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PubMed2026

Multiomic analysis identifies T cell subsets and mechanisms of epithelial interaction in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by progressive scarring and respiratory failure. While T cells are elevated in IPF lungs, their contributions to fibrosis beyond inflammation remain poorly understood. Here, we performed multiplex imaging and single-cell RNA and protein profiling on about 90,000 CD3+ T cells from control and fibrotic lungs, revealing 11 distinct subsets of CD4+ and CD8+ T cells, including a rare CD56+ regulatory T cell. In addition to increased T cell numbers in severely fibrotic lungs compared with non-diseased controls, we observed CD4+ and CD8+ T cells localized near epithelial cells and in niches of abnormal epithelium. CXCR4/MIF signaling emerged as a central axis mediating T cell-epithelial interactions, while epidermal growth factor receptor (EGFR) and TGF-β pathways dominated in multiple T cell subsets. Our findings support the concept that T cells in IPF adopt nonclassical activation patterns that are driven by epithelial interactions within the fibrotic microenvironment. These studies provide a foundation for exploring alternative therapeutic strategies in IPF lungs by modulating T cell behavior and communication networks.

Idiopathic Pulmonary Fibrosis↗
PubMed2026

Hepatic metabolic adaptation to endurance exercise: temporal and sex differences by multiomics integration and validation.

BACKGROUND: Although endurance exercise benefits liver health, sex-specific adaptive trajectories remain unclear. This study mapped dynamic liver adaptation in males and females during prolonged training and identified underlying molecular programs. METHODS: Using publicly available time-resolved liver multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), we established a computational pipeline for differential analysis of transcriptomic, proteomic, phosphoproteomic, and metabolomic data with FDR correction, followed by FGSEA pathway enrichment. Kinase activities were inferred through ortholog mapping and PhosphoSitePlus. Cross-omics co-expression networks were constructed using WGCNA and topological overlap to link omics features with physiological phenotypes. For experimental validation, liver tissues were collected from endurance-trained Sprague-Dawley rats, and key nodes were confirmed by Western blotting, qRT-PCR, and immunofluorescence/immunohistochemical staining. Public scRNA-seq data were further integrated to map multi-omics signals to single-cell resolution and assess functional changes in specific cell types. RESULTS: The hepatic response to exercise stress was stage-specific, shifting from early transcriptional activation to later proteomic and metabolic remodeling. Multi-omics integration revealed distinct sex-associated adaptive trajectories: males were more strongly associated with energy metabolism, redox-related programs, and amino acid/organic acid catabolism, whereas females showed prominent membrane lipid remodeling, proteostasis -related programs, and mitochondrial/ribosomal translational features. Single-cell analysis showed that tissue remodeling occurred without major lineage turnover, instead involving altered communication among pre-existing cell communities. Validation of PPP1R3G identified a protein-dominant exercise-responsive marker, supporting the contribution of post-transcriptional or protein-level regulation. CONCLUSIONS: Hepatic adaptation to endurance stress follows a cross-omics evolutionary pattern with sex-specific reprogramming of energy supply and homeostatic maintenance. This time-resolved framework clarifies how exercise improves liver function and supports sex-oriented metabolic interventions and therapeutic target discovery.

Animals↗
PubMed2026

Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.

Hirschsprung disease (HSCR) is characterized by absence of enteric ganglia (aganglionosis) along variable lengths of the distal intestine. This disorder results from deficient colonization of fetal intestine by enteric neural crest-derived cells (ENCDCs). HSCR exhibits complex, multifactorial inheritance with penetrance and severity varying widely even within families. SOX10 is among causal genes that predispose to aganglionosis. Yet, how gene interactions influence severity of HSCR aganglionosis is not understood. Prior mapping of aganglionosis modifiers was achieved in a standard F1-intercross utilizing the Sox10Dom HSCR mouse model. Here we deploy a novel strategy of genotyping an extended pedigree pedigree of Sox10Dom mice on a mixed genetic background. GWAS in this pedigree points to novel aganglionosis modifier intervals with replication and refinement of prior modifier regions. Complementary omics analysis of the developing Enteric Nervous System (ENS) enabled identification of multiple high-priority candidate genes within these modifier intervals based on gene expression, chromatin accessibility, and presence of conserved SOX10 binding motifs. We implemented a prioritization pipeline for ranking potential modifiers that generated candidate lists including several well-known for effects on ENS development as well as multiple novel genes. Among the novel genes, Dach1 ranked as a top priority candidate gene for modifying migration of ENCDCs and thus influencing aganglionosis severity. The results identify genome intervals with intrinsic genes that are logical candidates for modifying Sox10Dom aganglionosis severity. We also note that several human orthologs to aganglionosis modifier candidate genes are within linkage disequilibrium blocks containing genetic variants associated with human gut motility disorders, which offers opportunity for gaining biological insight into human HSCR severity.

Animals↗
PubMed2026

Reframing the asthma microbiome: Multikingdom, multisite, and multiomic perspectives.

The field of asthma microbiome research has shifted rapidly in recent years. Advances in sequencing technology have led to an increased ability to characterize multikingdom microbial species and integration with host -omics profiling to enhance future translational applications. Traditional bacteria-centric, cross-sectional studies are giving way to mechanistic frameworks that incorporate fungi, viruses, and host-immune interactions. In this state-of-the-art review of emerging concepts in microbiome asthma research, we first propose a structured framework to consider microbiome studies across 5 major domains-microbial kingdom, site of sampling, integration with host -omics, clinical outcome domain, and translational relevance-in order to synthesize recent high-impact human microbiome studies in asthma. We highlight emerging evidence that fungal and viral communities contribute independently to asthma risk and that human microbial communities are linked to distinct inflammatory and immune pathways shaped by host genetic susceptibility.

Asthma↗
PubMed2025

Integrative multiomic approaches reveal ZMAT3 and p21 as conserved hubs in the p53 tumor suppression network.

TP53, the most frequently mutated gene in human cancer, encodes a transcriptional activator that induces myriad downstream target genes. Despite the importance of p53 in tumor suppression, the specific p53 target genes important for tumor suppression remain unclear. Recent studies have identified the p53-inducible gene Zmat3 as a critical effector of tumor suppression, but many questions remain regarding its p53-dependence, activity across contexts, and mechanism of tumor suppression alone and in cooperation with other p53-inducible genes. To address these questions, we used Tuba-seqUltra somatic genome editing and tumor barcoding in a mouse lung adenocarcinoma model, combinatorial in vivo CRISPR/Cas9 screens, meta-analyses of gene expression and Cancer Dependency Map data, and integrative RNA-sequencing and shotgun proteomic analyses. We established Zmat3 as a core component of p53-mediated tumor suppression and identified Cdkn1a as the most potent cooperating p53-induced gene in tumor suppression. We discovered that ZMAT3/CDKN1A serve as near-universal effectors of p53-mediated tumor suppression that regulate cell division, migration, and extracellular matrix organization. Accordingly, combined Zmat3-Cdkn1a inactivation dramatically enhanced cell proliferation and migration compared to controls, akin to p53 inactivation. Together, our findings place ZMAT3 and CDKN1A as hubs of a p53-induced gene program that opposes tumorigenesis across various cellular and genetic contexts.

Animals↗
PubMed2025

Functional phenotyping of genomic variants using joint multiomic single-cell DNA-RNA sequencing.

Genetic variants (both coding and noncoding) can impact gene function and expression, driving disease mechanisms such as cancer progression. The systematic study of endogenous genetic variants is hindered by inefficient precision editing tools, combined with technical limitations in confidently linking genotypes to gene expression at single-cell resolution. We developed single-cell DNA-RNA sequencing (SDR-seq) to simultaneously profile up to 480 genomic DNA loci and genes in thousands of single cells, enabling accurate determination of coding and noncoding variant zygosity alongside associated gene expression changes. Using SDR-seq, we associate coding and noncoding variants with distinct gene expression in human induced pluripotent stem cells. Furthermore, we demonstrate that in primary B cell lymphoma samples, cells with a higher mutational burden exhibit elevated B cell receptor signaling and tumorigenic gene expression. SDR-seq provides a powerful platform to dissect regulatory mechanisms encoded by genetic variants, advancing our understanding of gene expression regulation and its implications for disease.

Humans↗
PubMed2025

Multiomic Analyses Reveal the Molecular Mechanisms of Arid Adaptation in a Desert Rodent Species.

Organisms living in desert habitats face multiple simultaneous pressures, such as high temperatures and arid, and the population dynamics and community diversity of small rodents are strongly affected by climate extremes. However, the potential mechanisms by which desert rodents adapt to arid remain largely unexplored. Here, we assembled a 3.18 Gb genome, including 25,812 protein-encoding genes, for Orientallactaga sibirica, which is widely distributed across both arid and semihumid environments in Eurasia. Orientallactaga sibirica has longer ears and hind limbs to enhance heat dissipation, which may be related to the positively selected genes, such as Fgf10, Fgf11, Hoxc4, Hoxd1, and Bmp4. The renal transcriptome revealed increased fat and carbohydrate metabolism for metabolic water production in O. sibirica residing in arid habitats. Pathways such as material metabolism, oxidative stress response, osmoregulation, and water and salt reabsorption were enriched in candidate genes, such as Avp, Ang, and Ace, under positive selection in O. sibirica. Moreover, amino acid replacement was observed in the protein sequences of seven candidate genes, including Aldh7a1, Lnpep, Wnk4, C1qc, and Awat2, and these specific amino acid replacements of genes such as Umod and Scnn1a were related to unique osmoregulation, osmotic protection, and water retention compensation mechanisms. Water deprivation under laboratory conditions induced the upregulation of Umod and Aldh7a1 expression, further supporting the results observed in the wild population. These findings demonstrate that the positively selected genes related to limb development and specific amino acid replacements in the genes Umod and Scnn1a for unique osmoregulation in the renal vascular system may contribute to arid adaptation in the desert rodent species O. sibirica. This study provides novel insights into the adaptive evolution of desert small mammals and can serve as a reference for future research on renal damage-related diseases, such as human kidney stones and salt-sensitive hypertension.

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 (&#x2264;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

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗
PubMed2026

Dual-Matrix Platform for Highly Specific Multi-Omics Profiling of Renal Cell Carcinoma.

Multiomics interrogation provides complementary information beyond single-omics approaches for improved disease characterization. To enable such multilayer profiling, we expanded the rapid functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform by designing two structurally homologous but functionally tailored fMNPs. This design enables efficient acquisition of both serum metabolic and peptide fingerprints from a total of only 2.05 &#x3bc;L of serum, with an LDI MS analysis time of approximately 90 s per sample, while addressing the limitation of single-matrix systems in simultaneously optimizing analytical performance for different biomolecular species. Through statistical analysis and machine learning-based feature selection, an integrated multiomics biomarker panel was established, comprising 5 peptides and 4 metabolites. Notably, this integrated panel outperformed both single-omics panels across all evaluation metrics in the validation set, improving the area under curve from 0.985 to 1.000 and increasing the classification accuracy from 0.947 (metabolites) and 0.930 (peptides) to 0.965, while showing consistent improvements in F1-score, precision, and recall. Collectively, these results demonstrate the robust performance of the dual-matrix design and multiomics integration for renal cell carcinoma classification, with potential relevance for broader applications in complex disease profiling.

Carcinoma, Renal Cell↗
PubMed2026

High-throughput single-cell proteomics and transcriptomics from same cells with a nanoliter-scale, spin-transfer approach.

Single-cell multiomic platforms provide a comprehensive snapshot of cellular states and cell types by offering critical insights into the spatiotemporal regulation of biomolecular networks at a systems level, thereby defining the basis of multicellularity. Here, we introduce nanoSPINS, an advanced platform that enables high-throughput profiling and integrative analysis of the transcriptome and proteome from the same single cells using RNA sequencing and isobaric labeling LC-MS-based proteomics, respectively. NanoSPINS can efficiently transfer mRNA-containing droplets across two microarrays via a centrifugation-based approach, while proteins are retained on the initial platform. Benchmarking of nanoSPINS on two cell lines demonstrates its ability to generate global proteomic and transcriptomic profiles that align well with previously established methodologies/platforms. The incorporation of isobaric TMTpro labeling into this single-cell multiomics platform significantly enhances the throughput of single-cell proteomic analyses. Through the high-throughput quantification of the proteome and transcriptome, nanoSPINS not only facilitates the identification of molecular features at both mRNA and protein level but also provides larger sample sizes for improved statistical power in clustering and differential abundance. Given the broad applicability of single-cell multiomics in biological research and clinical settings, we believe nanoSPINS represents a powerful platform for the characterization of heterogeneous cell populations.

Single-Cell Analysis↗
PubMed2026

Estimating protein isoform abundances with [Formula: see text].

A single gene can encode multiple versions of a protein, dubbed isoforms, with varying functionality. Cellular control of isoform abundances is critical for multiple aspects of biology and is only partially regulated by transcript levels. While long-read sequencing facilitates transcript quantification, quantifying the resulting protein isoforms on a large scale is a major challenge, complicating biological interpretation of transcript alterations. Standard "bottom up" mass spectrometry can assess only short portions of isoforms called peptides, and these peptides often map onto more than one isoform. We introduce [Formula: see text] (Protein isoform Abundance Quantification), a Bayesian method that leverages multiomic information from the peptidome and transcriptome to provide accurate estimates of isoform abundance even when peptide mapping is ambiguous. [Formula: see text] offers several advantages over existing methods in a unified framework. It provides uncertainty quantification, integrates multiomic information for improved accuracy, and provides a rigorous framework for hypothesis testing. Extensive simulations show that [Formula: see text] consistently outperforms competing methods in detecting differentially abundant protein isoforms and estimating their abundances. We use [Formula: see text] to investigate differences in isoform abundance levels between people with schizophrenia and control subjects, confirming a long-held hypothesis that levels of the C4A isoform of Complement Component 4 are increased in schizophrenia while C4B is not. These results demonstrate that [Formula: see text] can identify significant variations in isoform abundance levels not previously possible.

Protein Isoforms↗
PubMed2025

Evolutionary Process Underlying Receptor Gene Expansion and Cellular Divergence of Olfactory Sensory Neurons in Honeybees.

Olfaction is crucial for animals' survival and adaptation. Unlike the strict singular expression of odorant receptor (OR) genes in vertebrate olfactory sensory neurons (OSNs), insects exhibit complex OR gene expression patterns. In honeybees (Apis mellifera), a significant expansion of OR genes implies a selection preference for the olfactory demands of social insects. However, the mechanisms underlying receptor expression specificity and their contribution to OSN divergence remain unclear. In this study, we used single-nucleus multiomics profiling to investigate the transcriptional regulation of OR genes and the cellular identity of OSNs in A. mellifera. We identified three distinct OR expression patterns, singular OR expression, co-expression of multiple OR genes with a single active promoter, and co-expression of multiple OR genes with multiple active promoters. Notably, &#x223c;50% of OSNs co-expressed multiple OR genes, driven by polycistronic transcription of tandemly duplicated OR genes via a single active promoter. In these OSNs, their identity was determined by the first transcribed receptor. The divergent activation of the promoter for duplicated OR genes ensures the coordinated increased divergence of OSN population. By integrating multiomics data with genomic architecture, we illustrate how fundamental genetic mechanisms drive OR gene expansion and influence flanking regulatory elements, ultimately contributing to the cellular divergence of OSNs. Our findings highlight the interplay between gene duplication and regulatory evolution in shaping OSN diversity, providing new insights into the evolution and adaptation of olfaction in social insects. This study also sheds light on how genetic innovations contribute to the evolution of complex traits.

Animals↗
PubMed2026

Multi-omics-based study on the biological characteristics of kidney renal deficiency and blood stasis in ankylosing spondylitis.

OBJECIVE: To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine (TCM) syndromes in ankylosing spondylitis (AS) using multiomics analysis. METHODS: Patients with AS were categorized into kidney deficiency and blood stasis syndrome (SX group) and damp-heat stasis syndrome (SR group). Transcriptomic sequencing and quantitative plasma proteomics were performed on patients with AS and healthy volunteers. Multiomics integration was used to characterize the biological basis of AS with renal deficiency and blood stasis syndrome. Specific proteins were validated by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Transcriptomic sequencing identified 31 significantly upregulated genes in patients with AS compared to healthy controls. These genes were primarily involved in tumor necrosis factor, interleukin-17, and nuclear factor kappa-B signaling pathways, as well as osteoblast differentiation and various viral infection pathways. Differentially expressed genes, including intercellular adhesion molecule 1 (ICAM1), 6-phosphofructo-2-kinase, cyclin-dependent kinase inhibitor 1A, interleukin 1 receptor antagonist, integrin alpha IIb, and myosin light chain 9 were more upregulated in the SX group than in the SR group. Quantitative proteomics identified 723 differential proteins associated with the disease and 788 differential proteins between the SX and SR groups. Notable proteins such as myeloperoxidase, cluster of differentiation 14, macrophage simulating 1 (MST1), and Ras homolog enriched in brain may serve as characteristic proteins of the SX group. By integrating transcriptomic and proteomic data, 45 associated differential molecules involved in platelet activation, pathogenic intestinal flora infection, glycolysis/gluconeogenesis, and T-cell receptor signaling pathways were identified in patients with AS compared to healthy controls. Additionally, ICAM1, MST1, C-X-C motif chemokine ligand 8 (CXCL8), suppressor of cytokine signaling 3 (SOCS3), and insulin-like growth factor binding protein 1 (IGFBP1) were detected in TCM syndromes by RT-qPCR and ELISA, showing upregulation in AS renal deficiency and blood stasis syndromes, which is consistent with the proteomic and transcriptomic results. CONCLUSIONS: ICAM1, MST1, CXCL8, SOCS3, and IGFBP1 were identified as biomarkers of renal deficiency and blood stasis syndrome in AS. This study provides a biological basis for the differential diagnosis of TCM syndromes in AS, offering new insights into Chinese medicine evidence and more precise Chinese medicine treatments for AS.

Humans↗
PubMed2026

Pathogenesis of psoriasis and psoriatic arthritis: Insights from animal models and single-cell and spatial transcriptomic analyses of skin, synovium and entheses.

Psoriasis (PsO) and psoriatic arthritis (PsA) are immune-mediated diseases characterized by chronic systemic inflammation, including inflammation of the skin and joints. Recent advances in animal models, single-cell transcriptomics, spatial transcriptomics, and proteomics have greatly enhanced our understanding of disease pathogenesis. Mouse models exhibit key features of skin and joint inflammation, facilitating analysis of molecular pathways, and identification of therapeutic targets. Single-cell and spatial transcriptomic analyses have revealed cell-type-specific contributions to inflammation, highlighting interactions between keratinocytes, T cells, fibroblasts, and dendritic cells that drive psoriatic pathology. In psoriatic synovium, type 17 tissue-resident memory T cells, monocytes, and fibroblasts contribute to local inflammation and joint damage, whereas the roles of B cells and plasma cells are less clear. Proteomic and metabolomic profiling in patients with PsA has identified circulating protein signatures and metabolites associated with disease progression, sex-specific differences, and response to therapy. The integration of these multiomic approaches provides a detailed map of immune-stromal-epithelial crosstalk across skin, synovium, and entheses, uncovering mechanisms that were previously inaccessible. These insights have implications for predicting disease progression, identifying novel therapeutic targets, and optimizing treatment strategies. Collectively, advances in animal models and multiomic profiling are reshaping our understanding of PsO and PsA, providing a framework for future research, disease monitoring, and therapeutic development.

Animals↗
PubMed2025

Colorectal Liver Metastasis Pathomics Model: Integrating Single-Cell and Spatial Transcriptome Analysis With Pathomics for Predicting Liver Metastasis in Colorectal Cancer.

The liver is the primary target organ for hematologic metastasis of colorectal cancer (CRC), and CRC liver metastasis (CRLM) often precludes radical resection, making it the leading cause of death in patients with CRC. To improve the identification and prediction of liver metastasis risk, we identified a cell type of liver metastasis--triggering malignant cells (LMTMCs) through integrating single-cell RNA sequencing and spatial transcriptome analysis. Multiomics cell communication analysis indicated that the interaction between fibroblasts and LMTMCs through the COL1A1-CD44/SDC4 and LAMA4-CD44 signaling axes could promote CRLM. By applying the one-class logistic regression algorithm, we developed a CRLM scoring system in the bulk RNA-sequencing data according to the abundance of LMTMCs in each individual. Using the grouping labels derived from the CRLM scoring system in the bulk data and the corresponding whole-slide images without any manual annotations at the region or pixel level, processed via slide-level weakly supervised learning, a deep-learning model based on the ResNet18 architecture, called Colorectal Liver Metastasis Pathomics Model, was developed to predict the risk of liver metastasis in patients with CRC. The Colorectal Liver Metastasis Pathomics Model achieved an area under the curve of 0.84 at the internal test set of The Cancer Genome Atlas-CRC histology images. In the external independent validation sets, namely the Affiliated Hospital of Southwest Medical University and the Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University cohorts, the areas under the curve were 0.89 and 0.72, respectively, indicating effective classification performances. This study provided new insights and tools for the early identification of CRLM and demonstrated the potential of combining multiomics with deep learning-based pathomics in cancer research.

Humans↗
PubMed2026

Molecular biomarker profiling in noninfectious uveitis: a chronological review of discovery.

PURPOSE OR REVIEW: Noninfectious uveitis (NIU) encompasses a heterogeneous group of immune-mediated intraocular inflammatory diseases whose complexity has driven systematic molecular biomarker discovery. This review presents NIU molecular biomarkers organized by biological category; autoantigens, human leukocyte antigens (HLA) and genetic markers, cellular immune subsets, cytokines, chemokines, and multiomics platforms including proteomics, microbiome metagenomics, metabolomics, and single-cell transcriptomics with each category presented in strict chronological order of landmark discovery. RECENT FINDINGS: We present a review organized along two nested timelines. Categories are presented in the order they historically emerged in the field, and within each category, landmark discoveries appear in chronological sequence. This allows the reader to trace how each biomarker category evolved: from foundational autoantigen identification in experimental uveitis models, through the genomic revolution of HLA association studies, into cellular immunophenotyping, cytokine profiling of aqueous humor, chemokine mapping of intraocular trafficking, and finally the emerging omics platforms that may potentially anchor precision medicine in NIU. Each biomarker is paired in line with its linked targeted therapeutic. SUMMARY: Biomarker research has transformed the understanding of NIU from a clinically defined syndrome into a group of molecularly distinct immune disorders. Advances spanning autoantigens, genetics, immune-cell profiling, cytokines, chemokines, and multiomics have revealed novel pathogenic mechanisms and therapeutic targets. Integration of these biomarkers with targeted therapies may accelerate the transition toward precision medicine in uveitis care.

cytokines↗
PubMed2026

RAS Pathway Activation and Microenvironmental Adaptation as Hallmarks of Myeloid Sarcoma.

UNLABELLED: Myeloid sarcoma, an aggressive extramedullary subtype of acute myeloid leukemia (AML), occurs in approximately 20% of patients and remains strikingly understudied in large-scale genomic and multiomic investigations. The key drivers of its tumor evolution are largely unknown; timely detection in asymptomatic patients poses a clinical challenge, and effective treatment options are limited, as patients are often excluded from clinical trials, rendering it a largely neglected disease entity. In this study, we demonstrate that myeloid sarcoma evolves from medullary AML but exhibits distinct site-specific clonal evolution. This is supported by unique transcriptional signatures of myeloid sarcoma, reflecting adaptation to the extramedullary microenvironment. We establish a proof of concept that circulating tumor DNA (ctDNA) sequencing captures the molecular composition of myeloid sarcoma, offering a potential noninvasive approach for molecular profiling of extramedullary AML. Our findings highlight marked differences between medullary AML and myeloid sarcoma, including universal molecular evolution and RAS pathway activation as disease hallmarks. SIGNIFICANCE: We provide a comprehensive multiomic characterization of myeloid sarcoma, identifying key molecular pathways that contribute to its development, and suggest ctDNA as a noninvasive method of detection. We identify RAS pathway activation and transcriptional adaptation to the solid tissue microenvironment as cardinal features of myeloid sarcoma, suggesting novel therapeutic avenues.

Sarcoma, Myeloid↗