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PubMed2026

Identification and evaluation of glutamine-related gene characteristics based on multi-omics to predict the prognosis of patients with colorectal cancer.

BACKGROUND: Colorectal cancer (CRC), a prevalent malignancy of the gastrointestinal tract, ranks among the leading causes of cancer-related morbidity and mortality. Its clinical course is marked by high fatality and poor prognosis. Elucidating the mechanisms underlying CRC initiation and recurrence is therefore critical for identifying novel therapeutic targets. METHODS: This study incorporated two datasets, TCGA-CRC and GSE17537. A total of 84 glutamine metabolism-related genes (GMRGs) were identified, and differential expression analysis was conducted using the TCGA-CRC dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was applied to determine gene modules most strongly associated with GMRG scores. Single-cell RNA sequencing (scRNA-seq) was utilized to characterize key cellular clusters and to identify differentially expressed genes (DEGs) between high and low glutamine metabolism (GM) groups. Overlapping GMRGs were visualized using the ggVennDiagram package in R. A CRC risk prediction model was developed through Cox proportional hazards and LASSO regression analyses, with performance evaluated by ROC curves. Cell type enrichment across 64 immune and stromal populations was assessed via xCell, and intergroup differences were tested using the Wilcoxon rank-sum test. TIDE scores were used to estimate immunotherapy responsiveness, while oncoPredict facilitated drug sensitivity profiling. PCOLCE2 expression in CRC was validated by RT-qPCR and Western blotting. Its functional role was examined through CCK-8 assays, invasion and migration tests, flow cytometry, and glutamate quantification. RESULTS: ScRNA-seq analysis identified two key cell populations and 437 DEGs associated with GM status. WGCNA pinpointed the MEgreen module as most significantly correlated with GMRG scores, encompassing 1075 genes. Integration of DEGs, module genes, and GM-related DEGs yielded 60 candidate genes for downstream analysis. A GMRG-based prognostic model comprising six genes (SRPX, CXCL1, GPX3, PCOLCE2, CLU, SEMA3E) demonstrated strong predictive performance. Prognostic gene expression correlated with immune and stromal infiltration patterns, as indicated by Spearman correlation analysis. The high-risk group exhibited diminished predicted response to immunotherapy (TIDE scores). Drug sensitivity analysis identified four compounds—Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478—with elevated efficacy in high-risk CRC cases. PCOLCE2 expression was significantly reduced in CRC tissues. Functional assays revealed that PCOLCE2 knockdown did not substantially affect cell proliferation but significantly impaired invasion and migration in CRC cells, increased apoptosis, and suppressed both glutamine uptake and glutamate production—highlighting its oncogenic role. CONCLUSION: Six GMRGs—SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E—were identified as key components of a robust prognostic model for CRC. These findings offer valuable insights into CRC pathogenesis and potential therapeutic strategies. Notably, this study provides the first evidence implicating PCOLCE2 as a tumor-promoting factor in CRC.

Glutamine↗

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

COVID-19 multi-omics reveal organ-specific responses and biomarkers.

OBJECTIVE: Post-COVID-19 syndrome is characterised by persistent immune dysfunction and multi-organ sequelae. This study aimed to characterise the systemic blood molecular landscape induced by SARS-CoV-2 infection and identify prognostic markers linked to skeletal muscle mass loss, a key driver of poor outcomes. METHODS: We enrolled 30 healthy controls and 307 COVID-19 patients, collecting 422 plasma samples for integrated proteomic and metabolomic profiling to investigate organ-specific molecular alterations in COVID-19. RESULTS: We comprehensively mapped the molecular landscape of COVID-19, encompassing immune, tissue-specific, and metabolic perturbations, and delineated their interactions. Focusing on organ-damage-related molecular patterns associated with disease progression and mortality, we found that skeletal muscle mass loss contributed to poor clinical outcomes of COVID-19 (p < 0.0001). Dysregulated arginine metabolism emerged as a key metabolic signature in fatal COVID-19 cases, with GLUL, GOT1, and citrulline showing significant correlation with skeletal muscle mass loss. Longitudinal analyses further revealed that reduced citrulline levels underlie the poor outcome of COVID-19 patients with muscle mass loss. These findings were robustly supported through multiple approaches: Mendelian randomization confirmed causal relationships between citrulline depletion, sarcopenia/fat-free mass loss, and COVID-19 mortality (p < 0.05), transcriptomic analyses of SARS-CoV-2-infected golden hamsters (GSE231910) provided additional support in enrichment of arginine biosynthesis (FDR < 0.05), and in vitro experiments further demonstrated that citrulline depletion promotes pro-inflammatory M1 macrophage polarisation — a key immunological feature of critical COVID-19. Leveraging these insights, we developed a skeletal muscle loss-specific prognostic prediction model for COVID-19 using GLUL, GOT1, and citrulline. This model effectively stratified patients into high- and low-risk groups (p = 0.035). CONCLUSION: Our study advances the understanding of COVID-19-induced organ pathophysiology and provides a foundation for developing targeted therapeutic strategies for post-COVID sequelae.

COVID-19↗

PubMed2025

Multi-omics analysis reveals distinct spatial compartmentalization of lung repair niches in pediatric ARDS.

BACKGROUND: Pediatric acute respiratory distress syndrome (PARDS), often triggered by viral infections, is a life-threatening condition. Despite its severity, children demonstrate significantly better survival rates and superior lung repair compared to adults. However, the mechanisms underlying this age-specific advantage remain incompletely understood. PATIENTS AND METHODS: We conducted a pilot multi-omics study of influenza-associated PARDS integrating single-cell RNA sequencing (scRNA-seq) of pediatric lung tissue and bronchoalveolar lavage fluid (BALF), spatial transcriptomics, and plasma proteomics. Analyses were harmonized with the Human Lung Cell Atlas (HLCA) reference, reanalysis of public pediatric PARDS airway scRNA-seq, and contextual comparisons to adult lethal COVID-19 lung. RESULTS: Tissue scRNA-seq and spatial data indicated outcome-linked divergence in PARDS. Survivor showed spatially restricted repair with preserved alveolar type II (AT2) cells, AT2-to-alveolar type I (AT1) differentiation signatures, and higher KRT17, whereas fatal case and adults exhibited diffuse immune activation with pro-fibrotic and pro-apoptotic signaling. In BALF, KRT17-positive airway stress–repair epithelial cells (hillock-like) increased from the acute to recovery phase, and plasma proteomics showed higher circulating KRT17 in survivors. HLCA-based label transfer strengthened cell-type definitions and enabled pediatric–adult comparisons suggesting biological and developmental differences; the adult lethal COVID-19 atlas provided a benchmark with attenuated epithelial repair and prominent collagen CTHRC1-pathologic fibroblasts. Fibroblast programs were regionally compartmentalized, with injury-enriched CTHRC1+ states versus alveolar fibroblasts in preserved areas, and showed stronger injury–homeostasis anti-correlation in fatalities. Myeloid remodeling included BALF transitions from FCN1-high inflammatory states toward FABP4-positive resident-like states, consistent with public pediatric datasets showing reduced inflammatory and interferon-stimulated gene (ISG) modules and severity-linked increases in aged neutrophils. CONCLUSIONS: This pilot multi-omics case series outlines putative pediatric lung repair niches in influenza-associated PARDS. KRT17-positive transitional epithelium, preserved AT2 differentiation, and restoration of resident-like macrophages may align with recovery, whereas diffuse immune activation and CTHRC1-enriched fibroblast programs may accompany worse outcomes. HLCA-guided annotations and adult benchmarks indicate possible age-related differences, warranting validation in larger multi-center cohorts.

Humans↗

PubMed2025

Integrative multi-omics analysis proposes a metabolic classification of gliomas: distinct metabolic states, immune infiltration, and prognosis.

BACKGROUND: The tumor microenvironment (TME) of glioma harbors diverse cell types; however, cell metabolic heterogeneity remains to be explored. This study aims to characterize the metabolic features of different cell types in the TME by integrating multiple datasets, including genomics, bulk and single-cell transcriptomics, and metabolomics. METHODS: Unsupervised machine learning was used to construct an energy metabolic classifier based on the metabolic pathways identified from bulk RNA-seq of gliomas in the TCGA dataset. The classifier was externally validated using multiple datasets, including genomics, bulk RNA-seq, snRNA-seq, and the metabolomics data. Furthermore, metabolic heterogeneity associated with the classifier was further characterized at single-cell resolution. RESULTS: The energy metabolism-based classifier stratified patients into two prognostic clusters: patients in cluster 1 were characterized by high pathway activity of glycolysis, the pentose phosphate pathway (PPP), and fatty acid oxidation (FAO), whereas patients in cluster 2 exhibited higher activity in glutaminolysis. This metabolic classifier revealed both intratumoral and intertumoral metabolic heterogeneity, and the complexity was further validated by the metabolomics profiling and snRNA-seq data from the CPTAC dataset. Notably, OSMR, highly expressed in cluster 1, showed significant co-expression with key glycolytic enzyme genes. The OSM/OSMR/JAK1/STAT3 axis potently drives malignant progression of glioma cells, specially enhancing their invasive and migratory capabilities. Single-cell resolution analyses demonstrated that tumor metabolic heterogeneity is primarily driven by malignant cells rather than non-malignant components, while tumor microenvironment (TME) factors were also found to modulate malignant cell metabolism. Significantly, glycolytic activity in glioma cells increased during the phenotypic transition from PN (proneural) to MES (mesenchymal), with cluster 1 metabolic phenotypes predominating in the tumor core. Compared to cluster 2, cluster 1 patients exhibited higher mRNA expression of immunosuppressive checkpoint genes, which correlated with pronounced immunosuppression in the TME. Furthermore, various immune cells demonstrated distinct metabolic preferences at single-cell resolution. CONCLUSIONS: This study developed an energy metabolic-based classifier for gliomas with prognostic and therapeutic potential. Metabolic reprogramming was linked with the PN-to-MES transition of glioma cells and immunosuppression in the tumor microenvironment. Multi-omics data, especially snRNA-seq, offered insights into metabolism heterogeneity at single-cell resolution, enabling personalized treatment strategies.

Humans↗

PubMed2026

Multi-omics identifies lipid accumulation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cell lines: a case-control study.

BACKGROUND: In recent years, evidence has indicated a metabolic shift towards increased demand for lipids in various lymphoid cell populations from people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). We previously screened the mitochondrial function and gene expression of B cell-derived lymphoblastoid cell lines (LCLs) generated from the blood of people with ME/CFS to characterise a model for hypothesis discovery and testing, observing elevated expression of gene products facilitating amino acid and fatty acid degradation for energy. METHOD: In this follow-up study we have expanded this characterisation by profiling the polar metabolomes and non-polar lipidomes of an all-female cohort of 17 healthy control and 15 ME/CFS LCLs, and we integrated this new data with the previously generated proteomic and transcriptomic data. RESULTS: In the polar metabolome we detected no significantly altered individual features, while integrated multi-omic analysis by MetaboAnalyst indicated 15 dysregulated pathways. Next, in the non-polar lipidome, we identified that PC(O-38:4) had significantly reduced levels in ME/CFS LCLs and was almost entirely discriminative of ME/CFS status. Among all detected classes of lipids we found that triradylglycerolipids ("triglycerides"), diradylglycerolipids and fatty acids were the most significantly affected and were elevated, and that most lipids exhibited average levels higher than in healthy controls. BioPAN pathway analysis of the lipidomic data predicted a more-active gene product that we confirmed to be significantly elevated in both our proteomic and transcriptomic data, this being phosphatidylserine synthase 1 (PTDSS1), plus 7 other gene products that were concordantly altered in expression in the transcriptomic data. We also found that ME/CFS LCLs exhibited a significant tendency towards more saturated lipid content. CONCLUSIONS: LCLs generated from circulating B cells from people with ME/CFS show accumulation of lipids, skewed lipid profiles and altered activity of related metabolic enzymes such as PTDSS1. These findings will inform future hypothesis-driven studies of primary lymphoid cell populations from people with ME/CFS to dissect specific immunometabolic mechanisms that may be involved in the syndrome, particularly relating to intersections between lipid abnormalities and potential effects on immune cell effector functions.

Fatigue Syndrome, Chronic↗

PubMed2026

Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma.

BACKGROUND: The combination of immune checkpoint inhibitors (ICIs) with anti-angiogenic agents is the preferred first-line therapy option for patients with advanced hepatocellular carcinoma (HCC), yet only a subset of patients responds, urging the quest for prediction biomarkers. We aimed to integrate genomics with radiology to propose an immune-derived radiogenomics biomarker of response to such combination immunotherapy and evaluate its added value in clinical context. METHODS: We integrated bulk RNA sequencing (RNA-seq) and proteomics data of 994 HCC patients with single-cell RNA-seq data of 11 samples across multiple datasets to identify an immune-related signature (IRS) that may influence sensitivity or resistance to such combined immunotherapy strategy, followed by verification of selected marker genes using immunohistochemistry and cytological experiments. We then trained/validated a cross-modality radiogenomics biomarker using machine learning based on TCIA database that was further tested in multi-scale independent cohorts covering 754 HCC patients. RESULTS: Integrative multi-omics analysis identifed a parsimonious 2-gene prognostic signature including KPNA2 and SMG5 that was significantly associated with immune heterogeneity and response to combination immunotherapy. Machine-learning pipeline exported the optimal 4-feature radiogenomics biomarker using support vector machine that significantly discriminated prognosis (hazard ratio 1.415–1.890; p < 0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720–0.829) in independent retrospective series across major imaging modalities (computed tomography/magnetic resonance imaging). In a prospective neoadjuvant cohort, this biomarker also showed favorable performance for predicting pathological response and tumor recurrence, accompanied by biological validation through single-cell RNA-seq analysis of pre-treatment biopsies. CONCLUSIONS: Our study provides a cross-device-cross-modal radiogenomics biomarker that can improve patient selection for emerging ICI plus anti-angiogenic therapy with novel potential therapeutic targets in HCC.

Humans↗

PubMed2026

Epigenetic alterations of AKT1 orchestrate a metabolic reprogramming in advanced lipedema: translational insights from an integrated multi-omics study.

BACKGROUND: lipedema is a chronic, progressive adipose disorder predominantly affecting women, characterized by painful, symmetrical subcutaneous fat accumulation, and typically resistant to lifestyle interventions. The pathophysiology of advanced-stage lipedema remains poorly defined, and no validated biomarkers or targeted therapies are currently available. METHODS: in this observational study, we applied a comprehensive multi-omics approach to dissect the molecular and metabolic alterations underlying late-stage lipedema. RESULTS: Genome-wide DNA methylation profiling identified over 5,000 differentially methylated CpG sites affecting genes involved in receptor tyrosine kinase signaling, phospho-metabolism, and immune pathways. Transcriptomic analysis revealed profound downregulation of mitochondrial functions, including oxidative phosphorylation, the TCA cycle, and fatty acid β-oxidation, alongside disruption of the sirtuin pathway and extracellular matrix remodeling. Integrative analysis pinpointed AKT1 as a central regulatory node: its promoter region was hypomethylated, correlating with increased gene expression and protein phosphorylation. Metabolomic profiling confirmed AKT1-linked metabolic dysregulation, including altered levels of L-arginine, NADP+, ATP, guanosine, glycerol, and glutamate, indicating impaired redox balance and energy metabolism. Trans-omic network analysis positioned AKT1 at the intersection of multiple dysregulated pathways, suggesting its key role in advanced-stage lipedema. CONCLUSIONS: the consistent enhancing of AKT pathway signaling across omic layers highlights its potential not only as a biomarker for disease stratification but also as a putative druggable target for therapeutic intervention. These findings offer new mechanistic insights into lipedema pathophysiology and provide a rationale for future personalized treatment strategies guided by AKT1-centric molecular profiling.

Proto-Oncogene Proteins c-akt↗

PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve–based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC = 0.844). The NODM cohort was stratified into high- (n = 2,362) and low-risk (n = 5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗

PubMed2026

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans↗

PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗

PubMed2026

Multi-omics characterization of a GPRC5A+ epithelial subpopulation associated with malignant features in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) exhibits marked cellular heterogeneity, and the cellular context of malignancy-associated epithelial programs remains incompletely defined. METHODS: We integrated 2,993 CRC samples spanning bulk RNA-seq (n = 2,568; two OS/RFS cohorts), scRNA-seq (281,961 cells/152 specimens), spatial transcriptomics (n = 6), and proteomics (n = 267). Analyses included single-cell integration/annotation, GSVA/HALLMARK, interactome, pseudotime, and ligand-receptor mapping; functional CRISPR assays, EMT immunoblotting, and xenografts; TF profiling (SCENIC/JASPAR/ChIP-qPCR); and exploratory drug-response prediction (OncoPredict), cell-sensitivity assays, and docking/MD modeling. RESULTS: We constructed a stage-stratified single-cell atlas and resolved eleven malignant epithelial subsets, characterizing Epi_4 as late-stage-enriched with EMT, hypoxia, and inflammatory programs and adverse OS/RFS. GPRC5A marked this subset, which we define as GPRC5A+Epi; its expression rose from stage I→IV and was associated with poor outcomes across cohorts, with concordant spatial/proteomic observations. GPRC5A perturbation affected CRC proliferation, migration/invasion, EMT, and xenograft tumorigenicity, supporting a functionally important role in the tested models. SCENIC and ChIP-qPCR supported FOSL1 as an upstream regulator that occupies the GPRC5A promoter. Spatial and ligand-receptor analyses predicted close association and potentially reciprocal signaling between GPRC5A+Epi and POSTN+fibroblasts (COL1A1-SDC4, COL1A1/1A2-ITGA2/ITGB1, PPIA-BSG); concurrent high GPRC5A+Epi/POSTN+Fib signatures were associated with inferior OS/RFS. Drug-response analyses identified an association between GPRC5A status and trametinib sensitivity. Docking/MD produced a computational model of a possible trametinib-GPRC5A interaction, which remains experimentally unvalidated. CONCLUSIONS: GPRC5A⁺Epi is a malignancy-associated epithelial state in CRC, and GPRC5A is functionally important for malignant phenotypes in the tested models. Its inferred relationships with POSTN⁺ fibroblasts and the trametinib findings should be regarded as hypothesis-generating pending functional crosstalk, direct-binding, and therapeutic validation.

Humans↗

PubMed2026

Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.

Single-cell and spatial omics (SPOs) technologies have advanced how healthcare physicians characterise brain tumours by enabling detailed understanding of their cellular architecture, functional states, and microenvironmental dynamics. These approaches provide high-resolution detection of tumour heterogeneity and allow precise analysis of the brain tumour microenvironment. Their application has also led to the discovery of novel biomarkers used for early brain tumour detection, prognosis, and improved tumour stratification. Furthermore, integrative multi-omic analyses have revealed new therapeutic targets, clarified mechanisms of drug resistance, and uncovered molecular pathways underpinning treatment failure. By bridging cellular-level insights with spatial context, SPOs hold significant promise for advancing personalised diagnostics, predicting therapeutic response, and guiding the development of targeted interventions for brain tumours. Despite these advances, several limitations constrain the full translational potential of SPOs, including high experimental costs, substantial computational demands, lack of standardised protocols, and challenges in data integration and reproducibility. Addressing these barriers through scalable bioinformatic pipelines, consensus experimental frameworks, and cost-effective platforms remains critical for broadening accessibility and enabling clinical adoption.

Brain Neoplasms↗

PubMed2025

MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.

In single cell biology, the complexity of tissues may hinder lineage cell mapping or tumor microenvironment decomposition, requiring digital dissociation of bulk tissues. Many deconvolution methods focus on transcriptomic assay, not easily applicable to other omics due to ambiguous cell markers and reference-to-target difference. Here, we present MOADE, a multimodal autoencoder pipeline linking multi-dimensional features to jointly predict personalized multi-omic profiles and cellular compositions, using pseudo-bulk data constructed by internal non-transcriptomic reference and external scRNA-seq data. MOADE is evaluated through rigorous simulation experiments and real multi-omic data from multiple tissue types, outperforming nine deconvolution pipelines with superior generalizability and fidelity.

Humans↗

PubMed2026

Genetic and epigenetic underpinnings of biological aging: a multi-omics study integrating Mendelian randomization, spatial transcriptomics, and drug target discovery.

Inflammaging represents a hallmark of biological aging, yet the causal inflammatory mediators driving multi-dimensional epigenetic aging and their effector genes remain poorly characterized at the genetic level. We developed a four-tier analytical framework integrating causal screening, multi-omics effector gene mapping, spatial transcriptomics, and drug target evaluation. Two-sample Mendelian randomization (MR) of 91 circulating inflammatory proteins against six aging phenotypes identified IL-12B, IFNG, and IL-2 as the most robust pro-aging mediators with consistent effects across independent outcomes. Using multi-omics summary-based MR (SMR) as the core analytical engine, we integrated four-layer whole-blood molecular QTL resources eQTL (eQTLGen, n = 31,684), sQTL (GTEx, n = 755), pQTL (INTERVAL + SCALLOP, n = 34,232), and mQTL (McRae et al., n = 1,980) - with GWAS summary statistics for four epigenetic age acceleration measures. At a stringent threshold (P_SMR < 1×10⁻¹²), seven high-confidence effector genes were identified: NHLRC1, TPMT, SELP, and RIPPLY3 for IEAA; ZNF373A and PLDN for HannumAA; and EDARADD for PhenoAA. The chromosome 6p21 NHLRC1-TPMT locus, overwhelmingly driven by methylation QTL signals (-log₁₀P = 26.06), emerged as the dominant genetic node of epigenetic aging. Spatial projection via gsMap onto a mouse E16.5 embryo atlas (121,767 cells) revealed preferential enrichment in smooth muscle and lung, with EDARADD showing marked specificity in mucosal epithelium. Cross-database drug target mining classified TPMT and SELP as repurposable known targets and NHLRC1 as a high-priority novel druggable candidate. This study provides multi-omics convergent causal evidence for inflammation-driven epigenetic aging and delivers genetically anchored targets for precision anti-aging intervention.

Aging↗

PubMed2026

Association between the gut microbiome and plasma metabolites linked to vocalization-based temperament in Merino sheep.

BACKGROUND: Temperament, as a determinant of behavioural and emotional responses, has a substantial adaptive value in different environments. This study aims to investigate the association between the gut microbiota and temperament plasticity, and clarify the potential metabolic mechanism that underpins that association by running a multi-omics study in sheep. METHODS: The TrackSheep research cohort was generated using 200 healthy juvenile Merino ewes, and the rumen microbiota, plasma metabolome, and temperament phenotype was measured. RESULTS: Rumen metagenomic analysis identified 25 microbial species and 16 MetaCyc pathways that explained 37.5% and 11.1%, respectively, of the variation in temperament as estimated using the vocal reactivity to stress. Among these, the γ-aminobutyric acid (GABA) shunt and allantoin degradation pathways showed the strongest associations with vocal behaviour. Multi-omic integration linked these microbial pathways to plasma metabolites that are involved in neurotransmission, antioxidant defense, and energy metabolism, including acetyl-L-carnitine (ALCAR) and urocortisone, which partially mediated the effects of microbial pathways on vocalisations. Notably, functional genomic and mediation analyses indicated that the abundance of Cryptobacteroides sp902761655 was associated with the activity of GABA shunt pathway, where GABA co-occurred with succinate production, in turn correlating with reduced inhibitory effects of ALCAR on stress-susceptible temperament. Although plasma metabolite shifts observed immediately after behavioural tests reflected stress exposure, their associations with rumen microbiota highlight microbiome-metabolite interplay that could underly behavioural variation. CONCLUSIONS: Our study provides the first large-scale multi-omics evidence linking the rumen microbiome to a dimension of emotional reactivity in livestock, while underscoring the need for longitudinal and experimental validation to establish causal mechanisms. Video Abstract.

Animals↗

PubMed2026

Experimental insights in taxon-specific functional responses to droughts in glacier-fed stream biofilms.

BACKGROUND: Glacier-fed streams are predicted to face increasingly frequent and intense droughts. However, the impacts of drought events on benthic biofilm, including bacteria, eukaryotes, and viruses, the dominating life form in glacier-fed streams, remain poorly understood. RESULTS: Using streamside flume mesocosms in the Swiss Alps, we grew glacier-fed stream biofilms over 103 days and exposed them to three droughts. Using a multi-omics approach (metagenomics, metatranscriptomics, and metaproteomics), we assessed the effects of a series of droughts on the taxonomy and metabolic activity of bacterial, eukaryotic, and viral metagenome-assembled genomes (MAGs). We found that the first drought (6 h) caused only minor changes, including mild upregulation of heterotrophic metabolism and signs of stress in diatoms. In contrast, the second drought (24 h) significantly altered both the composition and functionality of the microbiome, shifting phototrophic dominance from diatoms to Cyanobacteriota, while maintaining overall phototropic biomass and further upregulating the heterotrophic metabolism. Interestingly, a third 24 h drought had no detectable transcriptomic effect between pre- and post-drought conditions, suggesting a certain level of adaptive responses to droughts, but with the low diatom abundance being maintained. CONCLUSIONS: These findings indicate that glacier-fed biofilm microorganisms initially resisted short-term drought, but a second longer drought caused important shifts in their community structure, activity, and function. Climate-induced increases in drought frequency or duration may therefore have a lasting impact on microbial ecosystem functioning in glacier-fed streams. Video Abstract.

Biofilms↗

PubMed2026

Rumen microbiota-associated stress alleviation by creatine pyruvate in newly received cattle: a multi-omics study.

BACKGROUND: Stress experienced by newly received cattle is a significant challenge in the beef industry, frequently resulting in weakened immune responses and impaired growth. The rumen microbiota is essential to host health, and its imbalance can exacerbate stress. This study investigates the mechanisms by which creatine pyruvate (CrPyr) mitigates stress in newly received cattle through multi-omics approaches, including metagenomics, metabolomics, in vitro and in vivo experiments, and rumen microbiota transplantation (RMT) in mice. RESULTS: Our results revealed that CrPyr significantly reduces stress-related hormones (cortisol and adrenocorticotropic hormone) and inflammatory markers (IL-6, IL-1β, and TNF-α), and enhanced antioxidant capacity (SOD: 57.38 versus 46.93 U/mL, P < 0.05; GSH-Px: 305.87 versus 217.07 U/mL, P < 0.05; T-AOC: 9.62 versus 7.66 U/mL, P < 0.05). Metagenomic analysis demonstrated that CrPyr increased Prevotella abundance, a key rumen bacterium involved in volatile fatty acid (VFA) production, and enriches metabolic pathways associated with energy metabolism (ATP synthesis, and pyruvate metabolism) and antioxidant defense (glutathione metabolism, FC = 1.08, P < 0.05). In vitro and in vivo experiments, as well as RMT studies in mice, further validate these findings, demonstrating that CrPyr promote VFA synthesis and increased ATP production through the electron transport phosphorylation pathway. CONCLUSIONS: CrPyr modulates the abundance of ruminal Prevotella in transport-stressed cattle to enhance glutathione and VFA metabolism and to accelerate ATP and nucleotide synthesis, thereby alleviating stress in newly received cattle. This multimodal approach established CrPyr as an effective nutritional intervention that improves rumen function and increases livestock productivity. Video Abstract.

Animals↗