Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 911
Page 124 of 492-
High-resolution subtyping of fibroblasts in gastric cancer reveals diversity among fibroblast subsets and an association between the MFAP5-fibroblast subset and immunotherapy.
BACKGROUNDS: Gastric cancer (GC) remains a global health threat due to frequent treatment failures caused by primary or acquired resistance. Although cancer-associated fibroblasts (CAFs) have been implicated in this process, it is still unclear which specific subtype(s) of CAFs hinder T-cell infiltration and promote resistance to immunotherapy. METHODS: We analyzed the GC fibroblast atlas in detail by combining 63,955 single cells from 14 scRNA-seq datasets. We also performed RNA-seq data in a local GC cohort and examined 13 bulk RNA-seq datasets to understand the biological and clinical roles of different CAF subsets. Additionally, we conducted in vitro experiments to study the role of specific proteins in GC development. RESULTS: We identified a total of 17 fibroblast subsets in gastric cancer, nine of which did not fit into the existing CAFs classification. These subsets exhibited significant heterogeneity in distribution and biological characteristics (metabolism, cell-cell interactions, differentiation state), as well as clinical functions such as prognosis and response to immunotherapy. In particular, cluster 6 stood out for its high expression of MFAP5, CFD, and PI16; it was found to be negatively associated with both overall survival and response to immunotherapy in GC. This association was linked to an immunosuppressive microenvironment characterized by an increase in M2 macrophages but higher levels of T cell dysfunction and exclusion-a feature shared by tumors expressing MFAP5. Furthermore, the addition of human recombinant MFAP5 promoted proliferation and migration of HGC-27 cells by inducing the MFAP5/NOTCH1/HEY1 signaling pathway. CONCLUSION: We introduce a high-resolution GC fibroblast atlas. The 17 identified fibroblast clusters provide valuable opportunities for gaining deeper biological insights into the relationship between fibroblasts and GC development. Particularly, cluster 6 and its specific marker MFAP5 could serve as prognostic factors in GC and form a foundation for personalized therapeutic combinations to address primary resistance to ICIs.
-
Prognostic value of neuro-related genes in colorectal cancer and their potential implications for immunotherapy.
Colorectal cancer (CRC) remains a leading cause of cancer mortality. This study investigates the prognostic and immunological role of neural-related genes (NRGs) in CRC. Using The Cancer Genome Atlas (TCGA) data, a four-gene signature (POU4F1, KLC3, ATP2A1, CALB2) was constructed via Cox and least absolute shrinkage and selection operator (LASSO) regression. This signature independently predicted patient survival. High-risk patients showed enrichment in cytokine signaling and oxidative phosphorylation. Importantly, CALB2 expression correlated with increased infiltration of NKT and central memory CD4+ T cells, while high-risk scores were associated with greater potential for immune evasion. Single-cell analysis revealed epithelial cells as the primary source of these NRGs. This NRG-based signature provides a novel biomarker for prognosis and immunotherapy optimization in CRC.
-
Transcriptional Atlas of Intestinal Immune Cells Reveals that Neuropeptide α-CGRP Modulates Group 2 Innate Lymphoid Cell Responses.
Signaling abnormalities in immune responses in the small intestine can trigger chronic type 2 inflammation involving interaction of multiple immune cell types. To systematically characterize this response, we analyzed 58,067 immune cells from the mouse small intestine by single-cell RNA sequencing (scRNA-seq) at steady state and after induction of a type 2 inflammatory reaction to ovalbumin (OVA). Computational analysis revealed broad shifts in both cell-type composition and cell programs in response to the inflammation, especially in group 2 innate lymphoid cells (ILC2s). Inflammation induced the expression of exon 5 of Calca, which encodes the alpha-calcitonin gene-related peptide (α-CGRP), in intestinal KLRG1+ ILC2s. α-CGRP antagonized KLRG1+ ILC2s proliferation but promoted IL-5 expression. Genetic perturbation of α-CGRP increased the proportion of intestinal KLRG1+ ILC2s. Our work highlights a model where α-CGRP-mediated neuronal signaling is critical for suppressing ILC2 expansion and maintaining homeostasis of the type 2 immune machinery.
-
The eSNV-detect: a computational system to identify expressed single nucleotide variants from transcriptome sequencing data.
Rapid development of next generation sequencing technology has enabled the identification of genomic alterations from short sequencing reads. There are a number of software pipelines available for calling single nucleotide variants from genomic DNA but, no comprehensive pipelines to identify, annotate and prioritize expressed SNVs (eSNVs) from non-directional paired-end RNA-Seq data. We have developed the eSNV-Detect, a novel computational system, which utilizes data from multiple aligners to call, even at low read depths, and rank variants from RNA-Seq. Multi-platform comparisons with the eSNV-Detect variant candidates were performed. The method was first applied to RNA-Seq from a lymphoblastoid cell-line, achieving 99.7% precision and 91.0% sensitivity in the expressed SNPs for the matching HumanOmni2.5 BeadChip data. Comparison of RNA-Seq eSNV candidates from 25 ER+ breast tumors from The Cancer Genome Atlas (TCGA) project with whole exome coding data showed 90.6-96.8% precision and 91.6-95.7% sensitivity. Contrasting single-cell mRNA-Seq variants with matching traditional multicellular RNA-Seq data for the MD-MB231 breast cancer cell-line delineated variant heterogeneity among the single-cells. Further, Sanger sequencing validation was performed for an ER+ breast tumor with paired normal adjacent tissue validating 29 out of 31 candidate eSNVs. The source code and user manuals of the eSNV-Detect pipeline for Sun Grid Engine and virtual machine are available at http://bioinformaticstools.mayo.edu/research/esnv-detect/.
-
Prognostic and Immunotherapeutic Value of Regulatory T Cell Marker Gene Signature in Melanoma.
Regulatory T cells (Tregs) are central to establishing an immunosuppressive tumor microenvironment (TME), which promotes cancer progression and influences therapeutic outcomes. However, the prognostic significance of Treg-related genes (TRGs) in predicting immunotherapy response in melanoma remains insufficiently characterized. This study seeks to elucidate the role of TRGs in the antitumor immune response of melanoma. The ordinary transcriptome and single-cell RNA sequencing (scRNA-seq) data were obtained from the gene expression omnibus and the cancer genome atlas databases. A multi-tiered quality control process was applied to scRNA-seq data, followed by cell annotation, cell-cell communication, and enrichment analysis to investigate Treg function in the melanoma microenvironment. Weighted gene coexpression network analysis (WGCNA) was employed to identify modules associated with Treg infiltration. Key prognostic genes were identified using univariate Cox regression analysis and integrated into a prognostic model through least absolute shrinkage and selection operator and stepwise regression methods. The analysis revealed a Treg-related gene signature (TRGS) comprising CHD3, FOSB, SEMA4D, PSME1, FYN, PRKACB, and ARID5A. Higher TRGS-based risk scores were significantly associated with worse prognoses, immune cell infiltration, and stromal scores. TRGS was identified as an independent prognostic indicator for melanoma, offering novel insights into the role of Tregs in modulating the TME. This study highlights the potential clinical utility of TRGs in melanoma diagnostics and personalized immunotherapy, providing a robust foundation for future therapeutic strategies.
-
[Single-cell RNA sequencing of B cells reveals molecular typing in Sjögren syndrome].
OBJECTIVE: To establish a molecular classification framework for Sjögren syndrome (SS) by stratifying patients into distinct subtypes through unsupervised clustering of B cell single-cell RNA sequencing (scRNA-seq). This study characterizes subtype-specific gene signatures to construct protein-protein interaction (PPI) networks, thereby elucidating core regulatory mechanisms and potential therapeutic targets. Concurrently, it defines the clinical heterogeneity of SS by profiling autoantibodies and B-cell subset distributions across subtypes. METHODS: The scRNA-seq data from 24 SS patients and 4 healthy controls were obtained from the Gene Expression Omnibus (GEO) database. We constructed a B cell atlas and identified differential gene expression profiles between SS and healthy controls B cells. Unsupervised clustering was applied to stratify SS patients into different molecular subtypes. Functional enrichment analysis of subtype-specific gene signatures was performed to infer associated biological processes/pathways. PPI networks were constructed using the STRING database and Cytoscape software to identify core functions and potential therapeutic targets for subtype-specific genes. The prevalence of autoantibodies and proportions of B cell subsets were statistically analyzed across subtypes. RESULTS: The B cells were classified into eight subsets: transitional B cell, naïve B cell, memory B cell, double negative 1 (DN1) B cell, double negative 2 (DN2) B cell, VAV3+IRF1+ B cell, GP9+ B cell, and plasma cell. The FindAllMarkers function identified 792 differentially expressed genes (DEGs) between the SS patients and healthy controls. Unsupervised clustering stratified patients into three subtypes: (1) Inter-feron-dominant subtype characterized by enrichment in type Ⅰ/Ⅱ interferon and non-canonical nuclear factor kappa-B (NF-κB) signaling pathways. This subtype showed the highest proportions of naïve B cells and transitional B cells, along with the highest anti-Sjögren syndrome antigen A (SSA)/Sjögren syndrome antigen B (SSB) positivity. (2) B cell activation subtype characterized by enrichment in Fc receptor and B cell receptor signaling pathways. This subtype exhibited the highest proportions of memory B cells and DN1 B cells. (3) Endoplasmic reticulum stress subtype characterized by enrichment in protein folding and endoplasmic reticulum-associated degradation pathways. This subtype was marked by the highest proportion of VAV3+IRF1+ B cells. PPI networks identified subtype-specific hub genes regulating these core functions. CONCLUSION: Stratification of SS patients through clustering of B cell DEGs successfully defined three molecular subtypes (interferon-dominant, B cell activation, and endoplasmic reticulum stress subtypes). Each subtype exhibits distinct autoantibody profiles and B cell subset distributions. This molecular typing framework advances our understanding of SS heterogeneity and provides actionable insights for targeted therapy development.
-
Integration of single-cell and bulk RNA sequencing identifies and validates T cell-related prognostic model in hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is a lethal malignancy, and predicting patient prognosis remains a significant challenge in clinical treatment. T cells play a crucial role in the tumor microenvironment, influencing tumorigenesis and progression. In this study, we constructed a T cell-related prognostic model for HCC. Using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, we identified 6,281 T cells from 10 HCC patients and subsequently identified 855 T cell-related genes. Comprehensive analyses were conducted on T cells and their associated genes, including enrichment analysis, cell-cell communication, trajectory analysis, and transcription factor analysis. By integrating scRNA-seq and bulk RNA-seq data with prognostic information from The Cancer Genome Atlas (TCGA), we identified T cell-related prognostic genes and constructed a model using LASSO regression. The model, incorporating PTTG1, LMNB1, SLC38A1, and BATF, was externally validated using the International Cancer Genome Consortium (ICGC) database. It effectively stratified patients into high- and low-risk groups based on risk scores, revealing significant differences in immune cell infiltration between these groups. Differential expression levels of PTTG1 and BATF between HCC and adjacent non-tumor tissues were further validated by immunohistochemistry (IHC) in 25 patient tissue samples. Moreover, a Cox regression analysis was performed to integrate risk scores with clinical features, resulting in a nomogram capable of predicting patient survival probabilities. This study introduces a novel prognostic risk model for HCC patients, aimed at stratifying patients by risk, enhancing personalized treatment strategies, and offering new insights into the role of T cell-related genes in HCC progression.
-
Integration of single-cell and bulk RNA data to identify key cell types and biomarkers in viral myocarditis: An integrated bioinformatics analysis.
Viral myocarditis (VMC), a multifaceted pathological condition predominantly triggered by viral infections, has emerged as a major worldwide healthcare concern due to its intricate pathogenesis and substantial disease burden. Although the centrality of immune dysregulation in driving VMC progression is well-established, the precise identities of distinct immune cell subsets and their molecular mediators governing pathological progression continue to present significant knowledge gaps. Single-cell datasets were integrated using quality control, batch correction, and normalization. The FindAllMarkers function identified marker genes for each cluster. Cell types were annotated based on literature-derived marker genes. DESeq2 was employed to identify differentially expressed genes in T cells. CellChat was used to explore intercellular communication and identify key ligand-receptor signaling pathways. Receiver operating characteristic curves assessed the predictive performance of key factors in a validation cohort. T cells were re-annotated for higher-resolution subtyping, and pseudotime analysis depicted the cell trajectories of T cell subtypes. Integrated single-cell data revealed a comprehensive single-cell atlas of VMC. Mechanistic delineation of T cell as principal pathogenic effectors emerged through multi-omics interrogation incorporating single-cell transcriptomic profiling and pathological trajectory reconstruction; functional genomics analyses further substantiated their cardinal involvement in cardiotropic viral pathogenesis. Additionally, communication analysis highlighted CCL as a critical immune regulatory pathway for T cell interactions in VMC. Significant upregulation of CCL3 was confirmed in the validation cohort, establishing it as a potential biomarker and therapeutic target for VMC. Pseudotime analysis and re-annotation of T cell subpopulations revealed significant enrichment of T.8EFF.OT1LISO and T.Tregs in VMC. This study identifies T cells as key immune players in VMC, with CCL3 proposed as a novel biomarker for the condition.
-
Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression. METHODS: We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors. RESULTS: By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression. CONCLUSIONS: This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.
-
Identifying prognostic biomarkers in oral squamous cell carcinoma: an integrated single-cell and bulk RNA sequencing study on mitophagy-related genes.
Oral squamous cell carcinoma (OSCC) has an extremely poor prognosis. Recent studies have suggested that mitophagy-related genes (MRGs) are closely correlated with the development and occurrence of cancer, but the role they play in oral cancer has not yet been explained.We conducted a comprehensive analysis of integrated single-cell and bulk RNA sequencing (RNA-seq) data retrieved from Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) database. Multiple methods were combined to provide a comprehensive understanding of the genetic expression patterns and biology of OSCC, such as analysis of pseudotime series, CellChat cell communication, immune infiltration, Gene Ontology (GO), LASSO Cox regression, gene set variation analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), Tumor Mutation Burden (TMB) and drug sensitivity assessments. The findings of this study demonstrated significantly greater activity of MRGs in NK cells than in other cells in OSCC. A reliable prognostic model was developed using 12 candidate genes strongly associated with mitochondrial autophagy. T stage, N stage and risk score were revealed as independent prognostic factors. Distinctively enriched pathways and immune cells were observed in different risk groups. Notably, low-risk patients were more responsive to chemotherapy. In addition, a nomogram model with excellent predictive ability was established by combining the risk scores and clinical features. The activity of MRGs suggest the potential for the development of new targeted therapies. The construction of a robust prognostic model also provides reference value for individualized prediction and clinical decision-making in patients with OSCC.