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Identification of key necroptosis-related genes and immune landscape in patients with immunoglobulin A nephropathy

BMC Nephrology volume 25, Article number: 459 (2024) Cite this article

Abstract

Background

Immunoglobulin A nephropathy (IgAN) is a major cause of chronic kidney disease (CKD) and kidney failure. Necroptosis is a novel type of programmed cell death that has been proved to be associated with the pathogenesis of infectious disease, cardiovascular disease, neurological disorders and so on. However, the role of necroptosis in IgAN remains unclear.

Methods

In this study, we explored the role of necroptosis-related genes in the pathogenesis of IgAN using a comprehensive bioinformatics method. Microarray datasets GSE93798 and GSE115857 were downloaded from Gene Expression Omnibus (GEO). “limma” package of R software was employed to identify necroptosis-related differentially expressed genes (NRDEGs) between IgAN and healthy controls. GO and KEGG functional enrichment analysis was performed by Clusterprofiler. Least absolute shrinkage and selection operator (LASSO) regression analysis identified hub NRDEGs. We further established a diagnostic model consisting of 7 diagnostic hub NRDEGs and validated the efficacy by an external dataset. The expression of hub genes was confirmed in sc-RNA dataset GSE171314. Immune infiltration, gene set enrichment analysis and transcription factor binding motifs enrichment analysis were conducted to further uncover their roles.

Results

1076 differentially expressed genes were identified between healthy individuals and IgAN patients from RNA-seq dataset GSE9379. Then we cross-linked them with necroptosis-related genes to obtain 9 NRDEGs. LASSO regression analysis screened out 7 hub genes (JUN, CD274, SERTAD1, NFKBIA, H19, UCHL1 and EZH2) of IgAN. We further conducted functional enrichment analysis and constructed the diagnostic model based on dataset GSE93798. GSE115857 was used as the independent validation cohort and indicated a great predictive efficacy. Immune infiltration, gene set enrichment analysis and transcription factor binding motifs enrichment analysis revealed their potential function. Finally, we screened out four drugs that were predicted to have therapeutic value of IgAN.

Conclusions

In summary, we identified 7 hub necroptosis-associated genes, which can be used as potential genetic biomarkers for IgAN prediction and treatment. Four drugs were predicted as the potential therapeutic solutions. Collectively, we provided insights into the necroptosis-related mechanisms and treatment of IgAN at the transcriptome level.

Peer Review reports

Introduction

IgAN is the most prevalent glomerulonephritis globally, characterized by abnormal immunoglobulin A (IgA) accumulations in kidneys accompanied by a mesangial proliferative glomerulonephritis [1. 30%～40% of IgAN patients progress slowly to end-stage renal failure [2]. IgAN impairs the function of filtering in the kidney, leading to hematuria and proteinuria. The confirmed diagnosis is typically achieved through the collection of renal biopsy specimens and subsequent detection of IgA1 using immunofluorescent staining. Current therapy interventions mainly focus on supportive treatments as blood pressure control, proteinuria reduction and high-dose corticosteroid [3, 4], but optional individualized cures are limited. At present, the most common methods to predict and diagnose IgAN are histologic features and relevant parameters such as eGFR, BP, proteinuria, age, ethnicity [5]. The mechanism of IgAN remains elusive since the genetic landscape of IgAN is complex. Significant variations in symptoms and histological alterations are observed in various ethnic populations [6]. Therefore, it is imperative to explore new possible pathogenic mechanisms of IgAN and identify potential therapeutic targets.

Necroptosis is a newly discovered pathway of regulated cell death, morphologically characterized by the loss of plasma membrane integrity, leakage of intracellular contents and organelle swelling [7]. The process of necroptosis is triggered by immune ligands such as Fas, TNF and LPS [8]. The initiation of necroptosis requires the formation of necrosome, a complex consisting of receptor-interacting proteins 1(RIPK1), receptor-interacting proteins 3(RIPK3) and mixed-lineage kinase domain–like pseudokinase(MLKL) [9]. As a highly pro-inflammatory mode of cell death, necroptosis causes the release of DAMPs into the extracellular space, promotes inflammation and activate an immune response [10]. Necroptosis has been reported to be involved in the development of several diseases in multiple organs, including myocardial injury, inflammatory neurodegenerative disease and cancer [11, 12]. Recent studies suggested that necroptosis contributes to acute tubular damage, ischemic reperfusion injury and renal tubulointerstitial fibrosis [13]. However, whether necroptosis takes a part in IgAN needs further research.

By far, no studies have investigated whether necroptosis is associated with the pathogenesis and development of IgAN. In this study, we identified differentially expressed necroptosis-related genes that may play a role in IgAN using datasets downloaded from GEO, then selected 7 signature genes to construct a diagnostic model. We then screened out several potential therapeutic drugs.

Methods

The workflow of this study was demonstrated in Fig. 1. This study is a bioinformatics analysis and does not involve clinical trial registration.

Fig. 1
figure 1

The workflow of this study

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Data collection

Three microarray datasets of IgAN (GSE93798, GSE115857, GSE171314) were downloaded from the GEO database. GSE93798 contains glomerular compartment from 22 healthy individuals and 20 IgAN patients. GSE115857 contains renal biopsies from 7 healthy individuals and 55 IgAN patients. GSE171314 contains Single-cell RNA sequencing (scRNA-seq) information of kidney biopsies from 1 control subjects and 4 IgAN patients. Age, sex, and other clinical covariates were not available for control in their study.

Differentially expressed genes identification

The “limma” package [14] of R software was used to identify differentially expressed genes between IgAN patients and normal controls with the following criteria: P.Value < 0.05 and |log2FC| > 0.585. Then, we generated volcano and heat maps for differential gene analysis. Then we crossed them with necroptosis-related genes from GeneCards.

Functional enrichment analysis

Gene Ontology (GO) [15] and Kyoto Encyclopedia of Genes and Genomes (KEGG) [16] enrichment analysis were conducted by “ClusterProfiler"package [17] in R software. A gene set at p < 0.05 and q < 0.05 was regarded as significantly enriched.

Development of the diagnostic model of necroptosis-related genes

Differentially expressed necroptosis-related genes was selected and LASSO regression was used to construct the diagnostic model. After incorporating the expression values of each gene (denoted by x) and the estimated regression coefficients from the LASSO regression analysis (denoted by β), a risk score was calculated for each patient using the formula: Risk Score = β1⋅x1 + β2⋅x2+⋯+βp⋅xp. A The score for each patient was calculated based on the risk score formula. ROC curves were used to investigate the efficacy and reliability of model.

scRNA-seq data processing

Seurat R package [18] was used for downstream principal component analysis and t-distributed stochastic neighbor embedding (tSNE). The clusters were annotated by the celldex package (https://bioconductor.org/packages/release/data/experiment/vignettes/.

celldex/inst/doc/userguide.html). Marker genes for each cell sub-type from the single cell expression profile were selected by setting the parameter as P.Value adj < 0.05 (calculated using the Benjamini-Hochberg procedure)and |avg log2FC| >1.

Evaluation of immune cell infiltration

The abundance of immune cells in the environment was calculated by CIBERSORT [19]. We used CIBERSORT-based deconvolution combined with LM22 to measure the relative proportion of 22 types of immune subpopulations in GSE93798 and perform spearman correlation analysis of gene expression and immune cell subtypes.

Gene set enrichment analysis (GSEA)

DEGs were divided into two groups (high expression vs. low expression) based on the expression level (median expression) of 7 hub genes. GSEA was exploited to discover the enrichment terms to recognize potential pathways related to IgAN. Text was trimmed and organized using Java script.

Transcription factor binding motifs enrichment analysis

Transcription factor binding motifs (TFBMs) enrichment analysis was performed by The Bioconductor package RcisTarget [20]. The calculation was conducted based on motifs and enrichment score (NES) was normalized to the total number of motifs in the database. We further annotated files based on motif similarity and gene sequences. Firstly, to estimate the overexpression of each motif on a gene set, we calculated the area under the curve (AUC) for each motif-motif pair based on the recovery curve calculation of the gene set to motif ordering. The NES of each motif was calculated based on the AUC distribution of all motifs in the gene set.

Potential therapeutic drug prediction

The Connectivity Map (CMap) [21] is a gene expression profiling databased developed by the Broad Institute. It is used to reveal functional associations between small-molecule compounds, genes and disease states. It contains gene expression profiles of 1.5 million genes from 5,000 small-molecule compounds, and 3,000 genetic reagents, tested in multiple cell types, concentrations and treatment durations. This study used CMap to predict therapeutic agents through differentially expressed genes in IgAN.

Statistical analysis

Statistical analysis was conducted by R software (version 4.2.2) and P.Value < 0.05 was regarded as statistically significant.

Results

Identification of differentially expressed necroptosis-related genes in IgAN

Datasets GSE93798 was downloaded from the NCBI GEO database, including 22 healthy controls and 20 IgAN patients. The differently expressed genes were obtained by limma package with P.Value < 0.05 & |log2FC|>0.585. A total of 1076 differentially expressed genes were obtained, including 478 up-regulated genes and 598 down-regulated genes, as shown in Fig. 2a and b and Table S1. Subsequently, necroptosis-related genes with Relevance score > 1 were extracted from GeneCards database and intersected with the DEGs to obtain 9 necroptosis-related differentially expressed genes (NRDEGs), visualized using a Venn diagram (Fig. 2c). The relevance score is calculated by the GeneCards database and is based on term frequency/inverse document frequency (additional details on the scoring can be found here: https://www.genecards.org/Guide/Search#relevance).

Fig. 2
figure 2

Identification of DEGs. a A volcano plot showing 1076 DEGs in the dataset GSE93798. b The heatmap of DEGs. c 9 necroptosis-related differentially expressed genes (NRDEGs) were identified after intersection

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GO and KEGG Enrichment analyses

GO analysis revealed that NRDEGs were enriched in cellular response to reactive oxygen species, response to reactive oxygen species and cellular response to oxidative stress (Fig. 3a). The KEGG results indicated that NRDEGs were mainly enriched in IL − 17 signaling pathway, TNF signaling pathway and NOD − like receptor signaling pathway (Fig. 3b).

Fig. 3
figure 3

GO and KEGG enrichment analyses of NRDEGs. a GO enrichment of NRDEGs with bar-plot. b KEGG analyses of NRDEGs

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Construction and validation of a diagnosis-associated risk model

To screen diagnostic signature genes in IgAN, LASSO regression analysis was applied based on the 9 overlapping genes (Fig. 4a, b). LASSO regression analysis identified 7 genes as the characteristic genes for IgAN: JUN, CD274, SERTAD1, NFKBIA, H19, UCHL1 and EZH2 (Fig. 4c, Table S2). We used these 7 genes as hub genes for follow-up study and constructed a diagnostic model. The risk scores for this model were calculated by the following formula: RiskScore = JUN x (−0.23407192090164) + CD274x (−0.0971426727949052) + SERTAD1x (−0.064383446338459) + NFKBIA x (−0.00625176448515286) + H19 × 0.0213606147376945 + UCHL1 × 0.078096134570401 + EZH2 × 0.0943353752022954. As Fig. 4d showed, the area under the curve (AUC) reached 1, indicating the great predictive efficacy of the diagnostic signature in IgAN patients. To verify the stability and reliability of the diagnostic model, GSE115857 dataset from GEO was applied as the external validation. The area under the curve (AUC) was 0.966 (Fig. 4e), demonstrating the great predictive performance of the diagnosis-associated risk model.

Fig. 4
figure 4

LASSO regression analysis. a LASSO regression identified 7 diagnosis-related genes. b The optimal parameter (λ) in the LASSO model. c coefficient profiles of the 7 diagnosis-related genes. d ROC curves analysis of train set (GSE93798) e ROC curves analysis of validation set (GSE115857)

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Expression of NRDEGs in IgAN at cellular level

To explore the expression of the NRDEGs at single-cell level, we downloaded a sc-RNA (single-cell RNA) dataset GSE171314 from GEO and analyzed by Seurat. Cells were clustered by tSNE and annotated by SingleR [22]. Based on cell features, cells are assigned into Dendritic cells, Monocytes and Progenitors (Fig. 5a, Fig. S1). Figure 5b, c showed the expression level of JUN, CD274, SERTAD1, NFKBIA, H19, UCHL1 and EZH2 in each cell type.

Fig. 5
figure 5

scRNA analysis of hub genes. a Two-dimensional plot from unsupervised clustering by t-distributed stochastic neighbor embedding (tSNE) of the single-cell transcriptomes. Color coded based on cell types. b Illustration of the distribution of hub genes in each cell type

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Immune cell infiltration analysis

Microenvironment is composed of immune cells, extracellular matrix, growth factors as well as inflammatory factors, impacting the sensitivity of clinical diagnosis and treatment. To explore the underlying role of NRDEGs in IgAN, we analyzed the differential gene immune infiltration. Figure 6a, b demonstrated the composition of immune cells in each sample and the correlation between each type of immune cell. Compared to healthy controls, the proportion of NK cells activated, macrophages M1, dendritic cells resting, mast cells activated were significantly higher in IgAN patients (Fig. 6c). Moreover, several NRDEGs highly corelated to immune cells (Fig. 6d).

Fig. 6
figure 6

The landscape of immune infiltration in IgAN. a Relative proportion of 22 types of immune cells in IgAN. b Heatmap of the correlation between immune cells. c The violin plot revealed the immune cell expressions were different in two groups. d Correlation analysis between NRDEGs and immune cells

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The association between necroptosis-related DEGs and immune-characteristic molecules were analyzed via the TISIDB databases [23]. Figure 7a-e showed the high correlation between NRDEGs and immune and molecular subtypes

Fig. 7
figure 7

Analysis of immune infiltration in TISIDB

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.

Gene set enrichment analysis

GSEA (gene set enrichment analysis) was then employed to explore the functional roles of NRDEGs. CD274 was enriched in pathways such as aminoacyl tRNA-biosynthesis and insulin signaling. EZH2IS was enriched in pathways such as cell cycle and lysine degradation. H19 was enriched in pathways such as Fc epsilon RI signaling and homologous recombination. JUN was enriched in pathways such as ErbB signaling and histidine metabolism. NFKBIA was enriched in ErbB signaling pathway and histidine metabolism. SERTAD1 was enriched in pathways such as arginine and proline metabolism and adipocytokine signaling pathway. UCH1was enriched in pathways such as ECM (extracellular matrix) receptor interaction and nitrogen metabolism (Fig. 8).

Fig. 8
figure 8

Gene set enrichment analysis (GSEA) of hub genes

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Enrichment analysis for transcription factors

We used these 7 hub genes for transcription factor (TF) binding motif enrichment analysis and found that they were regulated by multiple common transcription factors. Cumulative recovery curves were used to enrich these transcription factors. Results showed that the motif with the highest normalized enrichment score (NES:6.19) was cisbp_M5081. Figure 8 demonstrates all enriched motifs and transcription factors (Fig. 9).

Fig. 9
figure 9

The enrichment analysis of transcription factors of hub genes

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The study of IgAN gene expression levels

To explore the change of gene expression levels in patients, 1840 IgAN-related genes were obtained from GeneCards. The expression of ACE, AGT, ALB, CD40LG, COL4A1, COL4A4, COL4A5, PIGR, SPRY2 and TGFB1 was significantly different between healthy controls and patients (Fig. 10a). In addition, expression of the 7 hub genes remarkably correlated with genes related to IgAN. Among which, the expression of JUN negatively correlated to COL4A1 (Pearson r=−0.724) and the expression of SERTAD1 positively correlated to SPRY2 (Pearson r = 0.83) (Fig. 10b). We then screened the miRcode database for the 7 hub genes and obtained 76 miRNA and 166 interactive RNA pairs (Fig. 11, Table S3).

Fig. 10
figure 10

The relationship of hub genes and the IgAN-related genes. The comparisons of the expression of multiple disease-related genes between the healthy and IgAN patients. (b) Bubble map for the pearson correlations between seven hub genes and IgAN-related

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Fig. 11
figure 11

miRNA-mRNA interaction network

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Potential drug candidates of NRDEGs Profile

The Connectivity Map (cMap) analysis was performed to identify possible drugs for IgAN using hub genes. DEGs were divided into upregulated and downregulated groups. Top100 genes in each group were uploaded to the CMAP database. Results showed that Tyrphostin_AG_126 (ERK1 and ERK2 phosphorylation inhibitor), Verrucarin_A (Protein synthesis inhibitor), Cephaeline (Protein synthesis inhibitor) and Homoharringtonine (Protein synthesis inhibitor) may be able to alleviate or reverse the disease states of IgAN (Fig. 12).

Fig. 12
figure 12

Potential therapeutic compounds identified by cMap

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Discussion

Traditionally, cell death can be categorized into two types: necrotic cell death and programmed cell death depending on whether the process is predefined. In recent years, other forms of cell death have been discovered, including necroptosis, ferroptosis, cuproptosis, oncosis and pyroptosis [24]. Necroptosis, as a highly regulated necrotic death, is mediated by RIPK1-RIPK3-MLKL cascade. Necroptosis participates in multiple diseases, including atherosclerosis cardiovascular disease, acute respiratory distress syndrome, liver injury, ocular disease, cancers and so on [25, 26]. In kidney disease, essential proteins of necroptosis such as RIPK1, RIPK3, MLKL and repulsive guidance molecules-b(RGM) contribute to AKI [27]. In the model of in the sepsis-induced acute kidney injury, blockade of RIPK1 and RIPK3 prevent kidney tubular injury [28, 29]. In patients with advanced CKD, necrotic renal tubular epithelia cells, as well as the expression level of RIP3 and MLKL, were significantly higher [30]. In the development of IgAN, necroptosis may play an important role. On one hand, mesangial cells are under pressure of inflammatory mediator, oxidative stress and cytokines, which is likely to cause necroptosis [31]. On the other hand, pathologic changes during IgAN progression may lead to necroptosis of mesangial cells [32].

This work identified the signature genes of necroptosis that were related to IgAN by analyzing the DEGs between IgAN patients and healthy individuals. 9 NRDEGs were obtained by cross-linking GSE93798 with necroptosis-related genes from GeneCards. Then, we performed GO and KEGG functional enrichment analysis to explore the GO terms and pathways that enriched of the NRDEGs. The response to oxidative stress and cellular response to reactive oxygen species were significantly enriched in the NRDEGs, indicating that NRDEGs were associated with oxidative stress, in line with former research which suggested oxidative stress correlated with serum Gd-IgA1 levels [33]. Previous studies revealed that oxidative stress may regulate the nephrotoxicity of aberrantly glycosylated IgA1 in IgAN [34]. Reactive oxygen species (ROS)are required for BV6/TNFα-induced necroptotic signaling and regulate it by stabilizing necrosome [35]. KEGG pathway analysis revealed that NRDEGs were mostly enriched in Epstein-Barr virus infection. The involvement of Epstein-Barr virus infection in IgAN has been reported in several studies [36]. In African American IgAN patients, enrichment of IgA-expressing B cells infected with Epstein-Barr virus (EBV) was observed, causing the production of galactose-deficient IgA1 [37].

We then performed LASSO regression analysis and identified 7 hub necroptosis-related genes (JUN, CD274, SERTAD1, NFKBIA, H19, UCHL1 and EZH2) and suggest their potential roles in IgAN. JUN is a key component of the transcription factor complex AP-1, regulating cell proliferation and stress response [38]. Research on the role of JUN in kidney diseases mainly focus on cystic kidney. During the progression of human acquired cystic kidney disease (ACKD), the activation of c-Jun is observed in glomerular and tubular cells [39]. In mouse PKD model, inhibition of Jnk1 and Jnk2 impair the nuclear accumulation of phospho c-Jun, thus decreasing proliferation and ameliorate the severity [40]. How JUN is related to IgAN needs more investigation. Drugs targeting CD274(PD-L1) has been applied to the treatment of kidney cancers including glomerulonephritis and metastatic renal cell carcinoma [41, 42]. Upregulation of PD-1 signaling is observed in focal segmental glomerulosclerosis in mouse model [43]. Our studies suggest a role of PD-L1 in IgAN but more research is needed to clarify the specific mechanism. SERTAD1 (serine/threonine-rich adaptor protein) is a Cdk4 (cyclin-dependent kinase 4) activator that plays a role in regulating cell growth, proliferation, and differentiation [44]. Differential expression of SERTAD1 was observed in several cancers, including breast cancer, colon cancer, lung cancer, brain tumors and renal cancer [45]. However, the relation between SERTAD1 and IgAN needs further studies to clarify. NFKBIA is an inhibitor of NF-κB. The relationship between NFKBIA and kidney disease is rarely discussed, but there are studies about its pro-inflammatory role. The expression of NFBIA significantly correlated to the production of inflammatory cytokines [46]. H19 is a long non-coding RNA that participates in oncogenic behaviors in many human cancers [47]. Expression of H19 positively correlated to the development of CaOx nephrocalcinosis-induced oxidative stress and renal tubular epithelial cell injury [48]. H19 has been reported as a potential therapeutic target in many studies. In diabetic mice model, suppression of H19 alleviates kidney fibrosis [49]. Overexpression of H19 significantly improves kidney function in I/R injury mice model [50]. During the transition of AKI to CKD, H19 promotes kidney fibrosis by regulating the miR-196a-5p/Wnt/β-catenin signaling pathway [51]. Little studies focus on the association between UCHL1 and kidney disease, but its role in inflammation has been reported. Knockdown of UCHL1 decreased the number of the key pro-inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α in macrophages [52].Enhancer of zeste homolog 2 (EZH2) acts as a methyltransferase responsible for inducing histone H3 lysine 27 trimethylation (H3K27me3) and is found to be upregulated in many cancer types [53]. In kidney disease, EZH2 is a potential therapeutic target. EZH2 promotes renal fibrosis by downregulating the expression of Smad7 and PTEN [53]. Inhibition of EZH2 alleviates IR-induced AKI through inactivation of p38 signaling [54]. In cisplatin-induced AKI mouse model, EZH2 inhibition attenuates inflammation by upregulating RKIP and blocking NF-κB p65 signaling [55]. We propose that EZH2 plays a role in IgAN, but the exact mechanism needs further elucidation.

Recent research has described that as the response to innate immune stimuli or genotoxic stress, “RIPoptosome” is assembled and mediates necroptotic signaling [56]. Nevertheless, the underlying mechanism between necroptosis and immunity remains elusive. By investigation the infiltration of immune cells in IgAN, we found that the proportion of NK cells activated, macrophages M1, dendritic cells resting and mast cells activated significantly higher compared to the control group. Macrophages are the predominant inflammatory cells in the renal interstitium [57]. Macrophage M1 facilitate Thl type inflammatory response by secretion of inflammatory factors such as IL-1, IL-6, IL-12 and ROS [58]. The number of macrophages was found increased in IgAN patients. The ability of dendritic cells to induce IgA production in naïve B cells was impaired in IgAN patients [59]. In mouse model, overexpression of indoleamine 2,3-dioxygenase (IDO) on dendritic cells reduced IgAN deposition in glomerular mesangium [60]. Among patients of primary and secondary glomerulonephritis with IgAN, the number of mast cell decreased patients whose renal function rarely deteriorated [61]. Mast cells were distributed in the fibrotic areas in interstitium. The number of mast cells significantly associated with renal functional parameters such as BUN, urinary protein excretion and creatinine clearance, suggesting its role in the progression of interstitial fibrosis [62].

MicroRNAs (miRNA) are reported to be involved in the pathogenesis of IgAN. For example, upregulated microRNA-21-5p (miR-21) leads to the accumulation of IgA1 by inhibiting SPRY1 and inducing Th17 polarization [63]. Several miRNAs serve as non-invasive markers of IgAN diagnosis, including miR-150, miR-204, miR-431 and miR-555 [64,65,66].

Taken together, this work highlighted the important role of necroptosis in IgAN. However, some limitations still remained in the study. Firstly, no other kidney disease control group was included. There is a possibility that DEGs were not specific to IgAN but generic symptoms like proteinuria. In addition, our conclusion is based on public databases, and it would be a great help to validate our results using clinical samples in the further.

Conclusion

In the study, we identified several necroptosis-related genes that are highly associated with IgAN pathogenesis. A well-validated diagnostic model was established on 7 hub genes and four drugs were screened out as the potential therapeutic molecules. These findings may provide a novel perspective for IgAN clinical diagnosis and treatment.

Data availability

The datasets were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO). GSE93798: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93798. GSE115857: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115857. GSE171314 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171314.

Abbreviations

IgAN:

Immunoglobulin A nephropathy

CKD:

C

hronic kidney disease

LASSO:

Least absolute shrinkage and selection operator

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

AUC:

The area under the curve

GSEA:

G

ene set enrichment analysis

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Funding

This work was supported by the National Natural Science Foundation of China (Grant No.82202063) .

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Author notes

  1. Ruikun Hu, Ziyu Liu and Huihui Hou contributed equally to this work.

Authors and Affiliations

  1. Affiliated Maternity and Child Health Care Hospital of Nantong University, Nantong, Jiangsu, 226001, China

    Ruikun Hu & Xiaorong Wang

  2. Department of Nephrology, Changzheng Hospital, Naval Medical University, Shanghai, 200003, China

    Ziyu Liu, Huihui Hou & Dechao Xu

  3. School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China

    Jingyu Li

  4. Department of Pharmacy, The First People’s Hospital of Nantong city, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, 226001, China

    Panfeng Feng

  5. Department of Nephrology, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200434, China

    Ming Yang

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  1. Ruikun Hu
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Contributions

Dechao Xu conceived the idea and designed the study. Ming Yang and Xiaorong Wang performed the data analysis. Ruikun hu and Jingyu Li performed the data analysis and wrote the main manuscript. Huihui Hou, Ziyu Liu and Panfeng Feng helped to polish the manuscript.

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Correspondence to Panfeng Feng, Xiaorong Wang or Dechao Xu.

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Hu, R., Liu, Z., Hou, H. et al. Identification of key necroptosis-related genes and immune landscape in patients with immunoglobulin A nephropathy. BMC Nephrol 25, 459 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12882-024-03885-4

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BMC Nephrology

ISSN: 1471-2369

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