Identification of inflammation-related genes signature to establish a prognostic model in MGMT unmethylated glioblastoma patients
Abstract Background Patients with unmethylated O6-methylguanine-DNA methyltransferase promoter (uMGMT) glioblastoma (GBM) have a poor prognosis. Inflammatory response can affect the prognosis, for it may have a significant impact on the tumor microenvironment (TME). This study aims to identify a pro...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01894-9 |
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| Summary: | Abstract Background Patients with unmethylated O6-methylguanine-DNA methyltransferase promoter (uMGMT) glioblastoma (GBM) have a poor prognosis. Inflammatory response can affect the prognosis, for it may have a significant impact on the tumor microenvironment (TME). This study aims to identify a prognostic signature of inflammation-related genes, which can predict the prognosis of uMGMT GBM patients. Methods We examined the gene expression, somatic mutations, and overall survival of 159 GBM patients with uMGMT using the TCGA and CGGA databases. We identified molecular subtypes of uMGMT GBM patients based on the expression of inflammation-related genes. Furthermore, we determined principal component analysis (PCA), gene ontology (GO) analysis, pathway analysis and immune infiltration analysis between high and low-inflammation subtypes. We also examined the spatial and longitudinal heterogeneity of these two subtypes. The LASSO-Cox analyses were used to develop an inflammation-related prognostic model. Results Our findings indicate that patients with uMGMT GBM can be divided into high-inflammation and low-inflammation subtypes. Patients with high levels of inflammation are more likely to develop an immunosuppressive microenvironment, which stimulates the production of immunosuppressive cytokines, immune checkpoints, and immunosuppressive cells. Nine inflammation-related genes (EREG, BDKRB1, DCBLD2, CD14, AHR, CLEC5A, LTA, SLC4A4, and LY6E) were found to have excellent predictive potential for patient survival in the prognostic model. Conclusions In conclusion, we created a new prognostic model including 9 inflammation-related genes. This model has produced meaningful results in evaluating patient prognosis, which may help with future therapeutic strategies for patients with uMGMT GBM. |
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| ISSN: | 2730-6011 |