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Assessment of cancer-associated fibroblast signature genes in ovarian cancer patients: impact on immunity, drug resistance, and prognosis.
Ovarian cancer (OC) is women's third most common gynecologic tumor and is highly lethal. Cancer-associated fibroblasts (CAFs) are associated with cancer at all stages of disease progression and are involved in biological processes, including inflammatory processes, tumor development occurrence, and immune rejection. This study aimed to construct prognosis-related CAFs regulatory factors to predict the survival of OC patients. Datasets of OC patients with complete clinical information were collected from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. First, we identified potential regulator factors of CAFs in OC based on the xCell algorithm and weighted gene co-expression analysis (WGCNA). Further screening using one-way cox regression analysis and LASSO regression models yielded 22 prognosis-related CAFs regulatory factors, using which a model was constructed. Subsequently, the diagnostic effectiveness of the model was assessed using receiver operating characteristic (ROC) curves, and the validity of the CAFs regulatory factors survival model was verified in three additional independent datasets and single cell data. Meanwhile, experimental validation was conducted using immunohistochemistry and Western blot. The results showed that GAS1 (Growth arrest specific 1) exhibited a higher expression pattern in fibroblasts from ovarian cancer patients. The assessment of resistance and immune checkpoint differences across various risk score groups indicates that the CAFs regulatory factor survival model is practical for guiding systemic treatment. In summary, this study establishes a prognostic model composed of 22 CAFs regulatory factors to predict the prognosis of ovarian cancer (OC), providing new perspectives for the clinical treatment of OC.
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Integrative multi-omics and network toxicology reveal candidate molecular targets of deoxynivalenol in melanoma.
Deoxynivalenol (DON) has been reported to exhibit skin toxicity and carcinogenic potential; however, its effects on melanoma remain unclear. We integrated network toxicology, single-cell transcriptomic analysis, molecular docking, and publicly available Human Protein Atlas immunohistochemical evidence to investigate the potential toxicological effects of DON on melanoma and identify candidate molecular targets. A total of 5283 DON-related genes were identified from the SwissTargetPrediction, CTD, and SEA databases, and 5101 melanoma-related genes were obtained from the GeneCards, TTD, and OMIM databases. Differentially expressed genes from the GSE15605 dataset were used for intersection validation. After further screening with three machine learning algorithms, MYEF2 was identified as a core target potentially involved in DON-induced melanoma. ROC analysis showed that MYEF2 achieved AUC values above 0.75 in both the training and validation cohorts. Single-cell transcriptomic analysis revealed the expression and distribution of MYEF2 across melanoma cell subtypes. Immune infiltration analysis further showed significant associations between MYEF2 and multiple immune cell populations. Molecular docking supported a stable interaction between DON and MYEF2, and HPA validation confirmed its elevated expression in melanoma. These findings suggest that MYEF2 may represent a candidate molecular target involved in DON-associated melanoma progression, providing a theoretical basis for future studies on the mechanisms and risk assessment of DON exposure.
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Integrative Single-Cell Transcriptomics, Multi-Omics Analyses, and Computational Pharmacology Reveal Macrophage Heterogeneity and the Putative THBS1-CD36 Axis in Coronary Atherosclerosis.
Coronary atherosclerosis (CA) is characterized by profound macrophage heterogeneity that drives plaque progression and vulnerability, yet the precise subpopulations and their niche-specific functions remain incompletely defined. Here, we constructed a single-cell transcriptomic atlas of human CA using dataset GSE131778 and identified six distinct macrophage subpopulations. Among these, the THBS1+ macrophage subset emerged as a terminally differentiated, hypoxia-adaptive population with elevated TGF-β signaling and FOSB-driven transcriptional regulation. High-dimensional weighted gene co-expression network analysis revealed 12 transcriptional modules, with module NEW10 serving as a specific molecular signature of THBS1+ macrophages. Cell-cell communication analysis predicted a THBS1-CD36 ligand-receptor interaction potentially contributing to crosstalk between THBS1+ macrophages and lymphatic endothelial cells. Molecular docking and 100-ns molecular dynamics simulations predicted a thermodynamically favorable and conformationally stable interaction between SMS121 and CD36, providing an initial computational rationale for further experimental evaluation. Finally, leveraging the NEW10 module genes, we trained 13 machine learning classifiers to distinguish ACS from sCAD and evaluated their performance in differentiating ruptured from stable plaques; Support Vector Machine with linear kernel and Naive Bayes achieved the highest accuracy in an independent testing cohort, both attaining an AUC of 0.9333. These findings illuminate macrophage heterogeneity and intercellular communication in CA, identify the predicted THBS1-CD36 interaction as a candidate for further mechanistic and pharmacological investigation, and establish a macrophage-derived transcriptional signature for precise risk stratification.
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Machine learning identifies PYGM as a macrophage polarization-linked metabolic biomarker in rectal cancer prognosis.
BACKGROUND: Macrophage polarization plays a pivotal role in shaping the tumor microenvironment and influencing rectal cancer progression. However, the metabolic and prognostic regulators governing this process remain largely undefined. METHODS: We constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). The prognostic performance of MPGS was evaluated across rectal and multiple other cancer types. Functional analyses, single-cell RNA sequencing, immunohistochemistry of clinical specimens, and in vitro cellular assays were employed to investigate the role of the MPGS hub gene, PYGM, in tumor biology and immune modulation. RESULTS: The MPGS exhibited robust prognostic capability and effectively predicted responses to immunotherapy and various chemotherapeutic agents. Both MPGS and its central metabolic component, PYGM, were closely linked to M2 macrophage infiltration, immunosuppressive tumor microenvironments, and poor clinical outcomes in rectal adenocarcinoma. Single-cell transcriptomic analysis revealed that malignant epithelial cells with elevated PYGM expression are metabolically active and closely interact with M2 macrophages. Clinical tissue analyses and functional assays confirmed that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization. CONCLUSIONS: This study firstly highlights PYGM as a key metabolic and immunological regulator in rectal cancer, with significant prognostic and therapeutic implications. MPGS and PYGM may serve as novel biomarkers for risk stratification and guide personalized treatment strategies in patients with rectal adenocarcinoma.
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MicroRNA-874 targets phosphomevalonate kinase and inhibits cancer cell growth via the mevalonate pathway.
The microRNA (miR) miR-874, a potential tumour suppressor, causes cell death via target gene suppression in various cancer types. Mevalonate pathway inhibition also causes cell death in breast cancer. However, the relationship between the mevalonate pathway and miR-874-induced apoptosis or its association with the tumour suppressor p53 has not been elucidated. We identified phosphomevalonate kinase (PMVK), a key mevalonate pathway enzyme, and sterol regulatory element-binding factor 2 (SREBF2), the master cholesterol biosynthesis regulator, as direct miR‑874 targets. Next-generation sequencing analysis revealed a significant miR-874-mediated downregulation of PMVK and SREBF2 gene expression and p53 pathway enrichment. Luciferase reporter assays showed that miR-874 directly regulated PMVK and SREBF2. miR-874-induced apoptosis was p53 dependent, and single-cell RNA sequencing analysis demonstrated that miR-874 transfection resulted in apoptosis and p53 pathway activation. Downregulation of PMVK expression also caused cell cycle arrest and p53 pathway activation, which was rescued by geranylgeranyl pyrophosphate (GGPP) supplementation. Analysis of The Cancer Genome Atlas (TCGA) database indicated a negative correlation between miR-874 and PMVK expression and between miR-874 and SREBF2 expression. These findings suggest that miR-874 suppresses the mevalonate pathway by targeting SREBF2 and PMVK, resulting in GGPP depletion, which activates the p53 pathway and promotes cycle arrest or apoptosis.
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Integrative network toxicology and experimental validation define mechanisms of perfluorodecanoic acid (PFDA) induced granulosa cell toxicity and attenuation by kaempferol in HGrC1 cells.
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants increasingly linked to female reproductive toxicity. Among them, perfluorodecanoic acid (PFDA) has been implicated in ovarian dysfunction, yet the molecular basis of its ovotoxicity remains insufficiently defined. Here, we investigated PFDA-induced granulosa cell injury and examined whether kaempferol, a naturally occurring flavonoid, could mitigate these effects using an integrative approach combining network toxicology, single-cell transcriptomic projection, and in vitro validation. Computational analysis identified 121 shared targets among PFDA, kaempferol, and ovotoxicity-related gene sets, with enrichment in oxidative stress, inflammatory, and nuclear receptor-associated pathways. Single-cell projection of PFDA- and kaempferol-associated gene sets onto a human ovarian reference atlas localized these signatures across multiple ovarian cell populations, including granulosa cell clusters. In the human granulosa cell line HGrC1, PFDA significantly reduced cell viability, increased reactive oxygen species (ROS), impaired nuclear factor erythroid 2-related factor 2 (NRF2)-associated antioxidant defense, shifted apoptosis-related gene expression toward a pro-apoptotic profile, and suppressed steroidogenic gene expression. Co-treatment with kaempferol alleviated several of these changes, lowering ROS levels and partially restoring antioxidant and steroidogenic markers. Together, these findings indicate that PFDA disrupts granulosa cell homeostasis through redox imbalance and modulation of steroidogenesis-related gene expression, while kaempferol mitigates part of this response in the present in vitro model. This study provides mechanistic insight into PFDA-induced granulosa cell toxicity and supports further investigation of bioactive flavonoids as modulators of pollutant-related ovarian injury.
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Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system.
Neuroendocrine prostate cancer (NEPC), a subtype of prostate cancer (PCa) with poor prognosis and high heterogeneity, currently lacks accurate markers. This study aims to identify a robust NEPC classifier and provide new perspectives for resolving intra- tumoral heterogeneity. Multi-omics analysis included 19 bulk transcriptomics, 14 single-cell transcriptomics, 1 spatial transcriptomics, 16 published NE signatures and 10 cellular experiments combined with multiple machine learning algorithms to construct a novel NEPC classifier and classification. A comprehensive single-cell atlas of prostate cancer was created from 70 samples, comprising 196,309 cells, among which 9% were identified as NE cells. Within this framework and in combination with bulk transcriptomics, a total of 100 high-quality NE-specific feature genes were identified and differentiated into NEPup sig and NEPdown sig. The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. Hierarchical clustering based on NEP100 revealed four distinct NEPC subtypes, designated VR_O, Prol_N, Prol_P, and EMT_Y, each of which presented unique biological characteristics. This allows us to select different targeted therapeutic strategies for different subtypes of phenotypic pathways. Notably, NEP100 expression correlated positively with neuroendocrine differentiation and disease progression, while the VR-NE phenotype dominated by VR_O cells indicated a propensity for treatment resistance. Furthermore, AMIGO2, a component of the NEP100 signature, was associated with chemotherapy resistance and a poor prognosis, indicating that it is a pivotal target for future therapeutic strategies. This study used multi-omics analysis combined with machine learning to construct a novel NEPC classifier and classification system. NEP100 provides a clinically actionable framework for NEPC diagnosis and subtyping.
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Spatial multiomics atlas reveals smooth muscle phenotypic transformation and metabolic reprogramming in diabetic macroangiopathy.
BACKGROUND: Diabetic macroangiopathy has been the main cause of death and disability in diabetic patients. The mechanisms underlying smooth muscle cell transformation and metabolic reprogramming other than abnormal glucose and lipid metabolism remain to be further explored. METHOD: Single-cell transcriptome, spatial transcriptome and spatial metabolome sequencing were performed on anterior tibial artery from 11 diabetic patients with amputation. Multi-omics integration, cell communication analysis, time series analysis, network analysis, enrichment analysis, and gene expression analysis were performed to elucidate the potential molecular features. RESULT: We constructed a spatial multiomics map of diabetic blood vessels based on multiomics integration, indicating single-cell and spatial landscape of transcriptome and spatial landscape of metabolome. At the same time, the characteristics of cell composition and biological function of calcified regions were obtained by integrating spatial omics and single cell omics. On this basis, our study provides favorable evidence for the cellular fate of smooth muscle cells, which can be transformed into pro-inflammatory chemotactic smooth muscle cells, macrophage-like smooth muscle cells/foam-like smooth muscle cells, and fibroblast/chondroblast smooth muscle cells in the anterior tibial artery of diabetic patients. The smooth muscle cell phenotypic transformation is driven by transcription factors net including KDM5B, DDIT3, etc. In addition, in order to focus on metabolic reprogramming apart from abnormal glucose and lipid metabolism, we constructed a metabolic network of diabetic vascular activation, and found that HNMT and CYP27A1 participate in diabetic vascular metabolic reprogramming by combining public data. CONCLUSION: This study constructs the spatial gene-metabolism map of the whole anterior tibial artery for the first time and reveals the characteristics of vascular calcification, the phenotypic transformation trend of SMCs, and the transcriptional driving network of SMCs phenotypic transformation of diabetic macrovascular disease. In the perspective of combining the transcriptome and metabolome, the study demonstrates the activated metabolic pathways in diabetic blood vessels and the key genes involved in diabetic metabolic reprogramming.
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Hybrid crosses reveal a cell-type-specific landscape of mouse regulatory variation.
Understanding the genetic architecture of gene expression is fundamental to evolutionary biology and medicine. As part of the IGVF Consortium, we present a single-nucleus RNA-seq resource of 5.3 million nuclei across eight tissue groups, featuring seven F1 hybrids from C57BL/6J dams crossed with the other Collaborative Cross founder strains for comparison against parental strains. We identify 25,864 genes (91% of those detected) exhibiting non-conserved regulatory behavior in at least one of 92 cell types in one or more crosses. Our results show that while cis-acting variation primarily drives divergence, trans-acting effects are substantially more cell-type specific and sensitive to tissue environment. Notably, bulk tissue analyses frequently mask these signals, particularly in smaller populations such as astrocytes. Furthermore, increasing genetic divergence primarily expands the landscape of cis-acting variation, while trans-acting effects remain stable across genetic distances within species. This atlas establishes a foundational framework for decoding the complex interplay between genetic variation and cell-type-specific regulation across the mammalian body.
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Systemic immune dysregulation in severe tuberculosis patients revealed by a single-cell transcriptome atlas.
Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb) infection, is currently the deadliest infectious disease in human that can evolve to severe forms. A comprehensive immune landscape for Mtb infection is critical for achieving TB cure, especially for severe TB patients. We performed single-cell RNA transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing of 213,358 cells from 27 samples, including 6 healthy donors and 21 active TB patients with varying severity (6 mild, 6 moderate and 9 severe cases). Two published profiles of latent TB infection were integrated for the analysis. We observed an obviously elevated proportion of inflammatory immune cells (e.g., monocytes), as well as a markedly decreased abundance of various lymphocytes (e.g., NK and γδT cells) in severe patients, revealing that lymphopenia might be a prominent feature of severe disease. Further analyses indicated that significant activation of cell apoptosis pathways, including perforin/granzyme-, TNF-, FAS- and XAF1-induced apoptosis, as well as cell migration pathways might confer this reduction. The immune landscape in severe patients was characterized by widespread immune exhaustion in Th1, CD8+T and NK cells as well as high cytotoxic state in CD8+T and NK cells. We also discovered that myeloid cells in severe TB patients may involve in the immune paralysis. Systemic upregulation of S100A12 and TNFSF13B, mainly by monocytes in the peripheral blood, may contribute to the inflammatory cytokine storms in severe patients. Our data offered a rich resource for understanding of TB immunopathogenesis and designing effective therapeutic strategies for TB, especially for severe patients.