Identification of key programmed cell death genes-necroptosis-related genes in systemic lupus erythematosus.

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Learn more: PMC Disclaimer | PMC Copyright Notice Clin Exp Med . 2026 Apr 19;26(1):230. doi: 10.1007/s10238-026-02144-0 Identification of key programmed cell death genes—necroptosis-related genes in systemic lupus erythematosus Lu Wang 2, # , Yuanyuan Li 1, # , Xinyi Liu 1 , Shichang Song 2 , Yichen Han 1 , Lingfei Mo 1 , Hanchao Li 1 , Xiaohao Wang 6 , Ying Pan 1 , Jian Sun 2, 3, 4, 5, ✉ , Jing Wang 1, ✉ Author information Article notes Copyright and License information 1 Department of Rheumatology and Immunology, First Affiliated Hospital of Xi’an JiaoTong University, Xi’an, 710061 PR China 2 Center for Innovative Research and Translation of Osteochondral Diseases, Xi’an Jiaotong University, Xi’an, 710061 Shaanxi China 3 Institute of Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, West Yanta Street No.76, Xi’an, 710061 Shaanxi People’s Republic of China 4 Key Laboratory of Trace Elements and Endemic Diseases, School of Public Health, National Health Commission, Xi’an Jiaotong University Health Science Center, Xi’an, People’s Republic of China 5 Key laboratory of environment and genes related to Diseases, Ministry of education, Xi’an, Shaanxi People’s Republic of China 6 Department of Spine Surgery, Honghui Hospital of Xi’an JiaoTong University, Xi’an, 710061 China ✉ Corresponding author. # Contributed equally. Received 2026 Jan 10; Accepted 2026 Mar 19; Issue date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13230332  PMID: 42001335 Abstract Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with challenging early diagnosis. Aberrant programmed cell death (PCD) is critical in SLE pathogenesis. This study aimed to identify key PCD genes for SLE diagnosis and disease activity assessment. Integrated bioinformatics analysis was conducted on three whole-blood transcriptomic datasets ( GSE65391 , GSE110174 , GSE154851 ) from GEO. Machine learning algorithms (LASSO, SVM-RFE) identified hub genes across 18 PCD types. ssGSEA and logistic regression determined the predominant PCD pathway. Immune infiltration and correlation analyses evaluated clinical relevance. Necroptosis was identified as the most discriminative PCD pathway (AUC = 0.950, 95% CI: 0.921–0.979). Seven necroptosis-related genes (STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B) were validated as key diagnostic biomarkers. A multivariate logistic regression model demonstrated robust performance across training and external validation cohorts, confirmed by internal validation (20 SLE patients and 16 healthy controls). The seven genes correlated significantly with dysregulated immune cells and were positively associated with SLEDAI, dsDNA levels, and neutrophil percentages. Necroptosis is a critical PCD pathway in SLE. The seven necroptosis-related genes represent promising diagnostic biomarkers and therapeutic targets. Supplementary Information The online version contains supplementary material available at 10.1007/s10238-026-02144-0. Keywords: Systemic lupus erythematosus, Programmed cell death, Necroptosis, Diagnosis, Disease activity Background Systemic lupus erythematosus (SLE) is a highly heterogeneous autoimmune disease characterized by substantial variation in clinical course and outcomes [ 1 ]. The typical manifestations encompass malar rash, alopecia, oral ulcers, arthritis, pleuritis, serositis, and lupus nephritis [ 2 ]. The presentation of symptoms and signs varies considerably among patients. This substantial clinical heterogeneity is presumably attributable to diverse underlying immune dysregulations driving SLE pathogenesis. At the cellular level, SLE is primarily driven by aberrant interactions between the adaptive and innate immune systems, leading to up-regulation of inflammatory cytokines, complement activation, immune complex deposition, and ultimately, inflammation aggravation and tissue damage [ 3 , 4 ]. The multifaceted and intricate pathogenesis of the condition is a significant contributing factor to the challenges encountered during the diagnostic process. The prevailing clinical diagnosis is predominantly informed by the 2019 EULAR/ACR classification criteria [ 5 ] and the 2012 SLICC classification criteria [ 6 ]. However, the multiple clinical and serological items comprising these criteria are complex and lack direct incorporation of specific immune mechanisms, potentially limiting their accuracy for early SLE diagnosis. The two cardinal pathological features of SLE are the breakdown of immune tolerance and the persistent production of autoantibodies targeting nuclear autoantigens. The dysregulation of cell death, in conjunction with defective clearance of the debris resulting from such cell death, is widely considered to represent a critical initiating factor for SLE. This process has been shown to result in the release of Damage-associated molecular patterns (DAMPs) [ 7 ], the amplification of inflammatory and immune responses, the generation and release of autoantigens, and the contribution to tissue injury [ 8 ]. It is evident that multiple distinct forms of Programmed cell death (PCD) are implicated in the pathogenesis of SLE, including apoptosis [ 9 ], necroptosis, pyroptosis [ 10 ], NETosis (NET-mediated cell death) [ 11 ], autophagy [ 12 ], and ferroptosis [ 13 ]. The exposure of SLE patients to triggers such as ultraviolet (UV) light, infections, or toxins can induce PCD, but the critical issue lies in the qualitative alterations of PCD processes that render them immunogenic and pro-inflammatory, rather than simply an increase in PCD quantity. These alterations, combined with defective clearance of cellular debris, result in the persistent exposure of autoantigens [ 14 ]. The failure to efficiently clear the resulting cellular debris provides a persistent source of autoantigens containing nuclear components, which strongly activate autoreactive B cells and promote the type I interferon (IFN-I) response, driving SLE. Necroptosis, a form of PCD that is independent of caspases [ 1 ], has recently emerged as a potential contributor to SLE occurrence. Dysregulated necroptosis has been increasingly recognized as a key mechanism in sterile inflammation and autoimmune diseases, including SLE [ 15 ]. The initiation of SLE can be triggered directly by receptors including TNFR, TLR3, TLR4, IFNRs, or the Z-DNA binding protein 1 (ZBP1), which promote the interaction of Receptor-Interacting Protein Kinase 1 (RIPK1) and RIPK3 via their RIP Homotypic Interaction Motif (RHIM) domains [ 16 – 19 ]. This results in RIPK3 activation and subsequent phosphorylation of the Mixed Lineage Kinase Domain-Like pseudokinase (MLKL) [ 20 – 22 ]. Phosphorylated MLKL translocates to the cytomembrane, resulting in the disruption of membrane integrity and the subsequent release of cellular contents and DAMPs [ 23 ]. This vigorous release of intracellular antigens and pro-inflammatory mediators positions necroptosis as a potent trigger for the loss of self-tolerance and systemic inflammation characteristic of SLE. It has been demonstrated that necroptosis can also induce NLRP3 inflammasome activation and subsequent IL-1β release, thereby further amplifying inflammation. Some evidence [ 24 ] indicated a potential role of necroptosis in the pathogenesis and progression of SLE. Increased necroptosis was observed in B cells from SLE patients [ 25 ]. Furthermore, sustained interferon (IFN) signalling contributed to the homeostatic expression of MLKL and facilitated the initiation of necroptosis. RIPK3-mediated necroptosis was associated with the onset of lupus nephritis in lupus mouse models (NZM 2328 and MRL/lpr) [ 26 ]. This link to end-organ damage, particularly in the kidney, highlighted the clinical relevance of this pathway beyond initial immune activation. In cutaneous lupus, necroptosis markers (p-RIPK3 and p-MLKL) was elevated in keratinocytes stimulated with IFN-γ and TNF-α, which suggested that necroptosis played a potential role to promote inflammation through the release of DAMPs and activation of the NLRP3 inflammasome, contributing to the development of SLE [ 27 ]. Despite these compelling findings, a comprehensive evaluation comparing necroptosis to other PCD pathways in human SLE, particularly at the transcriptomic level in clinical cohorts, was lacking. The definite gene signature of necroptosis in SLE and its correlation with SLE disease activity and specific immune cell infiltrates remained to be fully defined. To explore the role of PCD in SLE pathogenesis, bioinformatics analysis was used to evaluate the association between 18 distinct PCD pathways and SLE to select the most significantly death forms in SLE. We hypothesized that necroptosis would be a key PCD pathway in SLE and that its regulatory genes could serve as robust diagnostic biomarkers. Subsequent internal and external validation was used to confirm the specific PCD mechanisms, delineate their precise relationship with SLE pathogenesis and the diagnostic value of the key PCD genes. This study aimed to address this gap by systematically profiling PCD patterns to identify the most discriminative pathway and to develop and validate a diagnostic model based on necroptosis-related genes, thereby providing novel insights into SLE pathogenesis and potential therapeutic targets. Methods Datasets retrieval and processing Gene expression datasets were obtained from the NCBI Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/ ) using the search term \“Systemic Lupus Erythematosus\“. The inclusion criteria were: (1) gene expression profiles from human samples; and (2) the presence of both SLE patient samples and healthy control (HC) samples. Three datasets that met these criteria were selected: GSE65391 [ 28 ], GSE110174 [ 29 ], and GSE154851 [ 30 ]. These datasets included whole-blood gene expression profiles generated through microarray technology. Detailed information about the datasets is provided in Table 1 . Table 1. GEO Datasets Information Datasets Platform Samples Measurement Source Type References GSE65391 GPL10558 924 SLE and 72 Controls Microarray Whole Blood [ 28 ] GSE110174 GPL13158 144 SLE and 10 Controls Microarray Whole Blood [ 29 ] GSE154851 GPL16699 38 SLE and 32 Controls Microarray Whole Blood [ 30 ] Open in a new tab Abbreviation: SLE, systemic lupus erythematosus The study workflow was shown in Fig. 1 . Raw datasets were normalized using the limma package (version 3.54.0) in R (version 4.2.2). For probes mapping to the same gene symbol, the median expression value was assigned as the level of genes expression. This process generated precise expression matrices by converting probe identifiers to their corresponding official gene symbols. Due to missing clinical data entries in the GSE65391 dataset, imputation was performed using the k-nearest neighbors (KNN) algorithm (k = 10) to estimate missing values [ 31 ]. Fig. 1. Open in a new tab Flowchart of the study. Abbreviations: DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; SLE, systemic lupus erythematosus; ROC, receiver operating characteristic; ssGSEA, Single-sample Gene Set Enrichment Analysis; SVM-RFE, Support Vector Machine Recursive Feature Elimination Identification of programmed cell death-related DEGs To identify DEGs between SLE patients and HCs in the GSE65391 dataset, a comprehensive differential expression analysis was conducted using the limma package. Genes meeting the thresholds of an adjusted p -value < 0.05 and an absolute log2-fold change (|log2FC|) > 0.58 were designated as DEGs [ 32 ]. This robust empirical Bayes framework ensured precise detection of transcriptomic alterations with both statistical significance and biological relevance. To investigate the potential link between SLE pathogenesis and PCD mechanisms, up-regulated DEGs were systematically intersected with a curated gene set associated with 18 distinct PCD pathways (including 1560 genes). This comprehensive PCD gene repertoire was compiled from authoritative databases (GeneCards, Gene Ontology Consortium) and recent mechanistic literature (see Supplementary File for the complete gene list). Functional enrichment analysis (GO and KEGG) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis provided a comprehensive knowledge base for systematic gene function analysis [ 33 ]. Gene Ontology (GO) enrichment analysis, encompassing Biological Processes (BP), Molecular Functions (MF), and Cellular Components (CC) were used to identify the valuable insights into gene function [ 34 ]. Enrichment analyses were performed using the clusterProfiler R package (version 3.14.3). The top 3 significantly enriched terms within each category (KEGG, BP, MF, CC) were visualized using the ggplot2 R package. A significance threshold of P value < 0.05 was applied in this study. Machine learning algorithms and hub gene identification To identify hub genes, two machine learning algorithms were used for variable selection: LASSO Regression [ 35 ] (Least Absolute Shrinkage and Selection Operator), which included a penalty term (L1 regularization) to reduce the coefficients of less important features to zero, thus preventing overfitting, and Machine-Recursive Feature Elimination (SVM-RFE) [ 36 ], which was well-suited for smaller datasets, and iteratively removed features based on their importance ranking from an SVM model, keeping only the most relevant variables and eliminating redundant ones. Genes were identified as hub genes by both LASSO and SVM-RFE . Single-sample Gene Set Enrichment Analysis To quantify the activity levels of individual PCD pathways within each SLE sample, Single-sample Gene Set Enrichment Analysis (ssGSEA) [ 37 ] was performed to use the GSVA R package (version 1.46.0). This analysis leveraged the hub genes defining each PCD pathway. For each sample, ssGSEA calculated an Enrichment Score (ES), indicating the extent to which the genes within a specific programmed cell death (PCD) gene set were coordinately upregulated or downregulated in that sample. The ES served as a relative measure of activity level about the pathway. Logistic regression model construction The top five PCD pathways were identified by the normalized mean absolute enrichment scores. A multivariate logistic regression model was established using gene expression levels associated with these pathways. The diagnostic performance of the model was assessed through Receiver Operating Characteristic (ROC) curve analysis. The Area Under the Curve (AUC) and its 95% confidence interval (CI) were determined, with the model predicted probabilities serving as the input variable. Nomogram construction and internal validation A diagnostic nomogram was constructed using the regression equation from the logistic model in the GSE65391 dataset, visualized with the rms R package (version 6.2.0). This nomogram assigned scores to different expression levels of each feature based on their regression coefficients. The total score for each sample predicted its likelihood of being an SLE case. The nomogram’s predictive performance, which incorporated necroptosis-related genes, was evaluated through ROC curve analysis, calibration curves, and decision curve analysis (DCA). External validation of the nomogram and necroptosis-related genes External validation of the nomogram and the necroptosis-related gene signature was performed using the independent GSE110174 and GSE154851 datasets. GSE110174 included 154 whole-blood samples (144 SLE, 10 HC). GSE154851 included 70 whole-blood samples (38 SLE, 32 HC). ROC curves were used to evaluate the predictive accuracy in these external cohorts. Immune infiltration analysis The CIBERSORT algorithm, implemented via the CIBERSORT R package, was applied to the normalized gene expression matrix of the GSE65391 dataset to estimate the relative proportions of 22 infiltrating immune cell types [ 38 ]. Immune cell types with zero abundance across all samples were excluded. Bar plots were used to visualize the relative proportions of the remaining immune cell types in SLE patients versus healthy controls. Box plots compared and visualized differences in immune cell abundance between the groups. The corrplot R package was employed to visualize correlation patterns among the immune cell types. Additionally, correlations between the expression levels of necroptosis-related genes and the abundance of each immune cell type were assessed. Clinical correlation analysis Correlation analyses were conducted between the expression levels of necroptosis-related genes and key systemic lupus erythematosus (SLE) clinical indicators using data from the GSE65391 dataset, which contains both transcriptomic profiles and corresponding clinical parameters for SLE patients. A correlation heatmap was created to visualize these relationships. Scatter plots were used to depict the specific correlations between necroptosis-related genes and the following clinical parameters: SLE Disease Activity Index (SLEDAI), anti-double-stranded DNA (dsDNA) levels, neutrophil percentage, lymphocyte count, and lymphocyte percentage. Experimental validation of necroptosis-related gene expression via qPCR From January 1st 2025 to June 30st 2025, a total of 20 SLE patients and 16 HCs were enrolled in this study from the First Affiliated Hospital of Xi’an Jiaotong University, Shaanxi, China. The SLE patients fulfilled the Systemic Lupus International Collaborating Clinics (SLICC) 2012 criteria, which were the established standard when patient enrollment began. We note that the 2019 ACR/EULAR classification criteria have since been published and represent the current standard; future validation studies should apply these updated criteria. The exclusion criteria were as follows: (1) Other rheumatic connective tissue diseases; (2) Diagnosis of HIV, HCV and/or other virus infections; (3) Serious complication; (4) Patients are unwilling to undergo clinical trials or have incomplete data. The demographic and clinical characteristics of SLE patients were shown in Table S1 . The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (Approval No. XJTU1AF2023LSK-560). Total RNA was extracted from whole blood using an RNA isolation kit (5201050, Simgen, China). Reverse transcription was performed using a cDNA synthesis kit (RR037A, Takara, Japan). Quantitative real-time PCR (qPCR) was conducted using TB Green Premix Ex Taq II (RR820A, Takara, Japan) on a LightCycler 480 instrument (Roche, Switzerland). Primer sequences for target genes were asfollows: STAT1(Fwd:5’-ATGCTGGCACCAGAACGAATGAG-3’,Rev:5’-TCACCACAACGGGCAGAGAGG-3’);STAT2(Fwd:5’-CCAGGTCACAGAGTTGCTACAGCG-3’,Rev:5’-GAGTCTCACCAGCAGCCTTGTTC-3’);ZBP1(Fwd:5’-TTGTCCAGCCCTGCCGAGAG-3’,Rev:5’-GATGTCTTCCTCCCGTTGTTGGC-3’);CASP1(Fwd:5’-ACCCAGCTATGCCCACATCCTC-3’,Rev:5’-ACGTGTGCGGCTTGACTTGTC-3’);TNFSF10(Fwd:5’-ACCAACGAGCTGAAGCAGATGC-3’,Rev:5’-GCTGACGGAGTTGCCACTTGAC-3’);RIPK3(Fwd:5’-AAGAGGAGCAGGGCACAAGAGG-3’,Rev:5’-TTGAGGTAGGGCTGGGCATCTG-3’);IL1B(Fwd:5’-TCCGACCACCACTACAGCAAGG-3’,Rev:5’-GGGCAGGGAACCAGCATCTTC-3’);GAPDH(Fwd:5’-ATGACCCCTTCATTGACCTCA-3’,Rev:5’-GAGATGATGACCCTTTTGGCT-3’). The GAPDH gene was served as an internal control, and the relative expression of genes was determined using the 2^(-ΔΔCt) method. Statistical analysis Bioinformatics analyses and data visualization were performed using R (version 4.2.2) software. Statistical analyses were conducted using GraphPad Prism (version 10.1.2). Comparisons between two groups were used the Mann-Whitney U test. Comparisons across three or more groups were performed using the Kruskal-Wallis test, followed by Dunn’s test for post-hoc multiple comparisons. Correlations were assessed using Spearman’s rank correlation coefficient. A two-sided P value < 0.05 was considered statistically significant. Results Identification of programmed cell death-associated genes in SLE To identify key death-associated genes in SLE, DEGs between the SLE group and the HC group in the GSE65391 dataset were analyzed, shown in volcano and heatmap plots (Fig. 2 A and B). Using predefined significance criteria, a total of 824 SLE-related DEGs were identified, including 465 up-regulated and 359 down-regulated genes. Among these, 53 DEGs were associated with PCD (Fig. 2 C). Further functional enrichment analysis was conducted to explore the connection of these overlapping genes with various signaling pathways. Gene Ontology-Biological Process (GO-BP) analysis highlighted significant enrichment in responses to viruses, defense responses to symbionts, and defense responses to viruses. GO-Cellular Component (GO-CC) analysis revealed enrichment in secretory granule membranes, phagocytic vesicles, and inflammasome complexes. GO-Molecular Function (GO-MF) analysis indicated enrichment in lipopolysaccharide binding, pattern recognition receptor activity, and NAD(P)+ nucleosidase activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis demonstrated enrichment in the NOD-like receptor signaling pathway, necroptosis, and Influenza A (Fig. 2 D). These results suggested a correlation between infection, inflammation, and PCD in SLE. Fig. 2. Open in a new tab ( A ) Volcano plot of DEGs between SLE and HCs. ( B ) Heat map of DEGs between SLE and HCs (top 50). ( C ) Venn diagram of the intersection between up-regulated DEGs and cell death genes, yielding 53 intersecting genes. ( D ) Bubble chart of GO-KEGG enrichment analysis of intersecting genes. Abbreviations: DEGs, differentially expressed genes; SLE, systemic lupus erythematosus To identify key PCD genes in SLE, two machine learning algorithms were employed for feature selection. As shown in Table 2 , LASSO regression selected 31 genes at the minimum binomial deviance (Fig. 3 A). SVM-RFE identified 47 genes with the highest accuracy and lowest error rate (Fig. 3 B). Intersection of the genes from both algorithms identified 28 hub genes (Fig. 3 C). These 28 genes were classified based on PCD modes: 10 Apoptosis-related genes, 7 necroptosis-related genes, 6 autophagy-related genes, 5 anoikis-related genes, 5 pyroptosis-related genes, 3 ferroptosis-related genes, 2 immunogenic cell death-related genes, 1 NETosis-related gene, 1 lysosome-dependent cell death-related gene, 1 entoticcell death-related gene, and 1 entosis-related gene (Table 3 ). Table 2. Genes Selected by Two Machine Learning Algorithms Machine learning algorithm Gene Symbol Lasso regression CASP1, CASP5, IFI27, IL1B, PML, RIPK3, SHISA5, SP100, TFDP1, TNFSF10, AIM2, NOD2, ACSL1, SAT1, SLC40A1, MEFV, RRAGD, SNCA, RIM22, RIM5, TRIM6, STAT1, STAT2, ZBP1, CDC42, AB2, LGALS9, TLR2, CEACAM6, LTF, BST2. SVM-RFE IFI27, ZBP1, EIF2AK2, TRIM22, TRIM5, IFI16, IRF9, SHISA5, SAT1, TNFSF10, STAT1, DDIT3, TLR2, CHMP5, CEACAM1, STAT2, CASP1, P100, LY96, AIM2, RRAGD, RIPK3, TRIM21, MAZ, TFDP1, PML, BST2, ELANE, PLAUR, CEACAM3, LTF, TLR4, GAB2, SIAH2, ACSL1, IFI6, CEACAM6, MEFV, CDC42, CAMP, IL1B, NOD2, LRRK2, ASP5, MMP9, SLC40A1, FLCN. Open in a new tab Abbreviation: SVM-RFE, Support Vector Machine Recursive Feature Elimination Fig. 3. Open in a new tab ( A ) 31 genes were selected through LASSO regression under minimum binomial bias. ( B ) 47 genes were identified as features with the lowest error rate and highest accuracy through SVM-RFE. ( C ) 28 genes were cross-identified through the two machine learning algorithms Table 3. 28 genes were classified based on PCD modes Programmed cell deaths Genes Apoptosis CASP1, CASP5, IFI27, IL1B, PML, RIPK3, SHISA5, SP100, TFDP1, TNFSF10. Necroptosis STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B. Autophagy MEFV, RRAGD, TRIM22, TRIM5, CASP1, NOD2. Anoikis CEACAM6, LTF, BST2, TNFSF10, IFI27. Pyroptosis CASP5, NOD2, IL1B, AIM2, CASP1. Ferroptosis ACSL1, SAT1, SLC40A1. Immunogenic cell death CASP1, IL1B. NETosis TLR2. Lysosome-PCD GAB2. Entoticcelldeath CDC42. Entosis TNFSF10. Open in a new tab Abbreviation: PCD, programmed cell death Necroptosis as a key cell death pathway in SLE by ssGSEA and logistic regression To determine the most significant PCD mode in SLE, single-sample Gene Set Enrichment Analysis (ssGSEA) scoring was performed on the above-mentioned 28 hub genes in SLE samples. The top 5 PCD modes, ranked by their ssGSEA scores, were entosis, immunogenic cell death, necroptosis, ferroptosis, and lysosome-dependent cell death (Fig. 4 A). Subsequently, receiver operating characteristic (ROC) curves were generated for the five PCD modes using a logistic regression model. The necroptosis model yielded the highest area under the curve (AUC) value of 0.950 (95% CI: 0.921–0.979), outperforming the other PCD modes (Figs. 4 B-F). Herein, necroptosis was identified as the key cell death pathway in SLE. Fig. 4. Open in a new tab ( A ) Ranking of death modes based on ssGSEA scores for the 28 selected genes. ( B - F ) ROC curves for logistic regression models constructed using the top five genes associated with programmed cell death modes Construction and external validation of a necroptosis-related genes diagnostic model for SLE To evaluate the diagnostic value of necroptosis-related genes for SLE, three datasets were utilized: GSE65391 as the training set, and GSE110174 and GSE154851 as external validation sets. First, expression levels of necroptosis-related genes were significantly higher in SLE patients compared with HCs across all three datasets (Supplementary Fig. 1A and 2 A). Second, univariate logistic regression identified seven necroptosis-related hub genes (STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B) significantly associated with SLE (Fig. 5 A). Third, a visualized diagnostic nomogram was constructed using the GSE65391 dataset (Fig. 5 B), displaying points assigned for different expression levels of each gene contributing to the SLE diagnosis probability. Finally, the total score for each sample was calculated to predict the probability of SLE attack. ROC curve analysis indicated that the model exhibited valuable diagnostic performance, surpassing that of any single necroptosis-related gene. This superior predictive performance was consistently replicated in the two external whole-blood validation datasets ( GSE110174 and GSE154851 ) (Fig. 5 C, Supplementary Fig. 1B, 1 C, 2B, 2 C). Calibration curves demonstrated the relatively high accuracy of the nomogram compared with single gene (Fig. 5 D, Supplementary Fig. 1D and 2D ). Decision curve analysis (DCA) showed that the net benefit of using the model exceeded that of the default strategies (Fig. 5 E, Supplementary Fig. 1E,2E). The data indicated that the diagnostic model based on necroptosis-related genes demonstrated valuable diagnostic performance. Fig. 5. Open in a new tab ( A ) Forest plot showed the results of single-factor logistic regression using necroptosis-related genes. ( B ) The column chart showed the results of constructing a logistic regression model using necroptosis-related genes. ( C ) The ROC curve showed the results of 7 necroptosis-related genes. ( D ) The calibration curve showed the accuracy of the prediction. ( E ) The decision curve showed that the net benefit of the constructed model was superior to the default method Immune infiltration analysis and correlation with necroptosis-related genes The immune dysfunction played a pivotal role in SLE pathogenesis. To elucidate immune-cell death interactions in SLE, CIBERSORT analysis was employed. After excluding two cell types with zero abundance, the relative proportions of 20 immune cell types were determined (Fig. 6 A). Significant differences were observed in 12 immune cell types between SLE patients and HCs (Fig. 6 C), including naïve B cells, memory B cells, plasma cells, resting memory CD4 + T cells, activated memory CD4 + T cells, resting NK cells, M2 macrophages, resting dendritic cells (DCs), activated DCs, activated mast cells, and neutrophils. Furthermore, correlation analysis revealed significant associations between the necroptosis-related genes and specific immune cell types (Fig. 6 B). STAT1 showed a significant positive correlation with activated memory CD4 + T cells and a significant negative correlation with resting memory CD4 + T cells. STAT2 exhibited significant positive correlations with activated memory CD4 + T cells and activated DCs, and a significant negative correlation with resting memory CD4 + T cells. ZBP1, CASP1, TNFSF10, RIPK3, and IL1B all displayed significant positive correlations with neutrophils and significant negative correlations with resting memory CD4 + T cells. Fig. 6. Open in a new tab Immune infiltration analysis in SLE and the correlation between necroptosis-related genes and immune cells. ( A ) The relative percent of 20 immune cells in SLE. (B) Correlation analysis of immune cells with necroptosis-related genes. ( C ) The different expression of 20 immune cells between SLE and HCsl; ns: no significance; *: P < 0.05; **: P < 0.01; ***: P < 0.001 Association of necroptosis-related genes expression with SLE disease activity To evaluate the relationship between necroptosis-related genes and SLE disease activity, a correlation analysis was conducted using clinical data from the GSE65391 dataset. As shown in Fig. 7 A, all seven necroptosis-related genes (STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, and IL1B) indicated significant positive correlations with dsDNA levels, neutrophil percentage, and SLEDAI. Conversely, these genes demonstrated negative correlations with lymphocyte count and lymphocyte percentage (Fig. 7 B, Supplementary Figs. 3–6). Fig. 7. Open in a new tab ( A ) The heatmap displayed the correlation and significant difference between the expression patterns of 7 necroptosis-related genes and clinical Indicators. ( B ) The scatter plots demonstrated a significant correlation between the expression of the 7 necroptosis-related genes and SLEDAI score Expression levels of necroptosis-related genes in an internal validation cohort To validate the differential expression of necroptosis-related genes between SLE patients and healthy individuals, a total of 20 SLE patients and 16 HCs were enrolled to test the expression levels of STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, and IL1B. The demographic and clinical characteristics of the SLE patients are presented in Supplementary Table S1 . As shown in Fig. 8 , the significantly higher expression levels of all 7 genes were confirmed in SLE patients compared with HCs, consistent with the results in external validation cohorts. Fig. 8. Open in a new tab The expression differences of 7 necroptosis-related genes (STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B) between SLE and HCs; *: P < 0.05; **: P < 0.01; ***: P < 0.001. Abbreviations: HCs, Healthy controls; SLE, systemic lupus erythematosus Discussion The Aberrant PCD constituted a fundamental mechanism underlying the immune dysregulation in SLE. This study integrated machine learning algorithms, ssGSEA, and logistic regression modeling, and identified the predominant PCD genes, necroptosis-related genes. In internal and external validation, we confirmed that these necroptosis-related genes exhibited significantly higher expression in SLE patients compared with HCs. Furthermore, we validated the diagnostic utility of a model based on these genes and assessed their correlation with the clinical indicators, including the SLEDAI, dsDNA, neutrophil percentage, and lymphocyte count. Figure 9 provides a graphical summary of our proposed model, illustrating the seven key necroptosis-related genes, their roles in the necroptosis pathway, and the framework of the diagnostic model. Fig. 9. Open in a new tab Graphical summary of the proposed diagnostic model and molecular mechanisms of necroptosis in systemic lupus erythematosus (SLE) The multiple PCD pathways, such as cuproptosis, pyroptosis, and NETosis, have been involved in SLE pathogenesis [ 39 ]. To identify the crucial PCD form, we implemented stringent screening using both ssGSEA pathway activity scoring and logistic regression modeling, and indicated necroptosis as the most significantly associated PCD pathway in SLE. The external validation across independent datasets ( GSE110174 and GSE154851 ) consistently demonstrated elevated expression of these necroptosis-related genes in SLE patients compared with HCs, which was further confirmed in the internal validation with 20 SLE patients vs. 16 HCs. Subsequent analysis revealed significant correlations between the expression levels of the 7 necroptosis-related genes (including STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B) and established markers of SLE disease activity (SLEDAI, complement C3/C4 levels, dsDNA) and systemic inflammation (ESR and CRP). These data strongly implicated the value of 7 necroptosis-related genes in SLE pathogenesis, disease activity, and inflammatory level. Specifically, STAT1 was a crucial signal transducer and activator of transcription involved in the signaling pathways downstream of cytokines and growth factor receptors, and formed either homodimers or heterodimers after phosphorylation, which then moved to the nucleus to activate gene transcription [ 40 , 41 ]. STAT2, activated by interferons (IFNs), formed the ISGF3 complex with STAT1 and IRF9, functioning as a potent transcriptional activator for ISGs [ 42 ]. ZBP1 functioned as a potent sensor of nucleic acids, initiating PANoptosis—a coordinated cell death pathway involving pyroptosis, apoptosis, and necroptosis—by activating RIPK3, CASP8, and the NLRP3 inflammasome, thus amplifying inflammation [ 43 ].CASP1 was a member of the cysteine-aspartic acid protease family and played a crucial role in pyroptosis, which was responsible for the proteolytic maturation and secretion of pro-inflammatory cytokines such as IL-1β and IL-18 [ 44 ]. TNFSF10, primarily known for inducing apoptosis through death receptors, contributed to necroptotic signaling under specific pathological conditions by modulating cellular stress responses that influence RIPK pathway activation [ 45 ]. RIPK3 was the central kinase in necroptosis and phosphorylated MLKL, leading to MLKL oligomerization, plasma membrane disruption, and the release of DAMPs [ 46 ]. IL1B was a potent pro-inflammatory cytokine processed and released upon inflammasome activation, such as by CASP1 [ 47 ]. In summary, these genes had pro-pathogenic effects within the dysregulated immune and inflammatory environment of SLE. The multiple immune cells can be activated by the defective clearance of dying cell debris. In the current study, the immune infiltration analysis using CIBERSORT revealed significant correlations between the expression of these necroptosis-related genes and the abundance of specific immune cells, notably neutrophils, activated dendritic cells, resting CD4 + memory T cells, CD8 + T cells, and naïve B cells. Neutrophils, the most abundant circulating leukocytes, are traditionally viewed as first-line defenders against infection but also contribute to tissue damage. The necroptosis of neutrophils played a core role in the pathogenesis of SLE by releasing self-antigens, activating inflammatory pathways, and promoting organ damage [ 48 ]. Necrotic neutrophils released mitochondrial DNA and nucleosomes and other self-antigens, which were recognized by DC and B cells, activating TLR7/9 and cGAS-STING pathways, promoting the secretion of type I interferons, stimulating T cells to exert cytotoxic effects and secrete inflammatory cytokines, forming an ‘inflammation-autoimmunity’ positive feedback loop. In addition, SLE neutrophils exhibited an aberrantly activated phenotype and heightened propensity for NETosis, impaired NET clearance was a recognized defect in SLE patients [ 11 ]. To our knowledge, this is the first report to systematically compare multiple PCD pathways and support the correlation between a defined necroptosis-related gene signature and SLE diagnosis and disease activity. Our findings highlight the significance of necroptosis in SLE; however, several limitations of this study should be acknowledged. First, our findings are primarily derived from bioinformatic analyses of public datasets and our own cohort with a relatively small sample size for internal validation, which may limit statistical power for some subgroup analyses. Second, the participants were of single ethnicity, and future studies in multi-ethnic cohorts are necessary to verify the generalizability of our necroptosis-based diagnostic model. Third, the potential confounding effect of treatments on gene expression patterns was not fully controlled in our analysis. SLE patients in both the public datasets and our internal validation cohort received various immunosuppressive therapies, which may independently influence the expression of necroptosis-related genes. Future studies with detailed treatment stratification are needed to distinguish disease-related gene expression changes from treatment-induced modifications. Fourth, the immune cell proportions estimated by CIBERSORT represent computational inferences rather than direct measurements. While CIBERSORT is a widely validated deconvolution algorithm, its estimates may not fully capture the complexity of immune cell subsets in SLE, particularly for rare or atypical populations. Additionally, the reference signature matrix may not perfectly match the immune cell phenotypes present in SLE patients. Future studies incorporating flow cytometry or single-cell RNA sequencing would provide more direct and comprehensive immune profiling. Fifth, the transcriptomic datasets used in this study were derived from microarray platforms rather than RNA sequencing. Microarrays are limited to detecting predefined probe sets and may miss novel transcripts, splice variants, and low-abundance genes that could be relevant to SLE pathogenesis. RNA sequencing would provide more comprehensive transcriptome coverage and potentially identify additional necroptosis-related genes. Future studies utilizing RNA-seq data could validate and extend our findings. Most importantly, there is a lack of functional validation through cellular or animal models. Future studies are needed to provide mechanistic insights into the roles of these key genes (e.g., using RIPK3 knockout models to elucidate its specific contribution to renal pathology in lupus-prone mice) and to explore their potential as therapeutic targets. Conclusion Dysregulated programmed cell death (PCD) represents a cornerstone event in the pathogenesis of systemic lupus erythematosus (SLE), initiating autoimmunity through the release of self-antigens and pro-inflammatory mediators. This study systematically identified necroptosis as the predominant PCD pathway driving this process. We defined a signature of 7 necroptosis-related genes (STAT1, STAT2, ZBP1, CASP1, TNFSF10, RIPK3, IL1B) that were consistently dysregulated in SLE patients and correlated with disease activity and inflammation. These genes not only formed the basis of a robust diagnostic model but also emerged as promising candidates for therapeutic targeting. In future, we will focus on experimentally validating their functional roles in SLE mechanisms and translating this gene signature into a clinically applicable diagnostic or prognostic assay. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (3MB, docx) Acknowledgements The authors acknowledge all study population participating in this study. Author contributions Conceptualization: Jian Sun, Yuanyuan Li and Jing Wang; Data curation: Lu Wang and Shichang Song; Formal analysis: Lu Wang, Xinyi Liu and Xiaohao Wang; Funding acquisition: Jing Wang; Methodology: Jian Sun, Yuanyuan Li and Jing Wang; Project administration: Lu Wang, Hanchao Li, Lingfei Mo, Yichen Han, Ying Pan and Jing Wang; Resources: Jian Sun, Yuanyuan Li and Jing Wang; Validation: Xinyi Liu, Shichang Song and Xiaohao Wang; Writing-original draft: Lu Wang and Yuanyuan Li; Writing-review & editing: Jian Sun and Jing Wang. All authors took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Funding The work was supported by the National Natural Science Foundation of China (No. 82371811), and Shaanxi Province Natural Science Basic Research Program-Key Project (No.2025JC-QYCX-062). Data availability Datasets used in the study ( GSE65391 , GSE110174 , GSE154851 ) can be downloaded without restriction from the public GEO database. GSE65391 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391;GSE110174 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110174;GSE89408 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154851 . Upon reasonable request, data analysis procedures are available from the first and corresponding authors. Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and approved by the Medical Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (Ethical Approval Number: XJTU1AF2023LSK-560). 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials Supplementary Material 1 (3MB, docx) Data Availability Statement Datasets used in the study ( GSE65391 , GSE110174 , GSE154851 ) can be downloaded without restriction from the public GEO database. GSE65391 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65391;GSE110174 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE110174;GSE89408 : https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154851 . Upon reasonable request, data analysis procedures are available from the first and corresponding authors. 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