Investigation of hub genes involved in diabetic nephropathy using biological informatics methods

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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Bioinformatics analysis of genes related to iron death in diabetic nephropathy through network and pathway levels based approaches

PLoS ONE ◽

10.1371/journal.pone.0259436 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0259436

Author(s):

Yaling Hu ◽

Shuang Liu ◽

Wenyuan Liu ◽

Ziyuan Zhang ◽

Yuxiang Liu ◽

...

Keyword(s):

Principal Component Analysis ◽

Diabetic Nephropathy ◽

Transcription Factors ◽

Interaction Analysis ◽

Microvascular Complications ◽

Differentially Expressed Gene ◽

Principal Component ◽

Component Analysis ◽

Differential Analysis ◽

Hub Genes

Diabetic nephropathy is one of the common microvascular complications of diabetes. Iron death is a recently reported way of cell death. To explore the effects of iron death on diabetic nephropathy, iron death score of diabetic nephropathy was analyzed based on the network and pathway levels. Furthermore, markers related to iron death were screened. Using RNA-seq data of diabetic nephropathy, samples were clustered uniformly and the disease was classified. Differentially expressed gene analysis was conducted on the typed disease samples, and the WGCNA algorithm was used to obtain key modules. String database was used to perform protein interaction analysis on key module genes for the selection of Hub genes. Moreover, principal component analysis method was applied to get transcription factors and non-coding genes, which interact with the Hub gene. All samples can be divided into two categories and principal component analysis shows that the two categories are significantly different. Hub genes (FPR3, C3AR1, CD14, ITGB2, RAC2 and ITGAM) related to iron death in diabetic nephropathy were obtained through gene expression differential analysis between different subtypes. Non-coding genes that interact with Hub genes, including hsa-miR-572, hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-208a-3p, hsa-miR-153-3p and hsa-miR-29c-3p, may be related to diabetic nephropathy. Transcription factors HIF1α, KLF4, KLF5, RUNX1, SP1, VDR and WT1 may be related to diabetic nephropathy. The above factors and Hub genes are collectively involved in the occurrence and development of diabetic nephropathy, which can be further studied in the future. Moreover, these factors and genes may be potential target for therapeutic drugs.

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Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.767654 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guoqing Li ◽

Jun Zhang ◽

Dechen Liu ◽

Qiong Wei ◽

Hui Wang ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Network Analysis ◽

Disease Progression ◽

Signaling Pathway ◽

Microvascular Complications ◽

Pearson Correlation ◽

Mapk Signaling ◽

Diabetic Patients ◽

Hub Genes ◽

Regulatory Pathways

Diabetic nephropathy (DN) is one of the most common microvascular complications in diabetic patients, and is the main cause of end-stage renal disease. The exact molecular mechanism of DN is not fully understood. The aim of this study was to identify novel biomarkers and mechanisms for DN disease progression by weighted gene co-expression network analysis (WGCNA). From the GSE142153 dataset based on the peripheral blood monouclear cells (PBMC) of DN, we identified 234 genes through WGCNA and differential expression analysis. Gene Ontology (GO) annotations mainly included inflammatory response, leukocyte cell-cell adhesion, and positive regulation of proteolysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways mostly included IL-17 signaling pathway, MAPK signaling pathway, and PPAR signaling pathway in DN. A total of four hub genes (IL6, CXCL8, MMP9 and ATF3) were identified by cytoscape, and the relative expression levels of hub genes were also confirmed by RT-qPCR. ROC curve analysis determined that the expression of the four genes could distinguish DN from controls (the area under the curve is all greater than 0.8), and Pearson correlation coefficient analysis suggested that the expression of the four genes was related to estimated glomerular filtration rate (eGFR) of DN. Finally, through database prediction and literature screening, we constructed lncRNA-miRNA-mRNA network. We propose that NEAT1/XIST/KCNQ1T1-let-7b-5p-IL6, NEAT1/XIST-miR-93-5p-CXCL8 and NEAT1/XIST/KCNQ1T1-miR-27a-3p/miR-16-5p-ATF3 might be potential RNA regulatory pathways to regulate the disease progression of early DN. In conclusion, we identified four hub genes, namely, IL6, CXCL8, MMP9, and ATF3, as markers for early diagnosis of DN, and provided insight into the mechanisms of disease development in DN at the transcriptome level.

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P0971IDENTIFICATION OF HUB GENES ASSOCIATED WITH THE DEPOSITION OF EXTRACELLULAR MATRIX AND SPECIFIC FOR DIABETIC NEPHROPATHY BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p0971 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Songtao Feng ◽

Linli Lv ◽

Gao Yueming ◽

Cao Jingyuan ◽

Di Yin ◽

...

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Diabetic Nephropathy ◽

Network Analysis ◽

Expression Level ◽

Hub Genes ◽

Hub Gene ◽

Gene Screening ◽

Gene Modules ◽

Geo Database

Abstract Background and Aims Diabetic nephropathy (DN) and its most severe manifestation, end-stage renal disease (ESRD), remains one of the leading causes of reduced lifespan in people with diabetes. Identifying novel molecules that are involved in the pathogenesis of DN has both diagnostic and therapeutic implications. The gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology approach that provide the approach of association between gene modules and clinical traits to find the module involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DN from the aspect of whole gene transcripts analysis. Method Various types of chronic kidney diseases (CKD), including DN, microarray-based mRNA gene expression data, listed in the Gene Expression Omnibus (GEO) database, were analyzed. Next, we constructed a weighted gene co-expression network and identified modules distinguishing DN from normal or other types of CKD by WGCNA. Functional annotations of the genes in modules specialized for DN were analyzed by Gene Ontology (GO) enrichment analysis. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes specific for DN were obtained. Furthermore, we drew ROC curves to determine the diagnosis and differential diagnosis value to DN of hub genes. Finally, another study of microarray in the GEO database was selected to verify the expression level of hub genes and in the “Nephroseq” database, the correlation between the gene expression level and eGFR was analyzed. Results “GSE99339”, glomerular tissue microarray in 187 patients with a total of 10947 genes, was selected for analysis. After excluding the inappropriate cases, a total of 179 specimens were analyzed, including 14 cases of DN, 22 cases of focal segmental glomerulosclerosis (FSGS), 15 cases of hypertensive nephropathy (HT), 26 cases of IgA nephropathy (IgAN), 13 cases of minimal change disease (MCD), 21 cases of membranous nephropathy (MGN), 23 cases of rapidly progressive glomerulonephritis (RPGN), 30 cases of lupus nephritis (LN) and 14 cases of kidney tissue adjacent to tumor. Co-expression network analysis by WGCNA identified 23 distinct gene modules of the total 10947 genes and revealed “MEsaddlegreen” module was strongly correlated with DN (r=0,54), but not with other groups. GO functional annotation showed that these 64 genes in the “MEsaddlegreen” module mainly enriched in the deposition of extracellular matrix, which represents the specific and diagnostic pathophysiological process of DN. Further PPI and hub gene screening analysis revealed that LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD can be served as hub genes, which had been proved to play an important role in the deposition of extracellular matrix. Furthermore, we found that the expression of hub genes was the highest in DN group and for the diagnosis value of DN by each gene, the area under the ROC curve is about 0.75∼0.95. The external verification of another study showed that compared with the normal control group, the expression of these hub genes was the highest in the DN group, and their expression level was negatively correlated with eGFR. Conclusion Using WGCNA and further bioinformatics approach, we identified six hub genes that appear to be identical to DN development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.

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Multiple‐microarray analysis for identification of hub genes involved in tubulointerstial injury in diabetic nephropathy

Journal of Cellular Physiology ◽

10.1002/jcp.28313 ◽

2019 ◽

Vol 234 (9) ◽

pp. 16447-16462 ◽

Cited By ~ 6

Author(s):

Mengru Zeng ◽

Jialu Liu ◽

Wenxia Yang ◽

Shumin Zhang ◽

Fuyou Liu ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Microarray Analysis ◽

Hub Genes

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Identification of Lumican and Fibromodulin as Hub Genes Associated with Accumulation of Extracellular Matrix in Diabetic Nephropathy

Kidney & Blood Pressure Research ◽

10.1159/000514013 ◽

2021 ◽

pp. 1-11

Author(s):

Songtao Feng ◽

Yueming Gao ◽

Di Yin ◽

Linli Lv ◽

Yi Wen ◽

...

Keyword(s):

Extracellular Matrix ◽

Diabetic Nephropathy ◽

Clinical Importance ◽

Gene Expression Omnibus ◽

Potential Candidate ◽

Hub Genes ◽

Glomerular Diseases ◽

Go Analysis ◽

Diagnostic Potential ◽

Renal Biopsies

Introduction: Diabetic nephropathy (DN) remains a major cause of end-stage renal disease. The development of novel biomarkers and early diagnosis of DN are of great clinical importance. The goal of this study was to identify hub genes with diagnostic potential for DN by weighted gene co-expression network analysis (WGCNA). Methods: Gene Expression Omnibus database was searched for microarray data including distinct types of CKD. Gene co-expression network was constructed, and modules specific for DN were identified by WGCNA. Gene ontology (GO) analysis was performed, and the hub genes were screened out within the selected gene modules. In addition, cross-validation was performed in an independent dataset and in samples of renal biopsies with DN and other types of glomerular diseases. Results: Dataset GSE99339 was selected, and a total of 179 microdissected glomeruli samples were analyzed, including DN, normal control, and 7 groups of other glomerular diseases. Twenty-three modules of the total 10,947 genes were grouped by WGCNA, and a module was specifically correlated with DN (r = 0.54, p = 9e−15). GO analysis showed that module genes were mainly enriched in the accumulation of extracellular matrix (ECM). LUM, ELN, FBLN1, MMP2, FBLN5, and FMOD were identified as hub genes. Cross verification showed LUM and FMOD were higher in the DN group and were negatively correlated with estimated glomerular filtration rate (eGFR). In renal biopsies, expression levels of LUM and FMOD were higher in DN than IgA nephropathy, membranous nephropathy, and normal controls. Conclusion: By using WGCNA approach, we identified LUM and FMOD related to ECM accumulation and were specific for DN. These 2 genes may represent potential candidate diagnostic biomarkers of DN.

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Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

10.21203/rs.3.rs-136161/v1 ◽

2020 ◽

Author(s):

Si Xu ◽

Sha Wu ◽

Min Yang ◽

Xiaoning Li

Keyword(s):

Extracellular Matrix ◽

Diabetic Nephropathy ◽

Signaling Pathway ◽

Bioinformatic Analysis ◽

Molecular Therapy ◽

Akt Signaling Pathway ◽

Targeted Molecular Therapy ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Gene Modules

Abstract Background: To provide molecular markers and potential targeted molecular therapy for diabetic nephropathy by screening hubgenes based on bioinformatic analysis. Results: We found 91 differentially expressed genes (DEGs) between diabetic nephropathy tissues and normal kidney tissues. Majority DEGs were significantly enriched in the extracellular matrix structural constituent, collagen-containing extracellular matrix. KEGG pathway analysis showed that most of DEGs participated in PI3K-Akt signaling pathway, AGE-RAGE signaling pathway in diabetic complications. Five high relevant sub-networks and the top 16 genes according to 12 topological algorithms were screened out and also five co-expressed gene modules were identified by WGCNA. Eventually, 5 hub genes were identified by taking the intersection which might be involved in the progression of DN. And 11 microRNAs were associated with related genes in WebGestalt. Conclusions: We identified five hub genes, namely COL1A2, COL6A3, COL15A1, CLU and LUM, and their related microRNAs(especially miR29 and miR196), which might be used as diagnostic biomarkers and therapeutic targets for diabetic nephropathy.

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Identification of Hub Genes Associated With Clinical Characteristics and Potential Therapy of Diabetic Nephropathy by Bioinformatics Analysis.

10.21203/rs.3.rs-120610/v1 ◽

2020 ◽

Author(s):

Si Xu ◽

Xiaoning Li ◽

Sha Wu ◽

Min Yang

Keyword(s):

Diabetic Nephropathy ◽

Network Analysis ◽

Molecular Mechanism ◽

Protein Interaction ◽

Protein Interaction Network ◽

Theoretical Basis ◽

Interaction Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

Abstract Background: To provide theoretical basis for the molecular mechanism of the development of diabetic nephropathy and targeted molecular therapy by screening expressed genes based on bioinformatic analysis. Methods: We analyzed diabetic nephropathy microarray datasets derived from GEO database. Perl and R programming packages were used for data processing and analysis and for drawing. STRING online database and Cytoscape software were utilized for protein-protein interaction network analysis and screened for hub genes. Also, WebGestalt was used to analyze the relationship between genes and microRNAs. Nephroseq online tool was used to visualize the correlation between genes and clinical properties.Results: We found 91 differentially expressed genes between diabetic nephropathy tissues and normal control tissues. Protein-protein interaction network analysis screened out 5 key modules and a total of 14 hub genes were identified by integration, also11 microRNAs were associated with hub genes. Especially mir29 could regulate COL6A3 and COL15A1.Conclusions: The internal biological information in diabetic nephropathy can be revealed by integrative bioinformatical analysis, providing theoretical basis for further research on molecular mechanism and potential targets for diagnosis and therapeutics of diabetic nephropathy.

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Identification of Key Genes of Human Advanced Diabetic Nephropathy Independent of Proteinuria by Transcriptome Analysis

BioMed Research International ◽

10.1155/2020/7283581 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Fanghao Cai ◽

Xujie Zhou ◽

Yan Jia ◽

Weijian Yao ◽

Jicheng Lv ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Lipid Metabolism ◽

Pathway Analysis ◽

Differentially Expressed ◽

Hub Genes ◽

Protein Protein Interaction ◽

Limma Package ◽

Open Access Database ◽

Immune System Regulation ◽

Microarray Datasets

Background. Diabetic nephropathy (DN) is the leading cause of ESRD. Emerging evidence indicated that proteinuria may not be the determinant of renal survival in DN. The aim of the current study was to provide molecular signatures apart from proteinuria in DN by an integrative bioinformatics approach. Method. Affymetrix microarray datasets from microdissected glomerular and tubulointerstitial compartments of DN, healthy controls, and proteinuric disease controls including minimal change disease and membranous nephropathy were extracted from open-access database. Differentially expressed genes (DEGs) in DN versus both healthy and proteinuric controls were identified by limma package, 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 566 glomerular and 581 tubulointerstitial DEGs were identified in DN, which were commonly differentially expressed compared to normal controls and proteinuric disease controls. The upregulated DEGs in both compartments were significantly enriched in GO biological process associated with fibrosis, inflammation, and platelet dysfunction, and largely located in extracellular space, including matrix and extracellular vesicles. Pathway analysis highlighted immune system regulation. Hub genes of the upregulated DEGs negatively correlated with estimated glomerular filtration rate (eGFR). While the downregulated DEGs and their hub genes in tubulointerstitium were enriched in pathways associated with lipid metabolism and oxidation, which positively correlated with eGFR. Conclusions. Our study identified pathways including fibrosis, inflammation, lipid metabolism, and oxidative stress contributing to the progression of DN independent of proteinuria. These genes may serve as biomarkers and therapeutic targets.

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FC 091URINARY CCL5 MRNA, A POTENTIAL BIOMARKER FOR PROGRESSION OF TYPE 2 DIABETIC NEPHROPATHY

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab149.003 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Songtao Feng ◽

Yueming Gao ◽

Jingyuan Cao ◽

Di Yin ◽

Linli Lv ◽

...

Keyword(s):

Diabetic Nephropathy ◽

Mrna Expression ◽

Quantitative Pcr ◽

Protein Level ◽

Cox Regression ◽

Urinary Protein ◽

Hub Genes ◽

Urinary Protein Level ◽

Gene Modules

Abstract Background and Aims In recent years, it has been found that targeted mRNA detection of sediment cells in urine can be used as novel biomarkers for the diagnosis of diabetic nephropathy (DN). However, its value in predicting the progression of DN is not clear. The purpose of this study is to seek for urinary mRNA markers that evaluate the prognosis of DN through systems biological screening, clinical verification and prospective studies. Method GEO database and “Nephroseq” platform were searched, and the transcriptome data of DN glomeruli and tubules and their clinical information were obtained. Weighted gene co-expression network analysis (WGCNA) combined with gene ontology (GO) annotation and KEGG pathway enrichment analysis were used to screen the hub genes negatively related to eGFR, and the hub genes were used as candidate markers for mRNA detection in urine sediment in DN patients. A total of 91 patients with DN diagnosed by renal biopsy were included, and 60 patients with type 2 diabetes and 61 healthy people were selected as the control groups. The mRNA expression of candidate molecules was detected by Taqman probe quantitative PCR, and the correlation between mRNA expression and eGFR and urinary protein levels were analyzed. Patients with DN were followed up for a median time of 21 months, and the primary end point was defied as end stage renal disease or eGFR decreasing by more than 50%. Multivariate Cox regression was used to evaluate the value of mRNA in predicting DN progression. Results GSE30528 and GSE30529 datasets were selected for analysis, including mRNA expression data of 9 cases of DN and 13 cases of normal glomeruli; and 10 cases of DN and 12 cases of normal tubules respectively. The clinical data of the patients in this study, including gender, race, age and eGFR, were searched on the Nephroseq platform. The gene modules negatively related to eGFR were screened by WGCNA. GO and KEGG analysis showed that the main function of the gene modules in both datasets were related to the activation of inflammatory cells and chemokines pathway. Through the screening of hub genes and the comparison of expression levels, CCL5, CXCL1, CXCL6 and CXCL12 were finally obtained as candidate genes. Quantitative PCR showed that the levels of CCL5 and CXCL1 was significantly increased in DN group, CCL5 was negatively correlated with eGFR and positively correlated with urinary protein level, while CXCL1 was negatively correlated with eGFR, but had no significant correlation with urinary protein level. Multivariate Cox regression showed that eGFR, urinary protein level, degree of renal fibrosis and urinary CCL5 were independent risk factors of primary end point. Conclusion The activation of chemokine signal pathway in renal tissue is involved in the progression of DN. Urinary CCL5 mRNA can independently predict the prognosis of DN and may be served as a novel biomarker for the progression of DN.

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