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 161 из 492-
Investigation into the molecular mechanism of obesity: an integrated approach of multi-omics analysis, machine learning and experimental validation.
BACKGROUND: Obesity has emerged as a major global public health challenge and poses a significant threat to human health. Despite extensive research, the mechanisms underlying its pathological progression remain elusive. AIM: To systematically identify pivotal targets and underlying mechanisms affecting the pathological progression of obesity through integrated strategies. METHODS: Transcriptomic and single-cell RNA sequencing (scRNA-seq) datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify obesity-associated genes. Moreover, functional enrichment analysis was conducted to elucidate potential mechanisms, and immune cell infiltration was assessed using the CIBERSORT algorithm. Then, macrophage-related genes were screened and subjected to degree centrality assessment, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and random forest to identify hub genes. Furthermore, scRNA-seq was employed to systematically characterize key cell types and their gene expression profiles in the context of obesity. Finally, immunofluorescence (IF) and ELISA techniques were used to validate the expression of specific genes in the adipose tissue of obese mice. RESULTS: A total of 535 differentially expressed genes (DEGs) were identified, highlighting their significant role in the modulation of immune responses and inflammation. WGCNA was conducted to identify gene modules strongly correlated with obesity, and integration with differential expression analysis yielded 425 co-expressed DEGs. Pathway enrichment and immune cell infiltration analyses revealed that these genes were closely associated with the expression of macrophages. A total of 81 macrophage-related genes were further screened, and through protein-protein interaction (PPI) analysis combined with two machine learning algorithms, two hub genes (TREM2 and CXCR4) were ultimately identified. The Human Protein Atlas database and single-cell transcriptome analyses validated that TREM2 is specifically expressed in macrophages. Lastly, animal experiments verified the expression pattern of TREM2 in the adipose tissue of obese mouse models. CONCLUSION: This study identified TREM2 as a key effector in the regulation of obesity-related pathophysiological processes, with specific expression in macrophages. These findings collectively position TREM2 as a potential diagnostic biomarker for obesity.
-
Pyroptosis-related genes features on prediction of the prognosis in liver cancer: An integrated analysis of bulk and single-cell RNA sequencing.
OBJECTIVE: This study explores the impact of pyroptosis-related genes (PRG) on the prognosis of liver cancer (LC). METHODS: 421 samples (371 tumor samples and 50 normal samples) from the Cancer Genome Atlas (TCGA) were included in this study. GSE14520 dataset (data of RNA expression and relevant clinicopathological features), GSE125449 dataset (single-cell data in LC) and HCCDB18 dataset (validation on the reliability of the model) were downloaded as appropriate. Download the PRG and its corresponding pathway information from the gene set enrichment analysis (GSEA) website. The consensus clustering was performed by ConsensusClusterPlus package. Differentially expressed genes (DEGs) were identified using limma package, and prognostic features were constructed using un/multivariate and Lasso Cox regression. Pathway enrichment analysis was conducted by ssGSEA method. Receiver Operating Characteristic and the survival analysis were conducted by timeROC and Survminer packages. The Seurat package was used for single-cell RNA sequencing (scRNA-seq) analysis. For cellular validation, following the quantification on the key genes via reverse-transcription quantitative PCR, the Transwell and scratch assays were applied to evaluate the in-vitro invasion and migration of LC cells Huh-7. RESULTS: 12 prognosis-related genes were identified to be related to the progression of LC. Three subtypes including C1, C2 and C3 were categorized using the 12 prognosis-related genes and PRGs significantly related to the prognosis of LC patients. The worst and best prognosis was seen in C3 subtype and C2 subtype, respectively. Hallmark pathway enrichment analysis has shown the concurrent immunoactivation and immune escape in C3 subtype. A RiskScore model was constructed using 8 key genes (KPNA2, UCK2, FTCD, CBX2, RAB32, HMMR, S100A9 and ANXA10) from the DEGs of three subtypes. The RiskScore system as an independent prognostic factor dividing the patients into high and low risk groups, and patients of the high-risk group had poor prognosis in both test set and validation set. A nomogram model combining the risk score had the extreme higher benefit. Further, 6 subclusters were identified from scRNA-seq analysis, where the highest PYROPTOSIS score was seen in Monocytic-Macrophages. The quantification on the key genes has suggested the high expressions of KPNA2, UCK2, CBX2, RAB32, HMMR and S100A9 and the low expressions of FTCD and ANXA10 in LC cells Huh-7. Particularly, UCK2 knockdown evidently diminished the number of invaded and migrated LC cells in vitro. CONCLUSION: The risk model associated with pyproptosis is crucial for the tumor immunity of LC and may serve as a prognostic indicator for patients suffering from LC. Our findings will offer new perspectives for immunotherapies targeting LC.
-
Single-cell atlas of keratoconus corneas revealed aberrant transcriptional signatures and implicated mechanical stretch as a trigger for keratoconus pathogenesis.
Keratoconus is a common ectatic corneal disorder in adolescents and young adults that can lead to progressive visual impairment or even legal blindness. Despite the high prevalence, its etiology is not fully understood. In this study, we performed single-cell RNA sequencing (scRNA-Seq) analysis on 39,214 cells from central corneas of patients with keratoconus and healthy individuals, to define the involvement of each cell type during disease progression. We confirmed the central role of corneal stromal cells in this disease, where dysregulation of collagen and extracellular matrix (ECM) occurred. Differential gene expression and histological analyses revealed two potential novel markers for keratoconus stromal cells, namely CTSD and CTSK. Intriguingly, we detected elevated levels of YAP1 and TEAD1, the master regulators of biomechanical homeostasis, in keratoconus stromal cells. Cyclical mechanical experiments implicated the mechanical stretch in prompting protease production in corneal stromal cells during keratoconus progression. In the epithelial cells of keratoconus corneas, we observed reduced basal cells and abnormally differentiated superficial cells, unraveling the corneal epithelial lesions that were usually neglected in clinical diagnosis. In addition, several elevated cytokines in immune cells of keratoconus samples supported the involvement of inflammatory response in the progression of keratoconus. Finally, we revealed the dysregulated cell-cell communications in keratoconus, and found that only few ligand-receptor interactions were gained but a large fraction of interactional pairs was erased in keratoconus, especially for those related to protease inhibition and anti-inflammatory process. Taken together, this study facilitates the understanding of molecular mechanisms underlying keratoconus pathogenesis, providing insights into keratoconus diagnosis and potential interventions.
-
From a Multi-Omics Signature to a Therapeutic Candidate: Computational Prediction and Experimental Validation in Liver Fibrosis.
Background: Advanced liver fibrosis (LF) is a major determinant of prognosis across chronic liver diseases. Current biomarkers are often etiology-specific and lack cross-cohort robustness. Shared molecular drivers across etiologies remain incompletely defined, and effective anti-fibrotic therapies are limited. Methods: We developed a multi-algorithm consensus machine-learning framework to derive a robust LF progression signature. In the training non-alcoholic fatty liver disease (NAFLD) cohort GSE213621 (n = 368), samples were formulated as a binary classification task (mild fibrosis, F0-F2; advanced fibrosis, F3-F4). Candidate genes were screened in parallel using Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme Gradient Boosting (XGBoost). Genes selected by at least two algorithms were defined as a high-consensus pool, and genes consistently selected by all four algorithms were prioritized to construct a core signature. Model performance was evaluated by stratified cross-validation in the training cohort and externally validated in four independent cohorts of different etiologies (GSE49541, GSE84044, GSE130970, and GSE276114). Cellular sources of signature genes were characterized using single-cell RNA sequencing (scRNA-seq) datasets GSE136103 (human) and GSE172492 (mouse). For therapeutic discovery, the high-consensus expression profile was queried against the Connectivity Map (CMap) to prioritize compounds predicted to reverse the fibrotic transcriptional program. Withaferin A (WFA) was selected for experimental validation in a carbon tetrachloride (CCl4)-induced mouse LF model and in the transforming growth factor-β1 (TGF-β1)-stimulated human hepatic stellate cell line LX-2. Bulk liver RNA-seq profiling was performed to interrogate WFA-associated molecular changes in vivo. Results: We identified a six-gene signature (CLEC4M, COL25A1, ITGBL1, NALCN, PAPPA, and PEG3) that discriminated advanced from mild fibrosis, achieving a mean AUC of 0.890 in internal cross-validation and an average AUC of 0.864 across external validation cohorts. scRNA-seq analysis revealed cell-type-specific expression with prominent enrichment in fibroblast populations. In vivo, WFA markedly attenuated CCl4-induced fibrosis (p < 0.05) and reversed 1314 fibrosis-associated differentially expressed genes (adjusted p < 0.05), which were enriched in fatty acid metabolism and PPAR signaling, as well as extracellular matrix (ECM)-receptor interaction and focal adhesion (adjusted p < 0.05). In vitro, WFA suppressed TGF-β1-induced LX-2 activation, reducing α-SMA and Fibronectin expression (p < 0.05). Conclusions: We report a six-gene signature that robustly predicts advanced LF across etiologies, define its cellular context using single-cell atlases, and validate the anti-fibrotic activity of WFA in both in vivo and in vitro models. Bulk liver RNA-seq and cellular evidence further suggest that WFA-associated effects are linked to lipid metabolic programs, ECM remodeling, and attenuation of hepatic stellate cell activation.
-
Novel Approach for Pancreas Transcriptomics Reveals the Cellular Landscape in Homeostasis and Acute Pancreatitis.
BACKGROUND & AIMS: Acinar cells produce digestive enzymes that impede transcriptomic characterization of the exocrine pancreas. Thus, single-cell RNA-sequencing studies of the pancreas underrepresent acinar cells relative to histological expectations, and a robust approach to capture pancreatic cell responses in disease states is needed. We sought to innovate a method that overcomes these challenges to accelerate study of the pancreas in health and disease. METHODS: We leverage FixNCut, a single-cell RNA-sequencing approach in which tissue is reversibly fixed with dithiobis(succinimidyl propionate) before dissociation and single-cell preparation. We apply FixNCut to an established mouse model of acute pancreatitis, validate findings using GeoMx whole transcriptome atlas profiling, and integrate our data with prior studies to compare our method in both mouse and human pancreas datasets. RESULTS: FixNCut achieves unprecedented definition of challenging pancreatic cells, including acinar and immune populations in homeostasis and acute pancreatitis, and identifies changes in all major cell types during injury and recovery. We define the acinar transcriptome during homeostasis and acinar-to-ductal metaplasia and establish a unique gene set to measure deviation from normal acinar identity. We characterize pancreatic immune cells, and analysis of T-cell subsets reveals a polarization of the homeostatic pancreas toward type-2 immunity. We report immune responses during acute pancreatitis and recovery, including early neutrophil infiltration, expansion of dendritic cell subsets, and a substantial shift in the transcriptome of macrophages due to both resident macrophage activation and monocyte infiltration. CONCLUSIONS: FixNCut preserves pancreatic transcriptomes to uncover novel cell states during homeostasis and following pancreatitis, establishing a broadly applicable approach and reference atlas for study of pancreas biology and disease.
-
Single-Cell Transcriptomics Shows Cellular Heterogeneity, Intercellular Communication, and Extracellular Matrix Remodeling in Corneal Fibrosis In Vivo.
PURPOSE: Corneal fibrosis is a common clinical condition post ocular trauma/infection affecting 7% of world's population. This study characterized fibroblast heterogeneity, cellular trajectories, intercellular communications, and extracellular matrix (ECM) protein remodeling in fibrotic cornea in vivo via single-cell RNA sequencing (scRNA-seq). METHODS: Naïve and alkali-injured fibrotic corneas of New Zealand White rabbits were obtained under approved institutional animal use and care protocol. Multimodal in vivo eye imaging, single-cell RNA-sequencing, hematoxylin and eosin staining, and immunofluorescence analyzed corneas. Unsupervised clustering, subclustering of ECM remodeling cells, trajectory inference, CellChat-based intercellular communication mapping, and R-programming-generated transcriptomic atlas. RESULTS: Fourteen transcriptionally distinct cell clusters were identified via canonical marker genes. Basal epithelium and ECM remodeling clusters acted as communication hubs whereas differentiated epithelium had limited participation. Subclustering the ECM remodeling niche identified four distinct stromal cell populations, quiescent-keratocytes, activated-keratocytes, progenitor-like-keratocytes, and myofibroblasts. Transcriptional trajectories organized these cellular states into a bifurcating continuum; one aligned to fibroblast activation and myofibroblast formation whereas the other aligned to quiescent-keratocytes or progenitor-like-keratocytes. Eight pathways (MIF, NECTIN3, HGFα, POSTN, NAMPT, TWEAK, EPH, and VEGFC) demonstrated strong ligand-receptor connectivity and their protein expression corroborated predicted signaling. Stage-specific enrichment revealed temporal dynamics within ECM-remodeling cells and continuous landscape of stromal subpopulations from quiescent-keratocytes to proliferative-fibroblasts/myofibroblasts and progenitor-like-cells followed by late dominance of ECM organization, adhesion, and collagen biosynthesis. The progression trajectory appeared bidirectional along pseudotime. CONCLUSIONS: This study provides an integrative transcriptomic framework of stromal cell heterogeneity, intercellular signaling, and ECM remodeling trajectories in rabbit cornea in vivo and advances mechanistic understanding of corneal repair.
-
RBP7 Regulated by EBF1 Affects Th2 Cells and the Oocyte Meiosis Pathway in Bone Metastases of Bladder Urothelial Carcinoma.
BACKGROUND: Bladder urothelial carcinoma (BLCA) is a malignancy with a high incidence worldwide. One-third of patients may experience aggressive progression later on, and 70% of patients who have undergone surgical intervention will still suffer from metastasis. MATERIALS AND METHODS: RNA sequencing profiles of BLCA samples were obtained from The Cancer Genome Atlas (TCGA) database. Differential expression and univariate Cox regression analyses were performed to identify prognosis-related differentially expressed immune genes (DEIGs). Subsequently, a proportional hazards model of DEIGs was then constructed by univariate regression analysis. Differential expression and correlation analyses, CIBERSORT, Single Sample Gene Set Enrichment Analysis (ssGSEA), GSVA were conducted on transcription factors (TFs), immune cells/pathways and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The regulation network was then constructed. Eventually, ATAC-seq, ChIP-seq, scRNA-seq, and multiple online databases were employed for further validation. RESULTS: A proportional hazards model of 31 DEIGs was constructed and risk score was calculated and proven to be a independent prognostic factor. Then 5 immune genes were characterized to be significantly correlated with bone metastasis, stage and TF expression simultaneously. 4 TFs were identified to be significantly correlated with prognosis and RBP7 expression. 5 immune cells/pathways were revealed to be significantly correlated with RBP7 expression. Only 1 KEGG pathway was identified to be significant in Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) analyses. The regulatory relationship was then constructed, in which the correlation between EBF1 and RBP7 (R = 0.677, p < 0.001), Th2 cells and RBP7 (R = 0.23, p < 0.001), the oocyte meiosis pathway and RBP7 (R = 0.14, p = 0.042) were the most statistically significant. The results were further confirmed by Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq), Chromatin Immunoprecipitation sequencing (ChIP-seq), single-cell RNA sequencing (scRNA-seq), and multiple online databases validation. CONCLUSIONS: This study revealed that the EBF1-RBP7 regulatory relationship had potential importance in the bone metastasis in BLCA through Th2 cells and the oocyte meiosis pathway.
-
Single-Cell RNA-Seq Reveals the Cellular Diversity and Developmental Characteristics of the Retinas of an Infant and a Young Child.
The human retina, located in the innermost layer of the eye, plays a decisive role in visual perception. Dissecting the heterogeneity of retinal cells is essential for understanding the mechanism of visual development. Here, we performed single-cell RNA-seq to analyze 194,967 cells from the donors of infants and young children, resulting in 17 distinct clusters representing major cell types in the retina: rod photoreceptors (PRs), cone PRs, bipolar cells (BCs), horizontal cells (HCs), amacrine cells (ACs), retinal ganglion cells (RGCs), Müller glial cells (MGs), microglia, and astrocytes (ASTs). Through reclustering, we identified known subtypes of cone PRs as well as additional unreported subpopulations and corresponding markers in BCs. Additionally, we linked inherited retinal diseases (IRDs) to certain cell subtypes or subpopulations through enrichment analysis. We next constructed extensive intercellular communication networks and identified ligand-receptor interactions that play crucial roles in regulating neural cell development and immune homeostasis in the retina. Intriguingly, we found that the status and functions of PRs changed drastically between the young children and adult retina. Overall, our study offers the first retinal cell atlas in infants and young children dissecting the heterogeneity of the retina and identifying the key molecules in the developmental process, which provides an important resource that will pave the way for research on retinal development mechanisms and advancements in regenerative medicine concerning retinal biology.
-
Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis.
BACKGROUND AND AIMS: The recurrence and metastasis of hepatocellular carcinoma (HCC) are mainly caused by microvascular invasion (MVI). Our study aimed to uncover the cellular atlas of MVI+ HCC and investigate the underlying immune infiltration patterns with radiomics features. METHODS: Three MVI positive HCC and three MVI negative HCC samples were collected for single-cell RNA-seq analysis. 26 MVI positive HCC and 30 MVI negative HCC tissues were underwent bulk RNA-seq analysis. For radiomics analysis, radiomics features score (Radscore) were built using preoperative contrast MRI for MVI prediction and overall survival prediction. We deciphered the metabolism profiles of MVI+ HCC using scMetabolism and scFEA. The correlation of Radscore with the level of APOE+ macrophages and iCAFs was identified. Whole Exome Sequencing (WES) was applied to distinguish intrahepatic metastasis (IM) and multicentric occurrence (MO). Transcriptome profiles were compared between IM and MO. RESULTS: Elevated levels of APOE+ macrophages and iCAFs were detected in MVI+ HCC. There was a strong correlation between the infiltration of APOE+ macrophages and iCAFs, as confirmed by immunofluorescent staining. MVI positive tumors exhibited increased lipid metabolism, which was attributed to the increased presence of APOE+ macrophages. APOE+ macrophages and iCAFs were also found in high levels in IM, as opposed to MO. The difference of infiltration level and Radscore between two nodules in IM was relatively small. Furthermore, we developed Radscore for predicting MVI and HCC prognostication that were also able to predict the level of infiltration of APOE+ macrophages and iCAFs. CONCLUSION: This study demonstrated the interactions of cell subpopulations and distinct metabolism profiles in MVI+ HCC. Besides, MVI prediction Radscore and MVI prognostic Radscore were highly correlated with the infiltration of APOE+ macrophages and iCAFs, which helped to understand the biological significance of radiomics and optimize treatment strategy for MVI+ HCC.
-
Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma.
OBJECTIVES: In this study, we aimed to identify the metabolic genes associated with the metastasis and immunotherapy resistance of oral squamous cell carcinoma (OSCC) and to construct a metabolic gene-related predictive model for the prognosis of OSCC. METHODS: RNA-seq data were download from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the modules related to EMT, stemness, and checkpoint signatures in OSCC. Univariate Cox and the least absolute shrinkage and selection operator (LASSO) methods were used to construct the metabolic gene signature. Furthermore, the scRNA-seq data were obtained from Gene Expression Omnibus (GEO) database and analyzed using "Seurat" and "CopyKAT" packages. RESULTS: The risk prediction model was constructed using the 12 metabolic-related gene signature. Based on this model, risk score of each sample was calculated and used to divide the samples into low- and high-risk groups. Our model was effective as the risk score was significantly associated with clinical features and genetic mutations. Meanwhile, we found that lipid metabolism, glycolysis, amino acid metabolism, and drug metabolism differed between high- and low-risk groups. Pathways associated with malignant tumor and immunosuppression were enriched in high-risk group. Furthermore, low-risk group showed a more activated immune status and was predicted to have better response to immunotherapy. Finally, through single-cell transcriptome analysis, we assessed the expression of these 12 genes in tumor and non-tumor cells and verified the existence of two clusters of tumor cells with different degrees of malignancy at the cellular level. CONCLUSIONS: Our study demonstrates the clinical significance of metabolic related gene signature for the treatment of OSCC and suggests potential therapeutic targets and pathways for OSCC.