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Construction of TME and Identification of crosstalk between malignant cells and macrophages by SPP1 in hepatocellular carcinoma.
Liver cancer accounts for 6% of all malignancies causing death worldwide, and hepatocellular carcinoma (HCC) is the most common histological type. HCC is a heterogeneous cancer, but how the tumour microenvironment (TME) of HCC contributes to the progression of HCC remains unclear. In this study, we investigated the immune microenvironment by multiomics analysis. The tumour immune infiltration characteristics of HCC were determined at the genomic, epigenetic, bulk transcriptome and single-cell levels by data from The Cancer Genome Atlas portal and the Gene Expression Omnibus (GEO). An epigenetic immune-related scoring system (EIRS) was developed to stratify patients with poor prognosis. SPP1, one gene in the EIRS system, was identified as an immune-related predictor of poor survival in HCC patients. Through receptor-ligand pair analysis in single-cell RNA-seq, SPP1 was indicated to mediate the crosstalk between HCC cells and macrophages via SPP1-CD44 and SPP1-PTGER4 association. In vitro experiments further validate SPP1 can trigger the polarization of macrophages to M2-phenotype tumour-associated macrophages (TAMs).
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Single-cell transcriptome analysis reveals three sequential phases of gene expression during zebrafish sensory hair cell regeneration.
Loss of sensory hair cells (HCs) in the mammalian inner ear leads to permanent hearing and vestibular defects, whereas loss of HCs in zebrafish results in their regeneration. We used single-cell RNA sequencing (scRNA-seq) to characterize the transcriptional dynamics of HC regeneration in zebrafish at unprecedented spatiotemporal resolution. We uncovered three sequentially activated modules: first, an injury/inflammatory response and downregulation of progenitor cell maintenance genes within minutes after HC loss; second, the transient activation of regeneration-specific genes; and third, a robust re-activation of developmental gene programs, including HC specification, cell-cycle activation, ribosome biogenesis, and a metabolic switch to oxidative phosphorylation. The results are relevant not only for our understanding of HC regeneration and how we might be able to trigger it in mammals but also for regenerative processes in general. The data are searchable and publicly accessible via a web-based interface.
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A multi-omics cell atlas unlocks new frontiers in crop biotechnology.
Defining how plant cell types are specified and regulated has been a central challenge in biology. Previous single-cell studies in plants, relying on either RNA-seq or ATAC-seq, provided valuable insights but could not directly connect chromatin state to transcriptional programs. Writing in Nature, Wang et al. present the first multi-organ single-cell multi-omics atlas of rice. Profiling more than 116,000 nuclei across eight tissues, they delineate 56 distinct cell types with high resolution. Joint analysis of gene expression and chromatin accessibility reveals sharper cell-type boundaries, transient developmental states, and regulatory networks with unprecedented clarity. Importantly, the study links cell-specific regulatory programs to key agronomic traits, identifying candidate regulators of root architecture, photosynthesis, nitrogen metabolism, and yield. This atlas establishes both a foundational resource for comparative plant biology and crop biotechnology, providing a roadmap for precision breeding and resilient agriculture driven by cell-type insights.
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Integrative Analysis of Biomarkers for Cancer Stem Cells in Bladder Cancer and Their Therapeutic Potential.
Background: Cancer stem cells (CSCs) are key drivers of tumorigenesis and metastasis. However, the precise roles of CSC-associated genes in these processes remain unclear. Methods: This study integrates cancer stem cell biomarkers and clinical data from The Cancer Genome Atlas (TCGA) specific to bladder cancer (BLCA). By combining differentially expressed genes (DEGs) from TCGA-BLCA samples with CSC-related biomarkers, we conducted comprehensive functional analyses and developed an 8-gene prognostic signature through Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox regression. This model was validated with GEO datasets (GSE13507 and GSE32894), and the single-cell RNA seq dataset GSE222315 was subsequently analyzed to characterize the signature genes and elucidate their interactions. And a nomogram was created to stratify TCGA-BLCA patients into risk categories. The 'oncoPredict' algorithm based on the GDSC2 dataset assessed drug sensitivity in BLCA. Result: From the TCGA cohort, 665 CSC-related genes were identified, with 120 showing significant differential expression. The 8-gene signature (ALDH1A1, CBX7, CSPG4, DCN, FASN, INHBB, MYC, NCAM1) demonstrated strong predictive power for overall survival in both TCGA and GEO cohorts, as confirmed by Kaplan-Meier and ROC analyses. The nomogram, integrating age, tumor stage and risk scores, demonstrated high predictive accuracy. Additionally, the oncoPredict algorithm indicated varying drug sensitivities across patient groups. Based on retrospective data, we identified a novel CSC-related prognostic signature for BLCA. This finding suggests that targeting these genes could offer promising therapeutic strategies.
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Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data.
Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.
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Dissecting mammalian spermatogenesis using spatial transcriptomics.
Single-cell RNA sequencing has revealed extensive molecular diversity in gene programs governing mammalian spermatogenesis but fails to delineate their dynamics in the native context of seminiferous tubules, the spatially confined functional units of spermatogenesis. Here, we use Slide-seq, a spatial transcriptomics technology, to generate an atlas that captures the spatial gene expression patterns at near-single-cell resolution in the mouse and human testis. Using Slide-seq data, we devise a computational framework that accurately localizes testicular cell types in individual seminiferous tubules. Unbiased analysis systematically identifies spatially patterned genes and gene programs. Combining Slide-seq with targeted in situ RNA sequencing, we demonstrate significant differences in the cellular compositions of spermatogonial microenvironment between mouse and human testes. Finally, a comparison of the spatial atlas generated from the wild-type and diabetic mouse testis reveals a disruption in the spatial cellular organization of seminiferous tubules as a potential mechanism of diabetes-induced male infertility.
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A prognostic model for breast cancer based on mitochondria-associated endoplasmic reticulum membrane (MAM) signature genes.
BACKGROUND: Mitochondria-associated endoplasmic reticulum membranes (MAMs) have emerged as key regulators of breast cancer biology. Growing evidence indicates that MAM dysfunction affects tumor progression, immune regulation, and treatment response. However, their prognostic value and therapeutic relevance in breast cancer remain insufficiently clarified. METHODS: We curated 83 MAM-related genes from the literature and used TCGA breast cancer cohorts to construct a prognostic model through univariate Cox, LASSO, and multivariate Cox regression analyses. Protein-level validation was performed using the Human Protein Atlas (HPA). Functional enrichment, immune infiltration, immunotherapy response prediction, and drug sensitivity profiling were conducted between risk groups. Single-cell RNA-seq data (GSE255068) were analyzed to map gene expression within the tumor microenvironment. Finally, qRT-PCR and western blotting validated the expression of key genes in breast cancer cell lines. RESULTS: A four-gene signature (VDAC1, RYR2, PINK1, and TESPA1) robustly stratified breast cancer patients into high- and low-risk groups and independently predicted overall survival across multiple cohorts. A prognostic nomogram integrating the risk score with clinicopathological variables significantly improved survival prediction accuracy. Low-risk tumors were characterized by a more inflamed tumor microenvironment, with increased immune cell infiltration and enhanced immune-related pathway activity, and were predicted to respond more favorably to immune checkpoint blockade. In contrast, high-risk patients exhibited greater predicted sensitivity to multiple cytotoxic and targeted agents, a pattern that remained largely consistent across major molecular subtypes, highlighting the robustness of the signature beyond intrinsic subtype classification. Single-cell transcriptomic analyses further revealed distinct cell-type-specific expression patterns of the four genes within the tumor microenvironment. Finally, molecular experiments confirmed elevated expression of VDAC1, RYR2, and PINK1 in breast cancer cell lines, supporting their biological relevance in tumor progression. CONCLUSIONS: This MAM-based prognostic model provides new insights into breast cancer biology and has potential utility in guiding personalized treatment, particularly regarding immunotherapy and chemotherapy sensitivity. Experimental validation highlights VDAC1 as a promising biomarker.
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TMEM106A as a Macrophage-Associated Biomarker of Prognosis in IDH-Wildtype Glioma: Integrative Multi-Omics and Spatial Analyses.
INTRODUCTION: Gliomas remain aggressive despite current therapies, highlighting the urgent need for new biomarkers and targets. Transmembrane protein 106A (TMEM106A), implicated as a tumor suppressor in various cancers, has an unclear role in gliomas. We hypothesized that TMEM106A expression associates with tumor aggressiveness and may serve as a prognostic, microenvironmental biomarker. METHODS: We integrated TCGA and CGGA bulk RNA-seq, single-cell (GSE131928, GSE89567), spatial (Ivy Atlas, Visium), and immunohistochemistry (n = 79) to evaluate TMEM106A. Differential expression used limma. Survival used Kaplan-Meier and multivariable Cox models. Immune contexture used a 12-cell-state deconvolution and CIBERSORT. GSEA assessed hallmark pathways. Drug sensitivity was inferred using pRRophetic. Immunotherapy modeling combined TCGA expression with TCIA immunophenoscore and PD-L1. RESULTS: (1) TMEM106A mRNA is significantly upregulated in high-grade gliomas compared to lower-grade gliomas and normal brain. (2) In IDH-wildtype tumors, differential analyses highlight roles of TMEM106A and TMEM106C; high expression links to poorer prognosis. (3) TMEM106A is an independent prognostic factor associated with aggressive behavior, especially in IDH-wildtype astrocytomas. (4) Upregulation is confirmed by immunohistochemistry. (5) High TMEM106A associates with pro-inflammatory signatures and higher inferred fractions of myeloid cells and granulocytes. (6) Single-cell RNA-seq shows enrichment in myeloid lineages, and (7) CIBERSORT shows modest positive correlations with polarized macrophage signatures. (8) Spatial transcriptomics shows higher TMEM106A in myeloid-rich regions, consistent with a microenvironmental readout. (9) In IDH-wildtype tumors, pRRophetic predicts lower IC50 for multiple targeted agents in TMEM106A-high tumors. (10) TMEM106A-high IDH-wildtype tumors show higher IPS only when PD-1 is "on," suggesting a context-dependent, inflamed-but-suppressed state. CONCLUSION: TMEM106A independently predicts survival and correlates with myeloid-enriched transcriptional states in gliomas. Given its high expression in myeloid lineages in single-cell data, bulk upregulation is potentially driven by myeloid infiltration rather than tumor-cell intrinsic mechanisms. All findings are correlative; prospective studies are needed before any clinical use is considered.
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CeDR Atlas: a knowledgebase of cellular drug response.
Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.
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ELK4 Promotes Vasculogenic Mimicry in Oral Squamous Cell Carcinoma via Driving DHFR Transcriptional Activation.
BACKGROUND: The regulatory mechanisms governing vasculogenic mimicry (VM) in oral squamous cell carcinoma (OSCC) remain largely undefined. This study aimed to identify critical factors and elucidate the epigenetic mechanisms underlying VM in OSCC. METHODS: Bioinformatics analysis was performed utilizing single-cell RNA-seq, bulk RNA-seq, and histone H3 lysine 27 acetylation (H3K27ac) Chromatin Immunoprecipitation (ChIP)-seq data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. ChIP-qPCR was used to validate the binding of ETS transcription factor ELK4 (ELK4) to the dihydrofolate reductase (DHFR) enhancer. In vitro VM formation and invasion of OSCC cells were assessed using Matrigel-based tube formation and Transwell assays, respectively. RESULTS: Elevated expression of VM-related genes predicts unfavorable prognosis in OSCC patients. High-dimensional weighted gene co-expression network analysis (hdWGCNA) identified epithelial subcluster C4 as most strongly associated with VM and metastasis. Three co-expression modules within this subcluster exhibited significant positive correlations with both phenotypic traits. Among the 30 eigengenes from the three modules, DHFR emerged as a key regulator of VM and metastasis. Knockdown or inhibition of DHFR significantly suppressed VM formation and invasion in OSCC cells. Mechanistically, ELK4 activated DHFR transcription through direct binding to its enhancer. DHFR overexpression rescued VM and invasion impairment induced by ELK4 knockdown. CONCLUSION: DHFR was a pivotal enhancer-regulated gene driving VM and metastasis in OSCC. ELK4 directly binds to DHFR enhancer regions to activate its transcription, thereby promoting these malignant phenotypes. These findings identified the ELK4/DHFR axis as a promising therapeutic target for anti-angiogenic intervention in OSCC.