Search Results (1,604)

Structural and functional disruptions of the epigenome are hallmarks of breast and ovarian carcinogenesis. This review dissects the reciprocal regulatory networks co-operated by DNA methyltransferases (DNMTs), ten-eleven translocation enzymes (TETs), and key non-coding RNAs (microRNAs and lncRNAs). We map the precise molecular mechanisms through which these epigenetic modulators alter chromatin accessibility, drive transcriptional reprogramming, and promote phenotypic plasticity in hormone-dependent malignancies. By systematically contrasting the distinct yet overlapping epigenetic profiles of breast and ovarian tumors, we elucidate how these aberrations dictate clinical outcomes. This comprehensive synthesis offers critical insights into the dual utility of these epigenetic elements as dual-purpose diagnostic biomarkers and druggable therapeutic targets, laying the groundwork for next-generation targeted epigenetical therapies. Full article

In this paper, we consider the extremal trace problem for the coupled Laplacian on the sets homeomorphic to the Stiefel manifold defined on the complex Euclidean space. The study is implemented via various mathematical methods, including topological and probabilistic approaches. A detailed, comprehensive classification of the stationary points is given, which itself deserves to be considered as a general method in the framework of the optimization theory. Finally, an application to biologically meaningful integration of heterogeneous datasets, in which the structure of molecular interactions serves as a significant constraint for the mathematical model, is proposed. The main advantage of the elaborated method in comparison with the previously used ones is the absence of any conditions on the structure of the initial heterogeneous datasets. This paper is a continuation of a series of papers by our research group devoted to the development of new mathematical methods for integrating multi-omics data. Full article

Soybean is a strategic crop for global protein and vegetable oil supply chains; however, genetic improvement remains constrained by genotype-dependent regeneration, variable transformation efficiency, and regulatory concerns regarding stable transgene integration. This review synthesizes emerging DNA-free and genotype-independent genome-editing frameworks for soybean, where genotype independence is defined as the ability to recover fertile, non-chimeric edited plants across elite germplasm. We critically examine the soybean genome-editing toolbox, including CRISPR-Cas9, Cas12a, multiplex editing systems, base editing, and prime editing, and discuss persistent bottlenecks associated with target selection, off-target assessment, editability, and plant recovery. Particular emphasis is placed on artificial intelligence (AI)-assisted approaches that integrate genomic, epigenomic, chromatin-accessibility, and multi-omics datasets to improve target prioritization, guide RNA design, off-target prediction, and locus- and genotype-specific editability assessment. We further evaluate DNA-free genome-editing technologies, including CRISPR-Cas ribonucleoproteins, transient RNA-based systems, and nanocarrier-mediated delivery platforms, highlighting their potential to generate non-integrative edits while reducing prolonged nuclease exposure. In addition, we discuss regeneration reprogramming strategies based on developmental regulators and morphogenic modules, including BBM-WUS, GRF-GIF, de novo meristem induction, and somatic embryogenesis, as enabling technologies for overcoming cultivar-dependent regeneration barriers. Importantly, this review proposes an integrated AI-to-field framework that connects target discovery, editability prediction, DNA-free editing, regeneration reprogramming, phenotypic validation, and breeding deployment into a unified soybean improvement pipeline. We further highlight emerging opportunities in multi-omics-guided target discovery, genotype-aware prediction models, regeneration-aware editing strategies, and closed-loop machine-learning systems that continuously improve editing decisions through experimental feedback. Collectively, these convergent innovations provide a practical foundation for accelerating the development of climate-resilient, nutritionally enhanced, and industry-ready soybean cultivars. Full article

Stroke remains one of the leading causes of death and disability worldwide, yet its clinical management is constrained by substantial biological heterogeneity that single-biomarker and single-omics approaches fail to resolve. The integration of multiple molecular data layers, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and immunomics, offers a transformative framework for investigating the underlying neuroinflammatory mechanisms of different stroke subtypes and endotypes. In this review, we synthesize the current multi-omics evidence in stroke by examining how genetic variants propagate through regulatory and immune pathways and generate measurable molecular signatures and clinically relevant biomarkers. We investigate the roles of microglia, infiltrating monocyte-derived macrophages, astrocytes, neutrophils, T cells, and endothelial cells as interacting nodes in the neuroimmune network after stroke, and analyze how spatially resolved single-cell transcriptomics illuminate state-specific programs previously undetectable in bulk tissue analyses. We discuss how proteomics and metabolomics translate these cellular programs into actionable circulating biomarkers and examine emerging evidence on blood–brain barrier disruption and neurovascular unit remodeling as multi-omics-defined targets. We then explore AI and machine learning frameworks enabling the integration of heterogeneous, high-dimensional datasets for endotype classification, patient stratification, and therapeutic response prediction. Finally, we address translational barriers, including analytical standardization, multi-ancestry generalizability, and regulatory readiness, and propose a roadmap for precision stroke medicine based on systems immunology. The core conceptual point of this review is the shift from describing omics findings in stroke cases to redefining biologically meaningful neuroinflammatory endotypes and using multi-omics to enable precision cerebrovascular medicine. Full article

Chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, and interstitial lung diseases (ILDs), represent a major global health burden. Their significant clinical and biological heterogeneity complicates diagnosis and limits the efficacy of traditional, one-size-fits-all management approaches. The advent of high-throughput genomic and multi-omic technologies has initiated a paradigm shift from syndromic classification to molecular-based endotyping. A narrative review of the literature was performed, synthesising foundational and recent research in the genomics, epigenomics, and multi-omics of chronic respiratory diseases. Key studies were selected based on their relevance to genetic architecture, biomarker development, and translational applications in precision medicine. We discuss the complex genetic architecture of pulmonary conditions, highlighting the contribution of both rare, high-penetrance variants, such as SERPINA1, CFTR, and BMPR2, and polygenic risk from many common variants, such as HHIP, FAM13A, and IL33. We provide detailed analyses of polygenic risk scores (PRSs) for COPD and asthma, including their construction, validation across ancestries, and predictive performance. We detail how integrative multi-omic approaches, including transcriptomics, proteomics, and metabolomics, are successfully defining molecular endotypes, such as Type 2-high asthma, which, in turn, inform the use of targeted biologic therapies. Finally, we review the development of molecular diagnostics, including metagenomic sequencing of infections and liquid biopsies for lung cancer and the development of prognostic biomarkers. The genomic revolution is transforming pulmonary medicine through the discovery of novel disease pathways, precise molecular classification, and the recognition of new therapeutic targets. Despite major challenges in functional interpretation, data integration, and clinical–translational equity, these technologies hold the key to a new era of personalised respiratory health and precision medicine. Full article

Turner syndrome (TS), a disorder caused by the complete or partial absence of an X chromosome, exhibits significant clinical variability that cannot be fully explained by chromosomal anomalies alone. This narrative review highlights the crucial role of epigenetic mechanisms, particularly X-chromosome inactivation (XCI), in shaping the TS phenotype. The haploinsufficiency of genes that normally escape XCI is a primary driver of TS features. The specific epigenetic consequences depend on the chromosomal anomaly. In complete monosomy (45,X), the absence of escape-mediated dosage compensation genes from a second X chromosome amplifies haploinsufficiency across X-linked escape genes. Isochromosome Xq (i(Xq)) variants involve the loss of the short arm (Xp) and duplication of the long arm (Xq), creating a dual dosage imbalance with extreme XCI skewing. Carriers of i(Xq) also have a heightened risk for autoimmune disorders compared to those with 45,X TS. For ring-X chromosomes (r(X)), which are mitotically unstable, the functional status of the XIST gene is critical. If the ring is XIST-negative, it remains transcriptionally active, resulting in functional disomy and a more severe phenotype with pronounced neurodevelopmental and craniofacial features. Ultimately, the clinical heterogeneity in TS arises from a complex interplay of the specific chromosomal structure, tissue-specific mosaicism, XIST function, and variable escape from XCI, defining TS as a disorder of epigenetic and gene-regulatory imbalance. However, future research requires a better understanding of the complex mechanism of X-chromosome inactivation. Full article

The convergence of artificial intelligence (AI) and organoid technology represents a transformative advance toward precision and predictive medicine. Organoids derived from pluripotent stem cells or patient tissues provide physiologically relevant three-dimensional models that recapitulate key aspects of native organ architecture and function. However, intrinsic biological heterogeneity, high-content imaging outputs, and dynamic spatiotemporal processes pose significant analytical challenges that exceed the capacity of conventional approaches. Recent advances in AI and machine learning enable automated image segmentation, quantitative morphometric profiling, and predictive modeling of organoid growth, differentiation, and therapeutic response, thereby enhancing reproducibility and translational relevance. The integration of multimodal datasets, including imaging, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, has further enabled the development of organoid-based digital twins and in silico disease simulations to optimize personalized therapy. AI-enabled organoid-on-a-chip platforms, cloud-based analytics, and federated learning frameworks are accelerating the emergence of scalable, privacy-preserving, and data-driven biomedical ecosystems. Despite these advances, critical challenges persist, including data standardization, model interpretability, ethical governance, and clinical validation. In contrast to existing reviews that emphasize isolated AI applications, this study proposes a unified translational framework integrating AI-driven image analytics, multi-omics integration, digital twins, and organoid-on-a-chip systems within a precision medicine paradigm. By synthesizing current developments, methodological advances, and emerging trends, this study highlights how AI-powered organoid platforms can bridge experimental biology and clinical decision-making, with broad implications for drug discovery, disease modeling, and regenerative medicine. This review aims to provide a comprehensive overview of artificial intelligence–enabled organoid platforms by integrating advances in image analytics, multi-omics data integration, digital twins, and microphysiological systems, while highlighting their potential applications and future directions in precision medicine, drug discovery, and regenerative healthcare. Full article

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) representing the most common subtype. Despite major advances in immunotherapy, only a subset of patients benefits from current treatments, highlighting the need to better understand the tumor immune microenvironment (TIME) and the mechanisms underlying immune escape. In this context, extracellular vesicles (EVs) have emerged as key mediators of intercellular communication in lung cancer. This review summarizes the current knowledge on the role of EVs in NSCLC progression and immune regulation. We discuss how EVs contribute to primary tumor growth, dissemination, and pre-metastatic niche formation through the transfer of proteins, metabolites and nucleic acids. Particular attention is given to EV-mediated modulation of immune cells, highlighting their role in both immune suppression and immune activation. Furthermore, we provide an overview of the emerging therapeutic applications of EVs in lung cancer, including their use as drug-delivery systems and immunotherapeutic platforms. Full article

Circulating tumor DNA (ctDNA) was initially conceived as a minimally invasive surrogate for interrogating cancer biology; however, three decades of evidence have demonstrated that plasma is not a passive reservoir of tumor-derived material, but rather a dynamic and biologically heterogeneous milieu in which multiple competing genomic signals coexist. This review explores the level of interpretive rigor required to translate ctDNA detection into clinically actionable precision oncology. Clonal hematopoiesis of indeterminate potential (CHIP) is discussed not as an occasional confounder, but as an intrinsic source of biological background noise, underscoring the critical importance of matched leukocyte sequencing to discriminate tumor-derived alterations from hematopoietic variants, particularly in older individuals and in patients previously exposed to cytotoxic therapies. The widespread assumption that variant allele frequency (VAF) directly reflects tumor burden is critically re-evaluated through the mathematical relationships linking VAF to tumor fraction, local copy-number architecture, and mutation multiplicity. Within this framework, estimation of cancer cell fraction (CCF) and probabilistic discrimination between clonal and subclonal events are examined, including the emergence of reversion mutations as molecular evidence of therapy-driven evolutionary adaptation. The review also addresses the central paradox of ultra-sensitive sequencing technologies: although unique molecular identifiers and duplex sequencing can extend analytical sensitivity below 0.01% VAF, sensitivity in the absence of contextual specificity risks conflating technical artifacts and biologically insignificant alterations with clinically meaningful disease. Equal emphasis is placed on pre-analytical variables, highlighting how sample collection, stabilization, and processing protocols define the upper limit of downstream analytical reliability. Beyond single-nucleotide variants, fragmentomic and methylation-based approaches are presented as complementary orthogonal dimensions capable of revealing tumor-associated signals even when mutational evidence is limited or absent. Longitudinal ctDNA assessment is argued to provide substantially greater biological and clinical insight than isolated static measurements, while robust clinical reporting is shown to depend on transparent disclosure of assay limitations, residual uncertainty related to CHIP, and structured bidirectional communication between molecular laboratories and treating clinicians. Ultimately, the transition from a biomarker-centered model toward an integrated systems-based framework, combining genomics, epigenomics, fragmentomics, and evolutionary modeling, emerges as the defining challenge for the next generation of liquid biopsy in precision oncology. Full article

Diffuse large B-cell lymphoma (DLBCL) is a biologically heterogeneous mature B-cell neoplasm whose classification, prognosis, and therapy have been reshaped by advances in genomic, transcriptomic, epigenomic, single-cell, and spatial profiling technologies. This review focuses on how these approaches have refined the molecular landscape of DLBCL, including recurrent chromosomal translocations, tumor-suppressor alterations, oncogenic signaling pathways, and tumor-microenvironment programs. Cell-of-origin (COO) frameworks remain clinically useful. However, contemporary models extend beyond conventional germinal center categories by incorporating probabilistic genetic subtypes, expression-defined high-risk states, and spatially resolved lymphoma-cell and immune-cell ecosystems. These high-resolution methods clarify intratumoral heterogeneity, identify biologically distinct subgroups, and inform prognosis and therapeutic selection. The review also summarizes how tumor-intrinsic biology and the tumor-microenvironment (TME) shape responses to frontline therapy, targeted agents, antibody-drug conjugates, bispecific antibodies, and CD19-directed CAR T-cell therapy. Particular emphasis is placed on product-specific evidence in relapsed/refractory disease, rational sequencing of immunotherapies, and emerging biomarkers such as circulating tumor DNA-based measurable residual disease (ctDNA-MRD). Together, these developments support a shift from COO-centric classification toward dynamic, biology-driven models that incorporate tumor-intrinsic and microenvironmental determinants to guide personalized therapy in DLBCL. Full article

Moringa, a versatile tree species, has seen an increasing diversification of breeding objectives due to its rich nutritional value and potential applications in ecological restoration. The emergence and integration of multi-omics technologies have provided a revolutionary systems-level research framework for elucidating the fundamental biological mechanisms underlying Moringa’s agronomic traits and nutritional characteristics. This review systematically analyzes the application prospects and omics significance of Moringa based on research trends, and explores in depth the progress made in various omics studies of Moringa: the fourth iteration of the genome has identified specific genes encoding heat shock proteins (HSPs) in Moringa; phenomics reveals differential expression of Moringa under different environmental conditions; and the transcriptomics and metabolomics elucidate differential regulatory networks across different tissues and environments. In the future, multi-omics technologies can be integrated: genomics can further identify rare alleles and localize genetic loci for key agronomic traits; transcriptomics combined with epigenomics can elucidate the spatial regulation of gene expression and epigenetic mechanisms; and proteomics and metabolomics can be integrated to validate pathways and provide targets for improvement. Throughout the process, a high-throughput phenotyping platform utilizing drones will be introduced to dynamically monitor agronomic traits, enabling efficient breeding and accelerating genetic improvement. Full article

Recent advances in single-cell RNA sequencing (scRNA-seq) have transformed neuroscience research by enabling the identification of genes, cell types, and molecular pathways involved in brain development and function. However, scRNA-seq lacks spatial information regarding the anatomic location of gene expression. Emerging spatial genomics technologies, including MERFISH, CosMx, Stereo-seq, and Visium Spatial Gene Expression overcome this limitation by enabling transcriptomic and epigenomic profiling within intact tissue architecture. Integration of spatial genomics with scRNA-seq has revolutionized genomics and biomedical research by allowing gene expression to be mapped in situ at cellular and even subcellular resolution. These advances have facilitated the construction of brain spatial genomics atlases in several species, including mouse, human, non-human primate, and zebrafish. Spatial genomics technologies are particularly valuable for defining cellular heterogeneity across brain regions and characterizing the spatial organization of neuronal circuits when integrated with single-cell sequencing approaches. These reference atlases provide powerful resources for investigating brain development, function and disease, and for identifying region-specific molecular signatures associated with neurological disorders. Here, we review currently available brain spatial genomics atlases and the spatial genomics technologies used to generate these reference resources. Full article

Background: High body mass index (BMI) is a causal risk factor for endometrial cancer, but the tumor molecular mechanisms affected by adiposity remain poorly understood. Here, we characterize the tumor multi-omic landscape of endometrial cancers that have developed on a background of lifelong germline genetic liability to elevated BMI. Methods: We built a polygenic score (PGS) for BMI in women using data on independent, genome-wide significant variants associated with adult BMI in 434,794 women. We performed germline (blood) genotype quality control and imputation on data from 354 endometrial cancer cases from The Cancer Genome Atlas (TCGA). We assigned each case in this TCGA cohort their genetically predicted BMI based on the BMI PGS. Multivariable generalized linear models adjusted for age, stage, microsatellite status and genetic principal components were used to test for associations between the BMI germline PGS and endometrial cancer tumor genome-wide genomic, transcriptomic, proteomic, epigenomic and immune traits in TCGA. Results: High BMI germline PGS was associated with (i) upregulated tumor gene expression in IL6-JAK-STAT3 signaling (FDR = 4.2 × 10⁻⁷) and in other immune/inflammatory pathways; (ii) increased estimated intra-tumor activated mast cell infiltration (FDR = 0.008); and (iii) increased single base substitution (SBS) mutational signature 1 (FDR = 0.03), implicating age-related mutagenesis. In contrast, BMI at diagnosis associated with elevated progesterone receptor expression and alterations in estrogen and androgen signaling. Conclusions: Thus, we integrated germline, somatic and clinical data to identify associations between genetically predicted lifelong liability to higher BMI and endometrial cancer tumor molecular features. These associations inform our understanding of how high BMI may influence the development of this cancer, shaping endometrial tumor biology differentially over the long term. Full article

Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), involve the gradual loss of structure or function of neurons in the nervous system and are an increasing threat to the aging population worldwide. Although these disorders have different clinical features which affect cognition, movement and other vital body functions, they share key underlying molecular and cellular processes. This starts with protein misfolding and aggregation, mitochondrial dysfunction, oxidative stress, dysregulated protein homeostasis, neuroinflammation, and disrupted cell death pathways. Recent findings have added disease-specific processes, like amyloid-β and tau aggregates in AD, α-synuclein aggregation and mitophagy failure in PD’s, TDP-43-related impaired RNA metabolism in ALS, and mutant huntingtin causing transcription aberrations in HD. Protein interactome network analysis showed mechanistic crosstalk between pathogenic proteins of AD and PD. New evidence highlights how lysosomal dysfunction, endoplasmic reticulum stress, and microglial activation, act as a common axis in neurodegeneration. Advancements in genomics and epigenomics have found shared genetic risk loci and regulatory processes that affect how diseases develop and progress. Simultaneously, new biomarkers like circulating microRNAs, exosome-related pathological proteins, neurofilament light chain, inflammatory cytokines, and microglial activation markers are powering early diagnosis tools and disease variations. New imaging techniques also allow for the identification of protein aggregations before symptoms appear. Overall, these findings are accelerating targeted treatments and personalized medicine aimed at disease progression. This review highlights current insights into the molecular mechanisms of NDs and discusses new biomarkers and treatment targets that help future diagnostic and treatment strategies. Full article

Soil salinization represents one of the most severe abiotic constraints on global agricultural productivity, threatening crop yields and food security across increasingly large areas of cultivated land. Among the molecular mechanisms underlying plant physiological adaptation to salinity, histone methylation has emerged as a central epigenetic regulatory layer governing salt-responsive transcriptional reprogramming through the coordinated and opposing actions of histone methyltransferases, demethylases, and reader proteins at specific chromatin loci. Recent advances reveal how dynamic changes in activating marks, principally H3K4me3 and H3K36me3, and repressive marks, H3K9me2 and H3K27me3, orchestrate the activation of stress-responsive gene networks and the silencing of growth-incompatible programs under salt stress. How these modifications establish and sustain stress memory across somatic and transgenerational timescales is discussed. Recent technological advances, including single-cell epigenomics, CUT&RUN, CUT&Tag, and spatial transcriptomics, are assessed as future research priorities. The application of CRISPR/dCas9-based epigenome editing and epigenetic breeding strategies for improving crop salt tolerance is further explored. Full article