Dataset Information

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis.

ABSTRACT:

Background

Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear.

Method

Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network. Finally, the hub genes were verified, and candidate drugs were screened from the DsigDB database.

Results

A hundred DEGs and four hub genes (Atf3, Psmb6, Psmb8, and Psmb10) were screened out. Pathway enrichment analysis revealed that 100 DEGs were mainly enriched in apoptosis and the TNF signaling pathway. The four hub genes were verified in animal models and another dataset (GSE148420). Thereafter, a PPI network was used to identify the four hub genes (Atf3, Psmb6, Psmb8, and Psmb10). Finally, eight candidate drugs were identified as potential drugs.

Conclusion

Three hub genes (Psmb6, Psmb8, and Psmb10) were associated with RIRI and could be potential novel biomarkers for RIRI.

SUBMITTER: Pan Z

PROVIDER: S-EPMC9911259 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis.

Pan Ziwen Z Yang Yuanyuan Y Cao Rui R Qiu Yang Y Li Shanglin S Zhao Yuanyuan Y Chang Sheng S Chen Song S Chen Zhishui Z Zhang Weijie W Zhao Daqiang D

BioMed research international 20230202

<h4>Background</h4>Renal ischemia-reperfusion injury (RIRI) plays an important role in the poor prognosis of patients with renal transplants. However, the potential targets and mechanism of IRI are still unclear.<h4>Method</h4>Differential gene expression (DEG) analysis and weighted correlation network analysis (WGCNA) were performed on the GSE27274 dataset. Pathway enrichment analysis on the DEGs was performed. To identify the hub DEGs, we constructed a protein-protein interaction (PPI) network ...[more]

PMID: 36778059

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Project description:BackgroundAlthough AKI lacks effective therapeutic approaches, preventive strategies using preconditioning protocols, including caloric restriction and hypoxic preconditioning, have been shown to prevent injury in animal models. A better understanding of the molecular mechanisms that underlie the enhanced resistance to AKI conferred by such approaches is needed to facilitate clinical use. We hypothesized that these preconditioning strategies use similar pathways to augment cellular stress resistance.MethodsTo identify genes and pathways shared by caloric restriction and hypoxic preconditioning, we used RNA-sequencing transcriptome profiling to compare the transcriptional response with both modes of preconditioning in mice before and after renal ischemia-reperfusion injury.ResultsThe gene expression signatures induced by both preconditioning strategies involve distinct common genes and pathways that overlap significantly with the transcriptional changes observed after ischemia-reperfusion injury. These changes primarily affect oxidation-reduction processes and have a major effect on mitochondrial processes. We found that 16 of the genes differentially regulated by both modes of preconditioning were strongly correlated with clinical outcome; most of these genes had not previously been directly linked to AKI.ConclusionsThis comparative analysis of the gene expression signatures in preconditioning strategies shows overlapping patterns in caloric restriction and hypoxic preconditioning, pointing toward common molecular mechanisms. Our analysis identified a limited set of target genes not previously known to be associated with AKI; further study of their potential to provide the basis for novel preventive strategies is warranted. To allow for optimal interactive usability of the data by the kidney research community, we provide an online interface for user-defined interrogation of the gene expression datasets (http://shiny.cecad.uni-koeln.de:3838/IRaP/).

Project description:BackgroundIschemia-reperfusion injury (IRI) remains an inevitable and major challenge in renal transplantation. The current study aims to obtain deep insights into underlying mechanisms and seek prognostic genes as potential therapeutic targets for renal IRI (RIRI).MethodsAfter systematically screening the Gene Expression Omnibus (GEO) database, we collected gene expression profiles of over 1,000 specimens from 11 independent cohorts. Differentially expressed genes (DEGs) were identified by comparing allograft kidney biopsies taken before and after reperfusion in the discovery cohort and further validated in another two independent transplant cohorts. Then, graft survival analysis and immune cell analysis of DEGs were performed in another independent renal transplant cohort with long-term follow-ups to further screen out prognostic genes. Cell type and time course analyses were performed for investigating the expression pattern of prognostic genes in more dimensions utilizing a mouse RIRI model. Finally, two novel genes firstly identified in RIRI were verified in the mouse model and comprehensively analyzed to investigate potential mechanisms.ResultsTwenty DEGs upregulated in the process of RIRI throughout different donor types (living donors, cardiac and brain death donors) were successfully identified and validated. Among them, upregulation of 10 genes was associated with poor long-term allograft outcomes and exhibited strong correlations with prognostic immune cells, like macrophages. Furthermore, certain genes were found to be only differentially expressed in specific cell types and remained with high expression levels even months after RIRI in the mouse model, which processed the potential to serve as therapeutic targets. Importantly, two newly identified genes in RIRI, Btg2 and Rhob, were successfully confirmed in the mouse model and found to have strong connections with NF-κB signaling.ConclusionsWe successfully identified and validated 10 IRI-associated prognostic genes in renal transplantation across different donor types, and two novel genes with crucial roles in RIRI were recognized for the first time. Our findings offered promising potential therapeutic targets for RIRI in renal transplantation.

Project description:Myocardial ischemia-reperfusion injury (MIRI) is often part of clinical events such as cardiac arrest, resuscitation, and reperfusion after coronary artery occlusion. Recently, more and more studies have shown that the immune microenvironment is an integral part of ischemia-reperfusion injury (IRI), and CD4+ T-cell infiltration plays an important role, but there are no relevant molecular targets for clinical diagnosis and treatment. The transcriptome data and matched group information were retrieved from the Gene Expression Omnibus (GEO) database. The ImmuCellAI-mouse (Immune Cell Abundance Identifier for mouse) algorithm was used to calculate each symbol's CD4+ T cell infiltration score. The time period with the greatest change in the degree of CD4+ T cell infiltration [ischemia-reperfusion 6 hours (IR6h)-ischemia-reperfusion 24 hours (IR24h)] was selected for the next analysis. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were performed to screen out CD4+ T cell-related genes and from which the gene CLEC5A was screened for the highest correlation with CD4+ T cell infiltration. The potential regulatory mechanism of CD4+ T cells in MIRI was discussed through various enrichment analysis. Finally, we analyzed the expression and molecular function (MF) of CLEC5A and its related genes in MIRI. A total of 406 CD4+ T cell-related genes were obtained by intersecting the results of WGCNA and differential expression analysis. Functional enrichment analysis indicated that the CD4+ T cell-related genes were mainly involved in chemokine signaling pathway and cell cycle. By constructing a protein-protein interaction (PPI) network, a total of 12 hub genes were identified as candidate genes for further analysis. Through the correlation analysis between the 12 candidate genes found in the PPI network and CD4+ T cell infiltration fraction, we determined the core gene CLEC5A. Finally, a gene interaction network was constructed to decipher the biological functions of CLEC5A using GeneMANIA. In this study, RNA sequencing (RNA-Seq) data at different time points after reperfusion were subjected to a series of bioinformatics methods such as PPI network, WGCNA module, etc., and CLEC5A, a pivotal gene associated with CD4+ T-cells, was found, which may serve as a new target for diagnosis or treatment.

Dataset Information

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis.

Background

Method

Results

Conclusion

Publications

Identification and Verification of Potential Biomarkers in Renal Ischemia-Reperfusion Injury by Integrated Bioinformatic Analysis.

Similar Datasets

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