Identification of key biomarkers in diabetic nephropathy via bioinformatic analysis

Identifying MS4A6A+ macrophages as potential contributors to the pathogenesis of nonalcoholic fatty liver disease, periodontitis, and type 2 diabetes mellitus

Purpose

Concrete epidemiological evidence has suggested the mutually-contributing effect respectively between nonalcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and periodontitis (PD); however, their shared crosstalk mechanism remains an open issue.

Method

The NAFLD, PD, and T2DM-related datasets were obtained from the NCBI GEO repository. Their common differentially expressed genes (DEGs) were identified and the functional enrichment analysis performed by the DAVID platform determined relevant biological processes and pathways. Then, the STRING database established a PPI network of such DEGs and topological analysis through Cytoscape 3.7.1 software along with the machine-learning analysis by the least absolute shrinkage and selection operator (LASSO) algorithm screened out hub characteristic genes. Their efficacy was validated by external datasets using the receiver operating characteristic (ROC) curve, and gene expression and location of the most robust one was determined using single-cell sequencing and immunohistochemical staining. Finally, the promising drugs were predicted through the CTD database, and the CB-DOCK 2 and Pymol platform mimicked molecular docking.

Result

Intersection of differentially expressed genes from three datasets identified 25 shared DEGs of the three diseases, which were enriched in MHC II-mediated antigen presenting process. PPI network and LASSO machine-learning analysis determined 4 feature genes, of which the MS4A6A gene mainly expressed by macrophages was the hub gene and key immune cell type. Molecular docking simulation chosen fenretinide as the most promising medicant for MS4A6A+ macrophages.

Conclusion

MS4A6A+ macrophages were suggested to be important immune-related mediators in the progression of NAFLD, PD, and T2DM pathologies.

In silico identification and functional prediction of differentially expressed genes in South Asian populations associated with type 2 diabetes

Type 2 diabetes (T2D) is one of the major metabolic disorders in humans caused by hyperglycemia and insulin resistance syndrome. Although significant genetic effects on T2D pathogenesis are experimentally proved, the molecular mechanism of T2D in South Asian Populations (SAPs) is still limited. Hence, the current research analyzed two Gene Expression Omnibus (GEO) and 17 Genome-Wide Association Studies (GWAS) datasets associated with T2D in SAP to identify DEGs (differentially expressed genes). The identified DEGs were further analyzed to explore the molecular mechanism of T2D pathogenesis following a series of bioinformatics approaches. Following PPI (Protein-Protein Interaction), 867 potential DEGs and nine hub genes were identified that might play significant roles in T2D pathogenesis. Interestingly, CTNNB1 and RUNX2 hub genes were found to be unique for T2D pathogenesis in SAPs. Then, the GO (Gene Ontology) showed the potential biological, molecular, and cellular functions of the DEGs. The target genes also interacted with different pathways of T2D pathogenesis. In fact, 118 genes (including HNF1A and TCF7L2 hub genes) were directly associated with T2D pathogenesis. Indeed, eight key miRNAs among 2582 significantly interacted with the target genes. Even 64 genes were downregulated by 367 FDA-approved drugs. Interestingly, 11 genes showed a wide range (9-43) of drug specificity. Hence, the identified DEGs may guide to elucidate the molecular mechanism of T2D pathogenesis in SAPs. Therefore, integrating the research findings of the potential roles of DEGs and candidate drug-mediated downregulation of marker genes, future drugs or treatments could be developed to treat T2D in SAPs.

Identification and validation of disulfidptosis-related gene signatures and their subtype in diabetic nephropathy

Background: Diabetic nephropathy (DN) is the most common complication of diabetes, and its pathogenesis is complex involving a variety of programmed cell death, inflammatory responses, and autophagy mechanisms. Disulfidptosis is a newly discovered mechanism of cell death. There are little studies about the role of disulfidptosis on DN. Methods: First, we obtained the data required for this study from the GeneCards database, the Nephroseq v5 database, and the GEO database. Through differential analysis, we obtained differential disulfidptosis-related genes. At the same time, through WGCNA analysis, we obtained key module genes in DN patients. The obtained intersecting genes were further screened by Lasso as well as SVM-RFE. By intersecting the results of the two, we ended up with a key gene for diabetic nephropathy. The diagnostic performance and expression of key genes were verified by the GSE30528, GSE30529, GSE96804, and Nephroseq v5 datasets. Using clinical information from the Nephroseq v5 database, we investigated the correlation between the expression of key genes and estimated glomerular filtration rate (eGFR) and serum creatinine content. Next, we constructed a nomogram and analyzed the immune microenvironment of patients with DN. The identification of subtypes facilitates individualized treatment of patients with DN. Results: We obtained 91 differential disulfidptosis-related genes. Through WGCNA analysis, we obtained 39 key module genes in DN patients. Taking the intersection of the two, we preliminarily screened 20 genes characteristic of DN. Through correlation analysis, we found that these 20 genes are positively correlated with each other. Further screening by Lasso and SVM-RFE algorithms and intersecting the results of the two, we identified CXCL6, CD48, C1QB, and COL6A3 as key genes in DN. Clinical correlation analysis found that the expression levels of key genes were closely related to eGFR. Immune cell infiltration is higher in samples from patients with DN than in normal samples. Conclusion: We identified and validated 4 DN key genes from disulfidptosis-related genes that CXCL6, CD48, C1QB, and COL6A3 may be key genes that promote the onset of DN and are closely related to the eGFR and immune cell infiltrated in the kidney tissue.

The Role of V-Set Ig Domain-Containing 4 in Chronic Kidney Disease Models

V-set Ig domain-containing 4 (VSIG4) regulates an inflammatory response and is involved in various diseases. However, the role of VSIG4 in kidney diseases is still unclear. Here, we investigated VSIG4 expression in unilateral ureteral obstruction (UUO), doxorubicin-induced kidney injury mouse, and doxorubicin-induced podocyte injury models. The levels of urinary VSIG4 protein significantly increased in the UUO mice compared with that in the control. The expression of VSIG4 mRNA and protein in the UUO mice was significantly upregulated compared with that in the control. In the doxorubicin-induced kidney injury model, the levels of urinary albumin and VSIG4 for 24 h were significantly higher than those in the control mice. Notably, a significant correlation was observed between urinary levels of VSIG4 and albumin (r = 0.912, p < 0.001). Intrarenal VSIG4 mRNA and protein expression were also significantly higher in the doxorubicin-induced mice than in the control. In cultured podocytes, VSIG4 mRNA and protein expressions were significantly higher in the doxorubicin-treated groups (1.0 and 3.0 μg/mL) than in the controls at 12 and 24 h. In conclusion, VSIG4 expression was upregulated in the UUO and doxorubicin-induced kidney injury models. VSIG4 may be involved in pathogenesis and disease progression in chronic kidney disease models.

The landscape of immune cell infiltration in the glomerulus of diabetic nephropathy: evidence based on bioinformatics

Background

Increasing evidence suggests that immune cell infiltration contributes to the pathogenesis and progression of diabetic nephropathy (DN). We aim to unveil the immune infiltration pattern in the glomerulus of DN and provide potential targets for immunotherapy.

Methods

Infiltrating percentage of 22 types of immune cell in the glomerulus tissues were estimated by the CIBERSORT algorithm based on three transcriptome datasets mined from the GEO database. Differentially expressed genes (DEGs) were identified by the “limma” package. Then immune-related DEGs were identified by intersecting DEGs with immune-related genes (downloaded from Immport database). The protein–protein interactions of Immune-related DEGs were explored using the STRING database and visualized by Cytoscape. The enrichment analyses for KEGG pathways and GO terms were carried out by the gene set enrichment analysis (GSEA) method.

Results

11 types of immune cell were revealed to be significantly altered in the glomerulus tissues of DN (Up: B cells memory, T cells gamma delta, NK cells activated, Macrophages.M1, Macrophages M2, Dendritic cells resting, Mast cells resting; Down: B cells naive, NK cells resting, Mast cells activated, Neutrophils). Several pathways related to immune, autophagy and metabolic process were significantly activated. Moreover, 6 hub genes with a medium to strong correlation with renal function (eGFR) were identified (SERPINA3, LTF, C3, PTGDS, EGF and ALB).

Conclusion

In the glomerulus of DN, the immune infiltration pattern changed significantly. A complicated and tightly regulated network of immune cells exists in the pathological of DN. The hub genes identified here will facilitate the development of immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02906-4.

Upregulation of VSIG4 in Type 2 Diabetic Kidney Disease

Fibrosis is the final common finding in patients with advanced diabetic kidney disease. V-set Ig domain containing 4 (VSIG4) is related to fibrosis in several diseases. It also contributes to fibrosis under high-glucose conditions in renal tubule cells. To determine the role of VSIG4 in type 2 diabetes, we examined VSIG4 expression in a type 2 diabetic animal model and podocyte. Urinary excretion of albumin and VSIG4 was significantly higher in db/db mice than in the control group. Urine VSIGs levels for 6 h were about three-fold higher in db/db mice than in db/m mice at 20 weeks of age: 55.2 ± 37.8 vs. 153.1 ± 74.3 ng, p = 0.04. Furthermore, urinary VSIG4 levels were significantly correlated with urinary albumin levels (r = 0.77, p < 0.01). Intrarenal VSIG4 mRNA expression was significantly higher in db/db mice than in control mice (1.00 ± 0.35 vs. 1.69 ± 0.77, p = 0.04). Further, VSIG4 expression was almost twice as high in db/db mice at 20 weeks of age. Intrarenal VSIG immunoreactivity in db/db mice was also significantly higher than that in control mice. In cultured podocytes, both high glucose and angiotensin II significantly upregulated the expression of VSIG4 mRNA and protein. In conclusion, VSIG4 was upregulated in an animal model of type 2 diabetes and was related to albuminuria and pro-fibrotic markers. Considering these relationships, VSIG4 may be an important mediator of diabetic nephropathy progression.

Screening and Identification of Hub Genes in the Development of Early Diabetic Kidney Disease Based on Weighted Gene Co-Expression Network Analysis

Objective

The study aimed to screen key genes in early diabetic kidney disease (DKD) and predict their biological functions and signaling pathways using bioinformatics analysis of gene chips interrelated to early DKD in the Gene Expression Omnibus database.

Methods

Gene chip data for early DKD was obtained from the Gene Expression Omnibus expression profile database. We analyzed differentially expressed genes (DEGs) between patients with early DKD and healthy controls using the R language. For the screened DEGs, we predicted the biological functions and relevant signaling pathways by enrichment analysis of Gene Ontology (GO) biological functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways. Using the STRING database and Cytoscape software, we constructed a protein interaction network to screen hub pathogenic genes. Finally, we performed immunohistochemistry on kidney specimens from the Beijing Hospital to verify the above findings.

Results

A total of 267 differential genes were obtained using GSE142025, namely, 176 upregulated and 91 downregulated genes. GO functional annotation enrichment analysis indicated that the DEGs were mainly involved in immune inflammatory response and cytokine effects. KEGG pathway analysis indicated that C-C receptor interactions and the IL-17 signaling pathway are essential for early DKD. We identified FOS, EGR1, ATF3, and JUN as hub sites of protein interactions using a protein-protein interaction network and module analysis. We performed immunohistochemistry (IHC) on five samples of early DKD and three normal samples from the Beijing Hospital to label the proteins. This demonstrated that FOS, EGR1, ATF3, and JUN in the early DKD group were significantly downregulated.

Conclusion

The four hub genes FOS, EGR1, ATF3, and JUN were strongly associated with the infiltration of monocytes, M2 macrophages, and T regulatory cells in early DKD samples. We revealed that the expression of immune response or inflammatory genes was suppressed in early DKD. Meanwhile, the FOS group of low-expression genes showed that the activated biological functions included mRNA methylation, insulin receptor binding, and protein kinase A binding. These genes and pathways may serve as potential targets for treating early DKD.

Integrated Analysis of Multiple Microarray Studies to Identify Core Gene-Expression Signatures Involved in Tubulointerstitial Injury in Diabetic Nephropathy

Diabetic nephropathy is a leading cause of end-stage renal disease in both developed and developing countries. It is lack of specific diagnosis, and the pathogenesis remains unclarified in diabetic nephropathy, following the unsatisfactory effects of existing treatments. Therefore, it is very meaningful to find biomarkers with high specificity and potential targets. Two datasets, GSE30529 and GSE47184 from GEO based on diabetic nephropathy tubular samples, were downloaded and merged after batch effect removal. A total of 545 different expression genes screened with $\log 2\text{FC}>0.5$ were weighted gene coexpression correlation network analysis, and green module and blue module were identified. The results of KEGG analyses both in green module and GSEA analysis showed the same two enriched pathway, focal adhesion and viral myocarditis. Based on the intersection among WGCNA focal adhesion/Viral myocarditis, GSEA focal adhesion/viral myocarditis, and PPI network, 17 core genes, ACTN1, CAV1, PRKCB, PDGFRA, COL1A2, COL6A3, RHOA, VWF, FN1, HLA-F, HLA-DPB1, ITGB2, HLA-DRA, HLA-DMA, HLA-DPA1, HLA-B, and HLA-DMB, were identified as potential biomarkers in diabetic tubulointerstitial injury and were further validated externally for expression at GSE99325 and GSE104954 and clinical feature at nephroseq V5 online platform. CMap analysis suggested that two compounds, LY-294002 and bufexamac, may be new insights for therapeutics of diabetic tubulointerstitial injury. Conclusively, it was raised that a series of core genes may be as potential biomarkers for diagnosis and two prospective compounds.

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

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.

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

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.

Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease

Background

Chronic kidney disease (CKD) is characterized by progressive renal function loss, which may finally lead to end-stage renal disease (ESRD). The study is aimed at identifying crucial genes related to CKD progressive and constructing a disease prediction model to investigate risk factors.

Methods

GSE97709 and GSE37171 datasets were downloaded from the GEO database including peripheral blood samples from subjects with CKD, ESRD, and healthy controls. Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD.

Results

A total of 76 DEGs were screened from CDK vs. normal samples while 10,114 DEGs were identified from ESRD vs. CDK samples. For numerous genes related to ESRD, several GO biological terms and 141 signaling pathways were identified including markedly upregulated olfactory transduction and downregulated platelet activation pathway. The DEGs were clustering in three modules according to WGCNA access, namely, ME1, ME2, and ME3. By construction of the XGBoost model and dataset validation, we screened cohorts of genes associated with progressive CKD, such as FZD10, FOXD4, and FAM215A. FZD10 represented the highest score (F score = 21) in predictive model.

Conclusion

Our results demonstrated that FZD10, FOXD4, PPP3R1, and UCP2 might be critical genes in CKD progression.

Rapid isolation and proteome analysis of urinary exosome based on double interactions of Fe3O4@TiO2-DNA aptamer

There are accumulating evidence that proteins carried by exosomes in urine are most possibly used as biomarkers or therapeutic carriers for certain diseases. The isolation of exosomes is therefore highly desirable for aiding the downstream protein analysis. Particularly, urine is a dynamic biological fluid changing within a short time, resulting in that the separation of urinary exosome requires more efficient technology. Here, a new biocompatible material (denoted as Fe₃O₄@TiO₂-CD63 aptamer) is designed and synthesized for rapid exosome isolation from human urine, depending on the double interactions of TiO₂ with phosphate groups as well as aptamers with specific exosome proteins. Moreover, within 10 min, 92.6% exosomes with intact structure are captured from urine by Fe₃O₄@TiO₂-CD63 aptamers, from which 999 proteins are detected through LC-MS/MS.

Comprehensive Analyses of miRNA-mRNA Network and Potential Drugs in Idiopathic Pulmonary Arterial Hypertension

Introduction

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiopulmonary disease with a relatively low survival rate. Moreover, the pathogenesis of IPAH has not been fully recognized. Thus, comprehensive analyses of miRNA-mRNA network and potential drugs in IPAH are urgent requirements.

Methods

Microarray datasets of mRNA and microRNA (miRNA) in IPAH were searched and downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMIs) were identified. Then, the DEMI-DEG network was conducted with associated comprehensive analyses including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, while potential drugs targeting hub genes were investigated using L1000 platform.

Results

30 DEGs and 6 DEMIs were identified in the lung tissue of IPAH. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in antimicrobial humoral response and African trypanosomiasis, respectively. The DEMI-DEG network was conducted subsequently with 4 DEMIs (hsa-miR-34b-5p, hsa-miR-26b-5p, hsa-miR-205-5p, and hsa-miR-199a-3p) and 16 DEGs, among which 5 DEGs (AQP9, SPP1, END1, VCAM1, and SAA1) were included in the top 10 hub genes of the PPI network. Nimodipine was identified with the highest CMap connectivity score in L1000 platform.

Conclusion

Our study conducted a miRNA-mRNA network and identified 4 miRNAs as well as 5 mRNAs which may play important roles in the pathogenesis of IPAH. Moreover, we provided a new insight for future therapies by predicting potential drugs targeting hub genes.

Analysis of potential hub genes involved in the pathogenesis of Chinese type 1 diabetic patients

Background

Type 1 diabetes is an autoimmune disease strongly related to genetic factors. Although studies on T1D susceptibility genes have achieved great progress, the molecular mechanism of T1D remains to be explained.

Methods

To explore the underlying mechanisms of T1D, bioinformatic analysis based on a microarray database was used to determine the key biomarkers of T1D as well as their biofunctions and interactions. The microarray database GSE55100 was downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were processed by packages in R Software. The database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8) was used to conduct gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network was analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the module analysis was performed using Cytoscape.

Results

Seventy-eight DEGs and 13 hub genes were identified. The biofunctions and pathways of these DEGs were enriched in immune response, extracellular exosome, cytokine activity and antigen processing and presentation. Thirteen DEGs with MCODE score ≥2 were selected as hub genes including MMP9, ARG1, CAMP, CHI3L1, CRISP3, SLPI, LCN2, PGLYRP1, LTF, RETN, CEACAM1, CEACAM8 and MS4A3.

Conclusions

The identification and analyses of the DEGs and hub genes from database GSE55100 provide novel prospectives of the pathogenesis of T1D.

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia (VAP). MATERIAL AND METHODS GSE30385 from the Gene Expression Omnibus (GEO) database identified differentially expressed genes (DEGs) associated with patients with VAP. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment identified the molecular functions of the DEGs. The least absolute shrinkage and selection operator (LASSO) regression analysis algorithm was used to select key genes. Three modeling methods, including logistic regression analysis, random forest analysis, and fully-connected neural network analysis, also known as also known as the feed-forward multi-layer perceptron (MLP), were used to identify the diagnostic gene signature for patients with VAP. RESULTS Sixty-six DEGs were identified for patients who had VAP (VAP+) and who did not have VAP (VAP-). Ten essential or feature genes were identified. Upregulated genes included matrix metallopeptidase 8 (MMP8), arginase 1 (ARG1), haptoglobin (HP), interleukin 18 receptor 1 (IL18R1), and NLR family apoptosis inhibitory protein (NAIP). Down-regulated genes included complement factor D (CFD), pleckstrin homology-like domain family A member 2 (PHLDA2), plasminogen activator, urokinase (PLAU), laminin subunit beta 3 (LAMB3), and dual-specificity phosphatase 2 (DUSP2). Logistic regression, random forest, and MLP analysis showed receiver operating characteristic (ROC) curve area under the curve (AUC) values of 0.85, 0.86, and 0.87, respectively. CONCLUSIONS Logistic regression analysis, random forest analysis, and MLP analysis identified a ten-gene signature for the diagnosis of VAP.