Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation
BackgroundDiabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differenti...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1486251/full |
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| author | Han Chen Han Chen Enguang Chen Enguang Chen Ting Cao Feifan Feng Min Lin Xuan Wang Yu Xu |
| author_facet | Han Chen Han Chen Enguang Chen Enguang Chen Ting Cao Feifan Feng Min Lin Xuan Wang Yu Xu |
| author_sort | Han Chen |
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| description | BackgroundDiabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease’s pathogenesis and potential diagnostic tools.MethodsDR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms—LASSO regression, Random Forest, and SVM-RFE—were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments.ResultsDifferential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior.ConclusionThis study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and identifies potential biomarkers and therapeutic targets for the disease. |
| format | Article |
| id | doaj-art-f62d2ac2fa3a41e3aa3223d6dfd162f3 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-f62d2ac2fa3a41e3aa3223d6dfd162f32024-12-04T05:10:24ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-12-011510.3389/fimmu.2024.14862511486251Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validationHan Chen0Han Chen1Enguang Chen2Enguang Chen3Ting Cao4Feifan Feng5Min Lin6Xuan Wang7Yu Xu8Department of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaShanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaShanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Ophthalmology, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaBackgroundDiabetic retinopathy (DR) is a major complication of diabetes, leading to severe vision impairment. Understanding the molecular mechanisms, particularly PANoptosis, underlying DR is crucial for identifying potential biomarkers and therapeutic targets. This study aims to identify differentially expressed PANoptosis-related genes (DE-PRGs) in DR, offering insights into the disease’s pathogenesis and potential diagnostic tools.MethodsDR datasets were obtained from the Gene Expression Omnibus (GEO) database, while PANoptosis-related genes were sourced from the GeneCards database. Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. Three machine learning algorithms—LASSO regression, Random Forest, and SVM-RFE—were employed to identify hub genes. A diagnostic nomogram was constructed and its performance assessed via ROC analysis. The CIBERSORT algorithm analyzed immune cell infiltration. Hub genes were validated through RT-qPCR, Western blotting, immunohistochemistry, and publicly available datasets. Additionally, the impact of FASN and PLSCR3 knockdown on HUVECs behavior was validated through in vitro experiments.ResultsDifferential expression analysis identified 1,418 DEGs in the GSE221521 dataset, with 39 overlapping DE-PRGs (29 upregulated, 10 downregulated). Functional enrichment indicated that DE-PRGs are involved in apoptosis, signal transduction, and inflammatory responses, with key pathways such as MAPK and TNF signaling. Machine learning algorithms identified six PANoptosis-related hub genes (BEX2, CASP2, CD36, FASN, OSMR, and PLSCR3) as potential biomarkers. A diagnostic nomogram based on these hub genes showed high diagnostic accuracy. Immune cell infiltration analysis revealed significant differences in immune cell patterns between control and DR groups, especially in Activated CD4 Memory T Cells and Monocytes. Validation confirmed the diagnostic efficiency and expression patterns of the PANoptosis-related hub genes, supported by in vitro and the GSE60436 dataset analysis. Furthermore, experiments demonstrated that knocking down FASN and PLSCR3 impacted HUVECs behavior.ConclusionThis study provides valuable insights into the molecular mechanisms of DR, particularly highlighting PANoptosis-related pathways, and identifies potential biomarkers and therapeutic targets for the disease.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1486251/fulldiabetic retinopathyPANoptosismachine learningbioinformatics analysisdifferentially expressed genesbiomarkers |
| spellingShingle | Han Chen Han Chen Enguang Chen Enguang Chen Ting Cao Feifan Feng Min Lin Xuan Wang Yu Xu Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation Frontiers in Immunology diabetic retinopathy PANoptosis machine learning bioinformatics analysis differentially expressed genes biomarkers |
| title | Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation |
| title_full | Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation |
| title_fullStr | Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation |
| title_full_unstemmed | Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation |
| title_short | Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation |
| title_sort | integrative analysis of panoptosis related genes in diabetic retinopathy machine learning identification and experimental validation |
| topic | diabetic retinopathy PANoptosis machine learning bioinformatics analysis differentially expressed genes biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1486251/full |
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