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: 4 905
Страница 114 из 491-
Identification of PANoptosis-related genes in lung cancer and investigation of their role mechanisms in the immune microenvironment.
INTRODUCTION: Lung cancer remains a leading cause of cancer-related death globally, with high malignancy and poor prognosis. PANoptosis, a novel programmed cell death pathway combining apoptosis, pyroptosis, and necroptosis, has been increasingly implicated in disease pathogenesis, yet its role in lung cancer is poorly understood. This study integrated bulk and single-cell RNA-seq data from TCGA and GEO databases to identify and validate PANoptosis-related genes in lung cancer. We identified eight key genes (DAPK2, CAV1, PDK4, IL3RA, NOTCH1, CHMP4B, IRAK1, and SFN) that are significantly dysregulated in tumors and correlated with prognosis. Functional enrichment analysis implicated these genes in cell adhesion, cytokine signaling, necrotic pathways, and cell cycle regulation. Furthermore, immune infiltration analysis suggested roles for CHMP4B and IRAK1 in modulating the tumor immune microenvironment. Experimental validation via qPCR and IHC confirmed differential expression of these genes in cell lines and clinical samples. Drug sensitivity analysis also linked CHMP4B and PDK4 to response to targeted agents like Gefitinib. In conclusion, our results reveal the significance of PANoptosis-related genes in lung cancer pathogenesis and highlight their potential as prognostic biomarkers and therapeutic targets. BACKGROUND: Lung cancer is among the leading causes of cancer-related morbidity and mortality worldwide, characterized by high aggressiveness, early metastasis, and poor prognosis. PANoptosis, a recently recognized form of programmed cell death that integrates apoptosis, pyroptosis, and necroptosis, has been implicated in various diseases. However, comprehensive studies investigating PANoptosis-related genes in lung cancer remain limited, and their functional roles in tumorigenesis and progression are not fully understood. METHODS: In this study, we integrated transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases with single-cell RNA sequencing analysis to identify PANoptosis-related genes involved in lung cancer. A combination of differential gene expression analysis, functional enrichment analysis, immune infiltration profiling, and drug sensitivity prediction was performed to explore the biological significance and therapeutic potential of these genes. Furthermore, we validated the expression patterns of key genes using quantitative real-time PCR (qPCR) and immunohistochemistry (IHC) in lung cancer cell lines and tissue specimens. RESULTS: Eight PANoptosis-related genes (DAPK2, CAV1, PDK4, IL3RA, NOTCH1, CHMP4B, IRAK1, and SFN) were identified as being significantly associated with lung cancer. Their expression levels were notably altered in tumor tissues compared to normal controls and were significantly correlated with patient prognosis. Functional enrichment analysis revealed these genes were mainly involved in cell adhesion, cytokine signaling, necrosis-related pathways, and cell cycle regulation. Notably, CHMP4B and IRAK1 showed distinct expression patterns in different immune cell populations, indicating their potential roles in shaping the tumor immune microenvironment. Importantly, qPCR and IHC analyses confirmed the differential expression of these genes across lung cancer cell lines and clinical specimens. Drug sensitivity analysis further suggested that genes such as CHMP4B and PDK4 were associated with the response to targeted therapies including Gefitinib. CONCLUSION: This study provides a comprehensive landscape of PANoptosis-related gene dysregulation in lung cancer and highlights their potential as prognostic biomarkers and therapeutic targets. The integrated bioinformatics analysis combined with experimental validation supports their functional relevance in lung cancer development and treatment.
-
Exploring the role of pyroptosis in shaping the tumor microenvironment of colorectal cancer by bulk and single-cell RNA sequencing.
BACKGROUND: Emerging studies have shown that pyroptosis plays a non-negligible role in the development and treatment of tumors. However, the mechanism of pyroptosis in colorectal cancer (CRC) remains still unclear. Therefore, this study investigated the role of pyroptosis in CRC. METHODS: A pyroptosis-related risk model was developed using univariate Cox regression and LASSO Cox regression analyses. Based on this model, pyroptosis-related risk scores (PRS) of CRC samples with OS time > 0 from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database were calculated. The abundance of immune cells in CRC tumor microenvironment (TME) was predicted by single-sample gene-set enrichment analysis (ssGSEA). Then, the responses to chemotherapy and immunotherapy were predicted by pRRophetic algorithm, the tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms, respectively. Moreover, the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset (PRISM) were used to explore novel drug treatment strategies of CRC. Finally, we investigated pyroptosis-related genes in the level of single-cell and validated the expression levels of these genes between normal and CRC cell lines by RT-qPCR. RESULTS: Survival analysis showed that CRC samples with low PRS had better overall survival (OS) and progression-free survival (PFS). CRC samples with low PRS had higher immune-related gene expression and immune cell infiltration than those with high PRS. Besides, CRC samples with low PRS were more likely to benefit from 5-fluorouracil based chemotherapy and anti-PD-1 immunotherapy. In novel drug prediction, some compounds such as C6-ceramide and noretynodrel, were inferred as potential drugs for CRC with different PRS. Single-cell analysis revealed pyroptosis-related genes were highly expressed in tumor cells. RT-qPCR also demonstrated different expression levels of these genes between normal and CRC cell lines. CONCLUSIONS: Taken together, this study provides a comprehensive investigation of the role of pyroptosis in CRC at the bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) levels, advances our understanding of CRC characteristics, and guides more effective treatment regimens.
-
Single-cell transcriptional decoding of iron deficiency responses in maize root tips.
Maize roots respond to iron stress through cell type specificity and enhanced radial transport of Fe-DMA in root cells. Iron (Fe) deficiency, resulting from low Fe solubility in aerated soils, represents a major constraint for crop productivity. Maize (Zea mays), a Strategy II plant, acquires Fe through phytosiderophore (PS)-mediated chelation of rhizospheric Fe (III) and subsequent uptake of Fe (III)-PS complexes. However, cell-type-specific responses governing this process under Fe deficiency remain uncharacterized. Leveraging single-cell RNA sequencing (scRNA-seq), we constructed a root tip atlas using 15,306 high-quality cells from V3 stage primary roots under Fe-sufficient and Fe-deficient conditions, resolving seven distinct cell types. Under iron deficiency stress, significant changes were observed in the cell populations of cortex, epidermis, stele, and xylem. The cortex undergoes functional reprogramming following iron deficiency, with heme-binding and glutathione metabolism-related genes playing crucial roles in the iron deficiency response. Expression analysis of iron homeostasis genes revealed that iron-deficient root tips are associated with the biosynthesis of nicotianamine (NA) and 2'-deoxymugineic acid (DMA) and facilitated radial Fe-chelator transport. Notably, both scRNA-seq and bulk RNA-seq data revealed the downregulation of a FER-like iron deficiency-induced transcription factor (FIT) gene homologous to Arabidopsis AtFIT. This contrasts with the well-established role of AtFIT in Arabidopsis, where it acts as a positive regulator under iron-deficient conditions. Phylogenetic analysis suggests that AtFIT, OsFIT, and ZmFIT may share conserved functions, while their divergent expression patterns could be associated with differences between monocots and dicots. Through weighted gene co-expression network analysis (WGCNA), we identified cell-type-specific co-expression modules for the cortex, stele, epidermis, and xylem. The stele-specific module was significantly enriched with transcription factors, suggesting its role as a transcriptional regulatory hub for the iron deficiency response. Protein-protein interaction (PPI) network analysis revealed that core regulatory transcription factors (ZmWRKY76, ZmbHLH49, ZmMKK9) were distributed across various cell types including epidermis, xylem, phloem, and lateral root cap, indicating that the iron deficiency response is coordinated by a distributed regulatory network where the stele integrates signals and different cell types execute specific functions. In this study, we constructed a transcription map of iron-deficient maize root tips at single-cell resolution, uncovering fundamental adaptation strategies and potential targets for enhancing crop Fe efficiency.
-
A novel stemness-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity of clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction. METHOD: Gene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments. RESULTS: SRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells. CONCLUSION: We uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.
-
COL5A2 is a prognostic-related biomarker and correlated with immune infiltrates in gastric cancer based on transcriptomics and single-cell RNA sequencing.
BACKGROUND: There is still a therapeutic challenge in treating gastric cancer (GC) due to its high incidence and poor prognosis. Collagen type V alpha 2 (COL5A2) is increased in various cancers, yet it remains unclear how it contributes to the prognosis and immunity of GC. METHODS: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to download transcriptome profiling (TCGA-STAD; GSE84437), single-cell RNA sequencing (scRNA-seq) data (GSE167297) and clinical information. COL5A2 expression and its relationship with clinicopathological factors were analyzed. We conducted survival analysis and Cox regression analysis to evaluate the prognosis and independent factors of GC. Co-expressed analysis was also performed. To identify the underlying mechanism, we conducted analyses of differentially expressed genes (DEGs) and functional enrichment. The correlations between COL5A2 expression and immune cell infiltration levels and immune infiltrate gene marker sets were further explored. Additionally, we analyzed the association of COL5A2 expression with immunological checkpoint molecules. Furthermore, the relationship between COL5A2 expression and immunotherapy sensitivity was also investigated. RESULTS: COL5A2 expression was elevated in GC. More than this, the scRNA-seq analysis revealed that COL5A2 expression had a spatial gradient. The upregulated COL5A2 was associated with worse overall survival. A significant correlation was found between COL5A2 overexpression and age, T classification and clinical stage in GC. COL5A2 was found to be an independent factor for the unfortunate outcome in Cox regression analysis. The co-expressed genes of COL5A2 were associated with tumor stage or poor survival. Enrichment analysis revealed that the DEGs were mainly associated with extracellular matrix (ECM)-related processes, PI3K-AKT signaling pathway, and focal adhesion. GSEA analyses revealed that COL5A2 was associated with tumor progression-related pathways. Meanwhile, COL5A2 expression was correlated with tumor-infiltrating immune cells. Moreover, immunophenoscore (IPS) analysis and PRJEB25780 cohorts showed that patients with low COL5A2 expression were highly sensitive to immunotherapy. CONCLUSIONS: COL5A2 might act as a prognostic biomarker of GC prognosis and immune infiltration and may provide a therapeutic intervention strategy.
-
Nuclei multiplexing with barcoded antibodies for single-nucleus genomics.
Single-nucleus RNA-seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate or are frozen, and opens the way to human genetics studies, clinical trials, and precise cell atlases of large organs. However, such applications are currently limited by batch effects, processing, and costs. Here, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter recovered profiles. We develop DemuxEM, a computational tool that detects inter-sample multiplets and assigns singlets to their sample of origin, and validate its accuracy using sex-specific gene expression, species-mixing and natural genetic variation. Our approach will facilitate tissue atlases of isogenic model organisms or from multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.
-
Cross one single body 49 tissues single-cell transcriptome reveals detailed macrophage heterogeneity during pig pregnancy.
INTRODUCTION: Pregnancy involves complex physiological adaptations across maternal organs and the immune system to support fetal development. Macrophages play a dual role during pregnancy: defending against pathogens and supporting tissue adaptation. However, comprehensive and in-depth studies of cross-tissue transcriptional heterogeneity of macrophages during healthy pregnancy at the single-cell level remain elusive. METHODS: We performed single-cell RNA sequencing (scRNA-seq) to profile macrophages from a healthy pregnant pig across 49 tissues. Immunofluorescence was performed to verify the specific expression of transcription factors. RESULTS: In this study, we generated a macrophage atlas containing 114,881 macrophages from 49 tissues/organs within one single healthy pregnant pig, identified 33 subtypes, and revealed extensive tissue-specific diversity. We observed significant heterogeneity of macrophage subtypes across five different anatomical sites of adipose tissue. Notably, the Mφ MARCO+ subtype, primarily derived from mesenteric adipose tissue, showed higher activity in pattern recognition receptor signaling pathways compared to subtypes in other tissues, including different fat depots. Cross-tissue analysis revealed distinct expression patterns of transcription factors, cytokines, and cell surface receptors, including the transcription factor PLSCR1, specifically expressed in lung macrophages and verified by immunofluorescence. Cross-species analysis unveiled conservation and heterogeneity among macrophages in pigs, humans, and mice. CONCLUSION: We constructed a multiple-tissue single-cell transcriptome atlas of macrophages in one single healthy pregnant pig, revealing their molecular differences and commonalities across tissues and species. Our study provides a valuable resource for understanding macrophage diversity and tissue-specific macrophage adaptations during pregnancy in pigs.
-
The Potential Role of C4 MYH11+ Fibroblasts and the MDK-SDC2 Ligand-Receptor Pair in Lung Adenocarcinoma: Implications for Prognosis and Therapeutic Strategies.
BACKGROUND: Lung adenocarcinoma (LUAD) posed a significant threat to global human health. This study employed single-cell RNA sequencing (scRNA-seq) to analyze transcriptomic data from nine LUAD patients at different stages of tumor infiltration, aiming to elucidate the tumor microenvironment and key biological processes of LUAD. METHODS: In this study, we processed the scRNA-seq data using the Seurat package and sequentially applied principal component analysis followed by the Harmony package to effectively correct for batch effects, identifying 105,725 high-quality cells. Through cell clustering and gene expression profiling, we identified critical cell subpopulations and gene expression patterns in LUAD patients. RESULTS: Our analysis revealed that the C4 MYH11+ Fibroblasts subtype was primarily involved in biological processes related to muscle function. Further investigations uncovered the MDK-SDC2 ligand-receptor pair as a critical regulator of tumor cell invasion, proliferation, and migration, driving LUAD progression. Additionally, we developed a gene-based prognostic model that effectively predicted patient survival, providing valuable clinical insights. CONCLUSION: This study provided a comprehensive atlas of the LUAD tumor microenvironment, highlighted the role of the C4 MYH11+ Fibroblasts in tumor progression. It also proposed the MDK-SDC2 ligand-receptor pair as a novel mechanism, addressing a significant gap in this area of research. And presented a gene-based prognostic model as a novel perspective for research into immunotherapy and drug sensitivity in LUAD.
-
Single-cell transcriptomic analysis reveals T cell heterogeneity and metabolic reprogramming in human immune-mediated glomerulonephritis.
Immune-mediated glomerulonephritis (IMGN) is a major cause of kidney failure worldwide, yet the precise roles of T cells in its pathogenesis remain poorly understood. However, existing studies lack a comprehensive understanding of the characteristics and functional roles of IMGN T cells in the human context. Addressing this gap is crucial for advancing targeted therapies. By integrating single-cell RNA sequencing (sc-RNA-seq) data from three primary IMGN types-IgA nephropathy (IgAN), lupus nephritis (LN), and membranous nephropathy (MN)-we identified T cell subtype alterations at single-cell resolution. Utilizing advanced sc-RNA-seq computational pipelines, we constructed gene co-expression networks (GCNs), inferred T-cell differentiation trajectories, and assessed metabolic and intercellular signaling features. IMGN kidneys presented expanded T-cell compartments, with significant enrichment of cytotoxic natural killer T (NKT) cells and GZMK⁺ effector memory T (GZMK⁺ Tem) cells. Notably, LTB⁺ memory T cells (LTB⁺ Tm) were selectively elevated in IgAN and LN patients. A coexpression module centered on RGS1 was significantly correlated with 24-h proteinuria (p < 0.001). Metabolic profiling revealed subtype-specific disruptions in the glutathione (GSH) and 3-phospho-D-glyceroyl phosphate (3PD) pathways. Interaction analysis highlighted endothelial cells, mesangial cells, and fibroblasts as key mediators of pathogenic T-cell activation via defined ligand‒receptor pairs. This study provides the first comprehensive single-cell atlas of human IMGN T cells, revealing disease-specific T-cell states, metabolic signatures, and activation mechanisms. Our findings offer new insights into human renal immunopathology and identify promising therapeutic targets for IMGN.
-
Spatial organization of the mouse retina at single cell resolution by MERFISH.
The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina, little is known about their spatial organization. Here, we generated the single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.