Shifu Li1, Qian Zhang1, Zhou Chen1, Zheng Huang1, Longbo Zhang1,2, Fenghua Chen3. 1. Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China. 2. Departments of Neurosurgery and Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, 06520-8082, USA. 3. Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China. xyswcfh@csu.edu.cn.
Abstract
OBJECTIVES: This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA). METHODS: GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics. RESULTS: We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs. CONCLUSION: Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.
OBJECTIVES: This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA). METHODS: GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics. RESULTS: We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs. CONCLUSION: Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.
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