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At least 199 records · Page 11Linked to original sources
PubMed2026

Multi-Omics Integration Identifies a Five-Gene Metabolic Signature With Experimental Validation in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is hallmarked by profound metabolic reprogramming; however, its intricate crosstalk with the tumor immune microenvironment (TIME) and its clinical ramifications remain inadequately elucidated. This study aims to systematically decipher the metabolic-immune interplay in ccRCC through multi-omics integration, with the goal of identifying robust prognostic biomarkers and actionable therapeutic vulnerabilities. AIMS: This study aims to systematically decipher the metabolic-immune interplay in clear cell renal cell carcinoma (ccRCC) through multi‑omics integration, and to identify robust prognostic biomarkers and actionable therapeutic vulnerabilities that can inform precision risk stratification and individualized treatment strategies. METHODS: We integrated bulk transcriptomic, genomic, and clinical data from multiple ccRCC cohorts. Differential expression and functional enrichment analyses were performed to characterize metabolic pathway alterations. Mendelian randomization (MR) was employed to infer causal relationships between metabolic disorders and ccRCC risk. A machine learning-based prognostic framework, incorporating SHAP (SHapley Additive exPlanations) for feature interpretability, was constructed and rigorously validated. TIME heterogeneity was dissected using deconvolution algorithms, while drug sensitivity, tumor mutation burden (TMB), and TIDE scores were utilized to assess therapeutic responses and immune evasion. Candidate gene function was evaluated through in vitro gain- and loss-of-function assays, with expression validated via TCGA, HPA, western blot, and qRT-PCR. RESULTS: Enrichment analysis identified coordinated dysregulation in lipid metabolism, energy homeostasis, and hypoxia response pathways. MR analysis confirmed lipid metabolism disorders as a causal risk factor for ccRCC. Our machine-learning model, centered on five core SHAP-identified features (SUCLA2, ACAT1, PC, SUCLG1, and HMGCS2), demonstrated superior predictive accuracy over conventional clinical staging. Immune profiling unveiled dichotomous TIME states: the low-risk group retained active immune surveillance, whereas the high-risk group was enriched with immunosuppressive subsets. Drug sensitivity screening pinpointed LY2109761 and carmustine as high-risk-specific candidate agents. Furthermore, TMB and TIDE analyses stratified high-risk patients displaying genomic instability and immune evasion phenotypes. Functionally, SUCLA2 knockdown significantly enhanced ccRCC cell proliferation and invasion, while its overexpression suppressed these malignant phenotypes, corroborating its tumor-suppressive role. Expression patterns of the hub genes were consistently validated across multi-level datasets and experimental assays. CONCLUSION: This study establishes a precision oncology framework for ccRCC by functionally linking metabolic biomarkers, immunophenotypes, and stratified therapeutic strategies. Importantly, we identify SUCLA2 as a potential functional tumor suppressor and a promising target for further mechanistic and translational investigation.

Humans↗
PubMed2006

Plasma heart-type fatty acid binding protein is superior to troponin and myoglobin for rapid risk stratification in acute pulmonary embolism.

BACKGROUND: Irreversible right ventricular (RV) failure with myocardial damage may precipitate fatal outcome in acute pulmonary embolism (APE). Cytoplasmic heart-type fatty acid binding protein (H-FABP) is a sensitive and specific biomarker of myocardial damage. We assessed which biomarker of myocardial damage or RV stretching is the most useful for short-term risk stratification in APE. METHODS: We analyzed 77 patients (51 F, 26 M) aged 65.3+/-16.0 years with confirmed APE. On admission, systemic blood pressure and transthoracic echocardiography (for RV overload) were recorded and plasma concentrations of myoglobin (Mb), cardiac troponin T (cTnT), N-terminal fragment of proBNP (NT-proBNP) and H-FABP were evaluated. RESULTS: Fifteen (19.5%) patients died and 24 (31.2%) experienced complicated clinical course (CCC)-death/thrombolysis/cardiopulmonary resuscitation/intravenous vasopressors. Hazard ratio analysis demonstrated that plasma H-FABP, Mb, cTnT and NT-proBNP concentrations predicted fatal outcome. When only APE-related deaths were considered, plasma H-FABP concentrations indicated fatal outcome. Multivariate hazard ratio analysis revealed H-FABP as the only 30-day mortality predictor (HR 1.02 CI 95% 1.01-1.05). CONCLUSIONS: H-FABP measured on admission is useful for short-term risk stratification in APE. It appears to be superior to cTnT, NT-proBNP and Mb in the prediction of 30-day APE-related mortality.

Acute Disease↗
PubMed2003

Current management of non-ST-segment-elevation acute coronary syndrome: reconciling the results of randomized controlled trials.

AIMS: Patients presenting with non-ST-segment-elevation acute coronary syndrome represent a heterogeneous group with regard to the severity of coronary atherosclerosis and prognosis. The conventional approach to their treatment has involved admission to the hospital for pharmacologic stabilization, subsequent mobilization, and management by either a conservative or an invasive strategy. The choice of one approach over another is guided largely by local practice patterns and the availability of invasive facilities. METHODS AND RESULTS: However, recent randomized trials comparing the strategies have demonstrated a superiority of the invasive strategy, particularly in patients at higher risk. Furthermore, randomized trials have provided information on refining risk stratification. On the basis of these data, we outline criteria for assessing risk and recommend that stratification be conducted at presentation using clinical features, the electrocardiogram, and biomarkers. CONCLUSION: Higher-risk patients should be admitted for pharmacologic stabilization and assessed by coronary angiography within 48h with the aim of early revascularization, provided the risk of periprocedural complications is not prohibitive. Glycoprotein IIb/IIIa receptor inhibitors are indicated, particularly in patients requiring percutaneous coronary intervention. The conservative strategy remains appropriate for patients admitted to hospitals without invasive facilities. Patients not at highrisk may be observed in a facility with cardiac monitoring such as a chest pain unit and undergo subsequent stress testing. The adoption of such an early risk stratification and revascularization-based approach is likely to result in a reduction in recurrent myocardial infarction and ischaemia, duration of hospitalization, repeat hospitalization, and mortality.

Anticoagulants↗
PubMed2026

The future of blood-based biomarkers in liver cancer.

Liquid biomarkers hold substantial promise in liver cancer, with potential applications in risk stratification, surveillance and early detection, therapeutic decision-making, and treatment-response monitoring. In parallel with oncologic advances, liquid biopsy has gained increasing attention. However, despite expanded research efforts, prospective clinical validation remains limited. While cell-free DNA-based detection of actionable alterations has entered clinical practice in select contexts, most candidate liquid biomarkers still require rigorous evaluation through translational research embedded in clinical trials and prospective cohort studies. In this review, we summarise the current landscape of blood-based biomarkers across the cancer care continuum for individuals at risk, or diagnosed with hepatocellular carcinoma and biliary tract cancers, and discuss the key challenges and opportunities that lie ahead.

Humans↗
PubMed2026

Novel immunotherapeutic strategies for colorectal cancer treatment: Advances, challenges, and future directions.

Immunotherapy has reshaped the treatment landscape of colorectal cancer (CRC), with the clearest and most durable benefit established in mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) disease. However, framing CRC immunotherapy simply as "MSI-H responsive versus microsatellite stable (MSS) resistant" is no longer sufficient. Recent studies indicate that a subset of proficient mismatch repair (pMMR) colon cancers, particularly in the neoadjuvant setting, can mount clinically meaningful responses to immune checkpoint blockade, suggesting that disease stage, local immune organization, and treatment timing critically influence immunotherapy sensitivity. In parallel, emerging evidence has expanded the relevant immune landscape beyond the tumor bed itself, showing that spatially organized stromal and adipose niches can actively divert tumor-reactive lymphocytes and promote immune escape. These advances shift the central challenge in CRC immunotherapy from simply identifying new agents to defining when and in whom immune resistance is reversible, and which biological bottlenecks-such as vascular dysfunction, myeloid suppression, and spatial immune exclusion-must be overcome. In this context, alternative checkpoint inhibitors, bispecific antibodies, cellular therapies, vaccines, nanotechnology-enabled platforms, and microbiome-targeted approaches remain important, but their translational maturity and evidentiary support differ substantially. Biomarker development is likewise evolving from static genomic classification toward dynamic and mechanism-informed stratification incorporating circulating tumor DNA (ctDNA), chromosomal instability, immune architecture, and treatment-induced response trajectories. This review synthesizes recent advances in CRC immunotherapy while emphasizing evidence hierarchy, biomarker-guided patient selection, and the mechanistic basis of combination strategies. We argue that the next phase of CRC immunotherapy will depend less on the indiscriminate addition of novel agents and more on the rational deployment of immunotherapy across molecularly, spatially, and temporally defined disease states.

Humans↗
PubMed2005

Pregnancy-associated plasma protein-A levels in patients with acute coronary syndromes: comparison with markers of systemic inflammation, platelet activation, and myocardial necrosis.

OBJECTIVES: The goal of this study was to determine the predictive value of pregnancy-associated plasma protein-A (PAPP-A) in patients with acute coronary syndromes (ACS). BACKGROUND: Pregnancy-associated plasma protein-A is a zinc-binding matrix metalloproteinase abundantly expressed in eroded and ruptured plaques and may serve as a marker of plaque destabilization. METHODS: In 547 patients with angiographically validated ACS and in a heterogeneous emergency room population of 644 patients with acute chest pain, respectively, PAPP-A as well as markers of myocardial necrosis (troponin T [TnT]), ischemia (vascular endothelial growth factor [VEGF]), inflammation (high-sensitivity C-reactive protein [hsCRP]), anti-inflammatory activity (interleukin [IL]-10), and platelet activation (soluble CD40 ligand [sCD40L]) were determined. Patients were followed for the occurrence of death or myocardial infarction. RESULTS: In patients with ACS, elevated PAPP-A levels (>12.6 mIU/l) indicated an increased risk (odds ratio 2.44 [95% confidence interval (CI) 1.43 to 4.15]; p = 0.001). When the analysis was restricted to TnT-negative patients, PAPP-A still identified a subgroup of high-risk patients (odds ratio [OR] 2.72 [95% confidence interval (CI) 1.25 to 5.89]; p = 0.009). In a multivariable model, PAPP-A (OR 2.01; p = 0.015), sCD40L (OR 2.37; p = 0.003), IL-10 (OR 0.43; p = 0.003), and VEGF (OR 2.19; p = 0.018) were independent predictors. Prospective validation in patients with chest pain confirmed that PAPP-A levels reliably identify high-risk patients (adjusted OR 2.32 [95% CI 1.32 to 4.26]; p = 0.008). Patients negative for all three markers (TnT, sCD40L, and PAPP-A) were at very low cardiac risk (30 days: 3.0% event rate; no death). CONCLUSIONS: The PAPP-A level as a marker of plaque instability is a strong independent predictor of cardiovascular events in patients with ACS. Simultaneous determination of biomarkers with distinct pathophysiological profiles appears to remarkably improve risk stratification in patients with ACS.

Adult↗
PubMed2003

Histology of Barrett's esophagus and dysplasia.

This article explores issues related to the diagnosis of Barrett's esophagus (BE) in endoscopic biopsies and dysplasia in Barrett's epithelium. The definitions of BE, including long- and short-segment BE, are reviewed, with an emphasis on the significance of intestinal metaplasia (IM). IM of the gastroesophageal junction and cardia is reviewed and problems in its distinction from short-segment BE are discussed. In addition, the article reviews the classification of dysplasia in Barrett's mucosa, with reference to problematic areas, such as sampling error and interobserver variability. Biomarkers and their role in the diagnosis of dysplasia and stratification of risk are summarized.

Barrett Esophagus↗
PubMed2025

Exploring precision medicine by utilizing individual genetic information for the management of Alzheimer's disease.

Alzheimer's Disease (AD) represents a formidable challenge in neurology, characterized by progressive neurodegeneration and cognitive decline. Traditional therapeutic approaches have failed to deliver significant outcomes, underscoring the need for innovative paradigms such as precision medicine. The review explores integrating genomic, biomarker-driven, and individualized therapeutic strategies to tackle AD. It examines the role of key genetic factors, including APOE and MTHFR polymorphisms, in influencing disease susceptibility and treatment responses. Advances in biomarker technologies, such as blood-based and imaging biomarkers, are highlighted for their potential in early diagnosis and patient stratification. Additionally, the review underscores the importance of tailoring interventions across different stages of AD, incorporating lifestyle modifications and emerging tools like artificial intelligence & recent patented technologies. Precision medicine offers a transformative pathway, aiming to deliver personalized, effective care that addresses the complex and multifactorial nature of AD. The paradigm shift promises improved clinical outcomes and enhanced patient quality of life.

Humans↗
PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗
PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗
PubMed2026

Epigenetic alterations in rheumatoid arthritis: multilayer mechanisms and translational opportunities.

Rheumatoid arthritis (RA) is a chronic inflammatory disease driven by immune dysregulation, in which genetic susceptibility and environmental exposures promote persistent synovitis, progressive joint damage, and systemic comorbidities. Recent epigenomic studies show several recurring abnormalities. Many RA susceptibility variants lie outside protein-coding sequence and map to immune-cell and synovial fibroblast regulatory elements, linking inherited risk to enhancer activity, methylation quantitative trait effects, and distal gene control. Blood-based epigenome-wide association studies identify disease-associated DNA methylation signatures, but these signals require careful control for leukocyte composition, smoking, treatment exposure, and disease stage. RA fibroblast-like synoviocytes also display stable methylome remodeling, including relative hypomethylation at loci involved in inflammation, migration, matrix degradation, and apoptosis resistance, while TET3-associated 5-hydroxymethylcytosine has emerged as a functional contributor to chemokine production and invasive stromal behavior. Histone modifications, chromatin accessibility, and 3D genome organization define pathogenic regulatory states and connect non-coding risk loci to effector genes in immune and stromal compartments. Finally, miRNAs, lncRNAs, circRNAs, snoRNAs, extracellular RNAs, and m6A-related pathways add post-transcriptional and chromatin-linked layers with potential biomarker value. We synthesize these findings and discuss translational opportunities for diagnosis, stratification, flare monitoring, and therapeutic targeting, while emphasizing incomplete replication, uneven evidence across epigenetic layers, biospecimen variability, and the need for causal, longitudinal, cell-type-resolved validation.

Humans↗
PubMed2006

Association between blood glucose and long-term mortality in patients with acute coronary syndromes in the OPUS-TIMI 16 trial.

Hyperglycemia in the context of acute coronary syndrome (ACS) is a common observation, and existing data suggest that high glucose levels are associated with increased in-hospital mortality. We assessed the relation between random glucose and long-term mortality in 9,020 patients with ACS who were enrolled in the OPUS-TIMI 16 trial. A significant relation between glucose level and 10-month mortality was observed (2.7% in quartile 1 vs 7.0% in quartile 4, p <0.0001). After multivariable adjustment for co-morbidity, which included history of diabetes, this relation remained significant (quartile 4 vs 1, hazard ratio 1.70, 95% confidence interval 1.16 to 2.50, p = 0.006). These observations were similar in the TACTICS-TIMI 18 trial. In addition, we observed that B-type natriuretic peptide and troponin I levels increased across glucose quartiles in the OPUS-TIMI 16 trial (p values for trend = 0.002 and 0.0001, respectively) and the TACTICS-TIMI 18 trial (p values for trend = 0.006 and 0.0001, respectively). High blood glucose during ACS is an independent predictor of long-term mortality and is significantly correlated with prognostic biomarkers. Glucose levels during ACS may be an important addition to the risk stratification of patients with ACS and a potentially important target for therapy.

Biomarkers↗
PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve&#x2013;based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC&#x2009;=&#x2009;0.844). The NODM cohort was stratified into high- (n&#x2009;=&#x2009;2,362) and low-risk (n&#x2009;=&#x2009;5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗
PubMed2026

Mutational signature stratification of recurrent gliomas reveals distinct patterns of genomic traits.

BACKGROUND: Although temozolomide (TMZ) is widely used for glioma treatment, its therapeutic benefit is limited by acquired resistance and recurrence, facilitated by intratumor heterogeneity. Mutational signatures (MSs) inform tumor evolution and reveal alterations associated with treatment response. METHODS: We performed molecular analyses of 96 glioma recurrences with sufficient private single-nucleotide variants relative to their matched primary tumors, stratified by their dominant MS. RESULTS: Four groups were identified: MS11/TMZ-related (n&#x2009;=&#x2009;38), MS1/5/aging-related (n&#x2009;=&#x2009;32), MS6/15/21/26/microsatellite instability (MSI)-related (n&#x2009;=&#x2009;13), and other MS-related recurrences (n&#x2009;=&#x2009;13). MS11/TMZ-related recurrences showed higher acquired mutational counts than the other groups (1338 vs 59 (MS1/5/aging) vs 57 (MS6/15/21/26/MSI) vs 57 (other MSs); P&#x2009;<&#x2009;.01). Mutations in SYNE2, SZT2, and FBN3 were restricted to recurrences with dominant or second-dominant MS11/TMZ-related signature (n&#x2009;=&#x2009;41), and 85% (35/41) harbored mutations in these genes. In MS11/TMZ-related recurrences with RNA sequencing data (n&#x2009;=&#x2009;17), mRNA co-expression analyses identified SYNE2-ATAD5 and SZT2-MAPKBP1 associations. Among MS11/TMZ-related recurrences, MS23 was frequent (44%, 18/41) and associated with higher acquired mutational counts (2089 vs 1188; P&#x2009;=&#x2009;.018) and more IDH-wildtype tumors (67% vs 30%; P&#x2009;=&#x2009;.037). MAPKBP1 mutations were enriched in MS23-positive recurrences (56% (10/18) vs 0% (0/23); P&#x2009;<&#x2009;.001). MS1/5/aging-related recurrences showed more frequent acquired chromosome 16q losses (22% vs 8% (TMZ) vs 0% (MSI) vs 0% (other); P&#x2009;<&#x2009;.05), which were associated with an increased fraction of genome altered relative to 16q-diploid cases (15% vs 7%; P&#x2009;=&#x2009;.01). CONCLUSIONS: These findings show that MS-based stratification of recurrences refines molecular characterization after therapy and nominates candidate biomarkers and pathways for functional studies of treatment-associated glioma evolution.

mutational signatures↗