Search Results (3,080)

Intratumoral microbiota, once considered passive bystanders, are now recognized as active modulators of the tumor immune microenvironment (TIME)—the complex network of immune cells, stromal components, and signaling molecules within tumors—and ultimately shape immunotherapy outcomes in lung cancer. This review aims to elucidate the exact roles of intratumoral microbiota in lung cancer immuno-therapy responses and the potential mechanism, offering novel perspectives for overcoming resistance. We conducted a narrative review of the literature using a PubMed and Web of Science search of articles written in English from inception to November 2025. We summarize current evidence on the characteristics of intratumoral microbiota in lung cancer and their associations with patient outcomes following immune checkpoint inhibitor (ICI) treatment. We discuss how intratumoral microbes, their metabolites, and extracellular vesicles influence and remodel TIME, thereby either promoting or counteracting ICI efficacy. Furthermore, we explore the potential of microbial signatures as predictive biomarkers and highlight microbiota-targeted strategies—including probiotics, engineered bacteria, and rational antibiotic use—to overcome resistance and enhance clinical benefits. Collectively, available data support intratumoral microbiota as crucial modulators and promising therapeutic targets in lung cancer, and decoding their multifaceted interactions may inform precision microbiota-targeting strategies to improve patient outcomes. Full article

Breast cancer screening, while vital for reducing mortality, faces significant limitations in sensitivity and specificity, particularly in dense breasts. Current modalities primarily detect anatomical changes, often missing biologically aggressive tumors at their earliest stages. The altered metabolism of cancer cells establishes a characteristic inverted pH gradient that drives tumor invasion, metastasis, and treatment resistance. This makes tumor acidity a compelling, functional biomarker for early detection. This review synthesizes the emerging role of pH as a diagnostic biomarker and provides a critical evaluation of advanced imaging techniques for its non-invasive or minimal measurement. We detail the biological underpinnings of tumor acidosis, emphasizing its regulation through glycolytic reprogramming and dysregulated proton transport. Our analysis encompasses a broad spectrum of pH-sensitive imaging modalities, including magnetic resonance methods such as Chemical Exchange Saturation Transfer (CEST) MRI for extracellular pH mapping and multi-nuclear Magnetic Resonance Spectroscopy (MRS) using ¹H, ³¹P, and ¹⁹F nuclei to probe various cellular compartments. Furthermore, we examine hyperpolarized ¹³C MRI for real-time metabolic flux imaging, where metrics such as the lactate-to-pyruvate ratio demonstrate significant predictive value for treatment response. The review also assesses optical and photoacoustic imaging techniques, which offer high sensitivity but are often constrained to superficial tumors. Imaging tumor pH provides a powerful functional window into the earliest metabolic shifts in breast cancer, far preceding macroscopic anatomical changes. The ongoing development and evidence support the role of the pH-sensitive imaging techniques in diagnosis, lesion characterization, and therapy. Additionally, it holds promise for supplementing breast cancer screening by enabling earlier, more specific detection and personalized risk stratification, ultimately aiming to improve patient outcomes. Full article

Human missions into deep space will expose astronauts to the unique and complex radiation environment of galactic cosmic radiation (GCR), a mixed field of high-energy protons and heavy ions predicted to substantially increase long-term cancer risk. To support effective risk stratification, early detection, and mitigation strategies, there is a need to identify biomarkers indicative of GCR-induced cancer risk. Here, we applied a Tandem Mass Tag (TMT)-based quantitative proteomics approach to identify potential biomarkers associated with GCR-induced gastrointestinal (GI) and mammary tumorigenesis using the female Apc^Min/+ mouse, a well-established model of human colorectal and breast cancer. Eight- to ten-week-old Apc^Min/+ mice were exposed to 75 cGy of simulated GCR and serum and tissue samples were collected 100–110 days post-exposure for molecular and histopathological analyses. Tumor incidence was scored by blinded observers, and serum proteomes exhibiting a fold change > 1.2 or <0.83 with p < 0.05 were considered significantly altered. Bioinformatics analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and unsupervised clustering, were employed to delineate GCR-responsive molecular networks. Validation of differentially expressed proteins (DEPs) was performed using immunoblotting, ELISA, and enzyme activity assays. GCR exposure resulted in a significant increase in both GI and mammary tumor burden relative to controls. Proteomic profiling revealed 194 upregulated and 461 downregulated proteins, distinguishing GCR-exposed from control serum proteomes. Functional enrichment analyses highlighted alterations in metabolic processes, PI3K-AKT, HIF-1, and PPAR signaling pathways, alongside the suppression of antioxidant defense mechanisms. Notably, mice exposed to GCR exhibited elevated serum levels of TGF-β1 and MMP9, accompanied by reduced levels and enzymatic activities of key antioxidant defenses. Cross-referencing 36 GCR-induced serum SASP factors with the Human Protein Atlas revealed 11 SASP proteins associated with human breast and colorectal cancers. Together, these findings show that GCR exposure triggers a pro-tumorigenic serum proteomic signature that may serve as a biomarker for assessing cancer risk in astronauts during deep-space missions. Full article

Endometrial carcinoma (EC) continues to represent a major cause of gynecologic cancer–related mortality among women worldwide. Its multifactorial etiopathogenesis and underlying molecular heterogeneity have been the focus of extensive investigation. While traditional histological classification provides essential diagnostic insight, it is limited in predicting prognosis and therapeutic response due to significant interobserver variability. Recent advances in molecular biology and cancer genomics have profoundly enhanced understanding of EC pathogenesis. The Cancer Genome Atlas (TCGA) project delineated four distinct molecular subtypes of EC, POLE ultra-mutated, microsatellite instability hypermutated (MSI-H), copy number low (CNL) and copy number high (CNH), each defined by unique genomic alterations, histopathologic features, and clinical behaviors. These molecular groups demonstrate significant prognostic and therapeutic implications, correlating with differential outcomes and treatment responses. This review summarizes current evidence on the genomic landscape of endometrial carcinoma and underscores the pivotal role of molecular classification in improving diagnostic accuracy, prognostic stratification, and personalized therapy. Ongoing research into molecular biomarkers holds promise for refining patient management and optimizing clinical outcomes. Full article

Diabetic macular edema (DME) is a leading cause of vision loss, and predicting patients’ response to anti-vascular endothelial growth factor (anti-VEGF) therapy remains a clinical challenge. In this study, we developed an interpretable deep learning model for treatment prediction and biomarker analysis. We retrospectively analyzed 402 eyes from 371 patients with DME. The proposed DME-Receptance Weighted Key Value (RWKV) integrates optical coherence tomography (OCT) and ultra-widefield (UWF) imaging using Causal Attention Learning (CAL), curriculum learning, and global completion (GC) loss to enhance microlesion detection and structural consistency. The model achieved a Dice coefficient of 71.91 ± 8.50% for OCT biomarker segmentation and an AUC of 84.36% for predicting anti-VEGF response, outperforming state-of-the-art methods. By mimicking clinical reasoning with multimodal integration, DME-RWKV demonstrated strong interpretability and robustness, providing a promising AI framework for precise and explainable prediction of anti-VEGF treatment outcomes in DME. Full article

Head and neck cancer surgery has evolved from radical organ-sacrificing procedures to function-preserving approaches integrated within multidisciplinary frameworks. This comprehensive literature review, concentrating on studies from the past five years while incorporating relevant publications from the last three decades and landmark historical papers, examines the evolving role of surgery emphasizing diagnostic methodologies including comprehensive genomic profiling, validated imaging biomarkers, and their clinical integration for treatment selection and response prediction. Modern surgical practice demonstrates a paradigm shift toward precision medicine through validated diagnostic technologies. Comprehensive genomic profiling identifies clinically actionable alterations in over 90% of head and neck squamous cell carcinomas, with tumor mutational burden serving as a validated predictive biomarker for immunotherapy response. Programmed death-ligand 1 (PD-L1) combined positive score functions as a validated diagnostic biomarker for immunotherapy efficacy, demonstrating significant clinical benefit in biomarker-selected populations. Radiomics-based predictive models utilizing machine learning algorithms achieve diagnostic accuracies exceeding 85% for treatment response prediction when validated across independent cohorts. Quantitative ultrasound spectroscopy combined with magnetic resonance imaging radiomics demonstrates high sensitivity and specificity for radiation response prediction. Habitat imaging techniques characterizing tumor microenvironmental heterogeneity predict pathologic complete response to neoadjuvant chemoimmunotherapy with area under the curve values approaching 0.90 in validation studies. Integration of these diagnostic methodologies enables response-adaptive treatment strategies, with neoadjuvant chemotherapy facilitating mandibular preservation and adjuvant therapy omission in over half of human papillomavirus (HPV)-associated cases following surgical downstaging. Clinical validation of these diagnostic platforms enables accurate treatment response prediction and informed surgical decision-making, though standardization across institutions and demonstration of survival benefits through prospective trials remain essential for broader implementation. Full article

Background: Older adults face increased risks of influenza infection and related complications due to declining immunity and reduced vaccine responsiveness. Despite widespread vaccination, only 30–40% mount immune response due to immunosenescence. However, no biomarkers exist to identify potential non-responders, limiting the ability to target vaccine strategies, like high-dose or adjuvanted formulations, to those unlikely to benefit from standard options. Methods: We analysed publicly available baseline bulk RNA sequencing data from peripheral blood mononuclear cells of individuals aged ≥65 years to determine baseline transcriptomic signatures predictive of influenza vaccine response. Using two independent cohorts (discovery and validation), we classified individuals as triple responders (TRs) or triple non-responders (TNRs) based on hemagglutination inhibition assay titers at Day 0 and Day 28 for three components: A/H1N1, A/H3N2, and B/Yamagata. Results: We identified 1152 differentially expressed genes between TRs and TNRs at baseline. TRs exhibited enrichment of genes involved in B cell activation and protein synthesis, while TNRs showed enrichment of genes associated with innate immune responses and platelet activation. A response score derived from gene expression achieved high predictive accuracy in the discovery cohort (area under the curve [AUC] = 0.98). However, performance declined in the validation cohort (AUC = 0.69), and did not outperform clinical predictors, such as baseline titers, sex and vaccine dose. Conclusions: While baseline transcriptomic profiles may reveal mechanistic insights into vaccine responsiveness in the elderly, they offer limited predictive utility. Future work should prioritise higher-resolution or combined cell-specific approaches, such as single-cell RNA-sequencing or flow cytometry. Full article

The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment paradigm for hepatocellular carcinoma (HCC), establishing them as the cornerstone of systemic therapy for advanced stages of the disease. Nonetheless, the response rate remains limited, with only 15% to 20% of HCC patients benefiting from ICIs. Approximately 70% to 80% of cases exhibit resistance to anti-PD1 therapy. Therefore, exploring the biomarkers that can be used to identify the response of patients with HCC to immunotherapy and elucidating the potential mechanisms of immunotherapy resistance contribute to the development of predictive biomarkers and are significant for overcoming resistance and enhancing treatment efficacy. This review synthesizes the current understanding of both primary and acquired resistance mechanisms to ICIs in HCC. Compared with existing reviews, this article uniquely integrates the latest evidence on metabolic reprogramming and tumor immune microenvironment (TIME) remodeling in HCC. It also emphasizes the mechanistic crosstalk between oncogenic signaling, immunosuppression, and metabolic adaptation, providing an updated and more comprehensive framework for understanding ICI resistance. It provides a valuable reference for future research aimed at overcoming therapeutic resistance in this malignancy. Full article

Kiwifruit (Actinidia spp.) is valued for its sensory quality and nutritional richness but faces postharvest challenges such as rapid softening, chilling injury, and physiological disorders. Conventional management strategies help maintain quality yet insufficient to capture the complexity of ripening, stress physiology, and cultivar-specific variation. Recent research emphasizes the continuum from preharvest to postharvest, where orchard practices, harvest maturity, and handling conditions influence quality and storage potential. Omics-driven studies, particularly transcriptomics and metabolomics, have revealed molecular networks regulating softening, sugar–acid balance, pigmentation, antioxidant properties, and chilling tolerance. Integrated multi-omics approaches identify key biomarkers and gene–metabolite relationships linked to ripening and stress responses. Complementing omics, nondestructive estimation technologies, including hyperspectral imaging, near-infrared spectroscopy, acoustic profiling, and chemometric models are emerging as practical tools for real-time classification of maturity, quality, and storability. When calibrated with omics-derived biomarkers, these technologies provide predictive, non-invasive assessments that can be deployed across the supply chain. Together, the convergence of postharvest physiology, omics, and nondestructive sensing offers a pathway toward precision quality management and sustainable kiwifruit production. This review synthesizes recent advances across these domains, highlighting mechanistic insights, practical applications, and future directions for integrating omics-informed strategies with commercial postharvest technologies. Full article

Background/Objective: Central pathophysiological heterogeneity among Parkinson’s disease (PD) motor subtypes has been increasingly recognized, yet subtype-specific peripheral disturbances are limited. We aimed to characterize demographic, biochemical, and neurophysiological differences among PD motor subtypes, evaluate hematoinflammatory effects on peripheral and proximal motor conduction, and identify prognostic phenotypic biomarkers. Methods: A total of 110 participants (60 idiopathic PD patients (30 akinetic-rigid (AR), 30 tremor-predominant (TD), and 50 age- and sex-matched healthy controls (HCs)) were enrolled. Demographic data, nerve conduction studies (NCS) including detailed F-wave analysis, and hematoinflammatory markers were collected. Kruskal–Wallis, linear mixed models, multivariable regression, and ROC analyses were applied. Results: Hematoinflammatory indices were elevated in both subtypes compared with HCs, with more pronounced changes in AR (mean platelet volume (MPV) H = 4.367, p = 0.003; systemic inflammatory response index (SIRI) H = 3.929, p = 0.004). AR showed severe upper-limb–predominant motor involvement (median motor onset latency H = 55.30, p < 0.001; amplitude H = 50.52, p = 0.04; conduction velocity H = 49.15, p < 0.001), whereas TD showed milder, lower-limb–predominant changes (tibial motor onset latency H = 19.89, p < 0.001; amplitude H = 51.50, p = 0.02; velocity H = 15.39, p < 0.001). AR also demonstrated prolonged minimal (Fmin)/mean (Fmean) ulnar F-wave latencies versus TD (respectively, H = 10.51, p = 0.001; H = 8.79, p = 0.003), with both showing increased tibial Fmean/Fmax latencies. Platelet–eosinophil indices independently predicted ulnar F-latencies (B = 0.104–0.105; p = 0.001; model R² = 0.21–0.39). Select F-wave metrics yielded ROC AUCs ≈ 0.65–0.92 (ulnar Fmin AUC ≈ 0.92 vs. HCs); AR achieved sensitivity/specificity ≈ 70–74%. Conclusions: The AR subtype showed increased hematoinflammatory changes, specifically in MPV and SIRI, as well as a tendency toward more pronounced proximal motor and peripheral nerve conduction impairment compared with TD. Platelet–eosinophil indices and F-wave metrics may represent potential candidate markers for diagnostic or stratification purposes in PD subtyping and could possibly aid in prognostic estimation. Full article

Damage-associated molecular patterns (DAMPs) are endogenous molecules released during cellular stress or injury that trigger sterile inflammation. In perioperative settings, common triggers include surgical trauma, ischemia–reperfusion injury, cardiopulmonary bypass, blood transfusion, and mechanical ventilation. When released extracellularly, DAMPs activate innate immune receptors such as Toll-like receptors (TLRs) and the receptor for advanced glycation end products (RAGE), initiating signaling cascades that amplify inflammation, disrupt endothelial integrity, and promote coagulation and metabolic imbalance. This sterile inflammatory response may extend local tissue injury into systemic organ dysfunction, manifesting clinically as acute lung injury, acute kidney injury, myocardial dysfunction, disseminated intravascular coagulation, and perioperative neurocognitive disorders. Recognizing the central role of DAMPs reframes these complications as predictable consequences of endogenous danger signaling rather than solely as results of infection or hemodynamic instability. This understanding supports the use of established strategies such as protective ventilation and restrictive transfusion to minimize DAMP release. Emerging evidence also suggests that anesthetic agents may influence DAMP-mediated inflammation: propofol and dexmedetomidine appear to exert anti-inflammatory effects, whereas volatile anesthetics show variable results. Although clinical data remain limited, anesthetic choice and perioperative management may significantly affect systemic inflammatory burden and recovery. Future research validating DAMPs as biomarkers and therapeutic targets may inform precision anesthetic strategies aimed at modulating sterile inflammation, ultimately enhancing perioperative outcome. Full article

Background/Objectives: Immune checkpoint inhibitors (ICIs) show durable efficacy in tumors with deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H), yet clinical responses remain heterogeneous. This study aimed to define immune subgroups within dMMR/MSI-H tumors and develop a reproducible transcriptomic signature predictive of ICI response. Methods: Four MSI-H-enriched cancer types (UCEC, COAD, READ, STAD) from The Cancer Genome Atlas were analyzed. Tumors were stratified by immune cell infiltration (MCP-counter immune composite score) and T-cell-inflamed gene expression profiles (GEP score). Integrating these two axes defined four immune subgroups. Differential expression, random forest feature selection, and pathway enrichment were performed to identify immune programs. A 20-gene immune signature representing the most immune-active subgroup was developed and validated across TCGA, GEO (GSE39582), and IMvigor210 cohorts. Results: Among the four subgroups, the most immune-active group showed strong activation of interferon signaling, antigen presentation, and T-cell-mediated pathways. The 20-gene signature—including CD74, STAT1, TAP1, and HLA-class genes—achieved high reproducibility (mean AUC = 0.95 ± 0.02; accuracy ≈ 89%). In the IMvigor210 cohort, this signature identified tumors with higher PD-L1 blockade response (55.6% vs. 32.8%, p = 0.034) and improved survival trends in the TMB-high subset. Conclusions: The proposed 20-gene signature quantitatively captures immune heterogeneity in dMMR/MSI-H tumors and serves as a practical, interpretable biomarker to identify true ICI responders and guide precision immunotherapy. Full article

Background/Objectives: Cytarabine (Ara-C) remains central to acute myeloid leukemia therapy but is limited by unpredictable systemic toxicities. Preclinical studies have long documented multi-organ injury, yet findings remain fragmented. This systematic review synthesizes animal evidence to clarify the spectrum, dose–response patterns, and mechanisms of cytarabine-induced toxicity. Methods: Following PRISMA 2020 guidelines and PROSPERO registration (CRD420251081384), a comprehensive search of PubMed, MEDLINE, Scopus, Cochrane Library and Embase identified eligible in vivo animal studies. Data extraction covered animal models, dosing regimens, routes of administration, histopathological and biochemical endpoints and mechanistic findings. Risk of bias and study quality were assessed using SYRCLE’s tool, CAMARADES checklist and an adapted Newcastle–Ottawa Scale, with reporting benchmarked against ARRIVE 2.0. Results: Eighty-one studies (1964–2024) were included. Cytarabine produced dose- and regimen-dependent toxicities across multiple organs. Neurotoxicity was most frequently reported, followed by intestinal mucositis, ocular injury, alopecia, hepatotoxicity, nephrotoxicity, and developmental anomalies. Mechanistic analyses consistently implicated oxidative stress, inflammatory cascades, apoptosis, and epigenetic dysregulation. Study quality was moderate, with frequent deficiencies in randomization, blinding, and sample-size justification, raising concerns about reproducibility. Cardiotoxicity, despite clinical relevance, was virtually absent from preclinical evaluation. Conclusions: Preclinical evidence suggests cytarabine’s systemic toxicity as a multifactorial process extending beyond rapidly proliferating tissues. While animal studies provide mechanistic insights, methodological weaknesses and translational gaps limit predictive value. Future research must adopt rigorous design, systematically assess underexplored toxicities, and integrate molecular profiling to identify biomarkers and protective strategies. Full article

Background/Objectives: The polypeptide N-acetylgalactosaminyltransferase (GALNT) family initiates mucin-type O-glycosylation, a post-translational modification that plays a pivotal role in cellular signaling, adhesion, and immune evasion. Dysregulated GALNT expression has been increasingly implicated in carcinogenesis. Methods: We reviewed the literature on the expression, function, and clinical relevance of GALNT isoforms across various cancers, with a focus on their mechanistic roles, biomarker potential, and therapeutic implications. Results: Aberrant GALNT expression is observed in numerous malignancies, including breast, colorectal, gastric, lung, ovarian, and hepatocellular carcinomas. Isoforms such as GALNT1, -T2, -T3, and -T14 contribute to tumorigenesis by modulating the glycosylation of mucins such as Mucin-1 (MUC1), epithelial growth factor receptors (EGFR), and other signaling proteins. These alterations promote cancer cell proliferation, metastasis, epithelial–mesenchymal transition (EMT), and chemoresistance. Deranged GALNT expression is frequently associated with poor prognosis, and certain GALNT genotypes predict treatment response. However, functional redundancy among isoforms poses challenges for selective targeting. Conclusions: Despite their strong potential as modulators of cancer progression, GALNTs face substantial limitations in terms of substrate identification, mechanistic clarity, immune relevance, and therapeutic tractability. Overcoming these challenges requires advanced glycoproteomics, development of isoform-specific tools, and integrated studies across cancer and immunology to fully harness GALNT biology for clinical benefit. Full article

Background/Objectives: Anxiety disorders are highly prevalent and impairing psychiatric conditions. Conventional diagnostic approaches based on symptom checklists lack biological specificity and often fail to guide treatment decisions effectively. This study protocol outlines a multidimensional, prospective investigation designed to identify behavioral and neurobiological biomarkers predictive of treatment response in individuals with anxiety-related symptoms, grounded in the Research Domain Criteria framework. Methods: This observational, longitudinal study (NCT06773585) will include a transdiagnostic sample of clinical anxiety group alongside a healthy control group (185 participants, including 145 patients with anxiety disorders and 40 healthy controls). Participants will undergo comprehensive baseline assessments, including clinical interviews, self-report questionnaires, a virtual reality (VR)-based behavioral task, electroencephalography (EEG), electrocardiography (ECG), and structural and functional brain magnetic resonance imaging. Follow-up assessments will be conducted at 2, 6, and 12 months, with recruitment and data collection planned from 2024 to 2029. These complementary modalities are integrated to capture behavioral, physiological, and neural indicators of anxiety and its treatment response. Multimodal baseline features will be used to construct machine-learning models predicting treatment response, defined as ≥40% reduction in anxiety severity scores. Longitudinal analyses will examine symptom trajectories and neural mechanisms associated with response. Neurobiological comparisons will be made across timepoints and between responders, non-responders, and healthy controls. Conclusions: By identifying objective, biologically grounded markers of anxiety and treatment response, our findings will contribute to the development of personalized assessment tools and scalable digital interventions for psychiatric care. Full article