Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx
Background<p>Diabetic nephropathy (DN) is a major microvascular complication of diabetes, and its pathogenesis is closely associated with abnormal epigenetic regulation, particularly the silencing of tumor suppressor genes due to hypermethylation of promoter regions. This study was to investig...
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2025
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| _version_ | 1849927625232875520 |
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| author | Hui Miao (143177) |
| author2 | Yunke Zhu (13855527) Jiaqi Zheng (8551452) Chunfeng Deng (22684340) Yi Zeng (20895) Fei Tang (206071) Xi Liu (142824) |
| author2_role | author author author author author author |
| author_facet | Hui Miao (143177) Yunke Zhu (13855527) Jiaqi Zheng (8551452) Chunfeng Deng (22684340) Yi Zeng (20895) Fei Tang (206071) Xi Liu (142824) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hui Miao (143177) Yunke Zhu (13855527) Jiaqi Zheng (8551452) Chunfeng Deng (22684340) Yi Zeng (20895) Fei Tang (206071) Xi Liu (142824) |
| dc.date.none.fl_str_mv | 2025-11-25T22:01:42Z |
| dc.identifier.none.fl_str_mv | 10.3389/fgene.2025.1675592.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Table_11_Identification_and_experimental_validation_of_demethylation-related_genes_in_diabetic_nephropathy_xlsx/30715784 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics diabetic nephropathy demethylation machine Learning Biomarkers drug forecasting |
| dc.title.none.fl_str_mv | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background<p>Diabetic nephropathy (DN) is a major microvascular complication of diabetes, and its pathogenesis is closely associated with abnormal epigenetic regulation, particularly the silencing of tumor suppressor genes due to hypermethylation of promoter regions. This study was to investigate the workings of demethylation in diabetic nephropathy by applying bioinformatics methods.</p>Methods<p>DN-related datasets (GSE142153 and GSE154881) and demethylation-related genes (D-RGs) were included. Differentially expressed genes (DEGs) (DN vs. normal) were obtained. Candidate genes were obtained from the intersection of DEGs and D-RGs. To identify key genes, the Least absolute shrinkage and selection operator (LASSO) and Boruta algorithm, and expression validation were used for screening. The expression validation was used to identify biomarkers. The receiver operating characteristic (ROC) curve was subsequently utilized to assess the biomarkers’ capability to distinguish diseased from normal samples. Subsequently, a predictive nomogram was created to estimate the likelihood of developing DN. In addition, functional enrichment, immune infiltration, subcellular localization, correlation of biomarker expression with renal function, correlation for other diseases, network analysis of molecular interactions and computational drug prediction were carried out. Lastly, Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) was carried out to confirm the expression levels of biomarkers in blood samples.</p>Results<p>CXCL2 and MLF1 were determined to be biomarkers that exhibited notably elevated expression levels in the DN, in contrast to the normal group. Then the nomogram network was built, which had high prediction accuracy. Pathways most significantly enriched by CXCL2 and MLF1 included cytokine-cytokine receptor interaction and MAPK signaling pathway. Five types of immune cells were identified by immune infiltration analysis. In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. Finally drug prediction analysis found a total of 172 target drugs for CXCL2 and MLF1. RT-qPCR experiment revealed that both biomarkers showed a notable rise in the DN group relative to the normal group. RT-qPCR results revealed the DN exhibited notably increased expression levels of the two biomarkers (CXCL2 and MLF1) compared to the normal group.</p>Conclusion<p>CXCL2 and MLF1 were identified as diagnostic biomarkers for DN, offering a new reference for its treatment.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_028f0039837197eb99ab3fd0dc979f37 |
| identifier_str_mv | 10.3389/fgene.2025.1675592.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30715784 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsxHui Miao (143177)Yunke Zhu (13855527)Jiaqi Zheng (8551452)Chunfeng Deng (22684340)Yi Zeng (20895)Fei Tang (206071)Xi Liu (142824)Geneticsdiabetic nephropathydemethylationmachine LearningBiomarkersdrug forecastingBackground<p>Diabetic nephropathy (DN) is a major microvascular complication of diabetes, and its pathogenesis is closely associated with abnormal epigenetic regulation, particularly the silencing of tumor suppressor genes due to hypermethylation of promoter regions. This study was to investigate the workings of demethylation in diabetic nephropathy by applying bioinformatics methods.</p>Methods<p>DN-related datasets (GSE142153 and GSE154881) and demethylation-related genes (D-RGs) were included. Differentially expressed genes (DEGs) (DN vs. normal) were obtained. Candidate genes were obtained from the intersection of DEGs and D-RGs. To identify key genes, the Least absolute shrinkage and selection operator (LASSO) and Boruta algorithm, and expression validation were used for screening. The expression validation was used to identify biomarkers. The receiver operating characteristic (ROC) curve was subsequently utilized to assess the biomarkers’ capability to distinguish diseased from normal samples. Subsequently, a predictive nomogram was created to estimate the likelihood of developing DN. In addition, functional enrichment, immune infiltration, subcellular localization, correlation of biomarker expression with renal function, correlation for other diseases, network analysis of molecular interactions and computational drug prediction were carried out. Lastly, Real-Time Quantitative Reverse Transcription Polymerase Chain Reaction (RT-qPCR) was carried out to confirm the expression levels of biomarkers in blood samples.</p>Results<p>CXCL2 and MLF1 were determined to be biomarkers that exhibited notably elevated expression levels in the DN, in contrast to the normal group. Then the nomogram network was built, which had high prediction accuracy. Pathways most significantly enriched by CXCL2 and MLF1 included cytokine-cytokine receptor interaction and MAPK signaling pathway. Five types of immune cells were identified by immune infiltration analysis. In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. Finally drug prediction analysis found a total of 172 target drugs for CXCL2 and MLF1. RT-qPCR experiment revealed that both biomarkers showed a notable rise in the DN group relative to the normal group. RT-qPCR results revealed the DN exhibited notably increased expression levels of the two biomarkers (CXCL2 and MLF1) compared to the normal group.</p>Conclusion<p>CXCL2 and MLF1 were identified as diagnostic biomarkers for DN, offering a new reference for its treatment.</p>2025-11-25T22:01:42ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fgene.2025.1675592.s001https://figshare.com/articles/dataset/Table_11_Identification_and_experimental_validation_of_demethylation-related_genes_in_diabetic_nephropathy_xlsx/30715784CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307157842025-11-25T22:01:42Z |
| spellingShingle | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx Hui Miao (143177) Genetics diabetic nephropathy demethylation machine Learning Biomarkers drug forecasting |
| status_str | publishedVersion |
| title | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| title_full | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| title_fullStr | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| title_full_unstemmed | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| title_short | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| title_sort | Table 11_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx |
| topic | Genetics diabetic nephropathy demethylation machine Learning Biomarkers drug forecasting |