Браузер не даёт сохранять данные (например, приватный режим): закладки пропадут после закрытия вкладки.
Поиск статей PubMed
Ключевые слова, автор, журнал, годы — результаты из PubMed, аннотации с переводом на русский, закладки в браузере.
Найдено: 4 925
Страница 183 из 493-
Single-Cell Gene Expression Analysis in Patients with Medullary Sponge Kidney and a Retrospective Study.
OBJECTIVE: To establish better diagnosis thinking and provide advanced understanding of MSK, the CT imaging features, clinical characteristics, and the expression of suspected genes in the kidney spatiotemporal immune zonation and fetal renal development were investigated. METHODS: 17 patients with MSK hospitalized in our hospital were selected as our research subjects. Human Phenotype Ontology, MalaCards: The Human Disease Database, GeneCards: The Human Gene Database, Human Protein Atlas, and Single Cell Expression Atlas were used to analyze this disease. RESULTS: In our 17 patients, the incidence of MSK tended to be the same in male and female, and the onset age of MSK was probably 31-50 years old. The top one related disease of MSK was nephrocalcinosis and the most frequent phenotype related to MSK was nephrolithiasis. In addition, the expression of HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 has been implicated in both human kidney immune zonation and fetal kidney development. CONCLUSIONS: HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 could be independent indicators for the diagnosis and preventive intervention of MSK patients, and abnormal kidney development due to mutations in key genes was the underlying cause of MSK.
-
Lactylation-driven gene signatures define breast cancer prognosis: a predictive model and insights into immune microenvironment dynamics.
BACKGROUND: Breast cancer is a leading cause of cancer-related mortality worldwide, with poor prognosis largely due to its invasive and metastatic nature. Tumor lactylation plays a crucial role in cancer progression by influencing immune modulation and metabolic reprogramming. This study aimed to identify lactylation-related gene signatures associated with breast cancer prognosis and develop a predictive survival model. METHODS: Bioinformatics analyses were performed using RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consensus clustering analysis was applied to classify breast cancer samples into distinct groups based on lactylation-related gene expression. Differentially expressed genes (DEGs) between clusters were identified and subjected to functional enrichment analysis. To assess immune-related differences between groups, the ESTIMATE and CIBERSORT algorithms were used, along with an analysis of human leukocyte antigen (HLA) and immune checkpoint molecule expression levels, to explore the relationship between lactylation and the breast cancer immune microenvironment. A prognostic model was constructed using univariate Cox and Lasso regression analyses, followed by validation. Machine learning techniques identified key biomarkers, which were further analyzed for clinical relevance. Additionally, single-cell clustering was performed to investigate the expression patterns of these genes within the breast cancer microenvironment. RESULTS: Consensus clustering identified two distinct groups: high and low lactylation. Differentially expressed genes were enriched in pathways related to cytokine-cytokine receptor interaction, immune response, cell activation, and adhesion. Lactylation-related genes were found to influence immune cell infiltration in the breast cancer microenvironment. Thirty-seven prognostic lactylation-related genes were identified through univariate Cox regression and used to develop a predictive model. The high-risk group was associated with poorer survival, and the model's performance was validated in the GEO cohort. Specific hub genes involved in immune modulation and malignant cell proliferation were also identified. CONCLUSION: We successfully developed a lactylation-based prognostic model that can assess breast cancer prognosis and potentially guide personalized treatment strategies.
-
Single-cell resolution landscape of equine peripheral blood mononuclear cells reveals diverse cell types including T-bet+ B cells.
BACKGROUND: Traditional laboratory model organisms represent a small fraction of the diversity of multicellular life, and findings in any given experimental model often do not translate to other species. Immunology research in non-traditional model organisms can be advantageous or even necessary, such as when studying host-pathogen interactions. However, such research presents multiple challenges, many stemming from an incomplete understanding of potentially species-specific immune cell types, frequencies, and phenotypes. Identifying and characterizing immune cells in such organisms is frequently limited by the availability of species-reactive immunophenotyping reagents for flow cytometry, and insufficient prior knowledge of cell type-defining markers. RESULTS: Here, we demonstrate the utility of single-cell RNA sequencing (scRNA-Seq) to characterize immune cells for which traditional experimental tools are limited. Specifically, we used scRNA-Seq to comprehensively define the cellular diversity of equine peripheral blood mononuclear cells (PBMC) from healthy horses across different breeds, ages, and sexes. We identified 30 cell type clusters partitioned into five major populations: monocytes/dendritic cells, B cells, CD3+PRF1+ lymphocytes, CD3+PRF1- lymphocytes, and basophils. Comparative analyses revealed many cell populations analogous to human PBMC, including transcriptionally heterogeneous monocytes and distinct dendritic cell subsets (cDC1, cDC2, plasmacytoid DC). Remarkably, we found that a majority of the equine peripheral B cell compartment is comprised of T-bet+ B cells, an immune cell subpopulation typically associated with chronic infection and inflammation in human and mouse. CONCLUSIONS: Taken together, our results demonstrate the potential of scRNA-Seq for cellular analyses in non-traditional model organisms and form the basis for an immune cell atlas of horse peripheral blood.
-
ACE2 and TMPRSS2 expression by clinical, HLA, immune, and microbial correlates across 34 human cancers and matched normal tissues: implications for SARS-CoV-2 COVID-19.
BACKGROUND: Pandemic COVID-19 by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) infection is facilitated by the ACE2 receptor and protease TMPRSS2. Modestly sized case series have described clinical factors associated with COVID-19, while ACE2 and TMPRSS2 expression analyses have been described in some cell types. Patients with cancer may have worse outcomes to COVID-19. METHODS: We performed an integrated study of ACE2 and TMPRSS2 gene expression across and within organ systems, by normal versus tumor, across several existing databases (The Cancer Genome Atlas, Census of Immune Single Cell Expression Atlas, The Human Cell Landscape, and more). We correlated gene expression with clinical factors (including but not limited to age, gender, race, body mass index, and smoking history), HLA genotype, immune gene expression patterns, cell subsets, and single-cell sequencing as well as commensal microbiome. RESULTS: Matched normal tissues generally display higher ACE2 and TMPRSS2 expression compared with cancer, with normal and tumor from digestive organs expressing the highest levels. No clinical factors were consistently identified to be significantly associated with gene expression levels though outlier organ systems were observed for some factors. Similarly, no HLA genotypes were consistently associated with gene expression levels. Strong correlations were observed between ACE2 expression levels and multiple immune gene signatures including interferon-stimulated genes and the T cell-inflamed phenotype as well as inverse associations with angiogenesis and transforming growth factor-β signatures. ACE2 positively correlated with macrophage subsets across tumor types. TMPRSS2 was less associated with immune gene expression but was strongly associated with epithelial cell abundance. Single-cell sequencing analysis across nine independent studies demonstrated little to no ACE2 or TMPRSS2 expression in lymphocytes or macrophages. ACE2 and TMPRSS2 gene expression associated with commensal microbiota in matched normal tissues particularly from colorectal cancers, with distinct bacterial populations showing strong associations. CONCLUSIONS: We performed a large-scale integration of ACE2 and TMPRSS2 gene expression across clinical, genetic, and microbiome domains. We identify novel associations with the microbiota and confirm host immunity associations with gene expression. We suggest caution in interpretation regarding genetic associations with ACE2 expression suggested from smaller case series.
-
Development and validation of a basement membrane-associated immune prognostic model for hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, has a poor prognosis primarily due to its invasive and metastatic characteristics. Cancer invasion through basement membrane (BM) is the pivotal initial step in tumor dissemination and metastasis. This study aimed to identify gene signatures associated with the BM to enhance the overall prognosis of HCC. METHODS: In this study, we performed multiple bioinformatics analyses based on the RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consistent cluster analysis was conducted on 370 HCC patients, categorizing them into two distinct groups based on the expression profiles of 222 BM-related genes. Differentially expressed genes (DEGs) between these clusters were identified, followed by functional enrichment analysis. To explore the differences between the groups, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms were applied, along with the analysis of immune checkpoint molecules and human leukocyte antigen (HLA) expression levels. This helped in understanding the relationship between the HCC immune microenvironment and BM-related genes. A prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, with its performance subsequently estimated and validated. Additionally, hub biomarkers genes were identified using machine learning techniques, followed by an analysis of their functions and relationships with clinical characteristics. Finally, single-cell clustering analysis was employed to further investigate the expression distribution of these genes within the HCC immune microenvironment. RESULTS: Following consistent cluster analysis, two groups were identified: the BM high group and the BM low group. Among the 6,221 DEGs between the two groups, 5,863 were upregulated and 358 were downregulated, with enrichment functions primarily associated with extracellular matrix (ECM) organization, cell adhesion, immune response, and metabolism. The expression levels of BM-related genes were found to regulate the HCC immune microenvironment. Using univariate Cox regression analysis, 60 prognostic BM-related genes were identified, leading to the establishment of an 11-gene prognostic model named BMscore to predict the overall survival (OS) of HCC patients. The high BMscore group indicated worse prognosis, and the model's predictive performance was validated using the GEO dataset. P3H1 and ADAMTS5 were identified as hub biomarkers, playing roles in cell proliferation and ECM metabolism, with their expression distributions mapped respectively. CONCLUSIONS: A prognostic model based on BM-related genes was successfully developed and shows promise for evaluating prognoses and offering personalized treatment recommendations.
-
Integrated Pan-Cancer Analysis and Experimental Verification of the Roles of Retinoid-Binding Proteins in Breast Cancer.
BACKGROUND: Retinoid-binding proteins (RBPs) regulate retinoid metabolism and signaling, but their roles across human cancers remain incompletely defined. METHODS: We conducted a comprehensive analysis using bioinformatics tools and experimental validations, examining RBP expression profiles across cancer types based on data from The Cancer Genome Atlas (TCGA). We employed survival analysis using the Kaplan-Meier method and utilized single-cell RNA sequencing (scRNA-seq) to investigate the roles of RBP4 and RBP7 in the tumor microenvironment. RESULTS: Our analysis revealed significant downregulation of RBPs in multiple cancers, with RBP4 and RBP7 showing notable expression variations linked to tumor stages and grades. Cox analysis identified RBP4 as a protective gene in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and mesothelioma (MESO), while RBP7 exhibited protective effects in breast cancer (BRCA) and uveal melanoma (UVM). CONCLUSIONS: This pan-cancer and single-cell integrative analysis highlights the complex roles of RBPs in cancer progression and their potential as prognostic biomarkers, particularly RBP4 and RBP7 in breast cancer. These findings warrant further investigation into the functional mechanisms of RBPs, which may provide valuable strategies for therapeutic interventions.
-
Longitudinal analysis of invariant natural killer T cell activation reveals a cMAF-associated transcriptional state of NKT10 cells.
Innate T cells, including CD1d-restricted invariant natural killer T (iNKT) cells, are characterized by their rapid activation in response to non-peptide antigens, such as lipids. While the transcriptional profiles of naive, effector, and memory adaptive T cells have been well studied, less is known about the transcriptional regulation of different iNKT cell activation states. Here, using single-cell RNA-sequencing, we performed longitudinal profiling of activated murine iNKT cells, generating a transcriptomic atlas of iNKT cell activation states. We found that transcriptional signatures of activation are highly conserved among heterogeneous iNKT cell populations, including NKT1, NKT2, and NKT17 subsets, and human iNKT cells. Strikingly, we found that regulatory iNKT cells, such as adipose iNKT cells, undergo blunted activation and display constitutive enrichment of memory-like cMAF+ and KLRG1+ populations. Moreover, we identify a conserved cMAF-associated transcriptional network among NKT10 cells, providing novel insights into the biology of regulatory and antigen-experienced iNKT cells.
-
ITGB5-mediated biomechanical regulation in pancreatic ductal adenocarcinoma stroma impacts tumor progression and prognosis.
BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by stromal remodelling andstiffening, a process involving extracellular matrix (ECM) depositionand cross-linking, which forms a dense physical barrier that limits the penetration of chemotherapeutic agents and restricts immune cell-mediated cytotoxicity. In addition, the tumor stroma modulates nutrients availability and fosters cancer cell invasion and metastasis. Therefore, therapeutic strategies aimed at modulating the tumor stroma hold promise for overcoming some of the major challenges in pancreatic cancer treatment. However, most clinical trials of stromal-targeted therapies have not demonstrated meaningful improvements in overall survival, underscoring an urgent need to identify novel stromal targets and approaches that modulate the tumor microenviroment in ways that enhance therapeutic response and improve patient outcomes. METHODS: Transcriptomic datasets of PDAC sampleswere obtained from The Cancer Genome Atlas (TCGA). By integrating immune infiltration algorithms with weighted gene co-expression network analysis (WGCNA), this study aims to identify hub genes associated with cancer-associated fibroblasts (CAFs) infiltration in PDAC, explore the prognostic relevance of CAF infiltration, and construct a gene‑expression-based prognostic mode. Furthermore, we will employ a multi-modal approach incorporating magnetic resonance elastography, atomic force microscopy, single-cell RNA sequencing (scRNA-seq), histological assays, cell-based functional experiments, and animal models to identify candidate core genes involved in CAF infiltration and stromal stiffening in pancreatic cancer, and to assess the potential therapeutic relevance of targeting these genes. RESULTS: In PDAC, increased stromal stiffness and poor prognosis are both strongly linked to activation of CAFs. WGCNA identified six hub genes-ANTXR2, ANXA1, BCAT1, PLAU, TGFβ1, and ITGB5-which were used to construct a predictive model for estimating CAF abundance and forecasting PDAC prognosis. Single-cell RNA sequencing analysis showed that ITGB5 is highly expressed in CAFs. Both in vitro and in vivo experiments demonstrated that targeting ITGB5 in CAFs reduces tumor stiffness, thereby inhibiting the malignant progression of PDAC. These findings suggest a potential therapeutic benefit, although impact on clinical prognosis remains to be established. CONCLUSION: The integration of magnetic resonance elastography, atomic force microscopy, and single-cell sequencing allows detailed research characterization of the biomechanical properties of PDAC. ITGB5 has been identified as an important regulator of the biomechanical features of PDAC. Targeting ITGB5 in CAFs may reduce the excessive stiffness of PDAC tissue and mitigate gemcitabine-associated stromal fibrosis, suggesting a potential strategy to improve treatment response. The effect on patient prognosis, however, requires confirmation in clinical studies. TRIAL REGISTRATION: This study is a prospective single-center clinical trial, which has been registered in the U.S. Clinical Trials Database ( https://clinicaltrials.gov/study/NCT06526442 ) with the trial registration number NCT06526442. The registration date was October 9th, 2020.
-
The developing mouse coronal suture at single-cell resolution.
Sutures separate the flat bones of the skull and enable coordinated growth of the brain and overlying cranium. The coronal suture is most commonly fused in monogenic craniosynostosis, yet the unique aspects of its development remain incompletely understood. To uncover the cellular diversity within the murine embryonic coronal suture, we generated single-cell transcriptomes and performed extensive expression validation. We find distinct pre-osteoblast signatures between the bone fronts and periosteum, a ligament-like population above the suture that persists into adulthood, and a chondrogenic-like population in the dura mater underlying the suture. Lineage tracing reveals an embryonic Six2+ osteoprogenitor population that contributes to the postnatal suture mesenchyme, with these progenitors being preferentially affected in a Twist1+/-; Tcf12+/- mouse model of Saethre-Chotzen Syndrome. This single-cell atlas provides a resource for understanding the development of the coronal suture and the mechanisms for its loss in craniosynostosis.
-
In Vitro and In Vivo Development of the Human Airway at Single-Cell Resolution.
Bud tip progenitor cells give rise to all murine lung epithelial lineages and have been described in the developing human lung; however, the mechanisms controlling human bud tip differentiation into specific lineages are unclear. Here, we used homogeneous human bud tip organoid cultures and identified SMAD signaling as a key regulator of the bud tip-to-airway transition. SMAD induction led to the differentiation of airway-like organoids possessing functional basal cells capable of clonal expansion and multilineage differentiation. To benchmark in vitro-derived organoids, we developed a single-cell mRNA sequencing atlas of the human lung from 11.5 to 21 weeks of development, which revealed high degrees of similarity between the in vitro-derived and in vivo airway. Together, this work sheds light on human airway differentiation in vitro and provides a single-cell atlas of the developing human lung.