Integrated transcriptomic and Mendelian randomization analysis identifies novel biomarkers for type 2 diabetes-associated cardiac dysfunction: cynaropicrin as a candidate intervention.
Jul 2026· Naunyn-Schmiedeberg's Archives of Pharmacology· 0 citations· 28 references
Medicine
TL;DR
Cynaropicrin demonstrates candidate cardioprotective and metabolic benefits in diabetic rats; however, its direct molecular targets and mechanism of action require further validation.
Abstract
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.
Familial hypercholesterolemia (FH) has been associated with an increased risk of major depressive disorder (MDD), but their shared molecular signatures remain unclear. This study aimed to identify candidate genes associated with FH-MDD co-occurrence through integrative transcriptomic analysis, machine learning, summary-data-based Mendelian randomization (SMR), and clinical validation. Transcriptomic datasets from the Gene Expression Omnibus were analyzed, including GSE6054 and GSE13985 for FH and GSE98793 for MDD. Differential expression analysis and weighted gene co-expression network analysis were used to identify shared candidate genes. Candidate biomarkers were screened using least absolute shrinkage and selection operator regression and Random Forest algorithms, followed by the construction of an exploratory nomogram. SMR analysis was performed to assess genetically supported associations between candidate-gene expression and MDD risk. Selected genes were validated by PBMC RT-qPCR in an independent four-group clinical cohort comprising healthy controls and participants with FH, MDD, or co-occurring FH and MDD. Flow cytometry was subsequently used to characterize the peripheral CD4+ T-cell profile. A total of 54 shared candidate genes were identified. CD4, MRPS21, and CRTC2 were selected by machine learning and incorporated into the nomogram, which showed good discrimination in the training set and moderate performance in external validation. Immune infiltration and enrichment analyses highlighted monocyte alterations, reduced T-cell-related signals, and enrichment of T-cell receptor and TNF-mediated pathways. SMR analysis indicated that genetically predicted higher CD4 expression was inversely associated with MDD risk, whereas RT-qPCR showed higher CD4 expression in patients with FH-MDD. Flow-cytometric analysis showed that FH-MDD participants had a higher circulating CD3+CD4+ T-cell proportion, an increased effector-memory CD4+ T-cell proportion, and a higher proportion of HLA-DR+ CD4+ T cells than participants in the comparison groups. These findings suggest that CD4 may represent a promising immune-related candidate signature associated with the co-occurrence of FH and MDD, warranting further functional validation.
Chenxi Liu, Yu-Ting Li, Xiang Cao et al.· International Journal of Mol...· 0 citations
INTRODUCTION
PCOS is a common endocrine disorder with elevated cardiometabolic risk, yet the role of the renin-angiotensin system (RAS)-iron metabolism axis in this comorbidity remains unclear. We explored its underlying mechanisms and evaluated the therapeutic potential of gentiopicroside.
METHODS
Integrated multi-omics analyses combining transcriptomics, single-cell RNA sequencing, Mendelian randomization, machine learning, molecular docking, and in vitro functional assays were performed to identify shared molecular pathways and therapeutic targets across PCOS, hypertension, NAFLD, and T2DM.
RESULTS
SLC11A2 was consistently dysregulated in PCOS transcriptomic datasets, and associated with iron metabolism, inflammatory response and oxidative stress pathways. Genetic analyses validated RAS-related regulation in hypertension susceptibility and revealed shared genetic architecture between PCOS and cardiometabolic traits. Network and single-cell analyses characterized SLC11A2-associated molecular patterns in disease-relevant cell types; machine learning identified disease-classifying molecular signatures. Gentiopicroside alleviated inflammatory and oxidative stress phenotypes, including reduced IL-6 expression and reactive oxygen species accumulation.
CONCLUSION
This study defines an RAS-SLC11A2 molecular framework linking iron metabolism dysregulation to PCOS-related cardiometabolic risk, elucidating the mechanisms connecting ovarian dysfunction, inflammation, oxidative stress and hypertension, and supports gentiopicroside as a promising therapeutic candidate.
Si-Han Zhang, Yu Xu, Tingting Cao et al.· Clinical and experimental hy...· 0 citations
This study integrates transcriptomic, genetic-causal, immunological, and structural analyses to prioritize STAT6 as a potentially druggable MS-associated target and strengthen the computational plausibility of STAT6-ligand interactions.
Zhongbo Xu, Ke Xiong, Dan Zhao· Computational biology and ch...· 0 citations
Background Sepsis and postoperative delirium (POD) may share inflammatory and neuroimmune mechanisms, but their common molecular basis remains unclear. This study aimed to identify candidate shared molecular targets and explore a clinically feasible pharmacological candidate linked to the prioritized target. Methods Multi-cohort transcriptomic data from the Gene Expression Omnibus were integrated. Marked test-statistic inflation in the sepsis discovery cohort GSE65682 was addressed using a Removal of Unwanted Variation 2 (RUV2)-style adjustment based on the 10% lowest-variance genes and four estimated unwanted factors. Transcriptome-wide directional concordance between sepsis and POD was assessed using a rank-rank hypergeometric overlap-like analysis. Separately, RUV2-corrected sepsis differentially expressed genes were intersected with nominally significant POD candidate genes, and directionally concordant genes underwent functional-enrichment and protein–protein interaction analyses. Candidate genes retained from the protein–protein interaction (PPI) network analysis were further evaluated through cross-cohort expression analysis and SHAP-based feature importance analysis. Following target prioritization, target-guided pharmacological assessment, molecular docking, and immune-cell signature analysis were performed. Results RUV2-style adjustment reduced the genomic inflation factor in GSE65682 from 41.49 to 6.09, although residual inflation remained. The corrected analysis identified 440 sepsis-associated differentially expressed genes. Forty-four genes overlapped with the exploratory POD candidate set, of which 22 showed concordant directions of change. PPI analysis identified seven network-connected candidates. MAPK14 showed consistent disease-associated upregulation across the sepsis discovery cohort, an independent sepsis cohort, and the POD cohort, and had the highest SHAP contribution. Following prioritization of MAPK14, pharmacological candidates linked to this target were evaluated, leading to the selection of losartan as a clinically feasible candidate for exploratory assessment. Molecular docking suggested structural plausibility but did not establish direct binding or functional inhibition. MAPK14 expression was also positively associated with a neutrophil-predominant immune signature. Conclusions These findings implicate MAPK14/p38α as a candidate shared molecular target between sepsis and POD. The losartan–MAPK14 association remains computational and hypothesis-generating and requires experimental and prospective clinical validation.
Weixin Kong, Jing-Xian Xue, Lei Yu et al.· PLoS ONE· 0 citations
Background Endometriosis (EMS) is an estrogen-dependent disease that can lead to chronic pain and infertility. Small ubiquitin-related modifier (SUMO)ylation, a key post-translational modification, has been demonstrated to be associated with endometrial dysfunction, but its role in EMS remains unclear. This study aims to identify SUMOylation-related genes as potential biomarkers for EMS utilizing bioinformatics approaches. Methods The GSE51981, GSE7305, and GSE214411 datasets and 194 SUMOylation-related genes were assessed. The differential expression analysis and weighted gene co-expression network analysis were performed to identify candidate genes associated with EMS and SUMOylation. Subsequently, the univariable and multivariable Mendelian randomization analyses were used to investigate causal relationships. The receiver operating characteristic curve and nomogram were constructed for assessing the performance of candidate genes. Furthermore, the biological roles of candidate biomarkers were evaluated through gene set enrichment analysis. Additionally, immune infiltration levels were analyzed and pseudo-time trajectory analysis was conducted to assess cell differentiation dynamics. Results CRYZ, FRMD4B, FZD6, and TPST1 were identified as biomarkers associated with SUMOylation in EMS, all with area under the curve values above 0.75, suggesting good diagnostic performance. The constructed nomogram, which incorporated these biomarkers, demonstrated exceptional predictive capabilities for EMS risk. Furthermore, these biomarkers functioned through common enriched pathways, including the cell cycle, neuroactive ligand-receptor interaction, and ubiquitin-mediated proteolysis. Notably, immune cell analysis revealed significant infiltration of regulatory T cells and memory B cells in EMS. Pseudo-time trajectory analysis indicated that epithelial cells displayed distinct expression patterns for the biomarkers across five differentiation stages. Conclusion This study identifies CRYZ, FRMD4B, FZD6, and TPST1 as potential diagnostic and therapeutic targets associated with SUMOylation in EMS. These findings provide potential clinical insights for the non-invasive detection and risk stratification of EMS, which might help improve the efficiency of early diagnosis and the development of personalized treatment strategies.
Qiu Meng, Xin Xie, Haiyan Wang et al.· International Journal of Wom...· 0 citations
Background Mitochondrial dysfunction has been implicated in the pathophysiology of sepsis. However, human genetic evidence linking mitochondria-related genes to sepsis susceptibility remains limited. This study aimed to identify mitochondria-related genes associated with sepsis risk using a multi-omics Mendelian randomization framework. Methods Summary-data-based Mendelian randomization (SMR) was applied using sepsis genome-wide association study (GWAS) summary statistics from the UK Biobank and FinnGen databases. Expression, methylation, single-cell, and protein quantitative trait loci (QTLs) were used as genetic instruments. Colocalization analyses were conducted to evaluate whether SMR associations were driven by shared genetic variants. Expression of prioritized candidate genes was further examined in clinical septic samples, and correlations with disease severity (SOFA scores) were assessed. Results SMR analysis prioritized 13 mitochondria-related genes associated with sepsis risk. Immune cell-specific eQTL analysis suggested that genetically predicted SURF1 expression in memory B cells and naïve T cells was associated with sepsis risk. Differential expression of 12 candidate genes was confirmed in septic patients by qPCR, and PPOX expression showed a negative correlation with SOFA scores. Integration of mQTL and eQTL data supported a regulatory relationship between methylation at cg06661924 and AK4 expression. Increased genetically predicted AK4 expression was associated with higher sepsis risk (OR = 1.21, 95% CI 1.02-1.42). Protein-level analysis identified DUT as a potential sepsis-associated candidate, with consistent evidence across streptococcal and pneumococcal septicemia subtypes. Subtype analyses also suggested heterogeneous genetic signals across different sepsis subtypes. Conclusion This study prioritized several mitochondria-related genes associated with sepsis susceptibility based on human genetic evidence. These findings provide candidate targets for further mechanistic and translational investigation.
Lu Wang, Zhen Gao, Chengjin Wang et al.· Frontiers in Immunology· 0 citations
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