BACKGROUND: Acute kidney injury is a common clinical problem with no sensitive and specific diagnostic biomarkers and definitive treatments. The underlying molecular mechanisms of acute kidney injury are unclear. Therefore, it is pivotal to explore the underlying mechanisms and screen for novel diagnostic biomarkers, and therapeutic targets. METHODS: The present study identified 15 hub genes by WGCNA analysis. LASSO-based logistic regression analysis was used to select key features and construct a diagnostic model of AKI. In addition, GO and KEGG analyses were performed and TF-mRNA and miRNA-mRNA network analysis and immune infiltration analysis of hub genes were performed to reveal the underlying mechanisms of AKI. RESULTS: A diagnostic model was constructed by LASSO-based logistic regression analysis and was validated by RT-qPCR based on 15 hub genes. GO and KEGG analyses revealed DEGs were enriched in oxidation-reduction process, cell adhesion, proliferation, migration, and metabolic process. The enriched TFs were BRD2, EP300, ETS1, MYC, SPI1, and ZNF263. The enriched miRNAs were miR-181c-5p, miR-218-5p, miR-485-5p, miR-532-5p and miR-6884-5p. The immune infiltration analysis showed that Macrophages M2 was decreasing significantly revealing a protective factor for further AKI treatment. CONCLUSIONS: The present study identified 15 hub genes based on WGCNA. Development and validation of a potentially diagnostic model based on 15 hub genes. In addition, exploring the interaction between transcriptional factors and 15 hub genes, and miRNA-mRNA relationship pairs. Furthermore, immune infiltration analysis was performed by analyzing gene expression profiles of AKI. Our study provides some basis for further experimental studies.
Comprehensive analysis of fifteen hub genes to identify a promising diagnostic model, regulated networks, and immune cell infiltration in acute kidney injury.
对 15 个枢纽基因进行综合分析,以确定急性肾损伤中具有前景的诊断模型、调控网络和免疫细胞浸润
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作者:Sun Tao, Cao Ying, Huang Tiancha, Sang Yiwen, Dai Yibei, Tao Zhihua
| 期刊: | Journal of Clinical Laboratory Analysis | 影响因子: | 2.900 |
| 时间: | 2022 | 起止号: | 2022 Nov;36(11):e24709 |
| doi: | 10.1002/jcla.24709 | 研究方向: | 细胞生物学 |
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