Construction And Validation of a Combined Immune and Metabolism-Related Genes Model for Prognosis Prediction in Glioma
Asare K
Published on: 2025-05-02
Abstract
Background: Glioma, characterized by its highly malignant nature and low survival rates, poses a significant challenge in terms of prognosis due to its complex etiology and high heterogeneity. Hence, there is a pressing need to identify new, reliable prognostic models for these tumors.
Methods: A comprehensive analysis was conducted by using public glioma gene expression datasets to identify immune and metabolism-related differentially expressed genes (IMRDEGs). The IMRDEGs were used for clustering glioma samples via non-negative matrix factorization (NMF) analysis, followed by differential analysis of the resulting clusters. A prognostic model was constructed using Cox regression and least absolute shrinkage and selection operator (LASSO) analysis. Internal and external validation of the model was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Additionally, a nomogram was developed to assess the predictive performance of the model. The biological functions of this model were elucidated through gene set enrichment analysis (GSEA). Furthermore, the expression levels of the five genes in gliomas were validated using the Human Protein Atlas (HPA) database and reverse transcription quantitative polymerase chain reaction (qRT-PCR) in glioma tissues.
Results: A predictive model was developed based on five IMRDEGs: AK7, RDH10, DTYMK, PLXNA3, and NMNAT2. This model exhibited robust prognostic prediction capabilities across training, test, and GEO datasets. Both univariate and multivariate Cox regression analyses confirmed the model as a novel independent predictor of outcomes in glioma patients. Additionally, a nomogram demonstrated excellent predictive performance in accurately estimating survival rates at 1, 3, and 5 years for glioma patients. Compared to existing models, our model demonstrated superior performance in distinguishing overall survival (OS) between high and low-risk patients. GSEA revealed enrichment of biological functions related to the cell cycle and ECM receptor interactions within this model. Furthermore, qRT-PCR analysis of glioma tissues indicated high expression levels of DTYMK, RDH10, AK7, and PLXNA3, while NMNAT2 expression was found to be low in glioma tissues.
Conclusion: The immune and metabolism-related genes (IMRGs) prognosis risk model and their nomogram have important value for survival prediction and risk assessment of glioma patients.
Keywords
Glioma; TCGA; IMRGs prognosis risk model; Immune and metabolism-related genesIntroduction
Glioma is the most common primary malignant brain tumor, accounting for 80% of all malignant brain tumors [1]; Among gliomas, glioblastoma (GBM) is the most common malignancy of the central nervous system, comprising 48.3% of cases and having a median survival period of approximately 14.6 months [2]. Treatment options for glioma typically include surgery, chemotherapy, and immunotherapy. However, the effectiveness of current immunotherapy for gliomas remains suboptimal due to several factors: significant tumor heterogeneity, immunosuppression within the tumor immune microenvironment, and low mutational burden [3, 4]. Consequently, ongoing research aims to identify new effective biomarkers and therapeutic targets to improve glioma treatment outcomes.
In recent years, metabolic reprogramming has emerged as a hallmark of malignancy [5]. Cancer cells adapt unfavorable microenvironment by altering their metabolism to sustain proliferation and survival [6-9]. Numerous studies have highlighted the close association between glioma growth and metabolic pathways [10,11]. One key feature in glioma malignant progression is the metabolic shift towards aerobic glycolysis, known as the Warburg effect [12]. Additionally, research by Lin H et al. has revealed that fatty acid oxidation serves as a significant energy source for the metabolism and proliferation of malignant glioma cells, challenging previous notions of the primary energy source in gliomas [13,14]. Simultaneously, the immune microenvironment plays a crucial role in tumor development [15,16]. Immune cells within the microenvironment can impede tumor recurrence, progression, and metastasis by modulating molecular signaling and activating immune responses [17]. However, some tumor cells evade immune surveillance, promoting invasion and metastasis [18]. Dysregulation of the tumor-induced immune status likely contributes to glioma progression, with immune components within the microenvironment serving as potential prognostic indicators [19, 20].
This insight suggests that models, particularly those focused on metabolic or immune-related factors, could aid in molecular stratification of gliomas, facilitating the development of personalized treatment strategies and the assessment of clinical survival outcomes. However, there have been relatively few studies investigating the interplay between immunity and metabolism in glioma. This study successfully identified and validated a five-gene immune and metabolism-related risk model using bulk glioma transcriptional data. The implementation of this model is poised to enhance our comprehension of the molecular underpinnings of immunity and metabolism in glioma, thereby offering insights to refine personalized immunotherapy strategies for glioma patients.
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