Integrated Bioinformatics and Experimental Validation Reveal Shared Molecular Targets for Diagnosis and Intervention in Type 2 Diabetes Mellitus and Diabetic Nephropathy.
Jul 2026· Current molecular medicine· Vol 26· 0 citations
Medicine
TL;DR
APP, RHEB, FRYL, and SOS1 represent shared molecular signatures of T2DM and DN and may offer potential targets for future mechanistic and therapeutic studies.
Abstract
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.
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.
Wei Du, Guiying Ma, Bingzu Li et al.· Journal of Visualized Experi...· 0 citations
Results indicate that CTSG and LTF may serve as promising diagnostic biomarkers for T1DM, and functional enrichment analysis showed that immune activation played an important role in T1DM.
Jiaci Li, Shuyue Zhang, Xue-Tao Wang et al.· PeerJ· 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
Alzheimer’s disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the molecular mechanisms of AD remain incompletely understood. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein–protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network construction analysis were performed to predict key microRNAs (miRNAs), transcription factors (TFs) and small drug molecules. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. Conduct molecular docking with hub genes and corresponding active molecules. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10,342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that miRNAs include hsa-mir-545-3p and hsa-miR-548f-5p, and TFs include PLAG1 and MEF2A might be involved in the development of AD. Drug molecules were predicted including Sulindac, Infliximab, Norfloxacin and Gemcitabine for treatment of AD. Molecular docking analysis revealed that Isocryptomerin and Macrophylloside D were the main active compounds with good binding activities to the HSP90AA1 and FN1. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.
Diabetes is a global public health problem with various complications, which can lead to disability and mortality. This study identified potential diagnostic markers for diabetes and explored the immunometabolic mechanisms in the pathological process. The gene expression of 17 diabetes cases and 16 normal controls were obtained from the Gene Expression Omnibus (GEO) database. The “limma” package was employed for screening differentially expressed genes (DEGs). Gene functions and enriched pathways of DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Candidate key genes were screened using the least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. The diagnostic effectiveness of identified markers was further verified via the receiver operating characteristic (ROC) curve. The compositional patterns of immune cell infiltration and signaling pathway enrichment associated with key genes were explored via single sample Gene Set Enrichment Analysis (ssGSEA) and GSEA analysis, respectively. Possible miRNAs interacting with key genes were predicted via miRcode database. B2M, FTL, SH3BGRL3, and SOD2 were recognized as diagnostic markers for diabetes based on LASSO regression and the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm. Analysis of immune cell infiltration demonstrated that the four key genes were related to B cells, neutrophils, macrophages, and CD8+ T cells. The diagnostic value of B2M, FTL, and SOD2 for diabetes was higher than that of SH3BGRL3 according to the ROC curve. Validation experiments indicated that the mRNA expression of B2M and FTL was increased in liver tissues of diabetic mice. B2M and FTL can act as diagnostic markers for diabetes and contribute to new understandings of the disease's molecular mechanisms.
Unknown authors· Brazilian journal of medical...· 0 citations
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
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