Cuproptosis Combined with lncRNAs Predicts the Prognosis and Immune Microenvironment of Breast Cancer

Zhang, Liangping; Zhang, Yujun; Bao, Jianhang; Gao, Wenshuo; Wang, Dong; Pan, Hao

doi:10.1155/2022/5422698

Cited by 11 publications

(11 citation statements)

References 46 publications

(50 reference statements)

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“…In these two studies, 11 and 37 CRLs were used to construct prognostic models of breast cancer, while only four CRLs were used in this study. It is worth noting that fewer CRLs were used as predictors in this study, but the accuracy of prediction was not much different from that in studies [ 13 , 14 ]. The comparison of the three models are shown in Table S4 .…”

Section: Discussionmentioning

confidence: 69%

“…Finally, we construct a nomogram with high accuracy to help the diagnosis of breast cancer. Cuproptosis-related lncRNAs’ prognostic model has only been reported in a few studies [ 13 , 14 ]. In these two studies, 11 and 37 CRLs were used to construct prognostic models of breast cancer, while only four CRLs were used in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Nonetheless, the role of CRLs in BRCA is still ambiguous. In the recent reports on breast cancer, only two articles [ 13 , 14 ] used cuproptosis-related lncRNAs to construct prognostic models. This suggest that more prognostic models may be needed to explain the role of CRLs in breast cancer.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis

Deng

Zhang

et al. 2023

Diagnostics

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Breast cancer (BRCA) has an undesirable prognosis and is the second most common cancer among women after lung cancer. A novel mechanism of programmed cell death called cuproptosis is linked to the development and spread of tumor cells. However, the function of cuproptosis in BRCA remains unknown. To this date, no studies have used machine learning methods to screen for characteristic genes to explore the role of cuproptosis-related genes (CRGs) in breast cancer. Therefore, 14 cuproptosis-related characteristic genes (CRCGs) were discovered by the feature selection of 39 differentially expressed CRGs using the three machine learning methods LASSO, SVM-RFE, and random forest. Through the PPI network and immune infiltration analysis, we found that PRNP was the key CRCG. The miRTarBase, TargetScan, and miRDB databases were then used to identify hsa-miR-192-5p and hsa-miR-215-5p as the upstream miRNA of PRNP, and the upstream lncRNA, CARMN, was identified by the StarBase database. Thus, the mRNA PRNP/miRNA hsa-miR-192-5p and hsa-miR-215-5p/lncRNA CARMN ceRNA network was constructed. This ceRNA network, which has not been studied before, is extremely innovative. Furthermore, four cuproptosis-related lncRNAs (CRLs) were screened in TCGA-BRCA by univariate Cox, LASSO, and multivariate Cox regression analysis. The risk model was constructed by using these four CRLs, and the risk score = C9orf163 * (1.8365) + PHC2-AS1 * (−2.2985) + AC087741.1 * (−0.9504) + AL109824.1 * (0.6016). The ROC curve and C-index demonstrated the superior predictive capacity of the risk model, and the ROC curve demonstrated that the AUC of 1-, 3-, and 5-year OS in all samples was 0.721, 0.695, and 0.633, respectively. Finally, 50 prospective sensitive medicines were screened with the pRRophetic R package, among which 17-AAG may be a therapeutic agent for high-risk patients, while the other 49 medicines may be suitable for the treatment of low-risk patients. In conclusion, our study constructs a new ceRNA network and a novel risk model, which offer a theoretical foundation for the treatment of BRCA and will aid in improving the prognosis of BRCA.

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Section: Discussionmentioning

confidence: 69%

Section: Discussionmentioning

confidence: 99%

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Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis

Deng

Zhang

et al. 2023

Diagnostics

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“…Based on previous literature, 16 genes associated with cuproptosis 8 , 15 , 16 , including NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, CDKN2A, DBT and DLST, were collected. A gene analysis was performed using the R package "limma" (version 3.52.4) to compare patients with PCOS with healthy controls 17 .…”

Section: Methodsmentioning

confidence: 99%

Identification and immune features of cuproptosis-related molecular clusters in polycystic ovary syndrome

et al. 2023

Sci Rep

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Polycystic ovary syndrome (PCOS), a common reproductive endocrine disease, has clinically heterogeneous characteristics. Recently, cuproptosis causes several diseases by killing cells. Hence, we aimed to explore cuproptosis-related molecular clusters in PCOS and construct a prediction model. Based on the GSE5090, GSE43264, GSE98421, and GSE124226 datasets, an analysis of cuproptosis regulators and immune features in PCOS was conducted. In 25 cases of PCOS, the molecular clusters of cuproptosis-related genes and the immune cell infiltration associated with PCOS were investigated. Weighted gene co-expression network analysis was used to identify differentially expressed genes within clusters. Next, we compared the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting for deciding the optimum machine model. Validation of the predictive effectiveness was accomplished through nomogram, calibration curve, decision curve analysis, and using other two datasets. PCOS and non-PCOS controls differed in the dysregulation of cuproptosis-related genes and the activation of immunoreaction. Two cuproptosis-related molecular clusters associated with PCOS were identified. Significant heterogeneity was noted in immunity between the two clusters based on the analysis of immune infiltration. The immune-related pathways related to cluster-specific differentially expressed genes in Cluster1 were revealed by functional analysis. With a relatively low residual error and root mean square error and a higher area under the curve (1.000), the support vector machine model demonstrated optimal discriminative performance. An ultimate 5-gene-based support vector machine model was noted to perform satisfactorily in the other two validation datasets (area under the curve = 1.000 for both). Moreover, the nomogram, calibration curve, and decision curve analysis showed that PCOS subtypes can be accurately predicted. Our study results helped demonstrate a comprehensive understanding of the complex relationship between cuproptosis and PCOS and establish a promising prediction model for assessing the risk of cuproptosis in patients with PCOS.

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“…The patients histological features including: Histology of uninvolved, clinically symptomless areas of skin is normal. Differential analysis of 19 CRGs (NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH, DLST) between psoriatic and non-psoriatic patients was performed to identify the significantly expressed genes (Tsvetkov et al, 2022;Zhang et al, 2022). These genes were then selected as key CRGs.…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis

Lin

Zhu

Chen

et al. 2023

Front. Mol. Biosci.

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Cuproptosis is a novel form of cell death linked to mitochondrial metabolism and is mediated by protein lipoylation. The mechanism of cuproptosis in many diseases, such as psoriasis, remains unclear. In this study, signature diagnostic markers of cuproptosis were screened by differential analysis between psoriatic and non-psoriatic patients. The differentially expressed cuproptosis-related genes (CRGs) for patients with psoriasis were screened using the GSE178197 dataset from the gene expression omnibus database. The biological roles of CRGs were identified by GO and KEGG enrichment analyses, and the candidates of cuproptosis-related regulators were selected from a nomogram model. The consensus clustering approach was used to classify psoriasis into clusters and the principal component analysis algorithms were constructed to calculate the cuproptosis score. Finally, latent diagnostic markers and drug sensitivity were analyzed using the pRRophetic R package. The differential analysis revealed that CRGs (MTF1, ATP7B, and SLC31A1) are significantly expressed in psoriatic patients. GO and KEGG enrichment analyses showed that the biological functions of CRGs were mainly related to acetyl-CoA metabolic processes, the mitochondrial matrix, and acyltransferase activity. Compared to the machine learning method used, the random forest model has higher accuracy in the occurrence of cuproptosis. However, the decision curve of the candidate cuproptosis regulators analysis showed that patients can benefit from the nomogram model. The consensus clustering analysis showed that psoriasis can be grouped into three patterns of cuproptosis (clusterA, clusterB, and clusterC) based on selected important regulators of cuproptosis. In advance, we analyzed the immune characteristics of patients and found that clusterA was associated with T cells, clusterB with neutrophil cells, and clusterC predominantly with B cells. Drug sensitivity analysis showed that three cuproptosis regulators (ATP7B, SLC31A1, and MTF1) were associated with the drug sensitivity. This study provides insight into the specific biological functions and related mechanisms of CRGs in the development of psoriasis and indicates that cuproptosis plays a non-negligible role. These results may help guide future treatment strategies for psoriasis.

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Cuproptosis Combined with lncRNAs Predicts the Prognosis and Immune Microenvironment of Breast Cancer

Cited by 11 publications

References 46 publications

Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis

Construct ceRNA Network and Risk Model of Breast Cancer Using Machine Learning Methods under the Mechanism of Cuproptosis

Identification and immune features of cuproptosis-related molecular clusters in polycystic ovary syndrome

Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis

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