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Systemic immune profiling analysis identifying M2-TAM related genes predicted colon cancer prognosis and chemotherapy responses.
Colon cancer (COAD) poses great challenges to clinical treatment due to its heterogeneity and complex immune microenvironment. M2-like macrophages significantly influence COAD's onset, progression, and treatment. Yet, existing M2-like macrophage markers are limited in prognostic efficacy, prompting the exploration of new M2 signatures. Extensive data analysis aimed to unveil prognosis-associated M2-derived signatures. Bulk transcriptome, single-cell RNA sequencing, and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus databases for patients with COAD were amassed. Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts identified immune cell infiltration, and the Kaplan-Meier test identified crucial immune populations associated with prognosis. Genetic signatures linked to M2 tumor-associated macrophage were crafted utilizing weighted gene coexpression network analysis, least absolute shrinkage and selection operator, and Cox regression. The M2 tumor-associated macrophage gene signature was validated in GSE17536. The expression profile of the M2 gene signature was investigated in single-cell RNA sequencing dataset GSE166555. Systemic immune profile identified that M2-like macrophage has the most significant prognostic significance in The Cancer Genome Atlas-COAD. The core genes related to M2 macrophage infiltration were extracted by weighted gene coexpression network analysis. Least absolute shrinkage and selection operator-stepwise COX regression-derived M2-derived signatures (snail family zinc finger 1, gastrin-releasing peptide, gamma-aminobutyric acid type A receptor delta subunit, cluster of differentiation 1B, poly(A)-binding protein cytoplasmic 2, manic fringe, and death-associated protein kinase 1) as a risk model, which was confirmed as independent prognosis factors, validated by external dataset. This M2-based prognostic model reflected M2 macrophage infiltration. Mendelian randomization established cytotoxic T lymphocyte associate protein-4 and cluster of differentiation 274 immune checkpoints' causality with COAD. In conclusion, our study developed novel markers for discriminating M2-like macrophages and predicting the prognosis of patients with COAD, offering fresh perspectives for clinical interventions.
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[Single-cell transcriptomic analysis reveals immune dysregula-tion and macrophage reprogramming in diabetic foot ulcers].
OBJECTIVES: To elucidate the underlying mechanisms of macrophage-mediated inflammation and tissue injury in diabetic foot ulcer (DFU). METHODS: Skin tissue samples were collected from patients with DFU and with non-DFU. A total of 79 272 high-quality cell transcriptomes were obtained using single-cell RNA sequencing. An unbiased clustering approach was employed to identify cell subpopulations. Seurat functions were used to identify differentially expressed genes between DFU and non-DFU groups, and gene ontology (GO) enrichment analysis was used to reveal gene function. Furthermore, cell-cell communication network construction and ligand-receptor interaction analysis were performed to reveal the mechanisms underlying cellular interactions and signaling regulation in the DFU microenvironment from multiple perspectives. RESULTS: The results revealed a significant expansion of myeloid cells in DFU tissues, alongside a marked reduction in structural cells such as endothelial cells, epithelial cells, and smooth muscle cells. Major cell types underwent functional reprogramming, characterized by immune activation and impaired tissue remodeling. Specifically, macrophages in DFU skin tissues exhibited a shift toward a pro-inflammatory M1 phenotype, with upregulation of genes associated with inflammation and oxidative stress. Cell communication analysis further demonstrated that M1 macrophages served as both primary signal receivers and influencers in the COMPLEMENT pathway mediated communication network, and as key signal senders and mediators in the secreted phosphoprotein 1 (SPP1) pathway mediated communication network, actively shaping the inflammatory microenvironment. Key ligand-receptor interactions driving macrophage signaling were identified, including C3-(ITGAM+ITGB2) and SPP1-CD44. CONCLUSIONS: This study establishes a comprehensive single-cell atlas of DFU, revealing the role of macrophage-driven cellular networks in chronic inflammation and impaired healing. These findings may offer potential novel therapeutic targets for DFU treatment.
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Cellular landscape of avian intestinal organoids revealed by single cell transcriptomics.
Studies of the avian gastrointestinal tract, where nutrient absorption and key host-pathogen interactions occur, have been strongly enabled by the development of intestinal organoid models. Here we report a single cell transcriptomic atlas of intestinal organoid cells derived from embryos of broiler and layer chickens, capturing mesenchymal, epithelial, endothelial, immune and neuronal cell lineages. Eight inferred mesenchymal subpopulations reflect anatomically distinct intestinal layers, including fibroblasts, telocytes, myofibroblasts, smooth myocytes, pericytes, and interstitial cells of Cajal. Identified heterogeneity within the epithelial lineage included enterocytes, goblet cells, Paneth cells, tuft cells, and diverse enteroendocrine cell subtypes. Additionally, we identified candidate macrophages, monocytes, γδ T cells, NK cells and granulocytes. Layer and broiler organoids showed significant differences in cell-specific transcriptome, most pronounced in epithelial cells, pointing to divergent selection on intestinal physiology. Our analysis finally provides a catalogue of novel cell marker genes to enable future research of chicken intestinal organoids.
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Vasculogenic mimicry regulates immune infiltration and mutational status of the tumor microenvironment in breast cancer to influence tumor prognosis.
BACKGROUND: Vasculogenic mimicry (VM) refers to the direct formation of microcirculatory ducts by invasive malignant tumors via cellular phenotypic transformation. However, there is a lack of VM-based biomarkers for breast cancer. METHODS: We obtained transcriptomic expression data, single cell sequencing data, and clinical data of patients from The Cancer Genome Atlas Program (TCGA) database and GEO database, performed single cell analysis to obtain specific type annotations of breast cancer cells and analyzed their spatial expression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analyses as well as Gene Set Enrichment Analysis (GSEA) analyses were performed to clarify the biological pathways and tumor functional enrichment relationships of the major expressed genes of VM in the breast cancer bulk data specimens. VM biomarkers were constructed. Meanwhile, the relationship between VM scores and tumor immune infiltration in breast cancer was analyzed using MCPcounter and ssGSEA methods. In addition, we assessed the specific relationship between NDRG1, a key VM gene in breast cancer, and tumor colonization, adhesion and invasion by biological experiments in breast cancer cell lines. RESULTS: The main cell types of breast cancer (BRCA) samples were annotated by single cell transcriptome analysis. Most of the VM-high group was present in epithelial cells, whereas the VM-low group was present in immune and stromal cells. Multiple tumor pathways such as TGFβ p53 and MAPK were closely associated with VM-mediated breast cancer infiltration and invasion. A prognostic model of breast cancer based on VM key genes was constituted. Prognostic stratification of breast cancer was successfully achieved for the TCGA-BRCA and GSE58812 datasets. Through immune infiltration analysis, we found that differential expression of VM markers was associated with multiple immune cell regulation. In MDA-MB-231 and MDA-MB-453 cell lines, we found that the NDRG1 gene significantly promoted colony formation of breast cancer cells. CONCLUSION: Our constructed VM-related gene-based model of breast cancer biology holds promise for prognostic prediction and patient stratification of breast cancer. This may provide a potentially clinically valuable aid in promoting a deeper understanding of the biological regulation of VM in breast cancer and exploring the specific mechanisms of tumor angiogenesis and breast cancer development.
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A pan-cancer single cell landscape reveals heterogeneity and functional diversity of double-negative T cells.
BACKGROUND: Double-Negative T (DNT) cells, lacking both CD4 and CD8 expression, play critical roles in cancer immunology, and have garnered increasing attention in cancer research. However, their heterogeneity and functional diversity within the tumor microenvironment (TME) remain underexplored. METHODS: In-house and publicly available single-cell RNA sequencing (scRNA-seq) data for different cancer types were integrated after quality control and batch effect correction, followed by DNT cells separation from CD3+ T cells subtypes. Functional characteristics, intercellular communication, differentiation trajectories, regulatory networks, and clinical relevance were analyzed among different DNT subsets. Key findings were validated using multiplex immunofluorescence and spatial transcriptomics to investigate the spatial localization of DNT subsets and their interactions within the TME. Impact of γδ T cells on immunotherapy response was also assessed using MC38-based murine tumor model. RESULTS: By integrating scRNA-seq data from 2,369 samples across 23 cancer types, we established a comprehensive single-cell atlas of 157,025 high-quality DNT cells. Fourteen distinct DNT subsets (6 αβ DNT and 8 γδ T cell subsets) were identified, demonstrating tumor both type-specific and shared distribution patterns, as well as unique cell-cell interaction network within the TME. These subsets displayed specialized functional profiles, including cytotoxicity, antigen presentation, and immune modulation, indicating that the functional diversity of DNT cells is largely subset-specific rather than a manifestation of multifunctionality within a single population. We also delineated divergent trajectories for αβ DNT and γδ T cell subsets, including the functional plasticity of gut-resident γδ T cells transitioning between cytotoxic and immunosuppressive states. Notably, several DNT subsets were significantly associated with favorable clinical treatment outcomes, including improved responses to cancer immunotherapy. Consistently, depletion of γδ T cells in the murine tumor model significantly decreased the efficacy of PD-1 blockade, underscoring their critical role in therapeutic response. CONCLUSIONS: Our study uncovers the previously underappreciated heterogeneity and functional diversity of DNT cells in the TME and demonstrates their profound impact on tumor progression and immunotherapy outcomes.
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Refining the adipose progenitor cell landscape in healthy and obese visceral adipose tissue using single-cell gene expression profiling.
Obesity is a serious health concern and is associated with a reduced quality of life and a number of chronic diseases, including diabetes, heart disease, stroke, and cancer. With obesity rates on the rise worldwide, adipose tissue biology has become a top biomedical research priority. Despite steady growth in obesity-related research, more investigation into the basic biology of adipose tissue is needed to drive innovative solutions aiming to curtail the obesity epidemic. Adipose progenitor cells (APCs) play a central role in adipose tissue homeostasis and coordinate adipose tissue expansion and remodeling. Although APCs are well studied, defining and characterizing APC subsets remains ambiguous because of ill-defined cellular heterogeneity within this cellular compartment. In this study, we used single-cell RNA sequencing to create a cellular atlas of APC heterogeneity in mouse visceral adipose tissue. Our analysis identified two distinct populations of adipose tissue-derived stem cells (ASCs) and three distinct populations of preadipocytes (PAs). We identified novel cell surface markers that, when used in combination with traditional ASC and preadipocyte markers, could discriminate between these APC subpopulations by flow cytometry. Prospective isolation and molecular characterization of these APC subpopulations confirmed single-cell RNA sequencing gene expression signatures, and ex vivo culture revealed differential expansion/differentiation capabilities. Obese visceral adipose tissue featured relative expansion of less mature ASC and PA subpopulations, and expression analyses revealed major obesity-associated signaling alterations within each APC subpopulation. Taken together, our study highlights cellular and transcriptional heterogeneity within the APC pool, provides new tools to prospectively isolate and study these novel subpopulations, and underscores the importance of considering APC diversity when studying the etiology of obesity.
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A single-cell atlas of RNA alternative splicing in the glioma-immune ecosystem.
BACKGROUND: Single-cell analysis has refined our understanding of cellular heterogeneity in glioma, yet RNA alternative splicing, a critical layer of transcriptome regulation, remains underexplored at single-cell resolution. Here, we present a pan-glioma single-cell alternative splicing analysis in both tumor and immune cells through integrating seven SMART-seq2 datasets of human gliomas to uncover overlooked isoform-level regulations shaping glioma progression and immune responses. RESULTS: Our analysis reveals lineage-specific alternative splicing regulation among glioma cellular states, with the most divergent alternative splicing landscapes observed between mesenchymal and neuronal-like glioma cells. Notably, this includes events in TCF12 exon 15 and PTBP2 exon 10, two key regulators of mesenchymal and neuronal transcriptomes. Inducing TCF12 exon 15 inclusion in glioma cells with a dCasRx-RBM25 system promotes neuronal gene expression, suppresses genes related to extracellular matrix organization, and enhances sensitivity to radiotherapy. Comparison of core and peripheral glioma cells highlights alternative splicing redox co-regulation of TPM1 and ACTN4, key genes involved in cytoskeletal organization. Further analysis of glioma-infiltrating immune cells reveals altered first exon usage of UGP2 in regulatory T cells and an association between MS4A7 alternative splicing in macrophages and clinical response to anti-PD-1 therapy. CONCLUSIONS: This study emphasizes the role of alternative splicing in glioma cellular heterogeneity, highlighting the importance of an isoform-centric approach to better understand the complex biological processes driving tumorigenesis.
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High Expression of ZNF208 Predicts Better Prognosis and Suppresses the Tumorigenesis of Breast Cancer.
BACKGROUND: Breast cancer (BRCA), the hormone related malignant tumor, is well-known for poor prognosis. ZNF208 mainly acts as a transcription factor in various tumors, and the single nucleotide polymorphisms (SNPs) of ZNF208 are related to telomere length. Nevertheless, its role in breast tumorigenesis is largely unknown. METHODS: We systematically investigated the gene expression, prognostic value, and promoter methylation of ZNF208 in BRCA with Gene Expression Profiling Interactive Analysis (GEPIA) and DNA Methylation Interactive Visualization Database (DNMIVD). Meanwhile, we clarified the association of ZNF208 with tumor-infiltrating immune cells (TICs) from Tumor Immune Estimation Resource (TIMER). Furthermore, we determined the biological process and functional enrichment from Cancer single-cell state atlas (CancerSEA). Finally, we verified our results with prognostic analysis and immunohistochemistry (IHC) assay. RESULTS: We discovered that ZNF208 was downregulated in breast cancer, and low expression of ZNF208 predicted worse prognosis of BRCA patients. The promoter methylation level of ZNF208 was obviously increased, and ZNF208 was associated with TlCs in BRCA. In addition, ZNF208 could inhibit the metastasis and invasion biological processes, and regulate the MAPK and RAS signaling pathways in BRCA. CONCLUSION: Our findings illustrate that ZNF208 can function as a tumor suppressor and predict prognosis of breast cancer.
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Identifying the signature of NAD+ metabolism-related genes for immunotherapy of gastric cancer.
NAD (Nicotinamide Adenine Dinucleotide) -related metabolic reprogramming in tumor cells involves multiple vital cellular processes. However, the role of NAD metabolism in immunity and the prognosis of gastric cancer (GC) remains not elucidated. Here we identified and clustered 33 NAD + metabolism-related genes (NMRGs) based on 808 GC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Survival analysis between different groups found a poor prognosis in the GC patients with high NMRGs expression. Gene SGCE, APOD, and PPP1R14A were identified and performed high expression in GC samples, while the qRT-PCR results further confirmed that their expression levels in GC cell lines were significantly higher than those from normal human gastric mucosa epithelial cells. Based on the single-cell analysis, Gene SGCE, APOD, and PPP1R14A can potentially be novel biomarkers of tumor-associated fibroblasts (CAFs). In parallel, the proliferation and migration of GC cells were significantly hampered following the knockdown of SGCE, APOD, and PPP1R14A, particularly APOD, we confirmed that APOD knockdown can inhibit β-catenin and N-cadherin expression, while promote E-cadherin expression. This study unveils a novel NMRGs-related gene signature, highlighting APOD as a prognostic biomarker linked to the tumor microenvironment. APOD drives GC cell proliferation and metastasis through the Wnt/β-catenin/EMT signaling pathway, establishing it as a promising therapeutic target for GC patients.
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Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.