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. 2023 May 19:14:1183871.
doi: 10.3389/fimmu.2023.1183871. eCollection 2023.

Integrative bioinformatics and validation studies reveal KDM6B and its associated molecules as crucial modulators in Idiopathic Pulmonary Fibrosis

Anning Chen  1 , Zhun Sun  1 , Donglin Sun  1 , Meiying Huang  1 , Hongwei Fang  2 , Jinyuan Zhang  3 , Guojun Qian  1
Affiliations

Integrative bioinformatics and validation studies reveal KDM6B and its associated molecules as crucial modulators in Idiopathic Pulmonary Fibrosis

Anning Chen et al. Front Immunol. .

Abstract

Background: Idiopathic Pulmonary Fibrosis (IPF) can be described as a debilitating lung disease that is characterized by the complex interactions between various immune cell types and signaling pathways. Chromatin-modifying enzymes are significantly involved in regulating gene expression during immune cell development, yet their role in IPF is not well understood.

Methods: In this study, differential gene expression analysis and chromatin-modifying enzyme-related gene data were conducted to identify hub genes, common pathways, immune cell infiltration, and potential drug targets for IPF. Additionally, a murine model was employed for investigating the expression levels of candidate hub genes and determining the infiltration of different immune cells in IPF.

Results: We identified 33 differentially expressed genes associated with chromatin-modifying enzymes. Enrichment analyses of these genes demonstrated a strong association with histone lysine demethylation, Sin3-type complexes, and protein demethylase activity. Protein-protein interaction network analysis further highlighted six hub genes, specifically KDM6B, KDM5A, SETD7, SUZ12, HDAC2, and CHD4. Notably, KDM6B expression was significantly increased in the lungs of bleomycin-induced pulmonary fibrosis mice, showing a positive correlation with fibronectin and α-SMA, two essential indicators of pulmonary fibrosis. Moreover, we established a diagnostic model for IPF focusing on KDM6B and we also identified 10 potential therapeutic drugs targeting KDM6B for IPF treatment.

Conclusion: Our findings suggest that molecules related to chromatin-modifying enzymes, primarily KDM6B, play a critical role in the pathogenesis and progression of IPF.

Keywords: Idiopathic Pulmonary Fibrosis; chromatin-modifying enzymes; disease biomarker; drug molecule; gene ontology; hub genes.

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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
Schematic representation of the workflow used in this study.
Figure 2
Figure 2
Identifying the DECMEGs in IPF. (A) Normalization of the samples selected from the GSE110147 dataset. (B) The DEGs identified from the GSE110147 dataset. (C) The DECMEGs of IPF. (D) The heatmap of the DECMEGs.
Figure 3
Figure 3
List of DECMEGs for functional enrichment analysis. (A) GO. (B) GO analysis network diagram. (C) KEGG. (D) KEGG analysis network diagram. (E) DO enrichment, green horizontal bar represents the items with valid P-values (<0.05).
Figure 4
Figure 4
Analysis of the PPI networks and hub genes. (A) PPI networks of DECMEGs, where bigger edge and node sizes imply higher degrees. (B) Connectivity ranks of the genes. (C) The primary module in the PPI network. (D) Biological processes of hub genes analyzed by ClueGO tool. (E) The six hub genes of IPF.
Figure 5
Figure 5
Analysis of the immune infiltration levels in the IPF. (A) The ratio of 22 immune cells in each IPF sample. (B) The relationship between each immune cell. (C) The proportion of immune cells in the control and IPF samples. *P < 0.05, **P < 0.01, ***P < 0.001.
Figure 6
Figure 6
The relationship between the immune cells and hub genes. (A) CHD4; (B) KDM6B; (C) KDM5A; (D) HDAC2; (E) SETD7; and (F) SUZ12.
Figure 7
Figure 7
Constructing the lncRNA–miRNA–mRNA ceRNA IPF network. (A) Venn diagram presents the miRNAs that target each hub gene. (B) The subcellular localization of lncRNAs of ceRNA. (C) The alluvial diagram presents the ceRNA network.
Figure 8
Figure 8
The GSEA of KDM6B. (A) Reactome activation of AMPK downstream of NMDARs. (B) Reactome IL-15 signaling pathway (C) WP cytokines and inflammatory response. (D) Croonquist IL-6 deprivation DN. (E) Baker hematopoiesis of STAT3 targets. (F) TP53 regulates the transcription of cell death genes.
Figure 9
Figure 9
Comprehensive analysis of KDM6B. (A) Subcellular localization of the KDM6B protein. Relationship between KDM6B and immune checkpoints such as (B) CTLA4; (C) PD-L2; (D) PD-L1.
Figure 10
Figure 10
Validation of identified hub genes using a bleomycin-induced mouse model of pulmonary fibrosis. (A) Schematic diagram of the experimental protocol used in the bleomycin (BLM)-induced lung fibrosis model in mice. (B) Representative MASSON-stained lung sections. (C) Expression of fibrosis-associated proteins and KDM6B was determined by Western Blot. (D) Semi-quantitative b-Actin of the Western Blot method. (E) Correlation between KDM6B and a-SMA and fibronectin. (F) Correlation between KDM6B and CD3e and CD8b. (G) Visualizing single-cell clustering results using t-SNE plots. (H) Analyzing KDM6B gene expression across diverse lung cell subsets. *P < 0.05 Results are expressed as mean ± SEM.
Figure 11
Figure 11
Construction of a diagnostic model using hub Genes and TGF-β1. (A) The diagram illustrates a diagnostic model built upon six hub genes in conjunction with TGF-β1. (B) Calibration curve (CC) representing the accuracy of the diagnostic model. (C) Decision curve analysis (DCA) illustrating the net benefit of the diagnostic model. (D) Clinical impact curve derived from the DCA, assessing the nomogram’s performance. (E) ROC curve for the training cohort (GSE110147). (F) ROC curve for the validation cohort (GSE33566).
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