, identified four distinct clusters, with cluster 1 showing significantly better overall survival. Machine learning techniques, including univariate Cox regression and LASSO regression, delineated a prognostic signature comprising six genes (ANXA1, CALD1, CP, IGFBP2, IGFBP5, and LOX). Multivariate Cox regression analysis substantiated the prognostic significance of a set of three optimal signature genes (CP, IGFBP2, and LOX). Using the hypoxia-related prognostic signature, patients were classified into high- and low-risk categories. Survival analysis demonstrated that the high-risk group exhibited inferior overall survival rates in comparison to the low-risk group. The prognostic signature showed good predictive performance, as indicated by the area under the curve (AUC) values for one-, three-, and five-year overall survival. Furthermore, functional enrichment analysis of the DEGs identified biological processes and pathways associated with hypoxia, providing insights into the underlying mechanisms of GBM. Delving into the tumor immune microenvironment, our analysis revealed correlations relating the hypoxia-related prognostic signature to the infiltration of immune cells in GBM. Overall, our study highlights the potential of a hypoxia-related prognostic signature as a valuable resource for forecasting the survival outcome of GBM patients. The multi-omics approach integrating bulk sequencing, single-cell analysis, and immune microenvironment assessment enhances our understanding of the intricate biology characterizing GBM, thereby potentially informing the tailored design of therapeutic interventions.