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

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Introduction: IgA nephropathy (IgAN) is the most common disease leading to end-stage renal disease, and tubular fibrosis represents an important risk factor for disease progression. However, research on early molecular diagnostic indicators of tubular fibrosis and the mechanisms underlying disease progression is still lacking. Methods: The GSE93798 dataset was downloaded from the GEO database. DEGs were screened and analyzed for GO and KEGG enrichment in IgAN. The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms were applied to screen for hub secretory genes. The expression and diagnostic efficacy of hub genes were confirmed by the GSE35487 dataset. ELISA was applied to detect the expression of APOC1 in serum. The expression and localization of hub genes in IgAN were verified by the expression of IHC and IF in human kidney tissues, and the correlation of expression with clinical data was verified in the Nephroseq database. Finally, cellular experiments clarified the role of hub genes in the signaling pathway. Results: A total of 339 DEGs were identified in IgAN, of which 237 were upregulated and 102 downregulated. The KEGG signaling pathway is enriched in the ECM-receptor interaction and AGE-RAGE signaling pathway. APOC1, ALB, CCL8, CXCL2, SRPX2, and TGFBI identified six hub secretory genes using the LASSO and SVM-RFE algorithms. In vivo and in vitro experiments demonstrated that APOC1 expression was elevated in IgAN. The serum concentration of APOC1 was 1.232 ± 0.1812 μg/ml in IgAN patients, whereas it was 0.3956 ± 0.1233 μg/ml in healthy individuals. APOC1 exhibited high diagnostic efficacy for IgAN (AUC of 99.091%, specificity of 95.455%, and sensitivity of 99.141%) in the GSE93798 dataset. APOC1 expression negatively correlated with eGFR (R ² = 0.2285, p = 0.0385) and positively correlated with serum creatinine (R ² = 0.41, p = 0.000567) in IgAN. APOC1 exacerbated renal fibrosis, possibly in part by activating the NF-κB pathway in IgAN. Conclusion: APOC1 was identified as the core secretory gene of IgAN, which was closely associated with blood creatinine and eGFR and had significant efficacy in the diagnosis of IgAN. Mechanistic studies revealed that the knockdown of APOC1 could improve IgAN renal fibrosis by inhibiting the NF pathway, which may be a potential therapeutic target for improving renal fibrosis in IgAN.

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis

Background

Diabetic nephropathy (DN), the most intractable complication in diabetes patients, can lead to proteinuria and progressive reduction of glomerular filtration rate (GFR), which seriously affects the quality of life of patients and is associated with high mortality. However, the lack of accurate key candidate genes makes diagnosis of DN very difficult. This study aimed to identify new potential candidate genes for DN using bioinformatics, and elucidated the mechanism of DN at the cellular transcriptional level.

Methods

The microarray dataset GSE30529 was downloaded from the Gene Expression Omnibus Database (GEO), and the differentially expressed genes (DEGs) were screened by R software. We used Gene Ontology (GO), gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify the signal pathways and genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The GSE30122 dataset was selected as the validation set. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive value of genes. An area under curve (AUC) greater than 0.85 was considered to be of high diagnostic value. Several online databases were used to predict miRNAs and transcription factors (TFs) capable of binding hub genes. Cytoscape was used for constructing a miRNA-mRNA-TF network. The online database 'nephroseq' predicted the correlation between genes and kidney function. The serum level of creatinine, BUN, and albumin, and the urinary protein/creatinine ratio of the DN rat model were detected. The expression of hub genes was further verified through qPCR. Data were analyzed statistically using Student's t-test by the 'ggpubr' package.

Results

A total of 463 DEGs were identified from GSE30529. According to enrichment analysis, DEGs were mainly enriched in the immune response, coagulation cascades, and cytokine signaling pathways. Twenty hub genes with the highest connectivity and several gene cluster modules were ensured using Cytoscape. Five high diagnostic hub genes were selected and verified by GSE30122. The MiRNA-mRNA-TF network suggested a potential RNA regulatory relationship. Hub gene expression was positively correlated with kidney injury. The level of serum creatinine and BUN in the DN group was higher than in the control group (unpaired t test, t = 3.391, df = 4, p = 0.0275, r = 0.861). Meanwhile, the DN group had a higher urinary protein/creatinine ratio (unpaired t test, t = 17.23, df = 16, p < 0.001, r = 0.974). QPCR results showed that the potential candidate genes for DN diagnosis included C1QB, ITGAM, and ITGB2.

Conclusions

We identified C1QB, ITGAM and ITGB2 as potential candidate genes for DN diagnosis and therapy and provided insight into the mechanisms of DN development at transcriptome level. We further completed the construction of miRNA-mRNA-TF network to propose potential RNA regulatory pathways adjusting disease progression in DN.

The Glomerulus Multiomics Analysis Provides Deeper Insights into Diabetic Nephropathy

Although diabetic nephropathy (DN) is the leading cause of the end-stage renal disease, the exact regulation mechanisms remain unknown. In this study, we integrated the transcriptomics and proteomics profiles of glomeruli isolated from 50 biopsy-proven DN patients and 25 controls to investigate the latest findings about DN pathogenesis. First, 1152 genes exhibited differential expression at the mRNA or protein level, and 364 showed significant association. These strong correlated genes were divided into four different functional modules. Moreover, a regulatory network of the transcription factors (TFs)-target genes (TGs) was constructed, with 30 TFs upregulated at the protein levels and 265 downstream TGs differentially expressed at the mRNA levels. These TFs are the integration centers of several signal transduction pathways and have tremendous therapeutic potential for regulating the aberrant production of TGs and the pathological process of DN. Furthermore, 29 new DN-specific splice-junction peptides were discovered with high confidence; these peptides may play novel functions in the pathological course of DN. So, our in-depth integrative transcriptomics-proteomics analysis provided deeper insights into the pathogenesis of DN and opened the potential avenue for finding new therapeutic interventions. MS raw files were deposited into the proteomeXchange with the dataset identifier PXD040617.

Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice

Background

Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD.

Materials and Methods

Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software "Limma", differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (-logFC- > 1, p-value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD.

Results

Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism-cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated).

Conclusion

Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.

An overview of the efficacy and signaling pathways activated by stem cell-derived extracellular vesicles in diabetic kidney disease

Diabetic kidney disease (DKD) is one of complications of diabetes mellitus with severe microvascular lesion and the most common cause of end-stage chronic kidney disease (ESRD). Controlling serum glucose remains the primary approach to preventing and slowing the progression of DKD. Despite considerable efforts to control diabetes, people with diabetes develop not only DKD but also ESRD. The pathogenesis of DKD is very complex, and current studies indicate that mesenchymal stromal cells (MSCs) regulate complex disease processes by promoting pro-regenerative mechanisms and inhibiting multiple pathogenic pathways. Extracellular vesicles (EVs) are products of MSCs. Current data indicate that MSC-EVs-based interventions not only protect renal cells, including renal tubular epithelial cells, podocytes and mesangial cells, but also improve renal function and reduce damage in diabetic animals. As an increasing number of clinical studies have confirmed, MSC-EVs may be an effective way to treat DKD. This review explores the potential efficacy and signaling pathways of MSC-EVs in the treatment of DKD.

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

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.

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

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.