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Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.
BACKGROUND: Accurate prognostic risk stratification for lung adenocarcinoma (LUAD) remains a critical challenge. This study employs single-cell pseudo-time series analysis to identify pseudo-time differential genes (PTDGs) associated with LUAD development and constructs tumor progression-related prognostic risk stratification for patients with LUAD. METHODS: This study conducted quality control for single-cell RNA sequencing (RNA-seq) data firstly. Then, malignant tumor cells of the epithelium were identified using cell-cluster markers and InferCNV. Trajectory analysis with Monocle identified PTDGs, further refined to 2,597 candidate genes. The prognostic signature of PTDGs was constructed by using Cox, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses. In addition, multicenter datasets and immunohistochemistry analysis were used to evaluate the expression of the identified PTDGs between the two groups. RESULTS: The developmental trajectory of the LUAD epithelium was plotted, and 13 PTDGs were identified to be highly associated with the prognosis of LUAD. Based on the prognostic risk score, The Cancer Genome Atlas (TCGA)-LUAD patients were well stratified into low- and high-risk groups. The PTDGs-based risk model demonstrated good performance in the training, internal and external validation datasets, as well as being compared with existing risk score formulas. In addition, multi-center datasets and immunohistochemistry revealed a significant up-regulation of DDIT4, FURIN, PTTG1 and RIPK2 at transcriptional and protein expression levels in LUAD tissues compared to normal tissues. CONCLUSIONS: PTDGs are potential biological markers for prognostic risk stratification of patients with LUAD.
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A new prognostic model for glioblastoma multiforme based on coagulation-related genes.
BACKGROUND: Glioblastoma multiforme (GBM) is the most aggressive, common, and lethal type of primary brain tumor. Multiple cancers have been associated with abnormalities in the coagulation system that facilitate tumor invasion and metastasis. In GBM, the prognostic value and underlying mechanism of coagulation-related genes (CRGs) have not been explored. METHODS: RNA sequencing (RNA-seq) and clinical information on GBM were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), respectively. Following the identification of differentially expressed CRGs (DECRGs) between GBM and control samples, the survival-related DECRGs were selected via univariate and multivariate Cox regression analyses to establish a prognostic signature. The prognostic performance and clinical utility of the prognostic signature were assessed by the Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve analysis, and a nomogram was constructed. The signature genes-related underlying mechanisms were analyzed according to gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-cell analysis. Finally, the difference in immune cell infiltration, stromal score, immune score, and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) score were compared between different risk groups. RESULTS: A 5-gene prognostic signature (PLAUR, GP6, C5AR1, SERPINA5, F2RL2) was established for overall survival (OS) prediction of GBM patients. The predicted efficiency of the prognostic signature was confirmed in TGGA-GBM dataset and validated in the CGGA-GBM dataset, revealing that it could differentiate GBM patients from controls well, and high risk score was accompanied with poor prognosis. Moreover, biological process (BP) and signaling pathway analyses showed that signature genes were mainly enriched in the functions of blood coagulation and tumor invasion and metastasis. Moreover, high-risk patients exhibited higher levels of immune cell infiltration, stromal score, immune score, and ESTIMATE score than that of low-risk patients. CONCLUSIONS: An analysis of coagulation-related prognostic signatures was conducted in this study, as well as how signature genes may affect GBM progress, providing information that might provide new ideas for the development of GBM-related molecular targeted therapies.
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Integrative analysis of miRNA and mRNA sequencing data reveals potential regulatory mechanisms of ACE2 and TMPRSS2.
Development of novel approaches for regulating the expression of angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) is becoming increasingly important within the context of the ongoing COVID-19 pandemic since these enzymes play a crucial role in cell infection. In this work we searched for putative ACE2 and TMPRSS2 expression regulation networks mediated by various miRNA isoforms (isomiR) across different human organs using publicly available paired miRNA/mRNA-sequencing data from The Cancer Genome Atlas (TCGA) project. As a result, we identified several miRNA families targeting ACE2 and TMPRSS2 genes in multiple tissues. In particular, we found that lysine-specific demethylase 5B (JARID1B), encoded by the KDM5B gene, can indirectly affect ACE2 / TMPRSS2 expression by repressing transcription of hsa-let-7e / hsa-mir-125a and hsa-mir-141 / hsa-miR-200 miRNA families which are targeting these genes.
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NVUAtlas: A Comprehensive Single-Nucleus RNA-Seq Resource for the Human Neurovascular Unit in Alzheimer's Disease.
BACKGROUND: Neurovascular unit (NVU) dysfunction is being recognized as one of the earliest contributors to Alzheimer's disease (AD) pathogenesis. However, systematic investigation of NVU dysfunction is currently limited by lack of access to molecular level information due to underrepresentation of vascular and mural cells in standard single-nucleus RNA sequencing (snRNA-seq) datasets. Consequently, existing transcriptomic atlases lack the resolution necessary to capture the coordinated intercellular signaling and dysfunction across vascular components of NVU, including endothelial cells and pericytes. METHODS: We constructed the Human NVU Atlas by integrating 11 publicly available snRNA-seq datasets, including vascular-enriched samples, from the human prefrontal cortex. This comprehensive dataset aggregates over 4.2 million nuclei from 748 donors, including AD patients and age-matched controls. We utilized a unified probabilistic pipeline based on deep generative models (SCVI) to perform batch-aware integration and employed an ensemble of supervised and deep-learning classifiers to rigorously re-annotate cell types. Differential expression and ligand-receptor interaction analyses were subsequently performed to identify cell-type-specific disruptions in males versus females. RESULTS: The atlas successfully curated vascular populations from 11 studies to assemble the largest publically available NVU cohort of endothelial cells (2.8%) and pericytes (1.9%) alongside astrocytes and neurons. Differential expression analysis revealed that while neurons predominantly exhibited gene downregulation in AD, vascular cells displayed a pattern of transcriptional hyperactivity with significant gene upregulation. We also identified pronounced sex-specific vulnerabilities; females exhibited distinct inflammatory signatures and downregulation of basement membrane collagen genes (e.g., COL4A1 , COL4A2 ) in pericytes, whereas these changes were not observed in males. Moreover, cell-cell interaction analysis revealed a widespread loss of collagen-integrin signaling between pericytes and neurons, suggesting the involvement of extracellular matrix disruptions in NVU dysfunction observed in AD. CONCLUSION: The Human NVU Atlas provides a high-resolution, integrated transcriptomic framework for dissecting the cellular heterogeneity of the neurovascular unit. By uncovering sex-specific vascular mechanisms and disrupted intercellular communication, this resource highlights the critical role of vascular cells in AD progression and serves as a foundational reference for investigating cerebrovascular contribution to AD.
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A single-cell multi-omics atlas defines the cellular and molecular landscape of gastric intestinal metaplasia.
BACKGROUND: Gastric intestinal metaplasia (GIM) is a precancerous lesion closely associated with gastric cancer (GC) risk. A systematic characterization of the molecular profiles across distinct cell types during GIM progression is crucial for devising new intervention strategies and enabling early prevention for GC. METHODS: This study integrates single-cell transcriptomics and proteomics data from gastric tissues of patients with mild chronic non-atrophic gastritis (CNAG) and moderate-to-severe GIM. RESULTS: We identified differentially expressed genes (DEGs) and proteins (DEPs) that show consistent changes at both transcriptional and protein levels across epithelial, stromal, and immune cells. Notably, most proteins were downregulated in GIM tissues, potentially linked to reduced expression of RNA splicing-related genes. Epithelial cells in GIM lesions exhibited intestinal-type subpopulations, including goblet and enterocytes, likely originating from gastric isthmus stem cells. Despite overall protein downregulation, the ubiquitin-like protein NEDD8 was markedly upregulated in metaplastic tissues, especially in H. pylori-positive tissues. Pathways involved in cytoskeleton maintenance, extracellular matrix stability, and cell adhesion were downregulated, while several macrophage-expressed DEGs/DEPs (e.g., BST2, CYBB, ITGB2) were elevated. CONCLUSIONS: This work delineates dynamic transcriptional and translational alterations during the progression from mild gastritis to GIM. It implies that chronic inflammatory injury causes suppress protein synthesis, further driving metaplastic transformation early gastric precancerous lesions.
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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.