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Prognostic significance of ZNF695 in uterine corpus endometrial carcinoma: single-cell and immune infiltration analyses.
BACKGROUND: Zinc Finger Protein 695 (ZNF695) has been reported as a prognostic indicator in several cancers; however, its clinical implications and functional contributions within uterine corpus endometrial carcinoma (UCEC) have not yet been elucidated. Herein, the prognostic significance of ZNF695 in UCEC and its potential involvement in immune infiltration were examined. METHODS: Bioinformatics analyses were conducted utilizing The Cancer Genome Atlas (TCGA) data to evaluate ZNF695 expression, its impact on survival, and patient clinicopathological features. ZNF695 protein expression and subcellular localization were validated using immunohistochemistry (IHC) on a UCEC tissue microarray. To dissect tumor heterogeneity and immune microenvironment, single-cell RNA-seq data from the Gene Expression Omnibus (GEO) were analyzed. The immune infiltration patterns were evaluated utilizing the CIBERSORT algorithm. Three prognostic nomograms were developed to assess UCEC patient survival at the 1-, 3-, and 5-year marks. Furthermore, in vitro cellular assays were conducted to validate ZNF695 expression and its biological roles in UCEC cells via siRNA knockdown. RESULTS: ZNF695 was significantly elevated in UCEC and associated with various clinicopathological features including age, body weight, menopause status, clinical stage, and histological grade. Functional enrichment analyses indicated that ZNF695 was involved in key biological functions, including endopeptidase regulator activity, estrogen signaling pathway and MYC targets. Additionally, single-cell subgroup analysis isolated different cell populations with high ZNF695 expression, underscoring its role in tumor microenvironment. Immune infiltration analyses indicated negative correlations between ZNF695 and various immunosuppressive immune cell subtypes. Elevated ZNF695 expression was strongly correlated with unfavorable clinical outcomes in UCEC. The nomograms for overall survival (OS), disease specific survival (DSS), and progression free interval (PFI) demonstrated promising predictive efficacy, with concordance indices (C-indexes) of 0.631, 0.719, and 0.787, respectively. In vitro functional assays demonstrated that siRNA-mediated ZNF695 knockdown markedly impaired the proliferation, migration, and invasion of UCEC cells, accompanied by cell-cycle arrest and altered apoptosis. CONCLUSION: ZNF695 serves as an independent prognostic biomarker for UCEC, with its potential as a molecular marker validated experimentally. These findings emphasize the critical role of ZNF695 in regulating the immune landscape of UCEC, warranting studies to elucidate underlying molecular mechanisms and explore its clinical translational implications.
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Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma.
BACKGROUND: Tumor-associated macrophages (TAMs) play pivotal roles in innate immunity and contribute to the advancement of lung cancer. We aimed to identify novel TAM-related biomarkers and significance of macrophage infiltration in lung adenocarcinoma (LUAD) through an integrative analysis of single-cell RNA-sequencing (scRNA-seq) data. To describe the cell atlas and construct a novel prognostic signature in LUAD. METHODS: The gene signature linked to TAMs was identified utilizing Scanpy from the scRNA-seq dataset GSE131907. Subsequent analysis involved evaluating the expression levels of these genes, their potential molecular mechanisms, and prognostic significance in LUAD using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We also constructed a risk score models through LASSO Cox regression for these genes. The underlying mechanism was further elucidated through the application of GSEA, ESTIMATE, TIDE, and other bioinformatic algorithms. RESULTS: Single-cell atlas was described by analyze 29 scRNA-seq samples from 19 LUAD patients. The TAMs-related gene signature (TGS) was identified as an independent prognostic factor by LASSO Cox regression analysis using differential expression genes (DEGs) derived from pro- and anti-inflammatory macrophage cells. Risk score model including nine TAMs-related genes (FOSL1, ZNF697, ADM, UBE2S, TICAM1, S100P, BIRC3, TLE1, and DEFB1) were obtained for prognosis construction. Moreover, the risk model underwent additional validation in four external GEO cohorts: GSE31210, GSE72094, GSE26939, and GSE30219. Interestingly, TGS-high tumors revealed enrichments in TGF-β signaling and hypoxia pathways, which shown low immune infiltration and immunosuppression by ESTIMATE and TIDE algorithm. The TGS-high risk group exhibited lower richness and diversity in the T-cell receptor (TCR) repertoire. CONCLUSION: This study introduces a novel TGS score developed through LASSO Cox regression analysis, utilizing DEGs in pro- and anti-inflammatory macrophage cells. High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. These results offer promising implications for the development of therapeutic strategies for LUAD.
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Single-cell RNA sequencing in cardiovascular development, disease and medicine.
Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.
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Spatial and single-cell expression analyses reveal complex expression domains in early wheat spike development.
BACKGROUND: Wheat is important for global food security. Understanding the molecular mechanisms driving spike and spikelet development can benefit the development of more productive varieties. RESULTS: Here we integrate single-molecule fluorescence in situ hybridization (smFISH) and single-cell RNA sequencing (scRNA-seq) to generate an atlas of cell clusters and expression domains during the early stages of wheat spike development. We characterize spatiotemporal expression of 99 genes by smFISH in 48,225 cells at early transition (W1.5), late double ridge (W2.5), and floret primordia stages (W3.5). These cells are grouped into 21 different expression domains, including four in the basal region of the developing spikelets and three different meristematic regions, which are consistent across spikelets and sections. Using induced mutants, we reveal functional roles associated with the specific expression patterns of LFY in intercalary meristems, SPL14 in inflorescence meristems, and FZP in glume axillae. Complementary scRNA-seq profiling of 26,009 cells from W2.5 and W3.5 stages identifies 23 distinct cell clusters. We use the scRNA-seq information to impute the expression of 74,464 genes into the spatially anchored smFISH-labelled cells and generate a public website to visualize them. We then use experimental and imputed expression profiles, together with co-expression studies and correlation matrices, to annotate the scRNA-seq clusters. From co-expression analyses, we identify genes associated with boundary genes TCP24 and FZP, as well as the meristematic genes AGL6 and ULT1. CONCLUSIONS: The smFISH and scRNA-seq studies provide complementary tools for dissecting gene networks that regulate spike development and identifying new co-expressed genes for functional characterization.
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AlzCode: a platform for multiview analysis of genes related to Alzheimer's disease.
MOTIVATION: Alzheimer's disease (AD) is a complex brain disorder with risk genes incompletely identified. The candidate genes are dominantly obtained by computational approaches. In order to obtain biological insights of candidate genes or screen genes for experimental testing, it is essential to assess their relevance to AD. A platform that integrates different types of omics data and approaches would facilitate the analysis of candidate genes and is in great need. RESULTS: We report AlzCode, a platform for multiview analysis of genes related to AD. First, this platform integrates a rich collection of functional genomic data, including expression data of AD samples (gene expression, single-cell RNA-seq data and protein expression), AD-specific biological networks (co-expression networks and functional gene networks), neuropathological and clinical traits (CERAD score, Braak staging score, Clinical Dementia Rating, cognitive function and clinical severity) and general data such as protein-protein interaction, regulatory networks, sequence similarity and miRNA-target interactions. These data provide basis for analyzing genes from different views. Second, the platform integrates multiple approaches designed for the various types of data. We implement functions to analyze both individual genes and gene sets. We also compare AlzCode with two existing platforms for AD analysis, which are Agora and AD Atlas. We pinpoint the features of each platform and highlight their differences. This platform would be valuable to the understanding of AD genetics and pathological mechanisms. AVAILABILITY AND IMPLEMENTATION: AlzCode is freely available at: http://www.alzcode.xyz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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A Spatiotemporal Organ-Wide Gene Expression and Cell Atlas of the Developing Human Heart.
The process of cardiac morphogenesis in humans is incompletely understood. Its full characterization requires a deep exploration of the organ-wide orchestration of gene expression with a single-cell spatial resolution. Here, we present a molecular approach that reveals the comprehensive transcriptional landscape of cell types populating the embryonic heart at three developmental stages and that maps cell-type-specific gene expression to specific anatomical domains. Spatial transcriptomics identified unique gene profiles that correspond to distinct anatomical regions in each developmental stage. Human embryonic cardiac cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of embryonic cardiac gene expression. In situ sequencing was then used to refine these results and create a spatial subcellular map for the three developmental phases. Finally, we generated a publicly available web resource of the human developing heart to facilitate future studies on human cardiogenesis.
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Dual Host-Pathogen Screening of the Lung During Severe Influenza Using Spatial Transcriptomics.
Here, we assessed whether spatially resolved capture of polyadenylated mRNA permitted simultaneous screening of host and viral responses in situ. Fresh-frozen lung samples from mice infected with H1N1 influenza A strain, PR8, were processed via genome-wide, spatial transcriptomics (ST) at 10-μm resolution. To determine if specific cell-types were detectable within the ST data, we first performed cellular deconvolution using publicly available, single-cell RNA-seq data of immune and non-immune cells from PR8-infected lungs. This revealed expected structures such as rings of epithelial cells lining airways, and at Day 10 post-infection (p.i.) unexpected structures dominated by B cells. We next screened ST data alone for groups of genes displaying similar spatial expression patterns. This again identified at Day 10 p.i. cellular aggregates enriched for genes associated with B cell biology. Mapping these gene signatures to a B cell transcriptomic atlas suggested plasmablasts had emerged early in the lung, which was confirmed by flow cytometry. Finally, by mapping ST data to the influenza A genome, we observed widespread expression of all 10 viral transcripts across samples at Day 4 but not Day 10 p.i., consistent with viral clearance. Cellular deconvolution analysis at Day 4 p.i. revealed proportions of every detected cell-type harbored viral transcripts, suggesting indiscriminate infection across the specific area of tissue examined. Subsequent whole-section immunohistochemical assessment further supported the phenomenon of localized indiscriminate infection, with these areas found adjacent to minimally infected zones. Thus, spatial screening of mRNA in tissues can simultaneously inform upon viral pathogenesis and host responses to infection.
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Single-cell transcriptome analysis reveals the metabolic changes and the prognostic value of malignant hepatocyte subpopulations and predict new therapeutic agents for hepatocellular carcinoma.
BACKGROUND: The development of HCC is often associated with extensive metabolic disturbances. Single cell RNA sequencing (scRNA-seq) provides a better understanding of cellular behavior in the context of complex tumor microenvironments by analyzing individual cell populations. METHODS: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data was employed to investigate the metabolic pathways in HCC. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analysis were applied to identify six cell subpopulations, namely, T/NK cells, hepatocytes, macrophages, endothelial cells, fibroblasts, and B cells. The gene set enrichment analysis (GSEA) was performed to explore the existence of pathway heterogeneity across different cell subpopulations. Univariate Cox analysis was used to screen genes differentially related to The Overall Survival in TCGA-LIHC patients based on scRNA-seq and bulk RNA-seq datasets, and LASSO analysis was used to select significant predictors for incorporation into multivariate Cox regression. Connectivity Map (CMap) was applied to analysis drug sensitivity of risk models and targeting of potential compounds in high risk groups. RESULTS: Analysis of TCGA-LIHC survival data revealed the molecular markers associated with HCC prognosis, including MARCKSL1, SPP1, BSG, CCT3, LAGE3, KPNA2, SF3B4, GTPBP4, PON1, CFHR3, and CYP2C9. The RNA expression of 11 prognosis-related differentially expressed genes (DEGs) in normal human hepatocyte cell line MIHA and HCC cell lines HCC-LM3 and HepG2 were compared by qPCR. Higher KPNA2, LAGE3, SF3B4, CCT3 and GTPBP4 protein expression and lower CYP2C9 and PON1 protein expression in HCC tissues from Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. The results of target compound screening of risk model showed that mercaptopurine is a potential anti-HCC drug. CONCLUSION: The prognostic genes associated with glucose and lipid metabolic changes in a hepatocyte subpopulation and comparison of liver malignancy cells to normal liver cells may provide insight into the metabolic characteristics of HCC and the potential prognostic biomarkers of tumor-related genes and contribute to developing new treatment strategies for individuals.
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Integrated bioinformatics analysis of prognostic significance and molecular regulation of mitochondrial metabolism-related genes in colorectal cancer.
BACKGROUND: The development and progression of colorectal cancer (CRC) are closely associated with metabolic reprogramming, in which mitochondrial metabolism is critically associated with tumor cell survival and proliferation. This study aims to provide novel insights for mitochondrial metabolism-related genes (MMRGs)-based prognosis prediction, biomarker discovery, and the development of targeted therapeutic strategies in CRC. METHODS: Based on multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) (including single-cell RNA-seq), we systematically analyzed MMRGs through differential expression screening, functional enrichment, least absolute shrinkage and selection operator (LASSO) regression modeling, and independent validation, supplemented by cell-cell communication, immune infiltration, drug sensitivity, and regulatory network analyses. RESULTS: We identified 7 major cell types and uncovered the immune microenvironment characteristics in CRC patients. We constructed a novel MMRGs-centric prognostic signature (CD36/ACOX1) validated in TCGA and GEO cohorts, effectively stratifying patients into distinct risk groups with significant survival differences. The risk model demonstrated independent prognostic value, correlated with distinct immune landscapes and genomic features. Regulatory network analysis revealed miRNA/TF interactions targeting CD36/ACOX1, while drug sensitivity screening identified 81 therapeutic agents with enhanced efficacy in high-risk patients. Pseudotime trajectory analysis further uncovered novel B-cell differentiation dynamics associated with prognostic gene expression. CONCLUSIONS: The mitochondrial metabolic signature CD36/ACOX1 enables robust survival stratification in CRC. This clinically applicable model informs tumor microenvironment dynamics and precision oncology frameworks for CRC.
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Single cell eQTL analysis identifies cell type-specific genetic control of gene expression in fibroblasts and reprogrammed induced pluripotent stem cells.
BACKGROUND: The discovery that somatic cells can be reprogrammed to induced pluripotent stem cells (iPSCs) has provided a foundation for in vitro human disease modelling, drug development and population genetics studies. Gene expression plays a critical role in complex disease risk and therapeutic response. However, while the genetic background of reprogrammed cell lines has been shown to strongly influence gene expression, the effect has not been evaluated at the level of individual cells which would provide significant resolution. By integrating single cell RNA-sequencing (scRNA-seq) and population genetics, we apply a framework in which to evaluate cell type-specific effects of genetic variation on gene expression. RESULTS: Here, we perform scRNA-seq on 64,018 fibroblasts from 79 donors and map expression quantitative trait loci (eQTLs) at the level of individual cell types. We demonstrate that the majority of eQTLs detected in fibroblasts are specific to an individual cell subtype. To address if the allelic effects on gene expression are maintained following cell reprogramming, we generate scRNA-seq data in 19,967 iPSCs from 31 reprogramed donor lines. We again identify highly cell type-specific eQTLs in iPSCs and show that the eQTLs in fibroblasts almost entirely disappear during reprogramming. CONCLUSIONS: This work provides an atlas of how genetic variation influences gene expression across cell subtypes and provides evidence for patterns of genetic architecture that lead to cell type-specific eQTL effects.