Search Results (1,523)

Prostate adenocarcinoma (PRAD) poses a significant challenge due to therapy resistance and an immunosuppressive tumor microenvironment (TME). Ferroptosis has emerged as a therapeutic vulnerability, yet its immunomodulatory role in PRAD remains elusive. Here, we employed a multi-omics approach—integrating bulk RNA-seq (498 tumors), single-cell RNA-seq (68,322 cells), and spatial transcriptomics (19,483 spots)—to decode the ferroptosis-immune landscape. We derived a robust 16-gene ferroptosis signature that predicted biochemical recurrence (C-index = 0.76) and validated it in two independent cohorts. Crucially, high-risk tumors exhibited a “cold” immunosuppressive TME enriched in regulatory T cells and M2 macrophages, alongside elevated immune checkpoints (HAVCR2, CTLA4, PDCD1). Single-cell and virtual knockout analyses revealed that cancer epithelial cells evade ferroptosis via NFE2L2-associated antioxidant defenses, which strongly correlates with immune exclusion. Spatial transcriptomics further demonstrated spatially organized vulnerabilities, with ferroptosis-resistant tumor cores and immune-infiltrated invasive margins. To identify therapeutic interventions, we utilized drug response modeling and molecular docking, prioritizing RSL3, Atovaquone (targeting NOX4 (NADPH oxidase 4)/DHODH), and Sorafenib (targeting TrxR1 (thioredoxin reductase 1, encoded by TXNRD1)) as potent agents with potential ferroptosis-modulatory activity. Collectively, our findings demonstrate that NFE2L2-associated ferroptosis resistance shapes immune evasion in PRAD. Targeting ferroptosis regulators provides a compelling therapeutic rationale to remodel the TME and synergize with immune checkpoint blockade. Full article

Understanding the development and differentiation of cardiac progenitor cells during the initial stages of embryogenesis is central to a complete understanding of vertebrate heart development. In zebrafish, cardiac specification begins during gastrulation; however, the single-cell transcriptional dynamics of initial cardiac lineage commitment remain not fully defined. In this case, we integrated single-cell RNA sequencing datasets of zebrafish embryos at 4 and 6 h post-fertilisation (hpf) to investigate early cardiac lineage specification. The unsupervised clustering of the integrated dataset identified 12 distinct cell clusters, which made it possible to identify a transcriptionally distinct population of cells characterised by the coordinated expression of transcription factors associated with cardiac development. A further subclustering of the cells expressing cardiac-associated transcription factors showed a significant level of early diversification of the cardiac progenitor group. A projection onto low-dimensional embedding revealed a structured transcriptional organisation of the cardiac subclusters, marked by the differential expression of key cardiac transcription factors, including Gata5, Gata6, Hand2, Nkx2.5, and Tbx5a. A pseudotemporal trajectory analysis uncovered a continuous developmental progression within the cardiac lineage and indicated the gene-specific dynamic regulation and temporal hierarchy of cardiac transcriptional programs. Collectively, these results indicate that zebrafish cardiac progenitors are transcriptionally diverse and acquire cardiac fate through a sustained, continuous regulatory process rather than an abrupt fate transition. This work provides an informative, high-resolution model of early cardiac lineage specification and highlights the power of single-cell transcriptomics for analysing dynamic events in vertebrate embryogenesis. Full article

Metabolic reprogramming is closely linked to tumor proliferation, invasion, and immune escape. Despite its central role in amino acid metabolism, the regulatory mechanisms of asparagine metabolism in hepatocellular carcinoma (HCC) progression remain poorly characterized. Rather than focusing on canonical metabolic genes, prognostic markers were identified from co-expression modules associated with asparagine metabolism signatures. Using the TCGA database and asparagine metabolism-related gene sets, a prognostic risk-scoring model was developed through differential expression analysis, univariate Cox regression, and the LASSO algorithm and externally validated with the GEO dataset (GSE14620). Survival analysis, ROC curve evaluation, nomogram construction, scRNA-seq, GSEA, and drug sensitivity analysis were performed to systematically delineate the molecular mechanisms by which asparagine metabolism drives HCC progression. A three-gene signature comprising BOP1, SAC3D1, and PDE2A effectively stratified patients into high- and low-risk groups. High-risk patients exhibited markedly poorer overall survival, enrichment in tumor proliferation-associated pathways, increased tumor purity, reduced immune cell infiltration, and a substantially higher TP53 mutation rate (38% vs. 13%). In contrast, the low-risk group showed enrichment in pathways linked to hepatoblastoma suppression and liver function, alongside improved predicted response to immunotherapy. Single-cell analysis identified NK cells and endothelial cells as central mediators of asparagine metabolism-driven HCC progression, with BOP1, SAC3D1, and PDE2A displaying dynamic expression patterns during differentiation. Furthermore, the high-risk group was predicted to be more sensitive to chemotherapeutics such as cyclophosphamide and 5-fluorouracil. These findings highlight a potential interplay between nitrogen metabolism and asparagine metabolism in HCC and suggest mechanisms by which these pathways may influence NK cell and endothelial cell function to promote disease progression. This study establishes a novel prognostic model and identifies potential chemotherapeutic vulnerabilities in high-risk patients, warranting further experimental and clinical validation. Full article

Acute kidney injury (AKI) lacks targeted pharmacological interventions. While the DanShen–DaHuang (DS-DH) herb pair shows clinical potential for AKI treatment, and our prior study has validated its nephroprotective efficacy in a cisplatin-induced murine model, its specific molecular targets within the renal microenvironment remain undefined. In this study, we integrated network pharmacology and weighted gene co-expression network analysis (WGCNA) to screen AKI-related targets of the DS-DH pair. A multi-algorithmic machine learning pipeline (including LASSO, Boruta, Random Forest, GBM, XGBoost, and Decision Trees) was utilized to calculate feature importance scores and rank core genes. Subsequently, single-cell RNA sequencing (scRNA-seq) data (GSE197266) were analyzed for transcriptomic mapping, pseudotime trajectory, and cell–cell communication. Finally, molecular docking evaluated theoretical binding affinities. After database screening, a total of 603 drug–disease intersecting targets were obtained. Subsequently, 917 module genes significantly associated with AKI were identified by WGCNA, and 62 core candidate genes were determined after intersecting with the above targets. Multi-algorithm machine learning ranked the importance of the 62 targets, with transketolase (TKT) ranking the highest. To elucidate the mechanism of TKT in AKI, scRNA-seq analysis was performed on 77,593 high-quality cells. The results showed that Tkt was specifically enriched in renal macrophages, with the highest expression in the M2-polarized subset. Pseudotime analysis further revealed that Tkt expression dynamics were highly synchronized with the differentiation trajectory of M2 macrophages and positively correlated with the repair markers Arg1 and Mrc1. Cell–cell communication analysis predicted that Tkt⁺ M2 macrophages act as active communication hubs via the Spp1 and Mif signaling axes. Molecular docking validated the favorable binding affinity between core DS-DH compounds and the TKT active pocket. This computational framework predicts that the DS-DH herb pair might mitigate AKI by potentially targeting TKT, a metabolic enzyme closely associated with macrophage M2 polarization. By prioritizing targets via multi-algorithmic scoring, we provide a data-driven rationale and candidate targets for future experimental validation. Full article

Idiopathic pulmonary fibrosis (IPF) and Crohn’s disease (CD) share overlapping immune and fibrotic processes, yet their convergent molecular mechanisms remain poorly defined. Here, we performed an integrative transcriptomic analysis of nine public datasets to identify shared transcriptional signatures across IPF and CD. The main discovery and validation analyses were based on bulk transcriptomic datasets and combined differential expression profiling, weighted gene co-expression network analysis, and machine-learning–based feature prioritization. We identified 28 shared disease-associated module genes, from which three core genes—ZNF395, EEF2K, and BAHD1—were prioritized based on reproducibility and biological consistency. Functional enrichment analysis revealed their involvement in immune regulation, protein homeostasis, and stress-response pathways. Immune deconvolution and supportive single-cell RNA-sequencing further suggested associations between these genes and T-cell and myeloid cell populations, suggesting coordinated immune-fibrotic regulation. Experimental validation in a repetitive bleomycin challenge model and TGF-β1-stimulated fibroblasts showed consistent downregulation of these genes during fibrotic remodeling, supporting their association with fibrosis-related transcriptional states. Collectively, our study identifies conserved immune–fibrotic transcriptional programs shared across intestinal inflammation and pulmonary fibrosis, providing a hypothesis-generating molecular framework for understanding extraintestinal pulmonary involvement in Crohn’s disease and prioritizing candidate genes for future mechanistic investigation. Full article

Esophageal squamous cell carcinoma (ESCC) progression involves dynamic cellular state transitions and tumor microenvironment remodeling, accompanied by extensive transcriptional regulation reprogramming. Here, we systematically mapped the TF-mediated regulatory landscape underlying ESCC progression at single-cell resolution by integrating stage-specific ESCC single-cell transcriptomic datasets comprising over 200,000 cells with TF–target interaction networks. Using a random walk algorithm combined with hypergeometric testing, we identified malignant progression-associated TFs (mpTFs) across multiple cell types and disease stages. Our analysis revealed extensive stage-dependent regulatory remodeling during ESCC progression. TCF4 was identified as an early-stage regulator associated with epithelial–mesenchymal transition activation and malignant invasive phenotypes. In immune lineages, BATF and IRF4 exhibited trajectory-associated activation during CD4⁺ T-cell differentiation and CD8⁺ T-cell exhaustion, suggesting critical roles in immunosuppressive T-cell state transitions. Additionally, mpTF-mediated remodeling of M2 macrophage subpopulations contributed to immunosuppressive tumor microenvironment formation during advanced ESCC progression. We further identified prognosis-associated cell-type-specific and shared mpTFs, including TFAP2C, which was associated with stabilized fibroblast and monocyte functional states and a less aggressive tumor microenvironment phenotype. Collectively, this study provides a comprehensive single-cell atlas of TF-mediated regulatory programs during ESCC progression and offers potential therapeutic targets for precision oncology. Full article

Alzheimer’s disease (AD) is characterized by progressive neurodegeneration and prominent neuroimmune remodeling, but the contribution of macrophage and myeloid states across disease severity remains incompletely defined. We integrated bulk transcriptomic, single-cell RNA sequencing (RNA-seq), and spatial transcriptomic datasets to characterize AD-associated myeloid immune changes across Braak stage and disease status. Across datasets, M2-like macrophage and myeloid signatures showed progressive enrichment with increasing neuropathological severity and were accompanied by pathway changes related to macrophage proliferation, TGF-β signaling, and myeloid homeostasis. Immune-feature-based classifiers identified macrophage-related variables among the informative features distinguishing AD from controls. CellChat analyses further inferred that M2-like myeloid populations occupied communication-enriched positions in single-cell and spatial interaction networks, including apolipoprotein E (ApoE), CX3C chemokine signaling, and fibronectin 1 (FN1)-associated signaling contexts. Collectively, these findings indicate that M2-like myeloid programs are consistently associated with AD severity and neuroimmune network remodeling. Rather than establishing a causal disease driver, this study highlights M2-like myeloid signatures as candidate neuroimmune components that warrant experimental validation in human-relevant systems. Full article

The tumor microenvironment (TME) is a highly adaptive and heterogeneous niche in which cancer stem cells (CSCs) promote immune evasion, metastatic dissemination, and therapy resistance. Among the mechanisms that support this phenotype, mitochondrial hijacking has emerged as a central strategy through which CSCs reprogram immune and stromal cells to favor tumor progression. This review synthesizes current evidence on how CSCs exploit mitochondrial transfer, particularly via tunneling nanotubes (TNTs) and extracellular vesicles (EVs), to impair antitumor immunity and remodel the metastatic niche. CSCs display marked metabolic plasticity, shifting between glycolysis and oxidative phosphorylation (OXPHOS) in response to environmental stress. They exploit this adaptability by transferring mitochondria and mitochondrial components to recipient cells, including tumor-associated macrophages (TAMs) and cytotoxic T cells, thereby disrupting ATP production, increasing oxidative stress, and skewing immune polarization. This mitochondrial hijacking contributes to an immunosuppressive milieu, stabilizes HIF-1α, and enhances PD-L1 expression, ultimately weakening T-cell activity and reinforcing CSC survival. EVs add another layer of regulation by transporting bioactive cargo, including oncogenic microRNAs (miRNAs) and mitomiRs such as miR-21, miR-210, and miR-34a. These molecules modulate mitochondrial gene expression, reshape immune signaling, and reinforce CSC phenotypes through autocrine and paracrine loops. Single-cell and spatial transcriptomic approaches have further revealed metabolic heterogeneity within CSC–immune synapses, identifying “metabolic hotspots” associated with profound immune dysfunction. Therapeutic strategies targeting OXPHOS, EV biogenesis, and miRNA activity are therefore being explored. In parallel, mitochondria-associated proteins such as TSGA10 may also contribute to CSC-driven immunometabolism regulation and deserve further investigation. Targeting downstream heterogeneity is like cutting the branches of a weed. Targeting the upstream mechanisms of mitochondrial hijacking and miRNA crosstalk aims to destroy the root (CSC plasticity) that generates the heterogeneity and drives therapy resistance in the first place. This review highlights mitochondrial hijacking and miRNA-mediated reprogramming as central determinants of CSC-driven immune escape and proposes a framework for precision interventions targeting CSC–immune interactions in metastatic cancer. Full article

Background: Small cell lung cancer (SCLC) carries a dismal prognosis with limited biomarkers for risk stratification. This study aimed to identify circulating prognostic biomarkers. Methods: We prioritized SCLC risk-associated genes using Summary-data-based Mendelian Randomization of pQTL/eQTL, differential expression, and weighted gene co-expression network analysis. Five machine learning approaches were compared to develop a diagnostic model based on ACE, AGER, and IL18R1, trained on GSE149507 and validated in GSE60052. We conducted single-cell transcriptomic analysis using public datasets (GSE150766 and GSE279570) and peripheral blood mononuclear cells (PBMCs) from our extensive-stage cohort. Finally, prioritizing the lead candidate IL18R1, we enrolled a prospective clinical cohort to assess its prognostic utility. A LASSO–Cox prognostic model incorporating plasma IL18R1 and clinical variables was internally validated (7:3 split) for progression-free survival (PFS) prediction. Results: Integrative multi-omics identified ACE, AGER, and IL18R1 as SCLC-protective genes. Elastic Net machine learning identified a two-gene predictive signature (AGER and IL18R1) with robust diagnostic accuracy. Single-cell RNA sequencing revealed the predominant downregulation of ACE, AGER, and IL18R1 in T cells and alveolar type II cells from SCLC patients. PBMC analysis further supported IL18R1 downregulation in CD8⁺ T cells, NK cells, and dendritic cells. In an independent prospective cohort (n = 300), lower plasma IL18R1 levels were independently associated with shorter PFS (HR = 0.997 per unit increase; 95% CI: 0.995–0.999; and p = 0.003), with time-dependent AUCs of 0.77–0.86. Performance in limited-stage disease was inconsistent and requires further validation. A prognostic model incorporating plasma IL18R1 and 11 clinical parameters stratified patients into distinct risk groups (HR = 5.19), showing a strong discriminative ability in extensive-stage SCLC. Conclusions: We identified ACE, AGER, and IL18R1 as protective factors against SCLC progression. Integration of plasma IL18R1 with clinical parameters provides a prognostic tool for extensive-stage SCLC. Full article

Meningiomas are the most common intracranial tumors. DNA methylation analysis in benign and aggressive meningiomas showed decreased MIR21 methylation and overexpression of hsa-miR-21-5p in atypical and anaplastic tumors. Transcriptomic analysis of distinct WHO grade meningiomas showed multiple predicted hsa-miR-21-5p target genes as differentially expressed. They were mainly related to processes of intercellular and intracellular signaling. Intercellular communication in meningioma was investigated using the deposited scRNA-seq dataset and deconvolution of our RNA-seq data. We found WHO grade-related differences in the microenvironment including inverse correlation between the count of border-associated macrophages (BAM) and the level of hsa-miR-21-5p. Single-cell transcriptomics suggests the role of interleukin 6 in direct communication between tumor cells and BAMs. IL6R and IL6ST are predicted targets of hsa-miR-21-5p downregulated in atypical/anaplastic meningiomas. IL6R downregulation was also confirmed by immunohistochemistry. Hsa-miR-21-5p enhanced proliferation and viability of KT21-MG1 meningioma cells and showed a regulatory effect on IL6R, IL6ST and other predicted target genes TIMP3, PIK3R, RHOB, and SASH1 by interacting with 3′UTRs. DNA hypomethylation-related overexpression of hsa-miR-21-5p contributes to aggressive meningioma growth by interaction with multiple target genes, and probably affects microenvironment communication between meningioma cells and BAMs by lowering the IL6R level in tumor tissue. Full article

Background: Septic cardiomyopathy (SCM) is a fatal sepsis-induced dysfunction. While Pueraria (Pue) exhibits protective effects in sepsis, its regulatory role regarding programmed cell death (PCD) in SCM remains unclear. This study aimed to identify Pue’s PCD-related targets in SCM and construct a validated diagnostic model. Methods: We analyzed 14 PCD modalities across seven GEO transcriptomic datasets. A robust machine learning framework integrating 171 algorithm combinations built a diagnostic signature. The immune landscape was profiled using single-cell RNA sequencing and enrichment analyses. Experimental validation utilized SCM patient blood samples and heart tissues from an LPS-induced murine model. Results: Nine PCD patterns were significantly altered in SCM. Intersection analysis and machine learning identified five core Pue targets: STAT3, RIPK2, GM2A, ALOX5, and DPP4. A diagnostic model constructed with these genes achieved high AUCs across all datasets. Single-cell analysis revealed cell-type-specific expression within the myocardial immune landscape. Differential expression of these five genes was validated in both human and animal samples, correlating significantly with cardiac function indices. Conclusions: Our results demonstrate that Pueraria mitigates SCM and restores cardiac function by modulating the expression of core PCD-related targets. These targets are closely associated with the localized inflammatory response, providing potential therapeutic avenues for SCM. Full article

Early diagnosis of AMI is crucial for improving patient outcomes, yet current clinical tools often lack the requisite sensitivity and specificity for reliable early detection. As neutrophils are the first innate immune responders mobilized following infarction, we employed an integrated multi-omics and machine learning approach to identify neutrophil-driven molecular signatures with diagnostic potential. By analyzing multiple peripheral blood transcriptomic datasets, we conducted differential expression and immune infiltration analyses, followed by machine learning-based feature selection to pinpoint key genes linked to neutrophil activity. Integration of these findings with single-cell transcriptomic data further clarified the neutrophil-specific expression patterns of candidate genes during AMI progression. Using a joint diagnostic model, we identified MCEMP1, NFE2, and AQP9 as the most informative predictors, with MCEMP1 emerging as the primary contributor. Experimental validation in a murine model of myocardial infarction (MI) confirmed rapid upregulation of MCEMP1 after injury, closely mirroring the kinetics of neutrophil infiltration. Collectively, these findings delineate a neutrophil-associated molecular profile of early AMI and highlight MCEMP1 as a promising noninvasive biomarker and a potential therapeutic target for modulating neutrophil-driven myocardial injury. Full article

Peach (Prunus persica L.) trees exhibit low propagation efficiency from cuttings, primarily due to the limited ability of their cuttings to develop adventitious roots (ARs). ARs originate from a single or a few cells and occur randomly and in varying numbers at the base of the cuttings. This poses a challenge for precise research on their regulatory mechanisms. Adventitious lateral roots (adLRs) are one kind of AR, which are induced from injured primary roots. In this study, we developed a method to induce adLRs by removing part of the primary root of young peach seedlings. The adLRs induced by this method are characterized by no obvious callus formation, a relatively stable number (2–4 roots), a fixed occurrence position (at the incision site), and a rooting rate of 100%. Using this system, we conducted transcriptome sequencing analysis during the early stage of adLR induction (0–24 h). The results showed that after the primary root was removed, the jasmonic acid (JA), wound, ethylene (ET), auxin, and salicylic acid (SA) signaling pathways were rapidly activated; subsequently, pathways related to root formation and development were significantly enriched. By screening early rapid-response genes, we successfully identified two key genes, PpF-box and PpERF13, that are involved in AR formation in peach. This study not only provides a reliable and efficient research system for analyzing the molecular regulatory mechanism of AR formation in peach, but also lays an important foundation for future in-depth studies using precise technologies such as single-cell sequencing and microscopic sampling. Full article

Prostate cancer (PCa) progression and treatment resistance are driven by tumor-intrinsic mechanisms and adaptive remodeling of the tumor microenvironment, in which cancer-associated fibroblasts (CAFs) play a crucial role. Although CAF biology is increasingly recognized, a major translational gap remains: CAFs are highly heterogeneous, and comprise distinct functional states with divergent effects on disease progression, immune regulation, and therapeutic resistance. To bridge this gap, we synthesize evidence from single-cell and spatial transcriptomic studies, tissue-based pathology, liquid biopsy assays, and molecular imaging to construct an evidence-tiered, decision-oriented translational framework that connects stromal mechanisms, translational measurement strategies, and therapeutic interventions in PCa. Single-cell and spatial transcriptomic analyses have consistently identified multiple CAF programs, including matrix-remodeling, inflammatory, immunoregulatory, antigen-presenting, and therapy-imprinted states, each with distinct functional outputs and clinical correlates. Tissue-based readouts, including reactive stromal grade (RSG) and fibroblast activation protein (FAP) immunohistochemistry, provide practical proxies for stromal activation and correlate with disease-specific mortality and imaging phenotypes. Circulating CAFs (cCAFs) represent an emerging liquid biopsy modality for longitudinal stromal monitoring, although technical standardization is required before clinical implementation. FAP-targeted PET imaging and emerging dual prostate-specific membrane antigen (PSMA)/FAP-targeted theranostic strategies provide noninvasive tools for patient selection and response assessment, particularly in PSMA-discordant or tracer-heterogeneous disease. Androgen receptor (AR)-targeted therapy can reprogram stromal states toward resistance-promoting circuits, highlighting the dynamic and plastic nature of the CAF compartment. A state-based CAF framework organizes stromal biology into testable translational hypotheses rather than immediate clinical standards. RSG and FAP-based tissue or imaging readouts are practical markers of stromal activation, whereas spatial CAF-immune signatures and cCAF assays remain investigational and require assay harmonization and prospective validation. Future trials should pre-specify stromal biomarkers as enrichment or pharmacodynamic variables when matched to the intervention and should avoid treating CAFs as a uniform therapeutic target. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification. Full article