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PubMed2026

The need for standardization and improved open (meta)data practices in metaproteomics.

Metaproteomics enables functional insight into microbial communities by identifying and quantifying proteins in complex samples. Yet, heterogeneous analytical workflows and the lack of standardization across experimental and bioinformatics stages hinder reproducibility and comparability, limiting integration with other omics data. We here present a community-developed reporting checklist tailored to the specific needs of metaproteomics. We also outline current efforts to enable structured and interoperable metadata capture, drawing on standards from proteomics and microbiome research wherever possible. By promoting transparent reporting and advancing metadata practices, our recommendations aim to align metaproteomics more closely with FAIR principles and support reproducible and interoperable research practices. Video Abstract.

Proteomics↗

PubMed2026

Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.

Cancer of Unknown Primary (CUP) presents substantial diagnostic and therapeutic challenges owing to its heterogeneous nature and the absence of an identifiable primary tumor site. This review provides a structured search of the pathogenesis, epidemiological characteristics, and limitations of traditional diagnostic and therapeutic approaches for CUP, with an emphasis on the evolution of Tissue of Origin (TOO) identification techniques. Recent advances in precision medicine have accelerated the development of machine learning-based TOO identification tools, representing a paradigm shift in CUP diagnostics. Deep learning (DL) algorithms that integrate multi-omics data (such as genomics and transcriptomics) with clinical features have markedly enhanced the accuracy of tracing tumor origin, and artificial intelligence (AI) driven TOO models are increasingly being incorporated into clinical practice, offering new insights for pathological diagnosis, treatment selection, and prognostic evaluation. Nevertheless, several challenges remain, including issues of data standardization, model generalizability, and interpretability. Ethical considerations related to data privacy, algorithmic fairness, and clinical implementation also warrant careful attention. Future research should focus on establishing standardized multi-center databases, developing more interpretable AI models, and fostering multidisciplinary collaborative strategies for CUP management. Through continued refinement of technical solutions and regulatory guidelines, TOO identification is anticipated to progress from research to routine clinical application, ultimately supporting precise and personalized care for patients with CUP.

Artificial intelligence↗

PubMed2026

Japanese encephalitis virus hijacks the host purine biosynthetic network to promote viral replication in neurons.

Japanese encephalitis virus (JEV) is an important neurotropic orthoflavivirus that poses a threat to both human and animal health. However, the mechanism underlying its rapid replication in the central nervous system (CNS) remains poorly understood. In this study, we conducted metabolomic profiling of JEV-infected mouse brains and neurons, revealing a profound reprogramming of central carbon metabolism, particularly an enhancement in nucleotide synthesis. Integrated multi-omics analyses confirmed that JEV infection transcriptionally upregulates key enzymes involved in de novo purine biosynthesis (DNPB), one-carbon (1C) metabolism, and the pentose phosphate pathway (PPP) in neurons. Pharmacological inhibition of the core DNPB enzymes potently suppressed JEV replication in neurons and reduced both viral loads and neuroinflammation in JEV-infected mice, suggesting the essential role of DNPB in JEV replication within CNS. Mechanistically, we delineated the critical functions of both the non-oxidative PPP and MTHFD2-mediated 1C metabolism, which jointly supply essential precursors, such as ribose-5-phosphate and formyl groups, for the de novo biosynthesis of purines required for viral RNA replication. These findings unveil a strategy by which JEV co-opts the host's purine biosynthetic machinery to fulfill the nucleotide demands for its genomic replication, establishing DNPB and its supporting pathways as promising therapeutic targets for infections caused by JEV and other neurotropic viruses.

Animals↗

PubMed2026

Epigenetic and metabolic reprogramming of innate immune cells establishes immunological memory in the Schistosomiasis vector snail Biomphalaria glabrata.

Innate immune memory enables non-vertebrates to mount faster and more effective immune responses upon re-exposure to a previously encountered pathogen, yet its cellular and molecular bases remain poorly understood. The freshwater snail Biomphalaria glabrata, intermediate host of the human parasite Schistosoma mansoni, provides a powerful model to investigate this phenomenon. Here, we show that innate immune memory in B. glabrata is carried by hemocytes and relies on profound metabolic and epigenetic reprogramming initiated during primary infection. Using an integrative multi-omics approach combining transcriptomics, chromatin accessibility profiling, whole-genome bisulfite sequencing and targeted metabolomics, we reveal that the first parasite encounter induces a stable rewiring of hemocyte metabolism and chromatin landscape. This reprogramming primes hemocytes for a massive and rapid transcriptional response upon secondary challenge, characterized by an immune shift toward highly specific humoral effector pathways. Metabolic analyses demonstrate an early switch toward aerobic glycolysis, altered tricarboxylic acid cycle activity and amino acid metabolism, consistent with a Warburg-like metabolic state previously described in vertebrate trained immunity. Notably, metabolic and epigenetic remodeling occurs primarily during the primary infection and remains stable upon secondary exposure, suggesting that immune memory is encoded prior to pathogen re-encounter. Together, our results identify conserved metabolic and epigenetic mechanisms underlying innate immune memory in a non-vertebrate host and provide direct evidence that hemocyte-mediated innate immune memory in B. glabrata shares core features with trained immunity described in vertebrates.

Animals↗

PubMed2026

Decoding age-stratified clinical and molecular heterogeneity in male breast cancer through multiomic profiling.

OBJECTIVE: Age-associated molecular heterogeneity is well described in female breast cancer but remains insufficiently characterized in male breast cancer (MBC). We profiled age-stratified clinical and molecular differences between younger (≤55 years) male breast cancer (YMBC) and older (>55 years) male breast cancer (OMBC). METHODS: We retrospectively analyzed 347 patients with MBC diagnosed at Fudan University Shanghai Cancer Center by integrating clinicopathological data, RNA sequencing, and whole-exome sequencing (WES). Survival, differential expression, and mutational signature analyses were performed. Tumor microenvironment features were inferred using xCell and ESTIMATE, and weighted gene co-expression network analysis (WGCNA) was conducted to identify age-associated co-expression modules. Candidate therapeutics were prioritized using the Genomics of Drug Sensitivity in Cancer (GDSC) resource and evaluated using patient-derived organoids (PDOs). RESULTS: Compared with OMBC, YMBC more frequently had human epidermal growth factor receptor 2 (HER2)-positive status (14.91% vs. 4.02%) and triple-negative tumors (4.92% vs. 1.78%), and had worse 5-year recurrence-free survival (hazard ratio=2.19, P=0.018). Transcriptomic analyses indicated enrichment of neural-related programs and reduced immune-related signaling in YMBC, and xCell/ESTIMATE supported lower immune infiltration. Consistently, WGCNA identified age-associated modules linking neural-related programs with reduced immune infiltration. Immunohistochemistry supported increased perineural invasion and lower CD8+ T cell infiltration in YMBC. GDSC-guided prioritization with PDO testing nominated sepantronium bromide (YM155) as a candidate vulnerability in YMBC. WES showed a higher NBPF10 mutation frequency in YMBC (54.5% vs. 14.3%, P<0.05). CONCLUSIONS: Integrated multi-omics profiling revealed age-stratified clinical and molecular heterogeneity in MBC. YMBC patients demonstrated inferior recurrence-free survival, neural signaling enrichment, an immune-cold microenvironment, and enriched NBPF10 mutations. These findings support age as a meaningful stratification variable in MBC risk assessment and treatment planning, and highlight the need for caution when considering treatment de-escalation in younger patients, while nominating YM155 as a candidate agent for prospective evaluation.

Male breast cancer↗

PubMed2026

Enhanced chromatin compaction is associated with de novo expression of a nuclear microprotein, global loss of H3 acetylation and local transcriptional changes in retinal rod photoreceptors.

We have limited understanding of how aging alters gene expression and remodels cellular architecture in post-mitotic neurons. The inverted nuclear organization of mouse rod photoreceptors provides a unique model to gain mechanistic insights into age-associated decline in neuronal function. We have generated and integrated multi-omic datasets including 3D-genome topology, histone modifications, chromatin accessibility, DNA methylation and transcriptome of rod photoreceptors from young- and aged-mice. We show that aging drives global chromatin compaction, with regional alterations enriched at active chromatin. Epigenomic and transcriptional changes broadly correlate with chromatin dynamics as validated by high resolution microscopy. We uncover a megabase-sized genomic region with multi-level alterations, including de novo transcription of Gm7239, which encodes a functional microprotein carrying histone acetyltransferase-inhibitor domain. Overexpression of Gm7239 is associated with global loss of histone H3 acetylation, highlighting a potential new axis of genomic regulation in aging. Finally, we identify multiple significant local transcriptional alterations in non-annotated regions and genes associated with age-related macular degeneration. Our studies link age-related chromatin landscape changes with gene expression that may influence rod function and vulnerability to diseases.

Journal Article↗

PubMed2026

Artificial Intelligence for Natural Products Discovery and Development.

Natural products (NPs) remain a cornerstone of modern drug discovery, offering stereochemical complexity and diverse bioactivities that precisely modulate therapeutic targets, refined through billions of years of evolution. However, their research has long been hindered by inefficient, empirical workflows, high resource consumption, structural complexity, and the "multicomponent, multi-target" nature of their mechanisms. The exponential growth of genomic, metabolomic, and spectral data has overwhelmed conventional analytical methods, exposing critical bottlenecks in handling high-dimensional, heterogeneous datasets that exceed human interpretive capacity. Artificial intelligence (AI) is emerging as a transformative paradigm to address these challenges, integrating multi-omics and chemical data to shift NP research from fragmented empiricism toward mechanism-driven, precision-oriented development. By leveraging deep learning architectures- including graph neural networks, Transformers, and diffusion-based generative models-AI enables systematic decoding of NP biosynthesis, automated structure elucidation, rational target identification, knowledge extraction from vast unstructured scientific literature, and de novo molecular design. This review comprehensively surveys recent advances in AI applications across the full NP discovery and development pipeline, encompassing genome mining, structure-based and ligand-based virtual screening, multimodal structural characterization, lead optimization, and biosynthetic pathway engineering. We further examine the emerging roles of protein-centric, molecule- centric, and multimodal foundation models, as well as large language models, in bridging genotype-to-chemotype gaps and unlocking unstructured scientific knowledge. Finally, we discuss critical challenges including data scarcity, representational limitations for complex stereochemistry, physical plausibility in generative models, and the urgent need for experimental validation, while outlining future directions toward autonomous experimentation, closed-loop optimization, and human-AI collaborative discovery.

Artificial intelligence↗

PubMed2006

Major advances in fundamental dairy cattle nutrition.

Fundamental nutrition seeks to describe the complex biochemical reactions involved in assimilation and processing of nutrients by various tissues and organs, and to quantify nutrient movement (flux) through those processes. Over the last 25 yr, considerable progress has been made in increasing our understanding of metabolism in dairy cattle. Major advances have been made at all levels of biological organization, including the whole animal, organ systems, tissues, cells, and molecules. At the whole-animal level, progress has been made in delineating metabolism during late pregnancy and the transition to lactation, as well as in whole-body use of energy-yielding substrates and amino acids for growth in young calves. An explosion of research using multicatheterization techniques has led to better quantitative descriptions of nutrient use by tissues of the portal-drained viscera (digestive tract, pancreas, and associated adipose tissues) and liver. Isolated tissue preparations have provided important information on the interrelationships among glucose, fatty acid, and amino acid metabolism in liver, adipose tissue, and mammary gland, as well as the regulation of these pathways during different physiological states. Finally, the last 25 yr has witnessed the birth of "molecular biology" approaches to understanding fundamental nutrition. Although measurements of mRNA abundance for proteins of interest already have provided new insights into regulation of metabolism, the next 25 yr will likely see remarkable advances as these techniques continue to be applied to problems of dairy cattle biology. Integration of the "omics" technologies (functional genomics, proteomics, and metabolomics) with measurements of tissue metabolism obtained by other methods is a particularly exciting prospect for the future. The result should be improved animal health and well being, more efficient dairy production, and better models to predict nutritional requirements and provide rations to meet those requirements.

Absorption↗

PubMed2026

Advanced and underlying therapeutic strategies in transformed small cell lung cancer.

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

advanced therapy↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗

PubMed2026

Genetic Analysis of Genomic and Methylomic Variation and Identification of Multi-Trait Mutants in Rice Carried on Chang'e-5.

Global food security is facing challenges from population growth to diminishing arable land. Space mutation breeding holds promise for overcoming the variation limitations in conventional breeding; however, the mutagenic effects of the deep-space environment on rice and the transgenerational inheritance patterns of induced variations remain unclear. In this study, rice seeds carried by the Chang'e-5 spacecraft were used as materials. Whole-genome sequencing and whole-genome bisulfite sequencing were performed on the first (SP1) and second generations (SP2) of space-mutagenized plants after their return to Earth. The results showed that the number of genomic variants in the SP2 generation increased significantly compared with SP1, and SNPs, homozygous sites, and variants in coding regions were more heritable. The genome-wide methylation level was elevated in the SP2 generation, and among differentially methylated cytosines, those in the CG context exhibited the highest heritability. Furthermore, large-scale screening for nitrogen efficiency, tolerance to PEG-induced stress, and germination-stage cold resistant mutants was conducted in the SP2 generation, and phenotypic validation was performed in the third generation (SP3). By integrating multi-omics analyses of representative mutants to mine candidate genes, a number of heritable elite mutants were obtained, and seven candidate genes for key traits were identified. This study systematically elucidates the transgenerational inheritance patterns of deep-space-induced variation in rice. The multi-trait mutants obtained provide valuable germplasm resources for gene cloning and breeding applications in rice.

DNA methylation↗

PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Spatially mapping the transcriptome and proteome in the same tissue section can significantly advance our understanding of heterogeneous cellular processes and connect cell type to function. Here, we present Deterministic Barcoding in Tissue sequencing plus (DBiTplus), an integrative multi-modality spatial omics approach that combines sequencing-based spatial transcriptomics and image-based spatial protein profiling on the same tissue section to enable both single-cell resolution cell typing and genome-scale interrogation of biological pathways. DBiTplus begins with in situ reverse transcription for cDNA synthesis, microfluidic delivery of DNA oligos for spatial barcoding, retrieval of barcoded cDNA using RNaseH, an enzyme that selectively degrades RNA in an RNA-DNA hybrid, preserving the intact tissue section for high-plex protein imaging with CODEX. We developed computational pipelines to register data from two distinct modalities. Performing both DBiT-seq and CODEX on the same tissue slide enables accurate cell typing in each spatial transcriptome spot and subsequently image-guided decomposition to generate single-cell resolved spatial transcriptome atlases. DBiTplus was applied to mouse embryos with limited protein markers but still demonstrated excellent integration for single-cell transcriptome decomposition, to normal human lymph nodes with high-plex protein profiling to yield a single-cell spatial transcriptome map, and to human lymphoma FFPE tissue to explore the mechanisms of lymphomagenesis and progression. DBiTplusCODEX is a unified workflow including integrative experimental procedure and computational innovation for spatially resolved single-cell atlasing and exploration of biological pathways cell-by-cell at genome-scale.

Journal Article↗

PubMed2024

Integration of Imaging-based and Sequencing-based Spatial Omics Mapping on the Same Tissue Section via DBiTplus.

Journal Article↗

PubMed2026

Bridging the airway microbiome and targeted therapy in bronchiectasis: multi-omics insights, endotypes and emerging therapies.

Bronchiectasis is a heterogeneous chronic airway disease primarily driven by persistent infection, microbial dysbiosis and dysregulated host immunity. While culture-based microbiology has historically informed clinical management, advances in high-throughput sequencing and multi-omic technologies have transformed our understanding of the airway ecosystem, revealing that disease activity is shaped not only by individual pathogens, but by complex and dynamic host-microbe interactions. Despite the breadth of descriptive microbiome data, translation into clinically actionable diagnostics or therapies has been limited. Importantly, cross-sectional correlations between microbiota and inflammation do not establish cause and effect, underscoring the need to embed host-microbiome profiling within both longitudinal and interventional therapeutic trials. In this review, we critically appraise current microbial and host multi-omics research in bronchiectasis, integrating microbiome studies with host inflammatory, proteomic and immunophenotyping data. We highlight themes emerging across cohorts, including low microbial diversity, pathogen dominance, loss of commensal networks and neutrophil-driven inflammation, and discuss how these features align with biological endotypes associated with exacerbations and treatment response. Drawing on lessons from host-directed therapeutic successes, we examine translational roadblocks limiting microbiome-guided care. We further review emerging microbiome-modulating strategies such as pathogen-specific biologics, bacteriophage therapy, live biotherapeutic products, biofilm-targeting adjuncts and precision antibiotic stewardship. Finally, we propose a roadmap toward microbiome-informed precision medicine through harmonised methodologies, integration of host and microbial biomarkers into clinical trials, and embedding multi-omics pipelines within large international registries. Collectively, these advances have the potential to shift bronchiectasis research and clinical management towards rationally designed, precision medicine-driven therapeutic strategies.

Humans↗

PubMed2026

SCMO: a deep learning model integrating the single-cell resolution TME ecosystem and multi-omics for survival prediction in CRC patients.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of global cancer mortality, highlighting the need for precise survival prediction to guide clinical decisions. Although tissue-level multi-omics is widely utilized for survival prediction, its limited resolution cannot capture tumor heterogeneity. Single-cell RNA sequencing (scRNA-seq) enables dissection of the tumor microenvironment (TME) at cellular resolution, supporting personalized prognostic assessment. METHODS: We collected 213 CRC scRNA-seq samples and established a CRC-specific TME atlas comprising 339,060 cells. Using this atlas as a reference, we deconvolved bulk RNA-seq data from TCGA-CRC cohort with the EcoTyper algorithm to reconstruct TME features. Clinical, genomic, and transcriptomic data were obtained from the Xena platform; microbial data were sourced from the BIC database. We integrated TME and multi-omics features through a self-normalizing neural network to construct a deep learning model (single-cell resolution TME ecosystem with multi-omics data [SCMO]) for survival prediction. To enhance interpretability, we utilized the Integrated Gradients algorithm and spatial transcriptomic data to analyze multi-omics and TME features. We performed anticancer drug screening with tumor necrosis factor receptor-associated protein 1 (TRAP1), a critical feature according to the Integrated Gradients algorithm, as a potential target. RESULTS: We identified 13 survival-related TME features from the CRC-specific atlas: 12 cell states and one multi-cellular ecosystem. SCMO, which combined TME and multi-omics features, improved survival prediction and outperformed existing methods, achieving a concordance index of 0.762. The SCMO demonstrated robust performance for long-term predictions, achieving areas under the curve (AUCs) of 0.752, 0.772, and 0.869 for 1-, 3-, and 5-year predictions in the training set, with corresponding test set AUCs of 0.639, 0.756, and 0.772. TME features from the SCMO model revealed that ecosystem density increased with CRC malignancy. Multi-omics features included TRAP1 as a potential drug target. Drug screening identified saikosaponin A as a novel TRAP1 inhibitor, and its anticancer activity was validated in vitro. We developed SCMO-Lite, a simplified model incorporating 12 high-attribution-weight multi-omics features, which demonstrated robust risk stratification. CONCLUSIONS: SCMO combines analytical precision with biological interpretability, offering novel insights for oncology survival prediction.

Humans↗

PubMed2026

Unlocking the Full Potential of Spatial Omics in Plants: Practical Challenges, Solutions, and a Path Forward.

Spatial omics technologies are providing new opportunities for plant biology by enabling molecular profiling within structurally intact tissues, revealing spatially organised cell states, developmental gradients, and regulatory interactions. While spatial transcriptomics has driven early advances, the field is rapidly expanding toward integrated spatial multi-omics by combining single-cell and spatial transcriptomic, epigenomic, proteomic, and metabolomic data. These approaches offer new opportunities to study development, physiology, and plant biotic and abiotic interactions in spatially preserved cellular contexts. However, despite rapid adoption, the field remains constrained by plant-specific challenges when applying technologies largely developed for animal systems. Compared with animal systems, plant tissues pose additional challenges due to rigid cell walls, and diverse chemistries, complicating sample preparation, cell and subcellular segmentation, signal detection, and data integration. As a result, many studies rely on bespoke protocols and analysis pipelines that are often difficult to reproduce or generalise. Here, we provide a practical, solution-oriented synthesis of current bottlenecks across experimental and computational pipelines, highlight emerging strategies to overcome these limitations, and propose a roadmap for community-driven protocol sharing, benchmarking, and integration across spatial and multi-omics modalities. Addressing these challenges will be essential to establish spatial omics as a routine and scalable tool for plant biology.

Journal Article↗

PubMed2025

Integrating necroptosis and immune landscapes: a multi-omics-derived NecropImmScore stratifies prognosis and therapy in ovarian cancer.

BACKGROUND: Ovarian cancer (OC) remains the deadliest gynecologic malignancy, largely due to its immunosuppressive tumor microenvironment (TME) and resistance to therapy. Necroptosis, a regulated lytic cell death pathway mediated by the RIPK1-RIPK3-MLKL axis, can trigger immunogenic cell death, but its specific role in shaping the OC immune landscape and its clinical translation potential are posorly understood. METHODS: We employed multi-omics analysis (transcriptomics, genomics, clinical data) from TCGA-OV (n = 380), ICGC OV-AU, and IMvigor210 cohorts, combined with rigorous in vitro functional validation using OC cell lines (SKOV3, HEY), macrophages (THP-1 derived), and T cells (Jurkat). Computational immunology approaches (ESTIMATE, CIBERSORT, ssGSEA) quantified immune infiltration. We identified MLKL-associated immune genes, performed survival analysis (Kaplan-Meier, Cox regression), and constructed a necroptosis-immune signature (NecropImmScore) using consensus clustering and PCA of 102 prognostic genes. Drug sensitivity was predicted via pRRophetic and CellMiner. RESULTS: MLKL emerged as a protective prognostic biomarker (p = 0.018), significantly correlated with enhanced immune infiltration (ImmuneScore, StromalScore, ESTIMATEScore; p < 2.22e-16), M1 macrophage polarization (p = 0.006), activated CD4 + T cells (p = 0.003), and elevated immune checkpoint expression (PD-L1, CTLA4, LAG3, TIGIT). In vitro, MLKL overexpression in OC cells promoted M1 polarization (p < 0.05), activated Jurkat T cells (upregulated CCR4/5/7/9, CD69, CD3D/E, GZMB; p < 0.05), and induced key chemokines (CXCL9/10/11/13) critical for immune cell recruitment. Integration of MLKL-related and immune-related DEGs (n = 632) revealed enrichment in T-cell activation, chemokine signaling, and antigen presentation pathways (FDR < 0.05). Consensus clustering based on 102 survival-associated genes defined three molecular subtypes (Clusters A-C) with divergent survival (p = 0.019), necroptosis activity, and immune infiltration (Cluster C: best prognosis, highest MLKL/ImmuneScore). The derived NecropImmScore robustly stratified patients: high-score correlated with superior overall survival (TCGA: p < 0.001; ICGC: p = 0.014), inflamed TME phenotype, elevated checkpoint expression, and improved response to anti-PD-L1 in IMvigor210. Critically, high NecropImmScore predicted higher BRCA1 mutation frequency (AUC = 0.802), synergy with BRCA1 status for prognosis, higher homologous recombination deficiency (HRD) score, sensitivity to cisplatin (p = 0.014), paclitaxel (p = 0.016), gemcitabine (p = 0.017), and provided superior prognostic stratification when combined with TMB and HRD score (p < 0.001). CONCLUSION: This study establishes MLKL as a master regulator of anti-tumor immunity in OC, driving chemokine-mediated immune cell recruitment and TME reprogramming. The novel NecropImmScore is a multifaceted biomarker that effectively predicts prognosis, immunotherapy response, BRCA1 deficiency, and chemosensitivity, offering significant potential for guiding precision therapeutic strategies in OC.

Humans↗

PubMed2025

Multi-omics signature of healthy versus unhealthy lifestyles reveals associations with diseases.

This multi-omics cross-sectional study investigated differences in metabolomics, proteomics, and epigenomics profiles between two groups of adults matched for age but differing in lifestyle factors such as body composition, diet, and physical activity patterns. Data from prior studies were utilized for a comprehensive integrative analysis. The study included 52 participants in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females). Using multi-omics integration software (OmicsNet and Pathview), 96 significantly (p < 0.05) enriched pathways were identified that differentiated the LIFE and CON groups. Top pathways significantly (p < 2.63 × 10-5) influenced by group status included fatty acid degradation, fatty acid elongation, glutathione metabolism, Parkinson disease, and central carbon metabolism in cancer. This study identified a distinct metabolic signature comprised of metabolites, proteins, and gene methylation sites associated with a healthy lifestyle. These findings provide unique, but complementary, results to previous single-omics analyses using metabolomics and proteomics procedures which showed that the LIFE group exhibited lower plasma bile acid levels, higher levels of beneficial fatty acids, reduced innate immune activation, enhanced lipoprotein metabolism, and increased HDL remodeling. The current multi-omics analysis builds on these previous results by providing a more holistic view of how metabolites, proteins, and methylation sites associated with a healthy lifestyle, providing a larger, more comprehensive list of altered pathways. Additionally, the integrated analysis revealed connections between lifestyle factors and conditions such as cancer and insulin resistance beyond what identified in the single-omics approaches, highlighting the broader metabolic impact of lifestyle on health. Overall, the signatures identified by this multi-omics approach provide a basis for developing more translational biomarkers, such as those that defined the cancer and insulin resistance pathways that can be used to assess one's state of health and provide guidance on behavior modifications that should be taken to lower disease risk.

Humans↗