Title : Construction of a glioma prognostic model and analysis of immune correlation based on NAT1-associated genes
Abstract:
The gene encoding the biological enzyme N-acetyltransferase 1 (NAT1) plays a crucial role in the malignant progression of various tumors. As the most common and poorly prognostic malignant brain tumor, this study explored the relationship between NAT1 expression changes in glioma tissues and survival prognosis, constructed and validated a prognostic prediction model based on NAT1-related genes (NRGs), and analyzed their role in the glioma immune microenvironment. Whole-gene expression data and clinical information of glioma were obtained from TCGA and GEO databases (TCGA-LGG, TCGA-GBM, and GSE4412) to analyze the expression differences of NRGs in glioma samples. GSVA analysis revealed that NRGs were enriched in key pathways such as cytokine-receptor interactions and hematopoietic cell signal regulation, suggesting their potential influence on the malignant progression of glioma. GO enrichment results revealed that these genes play a core role in chromatin covalent modification, histone modification, transcriptional co-regulatory activity, and histone binding. A survival prediction model was constructed based on 17 model genes (CSTA, CMTM6, TUBA1C, etc.) screened through univariate and multivariate Cox regression and Lasso regression, and the model's stable and effective performance was validated using the CGGA_325 dataset. Spearman's correlation test, TIMER, ESTIMATE, and CIBERSORT algorithms were used to investigate the correlation of differentially expressed genes with immune cells and stromal components. Immune microenvironment analysis found that the high-risk group had higher levels of immune cell infiltration in glioma tissues, while the low-risk group had significantly higher infiltration of follicular helper T cells, monocytes, macrophages, and other immune cells compared to the high-risk group. Further analysis of single-cell sequencing data of glioma from the TISCH2 database revealed the expression distribution of differentially expressed genes in different cell subpopulations. This study constructed a prognostic model for glioma based on NRGs, revealing that CSTA and other genes promote the malignant progression of glioma and affect patient prognosis by regulating M2 polarization of macrophages and influencing the glioma immune microenvironment, providing a theoretical basis for subsequent research.
Audience Take Away Notes:
- The crucial role of NAT1-related genes (NRGs) in the malignant progression and prognosis of glioma. The construction and validation of a prognostic prediction model for glioma based on NRGs.
- The involvement of NRGs in key biological pathways and processes related to glioma progression, such as cytokine-receptor interactions, hematopoietic cell signal regulation, chromatin modification, and histone binding.
- The correlation between NRGs and the immune microenvironment in glioma, particularly the infiltration of various immune cells.
- The potential of specific NRGs, such as CSTA, in promoting glioma malignancy by regulating macrophage M2 polarization.
- List all other benefits.
- For researchers, this study provides new insights into the role of NRGs in glioma progression and prognosis, opening up new avenues for further research and potential therapeutic target identification. Other faculty could use this research to expand their investigations into the molecular mechanisms of glioma and explore the potential of targeting NRGs or their associated pathways for glioma treatment.
- For clinicians, the prognostic model developed in this study could be a practical tool to predict patient survival and guide treatment decisions. By stratifying patients into high- and low-risk groups based on NRG expression, clinicians can design more personalized treatment plans and follow-up strategies, potentially improving patient outcomes.
- For bioinformaticians and data analysts, the analytical approaches employed in this study, such as GSVA, GO enrichment analysis, and various algorithms for investigating gene-immune cell correlations, could serve as a reference for designing similar studies or solving related problems in other cancer types or diseases.
- For educators, this study provides a comprehensive example of how bioinformatics and molecular biology techniques can be applied to investigate the role of specific genes in cancer progression and prognosis. This could be used as a teaching case to illustrate the process of translational research from data mining to potential clinical applications.
- Overall, this study contributes to a better understanding of glioma biology and provides new information that could assist in the development of more accurate prognostic tools and targeted therapies for glioma patients, ultimately improving patient care and outcomes in the field of neuro-oncology.
