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Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.
BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensively evaluates LOX/LOXL expression in cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance. METHODS: We assessed the association of LOX/LOXL expression with survival and drug sensitivity via analyzing public datasets (including bulk and single-cell RNA sequencing data of six datasets from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA) and Cancer Genome Atlas Program (TCGA)). We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction. RESULTS: The clinical significance of the LOX/LOXL family was evaluated across 33 cancer types. Overexpression of LOX/LOXL showed a strong correlation with tumor progression and poor survival, particularly in glioma. Therefore, we developed a novel prognostic model for glioma by integrating LOX/LOXL expression and its co-expressed genes. This model was highly predictive for overall survival in glioma patients, indicating significant clinical utility in prognostic assessment. Furthermore, our analysis uncovered a distinct LOXL2-overexpressing malignant cell population in recurrent glioma, characterized by activation of collagen, laminin, and semaphorin-3 pathways, along with enhanced epithelial-mesenchymal transition. Apart from glioma, our data revealed the role of LOXL3 overexpression in macrophages and in predicting the response to immune checkpoint blockade in bladder and renal cancers. Given the pro-tumor role of LOX/LOXL genes in most analyzed cancers, we identified potential therapeutic compounds, such as the VEGFR inhibitor cediranib, to target pan-LOX/LOXL overexpression in cancer. CONCLUSIONS: Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.
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Single-nucleus transcriptome profiling unveils cell-type-specific ethylene and TOR signaling in tomato.
Plant etiolation, a critical process for seedling emergence, is regulated by ethylene and target of rapamycin (TOR) signaling pathways. However, the cell-type-specific regulation of these pathways remains poorly understood. To address this, we generated a comprehensive single-nucleus RNA transcriptome atlas of etiolated apical hooks and hypocotyls in tomato seedlings treated with the ethylene precursor aminocyclopropane-1-carboxylic acid (ACC), the TOR inhibitor Torin2, or a mock treatment. In total, we obtained high-quality gene expression profiles for 117 929 nuclei across these tissues and treatments. Our analysis identified seven major cell types within each tissue, revealing distinct cellular compositions and transcriptional programs. ACC treatment increased the proportion of epidermal cells in apical hooks, while Torin2 had limited impact on cellular composition. Differential gene expression analysis demonstrated tissue-specific sensitivity to these treatments: apical hooks exhibited extensive ACC-responsive differentially expressed genes, whereas hypocotyls were highly responsive to Torin2. Cellular responsiveness analysis uncovered divergent ethylene/auxin pathway activities, such as ACC-repressed auxin transport in hook endodermis-like cells. Dynamic trajectory analysis indicated both treatments altered cell differentiation, authenticating epidermis as the key cell type for ethylene-mediated etiolated growth. Crucially, we identified JA1 (HD-ZIP I TF) as a negative ethylene regulator enriched in epidermis, and CRISPR knockout ja1 mutants exhibited hypersensitivity to ACC. This study deciphers cell-type-specific ethylene-TOR crosstalk, providing a robust single-cell RNA sequencing framework to dissect signaling networks in crops.
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An aging-related gene signature to predict the prognosis of hepatocellular carcinoma.
Aging increases the susceptibility of various diseases, including hepatocellular carcinoma (HCC). This study aimed to establish an aging-related prognostic model for HCC and to investigate the role of aging-related genes in HCC progression. Transcriptome and clinical information of HCC cases were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Aging-related prognostic genes were identified through univariate Cox regression analysis, protein-protein interaction analysis, and least absolute shrinkage and selection operator (LASSO) analysis. An aging-related risk signature was then constructed, including LDHA, MMP12, ATAD3A, CD8A, TPI1, CST3, and TPM1. The risk score was inversely associated with the overall survival of patients with HCC and correlated well with known prognostic factors. The area under the curve of 1-, 3-, and 5-year survival in the training dataset was 0.83, 0.83, and 0.84, respectively. Univariate and multivariate cox regression analysis verified that the aging-related risk signature independently predicted the overall survival in HCC. To increase the clinical utility of the prognostic model, a nomogram was developed by incorporating the risk score with key clinical features. Finally, single-cell transcriptomes of HCC were analyzed to elucidate the expression pattern of the prognostic genes across different tissues, pathologic stages, and cell types. Collectively, the aging-related prognostic model shed light on HCC pathogenesis and held potential for optimizing the management of HCC.
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A basophil-fibroblast pro-inflammatory axis fuels type 2 skin inflammation.
Chronic inflammatory skin diseases arise from dysregulated interactions between tissue-resident and infiltrating cells, the complexity of which hinders disease understanding and treatment. To address this, here, we present a single-cell spatiotemporal atlas of murine type 2 skin inflammation using MERFISH and scRNA-seq. Analyzing ∼430,000 cells during MC903- and oxazolone-induced dermatitis, we identify 39 cell types, including pro-inflammatory fibroblasts that resemble those in human atopic dermatitis. Spatial neighborhood analyses reveal basophils as potent activators of pro-inflammatory fibroblasts, with basophil-derived oncostatin-M (OSM) and IL-4 synergizing fibroblast-mediated feedforward basophil and immune recruitment. While fibroblast-specific deletion of the IL-4Rα receptor disrupts inflammation in vivo, the addition of pharmacologic gp130 inhibition, a core component of the OSM receptor, results in synergistic reduction of inflammation. Our study establishes a basophil-fibroblast circuitry as a critical regulator of type 2 skin inflammation, redefining basophil biology and positioning fibroblasts as dynamic immune regulators and therapeutic targets in inflammatory skin disease.
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ZIC3 expression identifies a developmental transcriptional axis in IDH-wildtype glioblastoma.
OBJECTIVE: IDH-wildtype glioblastoma exhibits substantial biological heterogeneity despite current molecular classification. Although dynamic malignant cell states have been described, molecular features that position tumors along these states remain incompletely defined. We investigated whether ZIC3 expression identifies a transcriptional state associated with clinical outcome in IDH-wildtype glioblastoma. METHODS: RNA sequencing and clinical data from The Cancer Genome Atlas glioblastoma cohort were analyzed after restriction to genomically confirmed IDH-wildtype tumors (n = 167). Survival analyses were performed in patients with available survival data (n = 161) using Cox proportional hazards regression and Kaplan-Meier analysis. Associations with MGMT promoter methylation, tumor microenvironment scores, differential expression, gene set enrichment, and single-cell RNA sequencing data were evaluated to characterize transcriptional programs associated with ZIC3 expression. RESULTS: Higher ZIC3 expression was associated with longer overall and progression-free survival and remained associated with a more favorable survival profile after adjustment for age, sex, radiotherapy, and temozolomide treatment. ZIC3 expression was not associated with MGMT promoter methylation or with ESTIMATE-derived stromal, immune, or composite scores. In the single-cell dataset, ZIC3-positive cells were more frequent in malignant tumor cells than in non-malignant cells. ZIC3-high tumors showed enrichment of neural lineage-associated and blood-brain barrier-related programs, whereas ZIC3-low tumors showed enrichment of hypoxia-related, glycolytic, and metabolic stress programs. Ordered heatmap analysis showed that lineage-associated genes and hypoxia-related genes varied inversely across tumors ordered by ZIC3 expression. CONCLUSIONS: ZIC3 expression appears to mark a developmental transcriptional axis in IDH-wildtype glioblastoma that links neural lineage-associated programs to hypoxia-adaptive tumor states.
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Cross-tissue atlas of mucosa-associated lymphoid tissue lymphomas reveals intratumoral heterogeneity and microenvironmental subtypes.
Mucosa-associated lymphoid tissue (MALT) lymphoma, a common primary extranodal B cell lymphoma that arises in diverse mucosal tissues, has a pathogenesis closely linked to the supportive lymphoma microenvironment (LME). However, its microenvironmental heterogeneity and therapeutic implications remain unclear. Here, we report a cohort of 89 MALT lymphoma samples from multiple anatomical sites, profiled by bulk (n = 89), single-nucleus (n = 16), and spatial (n = 9) RNA sequencing. Integrative analysis of 120 bulk transcriptomes from our cohort and two independent cohorts, guided by cell-type-resolved signatures derived from single-nucleus data, identifies three LME subtypes: mesenchymal, inflammatory, and depleted. Single-cell and spatial mapping further reveal distinct cellular compositions and interaction networks across these subtypes, particularly in relation to B cell activation and angiogenesis, suggesting distinct biological states and subtype-specific therapeutic vulnerabilities. This study establishes an LME-based classification framework for MALT lymphoma with biological and clinical relevance.
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High-resolution spatial transcriptomic atlas of mouse soleus muscle: Unveiling single cell and subcellular heterogeneity in health and denervation.
Skeletal muscle exhibits pronounced cellular and subcellular heterogeneity, but comprehensive spatial mapping has been constrained by cell/nuclei dissociation-based methods that lose tissue architecture and by spatial platforms with insufficient resolution or limited transcriptome coverage. Here, we present a high-resolution spatial transcriptomic atlas of mouse soleus muscle in longitudinal sections with unbiased whole-transcriptome coverage, enabling myofiber-resolved transcriptomes while preserving subcellular expression domains across the length of fibers. Combining histology-guided myofiber segmentation with unbiased grid-based mapping, we recover canonical fiber types and reveal widespread hybrid myofiber states in situ, including type IIb-associated signatures that are rare in soleus muscle and evident only when intramyofiber heterogeneity is assessed. At subcellular scale, we delineate the neuromuscular junction (NMJ) as a multicompartment niche comprising postsynaptic myonuclei and spatially distinct perisynaptic and myelinating Schwann cell-associated regions, each with characteristic gene programs. Applying this framework to denervation (3 and 7 days) identifies robust fiber-type-specific stress responses, coordinated remodeling of macrophage and fibroblast transcriptomes, and marked intramyofiber heterogeneity, including spatially nonuniform activation of damage-response genes along individual myofibers, with distinct transcriptional domains proximal and distal to the NMJ and associated degenerative histological features. Together, this atlas provides a high-resolution reference for muscle biology and clarifies how denervation reshapes myofiber, synaptic, and stromal-immune programs across cells and within cells in intact tissue.
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Integrated Transcriptome Profiling and Pan-Cancer Analyses Reveal Oncogenic Networks and Tumor-Immune Modulatory Roles for FABP7 in Brain Cancers.
Fatty acid binding protein 7 (FABP7) is a multifunctional chaperone involved in lipid metabolism and signaling. It is primarily expressed in astrocytes and neural stem cells (NSCs), as well as their derived malignant glioma cells within the central nervous system. Despite growing evidence for FABP7's tumor-intrinsic onco-metabolic functions, its mechanistic role in regulating the brain tumor immune microenvironment (TIME) and its impact on prognosis at the molecular level remain incompletely understood. Utilizing combined transcriptome profiling and pan-cancer analysis approaches, we report that FABP7 mediates the expression of multiple onco-immune drivers, collectively impacting tumor immunity and clinical outcomes across brain cancer subtypes. An analysis of a single-cell expression atlas revealed that FABP7 is predominantly expressed in the glial lineage and malignant cell populations in gliomas, with nuclear localization in their parental NSCs. Pathway and gene enrichment analysis of RNA sequencing data from wild-type (WT) and Fabp7-knockout (KO) mouse brains, alongside control (CTL) and FABP7-overexpressing (FABP7 OV) human astrocytes, revealed a more pronounced effect of FABP7 levels on multiple cancer-associated pathways. Notably, genes linked to brain cancer progression and tumor immunity (ENO1, MUC1, COL5A1, and IL11) were significantly downregulated (>2-fold) in KO brain tissue but were upregulated in FABP7 OV astrocytes. Furthermore, an analysis of data from The Cancer Genome Atlas (TCGA) showed robust correlations between the expression of these factors, as well as FABP7, and established glioma oncogenes (EGFR, BRAF, NF1, PDGFRA, IDH1), with stronger associations seen in low-grade glioma (LGG) than in glioblastoma (GBM). TIME profiling also revealed that the expression of FABP7 and the genes that it modulates was significantly associated with prognosis and survival, particularly in LGG patients, by influencing the infiltration of immunosuppressive cell populations within tumors. Overall, our findings suggest that FABP7 acts as an intracellular regulator of pro-tumor immunomodulatory genes, exerting a synergistic effect on the TIME and clinical outcomes in brain cancer subtypes.
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Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis.
Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.
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Computational discovery of novel colony-stimulating factor-1 receptor as a potential therapeutic biomarker in osteosarcoma and a novel inhibitor from herbal sources.
Osteoclast differentiation and activation pathways drive abnormally enhanced bone resorption in osteosarcoma, and have thus emerged as potential therapeutic targets for this malignancy. However, the diagnostic value of osteoclast differentiation-related genes (ODRGs) in osteosarcoma remains largely uncharacterized. The present study first analyzed the expression profiles of ODRGs in osteosarcoma using multiple Gene Expression Omnibus datasets. Analysis of single cell RNA-sequencing data from osteosarcoma tissues demonstrated that colony-stimulating factor-1 receptor (CSF1R), a key ODRG, was widely expressed in osteoblasts and monocyte/macrophage lineages within the osteosarcoma microenvironment. Subgroup analysis further revealed that patients with osteosarcoma in the low-CSF1R expression group exhibited significantly increased sensitivity to immune checkpoint inhibitors compared with those in the high-CSF1R expression group. Furthermore, pan-cancer analysis across The Cancer Genome Atlas datasets demonstrated that CSF1R expression was aberrantly regulated in multiple tumor types and strongly correlated with the expression levels of immune checkpoint markers and the infiltration levels of immune cells. Functional validation experiments confirmed that treatment with CSF1R-specific inhibitors significantly reduced the viability of the MG63 and Saos-2 cell lines. To identify novel, natural-product-derived CSF1R inhibitors, structure-based virtual screening was combined with in vitro experimental assays (CCK-8 and western blotting). Among the validated candidates, sarsasapogenin, a compound identified from the screen, effectively suppressed CSF1R protein expression and inhibited the proliferation of both MG63 and Saos-2 cells in a dose-dependent manner. Collectively, the present findings highlight CSF1R as a clinically promising diagnostic biomarker and therapeutic target for osteosarcoma.