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Constructing a disulfidptosis-related prognostic signature of hepatocellular carcinoma based on single-cell sequencing and weighted co-expression network analysis.
Hepatocellular carcinoma (HCC) ranks as the second leading cause of cancer-related deaths globally. Disulfidptosis is a newly identified form of regulated cell death that is induced by glucose starvation. However, the clinical prognostic characteristics of disulfidptosis-associated genes in HCC remain poorly understood. We conducted an analysis of the single-cell datasets GSE149614 and performed weighted co-expression network analysis (WGCNA) on the Cancer Genome Atlas (TCGA) datasets to identify the genes related to disulfidptosis. A prognostic model was constructed using univariate COX and Lasso regression. Survival analysis, immune microenvironment analysis, and mutation analysis were performed. Additionally, a nomogram associated with disulfidptosis-related signature was constructed to identify the prognosis of HCC patients. Patients with HCC in the TCGA and GSE14520 datasets were categorized using a disulfidptosis-related model, revealing significant differences in survival times between the high- and low-disulfidptosis groups. High-disulfidptosis patients exhibited increased expression of immune checkpoint-related genes, implying that immunotherapy and certain chemotherapies may be beneficial for them. Meanwhile, the ROC and decision curves analysis (DCA) indicated that the nomogram has satisfying prognostic efficacy. Moreover, the experimental results of GATM in this prognostic model indicated that GATM is low expressed in HCC tissues, and GATM knockdown promotes the proliferation and migration of HCC cells. By analyzing single-cell and bulk multi-omics sequencing data, we developed a prognostic signature related to disulfidptosis and explored the relationship between high- and low-disulfidptosis groups in HCC. This study offers a novel reference for gaining a deeper understanding of the role of disulfidptosis in HCC.
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Single-cell landscape of peripheral immune response in patients with anti-melanoma differentiation-associated gene 5 dermatomyositis.
OBJECTIVE: Anti-melanoma differentiation-associated gene 5 (Anti-MDA5)-positive DM is a rare but life-threatening autoimmune disorder that is associated with a high risk of developing rapidly progressive interstitial lung disease. Current empirical therapies offer limited benefit in terms of patient survival, as little is known about the aetiology of anti-MDA5 DM. To best understand its immune landscape, we applied single-cell RNA sequencing (scRNA-seq) to peripheral blood samples from DM patients and healthy controls. METHODS: Peripheral blood mononuclear cells (PBMCs) from eight DM patients (including three distinct subtypes of DM) and two healthy donors were sequenced using the 10X Genomics platform. Additional scRNA-seq data for four healthy donors were incorporated for further bioinformatic analysis. RESULTS: Aberrantly increased proportions of CD14+ monocytes and plasma cells were observed in anti-MDA5 DM PBMC samples. Moreover, we found an overactivated type I IFN response and antiviral immunity in both innate and adaptive immune cells derived from anti-MDA5 DM patients that was positively correlated with disease severity. Importantly, a unique subset of CD14+ monocytes that highly expressed IFN alpha-inducible protein 27 (IFI27), a biomarker for viral infection, and IFN induced with helicase C domain 1 (IFIH1, which encodes MDA5) was specifically identified in anti-MDA5 DM samples for the first time. CONCLUSION: Our study has illustrated the peripheral immune cell atlas of a number of DM subtypes, has provided compelling evidence for a viral infection-derived origin for anti-MDA5 DM, and has indicated potential targets for innovative therapeutic interventions.
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Type I interferon signaling facilitates resolution of acute liver injury by priming macrophage polarization.
Due to their broad functional plasticity, myeloid cells contribute to both liver injury and recovery during acetaminophen overdose-induced acute liver injury (APAP-ALI). A comprehensive understanding of cellular diversity and intercellular crosstalk is essential to elucidate the mechanisms and to develop therapeutic strategies for APAP-ALI treatment. Here, we identified the function of IFN-I in the myeloid compartment during APAP-ALI. Utilizing single-cell RNA sequencing, we characterized the cellular atlas and dynamic progression of liver CD11b+ cells post APAP-ALI in WT and STAT2 T403A mice, which was further validated by immunofluorescence staining, bulk RNA-seq, and functional experiments in vitro and in vivo. We identified IFN-I-dependent transcriptional programs in a three-way communication pathway that involved IFN-I synthesis in intermediate restorative macrophages, leading to CSF-1 production in aging neutrophils that ultimately enabled Trem2+ restorative macrophage maturation, contributing to efficient liver repair. Overall, we uncovered the heterogeneity of hepatic myeloid cells in APAP-ALI at single-cell resolution and the therapeutic potential of IFN-I in the treatment of APAP-ALI.
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Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis.
This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.
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A temporal single cell atlas of dynamic chromatin accessibility during murine lung maturation.
Lung maturation involves intricate cellular differentiation and structural remodeling, yet the underlying epigenetic dynamics remain poorly characterized at single-cell resolution. Here we present HTL-ATAC-seq, an ultra-high-throughput, ligation-based single-cell ATAC-seq approach. Applying HTL-ATAC-seq to mouse lung tissue across 15 continuous postnatal time points, we construct a temporal chromatin accessibility atlas of 1,185,619 nuclei, identifying 129 distinct cell subclusters and 713,987 candidate chromatin regulatory elements. Temporal analysis reveals three distinct cellular maturation stages and 12 dynamic chromatin accessibility patterns, capturing the transition from structural morphogenesis to immune maturation. We uncover distinct transcription factor regulatory patterns between alveolar type 1 and 2 cells and identify a putative Sprr1a enhancer specifically active in alveolar type 1 cells. Finally, we identify time- and cell-type-specific cis-regulatory elements associated with lung disease-related gene sets. In this work, we provide a comprehensive resource for understanding the epigenetic mechanisms underlying lung maturation and lung disease-associated regulatory programs.
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KYNU in macrophages contributes to the unique immune feature of LUAD via integrating single-cell and bulk RNA sequencing data: an exploratory analysis.
BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant subtype of lung cancer associated with an unfavorable prognosis. However, the roles of the tumor microenvironment (TME) and Kynureninase (KYNU) in LUAD remain largely unclear. This study aimed to investigate the potential role of KYNU in macrophages and LUAD. METHODS: All LUAD related data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The expression of KYNU was analyzed across different cell types following dimensionality reduction analysis. Immune cell infiltration and immunotherapy response prediction were performed using CIBERSORT and TIMER, respectively. Gene set variation analysis (GSVA) was employed for functional enrichment. RESULTS: Among all immune cells in LUAD, KYNU was primarily expressed in monocytes and macrophages. The upregulated genes in KYNU+macrophages group were significantly enriched in in gene ontology (GO) terms related to antigen processing and presentation. There were increased MHC-I/ MHC-II signal interactions between KYNU+macrophages and B cells as well as T cells. In LUAD patients with higher proportions of KYNU+macrophages, a significantly greater number of patients benefited from immunotherapy (p = 0.033). GSVA results indicated that the MHC pathway was significantly activated in high KYNU+macrophage group. CONCLUSIONS: KYNU is primarily in LUAD macrophages, contributing to the distinct immune features and correlating with the enhanced antigen presentation in LUAD. This study preliminarily confirms that KYNU may serve as a potential biomarker for immunotherapy.
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Identification of immune-related gene signature for predicting prognosis of glioblastoma patients.
BACKGROUND: Glioblastoma (GBM) is the most common and aggressive primary brain tumor with a poor prognosis due to its immunosuppressive microenvironment. Reliable prognostic biomarkers are still lacking. This study aims to construct an immune-related gene signature to predict the prognosis of GBM patients and explore the molecular characteristics of different immune subtypes. METHODS: The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Gene Expression Omnibus Series (GSE) 108474 datasets were integrated to classify cold and hot tumor subtypes via single-sample gene set enrichment analysis (ssGSEA) and partitioning around medoids (PAM) clustering. Prognostic genes were identified via weighted gene co-expression network analysis (WGCNA) and screened with 101 machine learning (ML) algorithms. A risk-scoring model was developed, and gene expression was validated via single-cell transcriptomics and immunohistochemistry (IHC). RESULTS: Seven core genes were identified, with area under the curve (AUC) values of 0.75-0.88 for distinguishing cold/hot subtypes. The model exhibited stable predictive performance in external validation cohorts (AUC =0.55-0.69). The survival rate of the high-risk cold tumor cohort was significantly lower than that of the low-risk hot tumor cohort. CONCLUSIONS: Our study identifies key genes and molecular mechanisms underlying the immune microenvironment of GBM. The prognostic model developed lays a theoretical foundation for personalized immunotherapy and stratified treatments.
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Integrated Single-cell and Transcriptome Sequencing Analyses Identified PREX1 as an Immune-related Prognostic Biomarker for Liver Hepatocellular Carcinoma.
Background: PtdIns (3,4,5) P3-dependent Rac exchanger 1 (PREX1), also known as PREX1, a member of the Rac guanine nucleotide exchange factors (Rac-GEF) family. Studies have suggested that PREX1 plays a role in mediating oncogenic pathway activation and controlling various biological mechanisms in different types of cancer, including liver hepatocellular carcinoma (LIHC). However, the function of PREX1 in the pathogenesis of LIHC and its potential role on immunological regulation is not clearly elucidated. Methods: The expression level and the clinical role of PREX1 in LIHC was analyzed based on database from the Cancer Genome Atlas (TCGA), TNM plotter and University of Alabama Cancer Database (UALCAN). We investigated the relationship between PREX1 and immunity in LIHC by TISIDB, CIBERSORT and single cell analysis. Immunotherapy responses were assessed by the immunophenoscores (IPS). Moreover, biological functional assays were performed to further investigate the roles of PREX1 in liver cancer cell lines. Results: Higher expression of PREX1 in LIHC tissues than in normal liver tissues was found based on public datasets. Further analysis revealed that PREX1 was associated with worse clinical characteristics and dismal prognosis. Pathway enrichment analysis indicated that PREX1 participated in immune-related pathways. Through CIBERSORT and single cell analysis, we found a remarkable correlation between the expression of PREX1 and various immune cells, especially macrophages. In addition, high PREX1 expression was found to be associated with a stronger response to immunotherapy. Furthermore, in vitro assays indicated that depletion of PREX1 can suppress invasion and proliferation of LIHC cells. Conclusion: Elevated expression of PREX1 indicates poor prognosis, influences immune modulation and predicts sensitivity of immunosuppression therapy in LIHC. Our results suggested that PREX1 may be a prognostic biomarker and therapeutic target, offering new treatment options for LIHC.
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Chromatin State Distinguishes Injury-Responsive from State-Stabilizing Transcriptional Programs in Hybrid Hepatocytes.
Liver injury induces rapid transcriptional responses in hepatocytes, yet the chromatin features that distinguish injured hepatocytes from healthy hepatocytes remain poorly understood. Using an integrated functional genomics approach combining bulk RNA-seq, ATAC-seq, and CUT&Tag profiling of H3K27ac and H3K27me3, we define the transcriptional and chromatin landscape of Sox9-expressing hepatocytes, which exhibit gene expression consistent with both hepatocyte and biliary identity. Under homeostatic conditions, Sox9+ hybrid hepatocytes (HybHeps) are rare and confined to the periportal space, while chronic injury induces an expansion of Sox9+ metaplastic hepatocytes (MetHeps). We identify three classes of differentially expressed genes associated with injury-responsive, state-associated, or shared regulatory programs and demonstrate that these classes are governed by distinct chromatin mechanisms. Injury-responsive transcription is driven primarily by dynamic chromatin accessibility remodeling at NF-κB- and AP-1-enriched regulatory elements, while state-associated and shared programs are reinforced through selective H3K27ac and H3K27me3 modification with comparatively stable accessibility. Relative to conventional hepatocytes, HybHeps encode a permissive chromatin landscape at injury-responsive loci under homeostatic conditions, consistent with epigenetic priming that facilitates rapid inflammatory activation. Projection of mouse-derived gene programs onto a human liver single-cell atlas encompassing both healthy and diseased hepatocytes confirms that SOX9-expressing hepatocytes preferentially engage injury-associated inflammatory modules while attenuating hepatocyte metabolic identity programs. Together, these findings define a chromatin-based regulatory dichotomy between inflammatory responsiveness and hybrid hepatocyte cell state stability, providing mechanistic insight into how differentiated epithelial cells integrate inflammatory signals while preserving cell state.
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Predicting survival and immune status of breast cancer patients based on prognostic features related to PANoptosis.
BACKGROUND: Breast cancer (BRCA) is a prevalent female malignancy. PANoptosis, integrating diverse cell death traits, is pivotal in BRCA, thus necessitating deeper study. METHODS: Data from Gene Expression Omnibus (GEO, GSE180286 and GSE20685) and The Cancer Genome Atlas (TCGA) were analyzed. Weighted gene co-expression network analysis (WGCNA) identified PANoptosis-related genes in BRCA patients from TCGA. Further refinement of these module genes was conducted through univariate Cox regression, LASSO regression (glmnet package), and stepwise multivariate regression analysis to derive the final biomarkers. Based on these biomarkers, a risk model was established, and in-vitro experiments (wound healing assay, Transwell assay, and qRT-PCR) were carried out to validate the accuracy of these biomarkers. The MCPcounter package and the oncoPredict package were used to assess immune cell infiltration and sensitivity to drugs in BRCA patients, respectively. RESULTS: This study identified 8 biomarkers (ACY3, CD83, CXCL13, KLHDC7B, NR1H3, SMCO4, TRPM2, and UPP1) and established a risk model. In-vitro experiments revealed significant differences in biomarker expression between BRCA cells and the control group, with TRPM2 knockdown inhibiting BRCA cell migration and invasion. Enrichment analysis showed metabolic pathways were activated in high-risk group. Additionally, immune analysis showed lower immune cell enrichment and significant enrichment of fibroblasts in the high-risk group. Drug sensitivity analysis linked 13 drugs to RiskScore. Finally, single-cell analysis identified six cell types (including cancer stem cells, fibroblasts, T-cells, macrophages, B/Plasma cells, and endothelial cells) for BRCA and found that macrophages had higher PANoptosis activity. CONCLUSION: The current research introduces a novel model for BRCA prognosis analysis but also provides a fresh perspective on BRCA treatment strategies.