Identification of renal ischemia reperfusion injury subtypes and predictive strategies for delayed graft function and graft survival based on neutrophil extracellular trap-related genes

Prognostic significance of neutrophil extracellular trap-related genes in childhood acute lymphoblastic leukemia: insights from multi-omics and in vitro experiment

BACKGROUND

This study aimed to develop a prognostic model based on extracellular trap-related genes (NETRGs) for patients with cALL.

METHODS

Data from the TARGET-ALL-P2 and TARGET-ALL-P3 cohorts in the Genomic Data Commons database, the transcriptome dataset GSE26713, the single-cell transcriptome dataset GSE130116 from the Gene Expression Omnibus database and 306 NETRGs identified were analysed. Differentially expressed genes (DEGs) were identified from GSE26713 and differentially expressed NETRGs (DE-NETRGs) were obtained by overlapping DEGs with NETRGs. Functional analyses were conducted. Key feature genes were identified through univariate and least absolute shrinkage and selection operator (LASSO) regression. Prognostic genes were determined via multivariate Cox regression analysis, followed by the construction and validation of a risk model and nomogram. Additional analyses included immune profiling, drug sensitivity, functional differences, cell-type-specific expression, enrichment analysis and RT-qPCR.

RESULTS

A total of 1,270 DEGs were identified in GSE26713, of which 74 overlapped with NETRGs. Seven prognostic genes were identified using univariate, LASSO and multivariate Cox regression analyses. Survival analysis revealed lower survival rates in the high-risk group. Independent prognostic analysis identified risk scores and primary diagnosis as independent predictors of prognosis. Immune cell profiling showed significant differences in cell populations such as aDCs, eosinophils and Th2 cells between risk groups. Six cell subtypes were annotated, with prognostic genes predominantly expressed in myeloid cells. RT-qPCR revealed that PTAFR, FCGR2A, RETN and CAT were significantly downregulated, while TLR2 and S100A12 were upregulated in cALL.

CONCLUSION

TLR2, PTAFR, FCGR2A, RETN, S100A12 and CAT may serve as potential therapeutic targets.

Multiple omics-based machine learning reveals specific macrophage sub-clusters in renal ischemia-reperfusion injury and constructs predictive models for transplant outcomes

BACKGROUND

Ischemia-reperfusion injury (IRI) is closely associated with numerous severe postoperative complications, including acute rejection, delayed graft function (DGF) and graft failure. Macrophages are central to modulating the aseptic inflammatory response during the IRI process. The objective of this study is to conduct an analysis of the developmental and differentiation characteristics of macrophages in IRI, identify distinct molecules subtypes of IRI, and establish robust predictive strategies for DGF and graft survival.

METHOD

We analyzed scRNA-Seq data from GEO database to identify macrophage sub-clusters specific to renal IRI, and use the hdWGCNA algorithm to screen gene modules closely associated with this sub-cluster. Integrating these module genes with the results from bulk RNA-Seq differential analysis to obtain hub genes, and delineating the different IRI molecular subtypes through consensus clustering based on the expression profiles of hub genes. Innovatively, the gene expression matrix was transformed into a unique graphic pixel module and applied advanced computer vision processing algorithms to construct a DGF predictive model. Additionally, we also employed 111 combinations of 10 machine learning algorithms to develop a predictive signature for graft survival. Finally, we validated the expression of the key gene ANXA1 in a mouse IRI model using qRT-PCR, WB, and IHC.

RESULT

This study successfully identified a subset of macrophages closely associated with renal IRI, and cell communication and pseudo-time analysis implied that they may be instrumental in both the maintenance and exacerbation of the IRI process. Utilizing the expression patterns of hub genes, recipients can be clustered into two subtypes (CI and C2) with unique clinical and molecular features. We innovatively applied deep learning algorithms to construct a model for DGF prediction, which can effectively mitigate batch effects among IRI recipients. Compared to other existing models, our model demonstrated superior performance with AUC of 0.816 and 0.845 in the training and validation set. Furthermore, we also used the random survival forest algorithm to develop a high-precision predictive signature for graft failure. The mouse IRI model confirmed a marked upregulation of ANXA1 mRNA and protein expression in renal tissue following IRI.

CONCLUSION

This study successfully revealed the macrophage sub-cluster closely associated with renal IRI. Two distinct IRI subgroups with different characteristics were identified and robust strategies were constructed for predicting DGF and graft survival, which can offer potential therapeutic targets for the treatment of IRI and reference for early prevention of various postoperative complications.

Unraveling the Molecular Landscape of Neutrophil Extracellular Traps in Severe Asthma: Identification of Biomarkers and Molecular Clusters

Neutrophil extracellular traps (NETs) play a central role in chronic airway diseases. However, the precise genetic basis linking NETs to the development of severe asthma remains elusive. This study aims to unravel the molecular characterization of NET-related genes (NRGs) in severe asthma and to reliably identify relevant molecular clusters and biomarkers. We analyzed RNA-seq data from the Gene Expression Omnibus database. Interaction analysis revealed fifty differentially expressed NRGs (DE-NRGs). Subsequently, the non-negative matrix factorization algorithm categorized samples from severe asthma patients. A machine learning algorithm then identified core NRGs that were highly associated with severe asthma. DE-NRGs were correlated and subjected to protein-protein interaction analysis. Unsupervised consensus clustering of the core gene expression profiles delineated two distinct clusters (C1 and C2) characterizing severe asthma. Functional enrichment highlighted immune-related pathways in the C2 cluster. Core gene selection included the Boruta algorithm, support vector machine, and least absolute contraction and selection operator algorithms. Diagnostic performance was assessed by receiver operating characteristic curves. This study addresses the molecular characterization of NRGs in adult severe asthma, revealing distinct clusters based on DE-NRGs. Potential biomarkers (TIMP1 and NFIL3) were identified that may be important for early diagnosis and treatment of severe asthma.

NETs-related genes predict prognosis and are correlated with the immune microenvironment in osteosarcoma

Background

Osteosarcoma is the most common primary bone tumor. It has a high rate of early metastasis, and its treatment is one of the most challenging topics in the bone tumor field. Recent studies have shown that neutrophil extracellular traps play an important role in tumor metastasis and may provide new horizons for exploring metastasis in osteosarcoma.

Methods

OS data were downloaded from the TARGET database and Gene Expression Omnibus datasets. Univariate Cox regression was conducted to assess NETRGs. Patients were subsequently categorized into high- and low-risk groups on the basis of risk score values derived from multivariate Cox analysis, and prognostic models were established. The immune infiltration of relevant genes and drug sensitivity of key genes were also analyzed.

Results

A total of 15 NETs-related genes associated with osteosarcoma metastases were identified. Among them, a total of 4 genes were related to prognosis, namely, MAPK1, CFH, ATG7 and DDIT4, and a prognostic model based on these 4 genes was established. The prognosis was worse in the high-risk group, whose areas under the ROC curves (AUCs) were 0.857, 0.779, and 0.689 at 1, 3, and 5 years, respectively. The key genes were subsequently found to be associated with the infiltration of 20 types of immune cells. Finally, the small-molecule drug toxin c 10, an approximately 6700 mw protein, may target key genes. Finally, ATG7 was validated at the histological level by combining the results of the validation group dataset analysis.

Conclusions

A risk model based on 4 NETRDEGs is a reliable prognostic predictor for OS patients, and CFH and ATG7 may serve as a new diagnostic and therapeutic target.

Identification of novel neutrophil-extracellular-traps-related genes as biomarkers for breast cancer prognosis and immunotherapy

Background

Breast cancer (BC) ranks as one of the most prevalent malignancies among women globally. This study aimed to explore the involvement of neutrophil extracellular traps (NETs)-related genes (NETRGs) in BC pathogenesis, highlighting the critical role of NETs.

Methods

Differentially expressed NETRGs (DE-NETRGs) were identified by intersecting BC vs. control differentially expressed genes (DEGs) with the NETRG gene set from The Cancer Genome Atlas breast cancer (TCGA-BRCA) and GSE42568 datasets. Functional analysis elucidated their biological roles. Prognostic biomarkers were selected using least absolute shrinkage and selection operator (LASSO) and Cox regression, generating a predictive model, of which its prognostic predictive ability was evaluated through the Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) curve, and verified it in the test set and the validation set. Subsequently, the clinicopathological features were incorporated into the risk model for Cox independent prognostic analysis, and a nomogram was constructed to verify the predictive performance of the model. Finally, the mechanism of action of the biomarkers in BC was explored through immune infiltration, immunotherapy, and drug sensitivity. The biomarker expression validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

Functional analysis revealed 37 DE-NETRGs associated with leukocyte migration and the Interleukin (IL)-17 signaling pathway. Four biomarkers [F2RL2, AZU1, IL33, neutrophil elastase (ELANE)] were used to construct the prognostic model and it was validated by the test set and the validation set. The KM curve showed significant differences in prognosis between the high- and low-risk group, while the ROC curve showed that the model had good predictive performance. Radiation, age, tumor stage, pathologic N, and risk scores were identified as independent prognostic factors. Subgroups based on risk scores exhibited distinct immune cell infiltration patterns, with the risk score positively correlated with M0 macrophages and resting mast cells. The high-risk group demonstrated lower Tumor Immune Dysfunction and Exclusion (TIDE) scores. Drug sensitivity varied between risk subgroups, and qRT-PCR confirmed the expression of ELANE and IL33.

Conclusions

This study has reported four biomarkers related to BC prognosis, namely F2RL2, AZU1, IL33, and ELANE. Our study has offered new potential biomarkers for prognosis and has identified therapeutic targets for the treatment and prognosis prediction in BC patients.

Cytosolic nucleic acid sensing as driver of critical illness: mechanisms and advances in therapy

Nucleic acids from both self- and non-self-sources act as vital danger signals that trigger immune responses. Critical illnesses such as acute respiratory distress syndrome, sepsis, trauma and ischemia lead to the aberrant cytosolic accumulation and massive release of nucleic acids that are detected by antiviral innate immune receptors in the endosome or cytosol. Activation of receptors for deoxyribonucleic acids and ribonucleic acids triggers inflammation, a major contributor to morbidity and mortality in critically ill patients. In the past decade, there has been growing recognition of the therapeutic potential of targeting nucleic acid sensing in critical care. This review summarizes current knowledge of nucleic acid sensing in acute respiratory distress syndrome, sepsis, trauma and ischemia. Given the extensive research on nucleic acid sensing in common pathological conditions like cancer, autoimmune disorders, metabolic disorders and aging, we provide a comprehensive summary of nucleic acid sensing beyond critical illness to offer insights that may inform its role in critical conditions. Additionally, we discuss potential therapeutic strategies that specifically target nucleic acid sensing. By examining nucleic acid sources, sensor activation and function, as well as the impact of regulating these pathways across various acute diseases, we highlight the driving role of nucleic acid sensing in critical illness.

Identification and validation of neutrophil-related biomarkers in acute-on-chronic liver failure

Dysfunction of peripheral blood neutrophils occurs in acute-on-chronic liver failure (ACLF). However, the molecular mechanisms of neutrophils involved in the pathophysiology of the ACLF remains poorly understood. Data downloaded from the GEO database (GSE142255) was used to identify both ACLF and neutrophil-related genes with the help of the limma package and Weighted Gene Co-Expression Network Analysis (WGCNA) algorithms. The analysis identified 288 ACLF-related differentially expressed genes (DEGs) in the circulating blood cells. Among these, three genes were found to be related to neutrophils and were identified as diagnostic genes, exhibiting high diagnostic efficacy as evidenced by an area under the curve (AUC) value of 1. Among these, matrix metallopeptidase-9 (MMP9) and S100 calcium binding protein A12 (S100A12) were upregulated, whereas C-C chemokine ligand 5 (CCL5) was downregulated in circulating immune cells from patients with ACLF compared to those from healthy controls. These findings were corroborated using an additional GEO dataset, GSE156382. The expression levels of the three key genes demonstrated a correlation with both ferroptosis and cuprotosis. Among the three diagnostic genes, only MMP9 was validated as differentially expressed through both quantitative real-time PCR (qRT-PCR) and western blot. Moreover, a significant elevation in plasma MMP9 levels was observed in patients with ACLF compared to those with chronic hepatitis B (CHB) and acute decompensated cirrhosis (AD). Notably, ACLF patients exhibiting elevated MMP9 levels (>175.8 ng/mL) experienced higher short-term mortality rates within both 30 and 90 days (p<0.001). In addition, a total of 21 drugs targeting the three diagnostic genes were identified from the Drug Bank database. Finally, the Kinase-TF-mRNA-miRNA network was constructed utilizing Cytoscape software. This study represents the initial application of WGCNA algorithms to identify novel biomarkers related to neutrophils in ACLF. Our findings offer new perspectives on the role of neutrophil in the pathogenesis of ACLF. However, additional research is required to substantiate the effects of these key genes and therapeutic agents on ACLF.

Relationship of gene polymorphisms for complement components C3 and factor H and kidney allograft function

Complement plays a central role in organ ischemia/reperfusion injury (IRI) and allograft rejection. A retrospective observational study included a cohort of 73 non-diabetic deceased donor kidney allograft recipients. We collected data on donor and recipient demographic, clinical and laboratory parameters. The main outcomes of our study were delayed graft function (DGF) and kidney allograft function during five years posttransplant. Gene single nucleotide polymorphisms (SNPs) for complement components C3 (rs2230199, G_C) and FH (rs800292, G_A) were determined. The genotyping results for FH polymorphism (184G > A) showed a distribution of GG (71.2%) and GA (28.8%) genotypes, with the AA genotype not detected in the cohort. The genotype frequencies of the C3 polymorphism (304 C > G) were CC (71.2%), CG (26.0%) and GG (2.8%).Analysis of FH SNP demonstrated that patients with the GG genotype had a statistically higher frequency of DGF compared to those with the GA genotype (67.3% vs. 38.1%, p = 0.022). Univariate linear regression analysis confirmed that the FH GG genotype was the only significant determinant of DGF (p = 0.025). Analysis of C3 SNP showed that patients with the GC/GG genotype demonstrated significantly lower levels of creatinine clearance compared to those with the CC genotype at 1 year (p = 0.002), 3 years (p = 0.001) and 5 years (p = 0.010) posttransplant. These findings underscore the importance of genetic factors in influencing renal outcomes post-transplant.

Neutrophil-induced pyroptosis promotes survival in patients with hepatoblastoma

BACKGROUND

Hepatoblastoma (HB) is the predominant hepatic malignancy among children. Despite therapeutic options for HB were gradually refined in recent years, patients with metastasis suffer from an unsatisfactory prognosis. Pyroptosis is a type of programmed and inflammatory necrosis. Neutrophils are crucial in innate immunity, which were shown to be associated with tumor progression. Our study strived to unravel the relationship between neutrophil-induced pyroptosis (NIP) and HB.

METHODS

The clinical and bulk RNA sequencing data of 38 patients with HB were obtained from Shanghai Children's Medical Center. We established NIP score based on the LASSO regression. The single-cell RNA sequencing data (GSE186975) were used for for key genes identification, cellular communication, and differentiation trajectories of neutrophils. KEGG, GO, GSVA, and ssGSEA enrichment were used to analyze biological functions, including neutrophil extracellular traps (NETs), NOD-like receptors pathway, neutrophil activation, neutrophil-mediated cytotoxicity, and others.

RESULTS

We constructed a NIP score based on the expression of three genes related to neutrophil and pyroptosis, namely ELANE, CASP1, and NOD2, which was positively correlated with a favorable prognosis of HB. Moreover, we clarified the function of ELANE in HB microenvironmwnt. Immunohistochemistry and transcriptome analysis unraveled a significant correlation between NETs and pyroptosis in HB, suggesting the key role of NETs-related neutrophils in inducing pyroptosis and prolonging survival. We also found upregulated tumor-promoting and immunosuppression-related pathways in the HB microenvironment. In addition, we clarified the growth trajectories and phenotypic changes of neutrophils in the immune microenvironment of HB, which can serve as potential targets for immunotherapy.

CONCLUSIONS

The novel NIP score for patients with HB shows high predictive value for survival. Moreover, we identified biological function, cellular communication, and growth trajectories of neutrophils in HB. Our findings broaden insights into the treatment of HB.

Machine Learning and Experiments Revealed Key Genes Related to PANoptosis Linked to Drug Prediction and Immune Landscape in Spinal Cord Injury

Spinal cord injury (SCI) is a severe central nervous system injury without effective therapies. PANoptosis is involved in the development of many diseases, including brain and spinal cord injuries. However, the biological functions and molecular mechanisms of PANoptosis-related genes in spinal cord injury remain unclear. In the bioinformatics analysis of public data of SCI, the differentially expressed genes (DEGs) identified by GSE151371 were hybridized with PANoptosis-related genes (PRGs) to obtain differentially expressed PANoptosis-related genes (DE-PRGs). Through three machine learning algorithms, we obtained the hub genes. Then, we constructed functional analysis, drug prediction, regulatory network construction, and immune infiltrating cell analysis. Finally, the expression of the hub gene was verified in GSE93561, GSE45376, and qRT-PCR analysis. Through the above analysis, 14 DE-PRGs were obtained by intersecting 3582 DEGs with 46 PRGs. Five key hub genes, CASP4, GSDMB, NAIP, NLRC4, and NLRP3, were obtained by 3 machine learning algorithms. All five hub genes were enriched in phagocytosis mediated by FC GAMMA R. The 11 immune cells were significantly different between spinal cord injury (SCI) group and human control (HC) group, such as mast cell and gamma delta T cell. The transcription factor (TF)-hub gene network contained 10-nodes (4 hub genes and 6 TFs) and 8-edges. The miRNA-hub gene network consisting of 5-nodes (3 hub genes and 2 miRNAs) and 3-edges was constructed. Moreover, the CASP4 predicted 1 small molecule drug and NLRP3 predicted 9 small molecule drugs. Finally, the expression of 5 hub genes were significantly different in GSE45376 and GSE93561 (SCI vs. HC) and mice SCI model (Sham vs. SCI). Collectively, we identified 5 hub genes (CASP4, GSDMB, NAIP, NLRC4, and NLRP3) associated with PANoptosis, providing potential directions for treating spinal cord injury.

Neutrophil extracellular traps in tumor metabolism and microenvironment

Neutrophil extracellular traps (NETs) are intricate, web-like formations composed of DNA, histones, and antimicrobial proteins, released by neutrophils. These structures participate in a wide array of physiological and pathological activities, including immune rheumatic diseases and damage to target organs. Recently, the connection between NETs and cancer has garnered significant attention. Within the tumor microenvironment and metabolism, NETs exhibit multifaceted roles, such as promoting the proliferation and migration of tumor cells, influencing redox balance, triggering angiogenesis, and driving metabolic reprogramming. This review offers a comprehensive analysis of the link between NETs and tumor metabolism, emphasizing areas that remain underexplored. These include the interaction of NETs with tumor mitochondria, their effect on redox states within tumors, their involvement in metabolic reprogramming, and their contribution to angiogenesis in tumors. Such insights lay a theoretical foundation for a deeper understanding of the role of NETs in cancer development. Moreover, the review also delves into potential therapeutic strategies that target NETs and suggests future research directions, offering new perspectives on the treatment of cancer and other related diseases.

IL-33, a neutrophil extracellular trap-related gene involved in the progression of diabetic kidney disease

BACKGROUND

Chronic inflammation is well recognized as a key factor related to renal function deterioration in patients with diabetic kidney disease (DKD). Neutrophil extracellular traps (NETs) play an important role in amplifying inflammation. With respect to NET-related genes, the aim of this study was to explore the mechanism of DKD progression and therefore identify potential intervention targets.

METHODS

Hub NET-related DEGs were screened via differential expression analysis and three machine learning methods, namely, LASSO, SVM-RFE and random forest. Consensus clustering was performed to analyze NET-related subtypes in DKD patients. KEGG enrichment analysis, GSEA, GSVA, ssGSEA and ESTIMATE were conducted to explore the molecular features of DKD patient subtypes. Leveraging single-nucleus RNA-seq datasets, the "scissor" and "bisqueRNA" algorithms were applied to identify the composition of renal cell types in DKD patient subtypes. Soft clustering analysis was performed to obtain gene groups with similar expression patterns during the development and progression of DKD. The correlations between hub NET-related DEGs and clinical parameters were mined from the Nephroseq V5 database. The core gene among the hub NET-related DEGs was selected by calculating semantic similarity. "Cellchat" algorithm, immunostaining, ELISA and flow cytometry were performed to explore the expression and function of the core gene. The Drug-Gene Interaction Database (DGIdb) was searched to identify candidate drugs.

RESULTS

Six hub NET-related DEGs, namely, ACTN1, ITGB2, IL33, HRG, NFIL3 and CLEC4E, were identified. On the basis of these 6 genes, DKD patients were classified into 2 clusters. Cluster 1 patients, with higher NET scores, were evidently more immune-activating than those of cluster 2. Markedly increased numbers of immune cells, fibroblasts and proinflammatory proximal tubular cells were observed in cluster 1 but not in cluster 2. Cluster 1 also represented a more clinically advanced disease state. Among the 6 hub NET-related DEGs, the mRNA expression of ACTN1, ITGB2, IL33 and HRG was correlated with the eGFR. By semantic similarity analysis, IL33 was considered a central gene among the 6 genes. Cell-cell communication analysis further indicated that intercellular interactions via IL-33 were enhanced in DKD. Serum IL-33 concentration was negatively correlated with eGFR. IHC staining revealed that IL-33 expression was upregulated in the tubular epithelium in DKD patients. Supernatants from inflammatory tubular epithelial cells can increase MPO in neutrophils, whereas addition of anti-IL-33 antibody attenuated this phenotype.

CONCLUSIONS

We identified 2 distinct NET-related subtypes in DKD patients, in which one subgroup was apparently more inflammatory and associated with a more severe clinical state. A significantly increased level of IL-33 in this inflammatory patient subgroup may play a role in aggravating inflammation via the IL-33-ST2 axis.

Molecular mechanisms of ferroptosis in renal ischemia-reperfusion injury Investigated via bioinformatics analysis and animal experiments

Kidney transplantation is a pivotal treatment for end-stage renal disease. However, renal ischemia-reperfusion injury (IRI) during surgery significantly impacts graft function. Despite unclear molecular mechanisms, no specific therapies or preventative measures are available. Gene expression profiles from renal biopsies before and after IRI were downloaded from public databases. Differentially expressed genes were identified using the Wilcoxon rank-sum test and weighted gene co-expression network analysis. Ferroptosis-associated genes were screened using the FerrDb database. The genes with the highest connectivity were identified via the protein-protein interaction (PPI) network and upstream regulatory miRNAs were found through the gene-miRNA network. A mouse renal IRI model was constructed for transcriptome sequencing and quantitative real-time polymerase chain reaction (qRT-PCR) validation to elucidate the relationship between key ferroptosis genes and regulatory miRNAs in renal IRI. Differential analysis identified 15 ferroptosis-associated genes (TNFAIP3, IL6, KLF2, EGR1, JUN, ZFP36, GDF15, CDKN1A, HSPB1, BRD2, PDK4, DUSP1, SLC2A3, DDIT3, and CXCL2) involved in renal IRI regulation. In animal experiments, ferroptosis-related genes were also upregulated in the model group. Enrichment analysis and hematoxylin-eosin pathological staining suggested these genes are primarily involved in renal inflammatory responses. PPI network analysis revealed IL6 as the gene with the highest connectivity, and the gene-miRNA network indicated IL6 might be regulated by miR-let-7a. Animal experiments revealed decreased miR-let-7a and increased IL6 levels in the model group, identifying potential therapeutic targets. MiR-let-7a regulates ferroptosis in renal IRI by targeting IL6, highlighting IL6 as a crucial gene in the ferroptosis process of renal IRI.

Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing

BACKGROUND

The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft survival. KTx rejection poses a significant threat to allograft function and longevity of the kidney graft. The relationship between senescence and rejection remains elusive and controversial.

METHODS

Gene Expression Omnibus (GEO) provided microarray and single-cell RNA sequencing datasets. After integrating Senescence-Related Genes (SRGs) from multiple established databases, differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were applied to identify predictive SRGs (pSRGs). A cluster analysis of rejection samples was conducted using the consensus clustering algorithm. Subsequently, we utilized multiple machine learning methods (RF, SVM, XGB, GLM and LASSO) based on pSRGs to develop the optimal Acute Rejection (AR) diagnostic model and long-term graft survival predictive signatures. Finally, we validated the role of pSRGs and senescence in kidney rejection through the single-cell landscape.

RESULTS

Thirteen pSRGs were identified, correlating with rejection. Two rejection clusters were divided (Cluster C1 and C2). GSVA analysis of two clusters underscored a positive correlation between senescence, KTx rejection occurrence and worse graft survival. A non-invasive diagnostic model (AUC = 0.975) and a prognostic model (1- Year AUC = 0.881; 2- Year AUC = 0.880; 3- Year AUC = 0.883) for graft survival were developed, demonstrating significant predictive capabilities to early detect acute rejection and long-term graft outcomes. Single-cell sequencing analysis provided a detailed cellular-level landscape of rejection, supporting the conclusions drawn from above.

CONCLUSION

Our comprehensive analysis underscores the pivotal role of senescence in KTx rejection, highlighting the potential of SRGs as biomarkers for diagnosing rejection and predicting graft survival, which may enhance personalized treatment strategies and improve transplant outcomes.

Transcriptome analysis revealed that ischemic post-conditioning suppressed the expression of inflammatory genes in lung ischemia-reperfusion injury

Introduction

Ischemic post-conditioning (I-post C) is a recognized therapeutic strategy for lung ischemia/reperfusion injury (LIRI). However, the specific mechanisms underlying the lung protection conferred by I-post C remain unclear. This study aimed to investigate the protective mechanisms and potential molecular regulatory networks of I-post C on lung tissue.

Methods

Transcriptome analysis was performed on rat lung tissues obtained from Sham, ischemia-reperfusion (IR), and I-post C groups using RNA-seq to identify differentially expressed genes (DEGs). Subsequently, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted to elucidate significantly enriched pathways in the IR and I-post C groups. Additionally, protein-protein interaction (PPI) network analysis was carried out to examine associations among the DEGs. Pathological changes in lung tissues were assessed using hematoxylin-eosin (H&E) staining. The expression levels of CXCL1 and CXCL6 in the IR and I-post C groups were evaluated through immunofluorescence and Western blotting.

Results

Our results showed that I-post C significantly attenuated both pulmonary edema and inflammatory cell infiltration. Transcriptome analysis identified 38 DEGs in the I-post C group compared to the IR group, comprising 21 upregulated and 17 downregulated genes. Among these, seven inflammation-related DEGs exhibited co-expression patterns with the Sham and IR groups, with notable downregulation of Cxcl1 and Cxcl6. GO analysis primarily linked these DEGs to neutrophil activation, chemotaxis, cytokine activity, and CCR chemokine receptor binding. KEGG analysis revealed enriched pathways, including the IL-17, TNF, and NF-κB signaling pathways. GSEA indicated downregulation of neutrophil chemotaxis and the IL-17 signaling pathway, correlating with reduced expression of Cxcl1 and Cxcl6. Validation of Cxcl1 and Cxcl6 mRNA expression via immunofluorescence and Western blotting supported the RNA-seq findings. Furthermore, a PPI network was constructed to elucidate interactions among the 29 DEGs.

Conclusions

Through RNA-Seq analysis, we concluded that I-post C may reduce inflammation and suppress the IL-17 signaling pathway, thereby protecting against lung damage caused by LIRI, potentially involving neutrophil extracellular traps.

Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications

Background

Cancer cells induce neutrophil extracellular traps (NETs) to promote tumor progression and metastasis. However, only a few studies have focused on the role of NETs in Neuroblastoma (NB).

Methods

First, based on the expression of NET-related genes, consensus clustering analysis was conducted to cluster NB samples into different subtypes. Differential analysis was performed to identify DEGs between subtypes. Functional items and related pathways of DEGs were identified using enrichment analysis. Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. Furthermore, independent prognostic analysis was performed. Immune infiltration analysis was performed to identify differential immune cells. Finally, the verification of prognostic model genes were taken by the immunohistochemical staining and quantitative real-time PCR.

Results

Consensus clustering analysis demonstrated that NB samples were clustered into two subtypes. There were 125 DEGs between the two subtypes of NB. Moreover, the enrichment analysis results showed that the DEGs were mainly associated with 'external side of plasma membrane,' 'immune receptor activity' 'regulation of leukocyte migration' GO items. There were also several GO items related to neutrophils, such as regulation of neutrophil migration and differentiation. KEGG pathways revealed that the DEGs were correlated with in immunity-related activities, including 'Complement and coagulation cascades,' 'Neutrophil extracellular trap formation, 'T cell receptor signaling pathway,' 'PD-L1 expression and PD-1 checkpoint pathway in cancer' and so on. A total of five biomarkers,[Selenoprotein P1 (SEPP1), Fibrinogen-like protein 2 (FGL2), NK cell lectin-like receptor K1 (KLRK1), ATP-binding cassette transporters 6(ABCA6) and Galectins(GAL)], were screened, and a risk model based on the biomarkers was created. Furthermore, a nomogram for forecasting the survival rates of patients with NB was established based on the risk score, age at diagnosis, and MYCN status. Eight differential immune cells (CD8 + T cells, resting mast cells, etc.) were acquired between the two risk subgroups. The expression levels of five prognostic model genes at the protein and mRNA were verified and all results were consistent with the results of our bioinformatics analysis.

Conclusion

We initially found that five NET-related genes were significantly differentially expressed in NETs-associated molecular isoforms and two Netrg molecular isoforms were found to be associated with poorer prognosis. This stratification might provide insight into the prediction of prognosis and ideal immunotherapy strategies for patients with NB. However, we also noted that the formation of NETs is a complex biological process involving the regulation of multiple cytokines and cellular interactions. Therefore, the exact roles of these genes and their specific mechanisms in the formation of NETs and the development of NB still need to be further investigated.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Transcriptome sequencing and Mendelian randomization analysis identified biomarkers related to neutrophil extracellular traps in diabetic retinopathy

Summary

In the development of diabetic retinopathy (DR), neutrophil infiltration hastens the adhesion between neutrophils and endothelial cells, leading to inflammation. Meanwhile, neutrophil extracellular traps (NETs) produced by neutrophils could clear aging blood vessels, setting the stage for retinal vascular regeneration. To explore the mechanism of NETs-related genes in DR, the transcriptome of NETs from normal and DR individuals were analyzed with gene sequencing and mendelian randomization (MR) analysis. Five NETs-related genes were identified as key genes. Among these genes, CLIC3, GBP2, and P2RY12 were found to be risk factors for Proliferative DR(PDR), whereas HOXA1 and PSAP were protective factors. Further verification by qRT-PCR recognized GBP2, P2RY12 and PSAP as NETs-associated biomarkers in PDR.

Purpose

To investigate neutrophil extracellular traps (NETs) related genes as biomarkers in the progression of diabetic retinopathy (DR).

Methods

We collected whole blood samples from 10 individuals with DR and 10 normal controls (NCs) for transcriptome sequencing. Following quality control and preprocessing of the sequencing data, differential expression analysis was conducted to identify differentially expressed genes (DEGs) between the DR and NC groups. Candidate genes were then selected by intersecting these DEGs with key module genes identified through weighted gene co-expression network analysis. These candidate genes were subjected to mendelian randomization (MR) analysis, then least absolute shrinkage and selection operator analysis to pinpoint key genes. The diagnostic utility of these key genes was evaluated using receiver operating characteristic curve analysis, and their expression levels were examined. Additional analysis, including nomogram construction, gene set enrichment analysis, drug prediction and molecular docking, were performed to investigate the functions and molecular mechanisms of the key genes. Finally, the expression of key genes was verified by qRT-PCR and biomarkers were identified.

Results

Intersection of 1,004 DEGs with 1,038 key module genes yielded 291 candidate genes. Five key genes were identified: HOXA1, GBP2, P2RY12, CLIC3 and PSAP. Among them, CLIC3, GBP2, and P2RY12 were identified as risk factors for DR, while HOXA1 and PSAP were protective. These key genes demonstrated strong diagnostic performance for DR. With the exception of P2RY12, all other key genes exhibited down-regulation in the DR group. Furthermore, the nomogram incorporating multiple key genes demonstrated superior predictive capacity for DR compared to a single key genes. The identified key genes are involved in oxidative phosphorylation and ribosome functions. Drug predictions targeting P2RY12 suggested prasugrel, ticagrelor, and ticlopidine as potential options owing to their high binding affinity with this key genes. The qRT-PCR results revealed that the results of GBP2, PSAP and P2RY12 exhibited consistent expression patterns with the dataset.

Conclusion

This study identified GBP2, P2RY12 and PSAP as NETs-associated biomarkers in the development of PDR, offering new insights for clinical diagnosis and potential treatment strategies for DR.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

APOD: A biomarker associated with oxidative stress in acute rejection of kidney transplants based on multiple machine learning algorithms and animal experimental validation

BACKGROUND

Oxidative stress is an unavoidable process in kidney transplantation and is closely related to the development of acute rejection after kidney transplantation. This study aimed to investigate the biomarkers associated with oxidative stress and their potential biological functions during acute rejection of kidney transplants.

METHODS

We identified Hub genes using five machine learning algorithms based on differentially expressed genes (DEGs) in the kidney transplant acute rejection dataset GSE50058 and oxidative stress-related genes (OS) obtained from the MSigDB database, and validated them with the datasets GSE1563 and GSE9493, as well as with animal experiments; Subsequently, we explored the potential biological functions of Hub genes using single-gene GSEA enrichment analysis; The Cibersort algorithm was used to explore the altered levels of infiltration of 22 immune cells during acute rejection of renal transplantation, and a correlation analysis between Hub genes and immune cells was performed; Finally, we also explored transcription factors (TFs), miRNAs, and potential drugs that regulate Hub genes.

RESULTS

We obtained a total of 57 genes, which we defined as oxidative stress-associated differential genes (DEOSGs), after intersecting DEGs during acute rejection of kidney transplants with OSs obtained from the MSigDB database; The results of enrichment analysis revealed that DEOSGs were mainly enriched in response to oxidative stress, response to reactive oxygen species, and regulation of oxidative stress and reactive oxygen species; Subsequently, we identified one Hub gene as APOD using five machine learning algorithms, which were validated by validation sets and animal experiments; The results of single-gene GSEA enrichment analysis revealed that APOD was closely associated with the regulation of immune signaling pathways during acute rejection of kidney transplants; The Cibersort algorithm found that the infiltration levels of a total of 10 immune cells were altered in acute rejection, while APOD was found to correlate with the expression of multiple immune cells; Finally, we also identified 154 TFs, 12 miRNAs, and 12 drugs or compounds associated with APOD regulation.

CONCLUSION

In this study, APOD was identified as a biomarker associated with oxidative stress during acute rejection of kidney transplants using multiple machine learning algorithms, which provides a potential therapeutic target for mitigating oxidative stress injury and reducing the incidence of acute rejection in kidney transplantation.

Modified EBP-bFGF targeting endogenous renal extracellular matrix protects against renal ischemia-reperfusion injury in rats

Acute kidney injury (AKI) is a life-threatening disease primarily caused by renal ischemia-reperfusion (I/R) injury, which can result in renal failure. Currently, growth factor therapy is considered a promising and effective approach for AKI treatment. Basic fibroblast growth factor (bFGF), an angiogenic factor with potent activity, efficiently stimulates angiogenesis and facilitates regeneration of renal tissue. However, the unrestricted diffusion of bFGF restricts its clinical application in AKI treatment. Therefore, developing a novel sustained released system for bFGF could enhance its potential in treating AKI. In this study, we genetically engineered a multifunctional recombinant protein by fusing bFGF with a specific peptide (EBP). EBP-bFGF effectively binds to the extracellular matrix in the injured kidney, enabling slow release of bFGF in AKI. Furthermore, following orthotopic injection into I/R rats' ischemic kidneys, EBP-bFGF exhibited stable retention within the tissue. Additionally, EBP-bFGF suppressed apoptosis of renal cells, reduced renal fibrosis, and facilitated recovery of renal function. These findings suggest that EBP-bFGF delivery system represents a promising strategy for treating AKI.

Identification of neutrophil extracellular trap-related genes in Alzheimer's disease based on comprehensive bioinformatics analysis

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. There are currently no effective interventions to slow down or prevent the occurrence and progression of AD. Neutrophil extracellular traps (NETs) have been proven to be tightly linked to AD. This project attempted to identify hub genes for AD based on NETs. Gene expression profiles of the training set and validation set were downloaded from the Gene Expression Omnibus (GEO) database, including non-demented (ND) controls and AD samples. NET-related genes (NETRGs) were collected from the literature. Differential analysis identified 21 AD differentially expressed NETRGs (AD-DE-NETRGs) majorly linked to functions such as defense response to bacterium as well as pathways including IL-17 signaling pathway, as evidenced by enrichment analyses of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Protein-protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). Their diagnostic ability was validated in the validation set using receiver operating characteristic (ROC) curves and gene differential expression analysis. A total of 16 miRNAs and 132 lncRNAs were predicted through the mirDIP and ENCORI databases, and a lncRNA-miRNA-mRNA regulatory network was constructed using Cytoscape software. Small molecular compounds such as Benzo(a)pyrene and Copper Sulfate were predicted to target hub genes using the CTD database. This project successfully identified five hub genes, which may serve as potential biomarkers for AD, proffering clues for new therapeutic targets.

Neutrophil extracellular traps and neutrophil extracellular traps-related genes are involved in new-onset atrial fibrillation in LPS-induced sepsis

BACKGROUND

Sepsis is considered a high risk factor for new-onset atrial fibrillation (NOAF), with neutrophil extracellular traps (NETs) being implicated in the pathogenesis of numerous diseases. However, the precise role of NETs and NETs-related genes (NRGs) in the occurrence of NOAF in sepsis remains inadequately elucidated. The objective of this study was to identify hub NRGs connecting sepsis and AF, and to investigate the potential association between NETs and NOAF in sepsis.

METHODS

The AF and sepsis microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database for analysis of shared pathophysiological mechanisms and NRGs implicated in both sepsis and AF using bioinformatics techniques. The CIBERSORT algorithm was employed to assess immune cell infiltration and identify common immune characteristics in these diseases. Additionally, a rat model of lipopolysaccharide (LPS)-induced sepsis was utilized to investigate the association between NETs, NRGs, and sepsis-induced AF. Western blotting, enzyme-linked immunosorbent assay, hematoxylin-eosin staining, immunohistochemistry, and immunofluorescence were employed to assess the expression of NRGs, the formation of NETs, and the infiltration of neutrophils. Electrophysiological analysis and multi-electrode array techniques were utilized to examine the vulnerability and conduction heterogeneity of AF in septic rats. Furthermore, intervention was conducted in LPS-induced sepsis rats using DNase I, a pharmacological agent that specifically targets NETs, in order to assess its impact on neutrophil infiltration, NETs formation, hub NRGs protein expression, and AF vulnerability.

RESULTS

A total of 61 commonly differentially expressed genes (DEGs) and four hub DE-NRGs were identified in the context of sepsis and AF. Functional enrichment analysis revealed that these DEGs were predominantly associated with processes related to inflammation and immunity. Immune infiltration analysis further demonstrated the presence of immune infiltrating cells, specifically neutrophil infiltration, in both sepsis and AF. Additionally, a positive correlation was observed between the relative expression of the four hub DE-NRGs and neutrophil infiltration. In rats with LPS-induced sepsis, we observed a notable upregulation in the expression of four DE-NRGs, the formation of NETs, and infiltration of neutrophils in atrial tissue. Through electrophysiological assessments, we identified heightened vulnerability to AF, reduced atrial surface conduction velocity, and increased conduction heterogeneity in LPS-induced sepsis rats. Notably, these detrimental effects can be partially ameliorated by treatment with DNase I.

CONCLUSIONS

Through bioinformatics analysis and experimental validation, we identified four hub NRGs in sepsis and AF. Subsequent experiments indicated that the formation of NETs in the atria may contribute to the pathogenesis of NOAF in sepsis. These discoveries offer potential novel targets and insights for the prevention and treatment of NOAF in sepsis.

Identification and validation of diagnostic genes associated with neutrophil extracellular traps of type 2 diabetes mellitus

Background

Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear.

Methods

We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TF-miRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group.

Conclusion

Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM.

The role of neutrophil extracellular trap formation in kidney transplantation: Implications from donors to the recipient

Kidney transplantation remains the gold standard for patients with end-stage renal disease, but severe donor organ shortage has led to long waiting lists. The utilization of expanded criteria donor kidneys within the category of deceased donors has enlarged the pool of available kidneys for transplantation; however, these grafts often have an increased risk for delayed graft function or reduced graft survival following transplantation. During brain or circulatory death, neutrophils are recruited to the vascular beds of kidneys where a proinflammatory microenvironment might prime the formation of neutrophil extracellular traps (NETs), web-like structures, containing proteolytic enzymes, DNA, and histones. NETs are known to cause tissue damage and specifically endothelial damage while activating other systems such as coagulation and complement, contributing to tissue injury and an unfavorable prognosis in various diseases. In lung transplantation and kidney transplantation studies, NETs have also been associated with primary graft dysfunction or rejection. In this review, the role that NETs might play across the different phases of transplantation, already initiated in the donor, during preservation, and in the recipient, will be discussed. Based on current knowledge, NETs might be a promising therapeutic target to improve graft outcomes.

Histopathological Features and Role of Allograft Kidney Biopsy Among Recipients With Prolonged Delayed Graft Function: A Review

Delayed graft function (DGF) is an early posttransplant complication predictive of adverse outcomes. This "acute kidney injury of transplantation" is often defined as allograft dysfunction requiring renal replacement within 7 d posttransplantation. DGF is an important area of study because it is emerging with efforts to expand the donor pool and address the supply-demand gap in kidney transplantation. DGF is often caused by severe kidney injury mechanisms because of multiple donors, recipients, and immunologic factors. The role of kidney biopsy, particularly in prolonged DGF, is an ongoing area of research and inquiry for clinicians and researchers alike to better define, manage, and predict outcomes of this early posttransplant event. This review aims to provide an in-depth, comprehensive summary of the literature to date on the histopathology of DGF and the role of kidney transplant biopsies in prolonged DGF.

Analysis of neutrophil extracellular trap-related genes in Crohn's disease based on bioinformatics

Crohn's disease (CD) presents with diverse clinical phenotypes due to persistent inflammation of the gastrointestinal tract. Its global incidence is on the rise. Neutrophil extracellular traps (NETs) are networks released by neutrophils that capture microbicidal proteins and oxidases targeting pathogens. Research has shown that NETs are implicated in the pathogenesis of several immune-mediated diseases such as rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. The goal of this study was to identify a panel of NET-related genes to construct a diagnostic and therapeutic model for CD. Through analysis of the GEO database, we identified 1950 differentially expressed genes (DEGs) associated with CD. Gene enrichment and immune cell infiltration analyses indicate that neutrophil infiltrates and chemokine-related pathways are predominantly involved in CD, with other immune cells such as CD4 and M1 macrophages also playing a role in disease progression. Utilizing weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks, we identified six hub genes (SPP1, SOCS3, TIMP1, IRF1, CXCL2 and CD274). To validate the accuracy of our model, we performed external validation with statistical differences(p < 0.05). Additionally, immunohistochemical experiments demonstrated higher protein expression of the hub genes in colonic tissues from CD patients compared to healthy subjects (p < 0.05). In summary, we identified six effective hub genes associated with NETs as potential diagnostic markers for CD. These markers not only offer targets for future research but also hold promise for the development of novel therapeutic interventions for CD.

Transcriptomics-based exploration of shared M1-type macrophage-related biomarker in acute kidney injury after kidney transplantation and acute rejection after kidney transplantation

BACKGROUND

Macrophage type 1 (M1) cells are associated with both acute kidney injury (AKI) during kidney transplantation and acute rejection (AR) after kidney transplantation. Our study explored M1-related biomarkers involved in both AKI and AR and their potential biological functions.

METHODS

Based on the Gene Expression Omnibus (GEO) database, the immune cell infiltration levels and differentially expressed genes were examined in AKI and AR in the kidney transplantation; M1-related genes shared in AKI and AR were identified using weighted gene co-expression analysis (WGCNA) system. Subsequently, protein-protein interaction (PPI) networks and machine learning methods to identify Hub genes and construct diagnostic models. Both AKI model and AR rat models were built to validate the expressions of Hub genes and test the injury phenotype, oxidative stress markers, and inflammatory factors. Finally, the transcription factor (TF)-Hub gene and micro-RNA (miRNA)-Hub gene regulatory networks were constructed based on identified Hub genes.

RESULTS

Out of 2167 differential expression genes (DEGs) in AKI and 2100 DEGs in AR, four M1-related Hub genes were obtained by PPI networks and machine learning methods, namely GBP2, TYROBP, CCR5, and TLR8. The calibration curves in the nomogram diagnostic model for these four Hub genes suggested the same predictive probability as an ideal model for AKI and AR after kidney transplantation (AUC values of the area under the ROC curve were all >0.7). The same observations were confirmed in ischemia reperfusion injury (IRI) and AR rat models by identifying common four Hub genes (GBP2, TYROBP, TLR8, and CCR5). Western blots showed that these four Hub genes were significantly different in rat models of IRI and AR (all p<0.05). Compared with the control group, IRI and AR groups showed aggravated histopathological damage and increased secretion of oxidative stress markers and inflammatory factors in rat kidneys (all p<0.05). Finally, TF-Hub and miRNA-Hub gene regulatory networks were constructed to provide a theoretical basis for the regulation of Hub genes.

CONCLUSION

We identified four macrophage M1-related Hub genes shared among AKI and AR after kidney transplantation. These genes may be considered for diagnosis of AKI and AR after kidney transplantation.

S100A8/A9hi neutrophils induce mitochondrial dysfunction and PANoptosis in endothelial cells via mitochondrial complex I deficiency during sepsis

S100a8/a9, largely released by polymorphonuclear neutrophils (PMNs), belongs to the S100 family of calcium-binding proteins and plays a role in a variety of inflammatory diseases. Although S100a8/a9 has been reported to trigger endothelial cell apoptosis, the mechanisms of S100a8/a9-induced endothelial dysfunction during sepsis require in-depth research. We demonstrate that high expression levels of S100a8/a9 suppress Ndufa3 expression in mitochondrial complex I via downregulation of Nrf1 expression. Mitochondrial complex I deficiency contributes to NAD+-dependent Sirt1 suppression, which induces mitochondrial disorders, including excessive fission and blocked mitophagy, and mtDNA released from damaged mitochondria ultimately activates ZBP1-mediated PANoptosis in endothelial cells. Moreover, based on comprehensive scRNA-seq and bulk RNA-seq analyses, S100A8/A9hi neutrophils are closely associated with the circulating endothelial cell count (a useful marker of endothelial damage), and S100A8 is an independent risk factor for poor prognosis in sepsis patients.

Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis

Atherosclerosis (AS) is a pathological process associated with various cardiovascular diseases. Upon different stimuli, neutrophils release reticular complexes known as neutrophil extracellular traps (NETs). Numerous researches have indicated a strong correlation between NETs and AS. However, its role in cardiovascular disease requires further investigation. By utilizing a machine learning algorithm, we examined the genes associated with NETs that were expressed differently in individuals with AS compared to normal controls. As a result, we identified four distinct genes. A nomogram model was built to forecast the incidence of AS. Additionally, we conducted analysis on immune infiltration, functional enrichment and consensus clustering in AS samples. The findings indicated that individuals with AS could be categorized into two groups, exhibiting notable variations in immune infiltration traits among the groups. Furthermore, to measure the NETs model, the principal component analysis algorithm was developed and cluster B outperformed cluster A in terms of NETs. Additionally, there were variations in the expression of multiple chemokines between the two subtypes. By studying AS NETs, we acquired fresh knowledge about the molecular patterns and immune mechanisms implicated, which could open up new possibilities for AS immunotherapy.

Bioinformatics gene analysis for potential biomarkers and therapeutic targets of Parkinson's disease based on neutrophil extracellular traps

Introduction

Neutrophil extracellular traps (NETs) provide key innate immune mechanisms, and studies have shown innate immunity and adaptive immunity are directly linked to Parkinson's disease (PD) pathology. However, limited research has been conducted on NETs in the context of PD.

Methods

A differential analysis was implemented to acquire differentially expressed genes (DEGs) between PD and control as well as between high- and low-score groups determined by a gene set variation analysis (GSVA). Then, the genes within the critical module, obtained through a weighted gene co-expression network analysis (WGCNA), were intersected with the DEGs to identify the overlapping genes. Then, five kinds of algorithms in the protein-protein interaction (PPI) were performed to identify potential biomarkers. Subsequently, a nomogram for forecasting PD probability was created. An enrichment analysis and an immune infiltration analysis were performed on the identified biomarkers. qRT-PCR was performed to validate the expression trends of three biomarkers.

Results

We revealed 798 DEGs between PD and control groups as well as 168 DEGs between high- and low-score groups obtained by differential analyses. The pink module containing 926 genes was identified as the critical module. According to the intersection of these gene sets, a total of 43 overlapping genes were screened out. Furthermore, GPR78, CADM3, and CACNA1E were confirmed as biomarkers. Moreover, we found that biomarkers mainly participated in pathways, such as the 'hydrogen peroxide catabolic process', and 'cell cycle'; five kinds of differential immune cells between PD and control groups were identified. Finally, the qRT-PCR analysis demonstrated the up-regulation of GPR78, CADM3, and CACNA1E in the PD group.

Discussion

Our study authenticated GPR78, CADM3, and CACNA1E as the biomarkers associated with PD. These findings provide an original reference for the diagnosis and treatment of PD.

NET-Related Gene as Potential Diagnostic Biomarkers for Diabetic Tubulointerstitial Injury

Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear. Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD's tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury. Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury. Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD.

Bulk and single-cell RNA sequencing analysis with 101 machine learning combinations reveal neutrophil extracellular trap involvement in hepatic ischemia-reperfusion injury and early allograft dysfunction

BACKGROUND

Hepatic ischaemia-reperfusion injury (HIRI) is a major clinical concern during the perioperative period and is closely associated with early allograft dysfunction (EAD), acute rejection (AR) and long-term graft survival. Neutrophil extracellular traps (NETs) are extracellular structures formed by the release of decondensed chromatin and granular proteins following neutrophil stimulation. There is growing evidence that NETs are involved in the progression of various liver transplantation complications, including ischaemia-reperfusion injury (IRI). This study aimed to comprehensively analyse the expression patterns of NET-related genes (NRGs) in HIRI, identify HIRI subtypes with distinct characteristics, and develop a reliable EAD prediction model.

METHODS

Microarray, bulk RNA-seq, and single-cell sequencing datasets were obtained from the GEO database. Initially, differentially expressed NRGs (DE-NRGs) were identified using differential gene expression analyses. We then utilised a non-negative matrix factorisation (NMF) algorithm to classify HIRI samples. Subsequently, we employed machine learning algorithms to screen the hub NRGs related to EAD and developed an EAD prediction model based on these hub NRGs. Concurrently, we assessed the expression patterns of hub NRGs at the single-cell level using the HIRI. Additionally, we validated C5AR1 expression and its effect on HIRI and NETs formation in a rat orthotopic liver transplantation (OLT) model.

RESULTS

In this study, we identified 11 DE-NRGs in the HIRI context. Based on these 11 DE-NRGs, HIRI samples were classified into two distinct clusters. Cluster1 exhibited a low expression of DE-NRGs, minimal neutrophil infiltration, mild inflammation, and a low incidence of EAD. Conversely, Cluster2 displayed the opposite phenotype, with an activated inflammatory subtype and a higher incidence of EAD. Furthermore, an EAD prediction model was developed using the four hub NRGs associated with EAD. Based on risk scores, HIRI samples were classified into high- and low-risk groups. The OLT model confirmed substantial upregulation of C5AR1 expression in the liver tissue, accompanied by increased formation of NETs. Treatment with a C5AR1 antagonist improved liver function, reduced tissue inflammation, and decreased NETs formation.

CONCLUSIONS

This study distinguished two apparent HIRI subtypes, established a predictive model for EAD, and validated the effect of C5AR1 on HIRI. These findings provide novel perspectives for the development of advanced clinical strategies to enhance the outcomes of liver transplant recipients.

Comprehensive analyses and experimental verification of NETs and an EMT gene signature for prognostic prediction, immunotherapy, and chemotherapy in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy with high mortality and poor prognosis. Neutrophil extracellular traps (NETs) and the epithelial-mesenchymal transition (EMT) significantly influence on the progression of various cancers. However, the underlying relevance of NETs- and EMT-associated genes on the outcomes of patients with PAAD remains to be elucidated. Transcriptome RNA sequencing data, together with clinical information and single-cell sequencing data of PAAD were collected from public databases. In the TCGA-PAAD cohort, ssGSEA was used to calculate NET and EMT scores. WGCNA was used to determine the key gene modules. A risk model with eight NET- and EMT-related genes (NERGs) was established using LASSO and multivariate Cox regression analysis. Patients in the reduced risk (RR) group showed better prognostic values compared with those in the elevated risk (ER) group. The prognostic model exhibited reliable and robust prediction when validated using an external database. The distributions of risk genes were explored in a single-cell sequencing data set. Immune infiltration, immune cycle, and immune checkpoints were compared between the RR and ER groups. Moreover, potential chemotherapeutic drugs were examined. DCBLD2 was identified as a key gene in PAAD cell lines by qRT-PCR, and was highly expressed in PAAD tissues. GSEA demonstrated that DCBLD2 induced the EMT. Transwell assays and western blotting showed that cell invasion and EMT induction were significantly reduced after DCBLD2 knockdown. Collectively, we constructed a prognosis model based on a NET and EMT gene signature, providing a valuable perspective for the prognostic evaluation and management of PAAD patient.

Identification of Molecular Subtype and Prognostic Signature for Prostate Adenocarcinoma based on Neutrophil Extracellular Traps

Background: Prostate adenocarcinoma (PRAD) is one of the most common cancers in male. Increasing evidences pointed out that Neutrophil Extracellular Traps (NETs) play an important role in tumor angiogenesis, tumor metastasis and drug resistance. However, limited systematic studies regarding the role of NETs in PRAD have been performed. Identification of biomarkers based on NETs might facilitate risk stratification which help optimizing the clinical strategies. Methods: NETs-related genes with differential expressions were identified between PRAD and adjacent normal tissues in TCGA-PRAD dataset. Consensus cluster analysis was performed to determine the PRAD subtypes based on the different-expressed NETs-related genes. The difference of pathway enrichment, infiltrating immune cell and genomic mutation were also evaluated between subtypes. LASSO cox regression analysis was conducted to construct a NETs-related prognostic signature. Result: We identified 19 NETs related genes with differential expressions between PRAD and adjacent normal tissue in TCGA-PRAD dataset. Two significant subtypes were identified based on these 19 genes by consensus cluster analysis, namely subtype 1 and subtype 2. Significant differences in prognosis, immune infiltration and tumor mutation burden were observed in subtypes. LASSO Cox regression analysis identified a NETs-associated prognostic signature including 13 genes, and this signature had a good performance in predicting the progression-free survival of PRAD patients. Further integrated analysis indicated that MMP9 mostly expressed in Mono/Macrophage cells might play a role in regulating NETs formation via neutrophil activation in PRAD. Conclusion: To sum up, the current study identified two NETs-related molecular subtypes and based on which constructed a prognostic signature for PRAD.

Early post-operative lactate increase following kidney transplant is associated with delayed graft function: A retrospective cohort study

INTRODUCTION

Delayed graft function (DGF) is a frequent complication following kidney transplant. This study aimed to assess the association between early post-operative lactate variation and DGF.

METHODS

This was a single center, retrospective cohort study between February 2021 and December 2022 in Saint-Louis Hospital (APHP, France). Venous lactate levels were measured immediately (H0) and 4 h (H4) after kidney transplant. The primary outcome was the occurrence of DGF (need for renal replacement therapy between transplantation and day 7). Secondary outcome was the occurrence of complications (i.e., death, vascular thrombosis, hemorrhagic shock, urological complications (hematoma, urinoma), local or systemic infection) between transplant and day 7.

RESULTS

Two hundred 12 patients were included, and 38 (17.9%) developed DGF. Venous lactate variation between H0 and H4 was higher in patients who developed DGF (-30 (IQR -83, -6)% vs. -15 (IQR -62, -11)%, p = .037), but the variation of level was more often positive (corresponding to an increased lactate production over time between H0 and H4) in patients who developed DGF ((28(85%) vs. 94(62%), p = .011). In multivariate logistic regression, positive venous lactate level variation between H0 and H4 was strongly associated with a reduced risk of developing DGF (OR .30 [.09-.79], p = .024). We did not find any association between post-operative hyperlactatemia and occurrence of complications between transplant and day 7.

DISCUSSION

DGF is a frequent complication following kidney transplantation. Its early prediction could help physicians optimize treatment and protect the kidney. Early venous lactate variation after kidney transplant could help to predict the occurrence of DGF.

Characterizing hub biomarkers for post-transplant renal fibrosis and unveiling their immunological functions through RNA sequencing and advanced machine learning techniques

BACKGROUND

Kidney transplantation stands out as the most effective renal replacement therapy for patients grappling with end-stage renal disease. However, post-transplant renal fibrosis is a prevalent and irreversible consequence, imposing a substantial clinical burden. Unfortunately, the clinical landscape remains devoid of reliable biological markers for diagnosing post-transplant renal interstitial fibrosis.

METHODS

We obtained transcriptome and single-cell sequencing datasets of patients with renal fibrosis from NCBI Gene Expression Omnibus (GEO). Subsequently, we employed Weighted Gene Co-Expression Network Analysis (WGCNA) to identify potential genes by integrating core modules and differential genes. Functional enrichment analysis was conducted to unveil the involvement of potential pathways. To identify key biomarkers for renal fibrosis, we utilized logistic analysis, a LASSO-based tenfold cross-validation approach, and gene topological analysis within Cytoscape. Furthermore, histological staining, Western blotting (WB), and quantitative PCR (qPCR) experiments were performed in a murine model of renal fibrosis to verify the identified hub genes. Moreover, molecular docking and molecular dynamics simulations were conducted to explore possible effective drugs.

RESULTS

Through WGCNA, the intersection of core modules and differential genes yielded a compendium of 92 potential genes. Logistic analysis, LASSO-based tenfold cross-validation, and gene topological analysis within Cytoscape identified four core genes (CD3G, CORO1A, FCGR2A, and GZMH) associated with renal fibrosis. The expression of these core genes was confirmed through single-cell data analysis and validated using various machine learning methods. Wet experiments also verified the upregulation of these core genes in the murine model of renal fibrosis. A positive correlation was observed between the core genes and immune cells, suggesting their potential role in bolstering immune system activity. Moreover, four potentially effective small molecules (ZINC000003830276-Tessalon, ZINC000003944422-Norvir, ZINC000008214629-Nonoxynol-9, and ZINC000085537014-Cobicistat) were identified through molecular docking and molecular dynamics simulations.

CONCLUSION

Four potential hub biomarkers most associated with post-transplant renal fibrosis, as well as four potentially effective small molecules, were identified, providing valuable insights for studying the molecular mechanisms underlying post-transplant renal fibrosis and exploring new targets.

Neutrophil extracellular trap-associated risk index for predicting outcomes and response to Wnt signaling inhibitors in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a type of breast cancer with poor prognosis, which is prone to distant metastasis and therapy resistance. The presence of neutrophil extracellular traps (NETs) contributes to the progression of breast cancer and is an efficient predictor of TNBC. We obtained the bulk and single-cell RNA sequencing data from public databases. Firstly, we identified five NET-related genes and constructed NET-related subgroups. Then, we constructed a risk index with three pivotal genes based on the differentially expressed genes between subgroups. Patients in the high-risk group had worse prognosis, clinicopathological features, and therapy response than low-risk group. Functional enrichment analysis revealed that the low-risk group was enriched in Wnt signaling pathway, and surprisingly, the drug sensitivity prediction showed that Wnt signaling pathway inhibitors had higher drug sensitivity in the low-risk group. Finally, verification experiments in vitro based on MDA-MB-231 and BT-549 cells showed that tumor cells with low-risk scores had less migration, invasion, and proliferative abilities and high drug sensitivity to Wnt signaling pathway inhibitors. In this study, multi-omics analysis revealed that genes associated with NETs may influence the occurrence, progression, and treatment of TNBC. Moreover, the bioinformatics analysis and cell experiments demonstrated that the risk index could predict the population of TNBC likely to benefit from treatment with Wnt signaling pathway inhibitors.

Identification of Neutrophil Extracellular Trap-Related Gene Expression Signatures in Ischemia Reperfusion Injury During Lung Transplantation: A Transcriptome Analysis and Clinical Validation

Purpose

Ischemia reperfusion injury (IRI) unavoidably occurs during lung transplantation, further contributing to primary graft dysfunction (PGD). Neutrophils are the end effectors of IRI and activated neutrophils release neutrophil extracellular traps (NETs) to further amplify damage. Nevertheless, potential contributions of NETs in IRI remain incompletely understood. This study aimed to explore NET-related gene biomarkers in IRI during lung transplantation.

Methods

Differential expression analysis was applied to identify differentially expressed genes (DEGs) for IRI during lung transplantation based on matrix data (GSE145989, 127003) downloaded from GEO database. The CIBERSORT and weighted gene co-expression network analysis (WGCNA) algorithms were utilized to identify key modules associated with neutrophil infiltration. Moreover, the least absolute shrinkage and selection operator regression and random forest were applied to identify potential NET-associated hub genes. Subsequently, the screened hub genes underwent further validation of an external dataset (GSE18995) and nomogram model. Based on clinical peripheral blood samples, immunofluorescence staining and dsDNA quantification were used to assess NET formation, and ELISA was applied to validate the expression of hub genes.

Results

Thirty-eight genes resulted from the intersection between 586 DEGs and 75 brown module genes, primarily enriched in leukocyte migration and NETs formation. Subsequently, four candidate hub genes (FCAR, MMP9, PADI4, and S100A12) were screened out via machine learning algorithms. Validation using an external dataset and nomogram model achieved better predictive value. Substantial NETs formation was demonstrated in IRI, with more pronounced NETs observed in patients with PGD ≥ 2. PADI4, S100A12, and MMP9 were all confirmed to be up-regulated after reperfusion through ELISA, with higher levels of S100A12 in PGD ≥ 2 patients compared with non-PGD patients.

Conclusion

We identified three potential NET-related biomarkers for IRI that provide new insights into early detection and potential therapeutic targets of IRI and PGD after lung transplantation.

Identification and Validation of Hub Genes Related to Neutrophil Extracellular Traps-Mediated Cell Damage During Myocardial Infarction

Purpose

Studies have shown that neutrophil-mediated formation of neutrophil extracellular traps (NETs) leads to increased inflammatory response and cellular tissue damage during myocardial infarction (MI). We aimed to identify and validate possible hub genes in the process of NETs-mediated cell damage.

Methods

We performed an immune cell infiltration analysis of the MI transcriptome dataset based on CIBERSORT and ssGSEA algorithms. Gene expression profiles of NETs formation (GSE178883) were used to analyze the physiological processes of peripheral blood neutrophils after phorbol myristate acetate (PMA) stimulation. Bioinformatics and machine learning algorithms were utilized to find candidate hub genes based on NETs-related genes and transcriptome datasets (GSE66360 and GSE179828). We generated the receiver operating curve (ROC) to evaluate the diagnostic value of hub genes. Next, the correlation between hub genes and immune cells was analyzed using CIBERSORT, ssGSEA and xCell algorithms. Finally, we used quantitative real-time PCR (qRT-PCR) and immunohistochemistry to verify gene expression.

Results

Immune cell infiltration analysis revealed that inflammatory cells such as neutrophils were highly expressed in the peripheral blood of patients with MI. Functional analysis of differentially expressed genes (DEGs) in GSE178883 indicated that the potential pathogenesis lies in immune terms. Using weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, we finally identified the seven hub genes (FCAR, IL1B, MMP9, NFIL3, CXCL2, ICAM1, and ZFP36). The qRT-PCR results showed that IL-1B, MMP9, and NFIL3 mRNA expression was up-regulated in the MI group compared to the control. Immunohistochemical results showed high MMP9, IL-1B, and NFIL3 expression in the infarcted area compared to the non-infarcted area and sham-operated groups.

Conclusion

We identified seven hub genes associated with NETs-mediated cellular damage during MI. Our results may provide insights into the mechanisms of neutrophil-mediated cell injury during MI.

Identification and validation of molecular subtypes and prognostic signature for stage I and stage II gastric cancer based on neutrophil extracellular traps

Purpose

This study identified subtypes and prognostic signature of stage I and stage II gastric cancer based on neutrophil extracellular trap (NET)-related genes.

Methods

The gene expression data associated with stage I and stage II gastric cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. NET-related genes were obtained from previous reference. Differentially expressed NET-related genes were selected by consensus cluster analysis. The differences in immune infiltration between two subtypes were analyzed. Prognosis-related genes were further screened by univariate Cox regression analysis. Gene Set Enrichment Analysis (GSEA) of prognostic signatures was conducted with clusterprofiler. Finally, a miRNA-mRNA-transcription factor (TF) network was constructed.

Results

Total 43 differential NET-related genes were obtained and two subtypes were obtained based on these genes. Patients of cluster 2 had a better prognosis compared to cluster 1. Eight types of immune cells were differential in infiltration level between two subtypes. Following univariate Cox regression analysis, two genes of CXC chemokine receptor 4 (CXCR4) and nuclear factor, erythroid 2-like 2 (NFE2L2) significantly related to patient survival were selected. GSEA of single gene revealed that CXCR4 was associated with allograft rejection and NFE2L2 was associated with drug metabolism-cytochrome P450. A network with 421 miRNA-mRNA-TF regulatory pairs was constructed.

Conclusion

The present study identified two subtypes and a prognostic signature for stage I and stage II gastric cancer based on NET-related genes.

Comprehensive Overview of Innovative Strategies in Preventing Renal Ischemia-reperfusion Injury: Insights from Bibliometric and In silico Analyses

BACKGROUND

Ischemia-reperfusion Injury (IRI) is a complex pathophysiological process with severe consequences, including irreversible loss of renal function. Various intraoperative prevention methods have been proposed to mitigate the harmful effects of warm ischemia and kidney reperfusion.

AIM

This comprehensive analysis provides an overview of pharmacological agents and intraoperative methods for preventing and treating renal IRI.

METHODS

Our analysis revealed that eplerenone exhibited the highest binding affinity to crucial targets, including Aldehyde Dehydrogenase (AD), Estrogen Receptor (ER), Klotho protein, Mineralocorticoid Receptor (MR), and Toll-like Receptor 4 (TLR4). This finding indicates eplerenone's potential as a potent preventive agent against IRI, surpassing other available therapeutics like Benzodioxole, Hydrocortisone, Indoles, Nicotinamide adenine dinucleotide, and Niacinamide. In preventing kidney IRI, our comprehensive analysis emphasizes the significance of eplerenone due to its strong binding affinity to key targets involved in the pathogenesis of IRI.

RESULTS

This finding positions eplerenone as a promising candidate for further clinical investigation and consideration for future clinical practice.

CONCLUSION

The insights provided in this analysis will assist clinicians and researchers in selecting effective preventive approaches for renal IRI in surgical settings, potentially improving patient outcomes.

Identification of bladder cancer subtypes and predictive model for prognosis, immune features, and immunotherapy based on neutrophil extracellular trap-related genes

Bladder cancer is the most common malignant tumor of urinary system, and its morbidity and mortality are increasing rapidly. Although great advances have been made in medical technology in recent years, there is still a lack of effective prognostic and therapeutic methods for bladder cancer. NETs are reticulated DNA structures decorated with various protein substances released extracellularly by neutrophils stimulated by strong signals. Recently, it has been found that NETs are closely related to the growth, metastasis and drug resistance of many types of cancers. However, up to now, the research on the relationship between NETs and bladder cancer is still not enough. In this study, we aimed to conduct a comprehensive analysis of NRGs in bladder cancer tissues to evaluate the relationship between NRGs and prognosis prediction and sensitivity to therapy in patients with bladder cancer. We scored NRGs in each tissue by using ssGSEA, and selected gene sets that were significantly associated with NRGs scores by using the WCGNA algorithm. Based on the expression profiles of NRGs-related genes, NMF clustering analysis was performed to identify different BLCA molecular subtypes. For the differentially expressed genes between subtypes, we used univariate COX regression, LASSO regression and multivariate COX regression to further construct a hierarchical model of BLCA patients containing 10 genes. This model and the nomogram based on this model can accurately predict the prognosis of BLCA patients in multiple datasets. Besides, BLCA patients classified based on this model differ greatly in their sensitivity to immunotherapy and targeted therapies, which providing a reference for individualized treatment of patients with bladder cancer.

Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA

Background

As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment.

Methods

We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model.

Results

A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions.

Conclusions

Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.

Neutrophil diversity and plasticity: Implications for organ transplantation

Neutrophils, as the first defenders against external microbes and stimuli, are highly active and finely regulated innate immune cells. Emerging evidence has challenged the conventional dogma that neutrophils are a homogeneous population with a short lifespan that promotes tissue damage. Recent findings on neutrophil diversity and plasticity in homeostatic and disease states have centered on neutrophils in the circulation. In contrast, a comprehensive understanding of tissue-specialized neutrophils in health and disease is still lacking. This article will first discuss how multiomics advances have contributed to our understanding of neutrophil heterogeneity and diversification in resting and pathological settings. This discussion will be followed by a focus on the heterogeneity and role of neutrophils in solid organ transplantation and how neutrophils may contribute to transplant-related complications. The goal of this article is to provide an overview of the research on the involvement of neutrophils in transplantation, with the aim that this may draw attention to an underappreciated area of neutrophil research.