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Peroxisome proliferator-activated receptors signature reveal the head and neck squamous cell carcinoma energy metabolism phenotype and clinical outcome.
BACKGROUND: Peroxisome proliferator activating receptors (PPARs) are important regulators of nuclear hormone receptor function, and they play a key role in biological processes such as lipid metabolism, inflammation and cell proliferation. However, their role in head and neck squamous cell carcinoma (HNSC) is unclear. METHODS: We used multiple datasets, including TCGA-HNSC, GSE41613, GSE139324, PRJEB23709 and IMVigor, to perform a comprehensive analysis of PPAR-related genes in HNSC. Single-cell sequencing data were preprocessed using Seurat packets, and intercellular communication was analyzed using CellChat packets. Functional enrichment analysis of PPAR-related genes was performed using ClusterProfile and GSEA. Prognostic models were constructed using LASSO and Cox regression models, and immunohistochemical analyses were performed using human protein mapping (The Human Protein Atlas). RESULTS: Our single-cell RNA sequencing analysis revealed distinct cell populations in HNSC, with T cells having the most significant transcriptome differences between tumors and normal tissues. The PPAR features were higher in most cell types in tumor tissues compared with normal tissues. We identified 17 PPAR-associated differentially expressed genes between tumors and normal tissues. A prognostic model based on seven PPAR-associated genes was constructed with high accuracy in predicting 1, 2 and 3 year survival in patients with HNSC. In addition, patients with a low risk score had a higher immune score and a higher proportion of T cells, CD8+ T cells and cytotoxic lymphocytes. They also showed higher immune checkpoint gene expression, suggesting that they might benefit from immunotherapy. PPAR-related genes were found to be closely related to energy metabolism. CONCLUSIONS: Our study provides a comprehensive understanding of the role of PPAR related genes in HNSC. The identified PPAR features and constructed prognostic models may serve as potential biomarkers for HNSC prognosis and treatment response. In addition, our study found that PPAR-related genes can differentiate energy metabolism and distinguish energy metabolic heterogeneity in HNSC, providing new insights into the molecular mechanisms of HNSC progression and therapeutic response.
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Single-cell Profiling of Reprogrammed Human Neural Stem Cells Unveils High Similarity to Neural Progenitors in the Developing Central Nervous System.
BACKGROUND: Similar to induced pluripotent cells (iPSCs), induced neural stem cells (iNSCs) can be directly converted from human somatic cells such as dermal fibroblasts and peripheral blood monocytes. While previous studies have demonstrated the resemblance of iNSCs to neural stem cells derived from primary sources and embryonic stem cells, respectively, a comprehensive analysis of the correlation between iNSCs and their physiological counterparts remained to be investigated. METHODS: Nowadays, single-cell sequencing technologies provide unique opportunities for in-depth cellular benchmarking of complex cell populations. Our study involves the comprehensive profiling of converted human iNSCs at a single-cell transcriptomic level, alongside conventional methods, like flow cytometry and immunofluorescence stainings. RESULTS: Our results show that the iNSC conversion yields a homogeneous cell population expressing bona fide neural stem cell markers. Extracting transcriptomic signatures from published single cell transcriptomic atlas data and comparison to the iNSC transcriptome reveals resemblance to embryonic neuroepithelial cells of early neurodevelopmental stages observed in vivo at 5 weeks of development. CONCLUSION: Our data underscore the physiological relevance of directly converted iNSCs, making them a valuable in vitro system for modeling human central nervous system development and establishing translational applications in cell therapy and compound screening.
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Substantially Altered Local and Systemic Immunity in Ischemia-Free Versus Conventional Liver Transplantation.
Ischemia-free liver transplant (IFLT) has been developed to reduce ischemia-reperfusion injury (IRI). This study aims to investigate how this procedure impacts local and systemic immunity compared to conventional liver transplantation (CLT). Immunohistochemistry, immunofluorescence staining, single-cell RNA sequencing (scRNA-seq), and multiplex cytokine are used to illustrate distinct local and systemic immunity. In contrast to CLT, IFLT reduces neutrophil infiltration and neutrophil extracellular trap formation in grafts. By constructing an immune cell chimerism atlas, we reveal that IFLT reduces recipient-derived monocyte infiltration by suppressing ANXA1-FPR1 signaling through the STAT3-HIF-1α pathway, thereby attenuating inflammatory responses in graft monocytes. Additionally, IFLT confers graft protection by upregulating HMOX1 expression in monocytes and macrophages. Peripherally, IFLT significantly reduces the expression of MHC II molecules in circulating monocytes. Accordingly, CD8+ effector T cell composition, T helper 1 (Th1) and Th17 cytokine levels are reduced, while regulatory T cell (Treg) composition and Th2 cytokine levels are increased in IFLT versus CLT recipients. These results show that IFLT profoundly affects local and systemic immunity in liver transplantation. Recipient-circulating monocytes might play a key role in the interaction between graft IRI and allograft rejection.
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Identification of tumor antigens and immune subtypes in breast cancer for mRNA vaccine development.
BACKGROUND: Poor prognosis, resistance to chemotherapy, insensitivity to radiotherapy, and a high prevalence of adverse drug reactions remain urgent issues for breast cancer (BC) patients. Increased knowledge of tumor immunobiology and vaccine development suggests the possibility of cancer vaccination. Here, we investigated potential BC-associated antigens for the development of an anti-BC mRNA vaccine and populations suitable for mRNA vaccination. METHODS: Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). The single-cell sequencing data were obtained from the Single Cell Portal platform. cBioPortal was used to visualize and compare genetic alterations. Correlations between immune cell infiltration and antigen expression were visualized with the Tumor Immune Estimation Resource (TIMER). Immune subtypes were identified by consensus clustering and analysis of immune infiltration. Biomarkers for the assessment of mRNA vaccination suitability were investigated. RESULTS: Three tumor-associated antigens, CD74, IRF1, and PSME2, that showed overexpression, amplification, and mutation and were linked with prognosis and immune cell infiltration, were identified. Single-cell sequencing analysis showed the expression of the three tumor-associated antigens in different cells of BC. Three immune subtypes were identified among BC patients, with Cluster B patients having a tumor microenvironment conducive to immunotherapy. These subtypes also showed different expression patterns of immune checkpoints, immune cell death-promoting genes, and response to immune checkpoint inhibitor (ICI) therapy. Thus, we identified five biomarkers that could be applied for assessing vaccination suitability and predicted drugs that would be appropriate for patients unsuited for vaccination. CONCLUSIONS: Our findings suggest new directions for the development of mRNA vaccines against breast cancer.
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The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs.
The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular-resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.
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The Novel-Natural-Killer-Cell-Related Gene Signature Predicts the Prognosis and Immune Status of Patients with Hepatocellular Carcinoma.
The current understanding of the prognostic significance of natural killer (NK) cells and their tumor microenvironment (TME) in hepatocellular carcinoma (HCC) is limited. Thus, we screened for NK-cell-related genes by single-cell transcriptome data analysis and developed an NK-cell-related gene signature (NKRGS) using multi-regression analyses. Patients in the Cancer Genome Atlas cohort were stratified into high- and low-risk groups according to their median NKRGS risk scores. Overall survival between the risk groups was estimated using the Kaplan-Meier method, and a NKRGS-based nomogram was constructed. Immune infiltration profiles were compared between the risk groups. The NKRGS risk model suggests significantly worse prognoses in patients with high NKRGS risk (p < 0.05). The NKRGS-based nomogram showed good prognostic performance. The immune infiltration analysis revealed that the high-NKRGS-risk patients had significantly lower immune cell infiltration levels (p < 0.05) and were more likely to be in an immunosuppressive state. The enrichment analysis revealed that immune-related and tumor metabolism pathways highly correlated with the prognostic gene signature. In this study, a novel NKRGS was developed to stratify the prognosis of HCC patients. An immunosuppressive TME coincided with the high NKRGS risk among the HCC patients. The higher KLRB1 and DUSP10 expression levels correlated with the patients' favorable survival.
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A novel cuproptosis-related LncRNA signature: Prognostic and therapeutic value for acute myeloid leukemia.
BACKGROUND: Cuproptosis is a type of programmed cell death that is involved in multiple physiological and pathological processes, including cancer. We constructed a prognostic cuproptosis-related long non-coding RNA (lncRNA) signature for acute myeloid leukemia (AML). METHODS: RNA-seq and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) database. The cuproptosis-related prognostic lncRNAs were identified by co-expression and univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) was performed to construct a cuproptosis-related lncRNA signature, after which the AML patients were classified into two risk groups based on the risk model. Kaplan-Meier, ROC, univariate and multivariate Cox regression, nomogram, and calibration curves analyses were used to evaluate the prognostic value of the model. Then, expression levels of the lncRNAs in the signature were investigated in AML samples by quantitative polymerase chain reaction (qPCR). KEGG functional analysis, single-sample GSEA (ssGSEA), and the ESTIMATE algorithm were used to analyze the mechanisms and immune status between the different risk groups. The sensitivities for potential therapeutic drugs for AML were also investigated. RESULTS: Five hundred and three lncRNAs related to 19 CRGs in AML samples from the TCGA database were obtained, and 21 differentially expressed lncRNAs were identified based on the 2-year overall survival (OS) outcomes of AML patients. A 4-cuproptosis-related lncRNA signature for survival was constructed by LASSO Cox regression. High-risk AML patients exhibited worse outcomes. Univariate and multivariate Cox regression analyses demonstrated the independent prognostic value of the model. ROC, nomogram, and calibration curves analyses revealed the predictive power of the signature. KEGG pathway and ssGSEA analyses showed that the high-risk group had higher immune activities. Lastly, AML patients from different risk groups showed differential responses to various agents. CONCLUSION: A cuproptosis-related lncRNA signature was established to predict the prognosis and inform on potential therapeutic strategies for AML patients.
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Integration of Machine Learning, Bioinformatics, and Experimental Validation to Identify Novel Diagnostic and Prognostic Biomarkers Associated With Succinylation in Lung Adenocarcinoma.
Lung adenocarcinoma (LUAD) is the most prevalent and lethal subtype of non-small cell lung cancer (NSCLC), with its progression closely associated with aberrant succinylation modifications. This study aimed to systematically identify succinylation-related genes in LUAD and evaluate their diagnostic and prognostic significance. By integrating four Gene Expression Omnibus (GEO) datasets, 45 differentially expressed succinylation-related candidate genes were identified. Feature selection using three machine learning methods-Lasso regression, support vector machine recursive feature elimination (SVM-RFE), and Random Forest-yielded seven core genes: TIMP1, SLC2A1, JUP, F12, B3GALNT1, DSP, and MMP1. ROC analysis showed that all core genes achieved AUC values greater than 0.7, indicating strong diagnostic potential. A diagnostic model constructed from these seven genes achieved an AUC of 0.912 in the training cohort, significantly outperforming single-gene models, and was validated in The Cancer Genome Atlas (TCGA) cohort (AUC = 0.893). Prognostic analysis revealed that Kaplan-Meier curves for all seven core genes demonstrated p < 0.05 and HR > 1, indicating that high expression was associated with poor outcomes. A risk prediction nomogram was also developed based on these genes. SHAP analysis clarified each gene's contribution to the model, while drug enrichment and transcriptional regulatory network analyses provided further insights into potential therapeutic targets. Notably, JUP exhibited the highest diagnostic efficacy (AUC = 0.921) and was significantly correlated with immune cell infiltration and tumor microenvironment regulation. Molecular docking suggested stable binding between JUP and potential therapeutic compounds, single-cell analysis confirmed its marked overexpression in tumor and epithelial cells, and experimental validation further established its oncogenic role. In conclusion, this study systematically defines the diagnostic and prognostic value of seven succinylation-related core genes in LUAD, with JUP playing a particularly pivotal role. These findings provide robust evidence supporting its potential as a novel biomarker and therapeutic target.
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Melanoma cell-derived LAG-3 enhances CXCL1/8-driven MDSCs recruitment and immune escape via TRIM28-mediated IκBα degradation.
INTRODUCTION: Lymphocyte activation gene 3 (LAG-3) is recognized as an important inhibitory receptor on T cells, and LAG-3-targeted therapy has been approved for advanced melanoma treatment. Recently, LAG-3 has been reported to be expressed on tumor cells. However, the potential effects and underlying mechanisms of tumor cell-derived LAG-3 in mediating tumor growth and immunosuppression remain unclear. OBJECTIVES: This study aimed to evaluate the role of tumor cell-derived LAG-3 within the tumor microenvironment (TME) and the efficacy of anti-LAG-3 combination therapy against melanoma. METHODS: The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, single-nuclear RNA sequencing database and tissue microarray analyses were conducted to detect the LAG-3 gene and protein expressions. The biological functions and underlying mechanism of tumor cell-derived LAG-3 in tumor growth and immunosuppression were investigated using phospho-array, RNA-seq, co-immunoprecipitation, flow cytometry, co-culture systems, and various tumor-bearing mouse models. RESULTS: Our findings revealed that LAG-3 was present in a subpopulation of cancer cells and clinical melanoma specimens associated with poorer prognosis. High LAG-3 expression on tumor cells not only contributed to the proliferation and metastasis of melanoma cells, but also promoted the recruitment of myeloid-derived suppressor cells (MDSCs) and dysfunctional CD8+ T cells into the TME. Notably, depletion of MDSCs counteracted the pro-tumor activity of tumor cell-derived LAG-3. Mechanistically, LAG-3 overexpression triggered CXCL1 and CXCL8 production of melanoma cells by enhancing TRIM28-mediated IκBα ubiquitination and degradation, which in turn activated the NF-κB pathway and fostered tumor growth and an immunosuppressive microenvironment. Intriguingly, mice bearing LAG-3-overexpressing melanoma cells showed reduced survival but responded to anti-LAG-3 antibody therapy. Furthermore, inhibiting the CXCL1/8-CXCR2 axis sensitized LAG-3-overexpressing melanoma to anti-LAG-3 antibody. CONCLUSION: This study firstly identified LAG-3 as a pro-tumor factor and a predictor for anti-LAG-3 immunotherapy efficacy. Combined CXCL1/8-CXCR2 inhibitor and anti-LAG-3 treatment may represent a promising therapeutic strategy for LAG-3-driven tumor.
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Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer.
Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients. Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC). Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression. Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration.