Search Results (1,278)

Cancer-associated fibroblasts (CAFs) are crucial regulators of the tumor microenvironment (TME), promoting cancer progression, immune suppression, and therapy resistance. Single-cell transcriptomics has identified at least five distinct CAF subtypes: myofibroblastic (myCAFs), inflammatory (iCAFs), antigen-presenting (apCAFs), metabolic (meCAFs), and vascular/developmental (vCAFs/dCAFs), each with unique localization, signaling, and functions. While CAFs are well studied in epithelial cancers, their roles in sarcomas are less understood despite the shared mesenchymal origin of tumor and stromal cells. This overlap blurs the line between malignant and non-malignant fibroblasts, raising fundamental questions about the identity of CAFs in mesenchymal tumors. In this narrative review, we explore the heterogeneity and plasticity of CAFs across solid tumors, focusing on their role in immune evasion, epithelial-to-mesenchymal transition (EMT), and resistance to chemotherapy, targeted therapy, and immunotherapy. We highlight emerging evidence on CAF-like cells in sarcomas and their contribution to tumor invasion, immune exclusion, and metastatic niche formation. We also assess new strategies to target or reprogram CAFs and suggest that CAF profiling may serve as a potential biomarker for patient stratification. Understanding CAF biology across various tumor types, including those with dense stroma and immunologically cold sarcomas, is crucial for developing more effective, personalized cancer treatments. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by persistent synovial inflammation, progressive joint destruction, and systemic complications. Despite significant progress in targeted therapies, major clinical challenges persist, including heterogeneous treatment responses and therapeutic resistance. This review aims to critically evaluate emerging immunomodulatory strategies—focusing on immune checkpoints, microRNAs (miRNAs), and cell-based therapies—as potential diagnostic and therapeutic tools. Methods: This non-systematic literature review involved a comprehensive analysis of recent studies to investigate emerging immunomodulatory strategies in RA. Special attention was given to immune checkpoint pathways—cytotoxic T-lymphocyte antigen 4 (CTLA-4); programmed death-1 (PD-1) and its ligand, PD-L1; and inducible T-cell costimulator (ICOS)—as well as cell-based therapies. Additionally, miRNA-based interventions were examined for their diagnostic and therapeutic potential. Results: Immune checkpoint modulation has demonstrated preclinical efficacy in attenuating inflammatory responses and restoring immune tolerance. Concurrently, miRNAs have emerged as both biomarkers and therapeutic agents, with exosome-based delivery systems enhancing their function. Cell-based therapies have shown robust immunoregulatory effects with acceptable safety profiles. Notably, integrative strategies that combine checkpoint inhibitors, cell-based interventions, and miRNA delivery exhibit synergistic effects and offer a promising avenue for personalised treatment, when guided by molecular and transcriptomic profiling. The majority of these approaches remain at the preclinical or early translational stage. Conclusions: Targeted immunomodulation is poised to transform RA management. The integration of cell therapies, checkpoint inhibition, and miRNA manipulation with omics technologies holds promise for enhancing therapeutic precision and safety. Advancing towards personalised immunotherapy will necessitate a multidisciplinary and patient-centred effort. Full article

Long noncoding RNAs (lncRNAs) have emerged as key regulators of gene expression during seed development and physiology. This review examines the diverse roles of lncRNAs in key stages of seed development, including embryogenesis, maturation, dormancy, germination, and aging. It integrates the current understanding of the biogenesis and classification of lncRNAs, emphasizing their functional mechanisms in seeds, particularly those acting in cis and trans. These mechanisms include the scaffolding of polycomb and SWI/SNF chromatin remodeling complexes, the guidance of RNA-directed DNA methylation, the ability to function as molecular decoys, and the modulation of small RNA pathways via competitive endogenous RNA activity. This review highlights the regulatory influence of lncRNAs on abscisic acid (ABA) and gibberellin (GA) signaling pathways, as well as light-responsive circuits that control dormancy and embryonic root formation. Endosperm imprinting processes that link parental origin to seed size and storage are also discussed. Emerging evidence for epitranscriptomic modifications, such as m6A methylation, and the formation of LncRNA–RNA-binding protein condensates that maintain resting states and coordinate reserve biosynthesis are also reviewed. Advances in methodologies, including single-cell and spatial transcriptomics, nascent transcription, direct RNA sequencing, and RNA–chromatin interaction mapping, are expanding the comprehensive lncRNA landscape during seed development and germination. These advances facilitate functional annotation. Finally, possible translational research applications are explored, with a focus on developing lncRNA-based biomarkers for seed vigor and longevity. Full article

This study aimed to compare the therapeutic effects of fresh (CMF) and dried (CMD) Centipeda minima against allergic rhinitis (AR), elucidate their underlying molecular mechanisms, and identify the bioactive compounds responsible for their immunomodulatory actions. An ovalbumin-induced AR mouse model was treated with CMF or CMD extracts, followed by evaluation of nasal symptoms, serum biomarkers (IgE, histamine, cytokines), and nasal mucosa histopathology. Transcriptomics and widely targeted metabolomics were integrated with network pharmacology to identify differentially expressed genes and bioactive components, which were further validated in RAW264.7 and RBL-2H3 cells. CMF and CMD exhibited distinct anti-AR mechanisms: CMF predominantly suppressed Th2 responses (reducing IgE, IL-6, and histamine while elevating IL-10), whereas CMD enhanced Th1 activity (increasing IFN-γ). Metabolomic analysis revealed CMF was rich in amino acids while CMD contained higher flavonoids, with neocnidilide and 6-gingerol identified as key bioactive compounds that modulated TNF-α, IL-6, and IL-10 via NF-κB and JAK-STAT pathways. These findings demonstrate that CMF and CMD exert complementary anti-inflammatory effects through Th2 inhibition and Th1 activation, respectively, providing a molecular basis for the traditional use of Centipeda minima and highlighting its bioactive compounds as potential therapeutics for inflammatory diseases. Full article

Cutaneous T-cell Lymphomas (CTCLs) are a heterogeneous group of non-Hodgkin lymphomas that currently have an incompletely understood pathophysiology and several challenges in both diagnosis and management. Single-cell RNA sequencing (scRNA-seq) is a powerful tool that enables the analysis of gene expression at the individual-cell level, revealing cellular heterogeneity and a complex tumor microenvironment. As single-cell RNA sequencing has become increasingly utilized, we aimed to provide an update on recent notable applications of single-cell RNA sequencing in CTCL and their findings. The included studies highlight the intricate network of interactions in the tumor microenvironment that contributes to tumorigenesis. While CTCL is notoriously heterogeneous, our results identify key markers that prove promising for diagnosis, prognostication, and therapeutic targets. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly heterogeneous malignancy, characterized by low tumor cellularity, a dense stromal response, and intricate cellular and molecular interactions within the tumor microenvironment (TME). Although bulk omics technologies have enhanced our understanding of the molecular landscape of PDAC, the specific contributions of non-malignant immune and stromal components to tumor progression and therapeutic response remain poorly understood. Methods: We explored genome-wide DNA methylation and transcriptomic data from the Cancer Genome Atlas Pancreatic Adenocarcinoma cohort (TCGA-PAAD) to profile the immune composition of the TME and uncover gene co-expression networks. Bioinformatic analyses included DNA methylation profiling followed by hierarchical deconvolution, epigenetic age estimation, and a weighted gene co-expression network analysis (WGCNA). Results: The unsupervised clustering of methylation profiles identified two major tumor groups, with Group 2 (n = 98) exhibiting higher tumor purity and a greater frequency of KRAS mutations compared to Group 1 (n = 87) (p < 0.0001). The hierarchical deconvolution of DNA methylation data revealed three distinct TME subtypes, termed hypo-inflamed (immune-deserted), myeloid-enriched, and lymphoid-enriched (notably T-cell predominant). These immune clusters were further supported by co-expression modules identified via WGCNA, which were enriched in immune regulatory and signaling pathways. Conclusions: This integrative epigenomic–transcriptomic analysis offers a robust framework for stratifying PDAC patients based on the tumor immune microenvironment (TIME), providing valuable insights for biomarker discovery and the development of precision immunotherapies. Full article

Yajiangxue cattle (XF) is three-way crossbred cattle developed specifically for producing high-quality beef in the Tibetan Plateau by introducing the bloods of Tibetan yellow cattle (HF) and Angus cattle into Tibetan yak (MF). In the present study, we mainly focused on fat deposition and metabolism changes and used RNA-seq and LC-MS/MS-based metabolomics to partially explain the meat quality improvement in Yajiangxue cattle. Differential expression analysis revealed 1762, 2949, and 2931 different expression genes in XF vs. HF, XF vs. MF, and XF vs. cattle–yak (PF), respectively, such as BMP2, WISP2, FGF1, IL1B, IL6, and WNT5B. Immune response, oxidation–reduction processes, and fatty acid metabolism were markedly enriched. Furthermore, an initial identification revealed 319 metabolites using positive ion mode and 289 metabolites using negative ion mode in bovine adipose tissue across four breeds/populations. Of these, 143 were differential metabolites in positive ion mode, while 166 were in negative ion mode. The main pathways of metabolism affected by breed/population were unsaturated fatty acid biosynthesis, tryptophan and tyrosine biosynthesis, primary bile acid biosynthesis, cholesterol metabolism, beta-alanine metabolism, etc. Similarly, both the transcriptome and the metabolome results highlighted fatty acid metabolism. These results could help elucidate the biological mechanisms involved in fat deposition and identify valuable biomarkers for specific metabolite accumulation. Full article

Brain metastases are a significant complication of non-small-cell lung cancer (NSCLC), contributing to high morbidity and mortality rates. The introduction of immune checkpoint inhibitors (ICIs) has opened new therapeutic avenues for patients with NSCLC, including those with brain metastases. However, the distinct microenvironment of the brain presents unique challenges to the effectiveness of these treatments. This review examines the mechanisms by which ICIs impact brain metastases from NSCLC, with particular focus on immune cell trafficking across the blood–brain barrier (BBB), tumor microenvironment modulation, and transcriptomic evolution of brain-tropic tumor clones. Unlike prior reviews, we integrate emerging data from single-cell and spatial transcriptomic studies, BBB disruption mechanisms, and the tumor-supportive role of brain-resident glia. We also critically evaluate key clinical trials and real-world evidence, highlighting differences in ICI efficacy across patient subgroups and therapeutic contexts. Additionally, we address the evolving role of surgical resection, stereotactic radiosurgery, and cerebrospinal-fluid-based biomarkers in optimizing ICI-based treatment strategies. This synthesis provides a comprehensive, mechanistic, and clinically relevant framework for improving outcomes in patients with NSCLC brain metastases treated with immunotherapy. Full article

Background: Schizophrenia is a complex neuropsychiatric disorder whose pathophysiology may involve oxidative stress-induced neuronal damage and inflammation. We conducted a cross-species study to elucidate oxidative stress dysregulation in schizophrenia. Methods: We measured peripheral oxidative stress biomarkers (malondialdehyde [MDA], nitric oxide [NO], reduced glutathione [GSH], superoxide dismutase [SOD], catalase [CAT], advanced protein oxidation products [APOP]), and C-reactive protein (CRP) in antipsychotic-naïve schizophrenia patients and matched controls. We also assayed liver enzymes (ALP, ALT, AST) as indicators of systemic metabolic stress. In parallel, we re-analyzed published single-cell RNA-sequencing data from a Setd1a^+/–^ mouse model of schizophrenia, focusing on prefrontal cortex (PFC) cell types and oxidative stress-related gene expression. Results: Patients with schizophrenia showed markedly elevated MDA and NO (indicators of lipid and nitrosative stress) and significantly reduced antioxidant defenses (GSH, SOD, CAT) versus controls (p < 0.01 for all comparisons). Notably, urban patients exhibited higher oxidative stress biomarker levels than rural patients, implicating environmental contributions. Liver function tests revealed increased ALT, AST, and ALP in schizophrenia, suggesting hepatic/metabolic dysregulation. Single-cell analysis confirmed dysregulated redox pathways in the schizophrenia model; PFC neurons from Setd1a^+/–^ mice displayed significantly lower expression of key antioxidant genes (e.g., Gpx4, Nfe2l2) compared to wild-type, indicating impaired glutathione metabolism. Conclusions: Our integrative data identify convergent oxidative stress imbalances in schizophrenia across species. These findings advance a mechanistic understanding of schizophrenia as a disorder of redox dysregulation and inflammation. They also have translational implications as augmenting antioxidant defenses (for example, with N-acetylcysteine or vitamins C/E) could mitigate oxidative injury and neuroinflammation in schizophrenia, representing a promising adjunct to antipsychotic therapy. Full article

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by lung scarring, impaired function, and high mortality. Effective therapies to reverse fibrosis are lacking. This study aims to uncover the molecular mechanisms of IPF, explore diagnostic biomarkers, and identify therapeutic targets. Methods: Multi-omics data were integrated to identify biomarkers with causal associations to IPF using Mendelian randomization and transcriptomic analysis. Machine learning was employed to construct a diagnostic model, and single-cell transcriptomic analysis determined gene expression patterns in fibrotic lung tissue. Results: Seven core genes (GREM1, UGT1A6, CDH2, TDO2, HS3ST1, ADGRF5, and MPO) were identified, showing strong diagnostic potential (AUC = 0.987, 95% CI: 0.972–0.987). These genes exhibited distinct distribution patterns in fibroblasts, endothelial cells, epithelial cells, macrophages, and dendritic cells. Conclusions: This study highlights key genes driving IPF, involved in pathways related to metabolism, immunity, and inflammation. However, their utility as fluid-based biomarkers remains unproven and requires protein-level validation in prospective cohorts. By integrating genomic, immunological, and cellular insights, it provides a framework for targeted therapies and advances mechanism-based precision medicine for IPF. Full article

Human engagement in extreme activities, from spaceflight to deep-sea diving and extreme sports, presents unique physiological challenges. Understanding the molecular mechanisms underlying adaptations to these demands is crucial for developing strategies to enhance human performance and resilience in such environments. This review integrates multi-omics data across a range of extreme phenotypes, including astronauts, scuba divers, acute alcohol consumers, long-haul flight passengers, bodybuilders, and simulation racers. We analyze current literature in genomic, transcriptomic, proteomic, metabolomic, and metagenomic studies to identify common and phenotype-specific adaptations, highlighting potential biomarkers and pathways associated with resilience in harsh conditions. This integrated approach offers insights into human adaptability and provides a foundation for developing personalized strategies to mitigate risks and enhance performance in extreme environments, with particular relevance to extended spaceflight. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

Neuropathic pain (NP) is a debilitating chronic pain condition with complex molecular mechanisms and inadequate therapeutic solutions. This study aims to identify temporal transcriptomic changes in NP using multiple bioinformatics and machine learning algorithms. A total of 10 mouse samples (5 per group) were harvested at each time point (day three, day seven, and day fourteen), following spared nerve injury and a sham operation. Differentially expressed gene (DEG) analysis and an intersection among the three time-point groups revealed 54 common DEGs. The GO and KEGG analyses mainly showed enrichment in terms of immune response, cell migration, and signal transduction functions. In addition, the interaction of the LASSO, RF, and SVM-RFE machine learning models on 54 DEGs resulted in Ngfr and Ankrd1. The cyan module in WGCNA was selected for a time-dependent upward trend in gene expression. Then, 172 genes with time-series signatures were integrated with 54 DEGs, resulting in 11 shared DEGs. Quantitative RT-PCR validated the temporal expressions of the above genes, most of which have not been reported yet. Additionally, immune infiltration analysis revealed significant positive correlations between monocyte abundance and the identified genes. The TF-mRNA-miRNA network and drug-target network revealed potential therapeutic drugs and posttranscriptional regulatory mechanisms. In conclusion, this study explores genes with time-series signatures as biomarkers in the development and maintenance of NP, potentially revealing novel targets for analgesics. Full article

Background/Objectives: Cancer immunotherapy has revolutionized the field of oncology by harnessing the immune system to attack cancer cells, increasing survival in a broad spectrum of malignancies. However, despite its positive therapeutic benefit, immunotherapy is also associated with a spectrum of adverse events affecting various vital organs, including the cardiovascular system. Methods: We conducted a comprehensive review of the available literature on the epidemiology, pathophysiological mechanisms, and current management approaches for cardiovascular adverse events associated with cancer immunotherapy. In addition, we evaluated emerging personalized strategies and interventions aimed at mitigating these risks and improving patient outcomes. Results: Immunotherapy is associated with a broad spectrum of potentially serious cardiovascular adverse events, including immune-mediated myocarditis, heart failure, arrhythmias, pericarditis, and accelerated atherosclerosis. Among these, immune checkpoint inhibitor-associated myocarditis is the most well characterized and potentially fatal form of cardiotoxicity, with reported mortality rates approaching 50%. Similarly, chimeric antigen receptor T-cell therapy, despite its powerful antitumor efficacy, is frequently associated with cytokine release syndrome—a profound immune activation that can lead to significant systemic and cardiovascular complications. In response to these challenges, several personalized strategies are currently under development, including artificial intelligence and machine learning approaches, genetic and transcriptomic profiling, novel biomarker discovery, and integrated risk scoring systems, all aimed at enhancing risk stratification and improving patient care. Conclusions: Cancer immunotherapy has been associated with a range of immune-related cardiac adverse events, both non-severe and severe. As such, it is critically important to adopt a personalized approach to patient management before, during, and after the administration of immunotherapy. Early recognition through heightened clinical vigilance, along with the implementation of individualized risk assessment tools, is essential for identifying patients at high risk of immunotherapy-induced cardiotoxicity. These strategies are imperative for optimizing patient outcomes and ensuring safe and effective cancer treatment. Full article

Background: Chemokines play a pivotal role in the progression of osteoarthritis (OA), but their exact mechanisms remain unclear. This study aimed to identify potential chemokine-associated biomarkers and investigate their causal relationships with OA. Methods: Transcriptome and genome-wide association study (GWAS) data were obtained from public databases, while chemokine-related genes (CRGs) were sourced from the literature. Initially, CRGs were expanded, followed by Mendelian randomization (MR) analysis, differential expression analysis, machine learning, and receiver operating characteristic (ROC) curve plotting to identify potential biomarkers. The causal relationships between these biomarkers and OA, as well as their biological functions, were further explored. Results: Fourteen candidate genes were identified for machine learning analysis, with DDIT3, CEBPB, CX3CR1, and ARHGAP25 emerging as feature genes. CEBPB and CX3CR1, which exhibited AUCs > 0.7 in the GSE55235 and GSE55457 datasets, were selected as potential biomarkers. Notably, CEBPB expression was lower, while CX3CR1 expression was elevated in the case group. Furthermore, both genes were co-enriched in spliceosome, lysosome, and cell adhesion molecule pathways. MR analysis confirmed that CEBPB and CX3CR1 were causally linked to OA and acted as protective factors (IVW model for CEBPB: OR = 0.9051, p = 0.0001; IVW model for CX3CR1: OR = 0.8141, p = 0.0282). Conclusions: CEBPB and CX3CR1 were identified as potential chemokine-related biomarkers, offering insights into OA and suggesting new avenues for further investigation. Full article