Three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism are identified and considered hypothesis-generating and require validation in larger independent cohorts.
Abstract
Uric acid metabolism is associated with the development of type 2 diabetes mellitus (T2DM), cardiometabolic, and cardiovascular diseases. Additionally, T2DM patients often exhibit mild cognitive impairment (MCI). However, the underlying mechanisms remain unclear. This study aims to identify and validate biomarkers associated with uric acid metabolism in T2DM and MCI, with the goal of discovering potential diagnostic and therapeutic targets to improve the quality of life for T2DM patients. Transcriptomic data for T2DM, MCI and uric acid metabolism-related genes were sourced from public databases. Biomarkers were screened using machine learning and validated for expression. Subsequent analyses included functional enrichment, immune infiltration, subcellular localization, and drug prediction. Three biomarkers—HP, ITGB3, and SELP—were identified. All showed significantly elevated expression in the T2DM group (p < 0.05). HP and ITGB3 were primarily enriched in ribosome-related pathways, primary immunodeficiency, and adherens junction processes. Immune infiltration analysis revealed that immature B cells and plasmacytoid dendritic cells were significantly enriched in T2DM. HP showed the strongest positive correlation with plasmacytoid dendritic cells (cor = 0.65, FDR <0.05), while ITGB3 exhibited the strongest positive correlation with immature B cells (cor = 0.76, FDR <0.05). Several potential therapeutic drugs were predicted, including calcifediol (score = −99.93) and meclofenamic acid (score = −99.89). This study identified three candidate biomarkers co-dysregulated across T2DM and MCI transcriptomes and associated with uric acid metabolism. Given the exploratory sample sizes, these findings are considered hypothesis-generating and require validation in larger independent cohorts.
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
Type 2 diabetes (T2D) and prediabetes represent a progressive glycemic continuum associated with multi-pathway metabolic deterioration that often precedes clinical diagnosis. Early identification of molecular alterations underlying this transition is critical for prevention strategies. Untargeted gas chromatography–mass spectrometry (GC–MS) metabolomics combined with multivariate statistical analysis (PCA) was applied to serum samples from 188 participants stratified into Control (n = 48), Prediabetes (n = 113), and Diabetes (n = 27) groups according to ADA/WHO diagnostic criteria. Progressive metabolic alterations were observed across the glycemic continuum. Several amino acid-, lipid-, and organic acid-related features showed nominal differences between the study groups. However, none of the detected metabolomic features remained statistically significant after false discovery rate (FDR) correction, indicating that these findings should be considered exploratory. Diabetes was associated with widespread downregulation of amino acid-related features, long-chain and complex lipid species, and small organic acids relative to both control and prediabetes groups. PCA (PC1 = 35.9%) showed progressive metabolic stratification primarily driven by hyperglycemia, dyslipidemia, and blood pressure elevation. These exploratory findings suggest that the transition from prediabetes to diabetes may be accompanied by alterations in amino acid, lipid, and energy metabolism. The identified metabolomic features represent candidate metabolites that require validation in larger independent cohorts using targeted metabolomics.
M. Toishimanov, Ivan Voitsekhovskiy, A. Shokan et al.· International Journal of Mol...· 0 citations
BACKGROUND
Nonalcoholic fatty liver disease (NAFLD) is characterized by profound metabolic reprogramming. Recent evidence suggests that lactate, beyond its role as a metabolic waste product, may modulate histone lactylation, linking metabolic stress to the epigenetic regulation of disease progression. However, the specific lactate-related gene (LRG) signatures driving NAFLD remain to be elucidated.
METHODS
We integrated transcriptomic mining of the GSE164760 dataset with in vivo validation to decode the LRG landscape. Differentially expressed LRGs were identified using strict thresholds and subsequently intersected with the GeneCards database. Functional enrichment (GO/KEGG) and Protein-Protein Interaction (PPI) networks were constructed to screen for hub genes. Key biomarkers were subsequently corroborated in a high-fat diet (HFD)- induced rat NAFLD model via Western blotting.
RESULTS
A total of 98 differentially expressed LRGs were identified, which were primarily enriched in molecular catabolism and xenobiotic stress responses. From this network, eight pivotal hub genes were distilled: CREBBP, EP300, HDAC1, HIF1A, PARP1, SIRT1, STAT3, and TP53. Diagnostic modeling demonstrated their predictive value, with AUC scores ranging from 0.60 to 0.81, among which PARP1 and STAT3 exhibited high diagnostic potential (AUC > 0.80). Experimental validation in the rat model confirmed the disruption of this metabolic-epigenetic regulatory module, revealing significant dysregulation in protein expression that reflects a compensatory stress response to lipotoxicity.
DISCUSSION
These findings bridge the gap between metabolic lipotoxicity and epigenetic regulation, suggesting that lactylation acts as a critical driver in NAFLD pathogenesis.
CONCLUSION
This study delineates a preliminary, hypothesis-generating lactate-associated gene signature in NAFLD, highlighting a complex regulatory network governing hepatocyte metabolic plasticity and survival. These eight core biomarkers offer promising tissue-based targets for mechanistic intervention, though further validation in peripheral blood is required to establish their non-invasive diagnostic utility.
Qingxuan He, Xu Wang, Pengfei Wang et al.· Endocrine, Metabolic & Immun...· 0 citations
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
The objective of this study is to identify T2DM-associated biomarkers linked to diabetic cardiac dysfunction-related transcriptomic signatures through integrated transcriptomic and Mendelian randomization analysis and evaluate cynaropicrin as a candidate cardiometabolic intervention. Transcriptomic datasets from the GEO database related to diabetic cardiac dysfunction, heart failure, and cardiomyopathy-associated phenotypes were processed for differential expression analysis and WGCNA, with consideration of disease-definition and tissue-source heterogeneity. A two-sample Mendelian randomization analysis was used to evaluate genetically predicted associations between candidate gene expression and T2DM risk. External validation assessed diagnostic performance via ROC analysis. Multi-database screening and molecular docking identified candidate compounds. Streptozotocin-induced diabetic rats were treated with cynaropicrin for 8 weeks, with comprehensive cardiac and metabolic assessment. Analysis identified 30 candidate genes with functional enrichment in proteostasis and metabolism. Mendelian randomization prioritized three T2DM-associated genetically supported genes: PPIP5K2, RBM23, and IGF2BP2. Cynaropicrin significantly improved cardiac function, reduced fibrosis, and enhanced glycemic control in diabetic rats while suppressing pro-fibrotic signaling. PPIP5K2, RBM23, and IGF2BP2 represent T2DM-associated genetically supported biomarkers dysregulated in diabetic cardiac dysfunction-related datasets with diagnostic potential. Cynaropicrin demonstrates candidate cardioprotective and metabolic benefits in diabetic rats; however, its direct molecular targets and mechanism of action require further validation.
INTRODUCTION/OBJECTIVE
Atherosclerosis (AS), a chronic inflammatory disease characterized by arterial plaque formation, remains a leading global cause of cardiovascular mortality; however, the molecular pathways that contribute to AS, particularly the role of lactate metabolism-related genes (LMRGs), remain poorly understood. This study aims to identify novel biomarkers and diagnostic models for early AS diagnosis by examining LMRGs.
METHODS
The AS datasets GSE100927, GSE40231, and GSE28829 were acquired from the Gene Expression Omnibus (GEO) database. Using bioinformatics approaches, including differential gene expression, functional enrichment, gene set enrichment analysis, random forest algorithm, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and immune infiltration profiling, we analyzed integrated AS datasets, developed a diagnostic model using key LMRGs, and validated this model through quantitative polymerase chain reaction (qPCR) using clinical AS samples.
RESULTS
We identified 15 differentially expressed LMRGs (LMRDEGs) and developed a LASSO regression model with five genes predictive of AS. The diagnostic model achieved high accuracy (area under the curve (AUC) > 0.9). Functional enrichment indicated that LMRDEGs play roles in lactate metabolism, small molecule catabolism, and pathways such as HIF-1 signaling and glycolysis/gluconeogenesis. Immune-cell infiltration analysis further indicated notable immune-cell variations across risk categories, with activated B cells, CD4+ T cells, and natural killer T cells exhibiting strong positive correlations. Clinical validation via qPCR confirmed significant expression differences for two genes (DISC1 and PIK3C2A), achieving AUCs of 0.730 and 0.758 and supporting the model's clinical relevance.
DISCUSSION
This study demonstrates that LMRGs are critically involved in the pathogenesis of AS, providing a novel molecular framework for early diagnosis and mechanistic insight. Functional analyses underscore the role of lactate-driven metabolic reprogramming in linking immune inflammation to plaque instability. Furthermore, immune infiltration analysis indicates that LMRGs may regulate immune cell recruitment, further supporting the "metabolism-inflammation" cycle concept in AS. Clinical validation confirms the differential expression and diagnostic value of DISC1 and PIK3C2A, reinforcing their relevance in human AS pathology.
CONCLUSION
This study highlights potential diagnostic biomarkers for early-stage AS. The results also imply that lactate metabolism-related pathways are intertwined with inflammatory and immune responses, offering new insights into AS mechanisms.
Xiaoying Wang, Xiumin Hou, Hang Yu et al.· Current Medicinal Chemistry· 0 citations