Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma
Abstract Background Low-grade gliomas (LGG) are slow-growing brain tumors with limited treatment options, making prognosis challenging. Recent advancements in molecular profiling offer potential for better understanding of genetic and immune factors involved in LGG progression, guiding more effectiv...
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| Format: | Article |
| Language: | English |
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02639-4 |
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| author | Peng Liao Xiaocui Zhang Ruyan Yuan Tingjun Tang Feng Wang Baowei Geng |
| author_facet | Peng Liao Xiaocui Zhang Ruyan Yuan Tingjun Tang Feng Wang Baowei Geng |
| author_sort | Peng Liao |
| collection | DOAJ |
| description | Abstract Background Low-grade gliomas (LGG) are slow-growing brain tumors with limited treatment options, making prognosis challenging. Recent advancements in molecular profiling offer potential for better understanding of genetic and immune factors involved in LGG progression, guiding more effective treatments and improving patient outcomes. Objectives In this study, the risk of low-grade gliomas (LGG) was analyzed by gene expression profile to provide a reference for clinical treatment and prognosis observation. Methods We employed RNA sequencing information from the International Cancer Genome Consortium (ICGC-US), the Gene Expression Omnibus (GEO), and the Cancer Genome Atlas (TCGA). Analysis Portal and Immunology Database provided information on genes relevant to immunity. To find differential expression of prognostic genes and create signatures, multivariate Cox, univariate, and Lasso regression were utilized. Its capability was assessed using the calibration curve and the receiver operating characteristic. The relationship between the score of risk and the quantity of tumor-invasive immune cells was evaluated using TIMER and CIBERSORTx. The expression of HPSE2 in brain glioma cells was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). The growth and metastatic ability of glioma cells after down-regulation of HPSE2 was detected by biological function assay. Results We identified 37 differential genes associated with LGG prognosis. Thirteen prognostic genes were determined to be risk factors. Prognostic characteristics showed comparable accuracy for overall survival at three and five years in both external (ICGC-US) and internal (TCGA) verification lines. The scores of StromalScore, ImmuneScore, and ESTIMATEScore in the high-risk group were higher than those in the low-risk group. Macrophages M0, memory resting of T cells CD4, memory activated of T cells CD4, B cells naive, the memory of B cells, the level of Macrophages M1 is higher than the group of low-risk. In contrast, T cell CD8, T cells regulatory (Tregs), Monocytes, Mast cells activated levels are lower than the group of low-risk. HPSE 2 was highly expressed in glioma by qRT-PCR, and the growth and metastasis of glioma cells were inhibited by means of HPSE 2 downregulation. Conclusions Our research constructs a novel prognostic characteristic for LGG that assesses prognosis and is connected to immune infiltration. Downregulation of HPSE 2 inhibited the metastasis and growth of glioma cells. |
| format | Article |
| id | doaj-art-c6ab3cbc18a040dda8453e1c1e892e7e |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-c6ab3cbc18a040dda8453e1c1e892e7e2025-08-20T03:10:32ZengSpringerDiscover Oncology2730-60112025-05-0116111410.1007/s12672-025-02639-4Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade gliomaPeng Liao0Xiaocui Zhang1Ruyan Yuan2Tingjun Tang3Feng Wang4Baowei Geng5Department of Neurosurgery, Chongqing University FuLing HospitalDepartment of Nephrology, Chongqing University FuLing HospitalDepartment of Neurosurgery, Chongqing University FuLing HospitalDepartment of Neurosurgery, Chongqing University FuLing HospitalDepartment of Neurosurgery, Chongqing University FuLing HospitalDepartment of Neurosurgery, Chongqing University FuLing HospitalAbstract Background Low-grade gliomas (LGG) are slow-growing brain tumors with limited treatment options, making prognosis challenging. Recent advancements in molecular profiling offer potential for better understanding of genetic and immune factors involved in LGG progression, guiding more effective treatments and improving patient outcomes. Objectives In this study, the risk of low-grade gliomas (LGG) was analyzed by gene expression profile to provide a reference for clinical treatment and prognosis observation. Methods We employed RNA sequencing information from the International Cancer Genome Consortium (ICGC-US), the Gene Expression Omnibus (GEO), and the Cancer Genome Atlas (TCGA). Analysis Portal and Immunology Database provided information on genes relevant to immunity. To find differential expression of prognostic genes and create signatures, multivariate Cox, univariate, and Lasso regression were utilized. Its capability was assessed using the calibration curve and the receiver operating characteristic. The relationship between the score of risk and the quantity of tumor-invasive immune cells was evaluated using TIMER and CIBERSORTx. The expression of HPSE2 in brain glioma cells was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). The growth and metastatic ability of glioma cells after down-regulation of HPSE2 was detected by biological function assay. Results We identified 37 differential genes associated with LGG prognosis. Thirteen prognostic genes were determined to be risk factors. Prognostic characteristics showed comparable accuracy for overall survival at three and five years in both external (ICGC-US) and internal (TCGA) verification lines. The scores of StromalScore, ImmuneScore, and ESTIMATEScore in the high-risk group were higher than those in the low-risk group. Macrophages M0, memory resting of T cells CD4, memory activated of T cells CD4, B cells naive, the memory of B cells, the level of Macrophages M1 is higher than the group of low-risk. In contrast, T cell CD8, T cells regulatory (Tregs), Monocytes, Mast cells activated levels are lower than the group of low-risk. HPSE 2 was highly expressed in glioma by qRT-PCR, and the growth and metastasis of glioma cells were inhibited by means of HPSE 2 downregulation. Conclusions Our research constructs a novel prognostic characteristic for LGG that assesses prognosis and is connected to immune infiltration. Downregulation of HPSE 2 inhibited the metastasis and growth of glioma cells.https://doi.org/10.1007/s12672-025-02639-4GlioblastomaLow-grade gliomaPrognostic modelAtlas of cancer genomesOmnibus of gene expression |
| spellingShingle | Peng Liao Xiaocui Zhang Ruyan Yuan Tingjun Tang Feng Wang Baowei Geng Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma Discover Oncology Glioblastoma Low-grade glioma Prognostic model Atlas of cancer genomes Omnibus of gene expression |
| title | Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma |
| title_full | Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma |
| title_fullStr | Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma |
| title_full_unstemmed | Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma |
| title_short | Application of bioinformatics analysis to construct the prognostic model and immune-related gene characteristics of low-grade glioma |
| title_sort | application of bioinformatics analysis to construct the prognostic model and immune related gene characteristics of low grade glioma |
| topic | Glioblastoma Low-grade glioma Prognostic model Atlas of cancer genomes Omnibus of gene expression |
| url | https://doi.org/10.1007/s12672-025-02639-4 |
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