Identification of hub genes in diabetic kidney disease via multiple-microarray analysis

Mannose and glycine: Metabolites with potentially causal implications in chronic kidney disease pathogenesis

BACKGROUND

Chronic Kidney Disease (CKD) represents a global health challenge, with its etiology and underlying mechanisms yet to be fully elucidated. Integrating genomics with metabolomics can offer insights into the putatively causal relationships between serum metabolites and CKD.

METHODS

Utilizing bidirectional Mendelian Randomization (MR), we assessed the putatively causal associations between 486 serum metabolites and CKD. Genetic data for these metabolites were sourced from comprehensive genome-wide association studies, and CKD data were obtained from the CKDGen Consortium.

RESULTS

Our analysis identified four metabolites with a robust association with CKD risk, of which mannose and glycine showed the most reliable causal relationships. Pathway analysis spotlighted five significant metabolic pathways, notably including "Methionine Metabolism" and "Arginine and Proline Metabolism", as key contributors to CKD pathogenesis.

CONCLUSION

This study underscores the potential of certain serum metabolites as biomarkers for CKD and illuminates pivotal metabolic pathways in CKD's pathogenesis. Our findings lay the groundwork for potential therapeutic interventions and warrant further research for validation.

Single-cell transcriptomics and chromatin accessibility profiling elucidate the kidney-protective mechanism of mineralocorticoid receptor antagonists

Mineralocorticoid excess commonly leads to hypertension (HTN) and kidney disease. In our study, we used single-cell expression and chromatin accessibility tools to characterize the mineralocorticoid target genes and cell types. We demonstrated that mineralocorticoid effects were established through open chromatin and target gene expression, primarily in principal and connecting tubule cells and, to a lesser extent, in segments of the distal convoluted tubule cells. We examined the kidney-protective effects of steroidal and nonsteroidal mineralocorticoid antagonists (MRAs), as well as of amiloride, an epithelial sodium channel inhibitor, in a rat model of deoxycorticosterone acetate, unilateral nephrectomy, and high-salt consumption-induced HTN and cardiorenal damage. All antihypertensive therapies protected against cardiorenal damage. However, finerenone was particularly effective in reducing albuminuria and improving gene expression changes in podocytes and proximal tubule cells, even with an equivalent reduction in blood pressure. We noted a strong correlation between the accumulation of injured/profibrotic tubule cells expressing secreted posphoprotein 1 (Spp1), Il34, and platelet-derived growth factor subunit b (Pdgfb) and the degree of fibrosis in rat kidneys. This gene signature also showed a potential for classifying human kidney samples. Our multiomics approach provides fresh insights into the possible mechanisms underlying HTN-associated kidney disease, the target cell types, the protective effects of steroidal and nonsteroidal MRAs, and amiloride.

Mechanism of Huaiqihuang in treatment of diabetic kidney disease based on network pharmacology, molecular docking and in vitro experiment

BACKGROUND

This study aimed to investigate the active components, key targets, and potential molecular mechanisms Huaiqihuang (HQH) in the treatment of diabetic kidney disease (DKD) through network pharmacology, molecular docking, and in vitro experiments.

METHODS

The active components and potential targets of HQH were obtained from the TCMSP and HERB databases. The potential targets of DKD were obtained from the GeneCards, OMIM, DrugBank, and TTD databases. Protein interaction relationships were obtained from the STRING database, and a protein interaction network was constructed using Cytoscape software. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed using the Metascape database. Molecular docking was performed using AutoDock software to verify the binding between key compounds and core target genes. In vitro experiments were conducted using human renal proximal tubular epithelial cells and various methods, such as CCK8, RT-PCR, immunofluorescence, and western blot, to evaluate the effects of HQH on inflammatory factors, key targets, and pathways.

RESULTS

A total of 48 active ingredients, 168 potential targets of HQH, and 1073 potential targets of DKD were obtained. A total of 118 potential targets, 438 biological processes, and 187 signal pathways were identified for the treatment of DKD. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that HQH may exert its therapeutic effects on DKD by regulating the expression of inflammatory factors through the nuclear factor kappa B (NF-κB) signaling pathway. The molecular docking results showed that β-sitosterol and baicalein had the highest binding affinity with key targets such as AKT1, IL6, TNF, PTGS2, IL1B, and CASP3, suggesting that they may be the most effective active ingredients of HQH in the treatment of DKD. In vitro experimental results demonstrated that HQH could enhance the viability of human renal proximal tubular epithelial cells inhibited by high glucose, decrease the levels of AKT1, TNF, IL6, PTGS2, IL1B, and CASP3, reduce the expression of NF-κB-P65 (P < .01), inhibit NF-κB-p65 nuclear translocation, and decrease chemokine expression (P < .01).

CONCLUSION

HQH may exert its therapeutic effects on DKD by inhibiting the NF-κB signaling pathway, reducing the level of pro-inflammatory cytokines, and alleviating the high glucose-induced injury of renal tubular epithelial cells.

Prediction of cellular targets in diabetic kidney diseases with single-cell transcriptomic analysis of db/db mouse kidneys

Diabetic kidney disease is the leading cause of impaired kidney function, albuminuria, and renal replacement therapy (dialysis or transplantation), thus placing a large burden on health-care systems. This urgent event requires us to reveal the molecular mechanism of this disease to develop more efficacious treatment. Herein, we reported single-cell RNA sequencing analyses in kidneys of db/db mouse, an animal model for type 2 diabetes and diabetic kidney disease. We first analyzed the hub genes expressed differentially in the single cell resolution transcriptome map of the kidneys. Then we figured out the communication among the renal and immune cells in the kidneys. Data from this report may provide novel information for better understanding the cell-specific targets involved in the aetiologia of type 2 diabetic kidney disease and for cell communication and signaling between renal cells and immune cells of this complex disease.

Regulon active landscape reveals cell development and functional state changes of human primary osteoblasts in vivo

BACKGROUND

While transcription factor (TF) regulation is known to play an important role in osteoblast development, differentiation, and bone metabolism, the molecular features of TFs in human osteoblasts at the single-cell resolution level have not yet been characterized. Here, we identified modules (regulons) of co-regulated genes by applying single-cell regulatory network inference and clustering to the single-cell RNA sequencing profiles of human osteoblasts. We also performed cell-specific network (CSN) analysis, reconstructed regulon activity-based osteoblast development trajectories, and validated the functions of important regulons both in vivo and in vitro.

RESULTS

We identified four cell clusters: preosteoblast-S1, preosteoblast-S2, intermediate osteoblasts, and mature osteoblasts. CSN analysis results and regulon activity-based osteoblast development trajectories revealed cell development and functional state changes of osteoblasts. CREM and FOSL2 regulons were mainly active in preosteoblast-S1, FOXC2 regulons were mainly active in intermediate osteoblast, and RUNX2 and CREB3L1 regulons were most active in mature osteoblasts.

CONCLUSIONS

This is the first study to describe the unique features of human osteoblasts in vivo based on cellular regulon active landscapes. Functional state changes of CREM, FOSL2, FOXC2, RUNX2, and CREB3L1 regulons regarding immunity, cell proliferation, and differentiation identified the important cell stages or subtypes that may be predominantly affected by bone metabolism disorders. These findings may lead to a deeper understanding of the mechanisms underlying bone metabolism and associated diseases.

Integrative transcriptome analysis reveals TEKT2 and PIAS2 involvement in diabetic nephropathy

Cell heterogeneity has impeded the accurate interpretation of the bulk transcriptome data from patients with diabetic nephropathy (DN). We performed an analysis by integrating bulk and single-cell transcriptome datasets to uncover novel mechanisms leading to DN, especially in the podocytes. Microdissected glomeruli and tubules transcriptome datasets were selected from Gene Expression Omnibus (GEO). Then the consistency between datasets was evaluated. The analysis of the bulk dataset and single-nucleus RNA dataset was integrated to reveal the cell type-specific responses to DN. The candidate genes were validated in kidney tissues from DN patients and diabetic mice. We compared 4 glomerular and 4 tubular datasets and found considerable discrepancies among datasets regarding the deferentially expressed genes (DEGs), involved signaling pathways, and the hallmark enrichment profiles. Deconvolution of the bulk data revealed that the variations in cell-type proportion contributed greatly to this discrepancy. The integrative analysis uncovered that the dysregulation of spermatogenesis-related genes, including TEKT2 and PIAS2, was involved in the development of DN. Importantly, the mRNA level of TEKT2 was negatively correlated with the mRNA levels of NPHS1 (r = -.66, p < .0001) and NPHS2 (r = -.85, p < .0001) in human diabetic glomeruli. Immunostaining confirmed that the expression of TEKT2 and PIAS2 were up-regulated in podocytes of DN patients and diabetic mice. Knocking down TEKT2 resisted high glucose-induced cytoskeletal remodeling and down-regulation of NPHS1 protein in the cultured podocyte. In conclusion, the integrative strategy can help us efficiently use the publicly available transcriptomics resources. Using this approach and combining it with classical research methods, we identified TEKT2 and PIAS2, two spermatogenesis-related genes involved in the pathogenesis of DN. Furthermore, TEKT2 is involved in this pathogenesis by regulating the podocyte cytoskeleton.

Bioinformatics analysis identifies diagnostic biomarkers and their correlation with immune infiltration in diabetic nephropathy

Background

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD). Currently, microalbuminuria is mainly used as a diagnostic indicator of DN, but there are still limitations and lack of immune-related diagnostic markers. In this study, we aimed to explore diagnostic biomarkers associated with immune infiltration of DN.

Methods

Immune-related differentially expressed genes (DEGs) were derived from those at the intersection of the ImmPort database and DEGs identified from 3 datasets, which were based on the Gene Expression Omnibus (GEO). Functional enrichment analyses were performed; a protein-protein interaction (PPI) network was constructed; and hub genes were identified by Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). After screening the key genes using least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), a prediction model for DN was constructed. The predictive performance of the model was quantified by receiver-operating characteristic curve, decision curve analysis, and nomogram. Next, infiltration of 22 types of immune cells in DN kidney tissue was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Expression of diagnostic markers was analyzed in DN and control patient groups to determine the genes with the maximum diagnostic potential. Finally, we explored the correlation between diagnostic markers and immune cells.

Results

Overall, 191 immune-related DEGs were identified, that primarily positively regulated with cell adhesion, T cell activation, leukocyte proliferation and migration, urogenital system development, lymphocyte differentiation and proliferation, and mononuclear cell proliferation. Gene sets were related to the PI3K-Akt, MAPK, Rap1, and WNT signaling pathways. Finally, CCL19, CD1C, and IL33 were identified as diagnostic markers of DN and recognized in the 3 datasets [area under the curve (AUC) =0.921]. Immune cell infiltration analysis demonstrated that CCL19 was positively correlated with macrophages M1 (R=0.47, P<0.001) and macrophages M2 (R=0.75, P<0.001). CD1C was positively correlated with macrophages M1 (R=0.47, P<0.05), macrophages M2 (R=0.75, P<0.01), and monocytes (R=0.42, P<0.01). IL33 was positively correlated with macrophages M1 (R=0.45, P<0.05), macrophages M2 (R=0.74, P<0.01), and monocytes (R=0.41, P<0.01).

Conclusions

Our results provide evidence that CCL19, CD1C, and IL33, which are associated with immune infiltration, are the potential diagnostic biomarkers for DN candidates.

The role of CDH2 and MCP-1 mRNAs of blood extracellular vesicles in predicting early-stage diabetic nephropathy

Background

Extracellular vesicles (EVs), including exosomes and microvesicles, are involved in intercellular communication by transferring biomolecules such as mRNA, which has been shown to be as essential biomarkers for many physiological and pathological conditions such as diabetic nephropathy (DN). This study aimed to investigate the expression of CDH1, CDH2, MCP-1, and PAI-1 mRNAs in blood EVs of DN patients and to determine their accuracy in predicting early-stage DN.

Methods

We recruited 196 participants, including 35 overt DN patients, 53 incipient DN patients, 62 diabetic patients (DM), and 46 healthy individuals. Quantification of the mRNA profile of blood EVs was performed using the qRT-PCR method. The diagnostic performance of mRNA was evaluated using receiver operating characteristic analysis.

Results

The mRNA expression of CDH2 and MCP-1 was downregulated in overt DN group (0.22-fold change and 0.15-fold change, respectively) and incipient DN group (0.60-fold change and 0.43-fold change, respectively) compared to DM group (1.72-fold change and 2.77-fold change, respectively), while PAI-1 mRNA expression decreased in incipient DN group (0.70-fold change) and DM group (0.58-fold change) compared to control. However, the expression level of CDH1 mRNA was not significantly different among the four groups (p = 0.408). Moreover, CDH2 and MCP-1 mRNAs inversely correlated with creatinine (r = -0.370 and r = -0.361, p<0.001) and Alb/Cr ratio (r = -0.355 and r = -0.297, p<0.001). 1/CDH2 mRNA also predicted overt DN with an accuracy of 0.75 (95%CI: 0.65–0.85) and incipient DN with an accuracy of 0.61 (95%CI: 0.50–0.71) while 1/MCP-1 mRNA had an accuracy of 0.66 (95%CI: 0.55–0.77) for overt DN prediction and an accuracy of 0.61 (95%CI: 0.51–0.71) for incipient DN prediction.

Conclusion

CDH2 and MCP-1 mRNAs expression in blood EVs was decreased with the development of DN, suggesting the renoprotective effect of these mRNAs in diabetic individuals. Moreover, their quantifications could serve as diagnostic biomarkers for early-stage DN.

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Integrated analysis of potential gene crosstalk between non-alcoholic fatty liver disease and diabetic nephropathy

BACKGROUND

Growing evidence indicates that non-alcoholic fatty liver disease (NAFLD) is related to the occurrence and development of diabetic nephropathy (DN). This bioinformatics study aimed to explore optimal crosstalk genes and related pathways between NAFLD and DN.

METHODS

Gene expression profiles were downloaded from Gene Expression Omnibus. CIBERSORT algorithm was employed to analyze the similarity of infiltrating immunocytes between the two diseases. Protein-protein interaction (PPI) co-expression network and functional enrichment analysis were conducted based on the identification of common differentially expressed genes (DEGs). Least absolute shrinkage and selection operator (LASSO) regression and Boruta algorithm were implemented to initially screen crosstalk genes. Machine learning models, including support vector machine, random forest model, and generalized linear model, were utilized to further identify the optimal crosstalk genes between DN and NAFLD. An integrated network containing crosstalk genes, transcription factors, and associated pathways was developed.

RESULTS

Four gene expression datasets, including GSE66676 and GSE48452 for NAFLD and GSE30122 and GSE1009 for DN, were involved in this study. There were 80 common DEGs between the two diseases in total. The PPI network built with the 80 common genes included 77 nodes and 83 edges. Ten optimal crosstalk genes were selected by LASSO regression and Boruta algorithm, including CD36, WIPI1, CBX7, FCN1, SLC35D2, CP, ZDHHC3, PTPN3, LPL, and SPP1. Among these genes, LPL and SPP1 were the most significant according to NAFLD-transcription factor network. Five hundred twenty-nine nodes and 1,113 edges comprised the PPI network of activated pathway-gene. In addition, 14 common pathways of these two diseases were recognized using Gene Ontology (GO) analysis; among them, regulation of the lipid metabolic process is closely related to both two diseases.

CONCLUSIONS

This study offers hints that NAFLD and DN have a common pathogenesis, and LPL and SPP1 are the most relevant crosstalk genes. Based on the common pathways and optimal crosstalk genes, our proposal carried out further research to disclose the etiology and pathology between the two diseases.

Integrated bioinformatics analysis reveals novel key biomarkers in diabetic nephropathy

Objectives:

The underlying molecular mechanisms of diabetic nephropathy have yet not been investigated clearly. In this investigation, we aimed to identify key genes involved in the pathogenesis and prognosis of diabetic nephropathy.

Methods:

We downloaded next-generation sequencing data set GSE142025 from Gene Expression Omnibus database having 28 diabetic nephropathy samples and nine normal control samples. The differentially expressed genes between diabetic nephropathy and normal control samples were analyzed. Biological function analysis of the differentially expressed genes was enriched by Gene Ontology and REACTOME pathways. Then, we established the protein–protein interaction network, modules, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network. Hub genes were validated by using receiver operating characteristic curve analysis.

Results:

A total of 549 differentially expressed genes were detected including 275 upregulated and 274 downregulated genes. The biological process analysis of functional enrichment showed that these differentially expressed genes were mainly enriched in cell activation, integral component of plasma membrane, lipid binding, and biological oxidations. Analyzing the protein–protein interaction network, miRNA-differentially expressed gene regulatory network and transcription factor-differentially expressed gene regulatory network, we screened hub genes MDFI, LCK, BTK, IRF4, PRKCB, EGR1, JUN, FOS, ALB, and NR4A1 by the Cytoscape software. The receiver operating characteristic curve analysis confirmed that hub genes were of diagnostic value.

Conclusions:

Taken above, using integrated bioinformatics analysis, we have identified key genes and pathways in diabetic nephropathy, which could improve our understanding of the cause and underlying molecular events, and these key genes and pathways might be therapeutic targets for diabetic nephropathy.

Diabetic kidney disease-predisposing proinflammatory and profibrotic genes identified by weighted gene co-expression network analysis (WGCNA)

Diabetic kidney disease (DKD) is one of the most serious microvascular complications of diabetes. Despite enormous efforts, the underlying underpinnings of DKD remain incompletely appreciated. We sought to perform novel and informative bioinformatic analysis to explore the molecular mechanism of DKD. The gene expression profiles of GSE142025, GSE30528, and GSE30529 datasets were downloaded from the Gene Expression Omnibus database. After the GSE142025 data set was preprocessed, a gene co-expression network was constructed by weighted gene co-expression network analysis (WGCNA), and hub genes were selected in the key modules. Meanwhile, differentially expressed genes (DEGs) upregulated commonly were identified between the GSE30528 and GSE30529 datasets. Then, pathway and process enrichment analysis were performed for hub genes and commonly upregulated DEGs. Next, candidate targets were identified by comparing hub genes to commonly upregulated DEGs. Finally, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was carried out to validate the expression of candidate targets, and protein-protein interaction (PPI) network was constructed. A total of 17 modules were clustered by WGCNA, and the most significant turquoise module was selected. Based upon MM > 0.7 and GM > 0.7, 313 hub genes were screened out in turquoise module. Functional analysis of these 313 genes demonstrated their enrichment in pathways involved in leukocyte differentiation, cell morphogenesis, lymphocyte activation, vascular development, collagen synthesis, chemotaxis, and chemokine signaling. A total of 115 commonly upregulated DEGs were identified between the GSE30528 and GSE30529 datasets. Intriguingly, a total of six proinflammatory and profibrotic candidate targets were selected and validated in DKD mice in vivo, including CCR2, MOXD1, COL6A3, COL1A2, PYCARD, and C7. Based on WGCNA and DEG analysis of DKD datasets, six DKD-predisposing candidate targets were uncovered. The data suggest that inflammation and fibrosis are key mechanisms of DKD, and future studies may determine the causal link between the six proinflammatory and profibrotic genes and DKD.

Identification of ferroptosis-related genes as potential biomarkers of tongue squamous cell carcinoma using an integrated bioinformatics approach

Tongue squamous cell carcinoma (TSCC) is one of the deadliest cancers of the head and neck, but the role of the ferroptosis pathway in its development is still unknown. In this study we explored the pathogenetic mechanisms associated with ferroptosis in TSCC. We identified differentially expressed genes (DEGs) of TSCC patients and used gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) to annotate, visualize, and integrate these DEGs. Receiver operating characteristic curve (ROC) analysis was performed, and the STRING database was used to construct a protein–protein interaction network to evaluate the predictive value of ferroptosis‐related DEGs. A total of 219 DEGs were identified and GO, KEGG, and GSEA showed that extracellular matrix (ECM)‐receptor interaction and interleukin (IL)‐17 signaling pathways were substantially upregulated in TSCC. Univariate Cox analysis revealed that high expression of CA9, TNFAIP3, and NRAS were predictive of a worse outcome. We then constructed a prognostic model that predicted survival in the validation cohort at 1 year and 32 months. Finally, 60 cases of tongue carcinoma and normal tissues were collected, and immunohistochemistry was used to detect the expression of CA9. We found that CA9 was strongly expressed in tongue carcinoma tissues and absent in adjacent tissues. Overall, we found that ferroptosis‐related genes may affect TSCC prognosis through the ECM‐receptor interaction and IL‐17 signaling pathways. Additionally, immunohistochemistry confirmed that CA9 was highly expressed in tongue carcinoma tissues, and a model based on ferroptosis‐related genes showed a good ability to predict overall survival in TSCC.

Studying Kidney Diseases at the Single-Cell Level

BACKGROUND

The kidney is a highly complex organ that performs diverse functions that are essential for health. Kidney disease occurs when the kidneys are damaged and fail to function properly. Single-cell analysis is a powerful technology that provides unprecedented insights into normal and abnormal kidney cell types and will transform our understanding of the mechanism underlying common kidney diseases.

SUMMARY

Our understanding of kidney disease pathogenesis is limited by the incomplete molecular characterization of cell types responsible for kidney functions. Application of single-cell technologies for the study of the kidney has revealed cellular heterogeneity, gene expression signatures, and molecular dynamics during the onset and development of kidney diseases. Single-cell analyses of kidney organoids and allograft tissues offer new insights into kidney organogenesis, disease mechanisms, and therapeutic outcomes. Collectively, a better understanding of kidney cell heterogeneity and the molecular dynamics of kidney diseases will improve diagnostic accuracy and facilitate the identification of novel treatment strategies in nephrology.

KEY MESSAGE

In this review article, we summarize recent single-cell studies on kidney diseases and discuss the impact of single-cell technology on both basic and clinical nephrology research.

Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

Background

Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression.

Methods

The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed.

Results

After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes.

Conclusions

The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.