Novel Pathology-Related Hub Genes in Focal Segmental Glomerulosclerosis

Background: Focal segmental glomerulosclerosis (FSGS) is a type of nephrotic syndrome leading to end-stage renal disease, and this study aimed to explore the hub genes and pathways associated with FSGS to identify potential diagnostic and therapeutic targets.Methods: We downloaded the microarray datasets GSE121233 and GSE129973 from the Gene Expression Omnibus (GEO) database. The datasets comprise 25 FSGS samples and 25 normal samples. The differential expression genes (DEGs) were identified using the R package “limma”. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID) to identify the pathways and functional annotation of the DEGs. The protein–protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. The hub genes of the DEGs were then evaluated using the cytoHubba plugin of Cytoscape. The expression of the hub genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the FSGS rat model, and receiver operating characteristic (ROC) curve analysis was performed to validate the accuracy of these hub genes.Results: A total of 45 DEGs including 18 upregulated and 27 downregulated DEGs, were identified in the two GSE datasets (GSE121233 and GSE129973). Among them, five hub genes with a high degree of connectivity were selected. From the PPI network, of the top five hub genes, FN1 was upregulated, while ALB, EGF, TTR, and KNG1 were downregulated. The qRT-PCR analysis of FSGS rats confirmed that the expression of FN1 was upregulated and that of EGF and TTR was downregulated. The ROC analysis indicated that FN1, EGF, and TTR showed considerable diagnostic efficiency for FSGS.Conclusion: Three novel FSGS-specific genes were identified through bioinformatic analysis combined with experimental validation, which may promote our understanding of the molecular underpinning of FSGS and provide potential therapeutic targets for the clinical management.

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Identification of Crucial Degs and Hub Genes in Focal Segmental Glomerulosclerosis: A Bioinformatics Study

International Journal of Applied Biology and Pharmaceutical Technology ◽

10.26502/ijabpt.202112 ◽

2021 ◽

Vol 12 (04) ◽

Author(s):

Bhuvnesh Rai ◽

Prabhakar Mishra ◽

Mehar Hasan Asif ◽

Swasti Tiwari

Keyword(s):

Focal Segmental Glomerulosclerosis ◽

Hub Genes ◽

Segmental Glomerulosclerosis ◽

Bioinformatics Study

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Identification Candidate Diagnostic Biomarkers Between Minimal Change Disease and Focal Segmental Glomerulosclerosis by Bioinformatics Analysis

10.21203/rs.3.rs-100941/v2 ◽

2021 ◽

Author(s):

Yayu Li ◽

Yuanyuan Du ◽

Xue Jiang

Keyword(s):

Differential Diagnosis ◽

Differential Expression ◽

Focal Segmental Glomerulosclerosis ◽

Minimal Change Disease ◽

Bioinformatics Analysis ◽

Minimal Change ◽

Hub Genes ◽

U6 Snrna ◽

Segmental Glomerulosclerosis ◽

Healthy Control

Abstract Background: Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are common causes of nephrotic syndrome which have similar clinical as well as histologic magnification and hard to differentiate. This study aimed to identify novel biomarkers to distinguish FSGS and MCD through bioinformatics analysis and elucidate the possible molecular mechanism. Material and Methods: Based on the microarray datasets GSE104948 and GSE108113 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between FSGS vs healthy control, MCD vs healthy control were identified, and further defined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Hub genes were checked by protein-protein interaction networks. Results: A total of and 358 and 368 genes were identified in FSGS and MCD compared with healthy controls, among them, there were156 overlapping DEGs. GO analysis showed the DEGs in these two diseases were simultaneously enriched in mRNA splicing, RNA polymerase II transcription, mRNA export, insulin stimulus, integrin-mediated signaling pathway, viral process and phagocytosis. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with Spliceosome among FSGS and MCD. The top 10 hub genes analysis discovered that most of hub genes were same between two disease, while among these genes, CD2 cytoplasmic tail binding protein 2 (CD2BP2), U6 snRNA-associated Sm-like protein (LSM8) and Small nuclear ribonucleoprotein polypeptides B (SNRPB) only differential expression in FSGS and Splicing factor 3A, subunit 3 (SF3A3) only differential expression in MCD, which may be used for differential diagnosis of these two diseases in the future. Conclusions: We identified key genes and mainly pathway associated with FSGS and MCD. Our results provide a set of potential genes used for differential diagnosis of these two diseases.

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Identification Candidate Diagnostic Biomarkers Between Minimal Change Disease and Focal Segmental Glomerulosclerosis by Bioinformatics Analysis

10.21203/rs.3.rs-100941/v1 ◽

2020 ◽

Author(s):

Xue Jiang ◽

Laite Chen ◽

Yuanyuan Du ◽

Yayu Li

Keyword(s):

Differential Diagnosis ◽

Differential Expression ◽

Focal Segmental Glomerulosclerosis ◽

Minimal Change Disease ◽

Bioinformatics Analysis ◽

Minimal Change ◽

Hub Genes ◽

U6 Snrna ◽

Segmental Glomerulosclerosis ◽

Healthy Control

Abstract Background: Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are common causes of nephrotic syndrome which have similar clinical as well as histologic magnification and hard to differentiate. This study aimed to identify novel biomarkers to distinguish FSGS and MCD through bioinformatics analysis and elucidate the possible molecular mechanism.Methods: Based on the microarray datasets GSE104948 and GSE108113 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between FSGS vs healthy control , MCD vs healthy control were identified, and further defined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Hub genes were checked by protein-protein interaction networks.Results: A total of and 358 and 368 genes were identified in FSGS and MCD compared with healthy controls, among them, there were156 overlapping DEGs. GO analysis showed the DEGs in these two diseases were simultaneously enriched in mRNA splicing, RNA polymerase II transcription, mRNA export, insulin stimulus, integrin-mediated signaling pathway, viral process and phagocytosis. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with Spliceosome among FSGS and MCD. The top 10 hub genes analysis discovered that Most of hub genes were same between two disease, while among these genes, CD2 cytoplasmic tail binding protein 2 (CD2BP2), U6 snRNA-associated Sm-like protein (LSM8) and Small nuclear ribonucleoprotein polypeptides B (SNRPB) only differential expression in FSGS and Splicing factor 3A, subunit 3 (SF3A3) only differential expression in MCD, which may be used for differential diagnosis of these two diseases in the future.Conclusions: We identified key genes and mainly pathway associated with FSGS and MCD. Our results provide a set of potential genes used for differential diagnosis of these two diseases.

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