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Construction and experimental validation of a B cell senescence-related gene signature to evaluate prognosis and immunotherapeutic sensitivity in bladder cancer.
Senescent B cells exhibit reduced antibody production and enhanced proinflammatory cytokine and chemokine secretion, exerting non-negligible functions in antitumor immunity. This study aims to clarify the prognosis value of B cell senescence-related genes in bladder cancer (BLCA). Twelve B cell senescence-related genes were identified based on previous studies and the single-cell RNA sequencing of a BLCA sample from Gene Expression Omnibus (GEO). The Cancer Genome Atlas BLCA cohort was used as the training dataset. Three cohorts from GEO, 35 clinical samples from the local hospital, and in vitro cell experiments were used for validation. The unsupervised clustering based on the 12 genes was associated with the prognosis and the tumor immunity. Through least absolute shrinkage and selection operator regression and random forest algorithm, G protein subunit gamma 11 (GNG11) and inhibitor of DNA binding 1 (ID1) of the 12 genes were determined as significant prognosis predictors and then included in the multivariate Cox regression model. The model was a reliable and robust prognosis biomarker across multiple large-scale cohorts (pooled HR = 1.76, 95% CI = 1.41-2.20). The tight association between the model and BLCA malignant degree was demonstrated in the local cohort (P < 0.01). The model could also predict the immunotherapeutic sensitivity, which was confirmed by the tumor immune dysfunction and exclusion algorithm (P < 0.0001) and IMvigor210 cohort (P < 0.0001). At last, in vitro cell experiments in IM-9 and GM12878 B cells indicated that GNG11 and ID1 were involved in the cellular aging process. Collectively, a B cell senescence-related gene signature was constructed to evaluate the prognosis and immunotherapeutic response in BLCA, providing novel insights into the biological mechanisms.
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Cardio-oncology-relevant programmes in the cervical cancer tumour microenvironment: an exploratory multi-omics and endothelial-cell study.
BACKGROUND: Bevacizumab and immune-checkpoint inhibitors (ICIs) improve outcomes in advanced cervical cancer but may cause cardiovascular toxicity (CVT). Public cervical-cancer transcriptomic datasets do not contain cardiac tissue or adjudicated CVT outcomes; consequently, tumour-microenvironment (TME) expression cannot by itself establish a cardiac susceptibility mechanism. We reanalysed the available data to ask the narrower question of whether cervical tissues contain angiogenic, endothelial, inflammatory, T-cell-exhaustion, and TGF-β/fibrotic programmes that are biologically relevant to cardio-oncology. METHODS: All nine single-cell RNA-sequencing specimens in GSE208653 were analysed: two HPV-negative healthy cervix, two HPV-positive histologically normal cervix, two high-grade squamous intraepithelial lesions, two squamous-cell carcinomas, and one adenocarcinoma. After prespecified quality control, 74,457 cells were integrated with Harmony and annotated into broad lineages. Prespecified module scores and four ligand-receptor expression-product scores were aggregated by biological specimen; malignant versus non-malignant differences were evaluated with exact specimen-label permutation tests and Benjamini-Hochberg correction. Four GSE208654 Visium count matrices were analysed descriptively at spot level. Coexpression among the five programmes was assessed in 304 TCGA-CESC primary tumours. qRT-PCR summary data from HUVEC (ATCC PCS-100-010) and primary human aortic endothelial cells (HAEC; ATCC PCS-100-011) were reinterpreted as endothelial pharmacology, not cardiac validation. RESULTS: The scRNA-seq atlas contained 32 clusters spanning epithelial, immune, endothelial, and stromal lineages. None of the five specimen-level programmes differed significantly between malignant and non-malignant specimens after multiple-testing correction (all q ≥ 0.119). Likewise, none of the VEGFA-KDR, IL1B-IL1R1, TGFB1-TGFBR2, or LGALS9-HAVCR2 expression-product scores was significant (all q ≥ 0.143). The four spatial specimens showed heterogeneous spot-level expression but, with one specimen per histology and no slide coordinates in the supplied matrices, did not support group-level spatial inference. TCGA tumours showed coordinated intratumoural expression, strongest between angiogenesis and TGF-β/fibrosis (Spearman ρ=0.707; q = 2.24 × 10⁻46), but TCGA-CESC contained no CVT endpoint. VEGFA increased KDR, ICAM1, VCAM1, and EDN1 mRNA in HUVEC, and bevacizumab attenuated these changes; IL-1β induced inflammatory transcripts in HAEC, and anakinra attenuated IL6, TNF, and CXCL8. CONCLUSIONS: Cervical tissues contain cardio-oncology-relevant transcriptional programmes, but the available data neither demonstrate cardiac expression nor identify bevacizumab- or ICI-induced CVT susceptibility. The endothelial experiments show expected pathway responsiveness and pharmacological blockade, not treatment-induced cardiotoxicity. Prospective analyses must compare CVT-positive and CVT-negative treated patients using matched clinical outcomes and, ideally, blood or cardiac-relevant biospecimens.
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Identification and Validation of Epithelial Cell Centre Regulatory Transcription Factors in the Gastric Cancer Microenvironment.
PURPOSE: To identify the epithelial cell centre regulatory transcription factors in the gastric cancer (GC) microenvironment and provide a new strategy for the diagnosis and treatment of GC. METHODS: The GC single-cell dataset was downloaded from the Gene Expression Omnibus (GEO) database. The regulatory mechanisms of transcription factors in both pan-cancer and GC microenvironments were analysed using the Cancer Genome Atlas (TGCA) database. Real-time quantitative PCR (RT-qPCR) was used to determine the mRNA expression levels of Prospero homeobox gene 1 (PROX1) and Endothelial PAS domain-containing protein 1 (EPAS1) in the human gastric mucosal normal epithelial cell line (GES-1) and the GC cell line (AGS). Immunohistochemistry (IHC) was used to determine the amounts of PROX1 and EPAS1 protein expression in GC and adjacent tissues. GC patients' overall survival (OS) was tracked through outpatient, Inpatient case inquiry, or phone follow-up. RESULTS: The single-cell data from GSE184198 was re-annotated, resulting in nine cell subsets: T cells (13364), NK cells (606), B cells (2525), Epithelial cells (2497), DC cells (1167), Fibroblast cells (372), Endothelial cells (271), Neutrophils cells (246) and Macrophage cells (420). Analysis of cell subgroup signalling pathways revealed that communication intensity between epithelial cells and smooth muscle cells was highest. Transcription factors PROX1 and EPAS1 were notably active in epithelial cells. Cell communication analysis indicated that IFNG may interact with IFNGR1/2 and LIF with IL6ST and LIFR to regulate the downstream PROX1 and EPAS1. PROX1 and EPAS1 were upregulated and negatively correlated with tumour mutation burden (TMB). They also exhibited high positive correlations with immune checkpoints CTLA4 and PDCD1LG2, as well as with chemokines CCL24 and CXCL12 and their receptors CCR3 and CCR4. Additionally, PROX1 and EPAS1 were positively correlated with immunosuppressive factors ADORA2A, CD160, IL10, TGFBR1, KDR and CSF1R, as well as with immunostimulators CD276, PVR, TNFRSF25, ULBP1, CXCL12 and ENTPD1. In GC tissues and AGS, PROX1 and EPAS1 were both substantially expressed. In the meantime, they showed a positive correlation with clinicopathological features such TNM stage and degree of differentiation. In GC patients, the up-regulated group's PROX1 and EPAS1 prognosis was noticeably poorer than the down-regulated group's. CONCLUSION: PROX1 and EPAS1 are likely central regulatory transcription factors in the epithelial cells of the GC environment, regulated by IFNG and LIF. They may contribute to GC progression by modulating the tumour's immune microenvironment.
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Microbiota-Driven microglia reprogramming reverses depressive-like behaviors through Peripheral-to-Central immune crosstalk.
Major depressive disorder (MDD) is a major psychiatric condition associated with neuroimmune dysregulation. Microglia, the principal innate immune cells of the central nervous system, have been implicated in MDD. Nevertheless, the specific subpopulations involved and the mechanisms that govern their responses are still elusive. Here, we found that gut microbiota intervention using bedding from control non-stressed mice reverses chronic unpredictable mild stress (CUMS)-induced alterations in behavior. Using CD45+ immune cell sorting and single-cell RNA sequencing (scRNA-seq), we built an atlas of brain-resident microglia and non-microglial immune cells in the central and peripheral compartments. CUMS primarily induced transcriptional alterations in microglia and monocytes, while the reversed changes induced by bedding exchange mainly detected in microglia (85%). Among the microglial subtypes, homeostatic microglia 2 (HM2) primarily responded to CUMS. HM2 shaped microglial states through activation trajectory: the HM2→chemokine-related microglia 3→inflame microglia 1, which could be reversed by bedding exchange. Brain Mdcs served as the mainly immune cells interact with microglia. Furthermore, we systematically characterized alterations in the myeloid-derived cells across the brain, skull, and peripheral blood mononuclear cells (PBMC). Meanwhile, CUMS disrupted immune cell differentiation by suppressing chromatin openness in global accessibility sequencing analyses, and Krüppel-like factors Klf2 emerged as a master regulator expressed in monocytes and HM2 microglial subtype. Remarkably, microbiota remodeling partially reversed this epigenetic dysregulation, restoring immune cell migration and activation processes. In summary, this study revealed that gut microbiota intervention ameliorates depressive phenotypes by dynamically reprogramming microglia via the "periphery-CNS immune" axis, providing a novel entry for antidepressant strategies targeting neuro-immune interactions.
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Repression of enhancer RNA PHLDA1 promotes tumorigenesis and progression of Ewing sarcoma via decreasing infiltrating T-lymphocytes: A bioinformatic analysis.
Background: The molecular mechanisms of EWS-FLI-mediating target genes and downstream pathways may provide a new way in the targeted therapy of Ewing sarcoma. Meanwhile, enhancers transcript non-coding RNAs, known as enhancer RNAs (eRNAs), which may serve as potential diagnosis markers and therapeutic targets in Ewing sarcoma. Materials and methods: Differentially expressed genes (DEGs) were identified between 85 Ewing sarcoma samples downloaded from the Treehouse database and 3 normal bone samples downloaded from the Sequence Read Archive database. Included in DEGs, differentially expressed eRNAs (DEeRNAs) and target genes corresponding to DEeRNAs (DETGs), as well as the differentially expressed TFs, were annotated. Then, cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) was used to infer portions of infiltrating immune cells in Ewing sarcoma and normal bone samples. To evaluate the prognostic value of DEeRNAs and immune function, cross validation, independent prognosis analysis, and Kaplan-Meier survival analysis were implemented using sarcoma samples from the Cancer Genome Atlas database. Next, hallmarks of cancer by gene set variation analysis (GSVA) and immune gene sets by single-sample gene set enrichment analysis (ssGSEA) were identified to be significantly associated with Ewing sarcoma. After screening by co-expression analysis, most significant DEeRNAs, DETGs and DETFs, immune cells, immune gene sets, and hallmarks of cancer were merged to construct a co-expression regulatory network to eventually identify the key DEeRNAs in tumorigenesis of Ewing sarcoma. Moreover, Connectivity Map Analysis was utilized to identify small molecules targeting Ewing sarcoma. External validation based on multidimensional online databases and scRNA-seq analysis were used to verify our key findings. Results: A six-different-dimension regulatory network was constructed based on 17 DEeRNAs, 29 DETFs, 9 DETGs, 5 immune cells, 24 immune gene sets, and 8 hallmarks of cancer. Four key DEeRNAs (CCR1, CD3D, PHLDA1, and RASD1) showed significant co-expression relationships in the network. Connectivity Map Analysis screened two candidate compounds, MS-275 and pyrvinium, that might target Ewing sarcoma. PHLDA1 (key DEeRNA) was extensively expressed in cancer stem cells of Ewing sarcoma, which might play a critical role in the tumorigenesis of Ewing sarcoma. Conclusion: PHLDA1 is a key regulator in the tumorigenesis and progression of Ewing sarcoma. PHLDA1 is directly repressed by EWS/FLI1 protein and low expression of FOSL2, resulting in the deregulation of FOX proteins and CC chemokine receptors. The decrease of infiltrating T-lymphocytes and TNFA signaling may promote tumorigenesis and progression of Ewing sarcoma.
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Single-Cell Transcriptome Atlas of Newcastle Disease Virus in Chickens Both In Vitro and In Vivo.
Newcastle disease virus (NDV) is an avian paramyxovirus that causes major economic losses to the poultry industry around the world, with NDV pathogenicity varying due to strain virulence differences. However, the impacts of intracellular viral replication and the heterogeneity of host responses among cell types are unknown. Here, we investigated the heterogeneity of lung tissue cells in response to NDV infection in vivo and that of the chicken embryo fibroblast cell line DF-1 in response to NDV infection in vitro using single-cell RNA sequencing. We characterized the NDV target cell types in the chicken lung at the single-cell transcriptome level and classified cells into five known and two unknown cell types. The five known cell types are the targets of NDV in the lungs with virus RNA detected. Different paths of infection in the putative trajectories of NDV infection were distinguished between in vivo and in vitro, or between virulent Herts/33 strain and nonvirulent LaSota strain. Gene expression patterns and the interferon (IFN) response in different putative trajectories were demonstrated. IFN responses were elevated in vivo, especially in myeloid and endothelial cells. We distinguished the virus-infected and non-infected cells, and the Toll-like receptor signaling pathway was the main pathway after virus infection. Cell-cell communication analysis revealed the potential cell surface receptor-ligand of NDV. Our data provide a rich resource for understanding NDV pathogenesis and open the way to interventions specifically targeting infected cells. IMPORTANCE Newcastle disease virus (NDV) is an avian paramyxovirus that causes major economic losses to the poultry industry around the world, with NDV pathogenicity varying due to strain virulence differences. However, the impacts of intracellular viral replication and the heterogeneity of host responses among cell types are unknown. Here, we investigated the heterogeneity of lung tissue cells in response to NDV infection in vivo and that of the chicken embryo fibroblast cell line DF-1 in response to NDV infection in vitro using single-cell RNA sequencing. Our results open the way to interventions specifically targeting infected cells, suggest principles of virus-host interactions applicable to NDV and other similar pathogens, and highlight the potential for simultaneous single-cell measurements of both host and viral transcriptomes for delineating a comprehensive map of infection in vitro and in vivo. Therefore, this study can be a useful resource for the further investigation and understanding of NDV.
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Single-cell RNA landscape of intratumoral heterogeneity and immunosuppressive microenvironment in advanced osteosarcoma.
Osteosarcoma is the most frequent primary bone tumor with poor prognosis. Through RNA-sequencing of 100,987 individual cells from 7 primary, 2 recurrent, and 2 lung metastatic osteosarcoma lesions, 11 major cell clusters are identified based on unbiased clustering of gene expression profiles and canonical markers. The transcriptomic properties, regulators and dynamics of osteosarcoma malignant cells together with their tumor microenvironment particularly stromal and immune cells are characterized. The transdifferentiation of malignant osteoblastic cells from malignant chondroblastic cells is revealed by analyses of inferred copy-number variation and trajectory. A proinflammatory FABP4+ macrophages infiltration is noticed in lung metastatic osteosarcoma lesions. Lower osteoclasts infiltration is observed in chondroblastic, recurrent and lung metastatic osteosarcoma lesions compared to primary osteoblastic osteosarcoma lesions. Importantly, TIGIT blockade enhances the cytotoxicity effects of the primary CD3+ T cells with high proportion of TIGIT+ cells against osteosarcoma. These results present a single-cell atlas, explore intratumor heterogeneity, and provide potential therapeutic targets for osteosarcoma.
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Prognostic, molecular characterization, and immune infiltration analysis of NUDT16 in clear cell renal cell carcinoma.
BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common, aggressive tumor with treatment challenges due to varied patient responses and ineffective biomarkers. mRNA-based therapeutic approaches, particularly in cancer immunology, have become an important area of research. This study investigates nucleoside diphosphate linked moiety X hydrolase 16 (NUDT16), a key enzyme involved in mRNA quality control through 5’ cap-dependent decapping, focusing on its expression profile and prognostic implications in clear cell renal cell carcinoma (ccRCC). METHODS: Utilizing a comprehensive multi-omics strategy, we integrated mRNA expression profiles from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets (GSE68417 and GSE66270). We systematically evaluated NUDT16 expression across pan-cancer, ccRCC, and normal tissues, and assessed its association with clinical parameters. A prognostic model was developed using Cox proportional hazards regression and subsequently validated. Co-expressed genes of NUDT16 were subjected to functional enrichment analysis, and a protein-protein interaction network was constructed. We further investigated NUDT16’s role in immune infiltration, immunotherapy response, methylation status, and CpG site prediction in ccRCC. Single-cell omics analysis and functional assays were performed to elucidate NUDT16’s biological functions. RESULTS: NUDT16 expression was significantly downregulated in ccRCC compared to normal tissues, with progressive reduction observed across advancing pathological stages and histological grades. The prognostic model incorporating NUDT16 expression and clinicopathological features demonstrated robust predictive accuracy for ccRCC patient survival. Gene Set Enrichment Analysis (GSEA) of NUDT16 co-expressed genes revealed significant enrichment in biological processes related to proximal tubule transport and DNA methylation. NUDT16 expression exhibited significant correlations with immune infiltration patterns, particularly NK CD56bright, TReg, and Tcm cells. Reduced NUDT16 expression was associated with enhanced response to CTLA4(+)PD1(-) immune phenotype scores, increased tumor immune dysfunction and exclusion scores, and elevated tumor mutation burden. Promoter region hypermethylation of NUDT16 was significantly elevated in ccRCC and correlated with adverse survival outcomes. NUDT16 overexpression inhibited proliferation, colony formation, migration, and invasion in ccRCC cell lines (ACHN and 786-O). CONCLUSION: NUDT16 emerges as a potential prognostic biomarker in ccRCC, with its downregulation significantly associated with unfavorable clinical outcomes, altered immune infiltration profiles, and promoter region hypermethylation.
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The Expression and Prognostic Value of SUMO1-Activating Enzyme Subunit 1 and Its Potential Mechanism in Triple-Negative Breast Cancer.
Background: Triple-negative breast cancer (TNBC) is the most invasive and metastatic subtype of breast cancer. SUMO1-activating enzyme subunit 1 (SAE1), an E1-activating enzyme, is indispensable for protein SUMOylation. SAE1 has been found to be a relevant biomarker for progression and prognosis in several tumor types. However, the role of SAE1 in TNBC remains to be elucidated. Methods: In the research, the mRNA expression of SAE1 was analyzed via the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database. Cistrome DB Toolkit was used to predict which transcription factors (TFs) are most likely to increase SAE1 expression in TNBC. The correlation between the expression of SAE1 and the methylation of SAE1 or quantity of tumor-infiltrating immune cells was further invested. Single-cell analysis, using CancerSEA, was performed to query which functional states are associated with SAE1 in different cancers in breast cancer at the single-cell level. Next, weighted gene coexpression network (WGCNA) was applied to reveal the highly correlated genes and coexpression networks of SAE1 in TNBC patients, and a prognostic model containing SAE1 and correlated genes was constructed. Finally, we also examined SAE1 protein expression of 207 TNBC tissues using immunohistochemical (IHC) staining. Results: The mRNA and protein expression of SAE1 were increased in TNBC tissues compared with adjacent normal tissues, and the protein expression of SAE1 was significantly associated with overall survival (OS) and disease-free survival (DFS). Correlation analyses revealed that SAE1 expression was positively correlated with forkhead box M1 (FOXM1) TFs and negatively correlated with SAE1 methylation site (cg14042711) level. WGCNA indicated that the genes coexpressed with SAE1 belonged to the green module containing 1,176 genes. Through pathway enrichment analysis of the module, 1,176 genes were found enriched in cell cycle and DNA repair. Single-cell analysis indicated that SAE1 and its coexpression genes were associated with cell cycle, DNA damage, DNA repair, and cell proliferation. Using the LASSO COX regression, a prognostic model including SAE1 and polo-like kinase 1 (PLK1) was built to accurately predict the likelihood of DFS in TNBC patients. Conclusion: In conclusion, we comprehensively analyzed the mRNA and protein expression, prognosis, and interaction genes of SAE1 in TNBC and constructed a prognostic model including SAE1 and PLK1. These results might be important for better understanding of the role of SAE1 in TNBC. In addition, DNA methyltransferase and TFs inhibitor treatments targeting SAE1 might improve the survival of TNBC patients.
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Cuproptosis-related lncRNA signature as a prognostic tool and therapeutic target in diffuse large B cell lymphoma.
Cuproptosis is a newly defined form of programmed cell death that relies on mitochondria respiration. Long noncoding RNAs (lncRNAs) play crucial roles in tumorigenesis and metastasis. However, whether cuproptosis-related lncRNAs are involved in the pathogenesis of diffuse large B cell lymphoma (DLBCL) remains unclear. This study aimed to identify the prognostic signatures of cuproptosis-related lncRNAs in DLBCL and investigate their potential molecular functions. RNA-Seq data and clinical information for DLBCL were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Cuproptosis-related lncRNAs were screened out through Pearson correlation analysis. Utilizing univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis, we identified seven cuproptosis-related lncRNAs and developed a risk prediction model to evaluate its prognostic value across multiple groups. GO and KEGG functional analyses, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. Additionally, drug sensitivity analysis identified drugs with potential efficacy in DLBCL. Finally, the protein-protein interaction (PPI) network were constructed based on the weighted gene co-expression network analysis (WGCNA). We identified a set of seven cuproptosis-related lncRNAs including LINC00294, RNF139-AS1, LINC00654, WWC2-AS2, LINC00661, LINC01165 and LINC01398, based on which we constructed a risk model for DLBCL. The high-risk group was associated with shorter survival time than the low-risk group, and the signature-based risk score demonstrated superior prognostic ability for DLBCL patients compared to traditional clinical features. By analyzing the immune landscapes between two groups, we found that immunosuppressive cell types were significantly increased in high-risk DLBCL group. Moreover, functional enrichment analysis highlighted the association of differentially expressed genes with metabolic, inflammatory and immune-related pathways in DLBCL patients. We also found that the high-risk group showed more sensitivity to vinorelbine and pyrimethamine. A cuproptosis-related lncRNA signature was established to predict the prognosis and provide insights into potential therapeutic strategies for DLBCL patients.