Abstract
Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.
Highlights
Diffuse gliomas represent the most common type of primary tumor originating in the central nervous system
According to the criteria for differentially expressed genes (DEGs), we identified 7,223 DEGs between 523 The Cancer Genome Atlas (TCGA)-lowergrade gliomas (LGGs) samples and 105 normal brain cortex samples based on overlapping edgeR, limma, DESeq2 analysis results (Figure 1A)
A total of 87 iron metabolism-related genes (50 up-regulated and 37 downregulated) among the DEGs were selected for further analysis (Figure 1B)
Summary
Diffuse gliomas represent the most common type of primary tumor originating in the central nervous system. The median overall survival (OS) time of patients with WHO II and III gliomas is 78.1 months and 37.6 months, respectively [2]. Despite advances in diagnostic and treatment methods, LGG may progress into high-grade glioma in some patients, leading to reduced therapeutic responses and a poorer disease prognosis. In contrast to normal cells, many tumor cells become dependent on iron in order to grow faster and, are more susceptible to iron depletion. This phenomenon is known as iron addiction [3]. Iron participates in several types of cell death [11], especially ferroptosis [3]
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