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 177 of 492-
Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma.
Background: Up frameshift protein 1 (UPF1) is a key component of nonsense-mediated mRNA decay (NMD) of mRNA containing premature termination codons (PTCs). The dysregulation of UPF1 has been reported in various cancers. However, the expression profile of UPF1 and its clinical significance in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: In order to detect UPF1 expression in ccRCC and its relationship with the clinical features of ccRCC, bulk RNA sequencing data were analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. The impact of UPF1 on the immune microenvironment of ccRCC was evaluated by multiple immune scoring algorithms to identify the cell groups that typically express UPF1 using ccRCC single cell sequencing (scRNA) data. In addition, genes co-expressed with UPF1 were identified by the weighted gene correlation network analysis (WGCNA), followed by KEGG and Reactome enrichment analysis. A series of functional experiments were performed to assess the roles of UPF1 in renal cancer cells. Finally, pan-cancer analysis of UPF1 was also performed. Results: Compared with normal tissues, the expression levels of UPF1 mRNA and protein in tumor tissues of ccRCC patients decreased significantly. In addition, patients with low expression of UPF1 had a worse prognosis. Analysis of the immune microenvironment indicated that UPF1 immune cell infiltration was closely related and the ccRCC scRNA-seq data identified that UPF1 was mainly expressed in macrophages. WGCNA analysis suggested that the functions of co-expressed genes are mainly enriched in cell proliferation and cellular processes. Experimental tests showed that knockdown of UPF1 can promote the invasion, migration and proliferation of ccRCC cells. Lastly, pan-cancer analysis revealed that UPF1 disorders were closely associated with various cancer outcomes. Conclusions: UPF1 may play a tumor suppressive role in ccRCC and modulate the immune microenvironment. The loss of UPF1 can predict the prognosis of ccRCC, making it a promising biomarker and providing a new reference for prevention and treatment.
-
StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases.
Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.
-
Single-Nucleus Transcriptomics Reveals Glial Metabolic-Immune Rewiring and Intercellular Signaling Disruption in Chronic Migraine.
Chronic migraine (CM) is a debilitating neurological disorder, yet the glial-specific mechanisms underlying its pathophysiology in the trigeminal nucleus caudalis (TNC)-a critical hub for craniofacial pain processing-remain poorly understood. Here, we employed single-nucleus RNA sequencing (snRNA-seq) to resolve cell-type-specific transcriptional landscapes in a nitroglycerin (NTG)-induced CM rat model, with a particular focus on microglia and astrocytes. We identified 19 transcriptional clusters representing nine major cell types, among which reactive microglia (NTG-Mic) and astrocytes (NTG-Asts) were markedly expanded. The NTG-Mic displayed a glycolysis-dominant, complement-enriched state, whereas the NTG-Asts exhibited concurrent activation of amino acid transport and cytokine signaling pathways. Pseudotime trajectory analysis revealed bifurcated glial activation paths, with NTG driving both cell types toward terminal reactive states. Intercellular communication inference uncovered suppressed homeostatic interactions (e.g., CSF1-CSF1R) alongside enhanced proinflammatory signaling (e.g., FGF1-FGFR2, PTN-SDC4), particularly affecting neuron-glia and glia-glia crosstalk. Together, these findings define a high-resolution atlas of glial reprogramming in CM, implicating state-specific metabolic-immune transitions and dysregulated glial communication as potential targets for therapeutic intervention.
-
Neurotransmitter receptor-related gene signature as potential prognostic and therapeutic biomarkers in colorectal cancer.
Background: Colorectal cancer is one of the most common malignant tumors worldwide. A various of neurotransmitter receptors have been found to be expressed in tumor cells, and the activation of these receptors may promote tumor growth and metastasis. This study aimed to construct a novel neurotransmitter receptor-related genes signature to predict the survival, immune microenvironment, and treatment response of colorectal cancer patients. Methods: RNA-seq and clinical data of colorectal cancer from The Cancer Genome Atlas database and Gene Expression Omnibus were downloaded. Neurotransmitter receptor-related gene were collected from publicly available data sources. The Weighted Gene Coexpression Network Analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms were employed to construct the Neurotransmitter receptor-related gene prognostic signature. Further analyses, functional enrichment, CIBERSORTx, The Tumor Immune Single Cell Center (TISCH), survival analysis, and CellMiner, were performed to analyze immune status and treatment responses. Quantitative real-time polymerase chain reaction (qRT-PCR) assays were carried out to confirm the expression levels of prognostic genes. Results: By combining machine learning algorithm and WGCNA, we identified CHRNA3, GABRD, GRIK3, and GRIK5 as Neurotransmitter receptor-related prognostic genes signature. Functional enrichment analyses showed that these genes were enriched with cellular metabolic-related pathways, such as organic acid, inorganic acid, and lipid metabolism. CIBERSORTx and Single cell analysis showed that the high expression of genes were positively correlated with immunosuppressive cells infiltration, and the genes were mainly expressed in cancer-associated fibroblasts and endothelial cells. A nomogram was further built to predict overall survival (OS). The expression of CHRNA3, GABRD, GRIK3, and GRIK5 in cancer cells significantly impacted their response to chemotherapy. Conclusion: A neurotransmitter receptor-related prognostic gene signature was developed and validated in the current study, giving novel sights of neurotransmitter in predicting the prognostic and improving the treatment of CRC.
-
TRIM27 is an adverse prognostic biomarker and associated with immune and molecular profiles in right-sided colon cancer.
Right-sided colon cancer (RCC), as an independent tumor entity, shows a poor prognosis. It is imperative to detect immune microenvironment-related genes for predicting RCC patient prognosis and study their function in RCC. Tripartite motif-containing 27 (TRIM27) was identified as a risk signature from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets by using weighted gene co-expression network analysis, differentially expressed analysis, and univariate Cox analysis. It predicted a poorer overall survival and increased lymph node metastasis, which were then validated in our 48 clinical samples. Using immunohistochemistry, TRIM27 was found to be highly expressed in both cancer cells and surrounding immunocytes, and its expression in tumor or immune cells both predicted a poorer prognosis. Thereafter, the functional mechanism, immune and molecular characteristics of TRIM27 were investigated using gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and gene set variation analysis (GSVA) at the single-cell, somatic mutation, and RNA-seq level. Patients with highly expressed TRIM27 presented lower CD4+ T cell infiltration and activation of the mTORC1/glycolysis pathway. In addition, patients with highly expressed TRIM27 were characterized by hypermetabolism, higher tumor purity, more BRAF mutation, and more chromosomal instability. Collectively, TRIM27 is an important immune-related prognostic biomarker in patients with RCC. It may function via activating the mTORC1/glycolysis pathway and suppressing CD4+ T cells. These results indicated that TRIM27 could be a promising therapeutic target in RCC.
-
Decoding tumor heterogeneity: A spatially informed pan-cancer analysis of the tumor microenvironment.
Pan-cancer single-cell atlases explore the heterogeneity of cell types residing within the tumor microenvironment (TME). So far, atlases focused on individual cell types, failing to capture the full complexity of the TME. Here, we present a single-cell atlas that simultaneously considers heterogeneity in 5 cell types, collected from 230 treatment-naive samples across 9 cancer types. We identify 70 pan-cancer single-cell subtypes, investigate their patterns of co-occurrence and show an enrichment of specific subtypes in certain TMEs, e.g., immune-reactive versus immune-suppressive TME. We observe two TME hubs of strongly co-occurring subtypes: one hub resembling tertiary lymphoid structures (TLSs), another consisting of immune-reactive PD1+/PD-L1+ immune-regulatory T cells and B cells, dendritic cells and inflammatory macrophages. Subtypes belonging to each hub are spatially co-localized, while their abundance associates with early and long-term checkpoint immunotherapy response. We publicly share our atlas using a Shiny app, allowing others to explore TME heterogeneity in different biological contexts.
-
Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses.
BACKGROUND: Laryngeal cancer (LCA) is the second most common type of head and neck malignancy, characterized by high recurrence rates and poor overall survival (OS). However, progress in curing LCA through molecular-targeted diagnostics and therapies is slow and limited. The occurrence and progression of cancer are closely associated with metabolic reprogramming. Therefore, this study aimed to identify metabolism-related LCA subtypes through a comprehensive analysis of transcriptomic, mutational, methylation, and single-cell RNA sequencing, in hopes of finding factors which influences the prognosis of LCA. METHODS: First, to identify metabolism-related LCA subtypes, data from 114 patients with LCA from The Cancer Genome Atlas (TCGA) dataset were collected for an unsupervised clustering analysis, which focused on the expression characteristics of survival-related metabolic genes. Subsequently, prognostic and diagnostic models have been developed using machine learning techniques. Specifically, the prognostic model utilized the least absolute shrinkage and selection operator (LASSO) Cox regression, whereas the diagnostic model was built using the Random Forest (RF) algorithm. Furthermore, to ensure the reproducibility, the results of the subtypes and models were validated using three independent bulk RNA datasets and a scRNA-seq dataset. RESULTS: Two robust subtypes were identified and independently validated. Each subtype has a distinct prognostic outcomes and molecular features. Specifically, the LCA1 subtype exhibited better prognosis, enriched metabolic pathways, and higher mutation frequencies. Notably, significant damaging mutations in the methyltransferases NSD1 were observed in this subtype. In contrast, the LCA2 subtype was associated with poorer prognosis, higher immune infiltration, and elevated methylation levels. Moreover, in LCA2 tumors, higher levels of T cell/APC co-inhibition and inhibitory checkpoints were observed. In addition, the diagnostic model demonstrated strong performance, achieving an area under the curve (AUC) values of 1.000 in the training group and 0.947 in the validation group. The prognostic model effectively predicted patient outcomes, with the RiskScore emerging as an independent prognostic factor. CONCLUSION: This study offers new perspectives for patient stratification and presents opportunities for therapeutic development in LCA. Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.
-
Scarf enables a highly memory-efficient analysis of large-scale single-cell genomics data.
As the scale of single-cell genomics experiments grows into the millions, the computational requirements to process this data are beyond the reach of many. Herein we present Scarf, a modularly designed Python package that seamlessly interoperates with other single-cell toolkits and allows for memory-efficient single-cell analysis of millions of cells on a laptop or low-cost devices like single-board computers. We demonstrate Scarf's memory and compute-time efficiency by applying it to the largest existing single-cell RNA-Seq and ATAC-Seq datasets. Scarf wraps memory-efficient implementations of a graph-based t-stochastic neighbour embedding and hierarchical clustering algorithm. Moreover, Scarf performs accurate reference-anchored mapping of datasets while maintaining memory efficiency. By implementing a subsampling algorithm, Scarf additionally has the capacity to generate representative sampling of cells from a given dataset wherein rare cell populations and lineage differentiation trajectories are conserved. Together, Scarf provides a framework wherein any researcher can perform advanced processing, subsampling, reanalysis, and integration of atlas-scale datasets on standard laptop computers. Scarf is available on Github: https://github.com/parashardhapola/scarf .
-
Integrated transcriptomic analysis and machine learning identify immunogenic cell death genes as prognostic markers and therapeutic targets in non-small cell lung cancer.
BACKGROUND: Immunogenic cell death (ICD) is a regulated cell death that activates antitumor immunity, yet its prognostic role in non-small cell lung cancer (NSCLC) remains unclear. This study aimed to develop and validate a robust ICD-related gene (ICDRG) signature for predicting survival and characterizing the tumor immune microenvironment in NSCLC. METHODS: In this retrospective prognostic model development and validation study, transcriptomic and clinical data from 598 NSCLC patients in The Cancer Genome Atlas (TCGA) cohort were used for model training. External validation was performed using three independent Gene Expression Omnibus (GEO) cohorts [GSE11969, GSE68465 and GSE81089, total n=441 from GSE11969/GSE68465, with GSE81089 providing an additional RNA sequencing (RNA-seq)-based validation set]. Based on 34 literature-curated ICD genes, we identified prognostic candidates through single-cell sequencing analysis and weighted gene co-expression network analysis (WGCNA). An 8-gene prognostic signature based on the ICD-related risk score (ICDRS) was constructed using least absolute shrinkage and selection operator (LASSO)-Cox regression and validated with a machine learning (ML) ensemble framework (10 algorithms). Model performance was assessed using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curves, and multivariate Cox regression. Associations between the ICDRS and immune infiltration or drug sensitivity were further evaluated. RESULTS: The ICDRS model stratified patients into high- and low-risk groups with significantly different overall survival (OS) in both the training and validation cohorts (all log-rank P<0.05). The model demonstrated robust predictive accuracy for 1-, 3-, and 5-year survival [area under the curve (AUC) >0.70]. Multivariate analysis confirmed the ICDRS as an independent prognostic factor [hazard ratio (HR) >2.0, P<0.001]. Notably, consistent prognostic performance was observed across microarray-based (GSE11969/GSE68465) and RNA-seq-based (GSE81089) platforms, underscoring the signature's robustness to technical variations. Furthermore, the high-risk group was characterized by an immunosuppressive microenvironment and higher predicted resistance to common chemotherapeutic agents. CONCLUSIONS: We developed and validated an 8-gene ICD-related signature that serves as an independent prognostic biomarker for NSCLC. This model provides insights into the immunogenic landscape of tumors and offers a potential tool for personalizing immunotherapy strategies.
-
Single-cell atlases: shared and tissue-specific cell types across human organs.
The development of single-cell and spatial transcriptomics methods was instrumental in the conception of the Human Cell Atlas initiative, which aims to generate an integrated map of all cells across the human body. These technology advances are bringing increasing depth and resolution to maps of human organs and tissues, as well as our understanding of individual human cell types. Commonalities as well as tissue-specific features of primary and supportive cell types across human organs are beginning to emerge from these human tissue maps. In this Review, we highlight key biological insights obtained from cross-tissue studies into epithelial, fibroblast, vascular and immune cells based on single-cell gene expression data in humans and contrast it with mechanisms reported in mice.