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Pan-Cancer Analysis and Validation of Opioid-Related Receptors Reveals the Immunotherapeutic Value of Toll-Like Receptor 4.
INTRODUCTION: The relationship between the expression of opioid-associated receptors and cancer outcomes is complex and varies among studies. METHODS: This study focused on six opioid-related receptors (OPRM1, OPRD1, OPRK1, OPRL1, OGFR, and TLR4) and their impact on cancer patient survival. Bioinformatics analysis was conducted on 33 cancer types from The Cancer Genome Atlas database to examine their expression, clinical correlations, mechanisms in the tumor microenvironment, and potential for immunotherapy. Due to significantly lower expression of OPRM1, OPRD1, and OPRK1 compared to OGFR and TLR4, the analysis concentrated on the latter two genes. RESULTS: OGFR was highly expressed in 16 tumor types, while TLR4 showed low expression in 13. Validation from external samples, the Gene Expression Omnibus, and the Human Protein Atlas supported these findings. The diagnostic value of these two genes was demonstrated using the Genotype-Tissue Expression database. Univariate Cox regression models and Kaplan-Meier curves confirmed OGFR's impact on prognosis in a cancer type-specific manner, while high TLR4 expression was associated with a favorable prognosis. Analysis of the tumor microenvironment using a deconvolution algorithm linked OGFR to CD8+ T cells and TLR4 to macrophages. Single-cell datasets further validated this correlation. In 25 immune checkpoint blockade treatment cohorts, TLR4 expression showed promise as an immunotherapy efficacy predictor in non-small cell lung cancer, urothelial carcinoma, and melanoma. CONCLUSION: In a pan-cancer analysis of 33 tissues, OGFR was consistently highly expressed, while TLR4 had low expression. Both genes have diagnostic and prognostic significance and are linked to immune cells in the tumor microenvironment. TLR4 has potential as an immunotherapeutic response marker.
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Single-nucleus multi-omics dissection of dysregulated trophoblast development and disrupted immune microenvironment in complete hydatidiform moles.
Hydatidiform mole (HM) is driven by aberrant trophoblast proliferation, disrupting embryonic development and leading to pregnancy loss with increased risk of malignant progression. Despite its clinical significance, the cellular and molecular heterogeneity of HM remains poorly characterized. To address this, we integrated single-nucleus RNA sequencing (snRNA-seq), single-nucleus transposase-accessible chromatin sequencing (snATAC-seq), and spatial transcriptomics to construct a cell-resolved atlas of androgenetic complete HM compared with gestational age-matched controls. The three major trophoblast lineages exhibit distinct differentiation relationships and functional associations: villous cytotrophoblast (VCT) are trophoblast cells with stemness, which can differentiate into hormone-secreting syncytiotrophoblast (SCT) and invasive migratory extravillous trophoblast (EVT). Our findings demonstrate that imprinted genes exhibited cell-type-specific expression patterns in HM, with more pronounced dysregulation in trophoblasts compared to non-trophoblast cells. We identified a deficiency of the progenitor subpopulation VCT1 with inactivation of the stemness-maintaining core transcription factor TP63. The invasive and migratory capacities of EVT were enhanced in HM, along with hyperactivation of the transcription factor MYCN and intensified crosstalk with the immune microenvironment. The SCT-Mature1 compartment displayed impaired maturation and downregulation of placenta-specific hormones, including PSG, CSH, and PAPPA. Machine learning analysis identified RASA1 as a novel key regulator characterized by its specific low expression in HM, which was further validated using hTSC (human trophoblast stem cell) to be involved in SCT differentiation. Potential therapeutic targets, such as MYCN and RASA1, and the diagnostic utility of monitoring placenta-specific hormone levels, are expected. Together, our findings establish a framework for understanding HM-specific placental dysfunction and developing future targeted diagnoses and therapies.
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Integrative multi-omics analysis of gastric cancer evolution from precancerous lesions to metastasis identifies a deep learning-based prognostic model.
BACKGROUND: Gastric cancer progression involves complex interactions among tumor cells, immune components, and stromal elements within the tumor microenvironment. However, a comprehensive understanding of cellular heterogeneity, spatial organization, and cell-cell communication in gastric cancer remains incomplete. METHODS: Single-cell RNA sequencing was performed on 252, 399 cells from six tissue types, spanning gastritis, intestinal metaplasia, primary tumors, adjacent normal tissue, and metastatic lesions. Integration with spatial transcriptomics enabled spatial mapping of cellular interactions. Pseudotime, cell-cell communication, and transcriptional heterogeneity analyses were conducted. Tumor stage-associated gene modules were identified using Weighted Gene Co-expression Network Analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Finally, a deep learning-based prognostic model was developed and externally validated. RESULTS: Our analysis revealed dynamic remodeling of the tumor microenvironment during gastric cancer progression, characterized by the expansion of dysfunctional CD8+ T cells, pro-tumorigenic fibroblasts (e.g., ITGBL1+, PI16+, and ITLN1+), and altered myeloid populations. Stromal-immune crosstalk, particularly fibroblast-driven immunosuppressive signaling, was prominent. Spatial transcriptomics revealed the colocalization of immune and stromal cells, supporting spatially organized cellular interactions. WGCNA identified a gene module (657 genes) associated with T cell, myeloid, and stromal alterations, as well as tumor stage. A deep learning model based on this gene set accurately stratified patients according to survival in both TCGA and independent validation cohorts. Risk scores were correlated with clinical features, including tumor stage and therapeutic response. CONCLUSIONS: Our integrative single-cell, spatial, and computational analysis provides a high-resolution map of gastric cancer microenvironment remodeling. We identified key stromal and immune subpopulations, extensive cellular communication networks, and spatial structures that collectively drive tumor progression and metastasis. The derived gene signature and prognostic model have the potential for clinical risk stratification and therapeutic targeting in gastric cancer.
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Single-cell RNA sequencing analysis of the temporomandibular joint condyle in 3 and 4-month-old human embryos.
BACKGROUND: The temporomandibular joint (TMJ) is a complex joint consisting of the condyle, the temporal articular surface, and the articular disc. Functions such as mastication, swallowing and articulation are accomplished by the movements of the TMJ. To date, the TMJ has been studied more extensively, but the types of TMJ cells, their differentiation, and their interrelationship during growth and development are still unclear and the study of the TMJ is limited. The aim of this study was to establish a molecular cellular atlas of the human embryonic temporomandibular joint condyle (TMJC) by single-cell RNA sequencing, which will contribute to understanding and solving clinical problems. RESULTS: Human embryos at 3 and 4 months of age are an important stage of TMJC development. We performed a comprehensive transcriptome analysis of TMJC tissue from human embryos at 3 and 4 months of age using single-cell RNA sequencing. A total of 16,624 cells were captured and the gene expression profiles of 15 cell clusters in human embryonic TMJC were determined, including 14 known cell types and one previously unknown cell type, "transition state cells (TSCs)". Immunofluorescence assays confirmed that TSCs are not the same cell cluster as mesenchymal stem cells (MSCs). Pseudotime trajectory and RNA velocity analysis revealed that MSCs transformed into TSCs, which further differentiated into osteoblasts, hypertrophic chondrocytes and tenocytes. In addition, chondrocytes (CYTL1high + THBS1high) from secondary cartilage were detected only in 4-month-old human embryonic TMJC. CONCLUSIONS: Our study provides an atlas of differentiation stages of human embryonic TMJC tissue cells, which will contribute to an in-depth understanding of the pathophysiology of the TMJC tissue repair process and ultimately help to solve clinical problems.
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Single-cell transcriptomics reveals cellular evolution underlying pleomorphic adenoma recurrence and malignant transformation.
Pleomorphic adenoma (PA), the most common benign salivary gland tumor, harbors unpredictable risks of recurrence and malignant transformation into carcinoma ex pleomorphic adenoma (CXPA), posing significant clinical challenges. To better delineate the tumor transformation trajectory, we performed single-cell RNA sequencing of normal salivary gland, primary PA, recurrent PA (rPA), and CXPA. Cell trajectory reconstruction, differential expression gene identification, and key gene network analysis were integrated to characterize molecular transitions and intercellular crosstalk driving PA recurrence and malignant transformation. Immunohistochemistry was used to validate key findings. GALNT13 + myoepithelial cells were identified as CXPA-specific malignant progenitors, delineating early malignant conversion. Concurrently, MIF + myoepithelial cells exhibited enhanced tissue-destructive capabilities. Fibroblasts enforced fibrotic restraint in primary PA and drove extracellular matrix degradation in CXPA. The tumor microenvironment exhibited stage-specific adaptations, with CXPA favoring pro-inflammatory MIF-CD74/CD44 signaling and rPA adopting immunosuppressive traits. Stromal reprogramming and immune-editing dynamics collectively orchestrated microenvironmental adaptation, linking cellular heterogeneity to clinical aggressiveness. This study provides the first comprehensive molecular atlas of PA-to-CXPA transformation, revealing malignant specialization of the myoepithelial subpopulation, fibroblast-mediated stromal reprogramming, and immune-editing driven microenvironmental adaptation. These findings provide a framework for precision stratification of the malignant potential of PA, while positioning microenvironmental intervention as a cornerstone of future clinical strategies.
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Unraveling the Cross-Tissue Neuroimmune-Vascular Genetic Architecture of Migraine Using Integrated Multi-Omics, Single-Cell, and Spatial Transcriptomics: Prioritizing T-Cell Regulatory Networks and Peripheral Targets.
Migraine is a complex neurovascular disorder in which immune signaling intersects with vascular and neural circuits, yet the tissue and cell-type context of common genetic risk remains incompletely defined. We integrated large-scale migraine genome-wide association study (GWAS) summary statistics with Genotype-Tissue Expression (GTEx) v8 expression and splicing quantitative trait loci (eQTLs and sQTLs), Bayesian co-localization, single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from migraine cases and controls, a healthy single-cell multi-omics atlas (assay for transposase-accessible chromatin (ATAC) plus RNA), high-dimensional weighted gene co-expression network analysis (hdWGCNA), and embryo-level spatial transcriptomics. Genetic signals were enriched in peripheral arteries, heart, and blood, and gene-level enrichment highlighted mucosal-smooth muscle organs including the bladder and the cervix endocervix. Cell-type prioritization consistently implicated endothelial and vascular smooth muscle lineages, with additional support for inhibitory interneurons and bladder epithelium. In PBMC T cells, co-expression modules capturing cytotoxic/activation and T-cell receptor signaling programs contained migraine-prioritized genes, including PTK2B, nominating immune activation circuitry as a component of genetic susceptibility. Spatial projection further localized risk concordance to craniofacial/meningeal interfaces and visceral smooth muscle-mucosal structures. Together, these analyses delineate a systemic neuroimmune-vascular architecture for migraine and provide genetically anchored candidate pathways and targets for mechanistic and therapeutic follow-up.
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Assessing the clinical use of clear cell renal cell carcinoma molecular subtypes identified by RNA expression analysis.
OBJECTIVES: To evaluate the clinical use of recently published RNA-based molecular subtyping algorithms. Patients who undergo surgery for clinically localized clear cell renal cell carcinoma can experience very different outcomes, representing a longstanding challenge for the practicing urologist. Two recent publications suggest that molecular subtyping based on the expression of large panels of genes can help clinically localized clear cell renal cell carcinoma prognostication; however, the analyses was not adjusted for routinely collected clinicopathologic indices. METHODS AND MATERIALS: We obtained level 3 RNA-seq RPKM data and corresponding clinicopathologic features from The Cancer Genome Atlas (TCGA) and assigned patients to the TCGA subtypes as well as to the ccA/ccB subtypes. To determine the prognostic ability of molecular subtyping after adjusting for variables that are collected as routine medical care, we used Cox models and adjusted for the composite Mayo stage, size, grade, and necrosis (SSIGN) score. RESULTS: Both the TCGA and the ccA/ccB subtypes are significantly associated with tumor size, category, grade, and presence of necrosis. The association of these subtypes with overall survival is markedly attenuated following adjustment for the composite Mayo SSIGN score. CONCLUSIONS: Both the TCGA and the ccA/ccB subtypes are associated with overall survival after adjusting for the Mayo SSIGN score. However, the effect sizes are similar to what has been reported for single markers, and thus the clinical use and cost-effectiveness of these RNA-based whole-genome signatures are questionable.
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PuMA: PubMed gene/cell type-relation Atlas.
BACKGROUND: Rapid extraction and visualization of cell-specific gene expression is important for automatic cell type annotation, e.g. in single cell analysis. There is an emerging field in which tools such as curated databases or machine learning methods are used to support cell type annotation. However, complementing approaches to efficiently incorporate the latest knowledge of free-text articles from literature databases, such as PubMed, are understudied. RESULTS: This work introduces the PubMed Gene/Cell type-Relation Atlas (PuMA) which provides a local, easy-to-use web-interface to facilitate literature-driven cell type annotation. It utilizes a pretrained machine learning based named entity recognition model in order to extract gene and cell type concepts from PubMed, links biomedical ontologies, and suggests gene to cell type relations based on a ranking score. It includes a search tool for genes and cell types, additionally providing an interactive graph visualization for exploring cross-relations. Each result is fully traceable by linking the relevant PubMed articles. CONCLUSIONS: This work enables researchers to analyse and automatize cell type annotation based on PubMed articles. It complements manual curated marker gene databases and enables interactive visualizations. The evaluation shows that PuMA is competitive against an extensive manual curated database across three gold standard datasets and two species-mouse and human. The software framework is freely available and enables regular article imports for incremental knowledge updates.GitLab: https://imigitlab.uni-muenster.de/published/PuMA/.
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Single-nucleus RNA-seq reveals no increase in T cells in Alzheimer's disease prefrontal cortex or hippocampus.
BACKGROUND: Alzheimer's disease (AD) has long been associated with hallmark protein aggregates, yet increasing evidence suggests immune involvement may contribute to its progression. Prior studies have found increased T cell presence in AD brain tissue, raising the possibility of neuroimmune crosstalk. METHODS: We used single-nucleus RNA sequencing data from the Religious Orders Study and Memory and Aging Project (ROSMAP), the largest available postmortem AD cohort, to investigate T cell dynamics in prefrontal cortex (PFC) and hippocampus. RESULTS: Contrary to prior findings, we observed no significant increase in T cell frequency in individuals with pathologically confirmed AD in either region. We replicated these findings in dorsolateral PFC (DLPFC) using the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD). Notably, although we confirmed a prior finding of T cell expansion in middle temporal gyrus (MTG), the strength of this association was affected by donor age. Additionally, we detected no change in gene expression in T cells in the brain parenchyma from individuals with AD. IMPACT: These results suggest that T cell enrichment in AD may be regionally restricted and not as widespread as previously assumed. Our findings underscore the importance of brain region selection, analytical approach, and dataset composition in interpreting immune cell dynamics in neurodegenerative disease.
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Single-Cell Transcriptomic Analysis of the Potential Mechanisms of Follicular Development in Stra8-Deficient Mice.
Follicle development is a critical process in mammalian reproduction, with significant implications for ovarian reserve and fertility. Stra8 is a known key factor regulating the initiation of meiosis; however, oocyte-like cells still appear in Stra8-deficient mice. Nevertheless, the underlying mechanism remains unclear and requires further investigation. Therefore, we used single-cell RNA sequencing to construct a comprehensive transcriptional atlas of ovarian cells from both wild-type and Stra8-deficient mice at embryonic stages E14.5 and E16.5. With stringent quality control, we obtained a total of 14,755 single cells of six major cell types. A further fine-scale analysis of the germ cell clusters revealed notable heterogeneity between wild-type and Stra8-deficient mice. Compared to the wild-type mice, the deficiency in Stra8 led to the downregulation of meiosis-related genes (e.g., Pigp, Tex12, and Sycp3), and the upregulation of apoptosis-related genes (e.g., Fos, Jun, and Actb), thereby hindering the meiotic process. Notably, we observed that, following Stra8 deficiency, the expression levels of Sub1 and Stk31 remained elevated at this stage. Furthermore, an RNA interference analysis confirmed the potential role of these genes as regulatory factors in the formation of primordial follicle-like cells. Additionally, Stra8 deficiency disrupted the signaling between germ cells and pregranulosa cells that is mediated by Mdk-Sdc1, leading to the abnormal expression of the PI3K/AKT signaling pathway. Together, these results shed light on the molecular processes governing germ cell differentiation and folliculogenesis, emphasizing the complex role of Stra8 in ovarian function.