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Gender differences in smoking-induced changes in the tumor immune microenvironment.
Both gender and smoking are correlated with prevalence and outcomes in many types of cancers. Tobacco smoke is a known carcinogen through its genotoxicity but can also affect cancer progression through its effect on the immune system. In this study, we aim to evaluate the hypothesis that the effects of smoking on the tumor immune microenvironment will be influenced differently by gender using large-scale analysis of publicly available cancer datasets. We used The Cancer Genomic Atlas (TCGA) datasets (n = 2724) to analyze effects of smoking on different cancer immune subtypes and the relative abundance of immune cell types between male and female cancer patients. We further validated our results by analyzing additional datasets, including Expression Project for Oncology (expO) bulk RNA-seq dataset (n = 1118) and single-cell RNA-seq dataset (n = 14). Results of our study indicate that in female patients, two immune subtypes, C1 and C2, are respectively over and under abundant in smokers vs. never smokers. In males, the only significant difference is underabundance of the C6 subtype in smokers. We identified gender-specific differences in the population of immune cell types between smokers and never smokers in all TCGA and expO cancer types. Increased plasma cell population was identified as the most consistent feature distinguishing smokers and never smokers, especially in current female smokers based on both TCGA and expO data. Our analysis of existing single-cell RNA-seq data further revealed that smoking differentially affects the gene expression profile of cancer patients based on the immune cell type and gender. In our analysis, female and male smokers show different smoking-induced patterns of immune cells in tumor microenvironment. Besides, our results suggest cancer tissues directly exposed to tobacco smoke undergo the most significant changes, but all other tissue types are affected as well. Findings of current study also indicate that changes in the populations of plasma cells and their correlations to survival outcomes are stronger in female current smokers, with implications for cancer immunotherapy of women smokers. In conclusion, results of this study can be used to develop personalized treatment plans for cancer patients who smoke, particularly women smokers, taking into account the unique immune cell profile of their tumors.
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AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales.
Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.
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Single-cell transcriptional logic of cell-fate specification and axon guidance in early-born retinal neurons.
Retinal ganglion cells (RGCs), cone photoreceptors (cones), horizontal cells and amacrine cells are the first classes of neurons produced in the retina. However, an important question is how this diversity of cell states is transcriptionally produced. Here, we profiled 6067 single retinal cells to provide a comprehensive transcriptomic atlas showing the diversity of the early developing mouse retina. RNA velocities unveiled the dynamics of cell cycle coordination of early retinogenesis and define the transcriptional sequences at work during the hierarchical production of early cell-fate specification. We show that RGC maturation follows six waves of gene expression, with older-generated RGCs transcribing increasing amounts of guidance cues for young peripheral RGC axons that express the matching receptors. Spatial transcriptionally deduced features in subpopulations of RGCs allowed us to define novel molecular markers that are spatially restricted. Finally, the isolation of such a spatially restricted population, ipsilateral RGCs, allowed us to identify their molecular identity at the time they execute axon guidance decisions. Together, these data represent a valuable resource shedding light on transcription factor sequences and guidance cue dynamics during mouse retinal development.
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CCT4 as a prognostic biomarker correlated with immune infiltration and hepatocyte dedifferentiation in hepatocellular carcinoma.
BACKGROUND: CCT4 is a key molecular chaperone and its dysfunction may impair actin or microtubule assembly, potentially driving hepatocellular carcinoma (HCC) progression. However, its roles in multiple cell types and tumor microenvironment infiltration in HCC remain unclear. This study aimed to investigate the biological functions of CCT4 in HCC by integrating single-cell and bulk transcriptomic data. METHODS: CCT4 expression in HCC was analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Functional analyses were conducted via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Furthermore, Cox regression analysis and nomogram construction were employed to assess patient prognosis. Immune infiltration was evaluated with CIBERSORT, and drug sensitivity was tested using the CellMiner database. Hepatocyte features were analyzed using single-cell RNA-seq tools, including Cellular Trajectory Reconstruction Analysis using gene Counts and Expression (CytoTRACE) for stemness and CellChat for cell-cell communication. RESULTS: CCT4 showed significant overexpression in HCC tissues and was associated with clinical features such as age and tumor node metastasis (TNM) stage. Functional analyses linked co-expression genes of CCT4 to ribosomal biogenesis and protein folding. Cox regression and nomograms confirmed CCT4 as an independent prognostic factor for HCC. Correlations with nine immune cell types were observed through immune infiltration analysis. Drug sensitivity analysis revealed that HCC patients with high expression of CCT4 were resistant to sorafenib and other drugs. Single-cell analysis revealed that elevated CCT4 expression was enriched in pro-metastatic hepatocyte clusters, associated with increased hepatocyte proportions and poor differentiation, with weakened T/natural killer (NK) cell interactions in tumors. CONCLUSIONS: CCT4 is overexpressed in HCC, linked to poor prognosis, and may drive tumor progression via pro-metastatic hepatocyte dedifferentiation and immune shifts.
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Identification of T-cell exhaustion-related genes and prediction of their immunotherapeutic role in lung adenocarcinoma.
Background: Lung adenocarcinoma ranks as the second most widespread form of cancer globally, accompanied by a significant mortality rate. Several studies have shown that T cell exhaustion is associated with immunotherapy of tumours. Consequently, it is essential to comprehend the possible impact of T cell exhaustion on the tumor microenvironment. The purpose of this research was to create a TEX-based model that would use single-cell RNA-seq (scRNA-seq) and bulk-RNA sequencing to explore new possibilities for assessing the prognosis and immunotherapeutic response of LUAD patients. Methods: RNA-seq data from LUAD patients was downloaded from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). 10X scRNA sequencing data, as reported by Bischoff P et al., was utilized for down-sampling clustering and subgroup identification using TSNE. TEX-associated genes were identified through gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). We utilized LASSO-Cox analysis to establish predicted TEX features. External validation was conducted in GSE31210 and GSE30219 cohorts. Immunotherapeutic response was assessed in IMvigor210, GSE78220, GSE35640 and GSE100797 cohorts. Furthermore, we investigated differences in mutational profiles and immune microenvironment between various risk groups. We then screened TEXRS key regulatory genes using ROC diagnostic curves and KM survival curves. Finally, we verified the differential expression of key regulatory genes through RT-qPCR. Results: Nine TEX genes were identified as highly predictive of LUAD prognosis and strongly correlated with disease outcome. Univariate and multivariate analysis revealed that patients in the low-risk group had significantly better overall survival rates compared with those in the high-risk group, highlighting the model's ability to independently predict LUAD prognosis. Our analysis revealed significant variation in the biological function, mutational landscape, and immune cell infiltration within the tumor microenvironment of both high-risk and low-risk groups. Additionally, immunotherapy was found to have a significant impact on both groups, indicating strong predictive efficacy of the model. Conclusions: The TEX model showed good predictive performance and provided a new perspective for evaluating the efficacy of preimmunization, which provides a new strategy for the future treatment of lung adenocarcinoma.
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Single-cell transcriptomic landscape of peripheral blood cells provides insights into adaptation of red-eared sliders (Trachemys scripta elegans).
Red-eared sliders (Trachemys scripta elegans), as one of the 100 most threatening aliens, have stronger immunity than the native species in response to environmental stress. Blood cells are an important component of immunity in the body. However, the blood cell researches of turtle are still in the traditional blood cell classification and morphological structure observation. Furthermore, turtle granulocytes cannot be accurately identified using traditional methods. Single-cell RNA sequencing techniques have been successfully implemented to study cells based on the mRNA expression patterns of each cell. The present study profiled the transcriptomes of peripheral blood cells in red-eared sliders to construct a single-cell transcriptional landscape of the different cell types and explored environmental adaptation mechanism from the perspective of hematology. All 14 transcriptionally distinct clusters (platelets, erythrocytes1, erythrocytes2, CSF1R monocytes, POF1B monocytes, neutrophils, GATA2high basophils, GATA2low basophils, CD4 T cells, CD7 T cells, B cells, ACKR4 cells, serotriflin cells, and ficolin cells) were identified in the peripheral blood cells of the red-eared sliders. In particular, a subtype of erythrocytes (erythrocytes1) that expressed immune signals was identified. Peripheral blood cells were grouped into three lineages: platelet, erythroid/lymphoid, and myeloid cell lineages. Furthermore, based on differentiation trajectory and up-regulated gene expression, ACKR4 cells were newly identified as lymphocytes, and serotriflin and ficolin cells as granulocytes. The single-cell transcriptional atlas of the peripheral blood cells in red-eared sliders provided in the present study will offer a comprehensive transcriptome reference for the exploration of physiological and pathological hematology in this species.
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Identification and validation of a novel signature based on macrophage marker genes for predicting prognosis and drug response in kidney renal clear cell carcinoma by integrated analysis of single cell and bulk RNA sequencing.
Macrophages are found in a variety of tumors and play a critical role in shaping the tumor microenvironment, affecting tumor progression, metastasis, and drug resistance. However, the clinical relevance of marker genes associated with macrophage in kidney renal clear cell carcinoma (KIRC) has yet to be documented. In this study, we initiated a thorough examination of single-cell RNA sequencing (scRNA-seq) data for KIRC retrieved from the Gene Expression Omnibus (GEO) database and determined 244 macrophage marker genes (MMGs). Univariate analysis, LASSO regression, and multivariate regression analysis were performed to develop a five-gene prognostic signature in The Cancer Genome Atlas (TCGA) database, which could divide KIRC patients into low-risk (L-R) and high-risk (H-R) groups. Then, a nomogram was constructed to predict the survival rate of KIRC patients at 1, 3, and 5 years, which was well assessed by receiver operating characteristic curve (ROC), calibration curve, and decision curve analyses (DCA). Functional enrichment analysis showed that immune-related pathways (such as immunoglobulin complex, immunoglobulin receptor binding, and cytokine-cytokine receptor interaction) were mainly enriched in the H-R group. Additionally, in comparison to the L-R cohort, patients belonging to the H-R cohort exhibited increased immune cell infiltration, elevated expression of immune checkpoint genes (ICGs), and a higher tumor immune dysfunction and exclusion (TIDE) score. This means that patients in the H-R group may be less sensitive to immunotherapy than those in the L-R group. Finally, IFI30 was validated to increase the ability of KIRC cells to proliferate, invade and migrate in vitro. In summary, our team has for the first time developed and validated a predictive model based on macrophage marker genes to accurately predict overall survival (OS), immune characteristics, and treatment benefit in KIRC patients.
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Artificial sweeteners and male infertility: Network toxicology and experimental evidence reveal FGFR1 as the critical target.
BACKGROUND: Artificial sweeteners (ASs) are widely used sugar substitutes, but chronic exposure is linked to male infertility. We integrated computational prediction, network analysis, and wet-lab validation to explore mechanisms. METHODS: Seven ASs were screened in AdmetSAR 3.0; high-confidence positives were prioritized. Targets were predicted by SwissTargetPrediction, SEA, and TargetNet, and intersected with the top 50 % GeneCards testicular-injury genes to define candidate targets. STRING PPI and enrichment analyses were performed, followed by machine-learning feature selection and independent dataset validation. Ligand-target binding was evaluated by molecular docking and 100-ns molecular dynamics (MD) simulations. Single-cell RNA-seq (Male Health Atlas) mapped core-gene expression across testicular clusters. TM3 Leydig cells were exposed to aspartame (0.5-2 mM) for 48 h and analyzed by RT-qPCR. RESULTS: Aspartame, neotame, and sucralose showed high-confidence reproductive-toxicity signals. Ninety-one candidate targets were identified, and FGFR1 emerged as the core gene with good discrimination in two datasets. Docking and MD supported stable AS-FGFR1 binding, especially for aspartame. FGFR1 was enriched in Leydig and vascular-associated cells. Aspartame upregulated FGFR1, DUSP6, and SPRY2 and downregulated STAR. CONCLUSIONS: FGFR1-associated signaling may link AS exposure to impaired male reproductive function, warranting in vivo and protein-level validation.
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Assessing tissue-specific gene expression of essential genes from human and mouse.
A gene is defined as essential when its functional loss compromises an organism's viability. Identifying essential genes is critical for identifying the components that regulate a biological system. Advances in gene editing techniques like CRISPR-Cas9 provide a capacity to interrogate a genome to elucidate the genes that are essential. However, these techniques are often applied for a single-cell line and rarely probed at a level of a tissue or organ. The recent availability of large-scale single-cell RNA-sequencing (scRNA-seq) atlases provides an unprecedented opportunity to investigate essential gene expression in a more comprehensive context. Our study leverages information from benchmarking datasets, single-cell tissue atlases, and databases of essential genes, to develop a method, scEssentials, that uses a statistical framework to investigate the robustness and specificity of essential genes across multiple cell types. Using scEssentials, mouse and human models showed consistently high expression and exhibited limited variability across more than 60 cell types. We demonstrate a substantial number of significantly correlated gene pairs that produce densely connected co-expression networks with functional annotation. Finally, we develop a score to quantify the relative essentiality of genes within scEssentials, further validating their significant association with gene mutation frequency and chromatin accessibility. Using ageing as an application, we demonstrate how scEssentials identifies robust gene expression profiles. Only one-fifth of scEssentials genes showed significant ageing-related differential expression among age groups. Collectively, the robustness of scEssentials serves as a reference for analysing scRNA-seq data and provides insight into the heterogeneous nature such as ageing.
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Downregulation of OPCML is associated with activation of AKT signaling and aggressive phenotypes in glioblastoma cells.
BACKGROUND: OPCML (opioid-binding protein/cell adhesion molecule-like), a glycosylphosphatidylinositol (GPI)-anchored IgLON adhesion molecule with brain-enriched expression, has tumor-suppressive roles in several epithelial cancers; however, its role in glioblastoma (GBM) biology is unclear. METHODS: We integrated two bulk microarray cohorts to derive a reproducible GBM signature and reanalyzed single-cell RNA sequencing (scRNA-seq) data to localize OPCML at single-cell resolution. The tissue distribution and clinical associations were evaluated using the Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA), with survival modeling and a nomogram. The co-expression, STRING-based protein interaction, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses outlined the molecular context. Immune infiltration was profiled using single-sample gene set enrichment analysis (ssGSEA) and cross-checked on TIMER2. Functional validation used a single OPCML small interfering RNA (siRNA) with a non-targeting siRNA control (siNC) in U87 and U251 cells with Transwell invasion, wound healing, colony formation, CCK-8 proliferation, and Western blotting for p-AKT and p-mTOR, with LY294002 rescue. RESULTS: OPCML was consistently downregulated in GBM and across multiple cancers. Within GBM, a lower expression was associated with higher grade, older age, isocitrate dehydrogenase (IDH) wild type, and poorer overall survival. At the single-cell level, the OPCML transcripts were largely confined to a neuron-glia hybrid population and were scarce in classical malignant clusters. Genome-wide correlations in GBM showed positive links to extracellular matrix/synaptic programs and negative links to cell cycle/DNA replication pathways. In vitro, OPCML knockdown increased the invasion, migration, clonogenic growth, and CCK-8 readouts and was associated with elevated p-AKT and p-mTOR. PI3K inhibition reversed the signaling changes. Pan-cancer, OPCML tracked with denser immune signatures, whereas in GBM it was inversely correlated with multiple effector populations and with cancer-associated fibroblast (CAF) estimates across deconvolution methods. CONCLUSIONS: OPCML marks a neuron-leaning, less aggressive state and is associated with the regulation of PI3K-AKT-mTOR signaling in GBM. Loss of OPCML aligns with proliferative programs and a GBM-specific immune pattern. These data nominate OPCML as a prognostic marker and a surface-level modulator that could be leveraged alongside RTK/PI3K axis inhibitors in GBM.