Identification of biomarkers related to immune and inflammation in membranous nephropathy: comprehensive bioinformatic analysis and validation
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SUBMITTER: Zhang P
PROVIDER: S-EPMC10590909 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE: We aim to explore the detailed molecular mechanisms of membrane nephropathy (MN) related genes by bioinformatics analysis. METHODS: Two microarray datasets (GSE108109 and GSE104948) with glomerular gene expression data from 65 MN patients and 9 healthy donors were obtained from the Gene Expression Omnibus (GEO) database. After processing the raw data, DEGs screening was conducted using the LIMMA (linear model for microarray data) package and Gene set enrichment analysis (GSEA) was performed with GSEA software (v. 3.0), followed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network analysis was carried out to determine the hub genes, by applying the maximal clique centrality (MCC) method, which was visualized by Cytoscape. Finally, utilizing the Nephroseq v5 online platform, we analyzed subgroups associated with hub genes. The findings were further validated by immunohistochemistry (IHC) staining in renal tissues from MN or control patients. RESULTS: A sum of 370 DEGs (188 up-regulated genes, 182 down-regulated genes) and 20 hub genes were ascertained. GO and KEGG enrichment analysis demonstrated that DEGs of MN were preponderantly associated with cell damage and complement cascade-related immune responses. Combined with literature data and hub gene-related MN subset analysis, CTSS, ITGB2, and HCK may play important roles in the pathological process of MN. CONCLUSION: This study identified novel hub genes in MN using bioinformatics. We found that some hub genes such as CTSS, ITGB2, and HCK might contribute to MN immunopathological process, providing new insights for further study of the molecular mechanisms underlying glomerular injury of MN.
| S-EPMC9263269 | biostudies-literature
Project description:Immune cell infiltration (ICI) plays a pivotal role in the development of diabetic nephropathy (DN). Evidence suggests that immune-related genes play an important role in the initiation of inflammation and the recruitment of immune cells. However, the underlying mechanisms and immune-related biomarkers in DN have not been elucidated. Therefore, this study aimed to explore immune-related biomarkers in DN and the underlying mechanisms using bioinformatic approaches. In this study, four DN glomerular datasets were downloaded, merged, and divided into training and test cohorts. First, we identified 55 differentially expressed immune-related genes; their biological functions were mainly enriched in leukocyte chemotaxis and neutrophil migration. The CIBERSORT algorithm was then used to evaluate the infiltrated immune cells; macrophages M1/M2, T cells CD8, and resting mast cells were strongly associated with DN. The ICI-related gene modules as well as 25 candidate hub genes were identified to construct a protein-protein interactive network and conduct molecular complex detection using the GOSemSim algorithm. Consequently, FN1, C3, and VEGFC were identified as immune-related biomarkers in DN, and a related transcription factor-miRNA-target network was constructed. Receiver operating characteristic curve analysis was estimated in the test cohort; FN1 and C3 had large area under the curve values (0.837 and 0.824, respectively). Clinical validation showed that FN1 and C3 were negatively related to the glomerular filtration rate in patients with DN. Six potential therapeutic small molecule compounds, such as calyculin, phenamil, and clofazimine, were discovered in the connectivity map. In conclusion, FN1 and C3 are immune-related biomarkers of DN.
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Project description:BackgroundMembranous glomerulonephritis (MGN) is the most common cause of nephrotic syndrome in adult patients. Despite extensive evidences suggested that many immune-related genes could serve as effective biomarkers in MGN, the potential has not been sufficiently understood because of most previous studies have concentrated on individual gene and not the entire interaction network.MethodsHere, we integrated multiple levels of data containing immune-related genes, MGN-related genes, protein-protein interaction (PPI) networks and gene expression profiling data to construct an immune or MGN-directed neighbor network (IOMDN network) and an MGN-related genes-directed network (MGND network).ResultsOur analysis suggested that immune-related genes in the PPI network have special topological characteristics and expression pattern related to MGN. We also identified five network modules which showed tighter network structure and stronger correlation of expression. In addition, functional and drug target analyses of genes in modules indicated that the potential mechanism for MGN.ConclusionsCollectively, these results indicated that the strong associations between immune and MGN and showed the potential of immune-related genes as novel diagnostic and therapeutic targets for MGN.
| S-EPMC6056925 | biostudies-literature
Project description:BackgroundMembranous glomerulonephritis (MGN) is a common kidney disease. Despite many evidences support that many immune and inflammation-related genes could serve as effective biomarkers and treatment targets for MGN patients, the potential associations among MGN-, immune- and inflammation-related genes have not been sufficiently understood.MethodsHere, a global immune-, inflammation- and MGN-associated triplets (IIMATs) network is constructed and analyzed. An integrated and computational approach is developed to identify dysregulated IIMATs for MGN patients based on expression and interaction data.Results45 dysregulated IIMATs are identified in MGN by above method. Dysregulated patterns of these dysregulated IIMATs are complex and various. We identify four core clusters from dysregulated IIMATs network and some of these clusters could distinguish MGN and normal samples. Specially, some anti-cancer drugs including Tamoxifen, Bosutinib, Ponatinib and Nintedanib could become candidate drugs for MGN based on drug repurposing strategy follow IIMATs. Functional analysis shows these dysregulated IIMATs are associated with some key functions and chemokine signaling pathway.ConclusionsThe present study explored the associations among immune, inflammation and MGN. Some effective candidate drugs for MGN were identified based on immune and inflammation. Overall, these comprehensive results provide novel insights into the mechanisms and treatment of MGN.
| S-EPMC6947948 | biostudies-literature
Project description:ObjectivesMembranous nephropathy (MN) is an autoimmune nephropathy. The incidence of MN is increasing gradually in recent years. Previous studies focused on antibody production, complement activation and podocyte injury in MN. However, the etiology and underlying mechanism of MN remain to be further studied.MethodsGSE104948 and GSE108109 of glomerular expression profile were downloaded from Gene Expression Omnibus (GEO) database, GSE47184, GSE99325, GSE104954, GSE108112, GSE133288 of renal tubule expression profile, and GSE73953 of peripheral blood mononuclear cells (PBMCs) expression profile. After data integration by Networkanalyst, differentially expressed genes (DEGs) between MN and healthy samples were obtained. DEGs were enriched in gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG), and protein-protein interaction (PPI) networks of these genes were constructed through Metascape, etc. We further understood the function of hub genes through gene set enrichment analysis (GSEA). The diagnostic value of DEGs in MN was evaluated by receiver operating characteristic (ROC) analysis.ResultsA total of 3 genes (TP53, HDAC5, and SLC2A3) were screened out. Among them, the up-regulated TP53 expression may be closely related to MN renal pathological changes. However, the expression of MN podocyte target antigen was not significantly different from that of healthy controls. In addition, the changes of Wnt signaling pathway in PBMCs and the effects of SLC2A3 on the differentiation of M2 monocyte need further study.ConclusionIt is difficult to unify a specific mechanism for the changes of glomerulus, renal tubules and PBMCs in MN patients. This may be related to the pathogenesis, pathology and immune characteristics of MN. MN podocyte target antigen may not be the root cause of the disease, but a stage result in the pathogenesis process.
| S-EPMC9452341 | biostudies-literature
Project description:Anti-M-type phospholipase A2 receptor (anti-PLA2R) is a widely accepted biomarker for clinical idiopathic membranous neurophathy (IMN). However, its ability to differentiate between IMN and secondary MN (SMN) is controversial. The objective of this study was to assess clinical MN biomarkers in blood, tissue and urine samples from Chinese patients. In total, 195 MN patients and 70 patients with other glomerular diseases were prospectively enrolled in the study. Participants were followed up for average of 17 months (range 3-39 months). Anti-PLA2R and anti-THSD7A (thrombospondin type-1 domain-containing 7A) were detected only in MN patient sera and not in controls. Serum anti-THSD7A and THSD7A-positive biopsies were detected in 1/18 and 2/18 PLA2R-negative MN cases, respectively. PLA2R and THSD7A were detected in 72.27% and 40% of SMN cases, respectively. While serum positivity for both anti-PLA2R and anti-THSD7A at the time of renal biopsy was specific to MN patients, neither antigen could discriminate between primary and secondary MN. We also found that high urinary levels of retinol binding protein (RBP) predicted poor proteinuria outcomes in study participants. Patients with low or medium urinary RBP levels achieved remission more frequently than those with high RBP.
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Project description:Background: DNA methylation is an important form of epigenetic regulation and is closely related to atherosclerosis (AS). The purpose of this study was to identify DNA methylation-related biomarkers and explore the immune-infiltrate characteristics of AS based on methylation data. Methods: DNA methylation data of 15 atherosclerotic and paired healthy tissues were obtained from Gene Expression Omnibus database. Differential methylation positions (DMPs) and differential methylation regions (DMRs) were screened by the ChAMP R package. The methylation levels of DMPs located on CpG islands of gene promoter regions were averaged. The limma R package was used to screen differentially methylated genes in the CpG islands of the promoter regions. The diagnostic values of the methylation levels were evaluated using the pROC R package. The EpiDISH algorithm was applied to quantify the infiltration levels of seven types of immune cells. Subsequently, three pairs of clinical specimens of coronary atherosclerosis with Stary's pathological stage III were collected, and the methylation levels were detected by the methylation-specific PCR (MS-PCR) assay. Western blot was performed to detect the protein expression levels of monocyte markers. Results: A total of 110, 695 DMPs, and 918 DMRs were screened in the whole genome. Also, six genes with significant methylation differences in the CpG islands of the promoter regions were identified, including 49 DMPs. In total, three genes (GRIK2, HOXA2, and HOXA3) had delta beta greater than 0.2. The infiltration level of monocytes was significantly upregulated in AS tissues. MS-PCR assay confirmed the methylation status of the aforementioned three genes in AS samples. The Western blot results showed that the expression levels of the monocyte marker CD14 and M1-type macrophage marker CD86 were significantly increased in AS while M2-type macrophage marker protein CD206 was significantly decreased. Conclusion: This study identified potential DNA methylation-related biomarkers and revealed the role of monocytes in early AS.
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Project description:Increasing evidence has indicated that ferroptosis engages in the progression of Parkinson's disease (PD). This study aimed to explore the role of ferroptosis-related genes (FRGs), immune infiltration and immune checkpoint genes (ICGs) in the pathogenesis and development of PD. The microarray data of PD patients and healthy controls (HC) from the Gene Expression Omnibus (GEO) database was downloaded. Weighted gene co-expression network analysis (WGCNA) was processed to identify the significant modules related to PD in the GSE18838 dataset. Machine learning algorithms were used to screen the candidate biomarkers based on the intersect between WGCNA, FRGs and differentially expressed genes. Enrichment analysis of GSVA, GSEA, GO, KEGG, and immune infiltration, group comparison of ICGs were also performed. Next, candidate biomarkers were validated in clinical samples by ELISA and receiver operating characteristic curve (ROC) was used to assess diagnose ability. In this study, FRGs had correlations with ICGs, immune infiltration. Then, plasma levels of LPIN1 in PD was significantly lower than that in healthy controls, while the expression of TNFAIP3 was higher in PD in comparison with HC. ROC curves showed that the area under curve (AUC) of the LPIN1 and TNFAIP3 combination was 0.833 (95% CI: 0.750-0.916). Moreover, each biomarker alone could discriminate the PD from HC (LPIN1: AUC = 0.754, 95% CI: 0.659-0.849; TNFAIP3: AUC = 0.754, 95% CI: 0.660-0.849). For detection of early PD from HC, the model of combination maintained diagnostic accuracy with an AUC of 0.831 (95% CI: 0.734-0.927), LPIN1 also performed well in distinguishing the early PD from HC (AUC = 0.817, 95% CI: 0.717-0.917). However, the diagnostic efficacy was relatively poor in distinguishing the early from middle-advanced PD patients. The combination model composed of LPIN1 and TNFAIP3, and each biomarker may serve as an efficient tool for distinguishing PD from HC.
| S-EPMC10012699 | biostudies-literature
Project description:BackgroundPolycystic ovarian syndrome (PCOS) is a common endocrine and metabolic disorder in women of reproductive age. In this study, we aimed to investigate the potential prognostic value of mitophagy-related genes in PCOS patients and to analyze their role in immune infiltration during PCOS pathogenesis and progression.MethodsTraining datasets were used for differential expression genes. Gene ontology annotations and Kyoto encyclopedia of genes and genomes signaling pathway enrichment analysis were performed. The potential biomarkers of mitophagy-related and immune infiltration in PCOS were screened by protein-protein interaction network using different algorithms, and the area under the curve was calculated to analyze their diagnostic value. The test datasets were used to validate the expression of hub genes, and receiver operating characteristic curves were used to evaluate the predictive effect of hub genes.ResultsFive hub genes: CTSD, IGF2R, ATP13A2, NAPA and GRN were identified as the potential diagnostic biomarkers of immune-mitophagy-related PCOS through five algorithms. GRN and NAPA were validated to be significantly different in oocytes and granulosa cells between primary and secondary follicles, respectively. Based on the single sample Gene Set Enrichment Analysis score, the infiltration of 4 immune cell types in PCOS was associated with PCOS and mitophagy. Specifically, the hub gene GRN showed a positive correlation with monocytes and plasmacytoid dendritic cells, while hub gene NAPA was negatively correlated with gamma delta T cell.ConclusionsThe current study identified immune-mitophagy-related hub genes for prognostic biomarkers of PCOS, which provided an innovative insight for the prevention and treatment of PCOS.
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Project description: Not available
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