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Modulation of SRC by SNTB1 activates the Hippo-YAP pathway during colon adenocarcinoma metastasis.
BACKGROUND: Colon adenocarcinoma (COAD) ranks as the third most prevalent and lethal cancer in 2020, with metastasis being the primary cause of cancer-related mortality. A comprehensive understanding of the mechanism underlying distant metastasis is imperative for enhancing the prognosis and quality of life of patients with COAD. METHODS: This study employed gene set enrichment analysis (GSEA) on RNA-sequencing data from 408 patients with COAD in The Cancer Genome Atlas (TCGA) database. GSEA analysis was applied to find the significant hallmark gene set, and genes in the significant hallmark gene set was performed by univariate Cox regression to select the key gene. Then, multivariate Cox regression model was constructed. And various databases were utilized to validate and find the significant oncoprotein and hallmark gene set of the key gene. In addition, the expression of key regulators in para-carcinoma tissue, colon cancer and distant metastases samples were detected by real-time PCR, Immunohistochemistry and Western blot. Additionally, the biological functions were examined by in vitro and in vivo experiments. The scRNA-seq and CellphoneDB were performed to explore cell characters in COAD and the potential mechanism of metastasis. RESULTS: The regulatory network analysis revealed SRC/YAP1 as the most significant oncoprotein and signature gene set associated with SNTB1. Moreover, significant SNTB1 overexpression in COAD metastatic tissues was observed compared to para-carcinoma and primary COAD tissues. Co-immunoprecipitation assays demonstrated the formation of a complex between SNTB1 and SRC proteins. Furthermore, the overexpression of SNTB1 enhanced the proliferation, migration and invasion capacities of COAD cell lines. Caudal vein injection of COAD cells overexpressing SNTB1 in nude mice resulted in increased tumour growth and metastasis to the lung, liver and bone. Finally, single cell RNA-seq revealed alterations in the cellular subtypes of COAD, and CellphoneDB indicated that the interaction between cancer cells exhibiting high SNTB1 expression and enterocytes promoted EMT through cellular communications involving TGF-β, accelerating metastasis in COAD. CONCLUSION: This study postulates that SNTB1 interacts with SRC to activate the Hippo-YAP pathway, thereby promoting COAD metastasis. Furthermore, cellular communication with enterocytes promotes EMT, facilitating metastasis. These findings propose novel therapeutic targets for preventing or treating metastatic COAD.
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Integrative single-cell and bulk transcriptomic analyses identify a robust four-gene signature for risk stratification in neuroblastoma.
BACKGROUND: Neuroblastoma (NB) is a heterogeneous pediatric malignancy with highly variable outcomes. Traditional clinical factors, such as stage, MYCN status, and patient age, often fail to fully capture disease complexity. Recent advances in single-cell sequencing and integrative transcriptomic analyses provide an opportunity to identify more precise prognostic biomarkers and guide individualized therapies. This study aimed to develop and validate a robust prognostic model for NB by integrating single-cell and bulk transcriptomic data. METHODS: We integrated seven publicly available single-cell RNA sequencing (scRNA-seq) datasets to form the Neuroblastoma Atlas and stratified tumor cells into high-risk and intermediate/low-risk groups. Differentially expressed genes (DEGs) were identified using defined fold-change and expression thresholds. Candidate genes were further validated using bulk RNA-sequencing data (HRA002064) and intersected with essential genes defined by DepMap Computational estimation of gene dependency using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening (CERES) scores. From the resulting set, we constructed a multivariate Cox regression-based prognostic model using the GSE49710 dataset. The model's performance was evaluated via Kaplan-Meier curves, time-dependent receiver operating characteristic analysis, and decision curve analysis. External validation was performed in the E-MTAB-8248 dataset, and comparisons with standard clinical indicators (International Neuroblastoma Staging System, MYCN status, age) were conducted. RESULTS: Integrating scRNA-seq and bulk RNA-seq data identified 123 overlapping DEGs, of which seven genes (BIRC5, CDC2, GINS2, MAD2L1, ORC6L, RRM2, TOP2A) were further prioritized based on CERES dependency scores. Multivariate Cox regression and collinearity screening yielded a four-gene prognostic model (RiskScore) that significantly discriminated high- and low-risk patients in the GSE49710 cohort. Compared to traditional indicators, the four-gene model demonstrated superior time-dependent area under the curve (AUC) values and clinical decision-making benefits. External validation in E-MTAB-8248 confirmed the model's robust predictive performance, with high AUC values at 1-, 3-, and 5-year time points and consistent superiority over clinical parameters. CONCLUSIONS: This study presents a novel four-gene prognostic signature derived from integrative scRNA-seq and bulk RNA-seq analyses. The model outperforms established clinical factors in predicting NB outcomes and provides added clinical decision-making value. Further prospective validation and mechanistic investigations may facilitate the translation of this prognostic signature into routine clinical practice, enabling more refined risk stratification and personalized treatment strategies for children with NB.
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Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Defines N7-Methylguanosine (m7G)-Mediated Modifications' Role in Prognosis and the Tumor Immune Microenvironment in Hepatocellular Carcinoma.
BACKGROUND: RNA 7-Methylguanosine (m7G) modification is biologically important to tumorigenesis and progression. Knowledge regarding the effect of m7G modification on the clinical features of hepatocellular carcinoma and its prognosis, tumor immune microenvironment, and sensitivity to immunotherapy remains limited. METHOD: Single-cell RNA sequencing (scRNA-seq) data was sourced from the GEO repository. Transcriptomic datasets were procured from TCGA, GEO, and ICGC databases. Following this, we scrutinized 17 m7G-associated genes and executed unsupervised clustering to discern m7G modification profiles within HCC. Evaluation of immune cell infiltration levels and associated biological functions across varied m7G modification patterns was conducted using single-sample gene set enrichment analysis (ssGSEA) complemented by gene set variation analysis (GSVA). In addition, we employed principal component analysis methodologies to elucidate m7G modification profiles in individual tumors and subsequently devised the m7Gscore. Data pertaining to m7G-related protein and mRNA levels were sourced from the Human Protein Atlas (HPA) database. These findings were corroborated in both HCC and normal samples utilizing western blot analysis, immunohistochemical assays, and RT-qPCR evaluations. RESULTS: Our analysis unveiled diverse cellular subgroups within HCC, with a particular focus on T-cell populations, providing insights into the intricate interplay of immune activation and suppression within the tumor microenvironment (TME). HCC patients were stratified into three distinct m7G modification patterns: immune-desert, immune-inflamed, and immune-excluded. Subsequently, m7Gscores, derived from m7G-associated signature genes, categorized HCC patients into high and low m7Gscore groups. Patients in the high-score cohort exhibited significantly improved survival outcomes compared to those in the low-score cohort. Elevated m7Gscores also correlated with enhanced therapeutic responsiveness, leading to improved outcomes in both chemotherapy and immunotherapy regimens. Our findings were further validated through RT-qPCR, western blot analyses, and immunohistochemical assays, providing robust support for our bioinformatic observations. CONCLUSION: The m7G modification profiles play a pivotal role in prognosticating outcomes for HCC patients, intricately intertwined with microenvironmental heterogeneity and complexity. Furthermore, the analytical insights gained from the m7Gscore signature not only shed light on tumor microenvironment infiltration dynamics but also strengthen prognostic precision, paving the way for optimizing immunotherapeutic approaches in HCC.
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Multi-omics profiling identifies ectopic olfactory receptors as putative drivers of tumor progression and prognostic indicators in clear cell renal cell carcinoma.
Olfactory receptors (ORs) are a subclass of G-protein-coupled receptors (GPCRs) that are primarily expressed in olfactory sensory neurons. Furthermore, ORs have recently been identified in the tumor microenvironment (TME) of various cancers, suggesting potential involvement for ORs in tumor progression. However, the roles of ORs in clear cell renal cell carcinoma (ccRCC), the most prevalent and aggressive subtype of kidney cancer, characterized by limited therapeutic response and a 5-year survival rate of only 10% in advanced stages, have yet to be elucidated. In this study, an integrative multi-omics analysis combining bulk transcriptomic profiles from The Cancer Genome Atlas (TCGA) and single-cell RNA- and ATAC-sequencing datasets from ccRCC patients to characterize the context- and cell-type-specific functions of ORs within the TME. Notably, ORs exhibited distinct, cell-type-specific expression profiles within ccRCC TME. OR51E1 was predominantly expressed in pericytes and correlated with vascular remodeling and angiogenic activity, whereas OR2T10 was enriched in malignant epithelial cells and associated with invasive and metastatic potential, with each OR being associated with resistance to therapeutic agents. In addition, OR-based prognostic modeling and tumor clustering both identified unfavorable prognostic signatures associated with poor patient outcomes and TME-related immunosuppression. These findings highlight ectopic OR networks as clinically relevant molecular features of ccRCC progression and position ORs as potential actionable biomarkers and potential therapeutic targets, offering new avenues for precision oncology in ccRCC.
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Multi-omics characterization of metabolic and immune interactions in prostate cancer.
BACKGROUND: Prostate cancer (PCa) is a common malignancy among men, marked by pronounced clinical and molecular heterogeneity. Metabolic reprogramming and immune evasion are recognized as critical factors in PCa progression; however, the underlying regulatory mechanisms remain insufficiently characterized. This study aimed to elucidate the interaction between metabolic reprogramming and the immune microenvironment in PCa through a multi-omics approach, and to identify key metabolic biomarkers with prognostic significance. METHODS: A multi-omics analytical framework was used, integrating single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) data from publicly available datasets. Following quality control and clustering using Seurat and Signac, cell types were annotated. Key metabolic genes were identified through combined gene activity and chromatin accessibility analyses. Immune cell infiltration was estimated using Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and functional pathway enrichment was assessed using gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). Furthermore, using The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) cohort, a prognostic nomogram was constructed by integrating ENO1 and CKB expression with clinical parameters [age, pathological tumor stage (T stage), node stage (N stage)] via multivariate Cox regression. Gene expression differences across N stages (N0 vs. N1) were assessed using the Wilcoxon rank-sum test. RESULTS: Six distinct cell subtypes were delineated, along with enrichment of key metabolic pathways, particularly glycolysis and oxidative phosphorylation associated with tumor progression. The metabolic regulators ENO1 and CKB demonstrated significant involvement in both metabolic reprogramming and modulation of the immune microenvironment. Their expression levels were positively correlated with the infiltration of immune cells, including CD8+ T lymphocytes and macrophages. The prognostic nomogram demonstrated that CKB contributed substantially to the total points, indicating strong prognostic relevance. Stratified analysis revealed ENO1 was significantly upregulated in N1 tumors (P<0.01), while CKB was higher in N0 tumors (P<0.05). CONCLUSIONS: ENO1 and CKB serve as clinically meaningful markers-ENO1 indicating metastatic potential and CKB predicting overall prognosis-while also representing promising therapeutic targets. These findings bridge molecular metabolism-immune crosstalk with clinical outcomes, offering novel perspectives for integrating metabolic intervention and immunotherapy in PCa management.
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Single-cell RNA transcriptome uncovers distinct developmental trajectories in the embryonic skeletal muscle of Daheng broiler and Tibetan chicken.
Different chicken breeds exhibit distinct muscle phenotypes resulting from selective breeding, but little is known about the molecular mechanisms responsible for this phenotypic difference. Skeletal muscle is composed of a large number of heterogeneous cell populations. Differences in differentiation and interaction of cell populations play a key role in the difference of skeletal muscle phenotype. In the current study, we performed a single-cell RNA sequencing (scRNA-seq) on the leg muscle of Daheng broiler (DH, cultivated breed) and Tibetan chicken (TC, native breed) at embryonic (E) 10, E14 and E18. A comprehensive cell atlas of embryonic chicken skeletal muscle, consisting of 29,579 high-quality cells representing 6 distinct cell types was built. The differentiation trajectory of Myoblasts and fibro-adipogenic progenitors (FAPs) was constructed through pseudotemporal trajectory analysis. Our results revealed the different developmental trajectories and dynamic gene expression profiles in 3 subtypes of myoblasts and 5 FAPs subtypes of the two chicken breeds. Tibetan chicken showed earlier embryonic myogenesis and less myoblasts compared with Daheng broiler. By comparing the switch status and switch time of genes in the two breeds, SNRPG,SNRPE,EIF4EBP1 and HSP90AB1 were considered as potentially critical genes for embryonic myogenesis, and the genes MYOG,MYBPH,APOA1, and MGP played dominant roles in the embryonic adipogenesis. Intercellular interaction networks showed that strong and complex intercellular communication was contained during embryonic skeletal muscle growth and development. These findings revealed the differences of molecular mechanisms in the skeletal muscle development between TC and DH chickens. Our data provide a better understanding of skeletal muscle developmental differences between cultivated and native breeds and valuable information for genetic breeding of chicken.
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Single cell RNA sequencing (scRNA-Seq) deciphering pathological alterations in streptozotocin-induced diabetic retinas.
Diabetic retinopathy (DR) is an irreversible and progressive diabetic complication leading to visual impairment, even blindness. Due to the delicate and complicated structure of the retina, the pathology of DR has not been completely elucidated yet. We constructed a transcriptome atlas of >14,000 single cells from healthy and streptozotocin (STZ)-induced diabetic murine retinas to decipher pathological alterations of DR. We found four stress-inducible genes Cirbp, Rmb3, Mt1 and Mt2 commonly induced in most types of retinal cells. Bipolar cells were little affected on both number and gene expression. Diabetes increased expression of inflammatory factor genes in retinal microglia, and stimulated expression of immediate early genes (IEGs) in retinal astrocytes. A large number of genes were deregulated in diabetic vascular endothelial cells (ECs), and the differentially expressed genes were paired to the pathways functioning in metabolism, shear stress and vascular permeability. These pathways were mapped by more deregulated genes in a subpopulation of ECs specifically presented in diabetic retinas (diabetic retinal ECs, DRECs). Moreover, several inflammation pathways were activated in DRECs, and the most significant one is the IL-17 signaling pathway. According to the EC markers, DRECs were mainly capillary ECs, confirmed by immunofluorescent staining of S100a9, a target gene of the IL-17 signaling pathway. This study deciphered pathological alterations of DR, and provided clues for potential targets for DR therapy.
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Single-cell multiomics analysis reveals regulatory programs in clear cell renal cell carcinoma.
The clear cell renal cell carcinoma (ccRCC) microenvironment consists of many different cell types and structural components that play critical roles in cancer progression and drug resistance, but the cellular architecture and underlying gene regulatory features of ccRCC have not been fully characterized. Here, we applied single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) to generate transcriptional and epigenomic landscapes of ccRCC. We identified tumor cell-specific regulatory programs mediated by four key transcription factors (TFs) (HOXC5, VENTX, ISL1, and OTP), and these TFs have prognostic significance in The Cancer Genome Atlas (TCGA) database. Targeting these TFs via short hairpin RNAs (shRNAs) or small molecule inhibitors decreased tumor cell proliferation. We next performed an integrative analysis of chromatin accessibility and gene expression for CD8+ T cells and macrophages to reveal the different regulatory elements in their subgroups. Furthermore, we delineated the intercellular communications mediated by ligand-receptor interactions within the tumor microenvironment. Taken together, our multiomics approach further clarifies the cellular heterogeneity of ccRCC and identifies potential therapeutic targets.
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Integrating Tissue Microarray to GeoMx® Digital Spatial Profiler : Spatial Transcriptomics Assay with Bioinformatics Analysis.
Genome-wide or high-plex gene expression is important to understand the organism, tissue, and cellular mechanism. From microarrays to next-generation sequencing (RNA-Seq) at the bulk level and then to the single cell level, gene expression studies have undergone a long transition. The current bulk gene expression and pathway-centric approach toward disease and therapeutics is moving toward spatial transcriptomics, which makes it possible to profile gene expression from the cellular microenvironment without any loss of spatial information. Spatial transcriptomics allows us to understand cellular interactions, cell type abundance, and profile expression differences between the region of interest and its microenvironment. The technology is revolutionizing oncology, developmental biology, neuroscience, preclinical studies, and many therapeutic approaches, especially immunotherapy. Taking into consideration the diverse spatial transcriptomics technologies available, the current chapter aims to delineate the NGS assay protocol for Digital Spatial Profiler (DSP) and follow bioinformatics analysis. While the workflow itself has been detailed elsewhere, in this chapter we are focusing on the integration of tissue microarrays, bioinformatics pipelines, and statistical approaches specific to GeoMx RNA assays as well as common errors that can occur while running a DSP RNA assay. The descriptions of these methods refer to the current version of the GeoMX DSP guidebook and GeoMx Data Analysis Manual, which can be downloaded from the documents section of NanoString website. These documents and user guides are continuously improved and updated; hence, it is important to regularly check the company's website for the most recent version.
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Single-Cell Atlas of Spleen Remodeling Reveals Macrophage Subset-Driven ASFV Pathogenesis.
African swine fever virus (ASFV) causes global swine outbreaks, but its cellular pathogenesis is poorly understood. Using single-cell RNA data from ASFV-infected pig spleens across four timepoints, we identified macrophages as the primary viral reservoir, with infection driving lymphoid depletion and myeloid expansion. We characterized four functionally distinct macrophage subsets, including a metabolically reprogrammed SusceptibleMac population serving as the major viral niche and an AntiviralMac subset rapidly depleted during infection. Viral gene expression analysis revealed E165R as a central hub in viral replication networks, while host transcriptomics uncovered disruption of Netrin signaling pathways that may facilitate immune evasion. Pseudotime analysis revealed dynamic macrophage state transitions during infection. These findings provide a high-resolution cellular atlas of ASFV pathogenesis, revealing macrophage subset-specific responses that shape disease outcomes and identifying potential targets for therapeutic intervention.