Dataset Information

Identification of CCL19 as a Novel Immune-Related Biomarker in Diabetic Nephropathy.

ABSTRACT: Diabetic nephropathy (DN) is one of the major microvascular complications in diabetic patients and the leading cause of end-stage renal disease (ESRD). Previous studies found that immune-related genes and immune cell infiltration play important roles in the pathogenesis and development of DN. Therefore, this study aimed to explore immune-related biomarkers in DN. In this research, three microarray datasets that included 18 DN and 28 healthy tubule samples were downloaded and integrated as the training set to identify differentially expressed immune-related genes (DEIGs). A total of 63 DEIGs were identified, and most upregulated DEIGs were primarily involved in the inflammatory response and chemokine-mediated signaling pathways. The Microenvironment Cell Populations-counter (MCP-counter) algorithm was then used to estimate the abundance of infiltrated immune and stromal cell populations. According to DEIG, weighted gene coexpression network and protein-protein network analyses, CCL19 was identified as the hub immune-related biomarker. Moreover, the upregulated level of CCL19 was confirmed in other independent datasets as well as in in vitro experiments with high glucose. In summary, this study provides novel insights into the pathogenesis of diabetic nephropathy and identifies CCL19 as a potential critical gene of DN.

SUBMITTER: Chen H

PROVIDER: S-EPMC8864156 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Identification of CCL19 as a Novel Immune-Related Biomarker in Diabetic Nephropathy.

Chen Hanzhi H Zhang Zhijian Z Zhou Leting L Cai Ting T Liu Bin B Wang Liang L Yang Junwei J

Frontiers in genetics 20220209

Diabetic nephropathy (DN) is one of the major microvascular complications in diabetic patients and the leading cause of end-stage renal disease (ESRD). Previous studies found that immune-related genes and immune cell infiltration play important roles in the pathogenesis and development of DN. Therefore, this study aimed to explore immune-related biomarkers in DN. In this research, three microarray datasets that included 18 DN and 28 healthy tubule samples were downloaded and integrated as the tr ...[more]

PMID: 35222545

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Project description:BackgroundThe study aimed to identify core biomarkers related to diagnosis and immune microenvironment regulation and explore the immune molecular mechanism of diabetic nephropathy (DN) through bioinformatics analysis.MethodsGSE30529, GSE99325, and GSE104954 were merged with removing batch effects, and different expression genes (DEGs) were screened at a criterion |log2FC| >0.5 and adjusted P <0.05. KEGG, GO, and GSEA analyses were performed. Hub genes were screened by conducting PPI networks and calculating node genes using five algorithms with CytoHubba, followed by LASSO and ROC analysis to accurately identify diagnostic biomarkers. In addition, two different GEO datasets, GSE175759 and GSE47184, and an experiment cohort with 30 controls and 40 DN patients detected by IHC, were used to validate the biomarkers. Moreover, ssGSEA was performed to analyze the immune microenvironment in DN. Wilcoxon test and LASSO regression were used to determine the core immune signatures. The correlation between biomarkers and crucial immune signatures was calculated by Spearman analysis. Finally, cMap was used to explore potential drugs treating renal tubule injury in DN patients.ResultsA total of 509 DEGs, including 338 upregulated and 171 downregulated genes, were screened out. "chemokine signaling pathway" and "cell adhesion molecules" were enriched in both GSEA and KEGG analysis. CCR2, CX3CR1, and SELP, especially for the combination model of the three genes, were identified as core biomarkers with high diagnostic capabilities with striking AUC, sensitivity, and specificity in both merged and validated datasets and IHC validation. Immune infiltration analysis showed a notable infiltration advantage for APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. In addition, the correlation analysis showed that CCR2, CX3CR1, and SELP were strongly and positively correlated with checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation in the DN group. Finally, dilazep was screened out as an underlying compound for DN analyzed by CMap.ConclusionsCCR2, CX3CR1, and SELP are underlying diagnostic biomarkers for DN, especially in their combination. APC co-stimulation, CD8+ T cells, checkpoint, cytolytic activity, macrophages, MHC class I, and parainflammation may participate in the occurrence and development of DN. At last, dilazep may be a promising drug for treating DN.

Project description:BackgroundAging and immune infiltration have essential role in the physiopathological mechanisms of diabetic nephropathy (DN), but their relationship has not been systematically elucidated. We identified aging-related characteristic genes in DN and explored their immune landscape.MethodsFour datasets from the Gene Expression Omnibus (GEO) database were screened for exploration and validation. Functional and pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Characteristic genes were obtained using a combination of Random Forest (RF) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm. We evaluated and validated the diagnostic performance of the characteristic genes using receiver operating characteristic (ROC) curve, and the expression pattern of the characteristic genes was evaluated and validated. Single-Sample Gene Set Enrichment Analysis (ssGSEA) was adopted to assess immune cell infiltration in samples. Based on the TarBase database and the JASPAR repository, potential microRNAs and transcription factors were predicted to further elucidate the molecular regulatory mechanisms of the characteristic genes.ResultsA total of 14 differentially expressed genes related to aging were obtained, of which 10 were up-regulated and 4 were down-regulated. Models were constructed by the RF and SVM-RFE algorithms, contracted to three signature genes: EGF-containing fibulin-like extracellular matrix (EFEMP1), Growth hormone receptor (GHR), and Vascular endothelial growth factor A (VEGFA). The three genes showed good efficacy in three tested cohorts and consistent expression patterns in the glomerular test cohorts. Most immune cells were more infiltrated in the DN samples compared to the controls, and there was a negative correlation between the characteristic genes and most immune cell infiltration. 24 microRNAs were involved in the transcriptional regulation of multiple genes simultaneously, and Endothelial transcription factor GATA-2 (GATA2) had a potential regulatory effect on both GHR and VEGFA.ConclusionWe identified a novel aging-related signature allowing assessment of diagnosis for DN patients, and further can be used to predict immune infiltration sensitivity.

Project description:Background: Diabetic nephropathy (DN) was considered a severe microvascular complication of diabetes, which was recognized as the second leading cause of end-stage renal diseases. Therefore, identifying several effective biomarkers and models to diagnosis and subtype DN is imminent. Necroptosis, a distinct form of programmed cell death, has been established to play a critical role in various inflammatory diseases. Herein, we described the novel landscape of necroptosis in DN and exploit a powerful necroptosis-mediated model for the diagnosis of DN. Methods: We obtained three datasets (GSE96804, GSE30122, and GSE30528) from the Gene Expression Omnibus (GEO) database and necroptosis-related genes (NRGs) from the GeneCards website. Via differential expression analysis and machine learning, significant NRGs were identified. And different necroptosis-related DN subtypes were divided using consensus cluster analysis. The principal component analysis (PCA) algorithm was utilized to calculate the necroptosis score. Finally, the logistic multivariate analysis were performed to construct the necroptosis-mediated diagnostic model for DN. Results: According to several public transcriptomic datasets in GEO, we obtained eight significant necroptosis-related regulators in the occurrence and progress of DN, including CFLAR, FMR1, GSDMD, IKBKB, MAP3K7, NFKBIA, PTGES3, and SFTPA1 via diversified machine learning methods. Subsequently, employing consensus cluster analysis and PCA algorithm, the DN samples in our training set were stratified into two diverse necroptosis-related subtypes based on our eight regulators' expression levels. These subtypes exhibited varying necroptosis scores. Then, we used various functional enrichment analysis and immune infiltration analysis to explore the biological background, immune landscape and inflammatory status of the above subtypes. Finally, a necroptosis-mediated diagnostic model was exploited based on the two subtypes and validated in several external verification datasets. Moreover, the expression level of our eight regulators were verified in the singe-cell level and glomerulus samples. And we further explored the relationship between the expression of eight regulators and the kidney function of DN. Conclusion: In summary, our necroptosis scoring model and necroptosis-mediated diagnostic model fill in the blank of the relationship between necroptosis and DN in the field of bioinformatics, which may provide novel diagnostic insights and therapy strategies for DN.

Project description:BackgroundAs the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods.MethodsFirst, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related genesets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified.ResultsFinally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models.ConclusionMachine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

Dataset Information

Identification of CCL19 as a Novel Immune-Related Biomarker in Diabetic Nephropathy.

Publications

Identification of CCL19 as a Novel Immune-Related Biomarker in Diabetic Nephropathy.

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