Search Results (2,282)

Chronic kidney disease is a global public health concern, representing a critical global public health challenge with increasing morbidity and mortality rates. The disease is a long-term condition characterized by the progressive loss of renal function. Early detection of declining kidney health and timely intervention are crucial to slow disease progression and improve prognosis, mitigating complications, including cardiovascular events. Current diagnostic standards are unable to detect early stages of kidney disease, reflecting early signs of glomerular and tubular damage. This creates an urgent need to identify reliable biomarkers for early detection, prognosis and therapeutic monitoring of kidney diseases. Novel biomarkers, including urinary microRNA, exosomal components, proteomic signatures and integrated multi-omics profiles, facilitated by up-to-date technologies offer strong promise for enhancing early diagnosis, risk assessment and monitoring of the disease. We focus on the fundamental biological significance and clinical application of these markers, discussing a critical evaluation of novel methodologies and clinical evidence supporting their potential for earlier and more precise diagnosis. This review summarizes innovative urinary biomarkers and advanced analytical technologies that can provide a more comprehensive and accurate assessment of the kidney status towards early diagnosis, better prognosis and better quality of life for patients with chronic kidney disease. Full article

Aging is one of the most complex biological processes, which leads to a gradual decline in the function of organs, tissues and cells, and significant increases in the risks of many age-associated diseases, including cancer, neurodegenerative disorders, and cardiovascular diseases. Protein biomarkers have attracted increasing attention in research on aging and age-related diseases. Considering the fact that proteins are large heterogenous biomolecules due to coding polymorphisms, alternative RNA splicing and post-translational modifications (PTMs), including glycosylation, phosphorylation, and methylation, mass spectrometry (MS)-based top-down proteomics (TDP) is a powerful technology that allows for measuring proteins without proteolysis, thus characterizing intact forms of proteins, which provides information on primary sequences, including their modifications. This review provides an overview of TDP technologies, with a particular focus on the separation, ionization, and fragmentation of intact proteins and introduces the most recent applications of TDP to the discovery of proteoform-resolved biomarkers associated with aging and age-related diseases. Full article

Non-small-cell lung cancer (NSCLC) constitutes approximately all lung cancers (LCs), and metastasis remains a major challenge in its treatment, thus necessitating the detection of novel molecular players involved in this process. In this study, we performed a comprehensive analysis of microarray and RNA-seq cohorts extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) and associated them with metastasis-related genes involved in brain metastasis (BM) in NSCLC. We thus identified differentially expressed metastatic genes (DEMGs) and constructed a protein–protein interaction network (PPIN) using these DEMGs. These DEMGs were further analyzed for associations with patient age, gender, and tumor stage, and the significant impact of specific genes on overall survival (OS) was assessed to determine the prognostic significance of the identified targets. We finally constructed a three-node microRNA (miRNA) feed-forward loop (FFL) involving miR-23b-3p, CD44, and five transcription factors (TFs) [EOMES, FOS, FOSL1, GLIS3, TP63] specific to NSCLC metastasis. Further mutational analysis of these FFL elements revealed that all were altered in the patient samples analyzed. Thus, our study identified potential genomic drivers that may play crucial roles in NSCLC BM. Overall, it provides valuable insights for the discovery of novel therapeutic targets in the management of NSCLC metastasis. However, further in vitro and in vivo experimentations are needed to justify the prognostic role of NSCLC biomarkers in BM pathogenesis. Full article

Non-muscle-invasive bladder cancer (NMIBC) is characterized by high recurrence rates and necessitates lifelong cystoscopic surveillance, underscoring the need for minimally invasive biomarkers to improve early detection and risk stratification. Therefore, this study aimed to investigate the role of trimethylamine-N-oxide (TMAO) and its precursors as diagnostic biomarkers for NMIBC. A total of 50 male patients with NMIBC (25 pTa and 25 pT1) were included in this study. Additionally, 52 age-matched healthy individuals were included as controls. Serum TMAO and its dietary precursors were quantified using liquid chromatography–tandem mass spectrometry. Group differences were analyzed using nonparametric tests, associations were assessed using Spearman’s correlation, and diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. Multivariate logistic regression was performed to identify independent predictors, and a composite risk score was generated. Serum TMAO, carnitine, and choline levels were significantly higher in patients with NMIBC than in controls (p ≤ 0.0001), whereas betaine showed a nonsignificant trend toward higher levels (p ≥ 0.05). The pathological stage (pTa vs. pT1) showed the strongest correlation with TMAO levels. The ROC analysis revealed that TMAO had the highest individual diagnostic accuracy (area under the curve [AUC] = 0.875, 95% confidence interval [CI] 0.812–0.939), whereas carnitine and choline provided complementary diagnostic performance. In multivariate models, TMAO, carnitine, and choline remained independent predictors of NMIBC (p ≤ 0.0001). A composite risk score integrating all four metabolites demonstrated excellent discriminatory capacity (AUC = 0.958, 95% CI 0.926–0.991). The TMAO metabolic axis can be used as a minimally invasive biomarker panel for NMIBC. Further large, prospective, multicenter studies integrating metabolomic and microbiome profiling are needed to validate the findings. Full article

Background: Acute rejection remains a major complication following liver transplantation, yet reliable noninvasive biomarkers for its early prediction and diagnosis remain unidentified. This exploratory study characterized bile and serum metabolites associated with acute rejection in living donor liver transplantation using comprehensive metabolomic profiling combined with machine learning. Methods: Non-targeted metabolomics were performed on bile samples collected on post-operative day (POD) 1 (n = 38) and serum on POD 14 (n = 45) from liver transplant recipients. Partial least squares discriminant analysis-based variable selection was followed by logistic regression and least absolute shrinkage and selection operator models, which were evaluated via cross-validation in the discovery cohort to explore potential biomarkers for acute rejection. Results: A three-variable, bile-based model for predicting acute rejection achieved a mean cross-validated AUC of 0.872 (95% confidence interval: 0.814–0.930). Glycohyocholic acid and sulfolithocholylglycine were the main contributors. A nine-variable serum model for the Rejection Activity Index, including the change in γ-glutamyl transferase, showed a mean cross-validated R² of 0.728 (95% confidence interval: 0.609–0.846), with methionine, creatine, and oxidized fatty acids contributing prominently. Conclusions: These findings suggest that metabolomic profiling combined with machine learning may provide candidate biomarkers for acute rejection after liver transplantation. However, given the exploratory nature of the study and the lack of external validation, the clinical utility of these metabolite signatures remains to be determined. Therefore, external validation in larger, independent cohorts will be required. Full article

Background/Objectives: Wound infections cause significant morbidity, yet current diagnostics rely on time-consuming microbial culture. Volatile organic compounds (VOCs) from bacterial metabolism offer potential for early diagnosis. This study aimed to validate the volatile metabolites profiled by gas chromatography–ion mobility spectrometry (GC-IMS) combined with machine learning for rapid identification of wound infections and certain bacterial infections. Methods: Headspace of clinical wound samples were analyzed using GC-IMS. Volatile metabolite profiles were compared between infected and non-infected groups and between Escherichia coli (E. coli)-positive and negative samples. Partial least squares discriminant analysis (PLS-DA) and Mann–Whitney U test were used for preliminary screening with variable importance in projection (VIP) > 1 and p-value < 0.05. Three machine learning algorithms, namely support vector machine (SVM), logistic regression (LR), and random forest (RF), were trained on the selected features for classification, using 5-fold cross-validation with 10 repeated runs. Model performance was assessed using key evaluation metrics, including accuracy, sensitivity, specificity, the area under the curve (AUC) and feature importance ranking to identify the most relevant biomarkers. Results: A total of 19 volatile metabolites associated with clinical wound samples were identified. The RF model achieved 90.15% sensitivity and 0.91 AUC for bacterial infection detection. For E. coli identification, LR reached 85.35% sensitivity and 0.89 AUC. Potential volatile metabolic biomarkers including elevated 3-methyl-1-butanol, 2-methyl-1-butanol, and ethyl hexanoate for identifying bacterial infection were selected through the cross-validation results of the three algorithms. Conclusions: Untargeted metabolomics by GC-IMS effectively captures infection-specific volatile metabolic signatures in complex wound samples. Integration with machine learning enables rapid, high-accuracy diagnosis of bacterial infections and E. coli identification at point of care. This approach addresses clinical metabolomics translational challenges by providing a portable and cost-effective method, potentially reducing antibiotic misuse through more timely and targeted therapy. Full article

As essential branches of systems biology, metabolomics and lipidomics systematically reveal the composition, dynamic changes, and biological functions of small-molecule metabolites and lipids using high-throughput analytical techniques. This review examines the application of these omics technologies in evaluating livestock and poultry meat and egg quality, focusing on their roles in elucidating the molecular mechanisms behind key traits such as flavor, tenderness, and nutritional value. By identifying key metabolic markers—including glutamic acid, inosine monophosphate, and specific triglycerides—the intrinsic links between these markers and intramuscular fat deposition, flavor precursor formation, and antioxidant capacity are highlighted. Furthermore, this paper emphasizes the transformative impact of integrating multi-omics data with artificial intelligence (AI). AI-driven analytical frameworks are overcoming the limitations of traditional high-dimensional data processing, enabling robust biomarker discovery, predictive modeling for product quality, and reverse design for genetic improvement. Ultimately, the synergistic application of metabolomics, lipidomics, and AI will drive the development of modern animal husbandry toward intelligent, predictable, and precision-based production. Full article

Environmental pollutants, lifestyle factors, and intrinsic metabolism can amplify reactive oxygen and nitrogen species (ROS/RNS) generation beyond antioxidant capacity. The resulting oxidative stress damages macromolecules, perturbs redox signaling, and may accelerate biological aging. This review synthesizes evidence published mainly in 2020–2025 on how major pollutant classes (air pollutants, metals, pesticides, nanoparticles, and micro-/nanoplastics) induce ROS through shared nodes mitochondrial electron transport disruption, NADPH oxidase activation, and redox cycling/Fenton chemistry and how these signals propagate to epigenetic remodeling (DNA methylation, histone modifications, and non-coding RNAs). To move beyond descriptive cataloging, we grade the strength of evidence by study context (cell culture, animal models, human observational studies, and clinically oriented biomarker research), highlight convergent findings and unresolved controversies, and specify key methodological limits. We then compare oxidative-stress biomarker platforms by analytical specificity, pre-analytical susceptibility, and translational readiness, distinguishing validated markers from exploratory redox-epigenetic and multi-omics signatures. Finally, we discuss how exposomics and AI-assisted multi-omics integration may support biomarker discovery while emphasizing current constraints (confounding, batch effects, and limited prospective validation) that must be addressed for clinical translation. Full article

Background: Ectopic fat deposition has been demonstrated to play a critical role in the onset and progression of renal dysfunction. However, research on renal parenchymal fat deposition and its association with renal dysfunction in type 2 diabetes mellitus (T2DM) remains limited, particularly regarding its association with early kidney injury. The present study aimed to further investigate the relationship between renal fat fraction (FF) and biomarkers of kidney injury, thereby providing new evidence for the potential link between intrarenal fat accumulation and early renal impairment in T2DM. Methods: This cross-sectional study enrolled 60 patients with T2DM. Renal FF was quantitatively assessed using magnetic resonance imaging (MRI). Clinical characteristics, body composition parameters, and biochemical indices were collected. Levels of kidney injury biomarkers, including tumor necrosis factor receptors 1 (TNF-R1), tumor necrosis factor receptors 2 (TNF-R2), chitinase-3-like protein 1 (YKL-40), and kidney injury molecule-1 (KIM-1), were measured using enzyme-linked immunosorbent assay (ELISA). To evaluate the correlations between fat distribution and inflammatory biomarkers, Pearson correlation analysis was performed. Furthermore, linear regression analysis was conducted to explore the associations between renal FF and kidney injury biomarkers with adjustments for potential confounders such as smoking status, diabetes duration, and visceral fat. Lasso regression was used to screen variables. Results: The results demonstrated that renal FF was significantly positively correlated with serum YKL-40 (r = 0.3, p = 0.021), TNF-R1 (r = 0.246, p = 0.042), and urinary KIM-1 (r = 0.396, p = 0.004), indicating a close association between renal fat accumulation and early kidney injury biomarkers. In regression analyses adjusted for age, sex, and duration of diabetes, the associations between renal FF and these biomarkers remained significant. After further adjustment for potential confounders, including smoking history, alcohol consumption, hypertension, renin-angiotensin-aldosterone system (RAAS) inhibitors, sodium-dependent glucose transporters 2 (SGLT2) inhibitors, glucagon-Like Peptide-1 (GLP-1) receptor agonists, and lipid-lowering drugs, renal FF remained significantly associated with TNF-R1 (β = 0.327, p = 0.015), KIM-1 (β = 0.352, p = 0.021), and YKL-40 (β = 0.275, p = 0.025). Moreover, even after additional adjustment for visceral fat, the associations of renal FF with TNF-R1 and KIM-1 persisted. After using the Benjamini–Hochberg procedure for false discovery rate, the relationship between renal FF and KIM-1 had a significant difference. Variables of age and gender were excluded to build the parsimonious modeling using Lasso regression. It suggested that renal fat accumulation may contribute to kidney injury independently of visceral adiposity. Conclusions: The study systematically demonstrates a significant association between renal FF and early biomarkers of kidney injury in T2DM, which may suggest the potential role of renal fat accumulation in the pathogenesis of diabetic nephropathy. These findings provide clinical data support for the development of a fat-targeted intervention study. Future research should further elucidate the long-term mechanistic role of renal FF in diabetic nephropathy, as well as its potential value in early diagnosis and therapeutic applications. Full article

Benign prostatic hyperplasia (BPH) is an age-related disorder increasingly linked to chronic inflammation and redox imbalance, yet its systemic oxidative and nitrosative profile remains insufficiently characterized. In this cross-sectional study, fasting serum samples were collected from 47 men with clinically confirmed BPH scheduled for transurethral resection of the prostate and 40 healthy controls. We assessed antioxidant status (thiols, total antioxidant capacity), lipid peroxidation (malondialdehyde, 4-hydroxynonenal), protein nitration (3-nitrotyrosine), glycoxidation markers (Amadori products, advanced glycation end products (AGE)-associated fluorescence), and tryptophan metabolism indices (tryptophan, kynurenine, N′-formylkynurenine). Compared with controls, BPH patients showed significantly lower antioxidant capacity and thiol levels, together with increased lipid peroxidation and protein nitration. AGE-associated fluorescence was modestly elevated, whereas Amadori products and advanced oxidation protein products did not differ significantly. Tryptophan metabolism was markedly altered, with lower tryptophan and higher kynurenine and N′-formylkynurenine, indicating activation of the kynurenine pathway. After false discovery rate correction, most redox biomarkers remained significant. Multivariable logistic regression confirmed independent associations of lipid peroxidation, nitrosative stress, and kynurenine pathway activation with BPH after adjustment for age and metabolic parameters. These findings support a role for systemic oxidative and inflammatory mechanisms in BPH pathophysiology, although confirmation in age-matched and longitudinal studies is needed. Full article

Preeclampsia remains a major cause of maternal and perinatal morbidity and mortality worldwide, yet progress in biomarker discovery and predictive modeling has translated only modestly into clinically meaningful risk stratification. Over the past two decades, numerous biomarkers and predictors reflecting placental–angiogenic dysfunction, maternal cardiovascular maladaptation, and inflammatory–metabolic stress have been proposed, alongside increasingly sophisticated statistical and machine learning approaches. However, many predictive strategies continue to treat preeclampsia as a single disease entity and rely on static thresholds applied at isolated gestational time points. Accumulating biological and clinical evidence instead suggests that preeclampsia represents a heterogeneous syndrome composed of partially overlapping mechanistic phenotypes whose relative contributions vary across pregnancy and across individuals. In this narrative review, we argue that further progress in prediction is likely to depend less on the identification of additional biomarkers and more on how biological heterogeneity and temporal dynamics are integrated into predictive frameworks. We synthesize current evidence supporting multimarker approaches, phenotype-informed frameworks, and longitudinal risk trajectories that conceptualize prediction as a dynamic process rather than a binary classification task. We also examine the complementary roles of classical statistical models and machine learning, emphasizing that calibration, external validation, interpretability, transportability, and clinical usability are essential, alongside discrimination, for successful clinical implementation. Finally, we outline key research priorities for the next generation of predictive studies, including mechanistically grounded phenotyping, dynamic risk updating across gestation, rigorous evaluation across diverse populations, and explicit linkage of risk stratification to preventive interventions and clinical decision-making. Together, these directions support a shift toward an integrative, longitudinal, and clinically anchored approach to preeclampsia prediction. Full article

Background/Objectives: Oral squamous cell carcinoma (OSCC) commonly arises from oral potentially malignant disorders (OPMDs), yet reliable molecular biomarkers that predict malignant transformation remain scarce. Because epithelial carcinogenesis follows similar multistep trajectories across multiple organs, pan-cancer transcriptional analyses may reveal conserved pathways relevant to early oral tumorigenesis. This study aimed to identify shared transcriptional signatures across carcinomas and evaluate their applicability to precancerous-to-carcinoma progression. Methods: Bulk RNA-seq data from five carcinomas (lung, colon, breast, prostate, and head and neck squamous cell carcinoma, HNSCC) were obtained from TCGA to identify shared differentially expressed genes (DEGs) (|log₂FC| ≥ 2; FDR < 0.05). Functional enrichment, clustering, and gene–pathway network analyses characterized conserved biological processes. Independent GEO datasets containing premalignant and malignant samples, including OPMD and OSCC cohorts, were examined to assess early-stage relevance. Results: A conserved 45-gene signature was identified, enriched for transcriptional regulation, chromatin organization, and RNA polymerase II-mediated processes. Regulatory hubs, including ZIC5, MYBL2, ONECUT2, POU4F1, and PDX1, and strong upregulation of cancer-testis antigens (MAGEA3, MAGEA6, MAGEC2) were notable. Integration with premalignant datasets revealed 13 genes consistently dysregulated across early lesions, involving pathways such as cell differentiation, apoptosis, and lipid transport. Several genes remained altered from normal tissue through OPMD to OSCC, supporting their potential as stable biomarkers. Conclusions: This study identifies conserved transcriptional programs shared across epithelial cancers and detectable in OPMDs. These findings highlight promising biomarker and regulatory candidates for improving early detection and risk stratification of oral precancer, addressing a critical unmet need in OSCC prevention and clinical management. Full article

Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation. Full article

Neurofilament light chain (NfL) is a promising biomarker of axonal injury across acute and chronic neurodegeneration, which can improve drug discovery and disease monitoring models. Traditional in vivo animal models cannot fully mimic human pathophysiology of neurodegenerative diseases (NDDs), but in vitro models based on human cells solve this problem, reducing the time and cost of drug testing. We developed an electrochemical immunosensor for NfL detection in cell culture media to monitor acute neuronal injury in in vitro models. The biosensor was designed in two configurations: the label-free system, which directly detects NfL in the sample via the antibody–antigen interaction, and the sandwich configuration, which incorporates two additional antibodies. Detection was examined using electrochemical techniques, including cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and chronoamperometry (CA). The sensor demonstrated a detection limit of 3–9 pg mL⁻¹, and a dynamic working range spanning from 10 up to 10⁷ pg mL⁻¹. Importantly, NfL was successfully detected in physiological media collected from cultured neurons that were differentiated from the long-term human neuroepithelial-like stem cells. This discovery highlights the platform’s applicability for in vitro neurodegenerative models. The immunosensor offers a sensitive, scalable, and cost-effective alternative for neurodegeneration detection in drug testing applications. Full article

Preeclampsia (PE), pregnancy-associated high blood pressure linked to organ damage, affects 3–8% of all pregnancies and results worldwide in 70,000 maternal and 500,000 perinatal deaths each year. Untreated PE may progress to eclampsia with long-term health implications for both mother and child. Non-invasive prenatal diagnosis or screening applies cell-free DNA approaches and offers a less invasive and more economical method for early diagnosis and prediction of various pregnancy complications. Recently, cell-free assays, particularly blood-based cell-free DNA and RNA analysis, have shown great potential in early PE prediction and diagnosis. Here, we provide an updated review of the current understanding and discoveries of PE, focusing on recent publications (1 January 2019–30 December 2025) of liquid biopsy-derived circulating fetal cells (circulating trophoblasts and fetal nucleated red blood cells), cell-free DNA, cell-free RNA and small extracellular vesicles (i.e., exosomes). We aim to discuss the conceptual framework and technical evolution of liquid biopsy applications in preeclampsia pathogenesis, prediction and diagnosis. Progressing novel screening and diagnostic molecular biomarkers have high potential to facilitate early detection for patients at risk of PE. Liquid biopsy-based screening strategies may aid in providing timely intervention and treatment. Full article