Identification of Prognostic Aging-Related Genes Associated with Immune Cell Infiltration in Glioblastoma

Autor: Xiaopeng Zhu, Xun Chen, Zhao Liu, Bihui Yang, Yuxiang Zhou, Sian Pan, Yongkai Huang, Deqing Han, Yiqian Zeng, Chao Liu
Rok vydání: 2023
Předmět:
Zdroj: Journal of Oncology. 2023:1-13
ISSN: 1687-8469
1687-8450
Popis: Background. Aging is recognized as a main tumor risk factor, and thus aging has become a field of interest in the tumor research field. Glioblastoma multiforme represents the most typical primary malignant intracranial tumor, particularly in the elderly. However, the association between aging-related genes (AGs) and GBM prognosis remains unknown. As a result, the primary goal of this study was to determine the association among AGs and the prognosis of GBM. Methods. A total of 307 human AGs were downloaded from the HAGR database, while the expression profiles of GSE4290 and GSE4412 were obtained from the GEO database. Furthermore, data on GBM expression profiles were obtained from the Chinese Glioma Genome Atlas (CGGA) database. The DEAGs that were differentially expressed among the AG and GBM gene expression profiles derived from GSE4290 were then identified, followed by functional analysis of the DEAGs. The survival-related AGs were then screened using univariate Cox regression analysis , which was used to build and validate a prognostic risk model. Furthermore, the ESTIMATE and CIBERSORT algorithms were utilized to explore the association between the survival-related AGs and the tumor immune microenvironment. Results. In entire, 29 DEAGs were identified in the GSE4290. This was monitored by the construction of the prognosis risk model using four DEAGs from the CGGA training set, including C1QA, CDK1, EFEMP1, and IGFBP2. Next, the risk model was confirmed in the CGGA experiment set and the GSE 4412 dataset. Results showed that C1QA, CDK1, EFEMP1, and IGFBP2 levels were remarkably higher in the high-risk score groups, and they had a good association with immune and stromal scores. Conclusion. A robust prognostic risk model was constructed and validated using four AGs, including C1QA, CDK1, EFEMP1, and IGFBP2, which had a close relationship with the immune microenvironment of GBM. This study offers a new reference to further explore the pathogenesis of GBM and recognize new and more effective GBM treatments.
Databáze: OpenAIRE
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