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: 4 924
Страница 177 из 493-
Construction and clinical application of a risk model based on N6-methyladenosine regulators for colorectal cancer.
BACKGROUND: Colorectal cancer (CRC) shows a high incidence in developed countries. This study established a prognosis signature based on N6-methyladenosine (m6A) regulators involved in CRC progression. METHOD: The bulk RNA-seq data from the Atlas and Compass of Immune-Colon cancer-Microbiome interactions (AC-ICAM) and GSE33113 CRC datasets were obtained from the cBioportal and GEO databases, and a total of 21 m6A regulators genes were collected from a previous study. The scRNA-seq analysis of the GSE146771 cohort was conducted applying the Seurat and harmony R packages. Consensus clustering based on the expressions of m6A regulators was performed with the ConsensusClusterPlus package. The ggGSEA package was used for the Gene Set Enrichment Analysis (GSEA). The un/multivariate and LASSO Cox analysis were performed applying the "survival" and "glmnet" packages for developing a risk model. The pRRophetic and GSVA packages were utilized to analyze potential drugs for CRC and immune infiltration in different risk groups, followed by the Kaplan-Meier (KM) survival and ROC analysis with the "survival" and "timeROC" packages. In vitro assays included the quantitative polymerase chain reaction (qPCR), wound healing and transwell were performed. RESULTS: CRC patients in the AC-ICAM cohort were assigned into three molecular subtypes (S1, 2 and 3) based on nine m6A regulator genes. Specifically, the prognostic outcome of the S3 was the most favorable, while that of the S1 was the worst and this subtype was associated with the activation of NF-kB, TNF-α and hypoxia pathways. Three key genes, namely, methyltransferase-like 3 (METTL3), insulinolike Growth Factor2 mRNA-Binding Protein 3 (IGF2BP3) and YTH domain-containing protein 2 (YTHDC2), selected from the 9 m6A regulator genes were combined into a RiskScore, which showed a high classification effectiveness in dividing the patients into high- and low-risk groups. Inhibition of the expression of METTL3A or that of IGF2BP3 suppressed the invasion and migration of CRC cells. Notably, the high-risk patients had higher immune cell infiltration to support the activation of multiple immune responses and exhibited significantly poor prognosis. Meanwhile, a nomogram with practical clinical value was developed based on the RiskScore and other clinical features. Finally, eight potential drugs associated with the RiskScore were identified, and CD4+ cells and Tregs were found to be closely associated with CRC progression. CONCLUSION: The RiskScore model developed based on m6A regulators played a critical role in CRC development and can be considered as a prognosis predictor for patients with the cancer. The present discoveries will facilitate the diagnosis and clinical management of CRC patients.
-
Prediction of prognostic biomarkers for hepatocellular carcinoma and immune microenvironment infiltration based on single-cell sequencing and RNA-Seq integration.
OBJECTIVE: Early diagnosis and prognostic evaluation of hepatocellular carcinoma (HCC) remain significant challenges in clinical management. This study aims to identify prognostic biomarkers in HCC and to explore their implications in immune microenvironment infiltration. METHODS: In this study, we constructed a single-cell transcriptomic atlas of HCC, focusing on the expression profiles of T cell-related genes. Analytical approaches included cell-cell communication analysis and pseudotime trajectory analysis. To further predict and validate T cell-associated prognostic genes, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Patients were stratified into high- and low-risk groups based on a prognostic model derived from these biomarkers. Immune infiltration levels in the tumor microenvironment were then evaluated across risk groups. RESULTS: A total of eight primary tumor samples and seven normal tissue samples were included as raw data in this study. Following stringent quality control and filtering, 53,477 cells were retained for downstream analysis. From these, we isolated 12,333 T cells, which were subjected to further clustering and annotation. The T cell subpopulations identified included 6314 natural killer T cells (NK T cells), 5199 effector memory CD4+ T cells, and 820 central memory CD8+ T cells. By integrating transcriptomic data from TCGA-LIHC and GEO datasets, we identified six prognostic biomarkers: LYZ, SPP1, EGR1, MARCO, FCN3, and PTTG1. A prognostic model was developed based on these biomarkers, enabling risk stratification into high- and low-risk groups. The model demonstrated robust predictive performance in estimating patient survival rates and immune cell infiltration levels within the tumor microenvironment. CONCLUSION: This study identified and validated prognostic biomarkers in HCC that effectively predict patient survival rates and immune infiltration characteristics. These findings provide a potential foundation for precision medicine strategies in HCC, offering novel insights into the tumor-immune microenvironment and its clinical implications.
-
Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma.
BACKGROUND: Cuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients. METHODS: RNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB). RESULTS: We have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group.
-
CLIC6 and ANLN: novel exosome-related prognostic markers and therapeutic targets in lung adenocarcinoma.
BACKGROUND: Exosomes can promote tumor development and regulate tumor immune responses, making them of significant value in Lung Adenocarcinoma (LUAD) management. In-depth exploration of exosome-related genes in LUAD is of great significance for expanding LUAD clinical treatment options. METHODS: Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. Differential expression analysis (limma package), consensus clustering (ConsensusClusterPlus), and Least Absolute Shrinkage and Selection Operator (LASSO) regression (glmnet package) were used to build a prognostic model. Immune infiltration was assessed with TIMER, MCPcounter, and single-sample gene set enrichment analysis (ssGSEA). Tumor Immune Dysfunction and Exclusion (TIDE) algorithm evaluated immunotherapy response. Single-cell RNA sequencing (scRNA-seq) data were processed using Seurat. LUAD cell lines (A549, NCI-H838) were used for quantitative real-time PCR (qRT-PCR), Cell Counting Kit-8 (CCK-8), Transwell, and wound-healing assays. RESULTS: Four genes associated with exosomes were identified as key genes significantly influencing LUAD prognosis, namely CLIC6, ANLN, FAM83A, and RHOV. A LUAD prognosis model was constructed based on these genes, and the ROC curve confirmed the model's excellent predictive performance. Immune infiltration analysis revealed immune cell infiltration differences between low- and high-RiskScore groups in LUAD, with significant differences in infiltration observed between groups for cells including eosinophils, T cells, myeloid dendritic cells, and B lineage (P < 0.05). CD274, PDCD1 and LAG3 were highly expressed in the high-risk group of LUAD (P < 0.01). Exosome-related pathways were significantly enriched in epithelial cells and monocyte/macrophage cells. Single-cell analysis reveals that CLIC6 exhibits higher expression levels in epithelial cells. ANLN regulates the malignant phenotype of LUAD cell lines. CONCLUSION: A four-gene exosome-related signature was identified that can effectively separate LUAD patients into risk subgroups having distinct immune characteristics and immunotherapeutic benefits. Which occurred when ANLN was validated as an oncogene, coupled to the model being significantly associated with immune evasion mechanisms, suggests these biomarkers could enhance prognostic assessment and allow clinicians to identify patients who may be less likely to respond to immunotherapy, thereby informing personalized treatment strategies.
-
Single-cell atlas of Leishmania development in sandflies reveals the heterogeneity of transmitted parasites and their role in infection.
Sandfly vectors transmit Leishmania through egestion of parasites into the host skin. The transmissible dose is shaped by Leishmania development in the sandfly gut, described as a sequential differentiation of promastigote morphotypes. Apart from isolated mammal-infective metacyclic promastigotes, little is known about the transcriptional programs and molecular markers for other stages coinhabiting the midgut in mature infections and cotransmitted by the sandfly bite. Here, we elucidate the single-cell transcriptomic complexity of Leishmania major colonizing its natural vector Phlebotomus duboscqi at early and late infection, providing markers for three transmissible stages. Contrary to prevailing models, our analyses indicate a nonlinear developmental progression, with bifurcation into either replicating early metacyclics or attached and detached haptomonads. We demonstrate that haptomonads constitute a key component of the transmitted inoculum and, along with nondividing late metacyclics, are infectious to and exacerbate the pathology in mice. Our single-cell analysis and validated markers will facilitate further studies on the Leishmania life cycle and its implications for vector-to-host transmission dynamics.
-
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.