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. 2023 Feb 6:14:1111976.
doi: 10.3389/fgene.2023.1111976. eCollection 2023.

Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis

Peng Yan  1 , Ben Ke  1 , Jianling Song  1 , Xiangdong Fang  1
Affiliations

Identification of immune-related molecular clusters and diagnostic markers in chronic kidney disease based on cluster analysis

Peng Yan et al. Front Genet. .

Abstract

Background: Chronic kidney disease (CKD) is a heterogeneous disease with multiple etiologies, risk factors, clinical manifestations, and prognosis. The aim of this study was to identify different immune-related molecular clusters in CKD, their functional immunological properties, and to screen for promising diagnostic markers. Methods: Datasets of 440 CKD patients were obtained from the comprehensive gene expression database. The core immune-related genes (IRGs) were identified by weighted gene co-expression network analysis. We used unsupervised clustering to divide CKD samples into two immune-related subclusters. Then, functional enrichment analysis was performed for differentially expressed genes (DEGs) between clusters. Three machine learning methods (LASSO, RF, and SVM-RFE) and Venn diagrams were applied to filter out 5 significant IRGs with distinguished subtypes. A nomogram diagnostic model was developed, and the prediction effect was verified using calibration curve, decision curve analysis. CIBERSORT was applied to assess the variation in immune cell infiltration among clusters. The expression levels, immune characteristics and immune cell correlation of core diagnostic markers were investigated. Finally, the Nephroseq V5 was used to assess the correlation among core diagnostic markers and renal function. Results: The 15 core IRGs screened were differentially expressed in normal and CKD samples. CKD was classified into two immune-related molecular clusters. Cluster 2 is significantly enriched in biological functions such as leukocyte adhesion and regulation as well as immune activation, and has a severe immune prognosis compared to cluster 1. A nomogram diagnostic model with reliable prediction of immune-related clusters was developed based on five signature genes. The core diagnostic markers LYZ, CTSS, and ISG20 were identified as playing an important role in the immune microenvironment and were shown to correlate meaningfully with immune cell infiltration and renal function. Conclusion: Our study identifies two subtypes of CKD with distinct immune gene expression patterns and provides promising predictive models. Along with the exploration of the role of three promising diagnostic markers in the immune microenvironment of CKD, it is anticipated to provide novel breakthroughs in potential targets for disease treatment.

Keywords: IRGs; biomarker; chronic kidney disease; immune; machine learning; molecular clusters.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Work flowchart of the study.
FIGURE 2
FIGURE 2
Identification of core IRGs in CKD. (A, B) PCA results of sample clustering before (A) and after (B) batch calibration. (C) Filtering of soft thresholds. (D) Cluster trees of co-expressing genes. (E) Division of gene modules, heat map of the relationship between building blocks and characteristics. (F) Representative heatmap of 15 differentially expressed IRGs. (G) Diagram of the gene relationship network for 15 IRGs with differential expression. (H) Representative correlation diagram of 15 differentially expressed IRGs. Blue is a positive correlation and red is a negative correlation.
FIGURE 3
FIGURE 3
Analysis of GSEA enrichment and immunoinfiltration among the normal and CKD patients (A) Six enriched KEGG pathways in normal samples. (B) Six enriched KEGG pathways in CKD samples. (C) Barplot of the relative frequency of 22 different immune cell infiltration in normal and CKD samples. (D) Vioplot of the difference of 22 different immune cells in normal and CKD samples.
FIGURE 4
FIGURE 4
Unsupervised consensus clustering in CKD samples based on IRGs. (A) Consensus clustering matrix displaying the two CKD sample clusters with k = 2. (B) Cumulative distributive function for k = 2 to 9. (C) Delta graph displaying the change in the CDF curve’s area from k = 2 to 9. (D) PCA scatter plot based on the results of cluster analysis. (E, F) Heatmap (E) and box plots (F) of 15 IRGs among the two clusters. ***p < 0.001; **p < 0.01; and *p < 0.05.
FIGURE 5
FIGURE 5
Identification of the DEGs and enrichment analysis between the two molecular subtypes.(A) Volcano plot of the DEGs among cluster one and 2. (B, C) GO analysis of upregulated DEGs in cluster 1 (B) and cluster 2 (C). (D, E) KEGG analysis of the upregulated DEGs in cluster 1 (D) and cluster 2 (E).
FIGURE 6
FIGURE 6
Identification of diagnostic biomarkers from subtypes using machine learning algorithms. (A) The LASSO coefficient profiles analysis. (B) Selecting the appropriate lambda value for a LASSO regression model. (C) Random forest trees constructed by cross-validation and gene ranking by importance score. (D) Estimation of 10-fold cross-validation error using SVM-RFE method. (E) Venn plot illustrating the key genes among LASSO, RF, and SVM-RFE.
FIGURE 7
FIGURE 7
Construction of diagnostic Nomogram model. (A) Nomogram for predicting risk of CKD subtypes based on 5 key IRGs. (B, C) Calibration curve (B) and DCA (C) to estimate the predictive efficiency of the Nomogram model. (D) Clinical impact curves (CIC) to estimate the clinical validity of Nomogram model based on DCA curve. (E) ROC curves to evaluate the discrimination ability.
FIGURE 8
FIGURE 8
PPI network construction of immune-related DEGs among subtypes. Nodes with rose-colored outlines represent key candidate genes, green nodes (negative logFC) represent genes upregulated in Cluster1, and red nodes (positive logFC) represent genes upregulated in Cluster2. FC, fold change.
FIGURE 9
FIGURE 9
Validate clinical diagnostic capabilities of key biomarkers. (A, B) Expression levels (A) and diagnostic potency (B) of key genes in the models in the four combined datasets. (C, D) Expression levels (C) and diagnostic potency (D) of key genes in the models in the dataset GSE66494.
FIGURE 10
FIGURE 10
Immune cell infiltration differences among clusters. (A) Immune microenvironment of cluster1 and cluster2. (B) Box plots showing variation in immune cell infiltration among clusters. (C) Relevance heatmap of immune cells with disparities. (D) Estimated Immunoscore among subtypes.
FIGURE 11
FIGURE 11
Relationship between three key diagnostic biomarkers and immune cell infiltration. (A) Visualization of immune cells or pathways in relation to three biomarkers by GSVA. (B–D) Correlation between immune cell infiltration and (B) CTSS, (C) LYZ, (D) ISG20 gene expression. ***p < 0.001; **p < 0.01; and*p < 0.05.
FIGURE 12
FIGURE 12
Correlation of IRG-DEGs with immune-related genes. (A–C) Correlation of IRG-DEGs with HLA (A), immune checkpoint genes (B), and immune receptor genes (C).
FIGURE 13
FIGURE 13
Validation of the three identified biomarkers and clinicality analysis.(A–C) Expression patterns of identified biomarkers CTSS (A), LYZ (B), ISG20 (C). (D–F) Correlation between the expression of biomarkers CTSS (D), LYZ (E), ISG20 (F) and renal function indicators. ***p < 0.001; **p < 0.01; and *p < 0.05.
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