Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis
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 be...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1115091/full |
| _version_ | 1797851006896177152 |
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| author | Qingyuan Lin Qingyuan Lin Jinchao Zhu Jinchao Zhu Jun Chen Shouqiang Jia Shengdong Nie |
| author_facet | Qingyuan Lin Qingyuan Lin Jinchao Zhu Jinchao Zhu Jun Chen Shouqiang Jia Shengdong Nie |
| author_sort | Qingyuan Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T19:09:46Z |
| format | Article |
| id | doaj.art-f018b436ebb340959246a57899743b44 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-09T19:09:46Z |
| publishDate | 2023-04-01 |
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| spelling | doaj.art-f018b436ebb340959246a57899743b442023-04-07T04:29:21ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2023-04-011010.3389/fmolb.2023.11150911115091Significance of cuproptosis- related genes in the diagnosis and classification of psoriasisQingyuan Lin0Qingyuan Lin1Jinchao Zhu2Jinchao Zhu3Jun Chen4Shouqiang Jia5Shengdong Nie6School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Pathology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Pathology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaDepartment of Imaging, Jinan People’s Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, ChinaCuproptosis 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.https://www.frontiersin.org/articles/10.3389/fmolb.2023.1115091/fullpsoriasiscuproptosisdiagnostic markersdrug sensitivity analysisprincipal component analysis |
| spellingShingle | Qingyuan Lin Qingyuan Lin Jinchao Zhu Jinchao Zhu Jun Chen Shouqiang Jia Shengdong Nie Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis Frontiers in Molecular Biosciences psoriasis cuproptosis diagnostic markers drug sensitivity analysis principal component analysis |
| title | Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis |
| title_full | Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis |
| title_fullStr | Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis |
| title_full_unstemmed | Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis |
| title_short | Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis |
| title_sort | significance of cuproptosis related genes in the diagnosis and classification of psoriasis |
| topic | psoriasis cuproptosis diagnostic markers drug sensitivity analysis principal component analysis |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2023.1115091/full |
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