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: 4 918
Страница 155 из 492-
Development of a prognostic model to predict BLCA based on anoikis-related gene signature: preliminary findings.
BACKGROUND: The prevalence of bladder urothelial carcinoma (BLCA) is significant on a global scale. Anoikis is a type of procedural cell death that has an important role in tumor invasion and metastasis. The advent of single-cell RNA sequencing (scRNA-seq) approaches has revolutionized the genomics field by providing unprecedented opportunities for elucidating cellular heterogeneity. Understanding the mechanisms associated with anoikis in BLCA is essential to improve its survival rate. METHODS: Data on BLCA and clinical information were acquired from the databases of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). ARGs were obtained from Genecards and Harmonizome databases. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). A multivariate Cox regression analysis was carried out to identify eight prognostic ARGs, leading to the establishment of a risk model. The OS rate of BLCA patients was evaluated using Kaplan-Meier survival analysis. To explore the molecular mechanism in low- and high-risk groups, we employed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSVA). Immune infiltration landscape estimation was performed using ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms. Patients were categorized into different subgroups through consensus clustering analysis. We employed biological functional enrichment analysis and conducted immune infiltration analysis to examine the disparities in potential biological functions, infiltration of immune cells, immune activities, and responses to immunotherapy. RESULTS: We identified 647 ARGs and 37 survival-related genes. We further developed a risk scoring model to quantitatively assess the predictive capacity of ARGs. The high-risk score group exhibited an unfavorable prognosis, whereas the low-risk score group demonstrated a converse effect. We also found that the two groups of patients might respond differently to immune targets and anti-tumor drugs. CONCLUSION: The nomogram with 8 ARGs may help guide treatment of BLCA. The systematic assessment of risk scores can help to design more individualized and precise treatment strategies for BLCA patients.
-
Advances in Single-Cell Transcriptomics for Livestock Health.
RNA sequencing (scRNA-seq) has emerged as a transformative technology for dissecting cellular heterogeneity and immune complexity in livestock species. This review summarizes recent advances in the application of single-cell transcriptomics to livestock health, with a particular focus on immune system organization and host-pathogen interactions in cattle, pigs, poultry, and small ruminants. We highlight the development of large-scale, multi-tissue cell atlases-such as the Cattle Cell Atlas and resources generated through the Farm Animal Genotype-Tissue Expression (FarmGTEx) consortium-that provide foundational reference frameworks for livestock genomics. These atlases have enabled the identification of tissue- and species-specific immune cell populations, clarified cellular tropism of major bacterial and viral pathogens, and revealed distinctive immunological features, including the prominent role of γδ T cells in ruminant immunity. We discuss how single-cell immune receptor sequencing has advanced monoclonal antibody discovery and informed rational vaccine design. Key technical and analytical challenges, including incomplete genome annotations, tissue processing constraints, and cross-platform data integration, are critically assessed. Finally, we outline future directions integrating spatial transcriptomics and multi-omics approaches to further resolve immune function within tissue contexts. Collectively, these advances position single-cell transcriptomics as a central framework for improving disease resistance, vaccine efficacy, and translational research in livestock health.
-
A Single-Cell Atlas of Porcine Skeletal Muscle Reveals Mechanisms That Regulate Intramuscular Adipogenesis.
Porcine skeletal muscle development is closely linked to meat production efficiency and quality. The accumulation of porcine intramuscular fat is influenced by the hyperplasia and hypertrophy of adipocytes within the muscle. However, the cellular profiles corresponding to the two stages of muscle development remain undetermined. Single-nucleus RNA sequencing (snRNA-seq) can elucidate cell subsets in tissues, capture gene expression at the individual cell level, and provide innovative perspectives for studying muscle and intramuscular fat formation. In this study, a total of 78,302 nuclei and 9 clusters of cells, which included fibro/adipogenic progenitor (FAP), myonuclei, adipocytes, and other cell types, of Xidu black pigs, were identified on Day 1 and Day 180. The pattern of cell clustering varied between the two developmental stages. Notably, the percentage of adipocytes in the Day 180 group was higher than in the Day 1 group (0.51% vs. 0.15%). Pseudo-time sequence analysis indicated that FAPs could differentiate into adipocytes and myonuclei cells, respectively. The THRSP gene was identified as a biomarker for swine intramuscular fat cells, and its down-regulation resulted in significant reduction in lipid droplet formation in porcine preadipocytes. Our research provides new insights into the cellular characteristics of intramuscular fat formation, which may facilitate the development of novel strategies to enhance intramuscular fat deposition and improve pork quality.
-
The potential of MMP14 as a prognostic and immune biomarker in lung cancer.
BACKGROUND: Precision therapy and prognostic assessment are crucial for improving the quality of life of patients with lung cancer. While NSUN2-mediated m⁵C RNA methylation is involved in multiple malignancies, the role of its downstream target MMP14 in lung adenocarcinoma remains undefined. METHODS: Using the A549 cell line as the research object, we established a stable NSUN2 knockdown model. Differentially expressed gene MMP14 was identified through RNA-seq, and its expression was validated by quantitative real-time polymerase chain reaction (RT-qPCR). Combined with The Cancer Genome Atlas (TCGA) and multi-database integrated analysis, we thoroughly elucidated the molecular regulatory mechanisms (focusing on RNA methylation-related pathways) and clinical value of MMP14 in LUAD. RESULTS: Our findings demonstrate that MMP14 is overexpressed in LUAD and multiple other malignancies, and that its elevated expression is significantly associated with poor prognosis and advanced tumor stage. In LUAD, MMP14 RNA methylation levels are positively correlated with mRNA expression, while DNA promoter methylation is closely linked to tumor progression and lymph node metastasis. MMP14 expression is regulated by NSUN2, a key m⁵C methyltransferase, and functionally participates in diverse tumor-associated biological processes. Notably, MMP14 expression correlates with immune cell infiltration, including macrophages and T cells, as well as with immune checkpoint molecules such as PD-1 and PD-L1, highlighting its potential role in shaping the tumor immune microenvironment. CONCLUSION: MMP14, a key NSUN2-regulated molecule in LUAD, represents a potential target for precision diagnosis and therapy and may provide new insights into improving overall survival in LUAD.
-
Discovery of optimal cell type classification marker genes from single cell RNA sequencing data.
BACKGROUND: The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. METHODS: NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. RESULTS: By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions in simulation studies and on its ability to identify markers with higher On-Target Fraction values for closely related cell types in real data, and outperforms other marker gene selection approaches for cell type classification with significantly higher F-beta scores when applied to datasets from three human organs-brain, kidney, and lung. DISCUSSION: Finally, we discuss potential use cases of the NS-Forest marker genes, including for designing spatial transcriptomics gene panels and semantic representation of cell types in biomedical ontologies, for the broad user community.
-
A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer.
BACKGROUND: Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics. METHODS: All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments. RESULTS: Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies. CONCLUSIONS: We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.
-
Identification of Genes Related to 5-Fluorouracil Based Chemotherapy for Colorectal Cancer.
BACKGROUND: Colorectal cancer (CRC) is one of the most common malignancies and its incidence and mortality are increasing yearly. 5-Fluorouracil (5-FU) has long been used as a standard first-line treatment for CRC patients. Although 5-FU-based chemotherapy is effective for advanced CRC, the consequent resistance remains a key problem and causes the poor prognosis of CRC patients. Thus, there is an urgent need to identify new biomarkers to predict the response to 5-FU-based chemotherapy. METHODS: CRC samples were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). The immune-related genes were retrieved from the ImmPort database. Single-cell sequencing results from colorectal cancer were obtained by the ArrayExpress database. 5-FU resistance-related genes were filtered and validated by R packages. ESTIMATE algorithms were used to assess the tumor microenvironment (TME). KEGG and GO analysis were performed to explore the biological signaling pathway for resistant-response patients and sensitive-response patients in the tumor microenvironment. pRRophetic algorithms were used to predict 5-FU sensitivity. GSEA and GSVA analysis was performed to excavate the biological signaling pathway of the RBP7 gene. RESULTS: Nine immune-related genes were identified to be associated with 5-FU resistance and poor disease-free survival (DFS) of CRC patients and the signature of these genes was developed in a DFS-prognostic model. Four immune-related genes were determined to be associated with 5-FU resistance and overall survival (OS) of CRC patients. The signature of these genes was developed an OS-prognostic model. ESTIMATE scores showed a significant difference between 5-FU resistant and 5-FU sensitive CRC patients. Resistant-response patients and sensitive-response patients to 5-FU based chemotherapy showed different GO and KEGG enrichment on the tumor microenvironment. RBP7, as a tumor immune microenvironment (TIME) related gene, was found to have the potential of predicting chemotherapy resistance and poor prognosis of CRC patients. GSEA analysis showed multiple signaling differences between the high and low expression of RBP7 in CRC patients. Hypoxia and TNFα signaling via NFκB gene sets were significantly different between chemotherapy resistant (RBP7High) and chemotherapy sensitive (RBP7Low) patients. Single-cell RNA-seq suggested RBP7 was centrally distributed in endothelial stalk cells, endothelial tip cells, and myeloid cells. CONCLUSIONS: Immune-related genes will hopefully be potential prognostic biomarkers to predict chemotherapy resistance for CRC. RBP7 may function as a tumor microenvironment regulator to induce 5-FU resistance, thereby affecting the prognosis of CRC patients.
-
A comprehensive molecular atlas of the mesenchymal cell types in the mouse liver.
The liver plays crucial roles in many essential physiological processes, and its impaired function due to liver fibrosis from various causes is an increasingly significant health issue. The liver's functionality relies on the precise arrangement of its cellular structures, yet the molecular architecture of these units remains only partially understood. We created a comprehensive molecular atlas detailing the major cell types present in the adult mouse liver through deep single-cell RNA sequencing. Our analysis offers new insights into hepatic endothelial and mesenchymal cells, specifically highlighting the diversity of cells in the periportal microvasculature, the sinusoids, and the portal vein, the latter exhibiting a mixed arterio-venous phenotype. We identified distinct subpopulations of hepatic stellate cells, fibroblasts, and vascular mural cells located in different anatomical liver regions. Comparisons with transcriptomic data from disease models indicate that a previously unrecognized capsular population of hepatic stellate cells expands in response to fibrotic disease. Our findings reveal that various fibroblast subpopulations respond differently to pathological insults. This data resource will be relevant for the advancement of therapies targeting hepatic diseases.
-
Urinary EGF and MCP-1 and risk of CKD after cardiac surgery.
BACKGROUNDAssessment of chronic kidney disease (CKD) risk after acute kidney injury (AKI) is based on limited markers primarily reflecting glomerular function. We evaluated markers of cell integrity (EGF) and inflammation (monocyte chemoattractant protein-1, MCP-1) for predicting long-term kidney outcomes after cardiac surgery.METHODSWe measured EGF and MCP-1 in postoperative urine samples from 865 adults who underwent cardiac surgery at 2 sites in Canada and the United States and assessed EGF and MCP-1's associations with the composite outcome of CKD incidence or progression. We used single-cell RNA-Seq (scRNA-Seq) of AKI patient biopsies to perform transcriptomic analysis of programs corregulated with the associated genes.RESULTSOver a median (IQR) follow-up of 5.8 (4.2-7.1) years, 266 (30.8%) patients developed the composite CKD outcome. Postoperatively, higher levels of urinary EGF were protective and higher levels of MCP-1 were associated with the composite CKD outcome (adjusted HR 0.83, 95% CI 0.73-0.95 and 1.10, 95% CI 1.00-1.21, respectively). Intrarenal scRNA-Seq transcriptomes in patients with AKI-defined cell populations revealed concordant changes in EGF and MCP-1 levels and underlying molecular processes associated with loss of EGF expression and gain of CCL2 (encoding MCP-1) expression.CONCLUSIONUrinary EGF and MCP-1 were each independently associated with CKD after cardiac surgery. These markers may serve as noninvasive indicators of tubular damage, supported by tissue transcriptomes, and provide an opportunity for novel interventions in cardiac surgery.TRIAL REGISTRATIONClinicalTrials.gov NCT00774137.FUNDINGThe NIH funded the TRIBE-AKI Consortium and Kidney Precision Medicine Project. Yale O'Brien Kidney Center, American Heart Association, Patterson Trust Fund, Dr. Adam Linton Chair in Kidney Health Analytics, Canadian Institutes of Health Research, ICES, Ontario Ministry of Health and Long-Term Care, Academic Medical Organization of Southwestern Ontario, Schulich School of Medicine & Dentistry, Western University, Lawson Health Research Institute, Chan Zuckerberg Initiative Human Cell Atlas Kidney Seed Network.
-
MCRS1 is associated with immunosuppressive microenvironments in pan-cancer and promotes hepatocellular carcinoma malignant phenotypes.
BACKGROUND: Pan-cancer analyses focused on the immunological significance and therapeutic potential of microspherule protein 1 (MCRS1) remain unreported. This study aims to define the pan-cancer immunological significance and therapeutic potential of MCRS1, with focused mechanistic dissection of its epigenetic-driven roles in hepatocellular carcinoma (HCC) progression. METHODS: An integrated multi-omics approach was employed, encompassing bulk transcriptomics, single-cell RNA-sequencing (scRNA-seq), and functional validation. Data from public repositories including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Human Protein Atlas (HPA) were analyzed using tools such as SangerBox, University of Alabama at Birmingham Cancer (UALCAN), Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier plotter, and Tumor Immune Estimation Resource (TIMER). Expression patterns, prognostic significance, epigenetic regulation, interactions with immune infiltrates, and functional impact of MCRS1 on HCC malignant phenotypes were comprehensively assessed. RESULTS: MCRS1 was upregulated in 24 malignancies (including HCC) and correlated with advanced stage, poor differentiation, and reduced survival (P<0.001). Hypomethylation of the MCRS1 promoter drove its overexpression in HCC, strongly associating with tumor progression. MCRS1 recruited M2-polarized macrophages (Rho =0.423, P=1.90e-16) and myeloid dendritic cells (Rho =0.560, P=7.87e-30). Spatial mapping confirmed MCRS1+/CD68+ macrophage colocalization in tumor niches. MCRS1 knockdown suppressed HCC proliferation, migration, and invasion. scRNA-seq revealed MCRS1 enrichment in immunosuppressive clusters expressing VEGFA/TGFB1. CONCLUSIONS: Our pan-cancer analysis identifies MCRS1 as a key node linking epigenetic dysregulation and immunosuppression in HCC. Its promoter hypomethylation-driven overexpression is associated with an M2 macrophage-polarized, immune-resistant niche. These findings suggest that targeting MCRS1 may represent a strategy to overcome resistance to current immunotherapies.