A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Xu, Congcong; Chen, Hao

doi:10.3389/fgene.2021.697043

Cited by 14 publications

(13 citation statements)

References 59 publications

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“…The NOXs inhibitor diphenyleneiodonium and the NOX1/4-specific inhibitor GKT137831 prevent erastin-induced ferroptosis in Calu-1 and HT1080 cells [ 27 ], suggesting that members of the NOX family promote ferroptosis of cancer cells [ 28 – 30 ]. Besides, previous study confirmed that ALOX5 was a key enzyme for evoking ferroptosis in cancer diseases [ 31 ] or neuronal injury [ 32 ]. 5-lipoxygenase (ALOX5) can catalyze arachidonic acid (AA) to generate proinflammatory cytokine leukotrienes (LTs) and can also produce lipid peroxides and mediate lipid peroxidation; phospholipids, the main component of cell membrane, are prone to lipid peroxidation, resulting in membrane rupture and ferroptosis [ 33 ].…”

Section: Discussionmentioning

confidence: 87%

“…Besides, previous study confirmed that ALOX5 was a key enzyme for evoking ferroptosis in cancer diseases [31] or neuronal injury [32]. 5-lipoxygenase (ALOX5) can catalyze arachidonic acid (AA) to generate proinflammatory cytokine leukotrienes (LTs) and can also produce lipid peroxides and mediate lipid peroxidation; phospholipids, the main component of cell membrane, are prone to lipid peroxidation, resulting in membrane rupture and ferroptosis [33].…”

Section: Discussionmentioning

confidence: 95%

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Novel Diagnostic Biomarkers Related to Oxidative Stress and Macrophage Ferroptosis in Atherosclerosis

Xin

et al. 2022

Oxidative Medicine and Cellular Longevity

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Atherosclerosis (AS) is a chronic inflammatory disease, which has a complex interplay between altered immune metabolism and oxidative stress. Therefore, we aimed to determine the oxidative stress and immune-related biomarkers in AS. Differential gene expression analyses are based on the GSE100927 dataset in the Gene Expression Omnibus (GEO), and 389 oxidative stress (OS) genes are identified based on gene set enrichment analysis (GSEA). We identified 74 differentially expressed genes related to oxidative stress (DEOSGs). “CIBERSORT” and “WGCNA” R Packages were used to compare the differences in immune infiltration levels between AS and control samples. The DEOSGs ( N = 74 ) were intersected with the key module’s genes of WGCNA ( N = 972 ), and 27 differentially expressed immune-related oxidative stress genes (DEIOSGs) were obtained. To identify the pivotal genes, a protein-protein interaction (PPI) network was constructed using the STRING database and the Cytoscape software. MMP9, ALOX5, NCF2, NCF, and NCF4 were identified as diagnostic markers of AS, and we validated them in the GSE57691 dataset. The expression levels of the five diagnostic genes were significantly highly expressed in the AS group. Correlation analysis and single-cell analysis revealed that five diagnostic genes were mainly correlated with macrophages M1. We, respectively, intersected differentially expressed genes (DEGs) with ferroptosis gene set, necroptosis gene set, and pyroptosis gene set. The findings suggested that ALOX5 and NCF2 were differentially expressed genes of ferroptosis. High expression of five hub genes in RAW264.7 macrophages were confirmed by PCR. High ALOX5 and NCF2 expression levels in plaque tissues were confirmed by immunohistochemistry (IHC) and western blotting. Our study identified that MMP9, ALOX5, NCF2, NCF1, and NCF4 were diagnostic genes of AS and associated with oxidative stress. ALOX5 and NCF2 may be involved in the formation of the necrotic core in AS by regulating macrophage ferroptosis.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 95%

Novel Diagnostic Biomarkers Related to Oxidative Stress and Macrophage Ferroptosis in Atherosclerosis

Xin

et al. 2022

Oxidative Medicine and Cellular Longevity

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“…We also compared our model with others. Only two models based on immune or ferroptosis-related genes have been discovered so far [ 46 , 47 ]. However, in our analysis, for the first time, the incorporated ferroptosis and immune gene set were utilized to develop a melanoma-related prognostic model, which could play a consistent prognostic performance in diverse data sets that could be utilized as an independent prognosis-related predictive indicator for melanoma patients.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

Tang

Wang

et al. 2022

Journal of Oncology

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Background. Melanoma development and progression are significantly influenced by ferroptosis and the immune microenvironment. However, there are no reliable biomarkers for melanoma prognosis prediction based on ferroptosis and immunological response. Methods. Ferroptosis-related genes (FRGs) were retrieved from the FerrDb website. Immune-related genes (IRGs) were collected in the ImmPort dataset. The TCGA (The Cancer Genome Atlas) and GSE65904 datasets both contained prognostic FRGs and IRGs. The model was created using multivariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and the analysis and comparison between the expression patterns of ferroptosis and immune cell infiltration were done. Last but not least, research was conducted to assess the expression and involvement of the genes in the comprehensive index of ferroptosis and immune (CIFI). Results. Two prognostic ferroptosis- and immune-related markers (PDGFRB and FOXM1) were utilized to develop a CIFI. In various datasets and patient subgroups, CIFI exhibits consistent predictive performance. The fact that CIFI is an independent prognostic factor for melanoma patients was revealed. Patients in the CIFI-high group further exhibited immune-suppressive characteristics and had elevated ferroptosis gene expression levels. The results of in vitro research point to the possibility that the PDGFRB and FOXM1 genes function as oncogenes in melanoma. Conclusion. In this study, a novel prognostic classifier for melanoma patients was developed and validated using ferroptosis and immune expression profiles.

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“…Lasso regression was performed to characterize the high frequency features ( 34 ). Then, the univariate Cox regression analysis was performed to screen out the genes with significant correlation ( p < 0.05).…”

Section: Methodsmentioning

confidence: 99%

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

et al. 2022

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BackgroundProstate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.MethodsHigh-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.ResultsA total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.ConclusionThis new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

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A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Cited by 14 publications

References 59 publications

Novel Diagnostic Biomarkers Related to Oxidative Stress and Macrophage Ferroptosis in Atherosclerosis

Novel Diagnostic Biomarkers Related to Oxidative Stress and Macrophage Ferroptosis in Atherosclerosis

Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

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