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.
Abstract
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.
BACKGROUND
Atherosclerosis (AS) is a complex chronic disease caused by the development of atherosclerotic plaques. T cell proliferation exerts a vital influence on development of the AS. This research aimed to conduct a comprehensive analysis to computationally screen candidate T cell proliferation-related biomarkers in AS and to explore their potential molecular mechanisms.
METHODS
The transcriptional datasets of AS patients were obtained from the Gene Expression Omnibus (GEO) repository. To identify differentially expressed-T cell proliferation-related genes (DE-TPRGs), we integrated differential gene expression analysis with weighted gene co-expression network analysis (WGCNA), taking the intersection of DEGs and WGCNA-derived T cell proliferation-related module genes (TRMGs). Biomarkers were selected and validated through machine learning algorithms and expression levels. Moreover, a nomogram for predicting AS risk was developed based on the biomarkers. Enrichment analysis was employed to examine relevant pathways, while immune cell infiltration analysis was conducted to investigate the connection between immune cells and biomarkers. Finally, the construction of molecular regulatory and compound prediction networks, as well as molecular docking and molecular dynamic simulations, further validated the key regulatory roles of biomarkers in AS.
RESULTS
Overall, two candidate genes (CLU and GUCY1B3) were determined to be potentially associated with the progression of AS. The nomogram constructed from these biomarkers showed good performance in predicting AS risk. Reactome rRNA processing and reactome translation were notably enriched in the pathways related to two biomarkers. CLU and GUCY1B3 showed positive computational corrections with activated dendritic cells, suggesting a possible involvement of these immune processes in AS. Moreover, 50 miRNAs (like hsa-miR-4504) and 68 transcription factors (TFs) (like MYB and TAL1) were found to have relationships with biomarkers. Importantly, the results and molecular dynamics simulation analyses predicted potential binding interactions between these candidate genes and bisphenol A, with GUCY1B3 demonstrating relatively greater binding stability.
CONCLUSIONS
The bioinformatics findings suggested that CLU and GUCY1B3 may serve as candidate biomarkers warranting further experimental investigation in AS associated with T cell proliferation.
Wen Xiong, Xiang Long, Feng Lu et al.· Journal of Cardiothoracic Su...· 0 citations
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
Introduction Type 2 diabetes mellitus (T2DM) is among the most rapidly increasing metabolic disorders worldwide. Membrane proteins, integral components of biological membranes, are pivotal in insulin signal transduction and significantly contribute to the pathogenesis of T2DM. However, the systematic investigation of membrane proteins linked to T2DM remains insufficient. Methods The T2DM dataset and membrane protein-related genes were sourced from the GEO database and the Uniprot website, respectively. Bioinformatics methodologies, including Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis, were employed to assess the differentially expressed membrane protein-related genes between the normal control group and the T2DM group. Subsequently, machine learning algorithms, including Gaussian Mixture Model (GMM), Random Forest (RF), and Support Vector Machine (SVM), were utilized to identify hub genes. Following this, a clinical diagnostic model was constructed, and the receiver operating characteristic (ROC) curve was plotted. Candidate genes were further examined in palmitic acid (PA)-induced NES2Y and HepG2 cell models and in high-fat diet/streptozotocin-induced T2DM mice. Finally, Functional effects were assessed by gene knockdown, reverse transcription quantitative PCR (RT-qPCR), western blotting (WB), immunofluorescence (IF), co-immunoprecipitation, histological examination, and lipid staining. Results A total of 1,856 DEGs were identified between T2DM and control samples, including 599 upregulated genes and 1,257 downregulated genes, among which 42 were membrane proteinrelated T2DM DEGs. Machine learning algorithms were then applied to pinpoint two target genes: ICAM1 and EZR, the latter of which encodes Ezrin, a membrane–cytoskeleton linker protein. The ROC curve analysis showed that the diagnostic model exhibited strong predictive capability, with an AUC value of 0.95. PA treatment increased lipid accumulation and EZR/ICAM1 mRNA and Ezrin/ICAM1 protein expression in the cell models. Ezrin co-immunoprecipitated with the insulin receptor. In PA-treated HepG2 cells, ICAM1 or EZR knockdown increased p85α and AKT phosphorylation and GLUT4 protein expression. Ezrin and ICAM1 were also elevated in the livers of T2DM mice. Discussion ICAM1 and EZR may serve as potential diagnostic biomarkers and candidate therapeutic targets associated with T2DM. Furthermore, ICAM1 and EZR may be associated with insulin resistance by reducing glucose transport efficiency, potentially through modulation of the PI3K-AKT signaling pathway.
Tong Liu, Zhaoyuan Tang, Zulipikaer Aierken et al.· Frontiers in Endocrinology· 0 citations
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
Background Aging is a critical risk factor for the progression and complications of type 2 diabetes mellitus (T2DM). However, routine clinical indicators fail to accurately quantify biological senescence burden in T2DM patients. This study aimed to screen plasma protein signatures associated with metabolic senescence in T2DM using Olink targeted proteomics and to construct a novel biological aging evaluation model for diabetic populations. Methods A total of 21 healthy controls and 66 T2DM patients were enrolled. Plasma protein profiles were detected via Olink proteomics. Differentially expressed proteins (DEPs) were identified and subjected to GO and KEGG functional enrichment analyses. Random forest and LASSO regression analyses were applied to screen core T2DM-related protein molecules. T2DM patients were further stratified by a 55-year cutoff, a well-recognized critical threshold for metabolic senescence. After adjustment for sex, BMI, glycated hemoglobin (HbA1C), and hypoglycemic medication use, age-independent senescence-related proteins were identified to establish a multi-protein predictive model. A 1000-time Bootstrap resampling procedure was performed for internal validation to evaluate model discrimination and stability. Results A total of 84 DEPs (P < 0.05) were identified between T2DM patients and healthy controls, mainly enriched in cytokine–cytokine receptor interaction, lipid metabolism, and atherosclerosis pathways. Machine learning screened six core proteins with high discriminatory value for T2DM, including MERTK, BOC, TNFRSF10A, CCL3, AGRP, and PD-L2. Within the T2DM cohort, nine age-dependent plasma proteins were independently identified (FDR < 0.05), among which VSIG2, LPL, S100A11, and CST5 exhibited the most robust correlations (FDR < 0.01). A three-protein model comprising VSIG2, S100A11, and S100A5 achieved favorable discriminative performance (AUC = 0.874), which was superior to conventional clinical indicators including BMI (AUC = 0.572) and HbA1c (AUC = 0.593). Bootstrap validation confirmed reliable model stability with a correction optimism of −0.024, a bias-corrected AUC of 0.900, and an overfitting degree of −2.65%. Functional analyses indicated that candidate proteins were primarily involved in immune homeostasis, lipid remodeling, inflammatory responses, and calcium signaling. Conclusion This study identified novel T2DM-associated plasma biomarkers and established a robust three-protein signature (VSIG2, S100A11, S100A5) for age stratification and biological senescence evaluation in T2DM. The proposed model compensates for the limitations of routine clinical indices, providing a promising non-invasive serological tool for precise risk stratification and individualized intervention in elderly diabetic patients.
Yan Yang, Shi-Yu Liu, Chun-Guang Xie et al.· Frontiers in Endocrinology· 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.
