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Identification of a Transcriptional Prognostic Signature From Five Metabolic Pathways in Oral Squamous Cell Carcinoma.
Dysregulated metabolic pathways have been appreciated to be intimately associated with tumorigenesis and patient prognosis. Here, we sought to develop a novel prognostic signature based on metabolic pathways in patients with primary oral squamous cell carcinoma (OSCC). The original RNA-seq data of OSCC from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) database were transformed into a metabolic pathway enrichment score matrix by single-sample gene set enrichment analysis (ssGSEA). A novel prognostic signature based on metabolic pathways was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in both testing and validation cohorts. The optimal cut-off value was obtained using the Youden index by receiver operating characteristic (ROC) curve. The overall survival curves were plotted by the Kaplan-Meier method. A time-dependent ROC curve analysis with 1, 3, 5 years as the defining point was performed to evaluate the predictive value of this prognostic signature. A 5-metabolic pathways prognostic signature (5MPS) for OSCC was constructed which stratified patients into subgroups with favorable or inferior survival. It served as an independent prognostic factor for patient survival and had a satisfactory predictive performance for OSCC. Our results developed a novel prognostic signature based on dysregulated metabolic pathways in OSCC and provided support for aberrant metabolism underlying OSCC tumorigenesis.
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Single-cell analyses of intestinal epithelium reveal the dysregulation of gut immune microenvironment in systemic lupus erythematosus.
BACKGROUND: The small intestine harbors a rich array of intestinal intraepithelial lymphocytes (IELs) that interact with structural cells to collectively sustain gut immune homeostasis. Dysregulation of gut immune homeostasis was implicated in the pathogenesis of multiple autoimmune diseases, however, whether this homeostasis is disrupted in a lupus autoimmune background remains unclear. METHODS: We performed single-cell RNA sequencing (scRNA-seq) analyses to elucidate immune and structural milieu in the intestinal epithelium of MRL/Lpr lupus mice (Lpr mice) and MRL/Mpj control mice (Mpj mice). Comprehensive analyses including unsupervised clustering, trajectories, and cellular communication were performed. The primary findings from scRNA-seq were further validated by quantitative polymerase chain reaction (qPCR), flow cytometry, and in vivo experiments including selenium supplementation. RESULTS: We observed a significant reduction in CD8αα + IELs, accompanied by a marked increase in CD8αβ + IELs in Lpr mice. Additionally, subsets of CD8 + IELs exhibiting significantly enhanced effector functions were found to be markedly enriched in Lpr mice. Intercellular communication patterns within intestinal epithelial immune and structural cells were found to be specifically altered in Lpr mice. Moreover, scRNA-seq revealed significantly decreased intestinal TCRγδ T cells (γδT) associated with reduced aryl-hydrocarbon receptor repressor (AHRR) expression and subsequent oxidative stress and ferroptosis in Lpr mice. Antioxidant selenium effectively reversed the loss of γδT in Lpr mice, improved the gut barrier, and alleviated lupus symptoms. CONCLUSIONS: Our high-resolution single-cell atlas enhances the understanding of the immune and structural milieu of intestinal epithelium in lupus and provides new insights into lupus pathogenesis mediated by intestinal immune dysregulation.
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Spatially resolved C1QC+ macrophage-CD4+ T cell niche in colorectal cancer microenvironment: implications for immunotherapy response.
Colorectal cancer (CRC), including both microsatellite instability (MSI) and microsatellite stability (MSS) subtypes, frequently exhibits intrinsic resistance to immunotherapy. However, the spatial tumor microenvironment (TME) and its role in distinguishing immunotherapy responders from non-responders remain poorly understood. In this study, spatial multiomics, including imaging mass cytometry (n = 50 in-house), spatial proteomics (n = 50 in-house), and spatial transcriptomics (n = 9 in-house), were employed to elucidate the spatial TME of metastatic CRC (mCRC) patients receiving immunotherapy. These methodologies were integrated with single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and bulk proteomics for comprehensive analysis and validation. A spatial immune atlas containing 314,774 cells was constructed. We found that C1QC+ resident tissue macrophages (RTMs) were more abundant in responders regardless of microsatellite status. Co-localization of C1QC+ RTMs with CD4+ T cells was observed in responders, and MHC-II expression facilitated their interaction. In contrast, cancer-associated fibroblasts inhibited this interaction in non-responders. Moreover, whole genome screening identified key genes involved in antigen presentation in C1QC+ RTMs. Hence, our study highlights the importance of spatial immune mapping in revealing the complex spatial topology of CRC and corresponding immunotherapy response.
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Pan-cancer analysis of the prognostic and immunological role of Fanconi anemia complementation group E.
Fanconi anemia (FA) genes contribute to tumorigenesis by regulating DNA repair. Despite its importance for assembly and functionality of the FA core complex, no pan-cancer analysis of FANCE was performed. We aimed to provide a comprehensive understanding of the role of FANCE in cancers. Based on The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype Tissue-Expression (GTEx), Human Protein Atlas (HPA), Gene Expression Omnibus (GEO), and Cancer Single-cell Atlas (CancerSEA) databases, we investigated the carcinogenicity of FANCE using various bioinformatics methods, including FANCE expression and prognosis, immune invasion, tumor mutation burden, microsatellite instability, and neoantigens. We monitored Fance mutations in mice that caused tumorigenesis. FANCE expression and activity scores were upregulated in 15 and 21 cancers. High expression of FANCE affected shorter overall survival (OS) in seven cancers and longer overall survival in three cancers. It was correlated with shorter overall survival and progression-free interval (PFI) in endometrial cancer and longer overall survival and PFI in cervical cancer. FANCE expression negatively correlated with stromal/immune scores in 21 cancers including cervical cancer, endometrial cancer, and ovarian cancer. FANCE expression negatively correlated with CD8 T cells in endometrial cancer and positively correlated with M1 macrophages in cervical cancer, possibly related to cancer prognosis. FANCE positively correlated with immune checkpoint inhibitors PD-1, PD-L1, and CTLA4 in endometrial cancer and ovarian cancer. FANCE expression positively correlated with microsatellite instability, tumor mutational burden, and neoantigens in 7, 22, and five cancers, especially in endometrial cancer, potentially increasing the effectiveness of immunotherapy. Single-cell sequencing data showed FANCE was primarily expressed in cancer cells in cervical and ovarian cancer, and in fibroblasts in endometrial cancer. Fance heterozygous mutant mice had increased tumor incidences and shorter overall survival and tumor-free survival (TFS) than Fance homozygous mutant mice and wild-type mice. Conclusively, FANCE potential to serve as a biomarker for cancer prognosis and may predict cancer immunotherapy responses. Fance heterozygous mutant resulted in increased tumorigenesis and poor prognosis in mice.
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A single-cell genomic atlas for maturation of the human cerebellum during early childhood.
Inflammation early in life is a clinically established risk factor for autism spectrum disorders and schizophrenia, yet the impact of inflammation on human brain development is poorly understood. The cerebellum undergoes protracted postnatal maturation, making it especially susceptible to perturbations contributing to the risk of developing neurodevelopmental disorders. Here, using single-cell genomics of postmortem cerebellar brain samples, we characterized the postnatal development of cerebellar neurons and glia in 1- to 5-year-old children, comparing individuals who had died while experiencing inflammation with those who had died as a result of an accident. Our analyses revealed that inflammation and postnatal cerebellar maturation are associated with extensive, overlapping transcriptional changes primarily in two subtypes of inhibitory neurons: Purkinje neurons and Golgi neurons. Immunohistochemical analysis of a subset of these postmortem cerebellar samples revealed no change to Purkinje neuron soma size but evidence for increased activation of microglia in those children who had experienced inflammation. Maturation-associated and inflammation-associated gene expression changes included genes implicated in neurodevelopmental disorders. A gene regulatory network model integrating cell type-specific gene expression and chromatin accessibility identified seven temporally specific gene networks in Purkinje neurons and suggested that inflammation may be associated with the premature down-regulation of developmental gene expression programs.
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Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies.
Recent advancements in single-cell technologies have enabled detection of RNA, proteins, metabolites, and xenobiotics in individual cells, and the application of these technologies has the potential to transform pharmacological research. Single-cell data has already resulted in the development of human and model species cell atlases, identifying different cell types within a tissue, further facilitating the characterization of tumor heterogeneity, and providing insight into treatment resistance. Research discussed in this review demonstrates that distinct cell populations express drug metabolizing enzymes to different extents, indicating there may be variability in drug metabolism not only between organs, but within tissue types. Additionally, we put forth the concept that single-cell analyses can be used to expose underlying variability in cellular response to drugs, providing a unique examination of drug efficacy, toxicity, and metabolism. We will outline several of these techniques: single-cell RNA-sequencing and mass cytometry to characterize and distinguish different cell types, single-cell proteomics to quantify drug metabolizing enzymes and characterize cellular responses to drug, capillary electrophoresis-ultrasensitive laser-induced fluorescence detection and single-probe single-cell mass spectrometry for detection of drugs, and others. Emerging single-cell technologies such as these can comprehensively characterize heterogeneity in both cell-type-specific drug metabolism and response to treatment, enhancing progress toward personalized and precision medicine. SIGNIFICANCE STATEMENT: Recent technological advances have enabled the analysis of gene expression and protein levels in single cells. These types of analyses are important to investigating mechanisms that cannot be elucidated on a bulk level, primarily due to the variability of cell populations within biological systems. Here, we summarize cell-type-specific drug metabolism and how pharmacologists can utilize single-cell approaches to obtain a comprehensive understanding of drug metabolism and cellular heterogeneity in response to drugs.
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Identification of feature genes and molecular mechanisms involved in cell communication in uveal melanoma through analysis of single‑cell sequencing data.
Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.
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Spatiotemporally deciphering the mysterious mechanism of persistent HPV-induced malignant transition and immune remodelling from HPV-infected normal cervix, precancer to cervical cancer: Integrating single-cell RNA-sequencing and spatial transcriptome.
BACKGROUND: The mechanism underlying cervical carcinogenesis that is mediated by persistent human papillomavirus (HPV) infection remains elusive. AIMS: Here, for the first time, we deciphered both the temporal transition and spatial distribution of cellular subsets during disease progression from normal cervix tissues to precursor lesions to cervical cancer. MATERIALS & METHODS: We generated scRNA-seq profiles and spatial transcriptomics data from nine patient samples, including two HPV-negative normal, two HPV-positive normal, two HPV-positive HSIL and three HPV-positive cancer samples. RESULTS: We not only identified three 'HPV-related epithelial clusters' that are unique to normal, high-grade squamous intraepithelial lesions (HSIL) and cervical cancer tissues but also discovered node genes that potentially regulate disease progression. Moreover, we observed the gradual transition of multiple immune cells that exhibited positive immune responses, followed by dysregulation and exhaustion, and ultimately established an immune-suppressive microenvironment during the malignant program. In addition, analysis of cellular interactions further verified that a 'homeostasis-balance-malignancy' change occurred within the cervical microenvironment during disease progression. DISCUSSION: We for the first time presented a spatiotemporal atlas that systematically described the cellular heterogeneity and spatial map along the four developmental steps of HPV-related cervical oncogenesis, including normal, HPV-positive normal, HSIL and cancer. We identified three unique HPV-related clusters, discovered critical node genes that determined the cell fate and uncovered the immune remodeling during disease escalation. CONCLUSION: Together, these findings provided novel possibilities for accurate diagnosis, precise treatment and prognosis evaluation of patients with precancer and cervical cancer.
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Identification of differentiation markers and immune microenvironment in head and neck squamous cell carcinoma using machine learning combined with single-cell analysis.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common malignant tumor globally. Histopathological grading classifies HNSCC into well-differentiated and poorly differentiated types. Poorly differentiated tumors tend to exhibit greater aggressiveness, with higher risks of invasion, metastasis, and mortality. However, current methods for assessing differentiation lack reliable molecular markers for objective diagnosis. Furthermore, the differences in immune microenvironment between well-differentiated and poorly differentiated tumors are not fully understood. In this study, we integrate machine learning and single-cell transcriptomic analysis to identify molecular markers closely associated with differentiation and explore the immune microenvironment differences across various differentiation states. METHODS: We obtained transcriptomic data, including pathological differentiation and survival annotations, from The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) dataset. Weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with tumor differentiation. Pseudotime trajectory analysis was performed using the Monocle tool on single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database. Branch Expression Analysis Modeling (BEAM) analysis was subsequently conducted to pinpoint gene changes at differentiation branch points. The integration of these analyses led to the identification of differentiation-related genes and the development of a differentiation prediction model. Finally, key differentiation-related genes and immune microenvironment differences were validated through western blot, quantitative polymerase chain reaction, immunohistochemistry, spheroid formation assays, and multiplex immunofluorescence. RESULTS: This study identified cellular retinoic acid-binding protein 2 (CRABP2) as a key gene associated with well-differentiated HNSCC. We found that low expression of CRABP2 may facilitate the formation of poorly differentiated HNSCC and contribute to the development of an immune-suppressive microenvironment. Tumor immune microenvironment remodeling was characterized by increased infiltration of regulatory T cells (Tregs) and reduced neutrophil infiltration, which may be linked to the invasiveness and poorer prognosis of poorly differentiated tumors. CONCLUSIONS: Our findings highlight significant molecular differences between well-differentiated and poorly differentiated HNSCC, with low CRABP2 expression identified as a critical driver of poorly differentiated HNSCC and a central factor in the immune-suppressive tumor microenvironment. For the first time, we report that poorly differentiated HNSCC are associated with an immune-suppressive microenvironment, characterized by increased Treg infiltration and decreased neutrophil infiltration, which may contribute to their worse prognosis. This study identifies novel marker genes associated with HNSCC differentiation and offers new insights into the mechanisms driving tumor differentiation. These results pave the way for developing personalized therapeutic strategies targeting differentiation pathways and immune modulation, aiming to improve treatment outcomes for HNSCC patients.
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Single-cell transcriptomic Atlas of aging macaque ocular outflow tissues.
The progressive degradation in the trabecular meshwork (TM) is related to age-related ocular diseases like primary open-angle glaucoma. However, the molecular basis and biological significance of the aging process in TM have not been fully elucidated. Here, we established a dynamic single-cell transcriptomic landscape of aged macaque TM, wherein we classified the outflow tissue into 12 cell subtypes and identified mitochondrial dysfunction as a prominent feature of TM aging. Furthermore, we divided TM cells into 13 clusters and performed an in-depth analysis on cluster 0, which had the highest aging score and the most significant changes in cell proportions between the two groups. Ultimately, we found that the APOE gene was an important differentially expressed gene in cluster 0 during the aging process, highlighting the close relationship between cell migration and extracellular matrix regulation, and TM function. Our work further demonstrated that silencing the APOE gene could increase migration and reduce apoptosis by releasing the inhibition on the PI3K-AKT pathway and downregulating the expression of extracellular matrix components, thereby increasing the aqueous outflow rate and maintaining intraocular pressure within the normal range. Our work provides valuable insights for future clinical diagnosis and treatment of glaucoma.