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Fc gamma receptor IIb in tumor-associated macrophages and dendritic cells drives poor prognosis of recurrent glioblastoma through immune-associated signaling pathways.
Background: Among central nervous system tumors, glioblastoma (GBM) is considered to be the most destructive malignancy. Recurrence is one of the most fatal aspects of GBM. However, the driver molecules that trigger GBM recurrence are currently unclear. Methods: The mRNA expression data and clinical information of GBM and normal tissues were collected from the Chinese Glioma Genome Atlas The Cancer Genome Atlas (TCGA), and REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT) cohorts. The DESeq2 R package was used to identify the differentially expressed genes between primary and recurrent GBM. ClueGO, Kyoto Encyclopedia of Genes and Genomes (KEGG), Biological Process in Gene ontology (GO-BP), and the Protein ANalysis THrough Evolutionary Relationships (PANTHER) pathway analyses were performed to explore the enriched signaling pathways in upregulated DEGs in recurrent GBM. A gene list that contained potential oncogenes that showed a significant negative correlation with patient survival from The Cancer Genome Atlas was used to further screen driver candidates for recurrent GBM. Univariate Cox proportional hazards regression analyses were used to investigate the risk score for the mRNA expression of the candidates. Single-cell RNA sequencing (scRNA-Seq) analyses were used to determine the cell type-specific distribution of Fc gamma receptor II b (FcγRIIb) in GBM. Immunohistochemistry (IHC) was used to confirm the FcγRIIb-positive cell populations in primary and paired recurrent GBM. Results: Through DEG analysis and overlap analysis, a total of 10 genes that are upregulated in recurrent GBM were screened. Using validation databases, FcγRIIb was identified from the 10 candidates that may serve as a driver for recurrent GBM. FCGR2B expression, not mutation, further showed a highly negative correlation with the poor prognosis of patients with recurrent GBM. Furthermore, scRNA-Seq analyses revealed that tumor-associated macrophage- and dendritic cell-specific FCGR2B was expressed. Moreover, FcγRIIb also showed a strong positive correlation coefficient with major immune-associated signaling pathways. In clinical specimens, FcγRIIb-positive cell populations were higher in recurrent GBM than in primary GBM. Conclusion: This study provides novel insights into the role of FcγRIIb in recurrent GBM and a promising strategy for treatment as an immune therapeutic target.
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Comprehensive characterization of lysosome-dependent cell death reveals prognostic significance and immune landscape in colon adenocarcinoma.
Lysosome-dependent cell death (LDCD) is an emerging form of regulated cell death with critical implications in tumor development, immune modulation, and therapy responsiveness. However, the role of LDCD-related genes in colon adenocarcinoma (COAD) remains poorly understood. We comprehensively analyzed LDCD-related gene expression profiles using transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases (GEO). Unsupervised clustering was performed to identify molecular subtypes. A prognostic signature was developed using LASSO and Cox regression analyses. Immune infiltration characteristics and immunotherapy responses were assessed via multiple algorithms. Single-cell RNA sequencing (scRNA-seq) analysis was conducted to explore the cellular distribution of LDCD genes. Functional assays, including colony formation, Transwell migration, and western blotting, were performed to validate the role of key LDCD regulators in COAD cell line. LDCD-related genes were differentially expressed in COAD tissues and stratified patients into two distinct subtypes with significant differences in survival, immune infiltration, and biological pathways. A five-gene prognostic signature demonstrated robust predictive ability for overall survival and was associated with tumor immune microenvironment characteristics. High-risk patients showed distinct immune checkpoint profiles and predicted sensitivity to immune checkpoint blockade therapy. scRNA-seq analysis revealed cell type-specific expression patterns of LDCD genes. In vitro experiments confirmed that modulation of a key LDCD regulator, SLC11A1, affected the proliferation and invasiveness of COAD cells. Moreover, the impact of SLC11A1 on apoptosis and intracellular ROS level confirmed the potential association between SLC11A1 and COAD cell apoptosis. Our study reveals the prognostic value and immunological relevance of LDCD-related genes in COAD. These findings provide new insights into molecular classification and may aid in the development of personalized therapeutic strategies targeting LDCD pathways in colorectal cancer.
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Identification of multiomics and immune infiltration-associated biomarkers for early gastric cancer: a machine learning-based diagnostic model development study.
BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis. METHODS: We employed serum proteomics combined with bioinformatics to identify genes differentially expressed in the serum of non-metastatic GC patients. Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and immunohistochemical staining using the Human Protein Atlas (HPA) database were used to validate the differential expression of candidate genes in GC tissues and adjacent non-cancerous tissues. RESULTS: The proteomic analysis identified several genes upregulated in the serum of GC patients compared to healthy controls. Single-cell RNA sequencing analysis further revealed that these upregulated genes were associated with altered immune cell infiltration in the tumor microenvironment. The glmBoost + XGBoost model incorporating B2M, CFL1, CTSD, and HSP90AB1 demonstrated strong diagnostic performance (mean AUC = 0.792), with 101 algorithm combinations achieving an average AUC > 0.7. A nomogram integrating gene expression and clinical data was developed, validated through calibration and decision curve analyses, highlighting its potential for early GC diagnosis. Additionally, four genes—TAGLN2, HSP90AB1, SH3BGRL3, and CFL1—were found to be highly expressed in non-metastatic GC tissues and were significantly correlated with immune infiltration, including CD8 + T cells, monocytes, and myeloid-derived suppressor cells. These findings were validated by qRT-PCR and immunohistochemical analyses, confirming their elevated expression in GC tissues. CONCLUSIONS: TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. Machine learning model shows excellent diagnostic performance. These results provide a foundation for future studies to improve early diagnosis and individualized treatment strategies for GC.
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Integrated genetic analysis and single cell-RNA sequencing for brain image-derived phenotypes and Parkinson's disease.
BACKGROUND: Previous studies have reported Parkinson's disease (PD) patients usually have changes in brain image-derived phenotypes (IDPs). However, the role of genetic factors in their association and biological mechanism remains unclear. We aimed to unveil genetic and biological links between brain IDPs and PD. METHODS: Using genome-wide association study (GWAS) summary statistics and single-cell RNA sequencing (scRNA-seq) data, we performed a comprehensive analysis between 624 brain IDPs and PD. The genetic correlations and causality were examined by linkage disequilibrium score regression (LDSC), two-sample bidirectional Mendelian randomization (MR) and meta-analysis. Potential shared genes were identified using MAGMA and PLACO. Finally, pathway enrichment using FUMA and Metascape, and scRNA-seq analysis were performed to determine biological mechanisms and gene expression atlas across various cell types in brain tissue. RESULTS: LDSC revealed that 50 brain IDPs were genetically correlated with PD (P < 0.05), in which 5 IDPs, exhibited putative causality on PD through MR (P < 0.05). For instance, we identified that the increased volume of the right thalamus (IVW: OR = 2.08, 95 % CI: 1.33 to 3.25, PFDR = 0.03) was positively correlated with the risk of PD, which was also supported by replicated MR (IVW: OR = 1.63, 95 % CI: 1.17-2.26, PFDR = 0.02) in FinnGen and meta-analysis (OR = 1.78, 95 % CI: 1.36-2.31, PFDR = 5.00 × 10-4). Additionally, we identified 56 unique pleiotropic genes, such as FAM13A, with notable enrichment in neuronal cells. Biological mechanism analysis revealed these genes were enriched in brain tissues and a variety of pathways such as negative regulation of neuron apoptotic processes. CONCLUSION: We indicated the shared genetic architecture and biological mechanisms between brain IDPs and PD. These findings might provide insights on the therapeutic intervention and early prediction of PD at the brain imaging level.
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Decoding the role of lipid metabolism in NSCLC: From macrophage subtype identification to prognostic model development.
Lipid metabolism plays a pivotal role in shaping the tumor microenvironment, particularly by influencing macrophage function. This study aimed to identify lipid-associated macrophage (LAM) marker genes involved in the onset and progression of non-small cell lung cancer (NSCLC) through integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) analyses. Mutation and RNA-seq data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed to explore the relationship between lipid metabolism pathways and NSCLC progression. scRNA-seq analysis revealed macrophage subtypes closely associated with lipid metabolism, with three key marker genes-S100A10, HLA-DMB, and CTSL-identified as predictive factors for patient prognosis. A prognostic risk scoring model was constructed and validated using survival analysis and ROC curves, demonstrating high accuracy in stratifying NSCLC patients by risk. Further in vivo experiments using subcutaneous tumor xenografts and lung metastasis models showed that S100A10 and CTSL promoted tumor growth and metastasis, while HLA-DMB inhibited these processes. Immune infiltration analysis highlighted the immunological relevance of the identified marker genes, providing insights into their functional roles. This study underscores the critical influence of LAMs in NSCLC progression and highlights a robust prognostic model that offers potential therapeutic targets for improving patient outcomes.
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An 8-gene diabetes-related signature predicts survival and immunotherapy response in breast cancer.
OBJECTIVE: To develop a signature of Diabetes-Related Genes (DRGs) using data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to predict the prognosis of Breast Cancer (BRCA) patients and identify potential therapeutic targets. METHODS: A list of DRGs was sourced from the GeneCard database. Analyses of differential expression and consensus clustering were conducted to identify DRGs, and Cox regression and Least Absolute Shrinkage and Selection Operator (Lasso) regression were used to construct prognostic risk signatures based on DRGs. High- and low-risk groups were classified using median risk scores, and nomogram plots were created to visualize prognostic signatures. The relationship between immune infiltration, chemotherapeutic agents, and risk scores was evaluated, along with the expression of DRGs in individual immune cells. RESULTS: A total of 1231 RNA-seq samples (cancer: 1118, paraneoplastic: 113), 1046 clinical data, and 965 DRGs were obtained from the databases. Differential expression and consensus clustering analyses identified 224 differentially expressed DRGs. Cox regression and Lasso regression analyses led to the establishment of a prognostic signature based on 8 DRGs. Kaplan-Meier curves demonstrated that patients in the low-risk group had a significantly better prognosis compared to those in the high-risk group (p < 0.001). The calibrated curves of the nomogram at 1-, 2-, 3-, and 5-years all aligned with the diagonal, indicating that our nomogram has high predictive performance. Immune infiltration analysis showed significant correlations between our constructed signature and the relative abundance of immune cells (p < 0.05). Single-cell data analysis revealed that DRGs were predominantly expressed in CD4+ conventional T-cells and regulatory T-cells. CONCLUSION: This study has successfully developed a prognostic signature for DRGs, which can be utilized as an efficient tool for risk stratification and prognostic prediction in BRCA patients. Additionally, this research offers a novel basis for investigating potential immunotherapy targets for BRCA patients.
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Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex.
Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.
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ADAMDEC1 predicts prognosis and immunotherapy response in a pan-cancer study with experimental validation.
BACKGROUND: ADAMDEC1 (a disintegrin and metalloproteinase domain-like protein decysin-1) is linked to immune disorders and cancers, but its pan-cancer role remains unclear. METHODS: We analyzed ADAMDEC1 using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for expression, prognosis, immune infiltration, and therapy response. Single-cell RNA sequencing (scRNA-seq) and genomic alterations were examined. Gene set enrichment analysis (GSEA) assessed pathways. Immunohistochemistry (IHC) validated ADAMDEC1 in esophageal cancer (ESCA) tissue microarray (n = 48). Immunotherapy response was predicted using Tumor Immune Dysfunction and Exclusion (TIDE) and independent cohorts. RESULTS: ADAMDEC1 was dysregulated across cancers with context-dependent prognosis: high expression correlated with poor survival in lower-grade glioma (LGG) and uveal melanoma (UVM), but better outcomes in ovarian cancer (OV) and skin cutaneous melanoma (SKCM). Single-cell analysis revealed a dichotomy: in UVM, ADAMDEC1 was mainly in tumor-associated macrophages; in SKCM, it was broadly expressed across malignant, immune, and stromal cells. DNA methylation inversely correlated with expression in 20 cancers. ADAMDEC1 linked to immune infiltration and immunomodulatory genes including programmed cell death protein 1 (PD-1). In ESCA, ADAMDEC1 was elevated in metastasis and positively correlated with PD-1. ADAMDEC1 predicted immunotherapy response comparably to conventional biomarkers. CONCLUSIONS: ADAMDEC1 is a context-dependent immune regulator with prognostic direction determined by cellular origin, supporting its investigation as a therapeutic target and biomarker in precision oncology.
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Single-cell RNA sequencing reveals the transcriptomic landscape of kidneys in patients with ischemic acute kidney injury.
BACKGROUND: Ischemic acute kidney injury (AKI) is a common syndrome associated with considerable mortality and healthcare costs. Up to now, the underlying pathogenesis of ischemic AKI remains incompletely understood, and specific strategies for early diagnosis and treatment of ischemic AKI are still lacking. Here, this study aimed to define the transcriptomic landscape of AKI patients through single-cell RNA sequencing (scRNA-seq) analysis in kidneys. METHODS: In this study, scRNA-seq technology was applied to kidneys from two ischemic AKI patients, and three human public scRNA-seq datasets were collected as controls. Differentially expressed genes (DEGs) and cell clusters of kidneys were determined. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, as well as the ligand-receptor interaction between cells, were performed. We also validated several DEGs expression in kidneys from human ischemic AKI and ischemia/reperfusion (I/R) injury induced AKI mice through immunohistochemistry staining. RESULTS: 15 distinct cell clusters were determined in kidney from subjects of ischemic AKI and control. The injured proximal tubules (PT) displayed a proapoptotic and proinflammatory phenotype. PT cells of ischemic AKI had up-regulation of novel pro-apoptotic genes including USP47 , RASSF4 , EBAG9 , IER3 , SASH1 , SEPTIN7 , and NUB1 , which have not been reported in ischemic AKI previously. Several hub genes were validated in kidneys from human AKI and renal I/R injury mice, respectively. Furthermore, PT highly expressed DEGs enriched in endoplasmic reticulum stress, autophagy, and retinoic acid-inducible gene I (RIG-I) signaling. DEGs overexpressed in other tubular cells were primarily enriched in nucleotide-binding and oligomerization domain (NOD)-like receptor signaling, estrogen signaling, interleukin (IL)-12 signaling, and IL-17 signaling. Overexpressed genes in kidney-resident immune cells including macrophages, natural killer T (NKT) cells, monocytes, and dendritic cells were associated with leukocyte activation, chemotaxis, cell adhesion, and complement activation. In addition, the ligand-receptor interactions analysis revealed prominent communications between macrophages and monocytes with other cells in the process of ischemic AKI. CONCLUSION: Together, this study reveals distinct cell-specific transcriptomic atlas of kidney in ischemic AKI patients, altered signaling pathways, and potential cell-cell crosstalk in the development of AKI. These data reveal new insights into the pathogenesis and potential therapeutic strategies in ischemic AKI.
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Single-nucleus transcriptomic profiling reveals temporal dynamics of neuroinflammation and myelin repair after intracerebral haemorrhage.
BACKGROUND: Intracerebral haemorrhage (ICH) progresses rapidly with complex pathology and limited treatment options, making it a severe subtype of stroke. The extravasation of blood into the brain parenchyma triggers a cascade of inflammatory responses, contributing to secondary injury. Single-nucleus RNA sequencing (snRNA-seq) data have enabled more profound insights into the cellular heterogeneity and dynamic interactions within the haemorrhagic brain. Immune cells play a crucial role in shaping neuroinflammation. However, the lack of comprehensive longitudinal studies limits our understanding of the temporal evolution of these inflammatory processes, posing a challenge to the development of targeted therapeutic strategies. METHODS: We used snRNA-seq in collagenase-induced ICH mouse models at Days 1, 3, 7, 14 and 28 post-injury, alongside naive controls, to profile the dynamics of gene expression over time. RESULTS: We obtained 281 577 high-quality transcriptional profiles representing 21 distinct cell types. Co-expression network analysis revealed a prominent 'inflammation module' that remained active throughout ICH. Integrative single-cell transcriptomic and immunofluorescence staining suggested that the various Mif-expressing cells may contribute to local inflammation, potentially engaging macrophages via receptor-ligand pairs such as Cd44 and Cd74. Over time, microglia appeared to serve as key recipients of pro-inflammatory signals increasingly. During the resolution phase, oligodendrocytes exhibited transcriptional signatures consistent with enhanced maturation and remyelination, which T cell-mediated interactions may have facilitated. CONCLUSIONS: These findings offer a systems-level perspective on cell-type-specific responses and immune-mediated interactions during ICH progression and resolution. KEY POINTS: Establish intracerebral haemorrhage (ICH) mouse models at various time points (Days 1, 3, 7, 14, 28) and construct a high-quality single-nucleus RNA sequencing (snRNA-seq) atlas. Computational analyses suggest that macrophage recruitment in the early stage of ICH potentially involves migration inhibitory factor (MIF) signalling pathways. T cells may interact with myelin-forming oligodendrocytes during the resolution phase, potentially contributing to remyelination after ICH.