Fig 1.
Screening of DEGs and weighted gene co-expression network analysis.
(A) Volcano plot depicting the results of differential gene expression analysis. Orange represents up-regulated DEGs, and blue represents down-regulated DEGs. (B) The upper figure shows the determination of the optimal soft threshold in the gene co-expression network, and the lower figure shows the mean connectivity for different soft thresholds. Scale-free distribution is a concept in network science that describes the fact that the node degree (number of connections) of certain networks follows a power law distribution, R2 value is the coefficient of determination to assess the superiority of the fitted model (scale-free R2 = 0.85, power = 24). (C-D) Cluster dendrogram of gene modules with different colors. (E) Heatmap of the correlation between gene modules and GSE139061 samples, the numbers in the modules represent the correlation coefficients and p-values.
Fig 2.
Functional annotation and PPI analysis of turquoise module genes.
(A-D) GO and KEGG enrichment analysis of turquoise module genes. The X-axis represents the Gene Ratio; the Y-axis represents the GO Term or enriched pathway; the size of the dots represents the odds ratio; the color of the dots represents the level of the p-value. (E) PPI network of turquoise module genes using the MCODE algorithm, with nodes represent proteins or protein domains, while edges represent interactions between these proteins.
Fig 3.
Expression analysis and diagnostic potential of 26 candidate genes in the AKI Turquoise module.
(A) Expression analysis of 26 candidate genes in AKI samples compared to control samples using the GSE139061 dataset. (B) Validation of the expression patterns of 26 candidate genes in AKI samples using the GSE30718 dataset. (C-F) ROC curves describing the diagnostic potential of candidate genes based on the GSE139061 dataset (C: AFG3L2, D: COX7A2L, E: IDH2, F: TUFM). (G-J) ROC curves illustrating the diagnostic potential of candidate genes based on the GSE30718 dataset (G: AFG3L2, H: COX7A2L, I: IDH2, J: TUFM). *p<0.05. ** p<0.01, *** p<0.01.
Fig 4.
COX7A2L inhibits H/R-treated NRK-52E cell apoptosis and promotes cell proliferation.
(A) qRT-PCR detects the expression of COX7A2L in NRK-52E cells after H/R treatment. (B-C) qRT-PCR and WB detected the overexpression efficiency and knockdown efficiency of COX7A2L. (D) Flow cytometry to detect the apoptotic effects of COX7A2L knockdown and overexpression on H/R-treated NRK-52E cells. (E) WB analysis of the effect of COX7A2L on the expression of pro-apoptotic protein Bax and anti-apoptotic protein Bcl-2 in H/R-treated cells. (F) CCK-8 analysis of the effects of COX7A2L knockdown or overexpression on cell proliferation. *p<0.05.
Fig 5.
TCF4-mediated regulation of COX7A2L expression.
(A) JASPAR database analysis reveals putative TCF4 binding sites in the promoter region of differentially expressed genes. (B) WB analysis of TCF4 protein expression in H/R-treated NRK-52E cells. (C) WB analysis of TCF4 overexpression efficiency in H/R-treated NRK-52E cells. (D) Luciferase reporter assay results in HEK 293T cells. Constructs harboring either the wild-type (WT) or mutant (MT) COX7A2L promoter sequences were utilized. (E) ChIP assay of TCF4 binding to COX7A2L promoter, IgG as negative control. *p<0.05, ***p<0.001.
Fig 6.
Effects of TCF4 overexpression and COX7A2L Silencing on apoptosis, proliferation, and Wnt/β-catenin signaling pathway in NRK-52E cells.
(A-B) Flow cytometry analysis of apoptotic changes in NRK-52E cells after the following groups of treatments: vector, over-TCF4, over-TCF4+si-NC, over-TCF4+si-COX7A2L. (C) CCK-8 assay assesses NRK-52E cell proliferation in the following groups: vector, over-TCF4, over-TCF4+si-NC, over-TCF4+si-COX7A2L. (D-E) WB analysis of the regulation of apoptosis-related proteins (Bcl-2, Bax, caspase3) and Wnt/β-catenin signaling pathway proteins in H/R-treated NRK-52E cells by the following groups: vector, over-TCF4, over -TCF4+si-NC, over-TCF4+si-COX7A2L. *p<0.05.