The shared mechanisms and potential diagnostic markers for IgA nephropathy and celiac disease

Background: Immunoglobulin A nephropathy (IgAN) is the most common type of primary glomerulonephritis worldwide and a frequent cause of end-stage renal disease. The inflammation cascade due to the infiltration and activation of immune cells in glomeruli plays an essential role in the progression of IgAN. In this study, we aimed to identify hub genes involved in immune infiltration and explore potential prognostic biomarkers and therapeutic targets in IgAN.Methods: We combined the single-cell and bulk transcriptome profiles of IgAN patients and controls with clinical data. Through single-cell analysis and weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) enrichment analysis, and differentially expressed gene (DEG) analysis in the bulk profile, we identified cell-type-specific potential hub genes in IgAN. Real hub genes were extracted via validation analysis and clinical significance analysis of the correlation between the expression levels of genes and the estimated glomerular filtration rate (eGFR) in the external dataset. Gene set enrichment analysis was performed to predict the probable roles of the real hub genes in IgAN.Results: A total of eleven cell clusters were classified via single-cell analysis, among which macrophages showed a variable proportion between the IgAN and normal control samples. We recognized six functional co-expression gene modules through WGCNA, among which the black module was deemed an IgAN-related and immune-involving module via GO enrichment analysis. DEG analysis identified 45 potential hub genes from genes enriched in GO terms. A total of twenty-three potential hub genes were specifically expressed in macrophages. Furthermore, we validated the differential expression of the 23 potential hub genes in the external dataset and identified nine genes with prognostic significance as real hub genes, viz., CSF1R, CYBB, FPR3, GPR65, HCLS1, IL10RA, PLA2G7, TYROBP, and VSIG4. The real hub gens are thought to contribute to immune cell regulation, immunoreaction, and regulation of oxidative stress, cell proliferation, and material metabolism.Conclusion: In this study, we demonstrated that macrophages infiltrated the glomeruli and contributed to the inflammatory response in IgAN. Based on integrated bioinformatics analyses of single-cell and bulk transcriptome data, we highlighted nine genes as novel prognostic biomarkers, which may enable the development of innovative prognostic and therapeutic strategies for IgAN.

Sarcopenic obesity patients are likely to develop exacerbated metabolic dysfunction, while the mechanism linking sarcopenia and obesity is still unclear. This study aims to explore hub genes and immune-metabolic dysregulation related to the molecular pathogenesis of sarcopenia and obesity. We used a public Gene Expression Omnibus (GEO) dataset to identify hub genes associated with sarcopenia and obesity. Weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI), differentially expressed gene (DEG) analysis, GO/KEGG functional enrichment analyses and immune cell infiltration analysis were conducted to identify hub genes. Subsequently, these hub genes underwent multi-level validation. Integrated bioinformatics analysis identified 16 shared hub genes linked to the sarcopenia-obesity nexus. These genes were mainly enriched in immune-related pathways, as supported by immune infiltration profiling. External validation in independent cohorts confirmed CD52 as a common and central gene in both sarcopenia and obesity datasets, showing significant associations with immune cell characteristics. Mendelian randomization analysis indicated potential causal links between genetically predicted CD52 levels and reduced hand grip strength as well as increased body mass index, and these results were further supported by PCR assays in clinical samples. The integrative analysis indicates that CD52 may function as a novel immunometabolic mediator in SO pathogenesis, underscoring its potential as a candidate biomarker for further study.

This study aims to identify angiogenesis-associated genes (AAGs) in endometriosis (EM) by integrating bioinformatics analysis with machine learning, and to investigate their underlying mechanisms. Differentially expressed genes (DEGs) were screened from integrated EM-related gene sets in the Gene Expression Omnibus database. These DEGs were integrated with AAGs retrieved from the AMIGO2 database. Weighted gene co-expression network analysis (WGCNA) was then employed to identify potential EM-AAGs, followed by functional enrichment analysis using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes. Five machine learning algorithms – Random Forest, LASSO, XGBoost, Gradient Boosting Machine (GBM), and SVM-RFE – were utilized for cross-validated screening of hub genes. The diagnostic efficacy of these genes was evaluated through receiver operating characteristic curves, calibration curves, and decision curve analysis. Further analyses included single-gene gene set enrichment analysis (GSEA), immune infiltration profiling, prediction of regulatory transcription factors, and construction of a competitive endogenous RNA (ceRNA) network. This study identified FZD4, SRPX2, and COL8A1 as hub genes for angiogenesis in EM. These genes were significantly upregulated in EM patients and demonstrated excellent diagnostic efficacy. Immune infiltration analysis revealed their regulatory associations with immune cell subpopulations, including M1/M2 macrophages and neutrophils. Single-gene GSEA and competitive endogenous RNA (ceRNA) network construction further elucidated their core regulatory roles in cell cycle control and multi-tiered molecular networks. Integrated bioinformatics and machine learning revealed FZD4, SRPX2, and COL8A1 as hub genes of angiogenesis in EM, proposing novel anti-angiogenic therapeutic strategies targeting EM.

BackgroundEarly-onset schizophrenia (EOS) occurs between the ages of 13 and 17 years, and neurobiological factors leading to cognitive deficits and psychotic symptoms with varying degrees of positive and negative symptoms. Numerous studies have demonstrated a broad link between immune dysregulation and the central nervous system in EOS, and its pathogenesis involves immune dysfunction, but the exact biological mechanisms have not been elucidated. This study employs immune infiltration analysis and bioinformatics to unveil the pathogenic mechanisms of EOS and identify potential diagnostic biomarkers, aiming for more precise clinical interventions.MethodsIn this study, we recruited 26 EOS patients and 27 healthy controls (HCs), and microarray data were collected. Crossover genes were identified using weighted gene co-expression network analysis (WGCNA) and differential expression genes (DEGs) analysis. These genes were subjected to genome enrichment analysis (GSEA) and gene ontology (GO) analysis. Hub genes were identified through protein-protein interactions (PPIs) and the GeneMANIA database. The diagnostic potential of immune-associated hub genes was evaluated using ROC analysis. Immune infiltration in EOS was analyzed with CIBERSORT. Regulatory miRNAs for the hub genes were predicted using miRNet, and the correlation between mRNAs and miRNAs was analyzed and validated in clinical samples.ResultsBy WGCNA and DEGs analysis, 330 relevant genes were screened in EOS patients compared to HCs. Functional enrichment analysis using Metascape showed significant enrichment in immune system pathways. Subsequently, a PPI network was constructed to select the top 10 potential hub genes, and functional analysis was performed by GeneMANIA, resulting in the identification of four immune-related genes. In addition, significant differences were observed among the four immune cell types in the two groups of samples. ROC analysis showed clinical relevance of the immune-related hub genes, and the AUC of all genes was greater than 0.7. A miRNA-mRNA regulatory network was constructed from miRNA data, and three miRNAs were found to be significantly associated with the immune-related hub genes.ConclusionOur findings demonstrated that CCL3, IL1B, CXCL8, CXCL10 and miR-34a-5p may be biomarkers that play crucial roles in the underlying mechanisms of EOS immune-related pathways. These findings contribute to the understanding of EOS pathophysiology and may help identify new diagnostic and therapeutic targets.

(1) Background: IgA nephropathy (IgAN) is a leading cause of chronic kidney disease worldwide. Despite its prevalence, the molecular mechanisms of IgAN remain poorly understood, partly due to limited research scale. Identifying key genes involved in IgAN’s pathogenesis is critical for novel diagnostic and therapeutic strategies. (2) Methods: We identified differentially expressed genes (DEGs) by analyzing public datasets from the Gene Expression Omnibus. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to elucidate the biological roles of DEGs. Hub genes were screened using weighted gene co-expression network analysis combined with machine learning algorithms. Immune infiltration analysis was conducted to explore associations between hub genes and immune cell profiles. The hub genes were validated using receiver operating characteristic curves and area under the curve. (3) Results: We identified 165 DEGs associated with IgAN and revealed pathways such as IL-17 signaling and complement and coagulation cascades, and biological processes including response to xenobiotic stimuli. Four hub genes were screened: three downregulated (FOSB, SLC19A2, PER1) and one upregulated (SOX17). The AUC values for identifying IgAN in the training and testing set ranged from 0.956 to 0.995. Immune infiltration analysis indicated that hub gene expression correlated with immune cell abundance, suggesting their involvement in IgAN’s immune pathogenesis. (4) Conclusion: This study identifies FOSB, SLC19A2, PER1, and SOX17 as novel hub genes with high diagnostic accuracy for IgAN. These genes, linked to immune-related pathways such as IL-17 signaling and complement activation, offer promising targets for diagnostic development and therapeutic intervention, enhancing our understanding of IgAN’s molecular and immune mechanisms.

Spinal cord injury (SCI) significantly impacts patients, with mitochondrial dysfunction playing a critical role in its pathology. Identifying mitochondria-related genes may offer new therapeutic and prognostic insights. RNA sequencing data from the GEO database were analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses were conducted, and weighted gene coexpression network analysis (WGCNA) alongside machine learning algorithms was used to identify key mitochondria-related genes. Immune infiltration was assessed using the EPIC algorithm, and single-cell RNA sequencing (scRNA-seq) data were analyzed for cellular diversity. A total of 2566 upregulated and 2634 downregulated genes were identified in SCI versus control samples. GO and KEGG enrichment analyses revealed these DEGs were primarily involved in oxidative stress, mitochondrial function, and immune pathways, including necroptosis and T cell receptor signaling. Then, 1578 genes with the strongest correlation to SCI were selected by WGCNA. By integrating DEGs, WGCNA module genes, and mitochondria-related genes, 76 candidate genes were obtained and used to construct a PPI network. Six hub genes (NDUFB3, SLC25A24, SLC25A40, GSTZ1, MAOA, and MRPL12) were identified by machine learning, all showing strong diagnostic potential (AUC > 0.77). Immune infiltration analysis indicated reduced B and T cell infiltration and increased macrophage activity in SCI samples. scRNA-seq analysis further revealed higher expression of NDUFB3 in dendritic cells and MAOA in pro-B cells, suggesting their involvement in immune regulation and mitochondrial dysfunction. These six genes represent potential biomarkers and therapeutic targets for SCI, providing insights into its molecular mechanisms and immune response.

BackgroundNon-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility.MethodsNOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein–protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA–microRNA (miRNA)–transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression.ResultsTwo immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data.ConclusionIt appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

Both IgA nephropathy (IgAN) and lupus nephritis (LN) are immunity-related diseases with a complex, polygenic, and pleiotropic genetic architecture. However, the mechanism by which the genetic variants impart immunity or renal dysfunction remains to be clarified. In this study, using gene expression datasets as a quantitative readout of peripheral blood mononuclear cell (PBMC)- and kidney-based molecular phenotypes, we analyzed the similarities and differences in the patterns of gene expression perturbations associated with the systematic and kidney immunity in IgAN and LN. Original gene expression datasets for PBMC, glomerulus, and tubule from IgAN and systemic lupus erythematosus (SLE) patients as well as corresponding controls were obtained from the Gene Expression Omnibus (GEO) database. The similarities and differences in the expression patterns were detected according to gene differential expression. Weighted gene co-expression network analysis (WGCNA) was used to cluster and screen the co-expressed gene modules. The disease correlations were then identified by cell-specific and functional enrichment analyses. By combining these results with the genotype data, we identified the differentially expressed genes causatively associated with the disease. There was a significant positive correlation with the kidney expression profile, but no significant correlation with PBMC. Three co-expression gene modules were screened by WGCNA and enrichment analysis. Among them, blue module was enriched for glomerulus and podocyte (P < 0.05) and positively correlated with both diseases (P < 0.05), mainly via immune regulatory pathways. Pink module and purple module were enriched for tubular epithelium and correlated with both diseases (P < 0.05) through predominant cell death and extracellular vesicle pathways, respectively. In genome-wide association study (GWAS) enrichment analysis, blue module was identified as the high-risk gene module that distinguishes LN from SLE and contains PSMB8 and PSMB9, the susceptibility genes for IgAN. In conclusion, IgAN and LN showed different systematic immunity but similarly abnormal immunity in kidney. Immunological pathways may be involved in the glomerulopathy and cell death together with the extracellular vesicle pathway, which may be involved in the tubular injury in both diseases. Blue module may cover the causal susceptibility gene for IgAN and LN.

IntroductionEsophageal squamous cell carcinoma (ESCA) is one of the most common cancers worldwide. PANoptosis is an inflammatory programmed cell death pathway event regulated by the PANoptosome complex. Currently, there is limited research on the PANoptosis-related genes (PORGs) in ESCA. We aim to explore the prognostic biomarkers of PANoptosis in ESCA and their underlying mechanisms through comprehensive bioinformatics analysis.MethodsIn this study, we analyzed transcriptome and single-cell RNA sequencing (scRNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were used to identify PANoptosis-related differentially expressed genes (POR-DEGs) in esophageal cancer. Hub genes were screened by univariate and multivariate Cox regression combined with machine learning models to construct diagnostic and prognostic models. The potential mechanisms of hub genes in esophageal cancer were preliminarily explored through gene immune infiltration and functional enrichment analysis. The differences and driving factors between high- and low-risk subgroups, as well as the regulation of PANoptosis by hub genes related to macrophages, were further revealed by immune assessment, drug sensitivity analysis, single-cell analysis, and molecular docking. Finally, the accuracy of model genes was verified by immunohistochemistry in clinical samples.ResultsFirstly, 74 PANoptosis-related differentially expressed genes (POR-DEGs) were identified for further analysis. Among the 74 genes, CCT6A, GMNN, and HSPB6 were identified as hub genes, and the constructed diagnostic and prognostic models were valuable. The high-risk subgroup showed poor prognosis, immune exhaustion, significant activation of pDC-LILRA4 cells, poor response to immunotherapy, and moderate sensitivity to chemotherapy. Further exploration of the immune regulatory mechanism of prognostic biomarkers revealed that the three hub genes, CCT6A, GMNN, and HSPB6, were closely related to the ESCA immune microenvironment. The CCT6A targeted by the traditional Chinese medicine component quercetin may inhibit PANoptosis by promoting the differentiation of Mono-CD14 cells into TAM-SPP1 macrophages.DiscussionWe constructed prognostic and diagnostic models using PANoptosis-related prognostic biomarkers, analyzed the differences and treatments between high-risk and low-risk groups, and revealed a new mechanism by which CCT6A may inhibit PANoptosis by promoting TAM-SPP1 differentiation, providing new targets and biomarkers for ESCA treatment.

Uterine sarcoma is a gynecological mesenchymal tumor with an elusive pathogenesis. The uterine leiomyosarcoma (LMS) is the most common subtype of uterine sarcoma. LMS is a highly aggressive tumor with a poor prognosis. The genomic landscape of LMS remains unclear. Rare cases of LMS are observed to arise from leiomyoma (LM). We conducted a study to explore the genomic relationship between LMS and LM using public microarray data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Using bioinformatics analysis tools, we would like to provide molecular insight into the pathogenesis of LMS and to discover novel predictive biomarkers for this disease. LMS and LM differentially expressed genes (DEGs) were screened by analyzing GEO datasets; GSE764, GSE68312 and GSE64763; and TCGA data. A protein-protein interaction (PPI) network was constructed, and hub genes were identified utilizing the CytoHubba plug-in from Cytoscape software. In addition, weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes. We took the intersection of the hub genes generated from the PPI network and WGCNA. Subsequently, random forest (RF) and support vector machine (SVM) algorithms were used to screen for key genes as predictive biomarkers. Finally, we constructed a nomogram with these genes. A total of 37 hub genes were selected using WGCNA. A total of 245 DEGs were identified; 63 DEGs were upregulated, and 182 DEGs were downregulated. Functional enrichment analysis revealed that these genes were mainly associated with the cell cycle, extracellular matrix receptor interactions and oocyte meiosis. The final hub genes were CENPA, KIF2C, TTK, MELK and CDC20. Gene set enrichment analysis (GSEA) revealed that these genes were mostly enriched in the cell cycle, mismatch repair and amino sugar and nucleotide sugar metabolism. Tumor-infiltrating immune cell analysis indicated that these genes did not have an obvious correlation with immune cells. CENPA, KIF2C, TTK, MELK and CDC20 were key genes significantly associated with LMS and LM. Functional enrichment analysis and tumor-infiltrating immune cell analysis indicated that these genes might be correlated with tumor proliferation, which might shed light on the possible pathogenesis and predictive biomarkers of LMS.

Introduction: Pulmonary hypertension (PH) presents a significant global public health challenge, underscoring the need for novel biomarkers and therapeutic strategies. Objective: This study proposes a lactylation-related diagnostic model for PH, aiming to identify potential therapeutic targets. Methods: The GSE15197 dataset was analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis (GSEA), were conducted to explore underlying mechanisms. Weighted gene co-expression network analyses (WGCNA) identified two key gene modules. The intersection of significant WGCNA modules, DEGs, and lactylation-associated genes yielded candidate genes related to lactylation in PH. Machine learning methods, particularly random forest and support vector machine, were employed to identify hub genes, ultimately selecting aryl hydrocarbon receptor (AHR), polyribonucleotide nucleotidyltransferase 1 (PNPT1), and RAS p21 protein activator 1 (RASA1). These were incorporated into a diagnostic nomogram, evaluated through receiver operating characteristic curve and decision curve analyses. Immune cell infiltration was assessed using CIBERSORT and single-sample GSEA, while Enrichr was utilized to identify transcription factors and potential therapeutic agents. Molecular docking was performed to assess drug&amp;ndash;gene binding affinities. Results: A total of 1,504 genes were upregulated and 1,931 downregulated. Functional enrichment analyses revealed clustering of DEGs in pathways associated with cellular transport, protein degradation, DNA repair, and signal transduction. WGCNA identified two critical modules comprising 1,178 genes, from which 33 candidate genes were derived. Machine learning refined this list to three hub genes (AHR, PNPT1, and RASA1), which formed the basis of a novel lactylation-related diagnostic nomogram validated in an external cohort. Immune dysregulation was evident, and friend leukemia integration 1 was recognized as a key TF. Ten potential drugs demonstrated promising binding affinity to the hub genes. Conclusion: This work introduces a lactylation-based diagnostic model for PH with strong diagnostic potential, though further clinical validation is required.

Introduction: Accumulating evidence has disclosed that IgA nephropathy (IgAN) could present shortly after the second dose of COVID-19 mRNA vaccine. However, the undying mechanism remains unclear and we aimed to investigate the potential molecular mechanisms. Methods: We downloaded gene expression datasets of COVID-19 mRNA vaccination (GSE201535) and IgAN (GSE104948). Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to identify co-expression modules related to the second dose of COVID-19 mRNA vaccination and IgAN. Differentially expressed genes (DEGs) were screened, and a transcription factor (TF)-miRNA regulatory network and protein-drug interaction were constructed for the shared genes. Results: WGCNA identified one module associated with the second dose of COVID-19 mRNA vaccine and four modules associated with IgAN. Gene ontology (GO) analyses revealed enrichment of cell cycle-related processes for the COVID-19 mRNA vaccine hub genes and immune effector processes for the IgAN hub genes. We identified 74 DEGs for the second dose of COVID-19 mRNA vaccine and 574 DEGs for IgAN. Intersection analysis with COVID-19 vaccine-related genes led to the identification of two shared genes, TOP2A and CEP55. The TF-miRNA network analysis showed that hsa-miR-144 and ATF1 might regulate the shared hub genes. Conclusions: This study provides insights into the common pathogenesis of COVID-19 mRNA vaccination and IgAN. The identified pivotal genes may offer new directions for further mechanistic studies of IgAN secondary to COVID-19 mRNA vaccination.

Background and Aims Clinically, IgA nephropathy has a variety of symptoms including paroxysmal gross hematuria, nephritic syndrome and nephrotic syndrome. This study aimed at investigating hub geneand genes modular related to IgA nephropathy clinical characteristics by using weighted gene co-expression network analysis combining clinical, microarray and network database parameters. Method We collected 32 human samples from the European Renal cDNA Bank and used RMA method to preprocess the raw data and utilize the limma package to obtain differentially expressed gene in renal interstitium and glomeruli. And then, we used the WGCNA package to construct the gene co-expression of differential expression genes and identify the hub genes associated with clinical characteristics in renal interstitium and glomeruli, respectively. Gene ontology enrichment analysis and KEGG analysis for hub genes which associated with clinical characteristics were performed by DAVID, and PPI information was acquired from STRING with visualization by Cytoscape. Results For glomeruli, 1470 genes differentially expressed between IgA nephropathy patients and healthy control, containing 10 hub genes associated with age, 8 hub genes associated with sex, 48 hub genes associated with Bp enrichd in ERK1 and ERK2 cascade and Rap1 signaling pathway, 223 hub genes associated with BMI enrich in organic acid catabolic process and fatty acid degradation pathway, 136 hub genes associated GFR enriched in immune response and PI3K-Akt signaling pathway, 82 hub genes associated with proteinuria enriched in extracellular matrix organization and PI3K-Akt signaling pathway. In tubulointerstitium, there were 480 genes differentially expressed between IgA nephropathy patients and healthy control. Among 480 DEGs, 6 hub genes associated with age, 15 hub genes associated with sex, 35 hub genes associated with Bp enrichd in positive regulation of apoptotic process, 87 hub genes associated with GFR enriched in negative regulation of macromolecule metabolic process and RNA transport, 33 hub genes associated with proteinuria enriched in regulation of apoptotic process and FoxO signaling pathway. PPI enrichment analysis shown that all hub genes sets are biologically connected cluster. Conclusion We made a preliminary investigation on molecular mechanisms of relationship between IgA nephropathy and clinical characteristics and identified hub genes and pathways closely related with BMI, GFR and Proteinuria in IgA nephropathy by a series of bioinformatics analysis.

Introduction: Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Methods: Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. Results: Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e−15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. Conclusion: By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN.

BackgroundDiabetic kidney disease (DKD) is characterized by chronic inflammation and immune dysregulation. Multiple programmed cell death pathways contribute to tubulointerstitial injury, but their perturbations, crosstalk, and integrative impact in DKD remain unclear. PANoptosis—a coordinated program integrating pyroptosis, apoptosis, and necroptosis—has emerged as a key mechanism in inflammatory disorders, yet its role in DKD is not defined.MethodsWe integrated multiple renal tubulointerstitial transcriptomic datasets from DKD and control cohorts to identify differentially expressed genes, followed by functional enrichment analysis. PANoptosis-related gene sets were curated from MSigDB, and pathway crosstalk was evaluated using independent single-cell RNA-seq datasets. Hub genes were prioritized by combining weighted gene co-expression network analysis (WGCNA) with five machine-learning algorithms, and a PANoptosis-related risk score (PRS) was constructed and correlated with clinical parameters and immune infiltration. miRNA–mRNA and transcription factor–hub gene regulatory networks were inferred using ENCORI and hTFtarget, respectively. Druggability of hub genes was assessed using DrugnomeAI, and candidate compounds were retrieved from DGIdb. Key findings were validated in diabetic mouse models.ResultsApoptosis, pyroptosis, necroptosis and the integrated PANoptosis program were markedly activated in DKD. At the single-cell level, these pathways were frequently co-activated within tubular and interstitial cell types, with extensive molecular overlap. Six PANoptosis-related hub genes (YWHAH, PRKACB, PSMB9, FAS, GZMA, CASP1) were identified; their expression correlated negatively with glomerular filtration rate and positively with serum creatinine and immune-cell infiltration. The PRS robustly discriminated DKD from controls and identified a high-risk subgroup with heightened immune infiltration and impaired renal function. Regulatory network analysis revealed convergent miRNA and transcription factor control of key hub genes. Druggability profiling with DrugnomeAI highlighted CASP1, FAS, PSMB9 and PRKACB as experimentally tractable and pharmacologically actionable targets, and DGIdb suggested multiple repurposable agents against these nodes.ConclusionThis study delineates extensive perturbations and crosstalk among apoptosis, pyroptosis and necroptosis in DKD, positioning PANoptosis as a unifying driver of tubulointerstitial injury. The six PANoptosis hub genes and their derived PRS show strong diagnostic potential, while integrated regulatory and druggability analyses nominate CASP1, FAS, PSMB9 and PRKACB as promising biomarkers and therapeutic entry points for PANoptosis-centered interventions in DKD.