Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Gao, Zhuo; Aishwarya, S.; Li, Xiaomei; Li, Xin-lun; Sui, Lina

doi:10.3389/fendo.2021.721202

Cited by 10 publications

(10 citation statements)

References 49 publications

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“…Cardiofibrosis has a significant influence on the prognosis of heart diseases, which can be regulated by POSTN [ 47 , 48 ]. POSTN was also found to be associated with renal diseases, including diabetic kidney disease [ 49 ], immunoglobulin A nephropathy [ 50 ], and polycystic kidney disease [ 51 ]. Hence, regardless of the absence of a regulatory relationship of POSTN and FN1 with CRS, FN1 and POSTN may exert important biological functions in the pathogenesis of CRS.…”

Section: Discussionmentioning

confidence: 99%

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Liang

Huang

et al. 2022

Aging

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Cardiorenal syndrome (CRS), defined as acute or chronic damage to the heart or kidney triggering impairment of another organ, has a poor prognosis. However, the molecular mechanisms underlying CRS remain largely unknown. The RNA-sequencing data of the left ventricle tissue isolated from the sham-operated and CRS model rats at different time points were downloaded from the Gene Expression Omnibus (GEO) database. Genomic differences, protein-protein interaction networks, and short time-series analyses, revealed fibronectin 1 (FN1) and periostin (POSTN) as hub genes associated with CRS progression. The transcriptome sequencing data of humans obtained from the GEO revealed that FN1 and POSTN were both significantly associated with many different heart and kidney diseases. Peripheral blood samples from 20 control and 20 CRS patients were collected from the local hospital, and the gene expression levels of FN1 and POSTN were detected by real-time quantitative polymerase chain reaction. FN1 (area under the curve [AUC] = 0.807) and POSTN (AUC = 0.767) could distinguish CRS in the local cohort with high efficacy and were positively correlated with renal and heart damage markers, such as left ventricular ejection fraction. To improve the diagnostic ability, diagnosis models comprising FN1 and POSTN were constructed by logistic regression (F-Score = 0.718), classification tree (F-Score = 0.812), and random forest (F-Score = 1.000). Overall, the transcriptome data of CRS rat models were systematically analyzed, revealing that FN1 and POSTN were hub genes, which were validated in different public datasets and the local cohort.

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Section: Discussionmentioning

confidence: 99%

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Liang

Huang

et al. 2022

Aging

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“…Previous studies have reported that VCAN is up-regulated in DKD and is associated with immune cell injury in diabetic kidney tissue. 47,48 TIMP1 is a pro-fibrotic protein that belongs to the TIMP family that exists in the extracellular matrix and is involved in the inhibition of degradation in the extracellular matrix and the promotion of cell proliferation. 49 Animal experiments have shown that TIMP-1 promotes fibrotic progression in renal tubular cells by exerting toxic effects; 50 however, the potential association between TIMP1 and DKD has yet to be elucidated.…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Wang¹,

Zhao²,

Yu³

2022

IJGM

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This study aimed to generate a competitive endogenous RNA (ceRNA) network and identify novel biomarkers for diabetic kidney disease by combining single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data analysis. Methods: Four datasets associated with diabetic nephropathy were downloaded, and their differentially expressed genes were identified. Enrichment analysis was conducted using Metascape. Next, we identified co-expressed mRNA-lncRNA pairs, analyzed subcellular lncRNA localization, and constructed a ceRNA network. Next, a specific protein-protein interaction was used to identify key biomarkers. Then, we determined the expression profiles of key biomarkers from scRNA-seq data and validated in an external independent dataset. Finally, we explored the correlations between key biomarkers and the clinical features and analyzed the drug perturbation. Results: Thirteen cell types were identified in the scRNA-seq data, and 106 common differentially expressed genes were identified in both scRNA-seq data and bulk RNA-seq data. These genes were mainly enriched in the focal adhesion pathway of the extracellular matrix. Next, we overlapped these genes with genes predicted by differentially expressed miRNAs, identified co-expressed mRNA-lncRNA pairs and generated a ceRNA network containing 60 mRNAs, 10 miRNAs, and 5 lncRNAs. All lncRNAs were localized in the cytoplasm or cytosol. Analyses identified five key biomarkers (VCAN, TIMP1, TNC, C3 and CP); these proteins were mostly differentially expressed in fibroblasts and renal tubular cells. Furthermore, most of these proteins are associated with glomerular filtration rate, serum creatinine, and proteinuria. In total, seven potential therapeutic molecular medicines were predicted for the treatment of diabetic nephropathy. Conclusion:In summary, we identified a novel ceRNA-network and five key biomarkers in diabetic kidney disease, which may help to elucidate the mechanisms underlying diabetic nephropathy and facilitate new treatments.

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“…Integrative bioinformatics analysis with larger sample sizes and more minor potential biases is superior to finding new molecular signatures ( 9 ). Different bioinformatics analyses for DKD have been reported ( 10 – 18 ). The datasets involved in those studies are either from type 1 diabetic kidney disease (T1DKD) ( 10 – 13 ) or type 2 kidney disease (T2DKD) samples ( 14 – 16 ).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Gao et al. performed integrative bioinformatics analysis of 3 human datasets associated with early T2DKD (pathologic stages I-III), glomerular DKD, and tubular DKD, respectively, and identified 7 candidate genes (SPARC, POSTN, LUM, KNG1, FN1, VCAN, PTPRO) significantly associated with the progression of DKD ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Novel Key Molecular Signatures in the Pathogenesis of Experimental Diabetic Kidney Disease

Diao

Yang

et al. 2022

Front. Endocrinol.

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Diabetic kidney disease (DKD) is a long-term major microvascular complication of uncontrolled hyperglycemia and one of the leading causes of end-stage renal disease (ESDR). The pathogenesis of DKD has not been fully elucidated, and effective therapy to completely halt DKD progression to ESDR is lacking. This study aimed to identify critical molecular signatures and develop novel therapeutic targets for DKD. This study enrolled 10 datasets consisting of 93 renal samples from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). Networkanalyst, Enrichr, STRING, and Cytoscape were used to conduct the differentially expressed genes (DEGs) analysis, pathway enrichment analysis, protein-protein interaction (PPI) network construction, and hub gene screening. The shared DEGs of type 1 diabetic kidney disease (T1DKD) and type 2 diabetic kidney disease (T2DKD) datasets were performed to identify the shared vital pathways and hub genes. Strepotozocin-induced Type 1 diabetes mellitus (T1DM) rat model was prepared, followed by hematoxylin & eosin (HE) staining, and Oil Red O staining to observe the lipid-related morphological changes. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was conducted to validate the key DEGs of interest from a meta-analysis in the T1DKD rat. Using meta-analysis, 305 shared DEGs were obtained. Among the top 5 shared DEGs, Tmem43, Mpv17l, and Slco1a1, have not been reported relevant to DKD. Ketone body metabolism ranked in the top 1 in the KEGG enrichment analysis. Coasy, Idi1, Fads2, Acsl3, Oxct1, and Bdh1, as the top 10 down-regulated hub genes, were first identified to be involved in DKD. The qRT-PCR verification results of the novel hub genes were mostly consistent with the meta-analysis. The positive Oil Red O staining showed that the steatosis appeared in tubuloepithelial cells at 6 w after DM onset. Taken together, abnormal ketone body metabolism may be the key factor in the progression of DKD. Targeting metabolic abnormalities of ketone bodies may represent a novel therapeutic strategy for DKD. These identified novel molecular signatures in DKD merit further clinical investigation.

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Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Cited by 10 publications

References 49 publications

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Integrated Analysis of Single-Cell RNA-seq and Bulk RNA-seq in the Identification of a Novel ceRNA Network and Key Biomarkers in Diabetic Kidney Disease

Identification of Novel Key Molecular Signatures in the Pathogenesis of Experimental Diabetic Kidney Disease

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