Introduction: IgA nephropathy (IgAN) is a prevalent worldwide glomerular disease with a complex pathophysiology that has significant economic implications. Despite the lack of successful research, this study aims to discover the potential competing endogenous RNA (ceRNA) network of autophagy-associated genes in IgAN and examine their correlation with immune cell infiltration. Methods: Autophagy-related hub genes were discovered by assessing the GSE116626 dataset and constructing a protein-protein interaction network. Nephroseq v5 analysis engine was used to analyze correlations between hub genes and proteinuria, glomerular filtration rate (GFR), and serum creatinine levels. Then, a ceRNA network construction and the CIBERSORT tool for immune cell infiltration analysis were also performed. Additionally, the differentially expressed autophagy-related genes were used to predict potential targeted medications for IgAN. Results: Overall, 1,396 differentially expressed genes were identified in IgAN along with 25 autophagy-related differentially expressed messenger RNAs. Enrichment analysis revealed significant involvement of autophagy and apoptosis in biological processes. Next, we evaluated the top hub nodes based on their highest degrees. The ability of IgAN discrimination was confirmed in the GSE35487 and GSE37460 datasets by validating the five hub genes: SIRT1, FOS, CCL2, CDKN1A, and MYC. In the Nephroseq v5 analysis engine, the clinical correlation of the five hub genes was confirmed. Furthermore, the ceRNA network identified 18 circular RNAs and 2 microRNAs associated with hub autophagy-related genes in IgAN. Our investigation identified hsa-miR-32-3p and hsa-let-7i-5p as having elevated expression levels and substantial diagnostic value. Finally, four distinctively infiltrated immune cells were found to be associated with the hub autophagy-related genes, and 67 drugs were identified as potential therapeutic options for IgAN. Conclusion: This study sheds light on a novel ceRNA regulatory network mechanism associated with autophagy in IgAN development.

Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis globally and represents a significant cause of end-stage renal disease (ESRD). Approximately 20–40% of individuals diagnosed with IgAN experience renal failure within a span of 10–20 years from the initial manifestation of the disease [1]. At present, the management of IgAN involves a multifaceted approach that includes non-pharmacologic interventions aimed at reducing kidney injury risk, such as dietary modifications, exercise, weight control, and avoidance of nephrotoxic substances [2]. Pharmacologic therapy, in addition to strict blood pressure control, may include non-immunosuppressive medications, such as renin angiotensin system blockade and sodium-glucose cotransporter 2 (SGLT-2) inhibitors [3], immunosuppressive agents like corticosteroids, cyclophosphamide, azathioprine, and mycophenolate [4], as well as recently approved targeted drugs like the targeted release formulation budesonide (nefecon) and sparsentan, which reduce IgAN inflammation [2]. Regrettably, these treatments are still unsatisfactory. Currently, the diagnosis of IgAN predominantly depends on renal tissue biopsy due to the absence of specific, non-invasive biomarkers for early screening. This limitation results in a significant societal burden as the majority of IgAN patients progress to ESRD before diagnosis [5]. Therefore, it is imperative to elucidate the underlying mechanisms and identify key biomarkers to enhance diagnostics and treatment strategies.

Autophagy, a highly conserved intracellular catabolic process responsible for eliminating impaired or surplus organelles and biomacromolecules, plays a crucial role in maintaining cellular homeostasis across various cell types and is intricately linked to the pathogenesis of numerous disorders [6]. The Human Autophagy Database (HADb) is a Web-based tool that provides a thorough and up-to-date collection of human genes and proteins associated with autophagy [7]. Recent studies suggest that autophagy may play a significant role in the development of IgAN [8]. Studies have shown a decrease in autophagy in the podocytes of individuals with IgAN [9], leading to podocyte cell damage, dysfunction, and ultimately proteinuria [10]. Rivedal et al. and colleagues [11] have identified members of the APOL lipoprotein family as key players in inflammation, autophagy, and kidney disease. The inflammation linked to APOL has been found to contribute to podocyte dysfunction, potentially impacting the development of IgAN through interstitial damage. Xia et al. [12] have reported that activation of mTORC1 contributes to the proliferation of mesangial cells in IgAN by inhibiting autophagy. In addition, Zhao et al. [13] have demonstrated that triptolide enhances autophagy to suppress mesangial cell proliferation in IgAN via the CARD9/p38 MAPK pathway. Autophagy has the ability to limit inflammation through its interaction with specific signaling pathways and engulfing inflammation triggers [10]. These findings suggest that increasing autophagy could potentially have positive effects in the treatment of IgAN. Additionally, the infiltration of immune cells is crucial in the progression and pathogenesis of IgAN. The accumulation of B and T cells has been implicated in the pathogenesis of this disorder. B cells, responsible for producing Gd-IgA1, are pivotal in the pathogenesis of IgAN, and clinical trials for B-cell-targeted therapies are currently underway in phase 3 trials [14]. Huang et al. [15] have provided evidence suggesting that specific clones of T-cell receptor beta chain (TCRB) and immunoglobulin heavy chain (IGH) associated with IgAN could serve as potential diagnostic biomarkers. A previous study found an increase in renal infiltration of CD68+ and CD206+ macrophages in individuals with IgAN. The degree of macrophage infiltration in the glomeruli may serve as a potential indicator of the efficacy of immunosuppressive treatment in IgAN patients at high risk of disease progression [16]. These results indicated that the infiltration of immune cells could be crucial in the progression of IgAN. Moreover, studies have shown potential interactions between autophagy and the immune system, with autophagy serving important functions in cellular autonomic defense and multicellular immunity [17]. Nevertheless, the specific regulatory mechanisms underlying the crosstalk between autophagy and immunity in the development and maintenance of IgAN remain poorly elucidated.

Non-coding RNA (ncRNA) has been shown to play a crucial role in a variety of biological processes such as cell growth, differentiation, apoptosis, and autophagy, despite its lack of involvement in protein encoding. In the context of kidney diseases, the importance of ncRNA is particularly significant [18, 19]. Specifically, the ncRNA FGD5-AS1 has been found to inhibit cell proliferation and inflammatory response, while promoting apoptosis by targeting the PTEN-mediated JNK/c-Jun signaling pathway via miR-196b-5p in IgAN mice, ultimately leading to an improvement in pathological conditions. The FGD5-AS1-miR-196b-5p-PTEN ceRNA network represents a potential therapeutic target for IgAN [20]. Circular RNA (circRNA) is a unique form of ncRNA that lacks 5′ end caps and 3′ end poly (A) tails, forming a circular structure through covalent bonds. Recent research has demonstrated that circRNAs can function as competitive endogenous RNAs (ceRNAs) by acting as sponges that bind to miRNAs. This interaction allows them to regulate transcription and either repress or activate gene expression [21]. Investigations have shown that circRNAs also participate in ceRNA regulatory networks in the development of IgAN [22]. Likewise, it was observed that circRNAs play a role in the advancement of IgAN [23].

This study involved the construction of a protein-protein interaction (PPI) network of differentially expressed autophagy-related genes (DEARGs) and the identification of hub genes. Meanwhile, an IgAN-associated ceRNA network was established to explore the functions of DEARGs in IgAN. Furthermore, the CIBERSORT algorithm was employed to estimate the proportions of immune cell subgroups in IgAN samples, and co-expression analysis was performed with DEARGs. Correlation analysis was conducted to investigate the relationship between DEARGs and clinical as well as prognostic factors, aiming to unveil the underlying regulatory mechanisms in IgAN. Finally, the DEARGs were utilized to predict potential targeted medications for IgAN. Our analysis identified five DEARGs (SIRT1, FOS, MYC, CDKN1A, and CCL2) that may have significant implications in the pathogenesis of IgAN. This research offers the prospect of shedding light on the underlying mechanisms of IgAN, facilitating the discovery of diagnostic indicators and therapeutic targets for the disease.

Data Acquisition and Processing

Microarray datasets were retrieved from the GEO database. The expression profiles of circRNA, miRNA, and mRNA in IgAN were acquired from GSE154046 (consisting of 3 IgAN and 3 normal controls), GSE25590 (consisting of 7 IgAN and 7 normal controls), and GSE116626 (consisting of 52 IgAN and 7 normal controls), respectively. The fundamental information of these datasets is presented in Table 1. Differentially expressed messenger RNAs (DEmRNAs) were identified in GSE116626 based on p values <0.05 and |log2FC|>0.5 (fold change >0.5). DEmiRNAs and DEcircRNAs were collected by setting values of adj. p value <0.05 and |log2FC|>1 (fold change >1) in GSE25590 and GSE154046. The “ggplot2” package was used for visualization of the heatmap and volcano plot.

Table 1.

Fundamental details of 6 IgAN microarray datasets

ProfileRNA typePlatformSample sourceControlIgAN
GSE116626 mRNA GPL14951 Kidney tissue 52 
GSE35487 mRNA GPL96 Kidney tissue 27 
GSE37460 mRNA GPL14663 Kidney tissue 27 
GSE64306 miRNA GPL19117 Urine sediments 18 
GSE25590 miRNA GPL7731 Peripheral blood mononuclear cells 
GSE154046 circRNA GPL20301 Peripheral blood mononuclear cells 
ProfileRNA typePlatformSample sourceControlIgAN
GSE116626 mRNA GPL14951 Kidney tissue 52 
GSE35487 mRNA GPL96 Kidney tissue 27 
GSE37460 mRNA GPL14663 Kidney tissue 27 
GSE64306 miRNA GPL19117 Urine sediments 18 
GSE25590 miRNA GPL7731 Peripheral blood mononuclear cells 
GSE154046 circRNA GPL20301 Peripheral blood mononuclear cells 

Analysis of Differentially Expressed Autophagy-Related Genes

The HADb provided a list of 222 genes associated with human autophagy. Through comparison with the DEmRNAs identified in the GSE116626 dataset, 25 autophagy-related DEmRNAs were identified for further study.

Functional Enrichment Analysis

The DEARGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID 2021) [24] to explore the pathways associated with autophagy genes.

Construction of Protein-Protein Interaction Network

A PPI network was conducted to predict the interactions of DEARGs and gain a comprehensive understanding of the regulatory mechanism in IgAN. The Search Tool for the Retrieval of Interacting Genes (STRING database, v11.5) was utilized for this purpose [25]. Subsequently, biological networks and topological characteristics were analyzed and visualized using Cytoscape software after retrieving outcomes from the STRING database with a confidence score exceeding 0.7. Hub genes were then identified using CytoHubba, a Cytoscape plugin [26].

Construction Network of the circRNA-miRNA-mRNA

The microT-CDS software was utilized within DIANA Tools (http://www.microrna.gr/microT-CDS, v5.0) to identify mRNA molecules potentially binding to DEmiRNAs. The intersection of predicted target mRNAs and hub genes in IgAN yielded potential target miRNAs in the ceRNA network. Then, interactions between the resulting miRNAs and circRNAs were forecasted using the StarBase 3.0 Website (http://starbase.sysu.edu.cn/), with the prediction results overlapping circRNAs from GSE154046. Finally, a circRNA-miRNA-mRNA network was constructed through online data analysis Website (http://www.bioinformatics.com.cn/).

Immune Cell Infiltration Analysis

The CIBERSORT algorithm, developed by Newman et al. [27], was used to estimate the composition of immune cell based on gene expression profiles. Using CIBERSORT, we calculated the proportions of 22 immune cell subpopulations in IgAN and control samples. Further analyses were conducted only on cases with CIBERSORT outputs of p < 0.05. The Wilcoxon rank-sum test was employed to identify significant differences in immune infiltration cells subpopulations between the IgAN and control samples. Finally, Pearson correlation analysis was utilized to investigate the relationship between the DEARGs and immune cells. In the IgAN samples, we examined the correlation between the expression of DEARGs and distinct subgroups of immune cells.

Correlation between the Hub Genes and Clinical Features

The Nephroseq v5 analysis engine (https://v5.nephroseq.org) offers the gene expression characteristics and clinical data. Through Pearson correlation analysis employing the hub gene, we identified genes linked to proteinuria, glomerular filtration rate (GFR), and serum creatinine levels. A significance level of p < 0.05 was deemed statistically significant.

Correlation Analysis of the ARGs and Identification of the Potential Drugs

In our investigation, we observed co-expressions of SIRT1, FOS, MYC, CDKN1A, CCL2, and autophagy-related genes. Spearman was used to analyze the correlation between two genes. Moreover, the Drug Gene Interaction Database (DGIdb 4.2.0) (https//dgidb.org/) [28] was utilized to predict potential therapeutic drugs for IgAN, based on the identified key autophagy-related genes. Subsequently, the Cytoscape software was employed to build a network depicting the interaction between drugs and genes.

Statistical Analysis

Statistical analysis of our research findings was performed using Prism (GraphPad Software, La Jolla, CA), with significance assessed through t test and Kruskal-Wallis test (no significance; * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001).

Identification of DEARGs in IgAN

The flowchart analysis depicted in Figure 1 revealed the identification of 1,396 mRNAs with significantly different expression levels in 7 healthy samples and 52 IgAN tissues. Concurrently, the HADb was used to obtain 222 autophagic genes. Subsequently, the intersection of the 222 autophagic genes with the 1,396 DEmRNAs from GSE116626 yielded 25 DEARGs for further investigation (|logFC|>0.5 and p value <0.05; Fig. 2a). Additionally, the heatmaps presented in Figure 2b illustrate the gene expression patterns of the 25 DEARGs.

Fig. 1.

Analysis flowchart.

Fig. 1.

Analysis flowchart.

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Fig. 2.

Hierarchical clustering analysis of differentially expressed autophagy-related genes (DEARGs). a Intersection of DEmRNAs of GSE116626 with autophagy genes of HADb. b Clustered heatmaps of the DEARGs.

Fig. 2.

Hierarchical clustering analysis of differentially expressed autophagy-related genes (DEARGs). a Intersection of DEmRNAs of GSE116626 with autophagy genes of HADb. b Clustered heatmaps of the DEARGs.

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Function Enrichment for the DEARGs

In order to evaluate the biological function of the 25 DEARGs, GO and KEGG pathway analysis was conducted. The results of the GO analysis showed that the DEARGs significantly enriched in the biological process such as autophagy, cell cycle arrest, and the intrinsic apoptotic pathway. Furthermore, the DEARGs exhibited enrichment in cellular component including vesicle membrane, PML body, and late endosome. In addition, the analysis of the molecular function revealed a significant enrichment of DEARGs in kinase activator activity, activating transcription factor binding and LRR domain binding (Fig. 3a). The KEGG pathway enrichment analysis indicated that the DEARGs showed enrichment in pathways related to autophagy, cancer, the IL-17 signaling pathway, and the ErbB signaling pathway (Fig. 3b).

Fig. 3.

Function of DEARGs. a GO analysis based on DEARGs. b KEGG analysis based on DEARGs. BP, biological process; CC, cellular component; MF, molecular function.

Fig. 3.

Function of DEARGs. a GO analysis based on DEARGs. b KEGG analysis based on DEARGs. BP, biological process; CC, cellular component; MF, molecular function.

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Building the PPI Network and Identifying the Hub Genes

To facilitate the identification of main modules and hub genes in IgAN encoded proteins, an analysis and visualization of the PPI network was conducted using Cytoscape (Fig. 4a). Furthermore, the PPI network was examined to pinpoint the ten hub nodes with the highest degrees. The hub genes CDKN1A, FOS, MYC, CCL2, SIRT1, STK11, RPS6KB1, RELA, LAMP1, and WPI2, which comprised the ten hub nodes, were considered in relation to the genesis and progression of IgAN (Fig. 4b).

Fig. 4.

PPI network construction and hub genes selection. a PPI network constructed using STRING database for DEARGs. b Top 10 hub genes explored by CytoHubba.

Fig. 4.

PPI network construction and hub genes selection. a PPI network constructed using STRING database for DEARGs. b Top 10 hub genes explored by CytoHubba.

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Identification of Autophagy-Related miRNAs in IgAN

According to the ceRNA hypothesis, miRNA suppresses the expression of downstream gene. Hence, a total of 130 DEmiRNAs were identified in the GSE25590 dataset (|log2FC|>1 and adj. p < 0.05; Fig. 5a), and further analysis was conducted using the DIANA microT-CDS database. A total of 57 miRNAs matching the hub gene were identified from 130 DEmiRNAs (Table 2). Subsequently, the 57 autophagic miRNAs were intersected with the 236 DEmiRNAs found in the GSE64306 dataset. The hsa-miR-32-3p and hsa-let-7i-5p based on the results were suitable for further analysis (|log2FC|>1 and adj. p < 0.05; Fig. 5b). The consistency of the up-regulated expression pattern and high AUC values of hsa-miR-32-3p and hsa-let-7i-5p were confirmed through the analysis of two training sets, GSE25590 and GSE64306 (Fig. 5c, d). Finally, 2 autophagy-related miRNA-mRNA pairs (hsa-miR-32-3p/SIRT1 and hsa-let-7i-5p/CDKN1A) were identified.

Fig. 5.

Search for autophagy-related miRNAs and validate their expression pattern and evaluate the diagnostic efficacy of these miRNA using the GEO datasets. a Volcano plot of DEmiRNAs in GSE25590 (|log2FC|>1 and adj. p < 0.05). b Intersection of autophagy-related DEmiRNAs of GSE25590 and GSE64306. c In the GS25590 dataset, the miRNAs of hsa-miR-32-3p and hsa-let-7i-5p showed significant up-regulation; the ROC analysis was performed on hsa-miR-32-3p and hsa-let-7i-5p. d The GSE64306 dataset yielded consistent results with GSE25590, ROC analysis of hsa-miR-32-3p and hsa-let-7i-5p.

Fig. 5.

Search for autophagy-related miRNAs and validate their expression pattern and evaluate the diagnostic efficacy of these miRNA using the GEO datasets. a Volcano plot of DEmiRNAs in GSE25590 (|log2FC|>1 and adj. p < 0.05). b Intersection of autophagy-related DEmiRNAs of GSE25590 and GSE64306. c In the GS25590 dataset, the miRNAs of hsa-miR-32-3p and hsa-let-7i-5p showed significant up-regulation; the ROC analysis was performed on hsa-miR-32-3p and hsa-let-7i-5p. d The GSE64306 dataset yielded consistent results with GSE25590, ROC analysis of hsa-miR-32-3p and hsa-let-7i-5p.

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Table 2.

Target hub gene mRNAs of miRNA

Hub genemiRNA
MYC hsa-miR-34b-5p, hsa-miR-495-3p 
SIRT1 hsa-miR-34a-5p, hsa-miR-132-3p, hsa-miR-601, hsa-miR-513b-5p, hsa-miR-29b-1-5p 
hsa-miR-199b-5p, hsa-miR-502-5p, hsa-miR-128-3p, hsa-miR-622, hsa-miR-200a-3p 
hsa-miR-495-3p, hsa-miR-518c-5p, hsa-miR-494-3p, hsa-miR-200b-3p, hsa-miR-425-5p 
hsa-miR-513a-5p, hsa-miR-340-5p 
FOS hsa-miR-501-3p, hsa-miR-502-3p, hsa-miR-199a-3p, hsa-miR-221-3p, hsa-miR-362-3p 
CDKN1A hsa-miR-20b-5p, hsa-miR-17-5p, hsa-miR-93-5p, hsa-let-7i-5p, hsa-let-7b-5p, hsa-let-7a-5p 
hsa-let-7c-5p, hsa-miR-20a-5p, hsa-miR-423-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-98-5p 
hsa-miR-365a-3p, hsa-miR-505-5p, hsa-miR-362-3p 
CCL2 hsa-miR-601, hsa-miR-378a-5p 
STK11 hsa-miR-371a-5p 
RELA hsa-miR-765, hsa-miR-185-5p, hsa-miR-7-5p, hsa-miR-302b-3p, hsa-miR-564, hsa-miR-512-3p, hsa-miR-486-3p, hsa-miR-378a-5p 
LAMP1 hsa-miR-660-5p 
WIPI2 hsa-miR-15b-5p, hsa-miR-495-3p, hsa-miR-199b-5p, hsa-miR-503-5p, hsa-let-7f-5p 
hsa-let-7g-5p, hsa-let-7a-5p, hsa-let-7i-5p 
RPS6KB1 hsa-miR-338-5p, hsa-miR-200b-3p, hsa-miR-557, hsa-miR-340-5p, hsa-miR-7-5p 
hsa-miR-378a-5p, hsa-miR-128-3p, hsa-miR-29b-1-5p, hsa-miR-330-3p, hsa-miR-374b-5p 
hsa-miR-98-5p, hsa-miR-501-5p, hsa-miR-513a-5p, hsa-miR-409-3p, hsa-let-7c-5p 
Hub genemiRNA
MYC hsa-miR-34b-5p, hsa-miR-495-3p 
SIRT1 hsa-miR-34a-5p, hsa-miR-132-3p, hsa-miR-601, hsa-miR-513b-5p, hsa-miR-29b-1-5p 
hsa-miR-199b-5p, hsa-miR-502-5p, hsa-miR-128-3p, hsa-miR-622, hsa-miR-200a-3p 
hsa-miR-495-3p, hsa-miR-518c-5p, hsa-miR-494-3p, hsa-miR-200b-3p, hsa-miR-425-5p 
hsa-miR-513a-5p, hsa-miR-340-5p 
FOS hsa-miR-501-3p, hsa-miR-502-3p, hsa-miR-199a-3p, hsa-miR-221-3p, hsa-miR-362-3p 
CDKN1A hsa-miR-20b-5p, hsa-miR-17-5p, hsa-miR-93-5p, hsa-let-7i-5p, hsa-let-7b-5p, hsa-let-7a-5p 
hsa-let-7c-5p, hsa-miR-20a-5p, hsa-miR-423-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-miR-98-5p 
hsa-miR-365a-3p, hsa-miR-505-5p, hsa-miR-362-3p 
CCL2 hsa-miR-601, hsa-miR-378a-5p 
STK11 hsa-miR-371a-5p 
RELA hsa-miR-765, hsa-miR-185-5p, hsa-miR-7-5p, hsa-miR-302b-3p, hsa-miR-564, hsa-miR-512-3p, hsa-miR-486-3p, hsa-miR-378a-5p 
LAMP1 hsa-miR-660-5p 
WIPI2 hsa-miR-15b-5p, hsa-miR-495-3p, hsa-miR-199b-5p, hsa-miR-503-5p, hsa-let-7f-5p 
hsa-let-7g-5p, hsa-let-7a-5p, hsa-let-7i-5p 
RPS6KB1 hsa-miR-338-5p, hsa-miR-200b-3p, hsa-miR-557, hsa-miR-340-5p, hsa-miR-7-5p 
hsa-miR-378a-5p, hsa-miR-128-3p, hsa-miR-29b-1-5p, hsa-miR-330-3p, hsa-miR-374b-5p 
hsa-miR-98-5p, hsa-miR-501-5p, hsa-miR-513a-5p, hsa-miR-409-3p, hsa-let-7c-5p 

Verification of the Expression Pattern and Determination of the Diagnostic Value Effectiveness of Hub Genes

The expression of hub genes was further validated through analysis of the expression profile of GSE35487 and GSE37460 (Fig. 6a, b). The results revealed significant downregulation of genes including MYC, FOS, SIRT1, CDKN1A, and CCL2. Conversely, genes such as STK11, RELA, LAMP1, WIPI2, and RPS6KB1 were excluded from the subsequent cohort analysis due to lack of differences (online suppl. Fig. S1; for all online suppl. material, see https://doi.org/10.1159/000539571). ROC curve analysis was then utilized to forecast the diagnostic level of 5 downregulated autophagy-related genes. The AUC values for 5 key genes in GSE35487 were found to exceed 0.8 (Fig. 6c). These results suggested that the genes MYC, FOS, SIRT1, CDKN1A, and CCL2 could potentially serve as gene markers for distinguishing between IgAN and controls.

Fig. 6.

Validation of the expression pattern and assessment of diagnostic effectiveness of 5 clinically associated genes using the GEO datasets. a In the GSE35487 dataset, significantly down-regulated genes are MYC, FOS, SIRT1, CDKN1A, and CCL2. b In the GSE37460 dataset, the same results were observed for genes that were significantly downregulated as that in GSE35487. c ROC curve of 5 hub genes of IgAN in GSE35487.

Fig. 6.

Validation of the expression pattern and assessment of diagnostic effectiveness of 5 clinically associated genes using the GEO datasets. a In the GSE35487 dataset, significantly down-regulated genes are MYC, FOS, SIRT1, CDKN1A, and CCL2. b In the GSE37460 dataset, the same results were observed for genes that were significantly downregulated as that in GSE35487. c ROC curve of 5 hub genes of IgAN in GSE35487.

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Constructing the ceRNA Regulatory Network

The regulatory mechanism of mRNA in the aforementioned analysis was investigated through the construction of autophagy-related ceRNA networks. It was observed that miRNA plays a role in negatively regulating target RNA molecules, and a decrease in circRNA expression could lead to a reduced binding of miRNAs to circRNAs. Consequently, this would enhance the negative regulatory effect of miRNAs on mRNAs, ultimately resulting in a decrease in mRNA expression. In the GSE154046 dataset, a total of 1,586 circRNAs exhibited differential expression between the IgAN and control groups, comprising 1,045 upregulated circRNAs and 541 downregulated circRNAs (Fig. 7a). Then, the further interaction between miRNAs of hsa-miR-132-3p and hsa-let-7i-5p and circRNA were constructed using Starbase3.0. The candidate circRNAs were intersected with the downregulated circRNAs in GSE154046 (Fig. 7b). The results of circRNA-miRNA-mRNA network, depicted in Figure 7c, revealed significant associations involving the genes CDKN1A and SIRT1 with 2 miRNAs and 18 circRNAs. These data provide an initial insight into the regulatory network of the identified mRNAs.

Fig. 7.

Identification of ceRNA related to IgAN autophagy: ceRNA network of the CDKN1A and SIRT1 was shown using Sankey diagram. a Volcano plot of DEcircRNAs in GSE154046 (|log2FC|>1 and adj. p < 0.05). b Intersection of DEcircRNAs of GSE154046 and circRNAs related to has-miR-32-3p and has-let-7i-5p was constructed using Starbase 3.0. c CircRNA-miRNA-mRNA network.

Fig. 7.

Identification of ceRNA related to IgAN autophagy: ceRNA network of the CDKN1A and SIRT1 was shown using Sankey diagram. a Volcano plot of DEcircRNAs in GSE154046 (|log2FC|>1 and adj. p < 0.05). b Intersection of DEcircRNAs of GSE154046 and circRNAs related to has-miR-32-3p and has-let-7i-5p was constructed using Starbase 3.0. c CircRNA-miRNA-mRNA network.

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Detection of the Genes with Clinical Significance

A correlation analysis was conducted on the expression of MYC, FOS, SIRT1, CDKN1A, and CCL2 with various clinicopathological features using the Nephroseq v5 analysis engine, facilitating the examination of the relationship between gene expression levels and clinical characteristics. Notably, a significantly positive correlation was observed between the expression of MYC, FOS, SIRT1, CDKN1A, CCL2, and proteinuria (Fig. 8a–e). Conversely, MYC, FOS, and CDKN1A expression showed a negative correlation associated with glomerular filtration rate (GFR) (Fig. 8f–h). Additionally, MYC and CDKN1A expression was found to be positively correlated with serum creatinine level (Fig. 8i, j). These findings suggest that MYC, FOS, SIRT1, CDKN1A, and CCL2 may serve as a prognostic indicator for IgAN.

Fig. 8.

Correlation analysis of MYC, FOS, SIRT1, CDKN1A, and CCL2 expression with the clinicopathological features, including proteinuria (a-e), glomerular filtration rate (GFR) (f-h), and serum creatinine level (i, j).

Fig. 8.

Correlation analysis of MYC, FOS, SIRT1, CDKN1A, and CCL2 expression with the clinicopathological features, including proteinuria (a-e), glomerular filtration rate (GFR) (f-h), and serum creatinine level (i, j).

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Immune Cell Infiltration Analysis

To further investigate potential immune cell dysregulation, it was imperative to examine the autoimmune characteristic of IgAN. An analysis of immune cell infiltration was performed to compare individuals with IgAN to those in the control group. The histogram chart displayed the dominant immune cell subpopulations in IgAN, which were CD8+ T cells, monocytes, and macrophages M2 (Fig. 9a). The CIBERSORT algorithm indicated that the proportions of the T follicular helper cells (p = 0.0068), T cells regulatory (Tregs) (p = 0.0293), mast cells activated (p = 0.0356), and dendritic cells resting (p = 0.0027) were comparatively lower in the IgAN samples compared to the control samples. Conversely, the proportions of NK cells activated (p = 0.0073), monocytes (p = 0.0392), and macrophages M2 (p = 0.0079) were relatively higher in the IgAN samples (Fig. 9b). The results of the correlation analysis on immune cells are presented in Figure 9c. Subsequently, the association among MYC, FOS, SIRT1, CDKN1A, CCL2, and immune cells was investigated. The findings indicated a notable association between the expression levels of these five genes and dendritic cells resting, as well as macrophages M2 and NK cells activated in IgAN (Fig. 9d). These results suggested that specific immune cell populations are dysregulated in the context of IgAN.

Fig. 9.

Landscape of immune infiltration in IgAN. a Relative ratio of immune infiltration in IgAN. b Violin plots depicting significantly different immune cells in IgAN patients. p < 0.05 was considered statistically significant. c Pearson correlation analysis of different infiltrating immune cell subpopulations. d Correlation between MYC, FOS, CDKN1A, SIRT1, CCL2, and infiltration immune cells. *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 9.

Landscape of immune infiltration in IgAN. a Relative ratio of immune infiltration in IgAN. b Violin plots depicting significantly different immune cells in IgAN patients. p < 0.05 was considered statistically significant. c Pearson correlation analysis of different infiltrating immune cell subpopulations. d Correlation between MYC, FOS, CDKN1A, SIRT1, CCL2, and infiltration immune cells. *p < 0.05, **p < 0.01, ***p < 0.001.

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Co-Expression Analysis of DEARG and Autophagy-Related Genes and Prediction of Potential Therapeutic Drug

The study employed Pearson correlation analysis to investigate the underlying communication mechanisms of DEARGs and autophagy. In mammals, the autophagosome membrane formation is influenced by 23 proteins linked to autophagy, including WIPI1, ATG14, BECN1, ATG2B, ATG3, ATG4A, and others. The heatmap presented in Figure 10 indicated a mild to moderate correlation between the percentages of different autophagy-related genes. The findings showed that the SIRT1 expression level had notable connections with ATG16L2, BECN1, ATG10, ATG5, ATG3, ATG4C, ATG4D, ATG14, ATG9B, and WIPI. CDKN1A had relevant to ATG12, ATG4B, ATG7, and ATG9B. CCL2 had an important correlation with ATG4A, ATG4C, ATG5, ATG14, ATG2B, ULK2, WIPI1, and WIPI2. These findings suggested that SIRT1, CDKN1A, MYC, FOS, and CCL2 may be associated with autophagy genes and may have a specific regulatory function in formation of autophagosome membrane. Moreover, the DGIdb was utilized to forecast the medications linked to 5 hub genes of autophagy-related genes, leading to the discovery of 67 drugs (online suppl. Table S1). The network depicting the interaction between genes and drugs was shown in Figure 11. The majority of potential therapeutic drugs interact with MYC (40/67), followed by CDKN1A (18/67).

Fig. 10.

Co-expression analysis of hub genes and autophagy-related genes. DEARGs with negative correlation were indicated by red, while DEARGs with positive correlation were indicated by blue. A correlation heatmap depicting the relationship between multiple genes and multiple genes (or one gene). Genes are represented by the abscissa and ordinate, with different correlation coefficients indicated by various colors (blue for positive correlation and red for negative correlation). The intensity of the color indicates the strength of the relationship. Asterisks (*) stand for significance levels, **p < 0.01, *p < 0.05.

Fig. 10.

Co-expression analysis of hub genes and autophagy-related genes. DEARGs with negative correlation were indicated by red, while DEARGs with positive correlation were indicated by blue. A correlation heatmap depicting the relationship between multiple genes and multiple genes (or one gene). Genes are represented by the abscissa and ordinate, with different correlation coefficients indicated by various colors (blue for positive correlation and red for negative correlation). The intensity of the color indicates the strength of the relationship. Asterisks (*) stand for significance levels, **p < 0.01, *p < 0.05.

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Fig. 11.

Genes and drugs interaction of hub genes of IgAN from DGIdb.

Fig. 11.

Genes and drugs interaction of hub genes of IgAN from DGIdb.

Close modal

IgAN is an autoimmune disease; the deposition of poorly glycated IgA immune complexes in the glomerular mesangium with the subsequent progression of renal inflammation and fibrosis is a key factor in the development of IgAN [29]. Furthermore, research has indicated that autophagy exerts a negative control on inflammatory responses and minimizes inflammatory harm to various tissues and organs in IgAN [30].

In this current study, network analysis was applied to identify biologically significant DEARGs. A total of 25 DEARGs were detected, consisting of 11 upregulated and 14 downregulated genes, with 5 autophagy-related genes (SIRT1, FOS, CCL2, CDKN1A, and MYC) identified as diagnostic biomarkers. Functional enrichment analysis confirmed that the hub DEARGs primarily functioned in macroautophagy, cell cycle arrest, and autophagy. For example, the autophagy protein LC3 identifies SIRT1 as a target for degradation in the cytoplasmic autophagosome-lysosome pathway [31]. A recent investigation demonstrated that Tris DBA attenuated NLRP3 inflammasome activation through SIRT1 and SIRT3-mediated autophagy induction, thereby improving IgAN [32]. Another study showed that compound K is used to treat IgAN by enhancing autophagy and SIRT1 [33]. Our research further confirmed a significant decrease in SIRT1 expression in IgAN, suggesting a potential role for SIRT1 in slowing the progression of IgAN through autophagy protective cellular mechanism. The MYC proto-oncogene plays a crucial role in the regulation of autophagy and is frequently implicated in chromosomal translocation and gene amplification in numerous human cancers [34]. MYC induces autophagy by upregulating the stress-induced transcription factor ATF4, which activates the cysteine sulfinyl reductase Sesn2 [35]. Barbagallo et al. [36] demonstrated that MYC is significantly downregulated in biopsies of patients with membranous glomerular nephropathy, impacting cell cycle, proliferation, and apoptosis. Moreover, MYC is one of the key genes in WNT/β-catenin and PI3K/Akt signaling pathways, contributing to the pathogenesis of the disease and exhibiting abnormal expression at gene and protein levels in the IgAN patient [37]. Consistent with these studies, our study also revealed that MYC was decreased in the IgAN. Therefore, we propose that increased MYC levels could serve as a compensatory mechanism to protect against the progression of IgAN. FOS gene has been extensively reported in numerous types of cancer and inflammatory diseases, functioning as a regulator of cell proliferation, differentiation, autophagy, and migration [38, 39]. Studies have discovered that FOS plays a role in causing DNA damage, telomere injury, and neutrophil activation, all of which are associated with the progression and development of IgAN [40]. Furthermore, FOS proteins are linked to the disappearance of podocyte foot processes. Our study found a significant downregulation of FOS in IgAN, suggesting that diminished FOS levels may have a reduced protective effect on IgAN occurrence and progression. Correspondingly, the reversal of the decreased FOS expression could potentially slow down the advancement of IgAN. The function of CCL2 is to facilitate signal transduction by binding and activating CCR2, thereby regulating apoptosis, necrosis, and autophagy [41]. Despite our findings indicating a significant decrease in CCL2 expression in IgAN, several studies have demonstrated that the overexpression of CCL2 is implicated in podocyte injury. Feng et al. [42] found that the presence of exosomal CCL2 mRNA was linked to the severity of tubular atrophy and interstitial fibrosis. In addition, urinary CCL2 played a crucial part in predicting the outcome for individuals with IgAN who have ESRD [43]. Hence, further investigation is warranted to delve deeper into the role of CCL2 in the development of IgAN. CDKN1A codes for a cyclin-dependent kinase inhibitor that has the capability to attach to cyclin/cyclin-dependent kinase complexes, thereby restraining their activity and blocking cell cycle at the G2/M boundary. The regulation of autophagy and apoptosis is significantly influenced by CDKN1A [44]. Although the role of CDKN1A in IgAN remains unclear, prior research has indicated that the decrease in CDKN1A expression might be linked to the degeneration of podocytes in the progression of membranous nephropathy [36]. Other research suggests P21 encoded by the CDKN1A gene coordinates autophagy, proliferation, and apoptosis in response to metabolic stress in cancer [45]. Our study found that CDKN1A expression was downregulated in IgAN, with a high AUC value of 97.33%. CDKN1A shows potential as a promising diagnostic indicator for IgAN. Future research endeavors will focus on validating these findings with a larger sample size.

The ceRNA is an element that competes with mRNA for binding, while miRNAs are a type of RNA molecules that are capable of binding to target mRNAs. Certain circRNAs can competitively bind to miRNAs, reducing the effectiveness of miRNA binding. The binding of miRNAs to target mRNAs can lead to mRNA degradation or gene expression regulation through translation repression [46]. It is increasingly evident that ceRNAs play an integral role in the pathogenesis of kidney disease, particularly in the regulation of autophagy. Xu et al. [47] identified the SPAG5-AS1-miR-749-5p-YY1 axis as a key player in inhibiting autophagy and exacerbating podocyte apoptosis. Liao et al. [48] found the regulatory role of miR-27a-3p in cell proliferation, apoptosis, and inflammatory responses in IgAN. Our study also revealed significant upregulation of let-7i-5p and miR-132-3p in IgAN patients, although their specific roles in IgAN are unknown. Previous studies have indicated that miR-132-3p targets SIRT1 to promote apoptosis and inflammation response in renal tubular epithelial cells [49]. Let-7i-5p promoted a malignant phenotype in nasopharyngeal carcinoma by inhibiting autophagy [50]. In order to investigate the role of autophagy in IgAN, we constructed a ceRNA network consisting of circRNA-miRNA-mRNA interactions, with the goal of predicting the regulatory relationships within a potential ceRNA network targeting hub DEARGs. This proposed ceRNA network related to autophagy represents a novel avenue for identifying potential biomarkers and therapeutic targets, although further investigation is necessary to confirm its value.

Prior research have indicated a strong correlation between autophagy and immune infiltration in the pathogenesis of IgAN [30, 32]. Nevertheless, a comprehensive exploration to elucidate the interplay between autophagy and immune infiltration in IgAN patients has not been conducted. Consequently, we conducted a thorough analysis of the possible mechanisms of DEARGs and the presence of immune infiltrating cells in IgAN. The CIBERSROT algorithm was utilized to analyze four immune cell subpopulations (NK cells activated, follicular helper T cells, macrophages M2, and dendritic cells resting) related to IgAN progression. Recent report indicated that individuals with IgAN display heightened levels of CD56dimCD16+ NK cells in comparison to healthy individuals, suggesting a potential association between NK cells and IgAN pathogenesis [51]. A recent study has demonstrated a positive correlation between M2a macrophages and proteinuria as well as serum creatinine levels, suggesting a potential exacerbation of the progression of IgAN [52]. Moreover, Meng et al. [53] observed a notable decrease in the expression of a protein secreted by follicular dendritic cells, which exhibited a negative correlation with increased IgA production in the tonsils of IgAN patients. Nevertheless, there was a disparity in follicular helper T cells in comparison to the prior analysis. This discrepancy may be attributed to the different immune statuses in the development of IgAN in the different datasets. Further investigation is warranted to uncover the underlying mechanisms governing these associations of immune cells. Based on our correlation analysis of hub ARGs and immune cells, we hypothesize that SIRT1, MYC, and CDKN1A exhibit a positive correlation with resting dendritic cells. Conversely, SIRT1 demonstrates a negative correlation with NK cells activated and macrophages M2. Additionally, we also propose that FOS and CCL2 are negatively correlated with macrophages M2. The occurrence and progression of IgAN could potentially be influenced by these associations. Further research is needed to fully understand the complex relationships among the previously mentioned genes and immune cell infiltration.

From the DGIdb, a total of 67 drugs associated with IgAN hub genes were acquired in this study. Experimental evidence from both in vitro and in vivo studies has confirmed that vorinostat, a inhibitor of lysine deacetylase, can effectively reduce proteinuria and hinder the progression of tubulointerstitial injury [54]. Vorinostat could potentially offer a new treatment treating proteinuria and progressive tubulointerstitial injury in chronic kidney disease. Methylprednisolone, a steroid reported to be effective in treating IgAN [55, 56], remains a topic of controversy. Further investigation into the optimal dosage and mechanism of methylprednisolone is crucial for improving treatment outcomes for IgAN patients. Thrombin is a quick-acting local hemostatic agent, which can promote the mitosis of epithelial cells and accelerate wound healing. Bagang et al. [57] found that thrombin is involved in the activation of proteinase-activated receptors that regulate oxidation, inflammatory stress, immune cell activation, fibrosis, autophagy flux, and apoptosis in different renal diseases. Cyclosporine, an immunosuppressant primarily used in transplant medicine and for the treatment of autoimmune diseases, has demonstrated therapeutic efficacy in reducing proteinuria in IgAN through several clinical trials [58]. In addition, cyclosporine has been found to regulate autophagy and inhibit tumor growth [59], but the relationship between IgAN and autophagy has not been reported. The majority of these immunosuppressants are also used as anti-cancer drugs, with their primary mechanisms being antioxidative, anti-inflammatory, and anti-apoptotic in nature.

In recent times, autophagy has been acknowledged as a vital cellular mechanism for preserving cell homeostasis and may serve a protective role in IgAN. However, there are still certain limitations in our research. First, the limited availability of datasets restricts our ability to conduct comprehensive analyses. Although the GSE116626 dataset that we used contains a limited number of cases. We will supplement the analysis when the data are enriched in the future. As for additional experimental work, we are sorry that we could not perform experimental research due to the lack of our own follow-up data of IgAN patients at present. We would like to add this functional analysis in future work. Third, we currently lack the necessary data to analyze patients with varying immune statuses when studying the immune microenvironment in IgAN. Hence, further studies are necessary to validate our findings.

A ceRNA network related to autophagy was constructed and analyzed for immune infiltration to propose novel regulatory mechanisms in the pathogenesis of IgAN. Our study identified SIRT1, FOS, CCL2, CDKN1A, and MYC, along with immune cells such as activated NK cells, follicular helper T cells, M2 macrophages, and resting dendritic cells, as potentially critical factors in the development of IgAN. Furthermore, we predicted several candidate drugs with the potential to alleviate glomerular damage and improve outcomes in IgAN patients. This study aims to contribute to the identification of potential diagnostic biomarkers or therapeutic targets for IgAN patients by enhancing the understanding of the regulatory mechanism of DEARGs and immune cells in IgAN.

We would like to express our heartfelt thanks to the editors and anonymous reviewers for their valuable comments.

An ethics statement is not applicable because all the data in this study are available in public databases. Meanwhile, this study protocol was reviewed and approved by the Ethics Committee of Hangzhou Hospital of Traditional Chinese Medicine, Approval No. 33010610106746. Written informed consent was not required because no subjects participated in the study.

The authors have no conflicts of interest to declare.

This work was supported by Zhejiang Traditional Chinese Medical Scientific Technology Project (Grant No. 2023ZR118).

Conception and design: Bingjie Shui and Huaying Zhang; collection and assembly of data: Huaying Zhang and Huiai Lu; data analysis and interpretation: Huaying Zhang, Bicui Zhan, and He Shi; and manuscript writing: all authors.

All the information utilized in this investigation may be found in the public domain at the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). Further enquiries can be directed to the corresponding author.

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