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 172 из 493-
Integrating anoikis and ErbB signaling insights with machine learning and single-cell analysis for predicting prognosis and immune-targeted therapy outcomes in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored. METHODS: This study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated. RESULTS: The single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq). CONCLUSION: AERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.
-
Diversity and intratumoral heterogeneity in human gallbladder cancer progression revealed by single-cell RNA sequencing.
BACKGROUND: Gallbladder cancer (GC) is a malignant disease characterized with highly cellular heterogeneity and poor prognosis. Determining the intratumoral heterogeneity and microenvironment (TME) can provide novel therapeutic strategies for GC. METHODS: We performed the single-cell RNA sequencing on the primary and lymph node metastatic gallbladder tumors and the adjacent normal tissues of five patients. The transcriptomic atlas and ligand-receptor-based intercellular communication networks of the single cells were characterized. RESULTS: The transcriptomic landscape of 24,887 single cells was obtained and characterized as 10 cellular clusters, including epithelial, neuroendocrine tumor cells, T&NK cells, B cells, RGS5+ fibroblasts, POSTN+ fibroblasts, PDGFRA+ fibroblasts, endothelial, myeloid cells, and mast cells. Different types of GC harbored distinct epithelial tumor subpopulations, and squamous cell carcinoma could be differentiated from adenocarcinoma cells. Abundant immune cells infiltrated into adenocarcinoma and squamous cell carcinoma, rather than neuroendocrine neoplasms, which showed significant enrichment of stromal cells. CD4+/FOXP3+ T-reg and CD4+/CXCL13+ T helper cells with higher exhausting biomarkers, as well as a dynamic lineage transition of tumor-associated macrophages from CCL20hi /CD163lo , CCL20lo /CD163hi to APOE+, were identified in GC tissues, suggesting the immunosuppressive and tumor-promoting status of immune cells in TME. Two distinct endothelial cells (KDR+ and ACKR1+), which were involved in angiogenesis and lymphangiogenesis, showed remarkable ligand-receptor interactions with primary GC cells and macrophages in gallbladder tumors. CONCLUSIONS: This study reveals a widespread reprogramming across multiple cell populations in GC progression, dissects the cellular heterogeneity and interactions in gallbladder TME, and provides potential therapeutic targets for GC.
-
Neuroendocrine prostate cancer converges on a fetal pulmonary neuroendocrine-like program.
Advanced prostate cancers can relapse as neuroendocrine prostate cancer (NEPC), a treatment-resistant state that is independent of androgen receptor (AR) signaling and whose developmental identity remains unclear. We integrated single-cell, bulk, cistrome and spatial datasets across published cohorts and mapped malignant tumor states to human fetal atlases. In the discovery cohort (35,696 tumor cells, 20 patients), NEPC cells were malignant and shared copy-number architecture with adenocarcinoma (chromosome-level r = 0.64). NEPC most closely matched a fetal, lineage-restricted neuroendocrine program, best supported as pulmonary neuroendocrine-like among the alternatives tested (islet, chromaffin and sympathoblast), with the same direction across three cohorts; the pulmonary-versus-islet distinction was method-sensitive and remains provisional. Cross-sectional stage ordering and one illustrative single-tumor pseudotime analysis were consistent with luminal-program loss and neuroendocrine-program gain, while the specific ordering of individual transcription factors remained partly dataset-dependent. A regulatory screen using five methods nominated a circuit centered on ASCL1 whose convergent enhancers overlapped fetal pulmonary neuroendocrine chromatin and showed FOXA1 binding in cell-line and xenograft models (87.0% versus 33.8%; P = 0.016). In one NEPC Visium section, the program localized to malignant NEPC territory defined by copy number, with an area under the receiver operating characteristic curve (AUROC) of 0.85. Based on three NEPC patients with cross-cohort replication of direction, these data support NEPC as an adenocarcinoma-related malignant state converging on a fetal pulmonary neuroendocrine-like reference program and nominate regulatory and surface targets linked to ASCL1 for functional study.
-
Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies.
Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets.
-
Integrative genomics unveils basement membrane-related diagnostic markers and therapeutic targets in esophageal squamous cell carcinoma.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is often diagnosed at advanced stages due to the inherent limitations of current screening methodologies. Central to evaluating tumor invasion and prognostic assessment in ESCC is the integrity of the basement membrane (BM). However, current research on the implications of BM-related genes (BMRGs) in diagnosing ESCC remains sparse. METHODS: We performed a comprehensive analysis using single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, alongside gene expression profiles acquired from GEO and The Cancer Genome Atlas (TCGA) databases. This identified differentially expressed BMRGs in ESCC. Employing LASSO, RF, and SVM-RFE, we selected potential BM biomarkers and crafted a diagnostic nomogram for ESCC, validated by ROC curves and AUC values. We also explored immune infiltration and biological mechanisms through consensus clustering and GSVA, and utilized single cell trajectory analysis and GSCALite to study gene distributions and pathways. In vitro experiments further elucidated the role of these genes in ESCC carcinogenesis. RESULTS: Here, we discovered that ESCC cell types exhibited markedly elevated BM-related scores. Our analysis pinpointed seven BM genes upregulated and linked to immune infiltration, showcasing unique gene expression profiles and varying immune cell densities across the BM-related subtypes. Furthermore, a robust positive correlation was observed between these genes expression and EMT activity. The knockdown of BGN significantly suppressed cell proliferation, migration, invasion, while also augmenting cell viability following chemotherapy drug treatment. CONCLUSION: Our study identified seven key BMRGs (BGN, LAMB3, SPARC, MMP1, LUM, COL4A1, and NELL2) and established a diagnostic nomogram for ESCC. Of noteworthy significance is the discovery of BGN as a promising drug target, indicating a novel strategy for future clinical combination therapies in ESCC.
-
Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.
BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanisms linking OTA exposure to colorectal cancer (CRC) pathogenesis remain poorly understood. METHODS: We employed an integrative computational approach combining network toxicology, multi-cohort transcriptomic analysis, and machine learning to identify potential connections between OTA and CRC. Functional enrichment analysis was conducted to characterize the associated biological processes. Molecular docking and molecular dynamics simulations were used to computationally assess the predicted binding potential and conformational stability of OTA-target complexes. Immune cell infiltration patterns were assessed using CIBERSORT, and protein-level expression patterns were examined using publicly available Human Protein Atlas (HPA) data. Single-cell RNA-sequencing (scRNA-seq) data from three public cohorts were integrated to characterize cell-type-specific expression patterns and to explore intercellular communication via CellChat. scTenifoldKnk-based in silico perturbation analysis of MET and PLAU was performed in their respective high-expression cell populations to generate functional hypotheses. RESULTS: Intersection of OTA-predicted targets with CRC-associated genes identified 81 candidate genes enriched in metabolic and microenvironment remodeling pathways. Integration of machine learning frequency, SHAP importance, and PPI topological centrality pinpointed five core genes: MET, PLAU, TOP2A, FABP4, and ADH1B. Molecular docking and dynamics simulations suggested favorable predicted binding and conformational stability for selected OTA-target complexes. Immune infiltration analysis revealed a myeloid-enriched microenvironment, with MET, PLAU, and TOP2A positively correlating with pro-tumorigenic subsets, while FABP4 and ADH1B were associated with lymphoid populations. HPA immunohistochemistry data were consistent with the upregulation of MET, PLAU, and TOP2A, alongside the downregulation of FABP4 and ADH1B at the protein level. Single-cell analysis revealed distinct cell-type preferences: MET in colonic epithelial cells and fibroblasts, PLAU in dendritic cells, TOP2A in proliferative epithelial and exhausted CD8+ T cells, FABP4 in vascular endothelial cells, and ADH1B in conventional dendritic cells (cDC1). CellChat analysis revealed active PLAU-mediated communication between dendritic cells and stromal populations. scTenifoldKnk-based in silico perturbation of PLAU in its native high-expression cells perturbed immune pathways, whereas MET perturbation in its high-expression cells disrupted cell adhesion and ECM-interaction pathways, suggesting their complementary roles in immune modulation and tissue architecture maintenance. CONCLUSIONS: This integrated computational study prioritized a five-gene framework potentially linking OTA-related predicted targets with CRC-associated metabolic and immune-related molecular alterations. These findings are supported by immune contexture inference, external protein-level reference data, computational structural analyses, and single-cell-based in silico perturbation results, providing a hypothesis-generating framework for subsequent experimental investigations into the potential toxicological relevance of OTA in CRC.
-
Using single cell atlas data to reconstruct regulatory networks.
Inference of global gene regulatory networks from omics data is a long-term goal of systems biology. Most methods developed for inferring transcription factor (TF)-gene interactions either relied on a small dataset or used snapshot data which is not suitable for inferring a process that is inherently temporal. Here, we developed a new computational method that combines neural networks and multi-task learning to predict RNA velocity rather than gene expression values. This allows our method to overcome many of the problems faced by prior methods leading to more accurate and more comprehensive set of identified regulatory interactions. Application of our method to atlas scale single cell data from 6 HuBMAP tissues led to several validated and novel predictions and greatly improved on prior methods proposed for this task.
-
Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer.
BACKGROUND: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50%. Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristics in TNBC patients remains obscure. METHOD: Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by single-sample gene set enrichment analysis (ssGSEA). Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes. RESULT: TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3. Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3. The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3. Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response. CONCLUSION: In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtype-specific treatment of TNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which senescence-associated genes influence TNBC progression.
-
Integration of Bulk and Single-Cell RNA-Seq Data to Construct a Prognostic Model of Membrane Tension-Related Genes for Colon Cancer.
BACKGROUND: The plasma membrane provides a highly dynamic barrier for cancer cells to interact with their surrounding microenvironment. Membrane tension, a pivotal physical property of the plasma membrane, has attracted widespread attention since it plays a role in the progression of various cancers. This study aimed to identify a prognostic signature in colon cancer from membrane tension-related genes (MTRGs) and explore its implications for the disease. METHODS: Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) database, and then applied to the differentially expressed gene analysis. By implementing a univariate Cox regression and a LASSO-Cox regression, we developed a prognostic model based on four MTRGs. The prognostic efficacy of this model was evaluated in combination with a Kaplan-Meier analysis and receiver operating characteristic (ROC) curve analysis. Moreover, the relationships between the signature and immune cell infiltration, immune status, and somatic mutation were further explored. Lastly, by utilizing single-cell RNA-seq data, cell type annotation, pseudo-time analysis, drug sensitivity, and molecular docking were implemented. RESULTS: We constructed a 4-MTRG signature. The risk score derived from the model was further validated as an independent variable for survival prediction. Two risk groups were divided based on the risk score calculated by the 4-MTRG signature. In addition, we observed a significant difference in immune cell infiltration, such as subsets of CD4 T cells and macrophages, between the high- and low-risk groups. Moreover, in the pseudo-time analysis, TIMP1 was found to be more highly expressed with the progression of time. Finally, three small molecule drugs, elesclomol, shikonin, and bryostatin-1, exhibited a binding potential to TIMP-1. CONCLUSIONS: The novel 4-MTRG signature is a promising biomarker in predicting clinical outcomes for colon cancer patients, and TIMP1, a member of the signature, may be a sensitive regulator of the progression of colon cancer.
-
Dissociation and inDrops microfluidic encapsulation of human gut tissues for single-cell atlasing studies.
In droplet-based single-cell RNA-sequencing (scRNA-seq) experiments, cells, along with some of their surrounding buffer and ambient material, are encapsulated into droplets for mRNA capture and barcoding. This protocol details the steps for human gut tissue dissociation using cold active protease, and subsequent isolation of single epithelial cells, with enrichment of viability through washes. Next, the steps for encapsulation on the inDrops scRNA-seq platform are described. This procedure has been demonstrated to be applicable to polyps, cancers, and inflamed tissues. For complete details on the use and execution of this protocol, please refer to Chen et al. (2021).