Search Results (2,293)

Background/Objectives: Androgen deprivation therapy (ADT) is widely used to manage prostate cancer (PC), but the emergence of treatment resistance remains a major clinical challenge. Although the GST family has been implicated in drug resistance, the specific role of GSTM5 remains poorly understood. This study investigates whether GSTM5, alone or in combination with clinical variables, can improve patient stratification based on the risk of early treatment resistance. Methods: In silico analyses were performed to examine GSTM5’s role in protein interactions, molecular pathways, and gene expression. The rs3768490 polymorphism was genotyped in 354 patients with PC, classified by ADT response. Descriptive analysis and logistic regression models were applied to evaluate associations between genotype, clinical variables, and ADT response. GSTM5 expression related to the rs3768490 genotype and ADT response was also analyzed in 129 prostate tissue samples. Results: The T/T genotype of rs3768490 was significantly associated with a lower likelihood of early ADT resistance in both individual (p = 0.0359, Odd Ratios (OR) = 0.18) and recessive models (p = 0.0491, OR = 0.21). High-risk classification according to D’Amico was strongly associated with early progression (p < 0.0004; OR > 5.4). Combining genotype and clinical risk improved predictive performance, highlighting their complementary value in stratifying patients by treatment response. Additionally, GSTM5 expression was slightly higher in T/T carriers, suggesting a potential protective role against ADT resistance. Conclusions: The T/T genotype of rs3768490 may protect against ADT resistance by modulating GSTM5 expression in PC. These preliminary findings highlight the potential of integrating genetic biomarkers into clinical models for personalized treatment strategies, although further studies are needed to validate these observations. Full article

Background: There is growing interest in the potential role of manganese (Mn) in the development of Alzheimer’s Disease and related dementias (ADRD). Methods: In this nested pilot study of a ferroalloy worker cohort, we investigated the impact of chronic occupational Mn exposure on cognitive function through β-amyloid (Aβ) deposition and multi-omics profiling. We evaluated six male Mn-exposed workers (median age 63, exposure duration 31 years) and five historical controls (median age: 60 years), all of whom had undergone brain PET scans. Exposed individuals showed significantly higher Aβ deposition in exposed individuals (p < 0.05). The average annual cumulative respirable Mn was 329.23 ± 516.39 µg/m³ (geometric mean 118.59), and plasma Mn levels were significantly elevated in the exposed group (0.704 ± 0.2 ng/mL) compared to controls (0.397 ± 0.18 in controls). Results: LC-MS/MS-based pathway analyses revealed disruptions in olfactory signaling, mitochondrial fatty acid β-oxidation, biogenic amine synthesis, transmembrane transport, and choline metabolism. Simoa analysis showed notable alterations in ADRD-related plasma biomarkers. Protein microarray revealed significant differences (p < 0.05) in antibodies targeting neuronal and autoimmune proteins, including Aβ (25–35), GFAP, serotonin, NOVA1, and Siglec-1/CD169. Conclusion: These findings suggest Mn exposure is associated with neurodegenerative biomarker alterations and disrupted biological pathways relevant to cognitive decline. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background/Objectives: Heart transplantation (HTx) is a lifesaving procedure for end-stage heart failure patients; however, postoperative infections remain a major challenge due to immunosuppressive therapy and surgical complications. Traditional biomarkers such as C-reactive protein (CRP) and procalcitonin (PCT) have limitations in distinguishing infections from systemic inflammatory response syndrome (SIRS). Emerging markers such as Presepsin and interleukin-6 (IL-6) may improve diagnostic accuracy. This study aimed to evaluate the kinetics and reliability of these four inflammatory biomarkers in heart transplant recipients in the immediate postoperative period. Methods: This retrospective observational study included 126 patients who underwent HTx at Policlinic of Bari between January 2022 and November 2024. Patients were categorized into infected (n = 26) and non-infected (n = 100) groups based on clinical and microbiological criteria. Biomarkers (CRP, PCT, Presepsin, and IL-6) were measured preoperatively and on postoperative days (PODs) 1, 2, 3, 4, 5, and 10. Statistical analyses included the Mann–Whitney U test and logistic regression to identify the independent predictors of infection. Results: CRP and PCT levels differed significantly between the groups only on day 10, limiting their use as early infection markers. In contrast, Presepsin levels were significantly elevated in infected patients from day 1 (p < 0.001), whereas IL-6 levels showed significant differences from day 3 onward. Presepsin showed the strongest association with infection in the early postoperative phase. Conclusions: Presepsin and IL-6 outperformed CRP and PCT in detecting early postoperative infections in heart transplant recipients. Their early elevation supports their use as reliable markers for guiding timely clinical intervention and improving patient outcomes. Further research is needed to validate these findings in larger cohorts and with different immunosuppressive regimens. Full article

Background: This study presents an innovative approach to cardiovascular disease (CVD) risk prediction based on a comprehensive analysis of clinical, immunological and biochemical markers using mathematical modelling and machine learning methods. Baseline data include indices of humoral and cellular immunity (CD59, CD16, IL-10, CD14, CD19, CD8, CD4, etc.), cytokines and markers of cardiovascular disease, inflammatory markers (TNF, GM-CSF, CRP), growth and angiogenesis factors (VEGF, PGF), proteins involved in apoptosis and cytotoxicity (perforin, CD95), as well as indices of liver function, kidney function, oxidative stress and heart failure (albumin, cystatin C, N-terminal pro B-type natriuretic peptide (NT-proBNP), superoxide dismutase (SOD), C-reactive protein (CRP), cholinesterase (ChE), cholesterol, and glomerular filtration rate (GFR)). Clinical and behavioural risk factors were also considered: arterial hypertension (AH), previous myocardial infarction (PICS), aortocoronary bypass surgery (CABG) and/or stenting, coronary heart disease (CHD), atrial fibrillation (AF), atrioventricular block (AB block), and diabetes mellitus (DM), as well as lifestyle (smoking, alcohol consumption, physical activity level), education, and body mass index (BMI). Methods: The study included 52 patients aged 65 years and older. Based on the clinical, biochemical and immunological data obtained, a model for predicting the risk of premature cardiovascular aging was developed using mathematical modelling and machine learning methods. The aim of the study was to develop a predictive model allowing for the early detection of predisposition to the development of CVDs and their complications. Numerical methods of mathematical modelling, including Runge–Kutta, Adams–Bashforth and backward-directed Euler methods, were used to solve the prediction problem, which made it possible to describe the dynamics of changes in biomarkers and patients’ condition over time with high accuracy. Results: HLA-DR (50%), CD14 (41%) and CD16 (38%) showed the highest association with aging processes. BMI was correlated with placental growth factor (37%). The glomerular filtration rate was positively associated with physical activity (47%), whereas SOD activity was negatively correlated with it (48%), reflecting a decline in antioxidant defence. Conclusions: The obtained results allow for improving the accuracy of cardiovascular risk prediction, and form personalised recommendations for the prevention and correction of its development. Full article

Inflammatory biomarkers, including high-sensitivity C-reactive protein (hsCRP), serum amyloid A (SAA), and interleukin-6 (IL-6), have been associated with an increased risk of future cardiovascular events. While they provide valuable prognostic information, these associations do not necessarily imply a direct causal role. The combined prognostic utility of these markers, however, remains insufficiently studied. We analysed 3300 well-characterised participants of the Ludwigshafen Risk and Cardiovascular Health (LURIC) study, all of whom underwent coronary angiography. Participants were stratified based on their serum concentrations of hsCRP, SAA, and IL-6. Associations between biomarker combinations and mortality were assessed using multivariate Cox regression and ROC analysis. Individuals with elevated hsCRP and SAA or IL-6 showed higher prevalence rates of coronary artery disease, heart failure, and adverse metabolic traits. These “both high” groups had lower estimated glomerular filtration rate, higher NT-proBNP, and increased HbA1c. Combined elevations of hsCRP and SAA were significantly associated with higher all-cause and cardiovascular mortality in partially adjusted models. However, these associations weakened after adjusting for IL-6. IL-6 alone demonstrated the highest predictive power (AUC: 0.638) and improved risk discrimination when included in multi-marker models. The co-elevation of hsCRP, SAA, and IL-6 identifies a high-risk phenotype characterised by greater cardiometabolic burden and increased mortality. IL-6 may reflect upstream inflammatory activity and could serve as a therapeutic target. Multi-marker inflammatory profiling holds promise for refining cardiovascular risk prediction and advancing personalised prevention strategies. Full article

Current research reported that the number of clinical studies found for botulinum toxin (BoNT) key effects on biochemical biomarkers in head and neck chronic conditions linked to inflammation was very low. There are no systematic reviews of animal studies on this topic, and hence our review aimed to evaluate the quality of the preclinical evidence. We searched PubMed, Scopus, and Web of Science databases, and registries up to 29 January 2024. There were 22 eligible records, and data were available for 11 randomised controlled trials. There were concerns about the risk of bias and great variations of data obtained regarding chronic conditions, which included mostly trigeminal neuralgia. The leading biomarkers were proinflammatory cytokines (IL-1β, TNF-α) and synaptosomal-associated protein-25 (SNAP25), followed by neuron activation marker c-Fos and calcitonin gene-related peptide (CGRP). Overall, data found that BoNT significantly altered the under/over-expression of biomarkers evoked by the investigated disease models and had no effect when the levels of these biomarkers were not changed by the induced chronic conditions in animals. However, there were some mixed results and exceptions, and the certainty evidence found was very low to low. Although the sample sizes detected significant effect size (p < 0.05), most studies are based on male inferior animals, which may limit the recommendations for clinical trials. This study is registered on PROSPERO (CRD42023432411). Full article

The protein p53, often referred to as the “guardian of the genome,” is essential for preserving cellular balance and preventing cancerous transformations. As one of the most commonly altered genes in human cancers, its impaired function is associated with tumor initiation, development, and resistance to treatment. Exploring the diverse roles of p53, which include regulating the cell cycle, repairing DNA, inducing apoptosis, reprogramming metabolism, and modulating immunity, provides valuable insights into cancer mechanisms and potential treatments. This review integrates recent findings on p53′s dual nature, functioning as both a tumor suppressor and an oncogenic promoter, depending on the context. Wild-type p53 suppresses tumors by inducing cell cycle arrest or apoptosis in response to genotoxic stress, while mutated variants often lose these functions or gain novel pro-oncogenic activities. Emerging evidence highlights p53′s involvement in non-canonical pathways, such as regulating tumor microenvironment interactions, metabolic flexibility, and immune evasion mechanisms. For instance, p53 modulates immune checkpoint expression and influences the efficacy of immunotherapies, including PD-1/PD-L1 blockade. Furthermore, advancements in precision diagnostics, such as liquid biopsy-based detection of p53 mutations and AI-driven bioinformatics tools, enable early cancer identification and stratification of patients likely to benefit from targeted therapies. Therapeutic strategies targeting p53 pathways are rapidly evolving. Small molecules restoring wild-type p53 activity or disrupting mutant p53 interactions, such as APR-246 and MDM2 inhibitors, show promise in clinical trials. Combination approaches integrating gene editing with synthetic lethal strategies aim to exploit p53-dependent vulnerabilities. Additionally, leveraging p53′s immunomodulatory effects through vaccine development or adjuvants may enhance immunotherapy responses. In conclusion, deciphering p53′s complex biology underscores its unparalleled potential as a biomarker and therapeutic target. Integrating multi-omics analyses, functional genomic screens, and real-world clinical data will accelerate the translation of p53-focused research into precision oncology breakthroughs, ultimately improving patient outcomes. Full article

With a rapidly growing incidence and prevalence, Alzheimer’s disease (AD) is rapidly becoming one of the most disabling, lethal, and expensive diseases of the century. To diagnose AD as early as possible, the scientific world struggles to find reliable and non-invasive biomarkers that could predict the conversion of mild cognitive impairment to AD and delineate the ongoing pathogenic vicious pathways to be targeted with therapy. Research supports the use of blood biomarkers, such as Aβ_1-42/Aβ_1-40 ratio, phosphorylated tau181, and p-tau217 for diagnostic purposes, although the cut-offs are not clearly established and can depend on the assays used. For more accurate diagnosis, markers of neurodegeneration (neurofilament light) and neuroinflammation (glial fibrillary acidic protein) could be introduced in the biomarker panel. The recent approval of the Lumipulse G p-tau217/Aβ_1-42 plasma ratio by the FDA for the early detection of amyloid plaques associated with Alzheimer’s disease in adult patients, aged 55 years and older, exhibiting signs and symptoms of the disease represents a significant advancement in the diagnosis of Alzheimer’s disease, offering a more accessible and less invasive way to diagnose this devastating disease and allow potentially earlier access to treatment options. Full article

We conducted systematic glycomic and glycoproteomic profiling to characterize the dynamic N-glycosylation landscape of rat serum, with particular focus on sex- and time-dependent variations. MALDI-TOF-MS analysis revealed that rat serum N-glycans are predominantly biantennary, disialylated complex-type structures with extensive O-acetylation of Neu5Ac residues, especially in females. LC-MS/MS-based glycoproteomic analysis of albumin/IgG-depleted serum identified 87 glycoproteins enriched in protease inhibitors (e.g., serine protease inhibitor A3K) and immune-related proteins such as complement C3. Temporal analyses revealed stable sialylation in males but pronounced daily fluctuations in females, suggesting hormonal influence. Neu5Gc-containing glycans were rare and mainly derived from residual IgG, as confirmed by glycomic analysis. In contrast to liver-derived glycoproteins, purified IgG exhibited Neu5Gc-only sialylation without O-acetylation, underscoring distinct sialylation profiles characteristic of B cell-derived glycoproteins. Region-specific glycosylation patterns were observed in IgG, with the Fab region carrying more disialylated structures than Fc. These findings highlight cell-type and sex-specific differences in sialylation patterns between hepatic and immune tissues, with implications for hormonal regulation and biomarker research. This study provides a valuable dataset on rat serum glycoproteins and underscores the distinctive glycosylation features of rats, reinforcing their utility as model organisms in glycobiology and disease research. Full article

Neurodegenerative diseases (NDDs) such as Alzheimer’s, Parkinson’s, ALS, and Huntington’s pose a growing global challenge due to their complex pathobiology and aging demographics. Once considered as cellular debris, small extracellular vesicles (sEVs) are now recognized as active mediators of intercellular signaling in NDD progression. These nanovesicles (~30–150 nm), capable of crossing the blood–brain barrier, carry pathological proteins, RNAs, and lipids, facilitating the spread of toxic species like Aβ, tau, TDP-43, and α-synuclein. sEVs are increasingly recognized as valuable diagnostic tools, outperforming traditional CSF biomarkers in early detection and disease monitoring. On the therapeutic front, engineered sEVs offer a promising platform for CNS-targeted delivery of siRNAs, CRISPR tools, and neuroprotective agents, demonstrating efficacy in preclinical models. However, translational hurdles persist, including standardization, scalability, and regulatory alignment. Promising solutions are emerging, such as CRISPR-based barcoding, which enables high-resolution tracking of vesicle biodistribution; AI-guided analytics to enhance quality control; and coordinated regulatory efforts by the FDA, EMA, and ISEV aimed at unifying identity and purity criteria under forthcoming Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines. This review critically examines the mechanistic roles, diagnostic potential, and therapeutic applications of sEVs in NDDs, and outlines key strategies for clinical translation. Full article

Adenosine receptors (ARs) are G protein-coupled receptors that are widely expressed across tissues, traditionally associated with cardiovascular, neurological, and immune regulation. Recent studies, however, have highlighted their non-canonical functions, revealing critical roles in metabolism, immunometabolism, and epigenetic regulation. AR subtypes, particularly A2A and A2B, modulate glucose and lipid metabolism, mitochondrial activity, and energy homeostasis. In immune cells, AR signaling influences metabolic reprogramming and polarization through key regulators such as mTOR, AMPK, and HIF-1α, contributing to immune tolerance or activation depending on the context. Additionally, ARs have been implicated in epigenetic modulation, affecting DNA methylation, histone acetylation, and non-coding RNA expression via metabolite-sensitive mechanisms. Therapeutically, AR-targeting agents are being explored for cancer and chronic inflammatory diseases. While clinical trials with A2A antagonists in oncology show encouraging results, challenges remain due to receptor redundancy, systemic effects, and the need for tissue-specific selectivity. Future strategies involve biased agonism, allosteric modulators, and combination therapies guided by biomarker-based patient stratification. Overall, ARs are emerging as integrative hubs connecting extracellular signals with cellular metabolic and epigenetic machinery. Understanding these non-canonical roles may unlock novel therapeutic opportunities across diverse disease landscapes. Full article

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model’s performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model’s diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy. Full article

In acute lymphoblastic leukemia (ALL), neutropenia and fever of unknown origin may occur, indicating the use of antimicrobials to control a probable infection. However, in 60–70% of cases there is no obvious infectious focus so treatment is empirical, increasing the risk of developing systemic inflammatory response syndrome (SIRS). The construction of a prognostic model of fever and development of SIRS based on the identification of endogenous molecules, called alarmins or damage-associated molecular patterns (DAMPs) and inflammatory cytokines, can help identify children with ALL and fever or SIRS and who do not have an infection. A cohort of 30 children with recently diagnosed ALL and absence of infectious microorganisms before starting the remission induction phase was studied. Two groups were identified: (1) a group with SIRS (fever, tachycardia, tachypnea, and leukopenia, without focus of infection) and (2) a group without SIRS. The DAMPs, namely HMGB1 and S100A8 proteins, were quantified by ELISA and inflammatory mediators were determined by multiple protein analysis. The medians of DAMPs and inflammatory mediators in children with SIRS were higher than in children who did not have SIRS, and the delta values of the biomarkers studied in patients with and without SIRS showed important differences, with statistically higher medians in patients with SIRS compared to those without SIRS. HMGB1 together with IL-1β, IL-8, IL-10, and MCP-1 can serve as biomarkers to identify children with ALL and fever or SIRS who should not receive antimicrobial treatment because the origin of their fever is not due to an infectious agent. Full article

Traumatic brain injury (TBI) initiates a complex cascade of pathophysiological events that have far-reaching consequences beyond the initial injury. This review examines the current state of the literature on the mechanisms underlying neurotrauma and neuroinflammation, with particular emphasis on the molecular cross-talk between these disparate pathways that ultimately precipitates the development of chronic traumatic encephalopathy (CTE). We integrate this mechanistic knowledge with potential diagnostic biomarkers, such as glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and advances in neuroimaging and machine learning-based predictive tools. Finally, we discuss the current therapeutic approaches under investigation, and highlight which molecular targets have yet to be explored for potential therapeutic development. Full article