Identifying DUSP-1 and FOSB as hub genes in immunoglobulin A nephropathy by WGCNA and DEG screening and validation

Introduction

Immunoglobulin A nephropathy (IgAN), the most common primary glomerulonephritis, is characterized by the presence of IgA-containing immune complexes in the mesangial area of the glomerulus. Most IgAN patients will progress to end-stage kidney disease (Lai et al., 2016; Woo et al., 2010). The pathogenesis of IgAN is not very clear and there are not currently any effective pharmacological treatments. There is, therefore, an urgent need to identify key molecular targets involved in the intervention and treatment of IgAN.

A weighted gene co-expression network analysis (WGCNA) is a useful bioinformatics method of identifying and clustering genes related to diseases or clinical manifestations and forming different co-expression networks or co-expression modules (Langfelder & Horvath, 2008). WGCNA is also a powerful data analysis tool that can identify potential biomarkers for the intervention and treatment of different diseases, such as advanced kidney disease (Xin et al., 2020), Sjögren’s syndrome (Yao et al., 2019), acute myocardial infarction (Wu et al., 2021), and pancreatic cancer (Zhou et al., 2019).

In this study, we identified two IgAN hub genes in the GSE93798 dataset using WGCNA and DEG screening. The flowchart of the study is shown in Fig. 1. The final hub genes were validated in the GSE26316 and GSE104948 datasets and in kidney tissue from IgAN patients and kidney cancer adjacent sample tissues. This study identified two hub genes, Dual Specificity Phosphatase 1 (DUSP1) and FosB Proto-Oncogene (FOSB), that might be promising targets for the intervention and treatment of IgAN.

Materials and Methods

Results

Construction of the WGCNA network

All gene expression matrices of the GSE93798 dataset were sorted according to median deviation size and the top 5,000 genes were used for subsequent analyses. A cluster analysis was performed on the samples before we started constructing the WGCNA network and the results showed that there was no obvious heterogeneity between these samples (Fig. 2A). The power value (β) was set to 8 and the scale independence value was set to 0.85 (Figs. 2B and 2C). There was a negative correlation between the logarithm log(k) and the logarithm log(P(k)), and the correlation coefficient was greater than 0.8. These results proved that the constructed co-expression network met the requirements of scale-free topology (Figs. 2D and 2E). Therefore, β = 8 was chosen to build a hierarchical clustering tree.

Identifying the key module and hub genes

There were six gene co-expression modules in the WGCNA (Fig. 3A). The turquoise module, which contained 1,806 genes, was the one with the highest correlation coefficient (CC) with IgAN (Figs. 3B and 3C). A total of 400 genes were randomly selected for heat map display (Fig. 3D). The module and disease characteristic heat map showed that the turquoise module had the most significant correlation with IgAN (Fig. 3E). Genes that met the criteria that the value of GS and MM were both greater than 0.9 were determined to be hub genes (Fig. 3F).

Functional enrichment analysis of turquoise module genes

A GO enrichment analysis showed that these 1,806 genes in the turquoise module were mainly enriched in the BP of cell adhesion, leukocyte migration, inflammatory response, extracellular matrix organization, immune response, and adaptive immune response (Fig. 4A). A cross-check GO analysis showed that a large number of genes related to white blood cell migration, inflammation and inflammatory response were also enriched in extracellular matrix tissue, immune response, adaptive immune response and other BPs. This indicated that these cross-mapped genes were involved in the pathogenesis of IgAN in multiple BPs (Fig. 4B).

DEGs screening

There were 248 DEGs identified, including 107 up-regulated genes and 141 down-regulated genes, after screening the DEGs based on the GSE93798 dataset (Figs. 5A and 5B). A total of 34 hub genes in three clusters were screened using the MCODE method when the threshold of k-score > 2. The three clusters included 15 nodes (Fig. 5C), 11 nodes (Fig. 5D) and 18 nodes (Fig. 5E), respectively.

Identification and verification of final hub genes

Hub genes found in both the WGCNA and PPI network, namely FOSB and DUSP1, were identified as the final hub genes (Fig. 5F). Compared with the control group, FOSB and DUSP1 had low expression levels in the glomerulus of IgAN patients in the GSE93798 dataset and the two validation datasets, GSE104948 (Fig. 6A) and GSE37460 (Fig. 6B). To validate our results in clinical specimens, we used immunohistochemistry and semi-quantitative analyses, which showed that compared with adjacent tissues of renal cancer, FOSB and DUSP1 were down-regulated in the glomerulus of IgAN patients. This result was consistent with the expressions of these two genes seen in the transcriptome matrix data (Figs. 6C–6E).

ROC curve analysis of final hub genes

The ROC curve was drawn to evaluate the diagnostic value of FOSB and DUSP1 using the sample information in the GSE93798, GSE37460 and GSE104948 datasets (Fig. 7). All the AUC values were greater than 0.75, indicating their potential value as diagnostic markers for IgAN.

Discussion

IgAN is one of the main causes of end-stage renal disease worldwide. The pathogenesis of IgAN is unclear, but the current hypothesis is the multi-strike theory that various pathogenic factors lead to the increase of galactose-deficient O-glycan (Gd-IgA1) in the glomerulus, activating the mesangial cells, and ultimately promoting the occurrence and development of IgAN (Lai et al., 2016). According to this hypothesis, specific glomerular biological information could better identify the hub genes of IgAN, which supported our decision to use datasets that included glomerulus samples from IgAN patients in this study rather than those that only included peripheral blood samples (Liu et al., 2020; Jiang et al., 2020).

The co-expression network of diseases and modules constructed by WGCNA can identify biomarkers and possible therapeutic targets for a certain disease. In this study, we used WGCNA to identify the key module with the highest correlation coefficient with IgAN in the GSE93798 dataset. A GO enrichment analysis showed that the genes in the key module were mainly enriched in the BP of cell adhesion, leukocyte migration, inflammatory response, extracellular matrix organization, immune response, and adaptive immune response. This result indicated that the pathogenesis of IgAN is related to the activation of immune and inflammation-related pathways, but the inflammatory response plays a more important role in the development of IgAN than the immune response. IgAN is currently considered an autoimmune disease (Suzuki et al., 2011), but Sheng et al. (2018) found that Notch Receptor 1 (NOTCH 1) and NF-κB and other inflammatory pathway-related molecules were increased in the renal tissue of IgAN patients. Some animal experiments have also shown that certain drugs can affect the progression of IgAN by inhibiting NF-κB signaling and NLRP3 inflammasome activation (Bai et al., 2019). In clinical studies, some scholars have found that the neutrophil to lymphocyte ratio is an independent risk factor for poor prognosis in IgAN patients (Li et al., 2020). Inflammation thus plays an important role in the occurrence and development of IgAN. This may partly explain why immunosuppressive agents are ineffective in treating IgAN (Rauen et al., 2020).

To find the most significant hub gene, we defined GS > 0.9 and ME > 0.9 as target values in the key module of WGCNA and used MCODE to identify the most important gene clusters in the PPI network. There were five hub genes in the WGCNA analysis and 34 hub genes in the MCODE analysis. The final hub genes found in both the WGCNA and MCODE analysis were defined as the key hub genes; FOSB and DUSP-1 were identified as the final hub genes of IgAN. An ROC analysis showed that FOSB and DUSP-1 had good diagnostic performance. The validation results of these key genes in two other datasets, as well as in clinical specimens, were consistent with the results that the expression of FOSB and DUSP1 were down-regulated in the renal glomerulus of IgAN patients compared with people that did not have IgAN. Previous studies have shown that FOSB is related to human stress and is a potential therapeutic target for depression. FOS family gene polymorphisms, including FOSB, are related to the clinical phenotypes of IgAN patients (Park et al., 2014). In an animal model of renal ischemia/reperfusion, the expression of FOSB was up-regulated in interthalamic core cells (Mutoh, Ohsawa & Hisa, 2013). Liao et al. (2020) found that MicroRNA-27A-3P targeted FOSB to regulate cell function involved in the occurrence and development of IgAN. These studies show that FOSB is involved in the progression of IgAN, but the specific mechanism of FOSB in the pathogenesis of IgAN unclear. DUSP1 can regulate cell death, tumor growth and plays a role in both inflammation and chronic metabolic diseases. DUSP1 is highly expressed in tissues such as the heart and liver that could activate mitogen-activated protein kinase (MAPKs), extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and p38 to promote cell apoptosis (Johnson & Lapadat, 2002). Jin et al. (2018) showed that DUSP1 inhibited cardiac ischemia-reperfusion (IR) injury by regulating downstream JNK to inhibit mitochondrial fission. Low DUSP1 expression is related to glucose metabolism disorder, renal dysfunction and glomerular apoptosis; by affecting mitochondrial homeostasis, DUSP1 is able to protect against diabetic nephropathy progression (Sheng et al., 2019; Ge et al., 2019). There is currently no research related to DUSP1 in IgAN and its potential role in the pathogenesis of IgAN needs further study. In this study, we found two genes closely related to IgAN using bioinformatics and in vitro verification. These genes can be used as potential markers for the diagnosis of IgAN, and perhaps also as potential therapeutic targets.

This research has several highlights. First, we used datasets that include glomeruli from the biopsies of IgAN patients for our analysis in order to better identify candidate biomarkers and therapeutic targets, contributing to a more comprehensive understanding of IgAN. Second, WGCNA is a powerful tool for bioinformatics analysis. It can associate gene modules with disease traits. WGCNA plus a DEG analysis can find the most relevant value target genes. However, due to limited sample availability we were unable to verify the mRNA expression levels of FOSB and DUSP1. Instead, we analyzed kidney tissue slices using immunohistochemistry and achieved consistent results with the bioinformatics analysis.

Conclusions

In summary, our study identified FOSB and DUSP1 as final hub genes in IgAN through WGCNA and DEG analysis. These genes were verified to be significantly down-regulated in the renal glomerulus of IgAN patient kidney tissues and other IgAN datasets, compared with non-IgAN subjects. The specific mechanisms of these hub genes involved in IgAN pathogenesis also need to be further studied. Our results provided novel molecular targets as biomarkers and therapeutic targets for future research.

Supplemental Information