Identification of GLS as a cuproptosis-related diagnosis gene in acute myocardial infarction

Liu, Zheng; Wang, Lei; Xing, Qichang; Liu, Xiang; Hu, Yonggang; Li, Wencan; Yan, Qingzi; Liu, Renzhu; Huang, Nan

doi:10.3389/fcvm.2022.1016081

Cited by 35 publications

(19 citation statements)

References 66 publications

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“…It has been demonstrated that GLS is an anti-cuproptosis gene [ 45 ]. GLS has been identified as a genetic marker for the diagnosis of acute myocardial infarction [ 46 ] and has been less studied in other diseases, but results have been published in cancers such as glioma [ 47 ], breast cancer [ 48 ] and liver cancer [ 49 ].However, these two marker genes have not been studied in the subject of prematurity. It is worth our attention that these genes are related to human chromosome 2, which provides a direction for future genetic screening of embryos.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Jia

Zhang

et al. 2023

BMC Med Inform Decis Mak

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Background Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases. Methods and results First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944. Conclusion We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Jia

Zhang

et al. 2023

BMC Med Inform Decis Mak

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“…Only a few bioinformatics studies on non-tumour diseases have reported that the expression of some copper-induced death-related genes changed in IgA nephropathy, Alzheimer's disease, in ammatory bone disease, endometriosis, and acute myocardial infarction. These include CDKN2A, GLS, MTF1, PDHB, PDHA1, DLAT, DLD, LIPT1, LIAS and FDX1 [29][30][31][32][33][34], of which FDX1 is a key gene [35][36]. However, there are no detailed studies on copper-induced death in the aforementioned diseases.…”

Section: Discussionmentioning

confidence: 99%

Steroidogenic factor 1 promotes cuproptosis in renal tubular epithelial cells during acute kidney injury

et al. 2023

Preprint

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Programmed cell death of renal tubular epithelial cells (RTECs) is the main pathophysiological mechanism of acute kidney injury (AKI). Copper-induced death is a newly discovered form of programmed cell death, mainly attributed to fatty acylation in the TCA cycle, which leads to mitochondrial stress and cell death. However, the role of copper-induced death in the occurrence and development of AKI remains unclear. In this study, we first identified that the expression of ferredoxin 1 (FDX1), a key gene for copper-induced death, was significantly increased in the renal tissues of patients with AKI. The expression of copper-induced death-related genes and copper transporter 1 (CTR1) was significantly increased in various animal models of AKI. Compared with the control group, in the hypoxia-reoxygenation (H/R), lipopolysaccharide, and cisplatin injury groups, the concentration of copper ions increased in the NRK-52E cells, and the expression of FDX1 and CTR1 proteins increased significantly. After the NRK-52E cells were stimulated by copper (Cu2+), elesclomol (ES), and Cu2+ + ES, the expression of CTR1 and FDX1 increased in the Cu2+ + ES group, and that of lactate dehydrogenase increased significantly. In addition, the expression of CTR1 and FDX1 in the tubular epithelial cells in the H/R model and cell death reduced significantly after siRNA knockdown of the FDX1 gene or copper chelation therapy with tetrathiomolybdate. Preliminary mechanism investigation showed that the expression of CTR1 and FDX1 in H/R tubular epithelial cells and cell death was significantly reduced following siRNA knockdown of the steroidogenic factor 1 gene. Therefore, we speculate that copper-induced death in the RTECs may play an important role in the pathogenesis of AKI. Additionally, SF1 may be involved in the regulation of cuproptosis during AKI.

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“…We combined the top 10 potential hub genes and the genes involved in the most significant module to identify the overlapping genes, which were ultimately regarded as hub genes related to necroptosis. The correlations of the hub genes were analyzed using the Spearman correlation in the “corrplot” package, and differences with p < 0.05 were considered statistically significant [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Identification and Analysis of Necroptosis-Related Genes in COPD by Bioinformatics and Experimental Verification

Wang

Yin

et al. 2023

Biomolecules

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Chronic obstructive pulmonary disease (COPD) is a heterogeneous and complex progressive inflammatory disease. Necroptosis is a newly identified type of programmed cell death. However, the role of necroptosis in COPD is unclear. This study aimed to identify necroptosis-related genes in COPD and explore the roles of necroptosis and immune infiltration through bioinformatics. The analysis identified 49 differentially expressed necroptosis-related genes that were primarily engaged in inflammatory immune response pathways. The infiltration of CD8+ T cells and M2 macrophages in COPD lung tissue was relatively reduced, whereas that of M0 macrophages was increased. We identified 10 necroptosis-related hub genes significantly associated with infiltrated immune cells. Furthermore, 7 hub genes, CASP8, IL1B, RIPK1, MLKL, XIAP, TNFRSF1A, and CFLAR, were validated using an external dataset and experimental mice. CFLAR was considered to have the best COPD-diagnosing capability. TF and miRNA interactions with common hub genes were identified. Several related potentially therapeutic molecules for COPD were also identified. The present findings suggest that necroptosis occurs in COPD pathogenesis and is correlated with immune cell infiltration, which indicates that necroptosis may participate in the development of COPD by interacting with the immune response.

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Identification of GLS as a cuproptosis-related diagnosis gene in acute myocardial infarction

Cited by 35 publications

References 66 publications

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Steroidogenic factor 1 promotes cuproptosis in renal tubular epithelial cells during acute kidney injury

Identification and Analysis of Necroptosis-Related Genes in COPD by Bioinformatics and Experimental Verification

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