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ELMOD2 Overexpression Predicts Adverse Outcomes and Regulates Tumor Progression in Gliomas.
OBJECTIVE: Glioma is a highly heterogeneous and malignant intracranial tumor that presents challenges for clinical treatment. ELMO domain containing 2 (ELMOD2) is a GTPase-activating protein that regulates a range of cellular biological processes. However, its specific role and prognostic value in tumorigenesis are still unknown. This study aimed to assess the prognostic relevance and signaling function of ELMOD2 in gliomas. METHODS: The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases were utilized to conduct a comprehensive analysis of the expression profile of ELMOD2 in gliomas, elucidating its associations with clinicopathological parameters and patient prognosis. Single-cell analysis was performed to characterize ELMOD2 expression across distinct glioma cell subpopulations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and Gene Set Variation Analysis (GSVA) were employed to evaluate the potential biological functions of ELMOD2 in gliomagenesis. Specific small interfering RNAs (siRNAs) were used to knock down ELMOD2 in the glioma cell lines U251 and A172 to assess their cellular behaviors and examine the levels of multiple key signaling molecules associated with the occurrence of gliomas. RESULTS: ELMOD2 was overexpressed in gliomas, and this upregulation was correlated with tumor grade, isocitrate dehydrogenase mutation, and 1p/19q codeletion status. Notably, ELMOD2 expression was elevated in classical and mesenchymal subtypes, and single-cell resolution analysis revealed predominant enrichment within malignant cells. Functionally, ELMOD2 regulated cell cycle progression, and its overexpression was related to independent adverse outcomes. In vitro experiments revealed that ELMOD2 was located in the cytoplasm and nucleoplasm. Furthermore, ELMOD2 knockdown reduced proliferation, migration, and invasion and increased apoptosis in U251 and A172 cell lines. Finally, ELMOD2 knockdown significantly decreased p-Erk1/2. CONCLUSIONS: ELMOD2 expression in glioma is positively correlated with tumorigenesis and is a crucial independent prognostic marker. Thus, ELMOD2 is a promising biomarker and therapeutic target for glioma treatment.
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Transcriptome combined with single-cell sequencing explored prognostic markers associated with T cell exhaustion characteristics in head and neck squamous carcinoma.
Head and neck squamous cell carcinoma (HNSC) ranks among the most prevalent cancers worldwide, characterized by significant heterogeneity and a complex immune microenvironment. T cell exhaustion is pivotal in the pathogenesis of HNSC, where depleted T cells exhibit reduced proliferative capacity and diminished effector function, facilitating tumor immune escape and subsequent disease progression. A thorough understanding of the primary mechanisms driving T cell depletion within the tumor microenvironment is essential for enhancing the efficacy of immunotherapeutic approaches in HNSC, with profound implications for patient outcomes. In this study, a single-cell atlas of HNSC was constructed, enabling an in-depth analysis of T cell heterogeneity. The differentiation trajectory of T cells, transitioning from normal tissue to HNSC, was characterized, revealing a predisposition toward depletion in the C2 T cell subgroup. A subsequent cross-analysis of significantly upregulated differentially expressed genes in the C2 T cell subset identified five characteristics pertinent to T cell C2, which informed the development of a clinical prognostic model. Additionally, maximum half inhibitory concentration (IC50) values for various pharmacological agents were calculated, leading to the identification of eleven drugs relevant to the risk model, providing an intriguing starting point for further work in personalized cancer treatment. However, certain limitations of this study must be acknowledged. While T cell heterogeneity and differentiation trajectories were mapped, the interrelationships among these subpopulations remain poorly understood. Further research is required to elucidate the specific biological processes and molecular evolutionary mechanisms involved. The insights from this study provide a valuable foundation for future investigations into the molecular mechanisms and immune landscape associated with the progression from normal tissue to malignant squamous cell carcinoma.
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Single-Cell RNA Sequencing Reveals the Tumor Heterogeneity and Immunosuppressive Microenvironment in Urothelial Carcinoma.
Urothelial carcinoma (UC) can arise from either the lower urinary tract or the upper tract; they represent different disease entities and require different clinical treatment strategies. A full understanding of the cellular characteristics in UC may guide the development of novel therapies. Here, we performed single-cell transcriptome analysis from four patients with UC of the bladder (UCB), five patients with UC of the ureter (UCU), and four patients with UC of the renal pelvis (UCRP) to develop a comprehensive cell atlas of UC. We found the rare epithelial cell subtype EP9 with epithelial-to-mesenchymal transition (EMT) and cancer stem cell (CSC) features, and specifically expressed SOX6, which was associated with poor prognosis. We also found that ACKR1+ endothelial cells and inflammatory cancer-associated fibroblasts (iCAFs) were more enriched in UCU, which may promote pathogenesis. While ESM1+ endothelial cells may more actively participate in UCB and UCRP tumorigenesis by promoting angiogenesis. Additionally, CD8 + effector T cells were more enriched in UCU and UCRP patients, while Tregs were mainly enriched in UCB tumors. C1QC+ macrophages and LAMP3+ dendritic cells were more enriched in UCB, which is closely related to the formation of the heterogeneous immunosuppressive microenvironment. Furthermore, we found strong interactions between iCAFs, EP9, and Endo_ESM1, and different degrees of activation of the FGF-FGFR3 axis and immune checkpoint pathway were observed in different UC subtypes. Our study elucidated the cellular heterogeneity and the components of the microenvironment in UC arising from the upper and lower urinary tracts and provided novel therapeutic targets.
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Discovery and construction of prognostic model for clear cell renal cell carcinoma based on single-cell and bulk transcriptome analysis.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common malignant kidney tumor in adults. Single-cell transcriptome sequencing can provide accurate gene expression data of individual cells. Integrated single-cell and bulk transcriptome data from ccRCC samples provide comprehensive information, which allows the discovery of new understandings of ccRCC and the construction of a novel prognostic model for ccRCC patients. METHODS: Single-cell transcriptome sequencing data was preprocessed by using the Seurat package in R software. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were used to perform cluster classification. Two subtypes of cancer cells were identified, pseudotime trajectory analysis and gene ontology (GO) analysis were conducted with the monocle and clusterProfiler packages. Two novel cancer cell biomarkers were identified according to the single-cell sequencing and were confirmed by The Cancer Genome Atlas (TCGA) data. T cell-related marker genes according to single-cell sequencing were screened by a combination of Kaplan-Meier (KM) analysis, univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox analysis of TCGA data. Four survival predicting genes were screened out to develop a risk score model. A nomogram consisting of the risk score and clinical information was constructed to predict the prognosis for ccRCC patients. RESULTS: A total of 5,933 cells were included in the study after quality control. Fifteen cell clusters were classified by PCA and t-SNE algorithm. Two clusters of cancer cells with distinct differentiation status were identified. Besides, GO analysis revealed that biological processes were different between the two subgroups. Egl-9 family hypoxia-inducible factor 3 (EGLN3) and nucleolar protein 3 (NOL3) were specifically expressed in cancer cell clusters, bulk RNA sequencing data from TCGA confirmed their high expression in ccRCC tissues. GTSE1, CENPF, SMC2 and H2AFV were screened out and applied to the construction of risk score model. A nomogram was generated to predict prognosis of ccRCC by combing the risk score and clinical parameters. CONCLUSIONS: We integrated single-cell and bulk transcriptome data from ccRCC in this study. Two subtypes of ccRCC cells with different biological characteristics and two potential biomarkers of ccRCC were discovered. A novel prognostic model was constructed for clinical application.
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Extracellular Matrix-Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single-Cell Analysis.
The 5-year overall survival rate for hepatocellular carcinoma (HCC) patients remains below 20%. Alterations in the extracellular matrix (ECM) are increasingly recognized as central drivers of HCC initiation and progression. This study applied a system biology framework integrating omics data and machine learning to analyze gene expression and regulatory networks in HCC using The Cancer Genome Atlas. Eight ECM-associated genes (CSPG4, CD34, C1orf35, ESM1, MAPT, PLXDC1, STC2, and THBS4) were identified as upregulated diagnostic biomarkers with strong discriminatory power. Among them, MAPT, PLXDC1, and STC2 showed significant associations with poor overall survival, defining a prognostic subset. Validation in the GSE104310 and GSE144269 datasets confirmed consistent expression patterns across cohorts. Functional enrichment linked these genes to tissue remodeling and angiogenesis. Single-cell RNA sequencing revealed MAPT upregulation in T cells, PLXDC1 enrichment in cancer-associated fibroblasts, and mild STC2 elevation in tumor-associated macrophages and endothelial cells. These findings identify key ECM-based biomarkers with potential for early detection, prognosis, and therapeutic targeting in HCC.
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Comprehensive analysis of the role of immune-related PANoptosis lncRNA model in renal clear cell carcinoma based on RNA transcriptome and single-cell sequencing.
The high immune infiltration and heterogeneity of the microenvironment in clear cell renal cell carcinoma (ccRCC) result in the variability of prognosis and clinical response. While PANoptosis has strong immunogenicity and is worthy of further study. In this study, data from The Cancer Genome Atlas database was used to obtain immune-related PANoptosis lncRNAs with prognostic value. Subsequently, the role of these lncRNAs in cancer immunity, progression and the therapeutic response was analyzed, and a new prediction model was constructed. Additionally, we further explored the biological value of PANoptosis-related lncRNAs using single-cell data from the Gene Expression Omnibus database. PANoptosis-associated lncRNAs were significantly associated with clinical outcome, immune infiltration, antigen presentation and treatment response in ccRCC. Notably, the risk model, which is based on these immune-related PANoptosis lncRNAs, showed good predictive performance. Subsequent studies on LINC00944 and LINC02611 revealed their high expression in ccRCC and significant correlation with cancer cell migration and invasion. Single-cell sequencing further validated these results and revealed the potential association between LINC00944 and T-cell infiltration and programmed cell death. In conclusion, this study identified the role of immune-related PANoptosis lncRNAs in ccRCC and provided a new risk stratification approach. Furthermore, it highlights the potential of LINC00944 as a prognostic biomarker.
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Mapping neutrophil fate and function in ischemic stroke: A single-cell roadmap for translational insights.
BACKGROUND: Ischemic stroke (IS) accounts for 71 % of all strokes, whose diagnosis and prognosis require further exploration. Neutrophil extracellular traps (NETs) are produced by neutrophils, and there is already evidence that NETs play a role in IS, but further studies about crosstalk between immune cells, pathways and NETs are still needed. MATERIALS AND METHODS: To assess the expression of neutrophil extracellular traps (NETs), we utilized single sample Gene Set Enrichment Analysis. Stroke-associated NETs genes (SN genes) were identified through differential expression analysis combined with Weighted Correlation Network Analysis. Based on these SN genes, we developed a sophisticated diagnostic model incorporating machine learning techniques. Furthermore, we constructed a single-cell atlas of neutrophil transitions in post-stroke mice. Validation of our findings was conducted both in vitro and in vivo. In vitro, we employed oxygen-glucose deprivation (OGD) experiments to simulate ischemic conditions, facilitating the assessment of NETs formation and monitoring alterations in SN genes expression within neutrophils. In vivo, validation involved tracking changes in peripheral blood levels of these genes in a mouse model of transient middle cerebral artery occlusion (tMCAO) post-cerebral ischemia. RESULTS: A detailed single-cell landscape depicting the dynamic transitions of neutrophils within the cerebral microenvironment post-stroke has been elaborately constructed. NETs displayed significant differential expression between IS and control groups in peripheral blood, correlating strongly with the activities of neutrophils and macrophages. Pathways pertinent to IS and NETs were delineated. A diagnostic model incorporating two SN genes was developed, demonstrating an AUC greater than 0.98, effectively pinpointing the hyperacute phase of IS. Additionally, the ceRNA networks concerning IS and NETs were mapped out. In vitro validation with oxygen-glucose deprivation (OGD) experiments revealed marked changes in NET formation and SN genes expression in neutrophils, corroborating our computational predictions. In vivo validation using a mouse transient middle cerebral artery occlusion (tMCAO) model confirmed significant changes in peripheral blood levels of F12 and PLXDC2 after cerebral ischemia, proving the excellent predictive value of these markers for IS. CONCLUSION: This study elucidates the complex roles and dynamic changes of neutrophils within the cerebral microenvironment of mice from 3 h to 3 days following stroke onset. We have identified key genes, immune cells, signaling pathways, and ceRNA networks implicated in the formation of NETs in IS. Our study constructed a robust diagnostic model capable of detecting the hyperacute phase of IS, with an AUC value greater than 0.98. The inclusion of experimental validation for the SN genes F12 and PLXDC2 not only corroborates our model's predictive accuracy but also underscores its potential utility in clinical settings. These findings offer promising avenues for improving early diagnosis and potentially guiding therapeutic strategies in IS.
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Developmental single-cell atlas of coronary vessel growth and cardiomyocyte interaction in zebrafish.
Cardiac morphogenesis requires the intricate coordination of different cell types and molecular cues. Coronary vessel formation is essential for heart development, yet how coronary vessels grow and contribute to ventricular wall formation remains poorly understood. Here, we combine high-resolution imaging and new genetic tools to systematically analyze coronary vasculature development in zebrafish at each millimeter increment from 7 to 30 mm of body length and identify cellular and molecular milestones defining four distinct coronary network developmental stages. Our data show how coronary vessels expand, pattern, specify and act as vascular scaffolds to guide cardiomyocyte growth throughout development. Manipulating coronary network growth by Vegfa signaling perturbation through gain- and loss-of-function approaches disrupts coronary vessel-cardiomyocyte interactions and cardiomyocyte expansion. To gain further resolution into these processes, we build a single-cell RNA sequencing atlas by profiling 37,554 ventricular cells across the different developmental stages and identify new coronary markers, dynamic cellular interactions, and stage-specific endothelial-cardiomyocyte crosstalk. Overall, we present the first comprehensive roadmap of coronary vessel formation and coordinated cellular and molecular interactions with the developing cardiac muscle in zebrafish.
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Integrated Epigenetic Mapping of Human and Mouse Salivary Gene Regulation.
Significant effort has been applied to identify the genome-wide gene expression profiles associated with salivary gland development and pathophysiology. However, relatively little is known about the regulators that control salivary gland gene expression. We integrated data from DNase1 digital genomic footprinting, RNA-seq, and gene expression microarrays to comprehensively characterize the cis- and trans-regulatory components controlling gene expression of the healthy submandibular salivary gland. Analysis of 32 human tissues and 87 mouse tissues was performed to identify the highly expressed and tissue-enriched transcription factors driving salivary gland gene expression. Following RNA analysis, protein expression levels and subcellular localization of 39 salivary transcription factors were confirmed by immunohistochemistry. These expression analyses revealed that the salivary gland highly expresses transcription factors associated with endoplasmic reticulum stress, human T-cell lymphotrophic virus 1 expression, and Epstein-Barr virus reactivation. DNase1 digital genomic footprinting to a depth of 333,426,353 reads was performed and utilized to generate a salivary gland gene regulatory network describing the genome-wide chromatin accessibility and transcription factor binding of the salivary gland at a single-nucleotide resolution. Analysis of the DNase1 gene regulatory network identified dense interconnectivity among PLAG1, MYB, and 13 other transcription factors associated with balanced chromosomal translocations and salivary gland tumors. Collectively, these analyses provide a comprehensive atlas of the cis- and trans-regulators of the salivary gland and highlight known aberrantly regulated pathways of diseases affecting the salivary glands.
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LncCE: Landscape of Cellularly-elevated lncRNAs in Single Cells Across Normal and Cancer Tissues.
Long non-coding RNAs (lncRNAs) have emerged as significant players in maintaining the morphology and function of tissues and cells. The precise regulatory effectiveness of lncRNAs is closely associated with their spatial expression patterns across tissues and cells. Here, we propose the Cellularly-Elevated LncRNA (LncCE) resource to systematically explore cellularly-elevated (CE) lncRNAs across normal and cancer tissues at single-cell resolution. LncCE encompasses 87,946 entries of CE lncRNAs of 149 cell types by analyzing 181 single-cell RNA sequencing datasets, involving 20 fetal normal tissues, 59 adult normal tissues, 32 adult cancer types, and 5 pediatric cancer types. Two main search options are provided via a given lncRNA name or cell type. The results emphasize both qualitative and quantitative expression features of lncRNAs across different cell types, their co-expression with protein-coding genes, and their involvement in biological functions. In particular, LncCE provides quantitative visualizations of lncRNA expression changes in cancers compared to control samples, as well as clinical associations with patients' overall survival. Together, LncCE offers an extensive, quantitative, and user-friendly interface to create a CE expression atlas for lncRNAs across normal and cancer tissues at the single-cell level. The LncCE database is available at http://bio-bigdata.hrbmu.edu.cn/LncCE.