Aug 2026· Journal of Visualized Experiments· Vol 234· 0 citations
Medicine
TL;DR
An integrated bioinformatics analysis facilitated the screening of candidate therapeutic targets, mechanisms, and drugs for T2DM and ASCVD, offering new insights into molecular therapies for these conditions.
Abstract
This study examined the mechanisms underlying the comorbidity between type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease (ASCVD), while identifying potential therapeutic targets. Common differentially expressed genes (C-DEGs) between T2DM and ASCVD were extracted from the GSE78721 and GSE12288 datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, protein-protein interaction (PPI) network construction, hub gene identification, and Drug-Gene Interaction Database (DGIdb) analysis were conducted. The association between hub C-DEGs and immune-infiltrating cells was analyzed using the CIBERSORT method. Expression levels of hub C-DEGs were quantified through qRT-PCR and Western blot analyses. A total of 32 C-DEGs were identified, comprising 20 upregulated and 12 downregulated genes. C-DEGs were predominantly enriched in key pathways, including viral myocarditis, arrhythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, and dilated cardiomyopathy. PPI analysis revealed 29 nodes and 39 edges, leading to the identification of eight hub C-DEGs (HSP90B1, PLAU, SLPI, TOP3A, NCF4, PRF1, TUBA1C, and CS) across both datasets. Furthermore, hub C-DEGs (TOP3A, SLPI, NCF4, PRF1, and PLAU) demonstrated significant correlations with immune-infiltrating cell levels. Drugs specifically targeting these hub C-DEGs present promising candidates for the treatment of T2DM and ASCVD. Additionally, the expression of hub C-DEGs at both mRNA and protein levels was validated in patients with T2DM and ASCVD. An integrated bioinformatics analysis facilitated the screening of candidate therapeutic targets, mechanisms, and drugs for T2DM and ASCVD, offering new insights into molecular therapies for these conditions.
INTRODUCTION
Type 2 diabetes mellitus (T2DM) is a major risk factor for diabetic nephropathy (DN), yet the molecular mechanisms connecting these conditions remain unclear. Identifying shared hub genes and regulatory networks may provide insight into common pathogenic pathways.
METHODS
Four GEO microarray datasets associated with T2DM (GSE23343, GSE29226) and DN (GSE30528, GSE142153) were analyzed using limma in R to identify differentially expressed genes (DEGs). Overlapping DEGs were assessed using Venn analysis and integrated into a STRING-based protein-protein interaction network. Hub genes were identified in Cytoscape. Their expression under high-glucose conditions was validated in HK-2 and NRK-52E cells using RT-qPCR and Western blotting. Predicted miRNAs were obtained from TargetScan and evaluated experimentally. Functional assays assessed the effects of hub gene overexpression.
RESULTS
Thirty-six common DEGs were identified, with APP, RHEB, FRYL, and SOS1 exhibiting highest network connectivity. All four genes were consistently downregulated in patient datasets and high-glucose cell models. ROC analyses indicated moderate discriminatory capacity within datasets. Four candidate miRNAs (miR-26b-5p, miR-18a-5p, miR-199a-5p, miR-148a-3p) were elevated under highglucose conditions. Functional enrichment linked hub genes to mTOR, PI3K-Akt, and cytoskeletal pathways. Overexpression of hub genes reduced proliferation, clonogenicity, and migration in vitro.
DISCUSSION
The convergence of transcriptomic and experimental findings suggests that reduced expression of these hub genes may contribute to glucose-induced cellular dysfunction. However, miRNA-gene relationships remain correlative, and validation in additional renal cell types and independent patient cohorts is needed.
CONCLUSION
APP, RHEB, FRYL, and SOS1 represent shared molecular signatures of T2DM and DN and may offer potential targets for future mechanistic and therapeutic studies.
Syed Shah Zaman Haider Naqvi, Zhitong Li, Ruixue Duan et al.· Current molecular medicine· 0 citations
Background
Type 1 diabetes mellitus (T1DM) is a chronic disease that significantly impacts patients' quality of life. Its prevalence is rising globally each year. This study aims to identify potential biomarkers associated with T1DM through comprehensive bioinformatics analysis, further enhancing T1DM early diagnosis and treatment.
Methods
Transcriptome datasets from T1DM patients and the control group were from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) were identified and subsequently analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) network analysis. Hub genes were identified using Enzyme-Linked Immunosorbent Assay (ELISA) on clinical samples comprising 17 T1DM patients and 19 controls. Immune cell infiltration was estimated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm, while the diagnostic performance of the hub genes was evaluated via receiver operating characteristic (ROC) curve analysis.
Results
A total of 20 up-regulated and eight down-regulated DEGs were identified in the GEO database. Functional enrichment analysis showed that immune activation played an important role in T1DM. The expression levels of the hub genes, CTSG and LTF, were further validated in clinical samples. ROC analysis showed moderate diagnostic performance, with AUC values of 0.75 (training set) and 0.67 (validation set).
Conclusions
The results indicate that CTSG and LTF may serve as promising diagnostic biomarkers for T1DM. Our study is positioned as exploratory with moderate diagnostic relevance rather than definitive biomarker discovery. The findings are preliminary and require further validation before any clinical application.
Jiaci Li, Shuyue Zhang, Xuetao Wang et al.· PeerJ· 0 citations
INTRODUCTION
Characterized as an idiopathic primary myocardial disorder, dilated cardiomyopathy (DCM) predominantly affects children and elderly adults. However, a lack of specific clinical symptoms and reliable biomarkers impedes timely diagnosis and rational clinical management of DCM.
METHODS
Whole-genome expression profiles (GSE120895, GSE9800) were retrieved from the Gene Expression Omnibus database via the GEOquery R package. A series of bioinformatic methods were employed, including DEG, GSVA, WGCNA, GO/KEGG enrichment, PPI, and immune infiltration analysis. Key biomarkers were validated by qRT-PCR in a Doxorubicin (DOX)-induced DCM model.
RESULTS
In total, 629 differentially expressed genes (DEGs) were screened out between DCM and control groups. Combined analysis of DEGs and WGCNA outputs identified 13 hub genes overlapping with oxidative stress-associated gene modules. Receiver Operating Characteristic (ROC) curve analyses confirmed that these hub genes exhibit favorable diagnostic efficiency for DCM. Functional enrichment results showed that these genes are mainly enriched in transmembrane transport and nucleotide metabolism pathways. Immune infiltration analysis indicated significantly elevated infiltration levels of five immune cell subsets in DCM myocardial tissues. In vivo experiments verified the significant upregulation of five core hub genes in DOX-induced DCM mice. By screening hub genes with diagnostic potency based on public transcriptome datasets and validating their expression alterations in a DOX-induced DCM animal model, this study provides partial experimental evidence to support the above bioinformatic outcomes.
DISCUSSION
Through integrative analysis of multiple datasets and molecular biology validation, this study provides robust evidence supporting the involvement of these hub genes in DCM pathogenesis. Although validation was limited to a single murine model, the findings lay the groundwork for future mechanistic studies and clinical exploration of these candidate genes.
CONCLUSION
Hub genes including MVP, WISP1, FCN1, AMPD3, RARRES1, FTL, and KRT14 possess promising auxiliary diagnostic potential, which provides novel clues for subsequent clinical evaluation research on DCM.
Patients with kidney stones (KS) often have an increased risk of atherosclerosis (AS). Because endothelial dysfunction (ED) is closely associated with AS, its role in KS remains unclear. This study aimed to examine the roles and mechanisms of AS-related ED genes in KS. Three datasets (GSE73680, GSE117518, and GSE132651) were analyzed. Differential expression analysis was conducted to identify differentially expressed genes (DEGs). To identify potential biomarkers, least absolute shrinkage and selection operator (LASSO) regression analysis and expression validation were conducted. Further analyses including GeneMANIA, gene set enrichment analysis (GSEA), examination of biomarkers within immune cells and subcellular localization analysis, molecular regulatory network analysis, tissue specificity analysis, and competing endogenous (ceRNA) network analysis were employed to comprehensively explore the functions and regulatory mechanisms of the identified biomarkers. Moreover, drug prediction analysis was conducted. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was proceeded to verify the expression levels of the biomarkers. A total of 22 DEGs associated with KS and AS were identified. Lasso regression selected 4 candidate biomarkers (MMP10, UCHL1, NEK2, and HEY1), among which UCHL1 and NEK2 were validated as key biomarkers. GeneMANIA and GSEA analyses uncovered the potential involvement of these biomarkers in cell adhesion molecules, focal adhesion, and lysosome pathways. Analysis of immune cells and subcellular localization provided insight into the biological functions and intracellular distribution of the biomarkers. Transcription factor regulatory network and ceRNA network analyses elucidated potential upstream regulatory mechanisms. Drug prediction analysis identified 17 potential drugs, including pazopanib and palbociclib, that may target NEK2. RT-qPCR demonstrated that NEK2 was significantly overexpressed in KS samples. This study identified biomarkers associated with KS and AS and comprehensively analyzed their molecular regulatory networks. These findings provide novel understandings of the molecular mechanism underlying KS and lay the foundation for future personalized treatment and drug development.
Aina Li, Chenjing Liu, Xiangshen Liu et al.· PLoS ONE· 0 citations
BACKGROUND
Diabetic kidney disease (DKD) is the primary global cause of end-stage renal disease. However, the aging-related gene networks driving its progression remain unclear.
METHODS
In this study, we integrated bioinformatics and experiments to screen for age-related hub genes in DKD and explore their diagnostic and therapeutic values. Transcriptomic datasets and aging-related gene databases were combined to obtain candidate differentially expressed genes enriched in the PI3K-Akt and JAK-STAT pathways. LASSO and Random Forest algorithms were applied to screen core hub genes, and a logistic diagnostic model was constructed for verification. Single-cell sequencing was utilized to clarify the main cell populations expressing key genes. Molecular docking and dynamics simulations were performed to analyze the binding stability of candidate drugs and target genes. High-glucose cell models and DKD rat models were established, and RT-qPCR, western blot, and pathological staining assays were used to validate the expression changes of key genes and the regulatory effects of drugs.
RESULTS
Nine hub genes (CLU, EGF, SLC16A7, MYC, RPA1, RB1, APOC3, SYK, and NR3C1) were finally screened out. The constructed logistic diagnostic model achieved an AUC of 0.881 in external verification. Glomerular endothelial cells and mesangial cells were identified as the main cell populations expressing these hub genes. Molecular simulation results confirmed the stable binding of fostamatinib to SYK and selexipag to APOC3. In vivo and in vitro validation experiments demonstrated that the two candidate agents could suppress NF-κB pathway activation, inflammatory cytokine release, and cellular senescence.
CONCLUSION
In this study, we identified age-related hub genes, constructed a reliable diagnostic signature, and suggested SYK-fostamatinib and APOC3-selexipag as potential drug-repurposing candidates for DKD, which requires further experimental and clinical validation.
Xue Hu, Yingzhuo Li, Yang Wang et al.· Clinical Epigenetics· 0 citations
Diabetic nephropathy (DN) is the most common complication of diabetes, with immune-mediated inflammation playing a significant role in its pathophysiology. The complement system, a key proinflammatory factor, is implicated in DN. This study was aimed at identifying diagnostic biomarkers related to the complement system in DN using bioinformatics methods. We analyzed three datasets (GSE96804, GSE104948, and GSE1009) from public databases, employing differential expression analysis and machine learning to identify complement system-related genes (CSRGs) as potential biomarkers. A diagnostic nomogram was constructed based on these genes, and immune microenvironment differences between the DN and control groups were explored. Gene interaction networks, enrichment analysis, and drug predictions were also conducted. Mendelian randomization (MR) was used to examine the causal links between identified biomarkers and DN. Our analysis identified five biomarkers (CKB, ANXA1, HSPA1L, CYP27B1, and XYLT1) associated with DN. A diagnostic nomogram based on these biomarkers showed high accuracy (model correction slope close to 1 and AUC close to 1). Immune infiltration analysis revealed significant differences in immune cell subsets between the DN and control groups. Gene set variation analysis (GSVA) indicated that the oxidative phosphorylation (OXPHOS) pathway was activated in CKB, HSPA1L, CYP27B1, and XYLT1 while inhibited in ANXA1. MR confirmed HSPA1L as a risk factor for DN (OR = 1.625, 95% CI: 1.272-2.076, p = 9.96e - 05). In conclusion, these five CSRGs may play significant roles in DN progression, providing a foundation for further research into DN pathogenesis and potential molecular markers for clinical diagnosis.
Lifang Wei, Ye Li, Yuhui Lu et al.· Journal of Diabetes Research· 0 citations