Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 918
Page 157 of 492-
Omics integration identified CRIP2 as a key mediator of olaparib resistance in prostate cancer.
BACKGROUND: The mechanisms driving resistance to poly(ADP-ribose) polymerase inhibitors (PARPis), such as Olaparib, in castration-resistant prostate cancer (CRPC) that emerge after androgen deprivation therapy (ADT) remain largely undefined. This study aimed to systematically identify tumor epithelial cell subpopulations associated with Olaparib resistance by integrating single-cell transcriptomics, spatial transcriptomics (ST), and bulk RNA-sequencing (RNA-seq) data, and to construct a risk prognostic model. METHODS: Single-cell RNA-sequencing (scRNA-seq) data from six CRPC patients were subjected to quality control, clustering, annotation, and dimensionality reduction to identify distinct epithelial cell subpopulations. The Scissor algorithm, combined with Olaparib half-maximal inhibitory concentration (IC50) phenotype data from the Genomics of Drug Sensitivity in Cancer (GDSC) database, was employed to select the Scissor+ cell subpopulations significantly associated with drug resistance. The BayesPrism algorithm was then used to estimate the proportion of Scissor+ cells in the bulk RNA-seq samples, to explore their relationship with prognosis. ST data from the 10x Genomics platform were integrated to validate the spatial distribution of the Scissor+ cells in tissues. Additionally, bulk RNA-seq data from multiple cohorts [including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO)] were integrated to construct an olaparib resistance-associated gene (ORAG) risk model, which was optimized through various machine-learning algorithms. Finally, the core gene CRIP2 was validated through in vitro drug sensitivity assays to investigate its role in olaparib resistance. RESULTS: The Uniform Manifold Approximation and Projection (UMAP) analysis of the single-cell data revealed significant cellular heterogeneity in the CRPC tissues, and the Scissor algorithm identified the epithelial cell subpopulations (Scissor+) strongly linked to Olaparib resistance. The BayesPrism analysis showed that the increased proportion of Scissor+ cells in the bulk samples was significantly associated with a poor prognosis (P=0.04). ST further validated that this subpopulation was predominantly enriched in tumor regions, displaying a clear spatially specific expression pattern. In seven independent bulk RNA-seq cohorts, the ORAG model, which was built using least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF) algorithms, showed excellent prognostic predictive performance [with the highest concordance index (C-index) of 0.722]. CRIP2 was identified as a key risk gene, and found to be correlated with a worse survival prognosis, higher tumor stage (T stage), Gleason score, and prostate-specific antigen (PSA) levels. In vitro experiments confirmed that CRIP2 knockdown significantly increased the sensitivity of LNCaP and C4-2B cells to olaparib. CONCLUSIONS: This study was the first to systematically identify olaparib resistance-associated tumor epithelial cell subpopulations in CRPC by integrating single-cell, spatial, and bulk multi-omics data. The constructed ORAG risk-prediction model has great potential for clinical application. CRIP2, which was identified as a key regulatory factor of resistance, offers a novel strategy for personalized treatment and the study of resistance mechanisms in CRPC.
-
Single-cell analysis of tumor microenvironment immune homeostasis and identification of prognostic biomarkers in head and neck squamous cell carcinoma.
BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is the eighth most common cancer globally, characterized by its diverse primary sites and immunosuppressive tumor microenvironment (TME). The TME impairs anti-tumor immunity, with T cells playing a critical role. However, T cells often become "exhausted," diminishing their anti-tumor function. Understanding the dynamic changes within the HNSCC TME is crucial for identifying therapeutic targets. To delineate immune homeostasis within the HNSCC TME at single-cell resolution and to identify robust prognostic biomarkers for patient risk stratification. METHODS: This study utilized single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) to analyze the cellular composition and intercellular interactions in the HNSCC TME. We focused on the heterogeneity and differentiation trajectories of T cell subsets. Key genes involved in T-cell differentiation were identified and validated using data from The Cancer Genome Atlas-HNSCC (TCGA-HNSC) project. A prognostic risk model was constructed based on these genes. RESULTS: The study revealed that the differentiation trajectory of T cells from naïve to exhausted states is regulated by genes such as CCL5, FOXP3, and NKG7. Six key genes (SERPINH1, PLAU, INHBA, TNFRSF4, CXCL13, and STAG3) were identified as prognostic biomarkers. High-risk genes (SERPINH1, PLAU, INHBA) correlated with tumor invasiveness, while low-risk genes (TNFRSF4, CXCL13, STAG3) were associated with improved prognosis. The prognostic risk model, based on these genes, achieved an area under the curve (AUC) of 0.66 for predicting 3-year survival. CONCLUSIONS: This study provides valuable insights into the immune dynamics of HNSCC and identifies a prognostic risk model based on key immune-related genes, aiding in personalized treatment strategies. The findings offer a novel approach for precise prognostic evaluation and targeted therapy development in HNSCC, advancing clinical management and patient outcomes.
-
A human liver cell atlas reveals heterogeneity and epithelial progenitors.
The human liver is an essential multifunctional organ. The incidence of liver diseases is rising and there are limited treatment options. However, the cellular composition of the liver remains poorly understood. Here we performed single-cell RNA sequencing of about 10,000 cells from normal liver tissue from nine human donors to construct a human liver cell atlas. Our analysis identified previously unknown subtypes of endothelial cells, Kupffer cells, and hepatocytes, with transcriptome-wide zonation of some of these populations. We show that the EPCAM+ population is heterogeneous, comprising hepatocyte-biased and cholangiocyte populations as well as a TROP2int progenitor population with strong potential to form bipotent liver organoids. As a proof-of-principle, we used our atlas to unravel the phenotypic changes that occur in hepatocellular carcinoma cells and in human hepatocytes and liver endothelial cells engrafted into a mouse liver. Our human liver cell atlas provides a powerful resource to enable the discovery of previously unknown cell types in normal and diseased livers.
-
Single-Cell Atlas Reveals Fatty Acid Metabolites Regulate the Functional Heterogeneity of Mesenchymal Stem Cells.
Bone marrow mesenchymal stem cells (MSCs) are widely used clinically due to their versatile roles in multipotency, immunomodulation, and hematopoietic stem cell (HSC) niche function. However, cellular heterogeneity limits MSCs in the consistency and efficacy of their clinical applications. Metabolism regulates stem cell function and fate decision; however, how metabolites regulate the functional heterogeneity of MSCs remains elusive. Here, using single-cell RNA sequencing, we discovered that fatty acid pathways are involved in the regulation of lineage commitment and functional heterogeneity of MSCs. Functional assays showed that a fatty acid metabolite, butyrate, suppressed the self-renewal, adipogenesis, and osteogenesis differentiation potential of MSCs with increased apoptosis. Conversely, butyrate supplement significantly promoted HSC niche factor expression in MSCs, which suggests that butyrate supplement may provide a therapeutic approach to enhance their HSC niche function. Overall, our work demonstrates that metabolites are essential to regulate the functional heterogeneity of MSCs.
-
Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer.
BACKGROUND: Breast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression. METHODS: We obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival. RESULTS: The NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making. CONCLUSION: We identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.
-
Identification and genetic dissection of convergent persister cell states.
Persister cells, rare phenotypic variants that survive normally lethal levels of antibiotics, present a major barrier to clearing bacterial infections1. However, understanding the precise physiological state and genetic basis of persister formation has been a longstanding challenge. Here we generated a high-resolution single-cell2 RNA atlas of Escherichia coli growth transitions, which revealed that persisters from diverse genetic and physiological models converge to transcriptional states that are distinct from standard growth phases and instead exhibit a dominant signature of translational deficiency. We then used ultra-dense CRISPR interference3 to determine how every E. coli gene contributes to persister formation across genetic models. Among critical genes with large effects, we found lon, which encodes a highly conserved protease4, and yqgE, a poorly characterized gene whose product strongly modulates the duration of post-starvation dormancy and persistence. Our work reveals key physiologic and genetic factors that underlie starvation-triggered persistence, a critical step towards targeting persisters in recalcitrant bacterial infections.
-
Comprehensive analysis to develop a stromal senescence-associated gene signature for predicting hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) continues to pose a major health concern for global public health, characterized by pronounced clinical heterogeneity in survival outcomes and therapeutic efficacies. The stromal senescence constitutes a major impediment in constructing robust prognostic models. This study aimed to develop and validate a robust prognostic prediction model to accurately assess the prognostic risk of HCC patients and facilitate personalized precision treatment. METHODS: We obtained data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. Area Under the Curve Cell (AUCell) was used to quantify senescence in single-cell RNA sequencing (scRNA-seq) of HCC. A stromal-senescence prognostic signature was then built by using Cox and least absolute shrinkage and selection operator (LASSO) analyses. High- and low-risk stratification was interrogated with clusterProfiler, maftools, immune-checkpoint analysis, and oncoPredict. Signature expression was cross-validated in scRNA-seq, bulk RNA sequencing (RNA-seq), proteomic, and spatial transcriptomic datasets. Small-molecule ligands targeting DNASE1L3 were identified by virtual screening, and their binding was verified by molecular docking and dynamics simulations. RESULTS: It was found that HCC-associated stromal cells exhibited the most pronounced levels of senescence among cell populations. Using machine-learning algorithms, we successfully established DNASE1L3, NDRG2, ADAM15, IGFBP3, MMP14, and ANGPT2 as a stromal-senescence prognostic signature in HCC. Subsequent comparisons between patient subgroups based on this signature revealed distinct differences in long-term survival outcomes, mutational landscapes, immune checkpoint expression, and responsiveness to chemotherapeutic agents. Further, the stromal-senescence prognostic signature is significantly associated with metabolic reprogramming, immune inflammation, cell proliferation, and metastasis. Multi-omics data analyses were employed to validate the expression levels of this signature in both tumor and normal tissues. In addition, virtual screening identified three potential DNASE1L3 candidates: STL167676, STK598132, and STK119225. CONCLUSIONS: This study developed a robust stromal senescence-associated gene signature that accurately predicts survival outcomes in HCC patients and identified candidate stromal senescence-related molecules for more effective personalized therapy.
-
5 signature genes revealed by single-cell profiling identified unique immune subtypes affecting the prognosis of ovarian cancer.
OBJECTIVE: Ovarian cancer (OC) ranks among the most prevalent gynecological malignancies, with surgery, chemotherapy, and immunotherapy constituting primary treatment modalities. However, despite advancements, immunotherapy, particularly immune checkpoint inhibitors, has yielded suboptimal outcomes. The pressing need to identify biomarkers predictive of clinical prognosis underscores our objective. We aim to discern gene signatures and establish prognostic subgroups, specifically in the context of immunotherapy and chemotherapy, guiding clinical decision-making. MATERIALS AND METHODS: We used the Tumor Immunotherapy Gene Expression Resource (TIGER) and The Cancer Genome Atlas (TCGA) databases to extract signature genes of prognostic significance. Unsupervised consensus clustering was employed to classify patients based on these signature genes. The Tumor Immune Estimation Resource (TIMER) database, along with the R packages "maftools" and "ESTIMATE" facilitated immune infiltration estimation. Gene set variation analysis (GSVA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were implemented to probe immune-related cell signaling pathways among distinct subtypes. The Tumor Immune Dysfunction and Exclusion (TIDE) database was used to assess immunotherapy effects, while the R package "OncoPredict" evaluated drug sensitivity differences among subtypes. RESULTS: We identified five prognostically influential genes in ovarian cancer: IGFBP7, JCHAIN, CCDC80, VSIG4, and MS4A1. Utilizing these signature genes, we categorized TCGA-OV patients into five clusters, each associated with varying clinical prognoses. Notably, 2 clusters exhibited superior prognoses, accompanied by enhanced immune cell infiltration. KEGG enrichment analysis revealed their heightened enrichment in cellular immunity and immune cell interaction pathways. Given the elevated expression levels of multiple immune checkpoint molecules, these clusters may substantially benefit from immune checkpoint inhibitor therapy. Additionally, chemotherapy sensitivity analysis indicated their favorable responses to first or second-line chemotherapy regimens. CONCLUSIONS: We subclustered ovarian cancer patients by 5 signature genes obtained from the Single-cell RNA sequencing (scRNA-seq) dataset, which demonstrated a good typing effect. Patients in the two molecular subtypes showed better survival, higher immune cell infiltration, and higher drug sensitivity. This meticulous typing may help clinicians to quickly assess the prognosis of patients and the response to immunotherapy and chemotherapy.
-
Construction and Validation of a Prognostic Model of Metabolism-Related Genes Driven by Somatic Mutation in Bladder Cancer.
BACKGROUND: Metabolic reprogramming is an important player in the prognosis of cancer patients. However, metabolism-related genes (MRGs) that are essential to the prognosis of bladder cancer (BLCA) are nor yet fully understood. The purpose of this study is to use bioinformatics methods to establish prognostic models based on MRGs in BLCA to screen potential biomarkers. METHODS: Based on the transcriptomic data from BLCA patients in The Cancer Genome Atlas Program (TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed genes related to metabolism and analyzed the functional enrichment by edgeR package. A prognostic model was generated using univariate Cox regression analysis and validated using GEO dataset. The prognostic risk model was analyzed by the Kaplan-Meier curve. The single cell RNA sequencing (scRNA-seq) revealed the gene interaction networks and traced the development trajectories of distinct cell lineages. The levels of key metabolism-related biomarkers in vitro were verified by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: We screened 201 differentially expressed metabolism-related genes (DEMRGs), which were significantly enriched in oxidative phosphorylation. The risk model was constructed by 5 biomarkers. qRT-PCR analysis verified that there is a significant higher expression of FASN and MTHFD1L in carcinoma tissue. CONCLUSIONS: This study constructed a novel prognostic model based on a combination of clinical and molecular factors that related to metabolic reprogramming, which has the potential to improve the prediction of independent prognosis indicators and management of BLCA patients, leading to better treatment outcomes and survival rates.
-
Heterogeneity of active mast cells, endothelial cells, and fibroblasts in hemophilic arthritis defined by synovial single-cell sequencing.
Recurrent synovial bleeding is the major cause of Hemophilic Arthritis (HA). Up to now, the transcriptomic profiles of bleeding syvonium in HA patient have largely remained unknown. Exploring the transcriptomic characteristics of synovium obtained from HA patients undergoing total knee arthroplasty (TKA), and uncovering potential pathological mechanisms of HA synovium through bioinformatics analysis and differential analysis. Single-cell RNA sequencing (scRNA-seq) technology was utilized to identify distinct cellular subsets within HA synovium. Comparative analysis was conducted with scRNA-seq data from osteoarthritis (OA) and rheumatoid arthritis (RA) synovium to assess transcriptional differences. Histological evaluation, immunofluorescence (IF), immunohistochemistry (IHC) and in vitro cell testing were performed for validation. We observed that the single-cell transcriptomic characteristics of HA synovium differ significantly from those of OA and RA patients. Mast cells, identified as unique immune cells in HA synovium, are actively involved and may be the initiating factor for changes in vascular permeability or bleeding in HA synovium. The transcriptomic features of endothelial cells and fibroblasts, which are non-immune stromal cells, reveal the synovial microenvironment: repeated bleeding leads to iron deposition, which causes substantial ferroptosis within the synovium. Based on the pseudotime analysis of endothelial cells, along with IHC staining and in vitro cell assays, it was demonstrated that ferroptosis in endothelial cells induces vascular damage and triggers a significant migration of endothelial cells. The stress of ferroptosis leads to the differentiation of HMOX1+ endothelial cells. Cell interaction analysis shows that HMOX1+ endothelial cells can induce the SCARA5+ fibroblast subtypes via FTL/SCARA5. This may be a critical target for alleviating cellular ferroptosis in HA synovium. This study presents the first single-cell transcriptomic atlas of hemophilic arthropathy (HA) synovium, identifying activated perivascular mast cells and a novel SCARA5 + fibroblast subpopulation that regulates ferroptosis. These findings reveal a unique cellular interactome in HA and propose new potential targets for gene therapy aimed at recurrent bleeding.