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Identification of miRNA signature in cancer-associated fibroblast to predict recurrent prostate cancer.
BACKGROUND: Cancer-associated fibroblasts (CAFs) are one of the major components of prostate stromal cells, which play a crucial part in tumor development and treatment resistance. This study aimed to establish a model of CAFs-related microRNAs (miRNAs) to assess prognostic differences, tumor microenvironments, and screening of anticancer drugs by integrating data from single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (buRNA-seq). METHODS: scRNA-seq and buRNA-seq data of primary prostate cancer (PCa) were downloaded from Gene Expression Omnibus and The Cancer Genome Atlas databases. Statistical methods including Least absolute shrinkage and selection operator (Lasso), Lasso penalized, Random Forest, Random Forest Combination, and Support Vector Machine (SVM) were performed to select hub miRNAs. Pathway analyses and assessment of infiltrating immune cells were conducted using Gene Set Enrichment Analysis and the CIBERSORT algorithm. The expression of CAFs-related miRNAs in fibroblast cell lines were validated through quantitative real-time PCR. Cell Counting Kit 8 (CCK8), wound-healing, clone formation, and cell migration assays were used to explore cell proliferation, growth, and migration in vitro. A mouse xenograft model was established to investigate the effect of CAFs on tumor growth in vivo. RESULTS: Through single-cell transcriptomics analysis in 34 PCa patients, 89 CAFs-related mRNAs were identified. A prognostic model based on 9 CAFs-related miRNAs (hsa-miR-1258, hsa-miR-133b, hsa-miR-222-3p, hsa-miR-145-3p, hsa-miR-493-5p, hsa-miR-96-5p, hsa-miR-15b-5p, hsa-miR-106b-5p, and hsa-miR-191-5p) was established to predict biochemical recurrence (BCR). We have determined through two prediction methods that NVP-TAE684 may be the optimal targeted therapy drug for treating CAFs. Downregulation of hsa-miR-106b-5p in CAFs significantly suppressed cell proliferation, migration, and colony formation in vitro. In vivo studies using a xenograft model further confirmed that hsa-miR-106b-5p downregulation significantly reduced tumor growth. CONCLUSION: Our findings conducted an integrated bioinformatic analysis to develop a CAFs-related miRNAs model that provides prognostic insights into individualized and precise treatment for prostate adenocarcinoma patients. Downregulation of miR-106b-5p in CAFs significantly suppressed tumor growth, suggesting a potential therapeutic target for cancer treatment.
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Integrated analysis of single-cell and bulk RNA-sequencing reveals the poor prognostic value of ABCA1 in gastric adenocarcinoma.
PURPOSE: ATP-binding cassette A1 (ABCA1) is a potential prognostic marker for various tumor types. However, the biological effects and prognostic value of ABCA1 in gastric adenocarcinoma (GAC) remain unknown. METHODS: GAC-associated single-cell RNA and bulk RNA-sequencing (bulk-seq) data were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. The differential expression of ABCA1 between GAC and normal gastric tissues was analyzed based on the bulk-seq data. Additionally, the relationship between ABCA1 expression and various clinicopathological features was explored. Furthermore, Kaplan-Meier survival and Cox regression analyses were performed to establish the prognostic value of ABCA1. The relationships between ABCA1 expression and anti-tumor drug sensitivity and immune checkpoints were also explored. Finally, the biological functions of ABCA1 were evaluated at the single-cell level, and in vitro studies were performed to assess the effects of ABCA1 on GAC cell proliferation and invasion. RESULTS: ABCA1 expression is significantly elevated in GAC samples compared with that in normal gastric tissues. Clinical features and survival analysis revealed that high ABCA1 expression is associated with poor clinical phenotypes and prognosis, whereas Cox analysis identified ABCA1 as an independent risk factor for patients with GAC. Furthermore, high ABCA1 expression suppresses sensitivity to various chemotherapeutic drugs, including cisplatin and mitomycin, while upregulating immune checkpoints. ABCA1-overexpressing macrophages are associated with adverse clinical phenotypes in GAC and express unique ligand-receptor pairs that drive GAC progression. In vitro, ABCA1-knockdown GAC cells exhibit significantly inhibited proliferative and invasive properties. CONCLUSION: High ABCA1 expression promotes an adverse immune microenvironment and low survival rates in patients with GAC. Furthermore, ABCA1 and ABCA1-producing macrophages may serve as novel molecular targets in GAC treatment.
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Integrated single-cell RNA sequencing analysis reveals a mesenchymal stem cell-associated signature for estimating prognosis and drug sensitivity in gastric cancer.
BACKGROUND: Mesenchymal stem cells (MSCs) play an important role in regulating all stages of the immune response, angiogenesis, and transformation of matrix components in the tumor microenvironment. The aim of this study was to identify the prognostic value of MSC-related signatures in patients with gastric cancer (GC). METHODS: MSC marker genes were identified by analyzing single-cell RNA sequencing (scRNA-seq) data for GC from the Gene Expression Omnibus (GEO) database. Using bulk sequencing data from the Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD), as a training cohort, and data from GEO, as a validation cohort, we developed a risk model consisting of MSC prognostic signature genes, and classified GC patients into high- and low-MSC risk subgroups. Multifactorial Cox regression was used to evaluate whether MSC prognostic signature was an independent prognostic factor. An MSC nomogram was constructed combining clinical information and risk grouping. Subsequently, we evaluated the effect of MSC prognostic signature on immune cell infiltration, antitumor drugs and immune checkpoints and verified the expression of MSC prognostic signature by in vitro cellular assays. RESULTS: In this study, 174 MSC marker genes were identified by analyzing scRNA-seq data. We identified seven genes (POSTN, PLOD2, ITGAV, MMP11, SDC2, MARCKS, ANXA5) to construct MSC prognostic signature. MSC prognostic signature was an independent risk factor in the TCGA and GEO cohorts. GC patients in the high-MSC risk group had worse prognoses. In addition, the MSC nomogram has a high clinical application value. Notably, the MSC signature can induce the development of a poor immune microenvironment. GC patients in the high MSC-risk group were more sensitive to anticancer drugs and tended to have higher levels of immune checkpoint markers. In qRT-PCR assays, the MSC signature was more highly expressed in GC cell lines. CONCLUSIONS: The MSC marker gene-based risk signature developed in this study can not only be used to predict the prognosis of GC patients, but also has the potential to reflect the efficacy of antitumor therapies.
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Single-cell and spatial analysis reveals macrophage-T cell crosstalk in non-small cell lung cancer immunosuppression.
BACKGROUND: The tumor microenvironment (TME) is instrumental in facilitating immune evasion and promoting tumor progression in non-small cell lung cancer (NSCLC). However, the spatial heterogeneity and molecular interactions between immune and non-immune cells are not yet fully understood. This research amalgamated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic data to examine the cellular composition, ligand-receptor (LR) interactions, and immunosuppressive mechanisms of NSCLC. METHODS: ScRNA-seq was performed on NSCLC tumors and paired adjacent normal tissues to analyze immune and stromal cell heterogeneity. Spatial transcriptomics was applied to consecutive tissue sections to map the distribution of major cell types and their interactions. Cell clustering, a differential gene expression analysis, and LR interaction modeling were used to identify key immune regulatory pathways. In vitro co-culture experiments were performed for functional validation. RESULTS: A total of 28,496 high-quality single cells were analyzed, and significant differences in immune cell composition between the tumor and normal tissues were found. The NSCLC tumors exhibited a notable increase in tumor-associated macrophages (TAMs) and fibroblasts, as well as a decrease in the presence of cytotoxic T cells and natural killer (NK) cells. Notably, spatial transcriptomics revealed that the TAMs were predominantly localized in the tumor cores, where they interacted with T cells via immunosuppressive LR pairs. The SPP1-CD44 and NECTIN2-TIGIT axes were identified as major immunosuppressive pathways, with the SPP1 secreted by the TAMs contributing to immune evasion. Additionally, the HLA-E-CD8B interactions were significantly upregulated, suggesting a potential mechanism of antigen presentation modulation. The functional validation showed that SPP1 overexpression (OE) enhanced PDCD1 and CD160 expression, further confirming its role in shaping the immunosuppressive microenvironment. CONCLUSIONS: This study established a high-resolution immune atlas of NSCLC, identifying novel macrophage-T cell interactions that drive immune suppression. Our findings suggest that targeting the SPP1-CD44, NECTIN2-TIGIT, and HLA-E-CD8B pathways may improve immune responses in NSCLC. These findings provide novel therapeutic targets and highlight the potential for combination immunotherapies to overcome macrophage-mediated immune evasion.
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Single-cell analysis of mouse uterus at the invasion phase of embryo implantation.
BACKGROUND: Embryo implantation into the uterus is a crucial step for human reproduction. A hypothesis has been proposed that the molecular circuit invented by trophoblasts for invasive embryo implantation during evolution might be misused by cancer cells to promote malignancy. Unfortunately, our current understanding of the molecular mechanism underlying embryo implantation is far from complete. RESULTS: Here we used the mouse as an animal model and generated a single-cell transcriptomic atlas of the embryo implantation site of mouse uterus at the invasion phase of embryo implantation on gestational day 6. We revealed 23 distinct cell clusters, including 5 stromal cell clusters, 2 epithelial cell clusters, 1 smooth muscle cell cluster, 2 pericyte clusters, 4 endothelial cell clusters, and 9 immune cell clusters. Through data analysis, we identified differentially expression changes in all uterine cell types upon embryo implantation. By integrated with single-cell RNA-seq data from E5.5 embryos, we predicted cell-cell crosstalk between trophoblasts and uterine cell types. CONCLUSIONS: Our study provides a valuable resource for understanding of the molecular mechanism of embryo implantation.
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Single-Cell Comparison of Small Intestinal Neuroendocrine Tumors and Enterochromaffin Cells from Two Patients.
BACKGROUND: Several studies have attempted to identify the initiating drivers of small intestinal neuroendocrine tumor (SI-NET) development and the molecular mechanisms underlying their progression and metastatic spread. Previous gene expression studies have used bulk microarrays or RNA sequencing to compare tumor tissue with normal intestinal mucosa. However, the intestine comprises multiple distinct cell types, and bulk analyses are limited by this cellular heterogeneity, which can confound tumor-specific signals. METHODS: We performed single-cell RNA sequencing on primary SI-NETs and paired normal mucosa from two patients to directly compare tumor cells with their cells of origin, the enterochromaffin (EC) cells. To minimize type I errors, we applied a two-step validation strategy by overlapping differentially expressed genes with an external single-cell dataset and cross-referencing candidate genes for enteroendocrine expression in the Human Protein Atlas. RESULTS: For further distinction and characterization, ECs were subdivided into serotonergic and non-serotonergic clusters. This analysis revealed that the SI-NET cells are transcriptionally more similar to serotonergic ECs, consistent with serum metabolite profiles derived from clinical parameters. Our analyses uncovered a loss-of-expression program characterized by regulators of epithelial differentiation and in parallel, a gain-of-expression program displayed neuronal signaling gene induction, implicating functional reprogramming toward neuronal-like properties. Together, these specific losses and gains suggest that our patient-derived SI-NETs undergo adaptation through both loss of enteroendocrine functions and acquisition of neurobiological-promoting signaling pathways. CONCLUSIONS: These findings nominate candidate drivers for further functional validation and highlight potential therapeutic strategies in our patient cohort, including restoring suppressed Notch signaling and targeting aberrant neuronal signaling networks. However, even with a two-step validation procedure, the modest cohort size limits statistical power and generalizability, particularly for the proposed association to a serotonergic phenotype. Larger, multi-patient single-cell studies are required to confirm these mechanisms and establish their clinical relevance.
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Signatures of plasticity, metastasis, and immunosuppression in an atlas of human small cell lung cancer.
Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively). To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 155,098 transcriptomes from 21 human biospecimens, including 54,523 SCLC transcriptomes. We observe greater tumor diversity in SCLC than lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discover a PLCG2-high SCLC phenotype with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival. SCLC exhibits greater immune sequestration and less immune infiltration than lung adenocarcinoma, and SCLC-N shows less immune infiltrate and greater T cell dysfunction than SCLC-A. We identify a profibrotic, immunosuppressive monocyte/macrophage population in SCLC tumors that is particularly associated with the recurrent, PLCG2-high subpopulation.
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ZEBRA: a hierarchically integrated gene expression atlas of the murine and human brain at single-cell resolution.
The molecular causes and mechanisms of neurodegenerative diseases remain poorly understood. A growing number of single-cell studies have implicated various neural, glial, and immune cell subtypes to affect the mammalian central nervous system in many age-related disorders. Integrating this body of transcriptomic evidence into a comprehensive and reproducible framework poses several computational challenges. Here, we introduce ZEBRA, a large single-cell and single-nucleus RNA-seq database. ZEBRA integrates and normalizes gene expression and metadata from 33 studies, encompassing 4.2 million human and mouse brain cells sampled from 39 brain regions. It incorporates samples from patients with neurodegenerative diseases like Alzheimer's disease, Parkinson's disease, and Multiple sclerosis, as well as samples from relevant mouse models. We employed scVI, a deep probabilistic auto-encoder model, to integrate the samples and curated both cell and sample metadata for downstream analysis. ZEBRA allows for cell-type and disease-specific markers to be explored and compared between sample conditions and brain regions, a cell composition analysis, and gene-wise feature mappings. Our comprehensive molecular database facilitates the generation of data-driven hypotheses, enhancing our understanding of mammalian brain function during aging and disease. The data sets, along with an interactive database are freely available at https://www.ccb.uni-saarland.de/zebra.
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Malignant epithelial cell marker-driven risk signature enables precise stratification in esophageal cancer.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly heterogeneous malignancy. Traditional clinical staging systems have limited value in prognostic evaluation and treatment guidance, often failing to capture the profound impact of intratumoral diversity on patient outcomes. Single-cell RNA sequencing (scRNA-seq) provides new perspectives on the cellular makeup and conditions in tumor tissues, with promising implications for functional classification and personalized therapeutic strategies in esophageal cancer. METHODS: In this study, we integrated scRNA-seq data with bulk RNA-seq profiles from esophageal cancer tissues to construct a comprehensive cellular atlas, focusing on the transcriptional characteristics of epithelial cells. Malignant epithelial cells were identified based on copy number variation (CNV) features using inferCNV analysis. Their developmental states and regulatory mechanisms were further characterized via transcription factor activity inference (SCENIC) and pseudotime trajectory analysis (Monocle). Based on marker genes of malignant epithelial subpopulations, we developed a multi-gene risk scoring model using data from the TCGA and GEO (GSE53624) cohorts. The model's predictive value for immune landscape, mutational features, and drug sensitivity was also evaluated. Additionally, qRT-PCR was conducted to quantify the expression levels of model genes in ESCC tissue samples, further evaluating their biological relevance. Functional roles of the key gene HMGB3 were validated in vitro through CCK-8 proliferation assays, Transwell invasion assays, and colony formation assays following gene knockdown in ESCC cell lines. RESULTS: At the single-cell level, we identified ten major cell types and six distinct malignant epithelial subclusters, which exhibited pronounced heterogeneity in cell cycle states, transcriptional regulatory networks, and differentiation trajectories. High CNV scores and the enrichment of specific transcription factors (e.g., FOXC1, E2F1, RUNX1) suggested a proliferative and immune-evasive phenotype. A six-gene prognostic model (HMGB3, CHORDC1, CTSD, BTG2, MT1E, PHYHD1) showed strong predictive accuracy for overall survival in the TCGA and GSE53624 cohorts. Furthermore, the risk score showed a significant correlation with an immunosuppressive tumor microenvironment, increased tumor purity, and the activation of certain immune-related pathways. Analysis of drug sensitivity suggests that patients classified as low-risk could respond better to various targeted therapies and chemotherapeutic agents, underscoring their potential clinical relevance. Functional assays revealed that HMGB3 knockdown markedly suppressed ESCC cell proliferation, invasion, and colony formation, supporting its oncogenic role in esophageal cancer progression. CONCLUSION: This study systematically characterized epithelial cell heterogeneity in esophageal cancer at single-cell resolution and established a risk model based on malignant epithelial markers that effectively predicts prognosis, immune status, and potential drug response. Combined with experimental validation, our findings highlight the pivotal role of HMGB3 in ESCC progression and provide a novel theoretical and practical framework for functional tumor classification and individualized treatment strategies.
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Evolutionary and Developmental Specialization of Foveal Cell Types in the Marmoset.
UNLABELLED: In primates, high-acuity vision is mediated by the fovea, a small specialized central region of the retina. The fovea, unique to the anthropoid lineage among mammals, undergoes notable neuronal morphological changes during postnatal maturation. However, the extent of cellular similarity across anthropoid foveas and the molecular underpinnings of foveal maturation remain unclear. Here, we used high throughput single cell RNA sequencing to profile retinal cells of the common marmoset ( Callithrix jacchus ), an early divergent in anthropoid evolution from humans, apes, and macaques. We generated atlases of the marmoset fovea and peripheral retina for both neonates and adults. Our comparative analysis revealed that marmosets share almost all its foveal types with both humans and macaques, highlighting a conserved cellular structure among primate foveas. Furthermore, by tracing the developmental trajectory of cell types in the foveal and peripheral retina, we found distinct maturation paths for each. In-depth analysis of gene expression differences demonstrated that cone photoreceptors and Müller glia, among others, show the greatest molecular divergence between these two regions. Utilizing single-cell ATAC-seq and gene-regulatory network inference, we uncovered distinct transcriptional regulations differentiating foveal cones from their peripheral counterparts. Further analysis of predicted ligand-receptor interactions suggested a potential role for Müller glia in supporting the maturation of foveal cones. Together, these results provide valuable insights into foveal development, structure, and evolution. SIGNIFICANCE STATEMENT: The sharpness of our eyesight hinges on a tiny retinal region known as the fovea. The fovea is pivotal for primate vision and is susceptible to diseases like age-related macular degeneration. We studied the fovea in the marmoset-a primate with ancient evolutionary ties. Our data illustrated the cellular and molecular composition of its fovea across different developmental ages. Our findings highlighted a profound cellular consistency among marmosets, humans, and macaques, emphasizing the value of marmosets in visual research and the study of visual diseases.