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Transcriptomic analysis unveils bona fide molecular signatures of microglia under conditions of homeostasis and viral encephalitis.
Microglia serve as a front-line defense against neuroinvasive viral infection, however, determination of their actual transcriptional profiles under conditions of health and disease is challenging. Here, we used various experimental approaches to delineate the transcriptional landscape of microglia during viral infection. Intriguingly, multiple activation genes were found to be artificially induced in sorted microglia and we demonstrated that shear stress encountered during cell sorting was one of the key inducers. Post-hoc analysis revealed that publicly available large-scale single-cell RNA sequencing datasets were significantly tainted by aberrant signatures that are associated with cell sorting. By exploiting the ribosomal tagging approach, we developed a strategy to enrich microglia-specific transcripts by comparing immunoprecipitated RNA with total RNA. Such enriched transcripts were instrumental in defining bona fide signatures of microglia under conditions of health and virus infection. These unified microglial signatures may serve as a benchmark to retrospectively assess ex vivo artefacts from available atlases. Leveraging the microglial translatome, we found enrichment of genes implicated in T-cell activation and cytokine production during the course of VSV infection. These data linked microglia with T-cell re-stimulation and further underscored that microglia are involved in shaping antiviral T-cell responses in the brain. Collectively, our study defines the transcriptional landscape of microglia under steady state and during viral encephalitis and highlights cellular interactions between microglia and T cells that contribute to the control of virus dissemination.
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Single-Cell Transcriptomic Profiling of the O-Methyltransferase Characteristics of Flavonoid Biosynthesis in Ziziphus jujuba var. spinosa Fruit.
Sour jujube (Ziziphus jujuba var. spinosa) is a valuable medicinal and edible fruit owing to its health-promoting properties. Methoxylated flavonoids enhance their value by improving stability and bioavailability. However, the cellular basis of their biosynthesis remains unclear. We constructed a single-nucleus transcriptomic atlas of sour jujube fruit. Parenchyma cells were identified as the primary sites for flavonoid biosynthesis and their O-methylation, characterized by high expression of phenylpropanoid pathway genes and O-methyltransferases (OMTs). Among the 48 identified OMTs, four (ZjOMT1-4) exhibited high expression, phylogenetically clustered with known functional OMTs, and localized to the cytoplasm; they were further characterized by enzyme activity assays. Notably, ZjOMT1 demonstrates moderate substrate promiscuity, catalyzing the methylation of various substrates, including quercetin, quercitrin, and rutin. Our findings reveal the cellular architecture of flavonoid metabolism in sour jujube fruit, providing genetic resources for breeding varieties with enhanced flavonoid profiles to develop functional foods and nutraceuticals.
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Mast cell marker gene signature in head and neck squamous cell carcinoma.
BACKGROUND: Mast cells can reshape the tumour immune microenvironment and greatly affect tumour occurrence and development. However, mast cell gene prognostic and predictive value in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study was conducted to identify and establish a prognostic mast cell gene signature (MCS) for assessing the prognosis and immunotherapy response of patients with HNSCC. METHODS: Mast cell marker genes in HNSCC were identified using single-cell RNA sequencing analysis. A dataset from The Cancer Genome Atlas was divided into a training cohort to construct the MCS model and a testing cohort to validate the model. Fluorescence in-situ hybridisation was used to evaluate the MCS model gene expression in tissue sections from patients with HNSCC who had been treated with programmed cell death-1 inhibitors and further validate the MCS. RESULTS: A prognostic MCS comprising nine genes (KIT, RAB32, CATSPER1, SMYD3, LINC00996, SOCS1, AP2M1, LAT, and HSP90B1) was generated by comprehensively analysing clinical features and 47 mast cell-related genes. The MCS effectively distinguished survival outcomes across the training, testing, and entire cohorts as an independent prognostic factor. Furthermore, we identified patients with favourable immune cell infiltration status and immunotherapy responses. Fluorescence in-situ hybridisation supported the MCS immunotherapy response of patients with HNSCC prediction, showing increased high-risk gene expression and reduced low-risk gene expression in immunotherapy-insensitive patients. CONCLUSIONS: Our MCS provides insight into the roles of mast cells in HNSCC prognosis and may have applications as an immunotherapy response predictive indicator in patients with HNSCC and a reference for immunotherapy decision-making.
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Lipid Metabolism and Oxidative Stress in Uterine Corpus Endometrial Carcinoma: Prognostic and Immune Landscape Analysis.
OBJECTIVE: Uterine corpus endometrial carcinoma (UCEC) is a common gynecologic malignancy characterized by metabolic reprogramming and immune dysregulation. This study aimed to investigate the prognostic and diagnostic value of lipid metabolism- and oxidative stress-related genes (LMOSGs) in UCEC. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus were analyzed to identify differentially expressed LMOSGs associated with prognosis using univariate Cox regression. Molecular subtypes were defined by non-negative matrix factorization (NMF) clustering. Functional differences between subtypes were evaluated through Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment, immune cell infiltration analysis, and tumor microenvironment scoring. Prognostic genes were further refined using Least Absolute Shrinkage and Selection Operator regression to construct a risk signature. Gene expression was validated by reverse transcription quantitative real-time polymerase chain reaction, the Human Protein Atlas, and single-cell RNA sequencing data. A diagnostic model was developed using 12 machine learning algorithms. In vitro functional assays were conducted to assess the effects of key LMOSGs on cell proliferation, migration, and invasion. RESULTS: Fifty-nine LMOSGs were identified as significantly associated with prognosis. NMF clustering classified UCEC into two molecular subtypes with distinct immune landscapes and survival outcomes: Cluster 1 exhibited higher immune infiltration and a favorable prognosis, whereas Cluster 2 showed immune suppression and poorer survival. A robust 10-gene prognostic signature (PHGDH, MAPT, KCNK9, EPHX2, LIPH, SLC8A1, CREB3L4, PDCD1, MYLIP, and ORMDL2) demonstrated strong predictive performance. Among these, EPHX2, SLC8A1, ORMDL2, and MYLIP showed superior diagnostic discrimination and were further validated functionally as key regulators of tumor progression. CONCLUSION: LMOSGs play critical roles in UCEC heterogeneity, prognosis, and immune modulation. These genes represent promising biomarkers and potential targets for personalized therapeutic strategies. TRIAL REGISTRATION: Not applicable.
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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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Multiomic characterization, immunological and prognostic potential of SMAD3 in pan-cancer and validation in LIHC.
SMAD3, a protein-coding gene, assumes a pivotal role within the transforming growth factor-beta (TGF-β) signaling pathway. Notably, aberrant SMAD3 expression has been linked to various malignancies. Nevertheless, an extensive examination of the comprehensive pan-cancer impact on SMAD3's diagnostic, prognostic, and immunological predictive utility has yet to be undertaken. Bioinformatics methods were employed to systematically investigate the potential carcinogenic impact of SMAD3. We extensively harnessed data from authoritative sources, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, and various other databases. Our study encompassed a comprehensive analysis of the following aspects: differential SMAD3 expression and its association with prognosis across diverse cancer types, gene mutations, immune cell infiltration, single-cell sequencing analysis, DNA methylation patterns, and drug sensitivity profiles. In vitro experiments were conducted with the primary objective of appraising both the expression profile and the precise functional attributes of SMAD3 within the milieu of Liver Hepatocellular Carcinoma (LIHC). Our findings revealed significant variations in SMAD3 expression between cancerous and adjacent normal tissues. High levels of SMAD3 expression were consistently associated with unfavorable prognoses across multiple cancer types,. Additionally, our analysis of SMAD3 methylation patterns in human cancers unveiled a favorable prognosis linked to elevated DNA methylation levels in pan-cancer. Furthermore, we identified positive associations between SMAD3 expression and RNAm6A methylation-related genes in the majority of cancers. Moreover, SMAD3 expression displayed substantial correlations with immune cell infiltration. Notably, immune checkpoint genes exhibited significant associations with SMAD3 expression across diverse cancers. Single-cell sequencing results elucidated the pan-cancer single-cell expression landscape of SMAD3. Within specific cancer subtypes, SMAD3 expression exhibited a noteworthy positive association with distinctive facets of malignancy. Finally, in our comprehensive analysis of drug sensitivity, we discerned a catalog of prospective therapeutic agents. In our comprehensive analysis across multiple cancer types, we observed a significant disparity in SMAD3 expression compared to normal tissues, and this findings suggest that SMAD3 holds promise as both a prognostic biomarker and a therapeutic target against various cancers. Difference displayed a noteworthy association with patient prognosis.
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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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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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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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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.