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PubMed2026

Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer.

BACKGROUND/AIM: Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Although immunotherapy has improved outcomes for a subset of patients, its limited efficacy in many cases highlights the need for a more comprehensive understanding of the CRC immune microenvironment. This study aimed to characterize the molecular landscape of the CRC immune microenvironment using an integrated multi-omics approach and to identify candidate regulatory molecules associated with immune remodelling. MATERIALS AND METHODS: We integrated structural variation, DNA methylation, chromatin accessibility, proteomic, and phosphoproteomic data generated from an in-house CRC cohort with transcriptomic data from The Cancer Genome Atlas (TCGA). Analyses focused on 1,539 immune-related genes (IRGs) associated with CD4+ T cells, B cells, and natural killer (NK) cells. Multi-layered genomic and proteomic analyses were performed to identify altered immune-related pathways, hub genes, candidate transcription factors, and upstream kinases. RESULTS: Higher infiltration of CD4+ T cells, B cells, and NK cells was associated with CRC. IRGs exhibited widespread alterations across genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic levels. IL10, LEP, ITGAM, and EGFR emerged as candidate hub genes. EGFR phosphorylation at S991 and T693 was significantly decreased in CRC. STAT2 and HSF1 were identified as candidate upstream transcription factors, while CDK2 emerged as a candidate upstream kinase associated with immune infiltration and immune checkpoint expression. CONCLUSION: This study provides a systematic multi-omics characterization of immune microenvironment remodelling in CRC and identifies candidate molecular regulators that may serve as potential targets for future immunotherapy research.

Humans↗
PubMed2026

Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma exhibits striking intra-tumoral heterogeneity at morphological and genetic levels, complicating treatment and contributing to disease progression. CcRCCs with rhabdoid differentiation are highly aggressive tumors characterized by distinct histopathologies. However, the relationship between morphology, underlying molecular alterations, and tumor behavior remains largely unclear. Here, we present Deep Visual Multi-Omics, an approach integrating digital pathology, morphology-guided single-cell isolation, and ultra-sensitive multi-omics profiling to link cell morphologies to their molecular underpinnings. Across five tumors, we profiled ~40,000 AI-classified and expert-curated cells. We identified progressive molecular dysregulation across cells with increasing histopathological grade coexisting within heterogeneous tumors as well as distinct molecular alterations associated with aggressive rhabdoid ccRCC cells, including signatures consistent with enhanced FOXM1-driven proliferation, altered cell-matrix interactions, and a putative immunomodulatory phenotype. Notably, rhabdoid cells exhibited elevated expression of IFN-beta, PD-L1, CD38, ITGB2, and integrin signaling, suggesting that they themselves may act as a source of signals influencing the local immune microenvironment. Besides providing new insights into the biology of ccRCC and highlighting avenues for future translational studies, this illustrates the potential of Deep Visual Multi-omics to dissect cancer heterogeneity and characterize high-risk cell populations.

Humans↗
PubMed2026

SeqUIaSCOPE: multi-omics data integration platform for single-patient clinical oncology pathway exploration.

SUMMARY: SeqUIaSCOPE is an open-source platform designed for routine clinical oncology diagnostics through case-centric integration and visualization of genomic variants, fusion events, and expression profiles. The platform combines molecular-level validation via embedded genome browsing with systems-level interpretation through dynamic pathway visualization, enabling geneticists to assess how alterations converge across biological networks. Flexible reporting with customizable templates accommodates diverse institutional requirements, while secure cluster-based or local deployment ensures compliance with data protection policies, making advanced multi-omics diagnostics accessible to academic and clinical institutions. AVAILABILITY AND IMPLEMENTATION: SeqUIaSCOPE is freely available on GitHub at https://github.com/BioIT-CEITEC/sequiascope under the MIT license and archived at Zenodo (https://zenodo.org/records/21338445). Due to the sensitive nature of patient data, the repository provides simulated datasets that mimic the structure of real clinical data for testing and exploration. Documentation and a live demo accompany these datasets, allowing users to explore the application without any prior setup. The repository also includes a Helm chart for Kubernetes deployment and Docker containers for local deployment, ensuring compatibility across Linux, macOS, and Windows. No user registration is required, and all data remains on local or institutional infrastructure.

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&#xd7;10&#x207b;&#xb9;&#xb2;), 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 (-log&#x2081;&#x2080;P = 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

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

Multi-omics analysis reveals Protein Kinase A-associated regulatory remodeling during adaptation of Trichoderma reesei to lignocellulosic substrate.

The filamentous fungus Trichoderma reesei is a major industrial source of holocellulolytic enzymes, and its response to complex carbon sources is regulated by nutrient-sensing mechanisms, including the cyclic adenosine monophosphate (cAMP)-protein kinase A (PKA) signaling pathway. Here, we integrated transcriptomics, quantitative proteomics, and phosphoproteomics to analyze PKAc1-associated responses in the parental strain QM9414 and a &#x394;pkac1 strain cultivated under glucose or sugarcane bagasse conditions. Deletion of pkac1 was associated with altered growth-related phenotypes and reduced extracellular activities of selected biomass-depolymerizing enzymes. Multi-omics profiling revealed condition-dependent changes affecting subsets of carbohydrate-active enzymes (CAZymes) genes and proteins, nutrient transporters, stress-associated proteins, and regulatory factors. Phosphoproteomics identified phosphorylation-state changes associated with pkac1 deletion, including reduced phosphorylation at sites enriched for the PKA consensus motif. In silico peptide docking was used to prioritize candidate PKAc1-associated substrates for future validation, including a Sec 7-derived peptide with favorable docking behavior relative to the control peptide. Together, these data support a working model in which PKAc1 contributes to regulatory and phosphorylation-state remodeling during adaptation to sugarcane bagasse, with effects on the magnitude and/or timing of selected CAZyme-related outputs in T. reesei.

Trichoderma↗
PubMed2026

iModMix: integrative module analysis for multi-omics data.

SUMMARY: Integrative Module Analysis for Multi-omics Data (iModMix) is a biology-agnostic framework that enables the discovery of novel associations across any type of quantitative abundance data, including but not limited to transcriptomics, proteomics, and metabolomics. Instead of relying on pathway annotations or prior biological knowledge, iModMix constructs data-driven modules using graphical lasso to estimate sparse networks from omics features. These modules are summarized into eigenfeatures and correlated across datasets for horizontal integration, while preserving the distinct feature sets and interpretability of each omics type. iModMix operates directly on matrices containing expression or abundances for a wide range of features, including but not limited to genes, proteins, and metabolites. Because it does not rely on annotations (e.g., KEGG identifiers), it can seamlessly incorporate both identified and unidentified metabolites, addressing a key limitation of many existing metabolomics tools. iModMix is available as a user-friendly R Shiny application requiring no programming expertise (https://imodmix.moffitt.org), and as a Bioconductor R package for advanced users (https://bioconductor.org/packages/release/bioc/html/iModMix.html). The tool includes several public and in-house datasets to illustrate its utility in identifying novel multi-omics relationships in diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: iModMix is freely available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/iModMix.html), and the example dataset package (iModMixData) is also available from Bioconductor (https://bioconductor.org/packages/release/ data/experiment/html/iModMixData.html). The R package source code and Docker are available from GitHub: https://github.com/biodatalab/iModMix. Shiny application can be accessed at: https://imodmix.moffitt.org.

Multiomics↗
PubMed2025

Multi-omics Mendelian randomization integrating RNA-seq, eQTL and pQTL data revealed CPXM1 as a potential drug target for osteoporosis.

Osteoporosis, a prevalent skeletal disorder characterized by decreased bone mineral density and increased fracture risk, continues to be a major global health concern. Traditional treatments for osteoporosis have limited efficacy and safety profiles, highlighting the need for novel therapeutic targets. This study integrates multi-omics data, including RNA-seq, expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data, through Mendelian randomization (MR) to identify potential drug targets for osteoporosis. By leveraging bidirectional two-sample MR analysis, we identified CPXM1 (Carboxypeptidase X, M14 family member 1) as a novel gene that is causally linked to osteoporosis risk. Through transcriptomic and proteomic validation, we demonstrate that CPXM1 was upregulated in aged bone tissues and osteoporotic conditions in both human and murine models. Gene set enrichment analysis (GSEA) revealed significant dysregulation of bone homeostasis pathways, including increased extracellular matrix degradation and suppression of osteoblast differentiation in aged mice. Furthermore, phenome-wide association studies (PheWAS) confirmed minimal off-target effects of CPXM1, reinforcing its potential as a therapeutic target. Finally, computational drug repurposing predicted several promising drug candidates, including Doxorubicin, 5-Fluorouracil, and 2-Methylcholine, which may target CPXM1 pathways for osteoporosis treatment. These findings highlight CPXM1 as a potential biomarker and therapeutic target, offering new avenues for osteoporosis therapy.

Osteoporosis↗
PubMed2024

Integrated multi-omics approaches reveal the neurotoxicity of triclocarban in mouse brain.

Triclocarban (TCC) is an antimicrobial ingredient that commonly incorporated in many household and personal care products, raising public concerns about its potential health risks. Previous research has showed that TCC could cross the blood-brain barrier, but to date our understanding of its potential neurotoxicity at human-relevant concentrations remains lacking. In this study, we observed anxiety-like behaviors in mice with continuous percutaneous exposure to TCC. Subsequently, we combined lipidomic, proteomic, and metabolic landscapes to investigate the underlying mechanisms of TCC-related neurotoxicity. The results showed that TCC exposure dysregulated the proteins involved in endocytosis and neurodegenerative disorders in mouse cerebrum. Brain energy homeostasis was also altered, as evidenced by the perturbation of pyruvate metabolism, TCA cycle, and oxidative phosphorylation, which in turn caused mitochondrial dysfunction. Meanwhile, the changing trends of sphingolipid signaling pathway and overproduction of mitochondrial reactive oxygen species (mROS) could enhance the neural apoptosis. The in vitro approach further demonstrated that TCC exposure promoted apoptosis, accompanied by the overproduction of mROS and alteration in the mitochondrial membrane potential in N2A cells. Together, dysregulated endocytosis, mROS-related mitochondrial dysfunction and neural cell apoptosis are considered to be crucial factors for TCC-induced neurotoxicity, which may contribute to the occurrence and development of neurodegenerative disorders. Our findings provide novel perspectives for the mechanisms of TCC-triggered neurotoxicity.

Animals↗
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&#x2013;1.890; p&#x2009;<&#x2009;0.05 for all) and modestly predicted response to ICI plus anti-angiogenic therapy (area under the curve 0.720&#x2013;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↗
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&#x2009;=&#x2009;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&#x2009;=&#x2009;0.018), significantly correlated with enhanced immune infiltration (ImmuneScore, StromalScore, ESTIMATEScore; p&#x2009;<&#x2009;2.22e-16), M1 macrophage polarization (p&#x2009;=&#x2009;0.006), activated CD4&#x2009;+&#x2009;T cells (p&#x2009;=&#x2009;0.003), and elevated immune checkpoint expression (PD-L1, CTLA4, LAG3, TIGIT). In vitro, MLKL overexpression in OC cells promoted M1 polarization (p&#x2009;<&#x2009;0.05), activated Jurkat T cells (upregulated CCR4/5/7/9, CD69, CD3D/E, GZMB; p&#x2009;<&#x2009;0.05), and induced key chemokines (CXCL9/10/11/13) critical for immune cell recruitment. Integration of MLKL-related and immune-related DEGs (n&#x2009;=&#x2009;632) revealed enrichment in T-cell activation, chemokine signaling, and antigen presentation pathways (FDR&#x2009;<&#x2009;0.05). Consensus clustering based on 102 survival-associated genes defined three molecular subtypes (Clusters A-C) with divergent survival (p&#x2009;=&#x2009;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&#x2009;<&#x2009;0.001; ICGC: p&#x2009;=&#x2009;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&#x2009;=&#x2009;0.802), synergy with BRCA1 status for prognosis, higher homologous recombination deficiency (HRD) score, sensitivity to cisplatin (p&#x2009;=&#x2009;0.014), paclitaxel (p&#x2009;=&#x2009;0.016), gemcitabine (p&#x2009;=&#x2009;0.017), and provided superior prognostic stratification when combined with TMB and HRD score (p&#x2009;<&#x2009;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↗
PubMed2026

Trans-omics integration underscores distinct roles of polyunsaturated phospholipids in bidirectional offspring birth weight deviations.

BACKGROUND: Abnormal birth weights are associated with adverse pregnancy outcomes and future metabolic consequences. We aimed to examine cord blood lipidomes from low, normal and high birth weight (LBW, NBW, HBW) infants to identify core lipid signatures associated with non-optimum birth weight, and to derive biological insights through trans-omics data integration with placental proteome, maternal plasma lipidome and clinical phenome. METHODS: We conducted quantitative lipidomics of cord blood samples from two independent cohorts: a retrospective discovery cohort (n = 147) and a prospective validation cohort (n = 73). Integration with placental proteomics, maternal plasma lipidomics and clinical phenomics was conducted to elucidate potential biological implications. FINDINGS: We identified substantial reductions in cord blood polyunsaturated phospholipids (PUFA-PLs) (FDR <0.05) associated with placental vesicle trafficking and formation in LBW, and altered neutrophil degranulation in HBW. Combinatorial analyses of paired maternal plasma and cord blood samples indicated that cord blood PUFA-PL reductions were not attributable to deficient maternal supply, but rather to impeded assimilation (LBW) and increased utilisation (HBW). INTERPRETATION: Our findings provide biological insights that may inform targetable, lipid-oriented nutritional and/or pharmacological strategies to modulate foetal growth and development, with the goal of optimising clinical outcomes for both mother and child. FUNDING: This work was supported by the National Natural Science Foundation of China (82170854, 81870579, 81870545, 82571043, 2357308); National High Level Hospital Clinical Research Funding (2022-PUMCH-C-019); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0530200 and 2024ZD0530204); Beijing Municipal Science & Technology Commission (Z201100005520011); Peking University Clinical Scientist Training Program (No. BMU2023PYJH022); Beijing Municipal Natural Science Foundation (7202163, 7184252).

Humans↗
PubMed2026

Deciphering the Genetic Underpinnings of Liver Cirrhosis-Heart Failure Comorbidity Through Multi-Omics: CRIM1 as a Key Endothelial Mediator.

The co-occurrence of liver cirrhosis (LC) and heart failure (HF) poses considerable clinical challenges, yet the cellular and molecular determinants of this comorbidity remain poorly characterized. To address this, we developed an integrative multi-omics pipeline encompassing GWAS meta-analysis, gsMap-based spatial transcriptomic projection, GeneEnrich functional annotation, single-cell atlas construction, seismicGWAS and ECLIPSER cell-type scoring, eCAVIAR and fastenloc colocalization, hdWGCNA network inference, scTenifoldKnk in silico gene perturbation, and GCTA-COJO fine-mapping. Quality-controlled meta-analysis yielded 12,347,758 and 9,256,862 variant-level associations for LC and HF, respectively. Spatial projection confirmed preferential enrichment of disease signals within embryonic hepatic and cardiac compartments. Pathway analyses disclosed that LC-linked loci were concentrated in lipid metabolic programs, whereas HF-linked loci implicated mitochondrial bioenergetics and lysosomal degradation. At the cellular level, endothelial cells emerged as the dominant HF-associated population. Convergent evidence from five orthogonal algorithms pinpointed CRIM1 as the sole robustly supported shared gene, selectively enriched in HF endothelial cells; virtual perturbation further identified LCP1 and PTPRC as downstream regulatory nodes. Fine-mapping of the chromosome 2 locus harboring rs12476437 revealed multiple statistically independent signals in the vicinity of CRIM1. Collectively, these findings computationally prioritize the endothelial-CRIM1 axis as a previously unappreciated candidate mechanistic bridge between LC and HF requiring experimental validation.

Humans↗
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&#x2014;Dasatinib-51, WH-4-023-56, TWS-119-366, and LDN-193189-478&#x2014;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&#x2014;highlighting its oncogenic role. CONCLUSION: Six GMRGs&#x2014;SRPX, CXCL1, GPX3, PCOLCE2, CLU, and SEMA3E&#x2014;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

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗
PubMed2025

Dual genetic loci and flavonoid metabolism orchestrate fruiting body coloration in Flammulina filiformis: a multi-omic roadmap for fungal pigmentation.

BACKGROUND: The fruiting bodies of macrofungi exhibit diverse coloration, traditionally attributed to melanin and carotenoid biosynthesis. This study is the first to reveal that flavonoids, rather than these classical pigments, are the predominant contributors to yellow pigmentation in the Flammulina filiformis. OBJECTIVE: To uncover the genetic basis and key regulatory genes involved in pigment formation in F. filiformis fruiting bodies, and to establish a model framework for studying color genetics in macrofungi. METHODS: Metabolomic profiling was conducted on yellow and white F. filiformis fruiting bodies to identify key pigment components. A segregating population was constructed, followed by integrated multi-omics analyses-including bulk segregant analysis (BSA), genome-wide association study (GWAS), and transcriptomics-to map regulatory loci and candidate genes. Functional roles were validated via genetic transformation and protein structural modeling. RESULTS: Flavonoid accumulation was identified as the biochemical hallmark of pigmented fruiting bodies. Genetic analysis revealed a dual regulatory mechanism: a qualitative locus governing pigmentation presence and a quantitative trait determining color intensity. Combined BSA and GWAS pinpointed a major locus, Ffcrs, within a recombination-suppressed region. Transcriptomic analysis identified two key regulators, Ffakr (a transcriptional activator) and Ffpal (encoding phenylalanine ammonia-lyase). Functional verification via transformation, structural modeling, and metabolite profiling in transgenic lines confirmed their essential roles in flavonoid biosynthesis and pigmentation. CONCLUSION: This study uncovers a flavonoid-based pigmentation mechanism in F. filiformis and elucidates a complex genetic architecture shaped by both qualitative and quantitative loci, providing a new paradigm for understanding pigment formation in macrofungi. The identified regulatory factors establish a molecular foundation for the precise manipulation of economically important pigmentation traits in edible mushroom.

Flavonoids↗
PubMed2026

Polystyrene microplastics induce auditory neurotoxicity in mammals: Integrated multi-omics profiling reveals oxidative damage and synaptic molecular dysregulation.

Microplastics (MPs) are ubiquitous environmental pollutants, yet their neurotoxic effects on the auditory system remain poorly understood. This study develops an integrated multi-level analytical framework combining auditory neurophysiology, behavioral assessment, tissue biochemistry, transcriptomics, and proteomics to investigate polystyrene (PS)-MPs-induced auditory neurotoxicity in rats. PS-MPs infiltrate the auditory system and significantly impair auditory processing, with central dysfunction emerging earlier and more prominently than peripheral alterations. Multi-omics analyses reveal coordinated suppression of glutamatergic synapse and Wnt signaling pathways in the cochlear nucleus. Mechanistically, PS-MPs perturb the crosstalk between glutamatergic synaptic and Wnt signaling, promoting AMPA receptor (AMPAR) internalization and potentially affecting synaptic plasticity-related processes and neuronal responsiveness. In parallel, PS-MPs trigger oxidative stress, apoptosis, and glial activation, reflecting pronounced neuroinflammatory and redox imbalance. In primary cochlear nucleus neurons (PCNNs), these mechanisms were further validated in vitro, where activation of Wnt signaling by Wnt3a significantly alleviated oxidative injury and reduced AMPAR internalization. Collectively, these findings provide comprehensive preclinical evidence for the neurotoxic potential of MPs and reveal a previously unrecognized PS-MPs-induced auditory neurotoxicity, although further studies are needed for human relevance. Results from the rat model further implicate Wnt-mediated signaling as a potential modulatory pathway underlying MPs-induced synaptic molecular alterations and redox dysfunction.

Animals↗
PubMed2026

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans↗