Diabetic nephropathy (DN) is the main cause of endâstage renal disease, with epithelialâmesenchymal transition (EMT) serving a key role in its initiation and progression. Nevertheless, the precise mechanisms involved remain unidentified. The present study aimed to identify the involvement of EMTârelated genes in the advancement of DN. Using the Gene Expression Omnibus database and the dbEMT 2.0 database, EMTârelated differentially expressed genes (DEGs) associated with DN were identified. Key EMTârelated genes were subjected to weighted gene coâexpression network analysis, machine learning and proteinâprotein interaction network analyses and validated against validation datasets from GEO database. Receiver operating characteristic analysis was used to assess the diagnostic performance of these hub genes. To delve into their cellular clustering in DN, singleânucleus RNA sequencing was conducted using the Kidney Integrative Transcriptomics database. Additionally, the CIBERSORT algorithm was used to determine the proportion of immune cell infiltration in DN samples. Reverse transcriptionâquantitative PCR (RTâqPCR) was used to assess the mRNA expression of fibronectin 1 (FN1) in the kidney of mice and patients with DN. After silencing FN1, the expression changes of EMT markers (Eâcadherin and vimentin) were detected by RTâqPCR. FN1 was upregulated in DN, demonstrating good diagnostic performance according to ROC analysis. FN1 was associated with infiltration of immune cells. RTâqPCR confirmed the increased expression of FN1 in the kidney of mice with DN and in the renal biopsy samples of patients with DN. After silencing FN1, the expression of Eâcadherin was upregulated, while the expression of vimentin was downregulated, indicating that EMT was inhibited. The present study identified FN1 as a diagnostic marker for DN. FN1 may serve key roles in the initiation and progression of DN by participating in EMT and upregulating various types of immune cells.
Identification and validation of epithelialâmesenchymal transitionârelated genes for diabetic nephropathy by WGCNA and machine learning.
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作者:Tang Huidi, Li Kang, Wang Xiaojie
| 期刊: | Molecular Medicine Reports | 影响因子: | 3.500 |
| 时间: | 2025 | 起止号: | 2025 Sep |
| doi: | 10.3892/mmr.2025.13614 | ||
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