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PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 0 | Ulcerative colitis (UC) patients carry a 2.5-fold increased risk of colorectal cancer (CRC), yet the shared multi-scale genetic architecture remains poorly understood. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 1 | We constructed an integrative framework across tissue, cellular, and variant levels to systematically dissect the pathogenic evolution of this comorbidity across spatiotemporal dimensions. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 2 | We integrated GWAS data from 100,204 CRC cases and 12,160 UC patients with tissue-specific MAGMA enrichment, embryonic spatial mapping (gsMap), and multidimensional single-cell prioritization (ECLIPSER, CELLECT, scDRS). | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 3 | We further resolved cell-specific co-expression patterns using hdWGCNA and identified high-confidence causal variants and genes through Bayesian fine-mapping (eCAVIAR, fastenloc) and Open4Gene analysis. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 4 | Genetic susceptibility for both diseases was significantly enriched in the terminal ileum and transverse colon, anchored to E16.5 embryonic gut programs. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 5 | CD4 + T cells emerged as the core immune hub in UC, exhibiting profound immunometabolic polarization (Th17/IL-17 axis and Warburg effect), while progenitors were identified as the primary cellular origin for CRC malignancy. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 6 | Pathological progression was characterized by a transition from chronic inflammatory stress toward p53-mediated genomic instability, epithelial-mesenchymal transition (EMT), and vascular remodeling. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 7 | We prioritized Tier 1 candidate genes—ARPC5, PTGER4, CIB1, PREX1, and S100A10—as key mediators of the comorbidity association between inflammation and cancer. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 8 | These findings partially support a “genetic programming-microenvironment triggering” hypothesis, where regional vulnerabilities established by embryonic developmental programs are activated by postnatal insults, though its broad applicability warrants caution. | Abstract |
PMC13160972 | Multidimensional dissection of shared genetic susceptibility in ulcerative colitis and colorectal cancer: novel insights from integrative single-cell and multi-omics analysis | 9 | This study provides a comprehensive multi-scale molecular framework for understanding UC-CRC comorbidity, offering potential targets for risk stratification and therapeutic intervention. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 0 | Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, with therapeutic resistance largely driven by unresolved malignant epithelial heterogeneity within the tumor microenvironment. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 1 | However, the epithelial cell states that underlie poor prognosis and immunotherapy resistance remain incompletely defined. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 2 | We performed an integrative multi-omics analysis combining large-scale single-cell RNA sequencing, spatial transcriptomics, and bulk transcriptomic data with clinical outcomes. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 3 | The Scissor algorithm was applied to identify prognosis-associated epithelial cell states, followed by construction of a risk score model. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 4 | External validation was conducted across multiple independent cohorts, including immunotherapy-treated datasets. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 5 | We identified a prognostically relevant Scissor⁺ malignant epithelial cell state associated with adverse survival. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 6 | This state was characterized by activation of MYC, epithelial–mesenchymal transition, hypoxia, and NF-κB signaling, and was linked to an immunosuppressive tumor microenvironment. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 7 | Based on this state, we developed a Scissor⁺ epithelial cell–derived risk score (SERS), which demonstrated robust and reproducible prognostic performance across multiple cohorts and was associated with reduced responsiveness to immunotherapy. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 8 | Further analyses identified SLC2A1 as a key gene associated with this malignant epithelial state. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 9 | Functional experiments confirmed that SLC2A1 promotes tumor cell proliferation, migration, and invasion. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 10 | In addition, cell–cell communication analysis suggested a potential SLC2A1–CAF–collagen signaling axis linking epithelial cell states with stromal interactions. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 11 | This study defines a clinically relevant malignant epithelial cell state in LUAD and establishes a framework linking cell states, molecular features, and microenvironmental interactions. | Abstract |
PMC13219086 | A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response in lung adenocarcinoma | 12 | These findings provide potential biomarkers for prognostic stratification and immunotherapy response prediction in LUAD. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 0 | This review highlights recent advances in single-cell analysis technologies and their application in clarifying the cellular and molecular complexity of atherosclerosis, redefining our understanding of vascular biology and immune cell functioning within the atherosclerotic plaque. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 1 | Single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq, and spatial transcriptomics have revealed an unforeseen diversity of immune and vascular cell states in human and experimental prototypes of atherosclerosis. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 2 | These techniques have led to the finding of novel macrophage and smooth muscle cell (SMC) phenotypes, distinct endothelial dysfunction signatures, and oligoclonal T cell populations. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 3 | By integrating transcriptomic, epigenomic, proteomic, and spatial data, researchers have clarified key mechanisms of disease progression and identified cell-specific molecular pathways responsive to targeted therapy. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 4 | Single-cell methods are changing our understanding of atherosclerosis by determining the heterogeneity, plasticity, and functional states of plaque-resident cells. | Abstract |
PMC13279529 | Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities | 5 | These understandings contribute for the development of novel biomarkers, precision diagnostics, and targeted immunomodulatory strategies, with the ultimate goal of improving risk stratification and personalized treatment in atherosclerotic cardiovascular disease. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 0 | Over 25% of patients with ulcerative colitis (UC) develop extraintestinal manifestations (EIMs), resulting in significant systemic morbidity. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 1 | We define the shared genetic foundation of these manifestations as the UC-associated Multisystem Inflammatory Genetic Architecture (UC-MIGA). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 2 | This study aims to identify shared genomic drivers and actionable immunosenescence therapeutic targets across the UC-EIM spectrum. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 3 | We applied genomic structural equation modeling (SEM) to seven European-ancestry GWAS datasets (UC, deep vein thrombosis, ankylosing spondylitis, primary sclerosing cholangitis, pyoderma gangrenosum, interstitial lung disease, and erythema nodosum) to identify a shared latent genetic factor (F1). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 4 | Post-SEM analyses included FUMA mapping, SuSIE/FINEMAP fine-mapping, FUSION/FOCUS transcriptome-wide studies, MAGMA enrichment, CELLECT deconvolution, LDSC partitioned heritability, and single-cell eQTL Mendelian randomization (MR). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 5 | UC exhibited the highest standardized factor loading (0.9801) on F1, justifying its use as a representative proxy for UC-MIGA in downstream analyses. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 6 | UC-telomere relationships were assessed via tissue-specific eQTL/sQTL enrichment across 49 GTEx tissues, spatial transcriptomics (gsMap), single-cell profiling (GSE214695, GSE163974), hdWGCNA, and colocalization analyses (eCAVIAR, fastENLOC). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 7 | SEM identified substantial genetic overlap (CFI = 1.0, SRMR = 0.17). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 8 | Within the UC-MIGA framework, we identified 17,005 SNPs (P ≤ 1 × 10⁻2⁰⁰), 2,622 risk loci, and 152 high-confidence effector genes. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 9 | Pathways implicated Th17/Treg imbalance and inflammasome signaling. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 10 | Super-enhancer regions showed exceptional heritability enrichment (80.16%, fold = 4.79, p = 0.0007). | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 11 | MR identified 35 causal immune cell-gene associations. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 12 | UC-telomere analyses revealed convergence in colon-specific DNA repair-mitochondrial energetics-telomere maintenance pathways, with B cells prioritized as the core cell type. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 13 | Colocalization identified NKX2-3 and LINC01475 as high-confidence shared candidates. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 14 | Embryonic intestinal enrichment supported the developmental origins of this systemic axis. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 15 | UC-MIGA represents a genetically coherent architecture driven by super-enhancer-mediated epigenetic dysregulation, Th17/Treg imbalance, and immunosenescence features, including telomere dysfunction and B-cell exhaustion. | Abstract |
PMC13152973 | Multivariable genome-wide analysis elucidates the shared genetic architecture, immunosenescence features, and gut-origin therapeutic targets of ulcerative colitis-associated multisystem inflammation | 16 | The ‘developmental vulnerability-environmental trigger’ model explains the gut-origin inflammatory cascade underlying extraintestinal manifestations, with UC-telomere analysis providing a genomic foundation for systemic therapeutic strategies targeting the inflammation-aging nexus. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 0 | Spatial transcriptomics analysis is a powerful approach for dissecting the structure of tissue microenvironment and uncovering the mechanism of cell–cell communications. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 1 | However, existing technologies are limited by either spatial resolution or gene coverage. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 2 | Most single-cell-resolution technologies target only a few hundred preselected genes, whose choice plays an important role in the overall analysis. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 3 | It remains a challenge to optimally design a gene panel to maximize the utility of spatial transcriptomics profiling. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 4 | To fill this gap, we introduce a novel method, named ReconST, to automatically design optimal gene panels for spatial transcriptomics profiling. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 5 | ReconST leverages information from existing scRNA-seq data and identifies the optimal subset of genes by using a gated autoencoder. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 6 | By using a high-coverage mouse brain MERFISH dataset and a fetal lung dataset as the reference benchmarks, we showed that ReconST outperforms existing methods in terms of reconstruction accuracy, spatial pattern preservation, and computing efficiency. | Abstract |
PMC13273311 | Optimal gene panel selection for targeted spatial transcriptomics experiments | 7 | As such, ReconST provides a useful and generally applicable tool for optimal gene panel design, which in turn can significantly enhance the utility of spatial transcriptomics profiling in a wide range of biomedical investigations. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 0 | Alzheimer’s Disease (AD), the most common form of dementia, is pathologically defined by extracellular beta-amyloid (Aβ) plaques and intraneuronal neurofibrillary tangles (NFTs), accompanied by chronic neuroinflammation. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 1 | Recent advances in single-cell RNA sequencing (scRNA-seq/snRNA-seq) and spatial transcriptomics have provided unprecedented resolution for unraveling the cellular and molecular landscape of neuroinflammation in AD. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 2 | While scRNA-seq enables high-throughput profiling of cellular heterogeneity across brain regions, spatial transcriptomics preserves tissue architecture to map cell-type-specific gene expression within its anatomical context. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 3 | This review synthesizes the neuroinflammatory mechanisms of AD, outlines the technical evolution and comparative capabilities of single-cell and spatial omics platforms, including resolution, throughput, and compatibility with multiple sample types, and critically evaluates findings from studies in both animal models a... | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 4 | These approaches have revealed state-specific transitions in microglia and astrocytes, including shifts in transcriptional programs, metabolic reprogramming, and pro-inflammatory polarization across disease stages. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 5 | Notably, spatial transcriptomic analyses demonstrate pronounced regional heterogeneity: periplaque microenvironments exhibit distinct immune-cell compositions and gene expression signatures. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 6 | Collectively, these omics technologies are redefining the cellular basis of AD progression and hold the potential to impact the discovery of early diagnostic biomarkers and precision therapeutic targets. | Abstract |
PMC13257420 | Multi-Dimensional Transcriptomics Reveals the Prominent Role of Neuroinflammation in Alzheimer’s Disease | 7 | Keywords: Alzheimer’s disease, neuroinflammation, single-cell RNA sequencing, spatial transcriptomics, microglia, astrocyte | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 0 | Lung adenocarcinoma (LUAD), a common lung cancer subtype, is significantly influenced by the immune microenvironment. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 1 | Immune checkpoint inhibitors have shown limited efficacy in Lung adenocarcinoma due to the immunosuppressive tumor microenvironment (TME). | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 2 | Identifying predictive biomarkers for immunotherapy response remains an urgent clinical need. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 3 | Multi-Omics data of LUAD were analyzed to investigate the immune microenvironment in LUAD. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 4 | Single cell RNA-seq was used for exploring the intercellular communication mechanisms in TME. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 5 | Spatial transcriptomic analysis confirmed the spatial co-localization of SPP1+ Malignant and CXCR4+ TAM, while in vitro experiments validated the functions of biomarker. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 6 | This study delineated the cellular heterogeneity and dynamic shifts within the LUAD tumor microenvironment, resolving the malignant transformation trajectory. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 7 | Crucially, we identified SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a driver of exhaustion of CD8T, which induced poor immunotherapy response. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 8 | Moreover, this study indicated that the immune escape potential of patients in high infiltration of SPP1+ malignant and CXCR4+ TAM increased, and the efficacy of immune checkpoint suppressive therapy might be poorer. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 9 | Spatial transcriptomics confirmed co-localization of SPP1+ Malignant and CXCR4+ TAM, while in vitro experiments demonstrated that CXCR4 plays an important role in the functions of LUAD cells. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 10 | This study uncovers the SPP1⁺ malignant and CXCR4⁺ TAM crosstalk as a novel TME-driven resistance mechanism and provides a potential biomarker for stratifying LUAD patients likely to benefit from immunotherapy. | Abstract |
PMC13187092 | Multi-Omics reveals SPP1 + malignant and CXCR4+ TAM crosstalk predicts immunotherapy response in lung adenocarcinoma | 11 | Keywords: Tumor-associated macrophages, Prognosis, Immune microenvironment, Immunotherapy, Biomarker | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 0 | Breast cancer brain metastasis (BCBM) is a lethal complication of advanced disease, facilitated by a specialized neuro-immune microenvironment, whereas its molecular regulators and spatial dynamics remain poorly understood. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 1 | This study aimed to resolve cellular heterogeneity and intercellular signaling networks crosstalk in BCBM microenvironment by using integrated single-cell RNA sequencing (scRNA-seq) single-cell and spatial transcriptomics to identify key regulators, build a prognostic model, and computationally screen for potential the... | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 2 | We analyzed public scRNA-seq and 10× Visium spatial transcriptomics datasets. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 3 | Cell landscapes were resolved with Seurat, communication networks were inferred with CellChat, and spatial mapping was performed with Tangram. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 4 | A prognostic risk model was developed from bulk RNA sequencing (RNA-seq) data from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA), the Gene Expression Omnibus (GEO) datasets using Least Absolute Shrinkage and Selection Operator-Cox (LASSO-Cox) regression and validated in an independent cohort. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 5 | Potential small-molecule inhibitors were screened by molecular docking (AutoDock Vina) against AlphaFold-predicted target structures. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 6 | The BCBM microenvironment exhibited high heterogeneity, with macrophages serving as the central signaling hub. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 7 | A novel regulatory axis, interleukin-18 and myosin light chain kinase (IL-18/MYLK), was found to be specifically upregulated and co-localized at the tumor-brain interface. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 8 | A risk-score model based on the IL-18/MYLK axis demonstrated robust prognostic value in both training and validation cohorts. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 9 | The model further revealed that the upregulation of IL-18 and MYLK was associated with better overall survival. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 10 | Virtual screening identified the MEK inhibitor, PD98059, as a high-affinity dual inhibitor of IL-18 and MYLK. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 11 | This study delineates the neuro-immune landscape of BCBM progression and proposes the IL-18/MYLK axis as a critical driver of neuroinflammation and cytoskeletal remodeling. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 12 | The prognostic model and the candidate compound PD98059 provide avenues for novel targeted therapeutic strategies. | Abstract |
PMC13190926 | Integrating single-cell and spatial transcriptomics to decipher the neuro-immune microenvironment of breast cancer brain metastasis: a key regulatory axis, prognostic model, and drug discovery | 13 | Keywords: Breast cancer brain metastasis (BCBM), single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, interleukin-18 (IL-18), myosin light chain kinase (MYLK) | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 0 | Tumors are highly dynamic diseases characterized by significant heterogeneity. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 1 | They consist of multiple cellular populations with distinct properties that respond differently to therapeutic pressure. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 2 | This heterogeneity may arise from spatial variation across tumor regions (spatial heterogeneity) as well as from temporal changes during tumor evolution and treatment (temporal heterogeneity). | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 3 | As a consequence, drug-resistant subclones often emerge under therapy and contribute to treatment failure. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 4 | Advances in single-cell and spatial multi-omics technologies enable precise quantification of tumor heterogeneity, supporting detailed investigation of how heterogeneity contributes to chemoresistance and informing the development of personalized therapeutic strategies. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 5 | In this review, we summarize the evolutionary dynamics underlying the emergence of tumor drug resistance and examine the molecular mechanisms responsible for failure of targeted therapies. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 6 | We highlight how advances in single-cell and spatial multi-omics have significantly improved our ability to elucidate these processes. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 7 | We further suggest that addressing tumor drug resistance may require a shift from static, single-target approaches toward dynamic, biology-informed personalized strategies. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 8 | Integrating high-resolution multi-omics monitoring with functional validation could enable identification of subclonal vulnerabilities, support adaptive treatment adjustment, and contribute to more durable clinical responses. | Abstract |
PMC13244270 | Tumor heterogeneity as a driver of drug resistance and its implications for personalized therapy | 9 | Keywords: Tumor heterogeneity, drug resistance, single-cell omics, spatial omics, personalized therapy, targeted therapy | Abstract |
PMC13147954 | Spatial multi‐omics identifies a NOTCH3‐mediated capillary–mCAF crosstalk driving immune exclusion in hepatocellular carcinoma | 0 | Fibrosis induced immune exclusion is a hepatocellular carcinoma (HCC) hallmark, underscoring the key role of cancer‐associated fibroblasts (CAFs) in immune regulation. | Abstract |
PMC13147954 | Spatial multi‐omics identifies a NOTCH3‐mediated capillary–mCAF crosstalk driving immune exclusion in hepatocellular carcinoma | 1 | Through HCC spatial multi‐omics data and integrating pan‐cancer scRNA‐seq profiles of CAFs under immune checkpoint blockade (ICB) treatment, we characterized a potential crosstalk between capillaries and CAFs mediated by the NOTCH signaling pathway. | Abstract |
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Dataset Card for CeLLaTe Abstract Benchmark Dataset
Dataset summary
The CeLLaTe Abstract Benchmark Dataset is a curated collection of biomedical abstracts created as a benchmark set for evaluating CeLLaTe named entity recognition (NER) models. It was extracted from Europe PMC article XML sources, with a focus on open-access articles. The dataset is intended to support model development and testing by providing an independent evaluation set for demo production runs and for rigorously assessing the tagging capacity of the models.
The dataset is unannotated and therefore not suitable for supervised training or quantitative metric-based evaluation. Instead, it is designed for qualitative inspection of model outputs, including entity boundary behaviour, consistency, and error analysis.
Dataset variants
CeLLaTe Abstract Benchmark Dataset (this release) Contains article abstracts extracted from Europe PMC XML sources and prepared as a standalone benchmark set.
CeLLaTe Full-Text Benchmark Dataset (separate release available here) Contains fulltext articles extracted from Europe PMC XML sources and prepared as a standalone benchmark set.
Dataset Sources [optional]
- Curated from : Europe PMC
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Intended Uses
This dataset can be used for:
- qualitative benchmarking of NER model outputs
- manual inspection of tagging performance
- exploratory analysis of entity coverage and boundary consistency
- analysing entity coverage in abstract-level scientific writing
- unsupervised or semi-supervised use cases such as pretraining and representation learning
- sentence-level processing tasks such as sentence segmentation or sentence-based downstream analysis
Direct Use
- Qualitative performance analysis of Biomedical NER models
- Several Unsupervised NLP tasks such as Topic modelling, pretraining/domain-specific contuined pretraning, sentence-based downstream analysis etc
Out-of-Scope Use
This dataset cannot be used as:
- supervised training data for NER/NLP models
- validation data for metric reporting (e.g. F1, precision, recall)
- quantitative benchmarking against annotated gold labels
- a source of ground-truth entity spans
Data characteristics
Domain: Biomedical literature Source type: Europe PMC XML-derived article text Text type: Article abstracts Annotation status: Unannotated Primary purpose: Qualitative NER evaluation
Dataset Structure
The dataset is organised at the sentence level. Each record corresponds to a sentence extracted from an article abstract and includes the following fields:
- Sentence: the sentence-level text extracted from the abstract
- PMCID: the PMCID of the source article from which the sentence was generated
- Title: the title of the source article
- Sentence_ID: a sequential identifier for the sentence within the article
Dataset Creation
Curation Rationale
The dataset was curated to provide a representative abstract-level benchmark for qualitative evaluation of CeLLaTe NER models. Its purpose is to support:
- independent testing on unseen biomedical text
- demo-style production evaluation
- inspection of entity boundary behaviour
- assessment of tagging consistency across models
- comparison of model behaviour on abstract-level scientific writing
Evaluation notes
Because the dataset is unannotated, its primary value lies in qualitative analysis rather than formal supervised evaluation. Therefore, evaluation should focus on:
- qualitative error inspection
- entity boundary behaviour
- false positive and false negative patterns
- consistency across models and samples
- robustness of the tagging pipeline
Data Collection and Processing
The abstracts were collected from Europe PMC XML records for open-access biomedical articles. The source XML was parsed to extract article metadata and abstract text, including the article title and PMCID.
During preprocessing:
- irrelevant or non-informative XML sections were removed e.g acknowledgements, references etc
- whitespace and formatting artefacts were normalised
- abstract text was split into sentence-level units
- each sentence was linked back to its source article using the PMCID and Title fields
Versioning
Dataset name: CeLLaTe Abstract Benchmark Dataset Release type: Abstract-only benchmark set Current state: Unannotated Use case: Qualitative NER evaluation and demo testing
Limitations
- No gold-standard labels are available.
- Quantitative scores cannot be interpreted as absolute model performance on this dataset.
- Results depend on manual review and qualitative comparison rather than formal metric computation.
Citation and attribution
If this dataset is used in reports or publications, it should be described as a CeLLaTe benchmark dataset derived from Europe PMC article text and clearly identified as an unannotated qualitative evaluation set.
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