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Tumour-Derived sEVs Promote Triple-Negative Breast Cancer Progression Associated with HAVCR2 Upregulation in Macrophages.
Backgrounds: Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with a unique tumor microenvironment, and while Programmed cell death protein 1/Programmed cell death ligand 1 (PD-1/PD-L1) blockade represents a standard immunotherapy, most patients develop primary or acquired resistance, with few alternative immunotherapeutic targets currently available. Therefore, we aimed to identify potential immune checkpoint-related molecules involved in TNBC-macrophage crosstalk, clarify the underlying molecular mechanism mediated by small extracellular vesicles (sEVs), and provide a theoretical basis for the future development of novel immunotherapeutic targets against TNBC. Methods: Single-cell RNA-sequencing (scRNA-seq) datasets for various breast cancer subtypes were used. Pseudotime trajectory, cell‒cell communication and Tumor Immune Estimation Resource 2.0 (TIMER2) analyses were conducted to characterize the tumour microenvironment (TME). Immunochemistry and immunofluorescence were used to confirm the results of the above analyses. Single-nucleus RNA sequencing (snRNA-seq) was conducted on three pairs of TNBC tumour and adjacent normal tissues. The functions of tumour-associated macrophages (TAMs) and sEVs in TNBC metastasis were explored by Western blotting, flow cytometry and cell-based experiments. Results: A total of nearly 60,000 high-quality single cells were subjected to scRNA-seq analysis, from which seven major cell types were identified. An overall increase in immune cell proportion was observed in TNBC compared with other subtypes, with the immune cell fraction in TNBC tissues being ~1.8-fold higher than that in luminal A/HER2+ subtypes (p < 0.001). Cell‒cell communication analysis indicated that TNBC cells mainly interact with macrophages. Interestingly, HAVCR2, an immune checkpoint, is expressed mainly in macrophages in the TNBC TME. HAVCR2 is associated with macrophage pseudotime progression in TNBC, which was validated by immunofluorescence staining. Moreover, analysis of The Cancer Genome Atlas (TCGA) bulk RNA-seq data revealed that HAVCR2 expression is significantly correlated with M2-like macrophage gene signatures and computationally inferred macrophage infiltration levels in TNBC, and this tissue-level transcriptional correlation is associated with poor patient prognosis. Notably, bulk RNA-seq data cannot define discrete cell subsets, and the identification of HAVCR2+ M2 macrophage subsets was independently validated by scRNA-seq and snRNA-seq at the single-cell level. Furthermore, treatment with TNBC-derived sEVs is associated with concurrent increases in the expression of HAVCR2 and M2-associated markers (CD163, CD206) in macrophages. These findings reflect a correlative association rather than a demonstrated causal or regulatory relationship between HAVCR2 and M2-associated marker upregulation. Conclusion: sEVs derived from TNBC cells are associated with the upregulation of M2-associated markers and concomitant HAVCR2 upregulation in macrophages, both of which correlate with TNBC progression and metastasis. We propose that HAVCR2 may serve as a candidate prognostic marker associated with M2-like macrophage features in TNBC, and these foundational in vitro findings from Human acute monocytic leukemia cell line (THP-1) macrophages warrant further validation in primary human monocyte-derived macrophages and in vivo TNBC models.
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Integrated multi-omics profiling of immune microenvironment and drug resistance signatures for precision prognosis in prostate cancer.
Introduction: Prostate cancer (PCa) continues to be a significant cause of mortality among men, with treatment resistance often influenced by the complexity of the tumor microenvironment (TME). This study aims to develop an immune-centric prognostic model that correlates TME dynamics, genomic instability, and the heterogeneity of drug resistance in PCa. Methods: Multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were integrated, encompassing transcriptomic profiles of 554 TCGA-PRAD samples and 329 external validation samples. Immune cell infiltration was assessed using CIBERSORT and ESTIMATE. Weighted gene co-expression network analysis (WGCNA) was employed to identify immune-related modules. Single-cell RNA sequencing (ScRNA-seq) of 835 PCa cells uncovered subtype-specific resistance patterns. Prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) regression and subsequently validated experimentally in PCa cell lines. Results: Two immune subtypes were identified: high-risk subgroups displayed TP53 mutations, increased tumor mutation burden (TMB), and enriched energy metabolism pathways. ScRNA-seq delineated three PCa cell clusters, with high-risk subtypes being sensitive to bendamustine/dacomitinib and resistant to apalutamide/neratinib. A 10-gene prognostic model (e.g., MUC5B, TREM1) categorized patients into high/low-risk groups with distinct survival outcomes (log-rank P < 0.0001). Validation in external datasets confirmed the robust predictive accuracy (AUC: 0.854-0.889). Experimental assays verified subtype-specific drug responses and dysregulation of key model genes. Discussion: This study establishes a TME-driven prognostic framework that connects immune heterogeneity, genomic instability, and therapeutic resistance in PCa. By pinpointing metabolic dependencies and subtype-specific vulnerabilities, our findings provide actionable strategies to circumvent treatment failure, such as targeting energy metabolism or tailoring therapies based on resistance signatures.
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Molecular Landscape of the Coagulome of Oral Squamous Cell Carcinoma.
BACKGROUND: Hemostatic complications, ranging from thromboembolism to bleeding, are a significant source of morbidity and mortality in cancer patients. The tumor coagulome represents the multiple genes and proteins that locally contribute to the equilibrium between coagulation and fibrinolysis. We aimed to study the coagulome of Oral Squamous Cell Carcinoma (OSCC) and examine its link to the tumor microenvironment (TME). METHODS: We used data from bulk tumor DNA/RNA-seq (The Cancer Genome Atlas), single-cell RNA-seq data and OSCC cells in culture. RESULTS: Among all tumor types, OSCC was identified as the tumor with the highest mRNA expression levels of F3 (Tissue Factor, TF) and PLAU (urokinase type-plasminogen activator, uPA). Great inter- and intra-tumor heterogeneity were observed. Single-cell analyses showed the coexistence of subpopulations of pro-coagulant and pro-fibrinolytic cancer cells within individual tumors. Interestingly, OSCC with high F3 expressed higher levels of the key immune checkpoint molecules CD274/PD-L1, PDCD1LG2/PD-L2 and CD80, especially in tumor dendritic cells. In vitro studies confirmed the particularity of the OSCC coagulome and suggested that thrombin exerts indirect effects on OSCC cells. CONCLUSIONS: OSCC presents a specific coagulome. Further studies examining a possible negative modulation of the tumor's adaptive immune response by the coagulation process are warranted.
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Exploration of the Roles and Mechanisms Between Tumor-Infiltrating Lymphocytes and Hepatocellular Carcinoma Based on Single-Cell Transcriptomics.
Hepatocellular carcinoma (HCC) remains a major global health challenge, with limited effective treatment options, particularly in advanced-stage patients. The tumor immune microenvironment (TIME) plays a crucial role in HCC progression and treatment response, with tumor-infiltrating lymphocytes (TILs) being key modulators of immune activity. In this study, we investigated the immunosuppressive role of TIL-related genes in NASH-associated HCC (NASH-HCC) and identified their potential as independent prognostic factors. We employed Gene Set Enrichment Analysis (GSEA) and Weighted Gene Coexpression Network Analysis (WGCNA) to explore immune suppression in NASH-HCC and identify TIL-related gene modules. Machine learning approaches were utilized to construct a prognostic model, validated using multiple cohorts from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). The model's predictive power was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Furthermore, single-cell RNA sequencing (scRNA-seq) analysis was performed to examine the role of TIL-related genes in different immune cell populations within TIME. We identified 10 distinct cell types in HCC and demonstrated that T cells exhibited the highest TIL pathway activity, playing a critical role in cellular communication via MIF signaling. Our findings highlight the immunosuppressive nature of TILs in NASH-HCC and provide valuable insights into their prognostic significance, potentially guiding future immunotherapeutic strategies.
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Integrative single-cell meta-analysis reveals disease-relevant vascular cell states and markers in human atherosclerosis.
Coronary artery disease (CAD) is characterized by atherosclerotic plaque formation in the arterial wall. CAD progression involves complex interactions and phenotypic plasticity among vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but human cell phenotypes remain controversial. Here, we perform an integrated meta-analysis of 22 scRNA-seq libraries to generate a comprehensive map of human atherosclerosis with 118,578 cells. Besides characterizing granular cell-type diversity and communication, we leverage this atlas to provide insights into smooth muscle cell (SMC) modulation. We integrate genome-wide association study data and uncover a critical role for modulated SMC phenotypes in CAD, myocardial infarction, and coronary calcification. Finally, we identify fibromyocyte/fibrochondrogenic SMC markers (LTBP1 and CRTAC1) as proxies of atherosclerosis progression and validate these through omics and spatial imaging analyses. Altogether, we create a unified atlas of human atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.
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Multi-omics and Mendelian randomization identify S1PR5 as a causal protective gene and NK cell-mediated prognostic biomarker in lung adenocarcinoma.
BACKGROUND: Lung adenocarcinoma (LUAD) sustains an immunosuppressive tumor microenvironment (TME) via stromal-immune interactions. Efferocytosis regulates immune suppression and tissue homeostasis, yet biomarkers stratifying its TME states are lacking in LUAD, hindering precision therapy. This study aimed to investigate efferocytosis-associated immune regulation with both causal and prognostic relevance in LUAD, and to identify key biomarkers with potential implications for tumor stratification and therapeutic guidance. METHODS: Gene expression profiles from The Cancer Genome Atlas (TCGA) LUAD cohort and Genotype-Tissue Expression (GTEx) underwent differential expression analysis. Efferocytosis-related genes (ERGs) from GeneCards were intersected to identify LUAD-associated candidates. Mendelian randomization (MR) and colocalization evaluated causal ERG-LUAD relationships. Risk-related ERGs were systematically analyzed for expression, biological functions, prognosis, and immune interactions. Single-cell RNA sequencing (scRNA-seq) mapped cellular expression specificity of core ERGs. Machine learning prognostic models (S1PR5-enriched NK cell-related prognostic signature, SENRPS) were developed and validated across independent cohorts. RESULTS: S1PR5 expression was significantly lower in tumor tissues from LUAD patients compared to healthy lung tissue, at both the transcript and protein levels. We identified S1PR5 as a dual biomarker serving both as a protective factor against LUAD pathogenesis and a prognostic marker for survival outcomes, linked to favorable prognosis and enhanced therapy sensitivity. ScRNA-seq localized S1PR5 to natural killer (NK) cells, enhancing the anti-tumor activity of CD16+ NK cells and mediating interactions with antigen-presenting CEACAM8+ macrophages. The SENRPS model integrates molecular and cellular features for risk stratification and clinical decision-making. CONCLUSIONS: S1PR5 serves as a causal protective gene and prognostic biomarker governing cytotoxic immunity. SENRPS bridges TME dynamics to clinical risk prediction and therapeutic optimization, advancing LUAD precision oncology.
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Analysis of cuproptosis in hepatocellular carcinoma using multi-omics reveals a comprehensive HCC landscape and the immune patterns of cuproptosis.
Cuproptosis represents a novel copper-dependent regulated cell death, distinct from other known cell death processes. In this report, a comprehensive analysis of cuproptosis in hepatocellular carcinoma (HCC) was conducted using multi-omics including genomics, bulk RNA-seq, single cell RNA-seq and proteomics. ATP7A, PDHA1 and DLST comprised the top 3 mutation genes in The Cancer Genome Atlas (TCGA)-LIHC; 9 cuproptosis-related genes showed significant, independent prognostic values. Cuproptosis-related hepatocytes were identified and their function were evaluated in single cell assays. Based on cuproptosis-related gene expressions, two immune patterns were found, with the cuproptosis-C1 subtype identified as a cytotoxic immune pattern, while the cuproptosis-C2 subtype was identified as a regulatory immune pattern. Cuproptosis-C2 was associated with a number of pathways involving tumorigenesis. A prognosis model based on differentially expressed genes (DEGs) of cuproptosis patterns was constructed and validated. We established a cuproptosis index (CPI) and further performed an analysis of its clinical relevance. High CPI values were associated with increased levels of alpha-fetoprotein (AFP) and advanced tumor stages. Taken together, this comprehensive analysis provides important, new insights into cuproptosis mechanisms associated with human HCC.
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HMOX1: A pivotal regulator of prognosis and immune dynamics in ovarian cancer.
BACKGROUND: This study investigates the intricate role of Heme Oxygenase 1 (HMOX1) in ovarian cancer, emphasizing its prognostic significance, influence on immune cell infiltration, and impact on the malignant characteristics of primary ovarian cancer cells. MATERIALS AND METHODS: Our research began with an analysis of HMOX1 expression and its prognostic implications using data from The Cancer Genome Atlas (TCGA) dataset, supported by immunohistochemical staining. Further analyses encompassed co-expression studies, Gene Ontology (GO) annotations, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. We utilized the TIMER and TISIDB platforms to evaluate the immunotherapeutic potential of HMOX1. Additionally, in vitro studies that involved modulating HMOX1 levels in primary ovarian cancer cells were conducted to confirm its biological functions. RESULTS: Our findings indicate a significant overexpression of HMOX1 in ovarian cancer, which correlates with increased tumor malignancy and poorer prognosis. HMOX1 was shown to significantly modulate the infiltration of immune cells, particularly neutrophils and macrophages. Single-cell RNA sequencing (scRNA-seq) analysis revealed that HMOX1 is predominantly expressed in tumor-associated macrophages (TAMs), with a positive correlation to chemokines and their receptors. An increase in HMOX1 levels was associated with heightened levels of immunoinhibitors, immunostimulators, and MHC molecules. Functional assays demonstrated that HMOX1 knockdown promotes apoptosis, attenuating cell proliferation and invasion, while its overexpression yields opposing effects. CONCLUSION: HMOX1 emerges as a critical therapeutic target, intricately involved in immunomodulation, prognosis, and the malignant behavior of ovarian cancer. This highlights HMOX1 as a potential biomarker and therapeutic target in the fight against ovarian cancer.
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Exploring the Shared Gene Signatures and Molecular Mechanisms between Bladder Urothelial Carcinoma and Metabolic Syndrome.
BACKGROUND: The aim of this study was to investigate the common gene signatures and potential molecular mechanisms of bladder urothelial carcinoma (BLCA) and metabolic syndrome (MS). METHODS: Transcriptome data for BLCA and MS were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to identify co-expression networks associated with BLCA and MS, and five hub genes were further screened and validated using logistic least absolute shrinkage and selection operator (LASSO) regression models and receiver operating characteristic (ROC) curve, and external dataset for validation. The relationship between the hub genes and the clinicopathological characteristics and prognosis of BLCA patients was explored in the GEO and The Cancer Genome Atlas (TCGA)-BLCA cohorts, respectively. Differences in the immune microenvironment of BLCA and MS were analyzed using the database CIBERSORT and the R package "ssGSEA", and the correlation between hub genes and tumor microenvironment, immune score and targeted drugs was analyzed with the help of the TCGA-BLCA cohort. Finally, BLCA single-cell RNA (scRNA) data were used to analyze the expression levels of the hub genes in various cell types of BLCA and molecular mechanisms. RESULTS: Five hub genes were screened by WGCNA and LASSO regression analysis, namely AP2-associated protein kinase 1 (AAK1), ATP-binding cassette subfamily F member 2 (ABCF2), Mitochondrial ribosomal protein L42 (MRPL42), La-related protein 3 (SSB) and TATA-box binding protein-associated factor 10 (TAF10). Analyzed in the GEO and TCGA-BLCA cohorts, we found that the hub genes (TAF10 and ABCF2) were closely associated with the clinicopathological characteristics and prognosis of BLCA patients. In CIBERSORT, we discovered that the hub genes are closely linked to the immune microenvironment, immune score, and especially with dendritic cells (DCs). In the single-cell RNA sequencing (scRNA-seq) analysis of BLCA, we identified that SSB was significantly differentially expressed in BLCA and normal bladder tissues and that it plays an important role in the development of BLCA. CONCLUSIONS: The interaction of BLCA with MS may be associated with several cancer pathways being activated and identified TAF10 and ABCF2 as potential biomarkers and therapeutic targets for patients with BLCA and MS.
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Identification of hepatocellular carcinoma-related subtypes and development of a prognostic model: a study based on ferritinophagy-related genes.
BACKGROUND: Hepatocellular carcinoma still has a high incidence and mortality rate worldwide, and further research is needed to investigate its occurrence and development mechanisms in depth in order to identify new therapeutic targets. Ferritinophagy is a type of autophagy and a key factor in ferroptosis that could influence tumor onset and progression. Although, the potential role of ferritinophagy-related genes (FRGs) in liver hepatocellular carcinoma (LIHC) is unknown. METHODS: Single-cell RNA sequencing (scRNA-seq) data of LIHC were obtained from the Gene Expression Omnibus (GEO) dataset. In addition, transcriptome and clinical follow-up outcome data of individuals with LIHC were extracted from the The Cancer Genome Atlas (TCGA) dataset. FRGs were collected through the GeneCards database. Differential cell subpopulations were distinguished, and differentially expressed FRGs (DEFRGs) were obtained. Differential expression of FRGs and prognosis were observed according to the TCGA database. An FRG-related risk model was constructed to predict patient prognosis by absolute shrinkage and selection operator (LASSO) and COX regression analyses, and its prognosis predictive power was validated. Ultimately, the association between risk score and tumor microenvironment (TME), immune cell infiltration, immune checkpoints, drug sensitivity, and tumor mutation burden (TMB) was analyzed. We also used quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the expression of key genes in normal liver cells and liver cancer cells. RESULTS: We ultimately identified 8 cell types, and 7 differentially expressed FRGs genes (ZFP36, NCOA4, FTH1, FTL, TNF, PCBP1, CYB561A3) were found among immune cells, and we found that Monocytes and Macrophages were closely related to FRGs genes. Subsequently, COX regression analysis showed that patients with high expression of FTH1, FTL, and PCBP1 had significantly worse prognosis than those with low expression, and our survival prediction model, constructed based on age, stage, and risk score, showed better prognostic prediction ability. Our risk model based on 3 FRGs genes ultimately revealed significant differences between high-risk and low-risk groups in terms of immune infiltration and immune checkpoint correlation, drug sensitivity, and somatic mutation risk. Finally, we validated the key prognostic genes FTH1, FTL, using qRT-PCR, and found that the expression of FTH1 and FTL was significantly higher in various liver cancer cells than in normal liver cells. At the same time, immunohistochemistry showed that the expression of FTH1, FTL in tumor tissues was significantly higher than that in para-tumor tissues. CONCLUSION: This study identifies a considerable impact of FRGs on immunity and prognosis in individuals with LIHC. The collective findings of this research provide new ideas for personalized treatment of LIHC and a more targeted therapy approach for individuals with LIHC to improve their prognosis.