Jul 2026· Current Medicinal Chemistry· Vol 33· 0 citations
Medicine
TL;DR
It is demonstrated that LMRGs are critically involved in the pathogenesis of AS, providing a novel molecular framework for early diagnosis and mechanistic insight and imply that lactate metabolism-related pathways are intertwined with inflammatory and immune responses, offering new insights into AS mechanisms.
Abstract
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.
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
The potential roles of MMRGs in COPD are revealed and novel biomarkers are identified, providing new insights and research foundations for the early diagnosis, personalized treatment, and drug development of COPD.
Xiao-Dan Li, Jin Wang, Zhongbai Hu et al.· International Journal of COP...· 0 citations
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
COPD is a progressive respiratory disorder characterized by persistent airflow limitation and chronic inflammation, yet the role of N4-acetylcytidine (ac4C) RNA modification in its pathogenesis remains largely unexplored. This study aimed to systematically screen for ac4C-related genes (ac4C-RGs) from a published database and investigate their regulatory networks in COPD, thereby identifying potential biomarkers for further mechanistic studies without assuming a direct regulatory relationship between any specific gene and ac4C modification. Differentially expressed genes (DEGs) were identified from transcriptomic profiles, and weighted gene co-expression network analysis (WGCNA) was applied to uncover key co-expression modules. Cross-analysis among DEGs, significant modules, and ac4C-RGs was conducted. Key genes were screened using LASSO regression, XGBoost, and random forest algorithms, followed by logistic regression‑based diagnostic model construction. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) with 95% confidence intervals, calibration curve assessment, and decision curve analysis (DCA). A total of 160 overlapping genes were identified, and six hub genes (PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2) were consistently selected by all three machine learning algorithms. The diagnostic model demonstrated good discriminative performance, with AUCs of 0.766, 0.759, and 0.723 in the training, internal test, and external validation sets, respectively. Regulatory network analysis suggested potential ceRNA axes and transcription factor interactions, while immune infiltration profiling revealed significant correlations between key genes and multiple immune cell subsets. Drug-gene interaction analysis and molecular docking indicated that fluorouracil, capecitabine, and 5-benzylacyclouridine may exhibit favorable predicted binding affinities with UPP1. In conclusion, PTRF, PRKCDBP, UPP1, TOR3A, FAM168B, and B4GALT2 were identified as potential ac4C-related biomarkers in COPD, potentially involved in immune and metabolic regulation, providing a foundation for future functional investigations and therapeutic exploration.
Unknown authors· 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
Findings support the five genes as candidate PH biomarkers and VX-745 as a computational drug-repositioning hypothesis requiring experimental validation and VX-745 as a computational drug-repositioning hypothesis requiring experimental validation.
