Identification of potential biomarkers associated with cuproptosis and immune microenvironment analysis in acute myocardial infarction A diagnostic accuracy study

被引：0
作者：
Zhang, Jing [1 ]
Yue, Zhijie [1 ]
Zhu, Na [2 ]
Zhao, Na [3 ]
机构：
[1] Shanxi Med Univ, Shanxi Acad Med Sci, Shanxi Bethune Hosp,Tongji Shanxi Hosp, Dept Cardiovasc Internal Med,Hosp 3, Taiyuan 030032, Peoples R China
[2] Shanxi Med Univ, Shanxi Bethune Hosp, Tongji Shanxi Hosp,Shanxi Acad Med Sci, Dept Med Record Management,Hosp 3, Taiyuan, Peoples R China
[3] Shanxi Med Univ, Dept Imaging & Nucl Med, Hosp 2, Taiyuan, Peoples R China
来源：
MEDICINE | 2025年 / 104卷 / 04期
关键词：
acute myocardial infarction; bioinformatics; biomarkers; cuproptosis; immune cells; EUROPEAN-SOCIETY; EXPRESSION; GENE; INFECTION; TRAF3IP3; SURVIVAL; PACKAGE;
D O I：
10.1097/MD.0000000000040817
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
Acute myocardial infarction (AMI), a critical cardiovascular condition, is often associated with serious health risks. Recent studies suggest a link between copper-induced apoptosis and immune cell infiltration. Specifically, abnormal accumulation of copper ions can lead to intracellular oxidative stress and apoptosis, while also affecting immune cell function and infiltration. Nevertheless, studies exploring this relationship in the context of AMI are notably scarce, underscoring the necessity of identifying biomarkers associated with cuproptosis in AMI. Consensus clustering analysis was employed to classify distinct subtypes of AMI in the GSE66360 dataset. Concurrently, differential expression analysis was performed to identify differentially expressed genes (DEGs) across subtypes and between AMI and control samples. We employed Venn diagrams to validate the selection of cuproptosis-related DEGs in patients with AMI. A protein-protein interaction network was constructed to pinpoint potential candidate genes. Receiver operating characteristic curves were generated to identify promising biomarkers. The immune infiltration milieu was analyzed using CIBERSORT algorithms. Finally, the expression levels of identified cuproptosis-related biomarkers were validated at the transcriptional level. We classified AMI into 2 distinct cuproptosis-related subtypes, leading to the identification of 157 cuproptosis-related DEGs. Further analysis refined this list to 10 potential candidate genes. Among these, 5 emerged as significant biomarkers for AMI: granzyme A (GZMA), GTPase immunity-associated proteins (GIMPAs) GIMAP7, GIMAP5, GIMAP6, and TRAF3 interacting protein 3 (TRAF3IP3). A comprehensive examination of immune infiltration in AMI samples revealed significant differences in the levels of 11 types of immune cells, with GZMA displaying the highest correlation with activated mast cells and CD8 + T cells. We observed markedly lower expression levels of GZMA, GIMAP6, and TRAF3IP3 in the AMI group compared to controls. This study identified 5 cuproptosis-related biomarkers (GZMA, GIMAP7, GIMAP5, GIMAP6, and TRAF3IP3) associated with AMI, laying a theoretical foundation for the treatment of AMI.
引用
收藏
页数：11
相关论文
共 49 条
  • [21] Kumar L., Futschik M.E., Mfuzz: A software package for soft clustering of microarray data, Bioinformation, 2, pp. 5-7, (2007)
  • [22] Livak K.J., Schmittgen T.D., Analysis of relative gene expression data using real-Time quantitative PCR and the 2(-Delta Delta C(T)) method, Methods, 25, pp. 402-408, (2001)
  • [23] Huo Q., Ning L., Xie N., Identification of GZMA as a potential therapeutic target involved in immune infiltration in breast cancer by integrated bioinformatical analysis, Breast Cancer (Dove Med Press), 15, pp. 213-226, (2023)
  • [24] Zhou Z., He H., Wang K., Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells, Science, 368, (2020)
  • [25] Garzon-Tituana M., Sierra-Monzon J.L., Comas L., Granzyme A inhibition reduces inflammation and increases survival during abdominal sepsis, Theranostics, 11, pp. 3781-3795, (2021)
  • [26] Van Daalen K.R., Reijneveld J.F., Bovenschen N., Modulation of Inflammation by Extracellular Granzyme A, Front Immunol, 11, (2020)
  • [27] Hackler Y., Siebenhaar F., Maurer M., Munoz M., Virus-infected mast cells activate virus-specific CD8+ T cells, Scand J Immunol, 98, (2023)
  • [28] Filen S., Lahesmaa R., GIMAP proteins in T-lymphocytes, J Signal Transduct, 2010, (2010)
  • [29] Nitta T., Takahama Y., The lymphocyte guard-IANs: Regulation of lymphocyte survival by IAN/GIMAP family proteins, Trends Immunol, 28, pp. 58-65, (2007)
  • [30] Heinonen M.T., Laine A.P., Soderhall C., GIMAP GTPase family genes: Potential modifiers in autoimmune diabetes, asthma, and allergy, J Immunol, 194, pp. 5885-5894, (2015)
12345