Integrated bioinformatics and clinical validation of oxidative stress‑associated genes in atrial fibrillation and chronic kidney disease

Figures

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  Figure 1. Overview of the present study design. AF, atrial fibrillation; CKD, chronic kidney disease; OS, oxidative stress; DEG, differentially expressed genes; LASSO, least absolute shrinkage and selection operator; RF, random forest; SVM, support vector machine; BTG2, BTG anti-proliferation factor 2; NPPB, natriuretic peptide B; ZFP36, ZFP36 zinc finger CCCH-type; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI-protein-protein interaction; GSVA, gene set variation analysis; RT-qPCR, reverse transcription quantitative-PCR.

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  Figure 2. Codirectional DEGs shared by AF and CKD and identification of OS-associated candidates. Volcano plots depicting DEGs between (A) patients with AF and matched controls in the dataset GSE115574 and between (D) patients with CKD and controls in the dataset GSE37171. Red and blue points correspond to significantly increased and decreased transcripts, respectively, whereas gray points denote genes whose transcriptional changes did not meet the significance criteria (log2 FC>0.263; P<0.05). Heatmaps illustrating the expression patterns of DEGs in (B) AF vs. control atrial tissue and in (E) CKD vs. control samples. Higher and lower expression levels are indicated by red and blue shading, respectively. (C) Venn diagram illustrating the intersection of upregulated and downregulated DEGs in the AF and CKD datasets. A total of 75 codirectional overlapping genes were identified and defined as AF-CKD codirectional DEGs. (F) Venn diagram summarizing the intersection between AF-CKD codirectional DEGs and OS-associated genes. A total of 28 common genes were retained and regarded as the core gene set for subsequent analyses. AF, atrial fibrillation; CKD, chronic kidney disease; DEGs,…

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  Figure 3. Enrichment profiling and interaction landscape of the core genes. (A) Overrepresentation analysis of the core gene set in GO and KEGG. GO annotations are grouped into three domains: BP, CC and MF. Bar length reflects the Gene ratio and bar color denotes the corresponding GO/KEGG category. (B) Interaction map of the core genes at the protein level generated from the Search Tool for the Retrieval of Interacting Genes/Proteins resource and rendered in Cytoscape. Each node represents an individual gene product; nodes with deeper colors indicate higher degrees of connection within the network. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, biological process; CC, cellular component; MF, molecular function; ECM, extracellular matrix.

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  Figure 4. Screening AF- and CKD-associated target genes with integrated machine learning approaches. LASSO regression analysis of candidate genes in the AF dataset. (A) The tenfold cross-validation for parameter (λ) selection showing the minimum partial likelihood deviance. (B) LASSO coefficient profiles as a function of log (λ). (C) RF analysis identifying the top-ranking AF-associated genes based on the ‘Mean Decrease Gini’ index. LASSO regression for feature selection in the CKD dataset. (D) The tenfold cross-validation curve for optimal λ selection and (E) corresponding coefficient profiles. (F) RF analysis identifying the most important CKD-associated genes ranked by the ‘Mean Decrease Gini’ index. SVM analysis of the AF dataset. (G) Number of selected features vs. classification accuracy. (H) Cross-validation error curve showing the optimal feature subset. Support vector machine-recursive feature elimination analysis of the CKD dataset. (I) Number of selected features vs. classification accuracy. (J) Cross-validation error curve indicating the optimal number of features. (K) UpSet plot showing the intersection of gene sets identified by the LASSO, RF and SVM models in the AF and CKD…

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  Figure 5. Validation and diagnostic performance evaluation of core genes and construction of nomogram models for AF and CKD. Violin plots of BTG2, NPPB and ZFP36 expression in the (A) AF (GSE115574) and (B) CKD (GSE37171) datasets. Red indicates patients with diseases, and blue indicates controls. Statistical significance is indicated as follows: *P<0.05 and ***P<0.001. ROC analyses illustrating the diagnostic performance of the three core genes across numerous (C) AF (GSE115574, GSE14975, and GSE41177) and (D) CKD (GSE37171, GSE62792, and GSE66494) datasets. The AUC values for each gene are labeled in the plots. Development and evaluation of prediction nomograms derived from BTG2, NPPB and ZFP36 in (E) the AF (EGSE115574) and (F) CKD (SE37171) datasets. The left panels display the nomogram models, the middle panels show the calibration curves for assessing the predictive consistency and the right panels present the ROC curves for evaluating the discriminatory performance of the models. ROC, receiver operating characteristic; AUC, area under the curve; BTG2, BTG anti-proliferation factor 2; NPPB, natriuretic peptide B; ZFP36, ZFP36 zinc finger CCCH-type; AF, atrial fibrillation; CKD,…

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  Figure 6. Immune infiltration analysis and regulatory network prediction of target genes in AF and CKD. Stacked bar charts depicting the estimated fractions of 22 immune cell populations inferred by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts algorithm in the (A) AF (GSE115574) and (B) CKD (GSE37171) datasets. Different colors represent distinct immune cell subtypes. Boxplots comparing the levels of infiltrating immune cells between patients and controls in the (C) AF and (D) CKD cohorts. Red and blue indicate the disease and control samples, respectively. Statistical significance is indicated as follows: ***P<0.001, **P<0.01 and *P<0.05. Heatmaps displaying the correlations between target genes (BTG2, NPPB and ZFP36) and immune cell infiltration levels in the (E) AF and (F) CKD datasets. Red denotes a positive correlation and blue denotes a negative correlation. (G) Interaction network between target genes TFs predicted using the NetworkAnalyst platform. (H) An integrated regulatory map associating target genes, TFs, and microRNAs was generated in NetworkAnalyst. Nodes represent genes, TFs, and miRNAs, while edges reflect their predicted regulatory…

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  Figure 7. Screening candidate therapeutic agents using CMap. (A) Heatmap illustrating the ten most strongly enriched small-molecule compounds identified by CMap on the basis of the expression pattern of the target genes. The x-axis shows the numerous cell lines included in the CMap resource and the y-axis lists the corresponding compounds. The colors indicate connectivity scores, with red indicating a positive association and blue indicating a negative association to the gene signature associated with the disease. (B) Summary table of the 10 candidate drugs, including the Pert_id, compound name and pharmacological description of each. The identified compounds included HDAC inhibitors, CDK inhibitors, proteasome inhibitors and other signaling modulators, suggesting their potential therapeutic effects in atrial fibrillation and chronic kidney disease. (C) The two-dimensional molecular structures of predicted compounds (vorinostat, MG-132, BNTX, AZD-5438, R-547, alvocidib, 5-iodotubercidin, bortezomib, Genz-644282 and AT-7519), showing their chemical configurations. cMAP, Connectivity Map; HDAC, histone deacetylase; Pert_id, perturbation ID.

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  Figure 8. GSVA-based functional profiling of target genes in AF and CKD datasets. GSVA enrichment results for (A) ZFP36, (B) BTG2 and (C) NPPB in the AF dataset (GSE115574). The bars represent the top enriched KEGG pathways that were significantly correlated with each gene. Red bars correspond to pathways that were positively associated with gene expression, whereas blue bars denote pathways showing negative associations. GSVA enrichment results for (D) ZFP36, (E) BTG2 and (F) NPPB in the CKD dataset (GSE37171). The length of each bar reflects the enrichment score for the corresponding KEGG pathway. Red and blue, respectively, indicate pathways that were positively and negatively associated with the corresponding genes. GSVA, gene set variation analysis; AF, atrial fibrillation; CKD, chronic kidney disease; BTG2, BTG anti-proliferation factor 2; NPPB, natriuretic peptide B; ZFP36, ZFP36 zinc finger CCCH-type; KEGG, Kyoto Encyclopedia of Genes and Genomes.

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  Figure 9. Validation of BTG2, ZFP36, and NT-proBNP expression levels in peripheral blood. Quantitative PCR analysis of (A) BTG2 and (B) ZFP36 mRNA expression levels in human whole blood from the control, AF, CKD and AF + CKD groups. The data are presented as relative mRNA levels normalized to those in the internal control. (C) Serum concentrations of NT-proBNP, encoded by NPPB, measured in the same four groups. Statistical significance is indicated as follows: *P<0.05, **P<0.01, ***P<0.001 and ****P<0.0001. BTG2, BTG anti-proliferation factor 2; ZFP36, ZFP36 zinc finger CCCH-type; NPPB, natriuretic peptide B; AF, atrial fibrillation; CKD, chronic kidney disease.