Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv
Background<p>Diabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to explore the key role of metabolic reprogramming (MR) in the pathogenesis of DN.</p>Met...
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2025
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| _version_ | 1852017191571947520 |
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| author | Shan He (121220) |
| author2 | Yi Wei Chen (21684728) Jian Ye (19265) Yu Wang (12152) Qin Kai Chen (21684734) Si Yi Liu (21684737) |
| author2_role | author author author author author |
| author_facet | Shan He (121220) Yi Wei Chen (21684728) Jian Ye (19265) Yu Wang (12152) Qin Kai Chen (21684734) Si Yi Liu (21684737) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shan He (121220) Yi Wei Chen (21684728) Jian Ye (19265) Yu Wang (12152) Qin Kai Chen (21684734) Si Yi Liu (21684737) |
| dc.date.none.fl_str_mv | 2025-08-29T05:44:54Z |
| dc.identifier.none.fl_str_mv | 10.3389/fcell.2025.1630708.s010 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_7_Investigating_the_metabolic_reprogramming_mechanisms_in_diabetic_nephropathy_a_comprehensive_analysis_using_bioinformatics_and_machine_learning_csv/30008443 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cell Biology GEO database diabetic nephropathy metabolic reprogramming bioinformatics qRT-PCR |
| dc.title.none.fl_str_mv | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Diabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to explore the key role of metabolic reprogramming (MR) in the pathogenesis of DN.</p>Methods<p>In our study, three transcriptome datasets (GSE30528, GSE30529, and GSE96804) were sourced from the Gene Expression Omnibus (GEO) database. These datasets were integrated for batch effect correction and subsequently subjected to differential expression analysis to identify differentially expressed genes (DEGs) between DN and control samples. The identified DEGs were cross-referenced with genes associated with MR to derive MR associated differentially expressed genes (MRRDEGs). These MRRDEGs underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To identify key genes and develop diagnostic models, four machine learning algorithms were employed in conjunction with weighted gene co-expression network analysis (WGCNA) and the protein interaction tool CytoHubba. Gene set enrichment analysis (GSEA) and CIBERSORT analysis were conducted on the key genes to assess immune cell infiltration in DN. Additionally, a competitive endogenous RNA (ceRNA) network was constructed using the key genes. Finally, the expression levels of core genes in human samples were validated through quantitative real-time PCR (qRT-PCR).</p>Results<p>We identified 256 MRRDEGs, highlighting metabolic and inflammatory pathways in DN. KEGG analysis linked these genes to the MAPK signaling pathway, suggesting its key role in DN. Six key genes were pinpointed using WGCNA, PPI, and machine learning, with their diagnostic value confirmed by ROC analysis. CIBERSORT revealed a strong link between these genes and immune cell infiltration, indicating the immune response’s role in DN. GSEA showed these genes’ involvement in inflammatory and metabolic processes. A ceRNA network was predicted to clarify gene regulation. qRT-PCR confirmed the expression patterns of CXCR2, NAMPT, and CUEDC2, aligning with bioinformatics results.</p>Conclusion<p>Through bioinformatics analysis, a total of six potential MRRDEGs were identified, among which CUEDC2, NAMPT, CXCR2 could serve as potential biomarkers.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_1a7e00f3e95beb2ca0a01068d8bac3d0 |
| identifier_str_mv | 10.3389/fcell.2025.1630708.s010 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30008443 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csvShan He (121220)Yi Wei Chen (21684728)Jian Ye (19265)Yu Wang (12152)Qin Kai Chen (21684734)Si Yi Liu (21684737)Cell BiologyGEO databasediabetic nephropathymetabolic reprogrammingbioinformaticsqRT-PCRBackground<p>Diabetic nephropathy (DN) is a common complication of diabetes, characterized by damage to renal tubules and glomeruli, leading to progressive renal dysfunction. The aim of our study is to explore the key role of metabolic reprogramming (MR) in the pathogenesis of DN.</p>Methods<p>In our study, three transcriptome datasets (GSE30528, GSE30529, and GSE96804) were sourced from the Gene Expression Omnibus (GEO) database. These datasets were integrated for batch effect correction and subsequently subjected to differential expression analysis to identify differentially expressed genes (DEGs) between DN and control samples. The identified DEGs were cross-referenced with genes associated with MR to derive MR associated differentially expressed genes (MRRDEGs). These MRRDEGs underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. To identify key genes and develop diagnostic models, four machine learning algorithms were employed in conjunction with weighted gene co-expression network analysis (WGCNA) and the protein interaction tool CytoHubba. Gene set enrichment analysis (GSEA) and CIBERSORT analysis were conducted on the key genes to assess immune cell infiltration in DN. Additionally, a competitive endogenous RNA (ceRNA) network was constructed using the key genes. Finally, the expression levels of core genes in human samples were validated through quantitative real-time PCR (qRT-PCR).</p>Results<p>We identified 256 MRRDEGs, highlighting metabolic and inflammatory pathways in DN. KEGG analysis linked these genes to the MAPK signaling pathway, suggesting its key role in DN. Six key genes were pinpointed using WGCNA, PPI, and machine learning, with their diagnostic value confirmed by ROC analysis. CIBERSORT revealed a strong link between these genes and immune cell infiltration, indicating the immune response’s role in DN. GSEA showed these genes’ involvement in inflammatory and metabolic processes. A ceRNA network was predicted to clarify gene regulation. qRT-PCR confirmed the expression patterns of CXCR2, NAMPT, and CUEDC2, aligning with bioinformatics results.</p>Conclusion<p>Through bioinformatics analysis, a total of six potential MRRDEGs were identified, among which CUEDC2, NAMPT, CXCR2 could serve as potential biomarkers.</p>2025-08-29T05:44:54ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fcell.2025.1630708.s010https://figshare.com/articles/dataset/Data_Sheet_7_Investigating_the_metabolic_reprogramming_mechanisms_in_diabetic_nephropathy_a_comprehensive_analysis_using_bioinformatics_and_machine_learning_csv/30008443CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300084432025-08-29T05:44:54Z |
| spellingShingle | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv Shan He (121220) Cell Biology GEO database diabetic nephropathy metabolic reprogramming bioinformatics qRT-PCR |
| status_str | publishedVersion |
| title | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| title_full | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| title_fullStr | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| title_full_unstemmed | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| title_short | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| title_sort | Data Sheet 7_Investigating the metabolic reprogramming mechanisms in diabetic nephropathy: a comprehensive analysis using bioinformatics and machine learning.csv |
| topic | Cell Biology GEO database diabetic nephropathy metabolic reprogramming bioinformatics qRT-PCR |