PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Immune-metabolic profiles”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

7 recordsLinked to original sources
PubMed2025

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n&#x2009;=&#x2009;39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n&#x2009;=&#x2009;30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC&#x2009;>&#x2009;0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p&#x2009;<&#x2009;0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗
PubMed2026

ZBTB16-associated NK cell alterations reveal shared immunometabolic signatures linking primary Sj&#xf6;gren's syndrome and type 1 diabetes mellitus.

BACKGROUND: Primary Sj&#xf6;gren's syndrome (pSS) and type 1 diabetes mellitus (T1DM) share immune-inflammatory features, yet conserved pathogenic signatures linking these autoimmune disorders remain incompletely understood. The present research sought to uncover common molecular markers and dissect the underlying immune-metabolic cross-talk underlying pSS and T1DM. METHODS: Gene expression profiles of patients with pSS and T1DM were retrieved from the Gene Expression Omnibus database, normalized, and corrected for batch effects prior to downstream analyses. Overlapping potential biomarkers were screened by integrating differential expression analysis, weighted gene co-expression network analysis and least absolute shrinkage and selection operator regression. Functional enrichment based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases was implemented to interpret gene biological properties, and a protein-protein interaction network was further established afterwards. Diagnostic performance was evaluated using receiver operating characteristic analysis. Experimental validation was conducted in non-obese diabetic (NOD) mice using quantitative PCR, immunohistochemistry, and flow cytometry. The CIBERSORT algorithm was adopted to quantify immune cell infiltration levels. RESULTS: ZBTB16 was identified as a shared hub biomarker in both pSS and T1DM and exhibited favorable diagnostic performance. Experimental validation confirmed significantly reduced ZBTB16 expression in peripheral blood mononuclear cells, salivary gland tissues, and pancreatic tissues of NOD mice. Gene Set Enrichment Analysis indicated that ZBTB16-associated signatures were enriched in mitochondrial-related processes, neuroactive ligand-receptor interactions, and ribosome-related pathways. Immune infiltration analysis revealed that resting natural killer (NK) cells were positively correlated with ZBTB16 expression in both diseases. Flow cytometric analysis further confirmed a reduced proportion of resting NK cells in peripheral blood of NOD mice, consistent with the CIBERSORT-based prediction. CONCLUSION: This study identifies ZBTB16 as a shared biomarker linking pSS and T1DM. Reduced resting NK-cell abundance was consistently observed in both computational and experimental analyses, and bioinformatic correlation analysis suggested a positive association with ZBTB16 expression. These findings provide evidence for shared molecular and immunological signatures underlying the two autoimmune disorders and support further investigation of the biological role and diagnostic value of ZBTB16 in pSS and T1DM.

Sjogren's Syndrome↗
PubMed2026

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

We previously identified diverse genetic evolutionary patterns in whole-genome sequencing of paired normal tissue adjacent to tumor (NAT) and tumor tissues from Hong Kong breast cancer (HKBC) patients. Here, we investigated whether DNA methylation (DNAm) contributes to NAT heterogeneity and shapes the tumor microenvironment (TME). Genome-wide DNAm profiling was performed on paired NAT and tumor tissues from 188 HKBC patients using the Infinium 850&#x2009;K array. RNA-seq data were available for 76 NATs and 177 tumors. Cellular composition was inferred using MethylCIBERSORT, CIBERSORTx, and EpiDISH, and histopathologic features were assessed on 115 H&E-stained sections. Unsupervised clustering identified two distinct NAT subtypes with divergent TME characteristics. Cluster 1 (N&#x2009;=&#x2009;139) showed higher epithelial and fibroblast content and enrichment of estrogen response pathways. Cluster 2 (N&#x2009;=&#x2009;49) exhibited an immune-metabolic phenotype characterized by increased fat and immune cells, stromal disruption, inflammatory pathway activation, and greater macrophage infiltration. Cluster 2 patients also demonstrated significantly younger epigenetic age estimated using multiple epigenetic clocks. These DNAm-defined NAT subtypes and associated TME features were validated in 97 NAT samples from TCGA breast cancer patients. Overall, our findings identify DNAm-driven NAT heterogeneity with distinct TME landscapes, providing new insights into field cancerization and tumor evolution in breast cancer.

Journal Article↗
PubMed2025

Proteomic hub proteins CDKN2B, TRAPPC2L, WFS1, and ARPP19 drive biochemical recurrence and metastatic progression in prostate cancer: Protein macromolecule action.

The biological characteristics and metastasis mechanism of prostate cancer are complex, involving the important role of many proteins in cell transcriptional regulation. This study focused on the role of the proteomic hub proteins CDKN2B, TRAPPC2L, WFS1 and ARPP19 in the biochemical recurrence and metastasis progression of prostate cancer. Cross-platform transcriptome integration and differential expression analysis were used to evaluate transcriptome characteristics in a prostate cancer cohort. Functional enrichment analysis was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, and weighted gene co-expression network analysis (WGCNA) was used to investigate cancer progression subtypes. It was found that prostate cancer progression showed significant transcriptome heterogeneity, and low-expression genes dominated. We reveal the important role of epithelial-immune interactions and inflammatory signaling in transcriptional remodeling in prostate cancer. The co-expression network topology analysis showed that the immune-metabolic center module plays a central role in cancer progression. CDKN2B was identified as a key transcriptional determinant in prostate cancer typing, while TRAPPC2L and WFS1 acted as core transcriptional regulators, driving metastatic heterogeneity. ARPP19 and LOC650152 also show important transcriptional driving effects in advanced prostate cancer.

Humans↗
PubMed2026

Integrated analysis reveals the impact of obesity on triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly aggressive and heterogeneous breast cancer subtype with limited therapeutic options. While the prevalence of overweight/obese (OW/OB) women continues to rise, the impact of obesity on molecular features of TNBC remains incompletely understood. We investigated clinicopathological and molecular data (including genomic, transcriptomic, proteomic and metabolomic profiling) using our original multi-omics database of TNBC (N&#x202f;=&#x202f;465) for associations with patient body mass index (BMI). Multi-omics profiling revealed that OW/OB patients exhibited worse survival as well as elevated inflammation of tumor microenvironment, higher expression of immune checkpoints, and dysregulated lipid metabolism. Our in vivo experiments demonstrated that tumors in obese mice displayed faster growth rates, a higher proportion of PD-1+CD8+ T cells and enhanced responsiveness to anti-PD-1 treatment. In addition, we analyzed data from four independent clinical trials and discovered that OW/OB patients demonstrated higher pathological complete response rates and longer progression-free survival following anti-PD-1-based immunotherapy. In conclusion, our study systematically revealed that obesity is associated with coordinated immune-metabolic remodeling in TNBC, characterized by checkpoint enrichment and lipid dysregulation, which may help explain the enhanced anti-PD-1 responsiveness and should be taken into account in the field of precision medicine.

Immunity↗
PubMed2026

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans↗
PubMed2026

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P&#x2005;<&#x2005;.001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve&#x2005;&#x2265;&#x2005;0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗