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Expression characteristics, molecular mechanisms, and clinical significance of DICER1 in breast cancer.
OBJECTIVE: This study aims to investigate the expression patterns, molecular mechanisms, and clinical significance of DICER1 in breast cancer (BRCA), providing new biomarkers and therapeutic targets for prognosis assessment and personalized treatment of breast cancer. METHODS: By integrating RNA-seq data, clinical data, and tumor mutation burden (TMB) data from The Cancer Genome Atlas (TCGA) database, as well as single-cell transcriptomic data from the Gene Expression Omnibus (GEO) database, we analyzed the expression characteristics of DICER1 in breast cancer. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules associated with the breast cancer phenotype, and gene set enrichment analysis (GSEA) was performed to explore their biological functions. Cellular experiments were conducted to verify the effects of DICER1 on the proliferation, migration, and invasion of breast cancer cells. A nomogram model was constructed based on clinical data to evaluate its prognostic value. Additionally, the effects of DICER1 expression levels on drug sensitivity and the tumor immune microenvironment were analyzed. RESULTS: The expression of DICER1 in breast cancer tissues was significantly lower than that in normal tissues, and was significantly correlated with tumor stage, T stage, and TMB levels. The expression level of DICER1 was an independent prognostic factor for breast cancer patients. The nomogram model based on DICER1 expression and clinical features demonstrated good discriminative ability in predicting patient survival probability. Drug sensitivity analysis revealed that the high-expression group of DICER1 exhibited higher sensitivity to multiple drugs. Immune microenvironment analysis indicated that the low-expression group of DICER1 had higher immune-suppressive features and immune exclusion scores, suggesting potential resistance to immunotherapy. Single-cell transcriptomic analysis revealed heterogeneous expression of DICER1 in breast cancer cell populations and its potential role in cell-cell communication. CONCLUSION: DICER1 plays an important regulatory role in breast cancer, with its expression level closely related to tumor progression, the immune microenvironment, and drug sensitivity. DICER1 has the potential to become an important biomarker for prognosis assessment in breast cancer and may provide new targets for future immunotherapy and targeted therapy.
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Integrative Multi-Omics Analysis Identifies Thrombosis-Associated Molecular Features Linked to Germline Susceptibility and Immune Cell Communication in Gastric Cancer.
Emerging evidence indicates that coagulation-related molecular programs are associated with thrombosis, tumor progression, and molecular dysregulation in gastric cancer (GC). However, thrombosis-associated molecular features in GC and their potential links to inherited susceptibility remain insufficiently understood. Integrated analyses of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were performed to identify thrombosis-associated genes and establish a machine learning-based prognostic signature. Genome-wide association study (GWAS), expression quantitative trait loci (eQTL), transcriptome-wide association study (TWAS), and Mendelian randomization (MR) analyses were conducted to investigate susceptibility-associated transcriptional programs in GC. Functional assays were used to evaluate candidate genes associated with malignant phenotypes. Single-cell RNA sequencing (scRNA-seq) and cell-cell communication analyses were further performed to characterize cell-type-specific expression patterns and potential intercellular interactions. A total of 22 differentially expressed thrombosis-associated genes were identified, and a prognostic signature comprising 14 genes was established. The signature stratified patients into high- and low-risk groups and showed prognostic performance in both the training and validation cohorts. Integrative GWAS, eQTL, and TWAS analyses identified susceptibility-associated transcriptional programs that were positively correlated with the thrombosis-associated risk score. Silencing ACTN2 and CRYAB significantly reduced GC cell migration and invasion. scRNA-seq analysis revealed relatively high CRYAB expression in neutrophils, and CellChat analysis suggested potential neutrophil-B cell interactions involving COLLAGEN-related signaling. This integrative multi-omics study identified a thrombosis-associated molecular signature linked to prognosis and germline susceptibility-associated transcriptional programs in GC. ACTN2 and CRYAB may represent candidate genes associated with GC cell migration and invasion, while single-cell analysis suggested potential immune-related communication features.
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Mapping Genetic Susceptibility to Stenosis in the Proximal Airway.
OBJECTIVES: Recent translational scientific efforts in subglottic stenosis (SGS) support a disease model where epithelial alterations facilitate microbiome displacement, dysregulated immune activation, and localized fibrosis. Yet despite recent advances, the genetic basis of SGS remains poorly understood. We sought to identify candidate risk genes associated with an SGS phenotype, investigate their biological function, and identify the cell types enriched for their expression. METHODS: The Online Mendelian Inheritance in Man (OMIM) database was queried for single gene variants associated with an SGS phenotype. The functional intersections and molecular roles of the identified genes were explored using pathway enrichment analysis (PEA) computational methods. Cellular localization of the candidate risk genes was measured via transcriptional quantification in an established single cell RNA sequencing (scRNA-seq) atlas of the proximal airway. RESULTS: Twenty genes associated with SGS phenotype were identified. PEA resulted in 24 significantly enriched terms including "cellular response to TGF-β," "epithelial-to-mesenchymal transition," and "adherens junctions." Mapping the 20 candidate risk genes to the scRNA-seq atlas found 3 (15%) genes were enriched in epithelial cells, 3 (15%) in fibroblasts, and 3 (15%) in endothelial cells. 11 (55%) genes were expressed ubiquitously among tissue types. Interestingly, immune cells were not significantly enriched for candidate risk genes. CONCLUSION: We identify and provide biologic context for 20 genes associated with fibrotic disease of the proximal airway and form the foundation for future detailed genetic study. LEVEL OF EVIDENCE: N/A Laryngoscope, 133:3049-3056, 2023.
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ZNF556 serves as an immunological and prognostic biomarker in colon adenocacinoma.
OBJECTIVES: Colon adenocarcinoma (COAD) is one of the most common and deadliest digestive tract malignancies, but its diagnostic and prognostic markers still need further exploration as targets for risk stratification and treatment response. METHODS: COAD cohorts were retrieved from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Differences in gene expression were analyzed using relevant R packages, followed by gene function enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Next, the Kaplan-Meier method and LASSO regression analysis were employed to screen for prognostic signature genes associated with the survival of COAD patients. Furthermore, comprehensive analyses were conducted to assess the role of this signature gene in immunomodulation and genetic mutation. Single-cell RNA sequencing (scRNA-seq) analyses were performed using the GSE166555 dataset. Additionally, CCK-8, colony formation, wound healing and Transwell assays were used to evaluate the biological functions of the signature gene in COAD cells. RESULTS: Differential expression analysis and WGCNA led to the identification of 156 candidate genes, which were primarily enriched in functional pathways including the cell cycle, P53 signaling pathway, and cellular senescence. Subsequently, LASSO regression combined with survival analysis screened out ZNF556 as a characteristic prognostic gene for COAD. The expression level of ZNF556 correlates with the infiltration of plasma cells, mast cells, and monocytes, and is further associated with the expression of multiple immune checkpoint genes. Additionally, gene mutation analysis revealed a significant correlation between mutant TP53 and ZNF556 expression. ScRNA-seq analysis further indicated that, in the context of COAD, ZNF556 may exert a key regulatory role in the differentiation of epithelial cells, thereby facilitating tumor initiation and progression. Furthermore, subsequent experiments confirmed the cancer-promoting effect of ZNF556, whose expression was positively regulated by mutant TP53. CONCLUSION: ZNF556 was highly up-regulated in COAD tissues, with high expression correlating with poor prognosis and difference in TIICs. It functions as a diagnostic, immunological and prognostic biomarker for COAD, and a potential therapeutic target.
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Cella: 3D data visualization for plant single-cell transcriptomics in Blender.
AIMS: Recent advancements in single-cell transcriptomics have facilitated the possibility of acquiring vast amounts of data at single-cell resolution. This development has provided a broader and more comprehensive understanding of complex biological processes. The growing datasets require a visualization tool that transforms complex data into an intuitive representation. To address this challenge, we have utilized an open-source 3D software Blender to design Cella, a cell atlas visualization tool, which transforms data into 3D heatmaps that can be rendered into image libraries. Our tool is designed to support especially research on plant development. DATA RESOURCES GENERATED: To validate our method, we have created a 3D model representing the Arabidopsis thaliana root meristem and mapped an existing single-cell RNA-seq dataset into the 3D model. This provided a user-friendly visual representation of the expression profiles of 21,489 genes from two perspectives (42,978 images). UTILITY OF THE RESOURCE: This approach is not limited to single-cell RNA-seq data of the Arabidopsis root meristem. We provide detailed step-by-step instructions to generate 3D models and a script that can be customized to project data onto different tissues. KEY RESULTS: Our tool provides a proof-of-concept method for how increasingly complex single-cell RNA-seq datasets can be visualized in a simple and cohesive manner.
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Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women.
UNLABELLED: The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates in every menstrual cycle or upon tissue damage. Here we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of 5 healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and to propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and mRNA patterns of literature-based markers as a shared community resource. We find many subtypes show dynamic changes over different phases of the cycle and identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type, transitional cells that are upstream of other subtypes, and potential cross-lineage multipotent stromal progenitors that may be capable of replenishing the epithelial, stromal, and endothelial compartments. When compared to the healthy premenopausal samples, a postpartum and a postmenopausal uterus sample revealed substantially altered tissue composition, involving the rise or fall of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders. SIGNIFICANCE: We present single-cell RNA sequencing data from seven individuals (five healthy pre-menopausal women, one post-menopausal woman, and one postpartum) and perform an integrated analysis of this data alongside 15 previously published scRNA-seq datasets. We identified 39 distinct cell subtypes across four major cell types in the uterus. By using RNA velocity analysis and centroid-centroid comparisons we identify multiple computationally predicted progenitor populations for each of the major cell compartments, as well as potential cross-compartment, multi-potent progenitors. While the function and interactions of these cell populations remain to be validated through future experiments, the markers and their "dual characteristics" that we describe will serve as a rich resource to the scientific community. Importantly, we address a significant challenge in the field: reconciling multiple uterine cell taxonomies being proposed. To achieve this, we focused on integrating historical and contemporary knowledge across multiple studies. By providing detailed evidence used for cell classification we lay the groundwork for establishing a stable, consensus cell atlas of the human uterus.
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A longitudinal single-cell atlas of treatment response in pediatric AML.
Pediatric acute myeloid leukemia (pAML) is characterized by heterogeneous cellular composition, driver alterations and prognosis. Characterization of this heterogeneity and how it affects treatment response remains understudied in pediatric patients. We used single-cell RNA sequencing and single-cell ATAC sequencing to profile 28 patients representing different pAML subtypes at diagnosis, remission and relapse. At diagnosis, cellular composition differed between genetic subgroups. Upon relapse, cellular hierarchies transitioned toward a more primitive state regardless of subtype. Primitive cells in the relapsed tumor were distinct compared to cells at diagnosis, with under-representation of myeloid transcriptional programs and over-representation of other lineage programs. In some patients, this was accompanied by the appearance of a B-lymphoid-like hierarchy. Our data thus reveal the emergence of apparent subtype-specific plasticity upon treatment and inform on potentially targetable processes.
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A single cell atlas of the human liver tumor microenvironment.
Malignant cell growth is fueled by interactions between tumor cells and the stromal cells composing the tumor microenvironment. The human liver is a major site of tumors and metastases, but molecular identities and intercellular interactions of different cell types have not been resolved in these pathologies. Here, we apply single cell RNA-sequencing and spatial analysis of malignant and adjacent non-malignant liver tissues from five patients with cholangiocarcinoma or liver metastases. We find that stromal cells exhibit recurring, patient-independent expression programs, and reconstruct a ligand-receptor map that highlights recurring tumor-stroma interactions. By combining transcriptomics of laser-capture microdissected regions, we reconstruct a zonation atlas of hepatocytes in the non-malignant sites and characterize the spatial distribution of each cell type across the tumor microenvironment. Our analysis provides a resource for understanding human liver malignancies and may expose potential points of interventions.
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Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research.
BACKGROUND: Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. OBJECTIVE: Create an online portal of public omics datasets for AD research. METHODS: We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. RESULTS: Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. CONCLUSIONS: Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.
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Genomic evolution reshapes cell-type diversification in the amniote brain.
Over 320 million years of evolution, amniotes have developed complex brains and cognition through largely unexplored genetic and gene expression mechanisms. We created a comprehensive single-cell atlas of over 1.3 million cells from the telencephalon and cerebellum of turtles, zebra finches, pigeons, mice, and macaques, employing single-cell resolution spatial transcriptomics to validate gene expression patterns across species. Our study identifies significant species-specific variations in cell types, highlighting their conservation and diversification in evolution. We found pronounced differences in telencephalon excitatory neurons (EXs) and cerebellar cell types between birds and mammals. Birds predominantly express SLC17A6 in EX, whereas mammals express SLC17A7 in the neocortex and SLC17A6 elsewhere, possibly due to loss of function of SLC17A7 in birds. Additionally, we identified a bird-specific Purkinje cell subtype (SVIL+), implicating the lysine-specific demethylase 11 (LSD1)/KDM1A pathway in learning and circadian rhythms and containing numerous positively selected genes, which suggests an evolutionary optimization of cerebellar functions for ecological and behavioral adaptation. Our findings elucidate the complex interplay between genetic evolution and environmental adaptation, underscoring the role of genetic diversification in the development of specialized cell types across amniotes.