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: 4 939
Страница 380 из 494-
Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer.
BACKGROUND: Breast cancer (BRCA) is a significant threat to women's health worldwide, and its progression is closely associated with the tumor microenvironment and gene regulation. Lactylation modification, as a key epigenetic mechanism in cancer biology, has not yet been fully elucidated in the context of BRCA. This study examines the regulatory mechanisms of lactylation-related genes (LRGs), specifically PRDX1, and their prognostic significance in BRCA. METHODS: We integrated data from multiple databases, including Genome-Wide Association Study (GWAS) summary statistics, single-cell RNA sequencing, spatial transcriptomics, and bulk RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs associated with BRCA and comprehensively analysed the expression patterns of PRDX1, cell-cell communication networks, and spatial heterogeneity. Furthermore, we constructed and validated a prognostic model based on the gene expression profile of PRDX1-positive monocytes, evaluating it through Cox regression and LASSO regression analyses. RESULTS: PRDX1 was identified as a key LRG significantly associated with BRCA risk (p_SMR = 0.0026). Single-cell RNA sequencing analysis revealed a significant upregulation of PRDX1 expression in monocytes, with enhanced cell-cell communication between PRDX1-positive monocytes and fibroblasts. Spatial transcriptomics analysis uncovered heterogeneous expression of PRDX1 in the tumor nest regions, highlighting the spatial interaction between PRDX1-positive monocytes and fibroblasts. The prognostic model constructed based on the gene expression profile of PRDX1-positive monocytes demonstrated high accuracy in predicting patient survival in both the training and validation cohorts. High-risk patients exhibited immune-suppressive microenvironment characteristics, including reduced immune cell infiltration and upregulation of immune checkpoint gene expression. CONCLUSION: This study reveals the key role of PRDX1 in BRCA progression, mainly through the regulation of the tumor microenvironment and immune escape mechanisms. The survival prediction model based on PRDX1 shows robust prognostic potential, and future research should focus on integrating PRDX1 with other biomarkers to enhance the precision of personalised medicine.
-
Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis.
LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.
-
Development and validation of a prognostic prediction model for gastric cancer based on lipophagy-related genes.
BACKGROUND: Gastric cancer (GC), as a heterogeneous disease, lacks clear clinical indications, and existing methods based on histological classification are insufficient for individualized stratified treatment of GC patients. Lipophagy-related genes (LRGs) are involved in the progression of GC, but their role in GC remains unclear. This study aims to develop and validate a lipophagy-related prognostic model to improve the predictive ability for GC prognosis. METHODS: GC-related datasets from public databases were used, including The Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD, training set), Gene Expression Omnibus 183904 (GSE183904, single-cell dataset), and GSE15459 (external validation set). LRGs were retrieved from the Molecular Signatures Database (MSigDB) using "Lipophagy" as the keyword. Differential expression analysis on TCGA-STAD identified differentially expressed genes (DEGs). Weighted gene co-expression network analysis screened lipophagy-related module genes, overlapped with DEGs for candidate genes. Mendelian randomization (MR) analysis identified key genes with causal relationships to GC. Univariate and multivariate Cox regression analyses screened prognostic genes to construct and validate a risk model. Nomograms for 1-, 3-, and 5-year survival rates were built using significant clinical factors and risk scores. Immune and enrichment analyses compared high- and low-risk groups. Pseudotime processes of prognostic genes in cell clusters and inter-cluster communication were analyzed. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) verified prognostic gene expression. RESULTS: Thirty-five genes had causal relationships with GC; AKAP12, BST1, DCBLD1, PDK4, and SPART were selected as prognostic genes, mainly expressed in adipocytes, dendritic cells, and heart. A risk model and nomogram integrating risk scores and clinical features (age, gender, tumor stage) were constructed. Nomogram predicted values were consistent with observed ones. Survival curves in both sets showed significant differences (P<0.05). Area under the curve (AUC) values: TCGA-STAD (0.61 for 1 year, 0.64 for 3 years, 0.71 for 5 years); GSE15459 (0.67 for 1 year, 0.67 for 3 years, 0.69 for 5 years), indicating limited prognostic discrimination. AKAP12, BST1, DCBLD1, and PDK4 correlated positively with most immune cells. All 5 genes showed differential expression in fibroblasts between GC and normal samples and in pseudotime stages. Endothelial-intestinal epithelial cell interaction was enhanced in GC. RT-qPCR showed AKAP12, BST1, and DCBLD1 were up-regulated in GC samples. CONCLUSIONS: In this study, a risk model was constructed based on five prognostic genes (AKAP12, BST1, DCBLD1, PDK4, and SPART). A nomogram was established by integrating each patient's risk score and clinical features (age, gender, and tumor stage). This model exhibits preliminary prognostic discrimination ability in both the training set and the validation set; however, its predictive accuracy is limited and requires further optimization.
-
A novel fumaric acid metabolism-related prognostic signature associated with prognosis and immune infiltration landscape in laryngeal squamous cell carcinoma.
BACKGROUND: Laryngeal squamous cell carcinoma (LSCC) is an aggressive malignant tumor, characterized by high incidence and mortality. Metabolic pathways within cancer cells are frequently dysregulated; thus, exploring fumaric acid metabolism-related genes (FAMRGs) appears interesting. We aimed to identify a signature prognostic genetic profile to develop tailored management strategies for patients with LSCC. METHODS: Data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and GeneCards databases were used to identify differentially expressed genes related to fumaric acid (FA) metabolism in LSCC. To explore the underlying mechanisms, we conducted analyses using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, we employed Cox regression and the least absolute shrinkage and selection operator (LASSO) to develop a risk signature based on FAMRGs. This signature was validated in TCGA and GEO cohorts. The association of the risk score with clinical characteristics, microenvironmental characteristics, and drug sensitivity was explored by correlation analyses. Finally, expression of FAMRGs was validated using datasets from the Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (HPA) databases. Moreover, the robustness of our findings was further confirmed through molecular docking and single-cell sequencing. RESULTS: A FA metabolism-associated model for laryngeal cancer was constructed using seven genes (ABCC2, ADH7, AQP9, CXCL11, GPT, PAEP, and PLCG1). Functional analysis suggested that FAMRGs were strongly associated with the chemotaxis and cytokine-cytokine receptor interaction. High-risk score subgroups, as indicated by the Kaplan-Meier curves, demonstrated poorer outcomes in both TCGA and GEO cohorts. A predictive nomogram was developed for LSCC survival probability; FAMRGs were significantly associated with the immune checkpoints. Additionally, six small molecule drugs that appeared promising as therapeutic agents in combating LSCC were identified. Besides, CXCL11 and AQP9 exhibited significantly high expression in tumor tissues, while GPT showed low expression, as confirmed by the HPA and GEPIA databases. Molecular docking confirmed the interaction between the seven core genes and FA. This finding was corroborated by single-cell sequencing, which revealed significant expression differences across various cell clusters in LSCC. CONCLUSIONS: A prognostic model associated with FA metabolism was established for LSCC based on seven genes. This model can effectively predict LSCC prognosis. Additionally, six small molecule drugs with potential therapeutic value for LSCC were identified.
-
Differentiation Stage Predicts Radiosensitivity in Mesenchymal-Like Pancreatic Cancer.
PURPOSE: To derive a genomic classifier to predict radiosensitivity of pancreatic cancer cell lines and patients with pancreatic cancer to allow genomic-guided radiation therapy. METHODS AND MATERIALS: We collected a comprehensive data set of full clonogenic cell survival curves of 45 pancreatic cancer cell lines irradiated with clinical photon and proton beams. We derived classifiers based on data from human embryonic and fetal pancreas single-cell RNA-sequencing to distinguish between epithelial and mesenchymal cells and to predict pancreas cell-line differentiation stage. Independent testing was done with an embryonic mouse pancreas single-cell RNA-sequencing data set. We then used bulk RNA-seq profiles from the Cancer Cell Line Encyclopedia to classify our pancreatic cancer cell lines using our epithelial-mesenchymal and differentiation stage classifiers. We then correlated the differentiation stage classifier with the radiosensitivity of the pancreatic cancer cell lines as well as with pancreatic cancer patient data from The Cancer Genome Atlas. RESULTS: We found wide variability in radiosensitivity to both photons and protons among pancreatic cancer cell lines. We showed that the differentiation stage is predictive of radiosensitivity of mesenchymal pancreatic cancer cell lines but not epithelial pancreatic cancer cell lines. We found that chromatin compaction is associated with the differentiation stage and showed that the less differentiated mesenchymal pancreatic cancer cell lines tend to be radioresistant and with more compact chromatin than the radiosensitive differentiated cell lines. Patients with more differentiated tumors exhibit better overall survival. CONCLUSIONS: We found that mesenchymal-like undifferentiated pancreatic cancer cell lines are more radioresistant than mesenchymal-like differentiated ones and that patients with pancreatic cancer with mesenchymal-like undifferentiated tumors treated with radiation therapy tend to have lower overall survival compared with patients with mesenchymal-like differentiated tumors. We show that it is feasibility to use the differentiation stage of mesenchymal pancreatic cancer cells to predict tumor specific radiosensitivity.
-
A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.
BACKGROUND: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. METHODS: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. RESULTS: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. CONCLUSIONS: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .
-
Multi-omics reveals a monocyte-macrophage-fibroblast axis in post-COVID-19 fibroinflammatory lung remodelling.
Post-COVID-19 residual lung abnormalities (RLA) are associated with persistent respiratory symptoms and radiological changes, yet the underlying mechanisms remain unclear. We performed integrated multi-omic profiling of paired bronchoalveolar lavage and blood samples from patients with post-COVID-19 RLA and healthy controls, combining single-cell RNA sequencing, CITE-seq, single-cell T cell receptor sequencing, bronchoalveolar lavage fluid proteomics and functional fibroblast assays. In post-COVID-19 RLA lungs, we identified an increased abundance of profibrotic SPP1hi monocyte-derived alveolar macrophages, arising from an expanded circulating HLA-DRlowCD163+PDE4Dhi classical monocyte progenitor population, supporting a blood-lung myeloid axis. Cell-cell communication modelling positioned macrophages as central hubs of immune-stromal crosstalk, promoting monocyte recruitment with profibrotic priming, and fibroblast activation. Proteomic analysis of bronchoalveolar lavage fluid from post-COVID-19 RLA and idiopathic pulmonary fibrosis, compared with healthy controls, revealed shared and distinct signatures. These alveolar proteins in post-COVID-19 RLA were predominantly attributed to myeloid cells and predicted to engage fibroblast receptors. Bronchoalveolar lavage fluid induced fibroblast proliferation, differentiation and collagen deposition in vitro, with proliferation attenuated by the antifibrotic drug nintedanib. We also identified compartment-specific lymphoid dysregulation, including depletion of mucosal-associated invariant T (MAIT) cells in both the lung and blood, decreased natural killer (NK) cells with oligoclonal T cell expansion in the lung, and expansion of regulatory and cytotoxic T cells in the blood. These findings support a persistent monocyte-macrophage-fibroblast axis linking immune dysregulation to fibroproliferative remodelling after COVID-19 and highlights candidate therapeutic targets for post-viral lung fibrosis. We provide a publicly available atlas at x (TBA).
-
Multi-omics dissection of CPNE1 reveals its prognostic value and immune-regulatory function in liver cancer.
BACKGROUND: Hepatocellular carcinoma (HCC) is known for its high malignancy and poor prognosis. Despite some progress in treatment approaches in recent years, the prognosis of HCC remains unfavorable. CPNE1 (Copine 1), a calcium-dependent phospholipid-binding protein, has been shown to be involved in the development of various cancers. However, the precise role of CPNE1 in HCC and its underlying molecular mechanisms remain unclear. This study aims to elucidate the role of CPNE1 in HCC and investigate its relationship with the immune microenvironment. METHODS: In this study, we conducted a pan-cancer analysis of CPNE1 expression and prognosis using data from the Cancer Genome Atlas (TCGA). Using a series of bioinformatics analyses, including expression, correlation, and survival analyses, we predicted the upstream miRNAs and lncRNAs of CPNE1 and explored its regulatory network through the competitive endogenous RNA (ceRNA) mechanism. Subsequently, functional enrichment analysis was conducted to explore the related functions and signaling pathways of the target genes. Finally, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data from the GEO database, we analyzed the spatial distribution of CPNE1 in liver cancer tissues and its expression profiles in immune cells. RESULTS: Differential expression analysis revealed that CPNE1 expression is higher in hepatocellular carcinoma tissues than in adjacent non-cancerous tissues, and HCC patients with higher CPNE1 expression tend to have a worse prognosis. The SNHG6-hsa-miR-101-3p axis was identified as a promising CPNE1 upstream ncRNA pathway in HCC. Functional enrichment analysis revealed that CPNE1 is significantly enriched in several tumor-associated pathways. Single-cell sequencing and spatial transcriptomics analyses revealed that CPNE1 expression is elevated in malignant hepatocytes and immune cells, particularly in NK cells. Further cell communication analysis suggested that CPNE1 may influence the immune microenvironment of liver cancer through the MIF and PPIA-BSG signaling pathways. CONCLUSION: CPNE1 is linked to poor prognosis in hepatocellular carcinoma (HCC) and proposes the SNHG6/hsa-miR-101-3p/CPNE1 regulatory axis. The study investigates the role of CPNE1 in modulating the immune microenvironment of liver cancer and suggests that it may serve as a biomarker for poor prognosis in HCC patients.
-
Graphical-model framework for automated annotation of cell identities in dense cellular images.
Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here, we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in Caenorhabditis elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.
-
KNSTRN Is a Prognostic Biomarker That Is Correlated with Immune Infiltration in Breast Cancer and Promotes Cell Cycle and Proliferation.
Kinetochore-localized astrin/SPAG5-binding protein (KNSTRN) promotes the progression of bladder cancer and lung adenocarcinoma. However, its expression and biological function in breast cancer remain largely unknown. Therefore, this study aimed to analyze KNSTRN expression, prognoses, correlation with immune infiltration, expression-associated genes, and regulated signaling pathways to characterize its role in regulating the cell cycle using both bioinformatics and in vitro functional experiments. Analyses of The Cancer Genome Atlas, Gene Expression Omnibus, TIMER, and The Human Protein Atlas databases revealed a significant upregulation of KNSTRN transcript and protein levels in breast cancer. Kaplan-Meier survival analyses demonstrated a significant association between high expression of KNSTRN and poor overall survival, relapse-free survival, post-progression survival, and distant metastases-free survival in patients with breast cancer. Furthermore, multivariate Cox regression analyses confirmed that KNSTRN is an independent prognostic factor for breast cancer. Immune infiltration analysis indicated a positive correlation between KNSTRN expression and T regulatory cell infiltration while showing a negative correlation with Tgd and natural killer cell infiltration. Gene set enrichment analysis along with single-cell transcriptome data analysis suggested that KNSTRN promoted cell cycle progression by regulating the expression of key cell cycle proteins. The overexpression and silencing of KNSTRN in vitro, respectively, promoted and inhibited the proliferation of breast cancer cells. The overexpression of KNSTRN enhanced the expression of key cell cycle regulators, including CDK4, CDK6, and cyclin D3, thereby accelerating the G1/S phase transition and leading to aberrant proliferation of breast cancer cells. In conclusion, our study demonstrates that KNSTRN functions as an oncogene in breast cancer by regulating immune response, promoting G1/S transition, and facilitating breast cancer cell proliferation. Moreover, KNSTRN has potential as a molecular biomarker for diagnostic and prognostic prediction in breast cancer.