Pan-cancer genetic analysis of cuproptosis and copper metabolism-related gene set

Liu, Hengrui; Tang, Tao

doi:10.3389/fonc.2022.952290

Cited by 37 publications

(19 citation statements)

References 58 publications

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“…As blood is a much more accessible clinical sample than tissues, the investigation of the plasma ORM2 levels in TNBC patients would be conducive to the development of ORM2 as a diagnostic and prognostic biomarker of TNBC. Bioinformatic analysis has been widely used to evaluate the prognostic value of genes [49][50][51][52][53][54][55], in this study, our bioinformatic analysis revealed that ORM2 was a potential prognostic biomarker for TNBC but not for non-TNBC.…”

Section: Discussionmentioning

confidence: 85%

Quercetin Suppresses TNBC Cell by Targeting ORM2

Chen

2023

Preprint

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Background Triple-negative breast cancer (TNBC) is known for its aggressive nature, and Quercetin (QUE) has shown potential anti-cancer effects. Methods We determined the IC50 of QUE for inhibiting cell viability in multiple TNBC, non-TNBC, and normal breast cell lines. We compared the expression of ORM2 in TNBC clinical samples and normal tissues. Additionally, we measured ORM2 expression in TNBC and normal breast cell lines. We determined the IC50 of QUE for inhibiting cell viability after ORM2 knockdown. An orthotopic implantation mice model was used to evaluate the treatment effect of QUE. We also conducted molecular docking and amino acid exchange validation to model the binding of QUE to ORM2. Furthermore, we performed a protein-protein interaction network analysis and GO enrichment analysis of differentially expressed genes associated with ORM2 in TNBC. Results QUE inhibited the viability of both TNBC and non-TNBC cell lines, but it was specifically associated with worse survival in TNBC patients. We observed higher expression of ORM2 in breast cancer cells compared to normal breast cells. Knockdown of ORM2 reduced the viability of TNBC cells. Treatment with QUE inhibited ORM2 expression and decreased viability in TNBC cells. In the animal model, QUE improved survival and downregulated ORM2 expression in tumors. Enrichment analysis provided insights into the potential functions of ORM2. Conclusion Our findings indicate that QUE directly inhibits TNBC cell viability through its interaction with ORM2. These results contribute to our understanding of the anti-cancer mechanisms of QUE in TNBC and highlight ORM2 as a potential therapeutic target.

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Section: Discussionmentioning

confidence: 85%

Quercetin Suppresses TNBC Cell by Targeting ORM2

Chen

2023

Preprint

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“…Some researches have found that EGR2 is the target gene of miR-25 that can promote the proliferation of cancer cells, and knocking down EGR2 will promote the proliferation and spread of tumor tissues [ 58 ]. ATP7B is a type of copper effector transporter, which is a key protein in maintaining copper metabolism and copper homeostasis in cells, and is also associated with some cancer prognostic effects [ 59 ]. Cisplatin and carboplatin are common chemotherapy drugs, and ATP7B can mediate the resistance of cancer cells to platinum-based chemotherapy drugs, helping to alleviate the stress of cancer cells, resulting in poor benefits of traditional chemotherapy for patients [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

Liu,

Miao,

Yao

et al. 2024

Aging

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Background: Thyroid cancer (THCA) is the most common endocrine malignancy having a female predominance. The insulin-like growth factor (IGF) pathway contributed to the unregulated cell proliferation in multiple malignancies. We aimed to explore the IGF-related signature for THCA prognosis. Method: The TCGA-THCA dataset was collected from the Cancer Genome Atlas (TCGA) for screening of key prognostic genes. The limma R package was applied for differentially expressed genes (DEGs) and the clusterProfiler R package was used for the Gene Ontology (GO) and KEGG analysis of DEGs. Then, the un/multivariate and least absolute shrinkage and selection operator (Lasso) Cox regression analysis was used for the establishment of RiskScore model. Receiver Operating Characteristic (ROC) analysis was used to verify the model’s predictive performance. CIBERSORT and MCP-counter algorithms were applied for immune infiltration analysis. Finally, we analyzed the mutation features and the correlation between the RiskScore and cancer hallmark pathway by using the GSEA. Result: We obtained 5 key RiskScore model genes for patient’s risk stratification from the 721 DEGs. ROC analysis indicated that our model is an ideal classifier, the high-risk patients are associated with the poor prognosis, immune infiltration, high tumor mutation burden (TMB), stronger cancer stemness and stronger correlation with the typical cancer-activation pathways. A nomogram combined with multiple clinical features was developed and exhibited excellent performance upon long-term survival quantitative prediction. Conclusions: We constructed an excellent prognostic model RiskScore based on IGF-related signature and concluded that the IGF signal pathway may become a reliable prognostic phenotype in THCA intervention.

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“…(GSEA: Hedgehog pathway, MAPK pathway,TP53 pathway, Autophagy pathway, Wnt pathway; GSVA: PI3K_AKT pathway,Wnt_BETA pathway,IL6_JAK pathway,Notch pathway,Oxidative pathway,TGF_BETA pathway) Given the impact of immune cell in ltration on the body's immune response in sepsis, where the systemic in ammatory response is the central disease mechanism, we investigated the expression levels of seven CRGs (XPO7, VTI1B, TMEM145, MTF1, ILIRN, FLOT2, CACNA1E) in the sepsis dataset in relation to immune cell correlation of in ltration abundance, along with 16 immune cells potentially regulated by cuproptosis. Numerous studies have proven that the degree of CRG expression correlates strongly with the condition of immune cell in ltration in the microenvironment of sick tissues in different forms of cancer [70][71][72][73] . Similarly, we discovered that CRGs have a role in altered immune in ltration of the organism in non-cancerous illnesses.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of cuproptosis-related genes in sepsis: A novel model based on bioinformatics screening

Jia,

Tian,

Zhang

et al. 2023

Preprint

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Background: Sepsis is a life-threatening functional disorder of the organs resulting from a dysregulated host immune response to infection and is a leading cause of death and critical illness worldwide. Genetic diagnosis combined with big data analysis of existing biomarkers has great potential in the diagnosis and prognosis prediction of sepsis, and there is an urgent need to construct prognostic models that will improve the effectiveness of treatment decisions. Methods: We used data from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database to perform a comprehensive analysis of differential gene expression profiles associated with cuproptosis in sepsis. Combining the sepsis datasets (GSE131761 and GSE54514) as test sets, a total of 208 sepsis samples and 69 normal samples were used for the analysis of cuproptosis-related differentially expressed genes (CRDEGs), weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The gene sets from the Molecular Signatures Database (MSigDB) and were used to perform GSVA (Gene Set Variation Analysis) and GSEA (Gene-set Enrichment Analysis). The prognostic performance of the hub genes in the CRDEGs diagnostic model was examined in the validation set (GSE25504 and GSE26378), and receiver operating characteristic curves (ROC) were plotted. We constructed a Cox regression model and drew a nomogram based on the final screened CRDEGs. The prognostic Calibration and decision curve analysis were used to evaluate the model. Finally, we constructed a protein-protein interaction network (PPI network) and performed ceRNA network analysis and immune cell infiltration abundance correlation analysis. Results: We obtained two sepsis disease subtype groups based on clustering analysis of differentially expressed cuproptosis hub genes (LIPT1, PDHB, MTF1, GLS, SLC31A1). GO and KEGG analyses indicated that sepsis-related cuproptosis alterations were primarily enriched in cellular copper ion homeostasis, regulation of respiratory gaseous exchange by neurological system process, suckling behavior, protein-cofactor linkage. WGCNA yielded six cuproptosis-related gene co-expression modules and 202 CRDEGs between subgroups of sepsis. A total of 32 CRDEGs were extracted additionally based on LASSO analysis calculations, of which 23 CRDEGs were included in the optimized diagnostic gene labels used for constructing Cox regression models and plotting nomogram. Finally, in the immune infiltration analysis, there was a statistically significant relationship between the abundance of immune infiltration of 16 immune cells and the expression of CRDEGs. Conclusions: The diagnostic model we constructed on CRDEGs has promising predictive power, paving the way for further exploration of the mechanisms related to cuproptosis in sepsis and providing new ideas for discovering potential biomarkers and diagnostic patterns for sepsis.

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Pan-cancer genetic analysis of cuproptosis and copper metabolism-related gene set

Cited by 37 publications

References 58 publications

Quercetin Suppresses TNBC Cell by Targeting ORM2

Quercetin Suppresses TNBC Cell by Targeting ORM2

Investigating the clinical role and prognostic value of genes related to insulin-like growth factor signaling pathway in thyroid cancer

Diagnostic value of cuproptosis-related genes in sepsis: A novel model based on bioinformatics screening

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