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Single-cell landscape of peripheral and tumor-infiltrating immune cells in HPV-negative HNSCC.
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. HPV-negative HNSCC, arising in diverse upper airway mucosal niches, is particularly aggressive, with poor 5-y survival and a limited response to immune checkpoint inhibitors. A deeper understanding of the tumor-localized immune landscape is essential to uncover actionable immunotherapeutic targets. Here, we integrated two single-cell RNA sequencing (scRNA-seq) datasets from 29 samples totaling nearly 300,000 immune cells to dissect immune rewiring during tumor progression and lymph node metastasis in HPV-negative HNSCC. We identified distinct shifts in adaptive immune cell populations across 14 peripheral blood mononuclear cell (PBMC) and 21 tumor-infiltrating immune cell (TIC) states. Notably, TICs exhibited enriched interferon response and immunomodulatory gene signatures, in contrast to PBMCs, indicating tumor-specific immune imprinting. Ligand-receptor analysis revealed that immunosuppressive crosstalk between macrophages and cytotoxic cells was associated with advanced disease. To spatially validate these transcriptional states, we conducted multiplexed immunofluorescence profiling on nine locally invasive HPV-negative HNSCCs, all from the ventrolateral tongue mucosa. Spatial proteomics confirmed peritumoral enrichment of activated (CD107a+, ICOS+) NK and CD8+ T cells and intratumoral accumulation of exhausted (PD-1+, PD-L1+) phenotypes, mirroring pseudotime trajectories inferred from scRNA-seq. These findings highlight spatially localized cytotoxic cell exhaustion as a key immune evasion mechanism in HPV-negative HNSCC and underscore the value of integrating spatial and single-cell data to reveal therapeutic vulnerabilities.
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Single-cell atlas of epithelial and stromal cell heterogeneity by lobe and strain in the mouse prostate.
BACKGROUND: Evaluating the complex interplay of cell types in the tissue microenvironment is critical to understanding the origin and progression of diseases in the prostate and potential opportunities for intervention. Mouse models are an essential tool to investigate the molecular and cell-type-specific contributions of prostate disease at an organismal level. While there are well-documented differences in the extent, timing, and nature of disease development in various genetically engineered and exposure-based mouse models in different mouse strains and prostate lobes within each mouse strain, the underlying molecular phenotypic differences in cell types across mouse strains and prostate lobes are incompletely understood. METHODS: In this study, we used single-cell RNA-sequencing (scRNA-seq) methods to assess the single-cell transcriptomes of 6-month-old mouse prostates from two commonly used mouse strains, friend virus B/NIH jackson (FVB/NJ) (N = 2) and C57BL/6J (N = 3). For each mouse, the lobes of the prostate were dissected (anterior, dorsal, lateral, and ventral), and individual scRNA-seq libraries were generated. In situ and pathological analyses were used to explore the spatial and anatomical distributions of novel cell types and molecular markers defining these cell types. RESULTS: Data dimensionality reduction and clustering analysis of scRNA-seq data revealed that basal and luminal cells possessed strain-specific transcriptomic differences, with luminal cells also displaying marked lobe-specific differences. Gene set enrichment analysis comparing luminal cells by strain showed enrichment of proto-Oncogene targets in FVB/NJ mice. Additionally, three rare populations of epithelial cells clustered independently of strain and lobe: one population of luminal cells expressing Foxi1 and components of the vacuolar ATPase proton pump (Atp6v0d2 and Atp6v1g3), another population expressing Psca and other stem cell-associated genes (Ly6a/Sca-1, Tacstd2/Trop-2), and a neuroendocrine population expressing Chga, Chgb, and Syp. In contrast, stromal cell clusters, including fibroblasts, smooth muscle cells, endothelial cells, pericytes, and immune cell types, were conserved across strain and lobe, clustering largely by cell type and not by strain or lobe. One notable exception to this was the identification of two distinct fibroblast populations that we term subglandular fibroblasts and interstitial fibroblasts based on their strikingly distinct spatial distribution in the mouse prostate. CONCLUSIONS: Altogether, these data provide a practical reference of the transcriptional profiles of mouse prostate from two commonly used mouse strains and across all four prostate lobes.
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Identifying ARRB2 as a Prognostic Biomarker and Key Player in the Tumor Microenvironment of Pancreatic Cancer through scPagwas Methodology.
INTRODUCTION: Pancreatic Cancer (PC) is recognized as a highly aggressive malignancy and is anticipated to become the second leading cause of cancer-associated deaths across the United States by 2030. Owing to its late-stage diagnosis and the substantial risk of metastasis, current therapeutic strategies exhibit limited efficacy, resulting in a five-year survival rate below 10%. Consequently, identifying reliable biomarkers and therapeutic approaches remains imperative for enhancing treatment effectiveness. METHODS: In this study, using the data of Cancer Genome Atlas (TCGA) and Genome-Wide Association Study (GWAS), we identified macrophage subsets and key gene arrb2 closely related to the progression of Pancreatic Adenocarcinoma (PAAD) via the scpagwas method combined with single- cell and bulk transcriptome analysis. RESULTS: The results showed that in macrophage subpopulations closely related to disease progression, the ARRB2 gene was significantly associated with the prognosis of patients, and low expression suggested poor survival outcomes. The high-expression subgroup of ARRB2 exhibited higher sensitivity to a variety of drugs, and i-bet-762 showed strong molecular binding ability with ARRB2. The experimental detection further confirmed the low expression of ARRB2 in PAAD tissue, which provided the basis for its use as a prognostic marker and potential therapeutic target. DISCUSSION: The results of this study suggest that ARRB2 is not only a prognostic biomarker of PAAD but may also be involved in the regulation of metabolic and immune pathways, thereby affecting drug responsiveness. There was a significant difference in drug sensitivity between the high and low-expression subgroups of ARRB2, suggesting that there may be a potential mechanism between the signal pathway and the therapeutic effect, which warrants further functional research. Considering the heterogeneity of the macrophage population and its dual role in tumor promotion and inhibition, in-depth analysis of the context-dependent function of ARRB2 in the immune-rich microenvironment is expected to provide new insights for the development of combination therapy, especially the potential of combining it with BET inhibitors (such as i-bet-762). CONCLUSION: Macrophages, along with ARRB2, serve an essential function in the progression of PAAD and immune regulation. The identification of ARRB2 as a prognostic biomarker and its involvement in critical oncogenic pathways furnish a theoretical foundation for targeted therapeutic interventions. These discoveries contribute to the ongoing exploration of diagnostic, prognostic, and treatment strategies for PAAD.
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Targeting LHPP in neoadjuvant chemotherapy resistance of gastric cancer: insights from single-cell and multi-omics data on tumor immune microenvironment and stemness characteristics.
Gastric cancer (GC) is a highly heterogeneous and complex malignancy, often characterized by tumor stemness and immune evasion mechanisms, which contribute to a poor response to neoadjuvant chemotherapy (NAC) and treatment resistance. In this study, we performed a comprehensive analysis using single-cell and multi-omics approaches on 375 GC samples from The Cancer Genome Atlas database, along with 141 clinical samples from patients who underwent NAC. We identified key gene modules associated with stemness and immune traits, and developed a novel stem cell-immune risk score. This score effectively distinguished responders from non-responders to chemotherapy, and was significantly associated with overall survival. Through multi-omics analysis, we further elucidated the role of phospholysine phosphohistidine inorganic pyrophosphatase (LHPP) in the tumor immune microenvironment. Our findings showed that high LHPP expression was closely linked to the increased infiltration of antitumor immune cells, such as CD8+ T cells, and significantly suppressed the development of stemness characteristics in GC. Additionally, single-cell sequencing data revealed that tumor epithelial cells with low LHPP expression exhibited heightened stemness and demonstrated the strongest communication with CD8+-exhausted T cells. We also observed that LHPP inhibited stemness and chemotherapy resistance in GC cells by regulating the phosphorylation of GSK-3β. In conclusion, LHPP plays a critical regulatory role in the stemness features and tumor immune microenvironment of GC, presenting a promising biomarker and potential therapeutic target for personalized treatment of GC.
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The Regulation Network of Glycerolipid Metabolism as Coregulators of Immunotherapy-Related Myocarditis.
BACKGROUND: To date, immunotherapy for patients with malignant tumors has shown a significant association with myocarditis. However, the mechanism of metabolic reprogramming changes for immunotherapy-related cardiotoxicity is still not well understood. METHODS: The CD45+ single-cell RNA sequencing (scRNA-seq) of the Pdcd1-/-Ctla4+/- and wild-type mouse heart in GSE213486 was downloaded to demonstrate the heterogeneity of immunocyte atlas in immunotherapy-related myocarditis. The liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectrum metabolomics analysis detects the metabolic network differences. The drug prediction, organelle level interaction, mitochondrial level regulatory network, and phosphorylation site prediction for key regulators have also been screened via multibioinformatics analysis methods. RESULTS: The scRNA analysis shows that the T cell is the main regulatory cell subpopulation in the pathological progress of immunotherapy-related myocarditis. Mitochondrial regulation pathway significantly participated in pseudotime trajectory- (PTT-) related differential expressed genes (DEGs) in the T cell subpopulation. Additionally, both the gene set enrichment analysis (GSEA) of PTT-related DEGs and LC-MS/MS metabolomics analysis showed that mitochondrial-regulated glycerolipid metabolism plays a central role in metabolic reprogramming changes for immunotherapy-related cardiotoxicity. Finally, the hub-regulated protease of diacylglycerol kinase zeta (Dgkz) was significantly identified and widely played various roles in glycerolipid metabolism, oxidative phosphorylation, and lipid kinase activation. CONCLUSION: Mitochondrial-regulated glycerolipid metabolism, especially the DGKZ protein, plays a key role in the metabolic reprogramming of immunotherapy-related myocarditis.
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Rethinking the Estrogen Receptor Beta Dominance Hypothesis in Endometriosis: Insights from Single Cell RNA Sequencing Meta-analysis.
BACKGROUND: Endometriosis is a chronic, estrogen-dependent disease characterized by the presence of endometrial-like tissue growing outside the uterus. The molecular and clinical heterogeneity of endometriosis complicate diagnostic and treatment options -- diagnostic delays of seven to ten years are common and therapies often lack long-term efficacy. Estrogen signaling and estrogen receptor beta (ERβ) expression is thought to be increased in endometriosis, contributing to increased cell proliferation in lesions. The "ERβ dominance hypothesis" is a prevailing hypothesis in the field, setting ERβ as a high-priority therapeutic target. If effectively modulated, ERβ could be the first therapy to directly target lesion biology, rather than only managing symptoms. OBJECTIVES: We aimed to characterize ERβ's expression in endometriosis by cell type and evaluate its therapeutic relevance, primarily assessing the validity of the ERβ dominance hypothesis. STUDY DESIGN: We reanalyzed scRNAseq data from eight previously published studies. Our final filtered dataset included 557,061 cells, the largest endometriosis single cell atlas ever constructed. We quantified gene expression levels of ESR1 and ESR2, which encode ERɑ and ERβ respectively, across each tissue and cell type, to identify cell-type specific drivers of ESR2/ERβ expression across diseased and healthy tissues. To characterize the differences between cells that uniquely express ESR1 versus those that uniquely express ESR2, we performed differential gene expression and pathway enrichment analyses. RESULTS: Count and distribution analyses revealed no significant ESR2/ERβ dominance in any cell or tissue type by Fisher's Exact Tests and Wilcoxon Rank Sum Tests. Differential gene expression and pathway enrichment analyses suggest distinct roles of each estrogen receptor isoform. CONCLUSIONS: Overall, our results argue against a simplified model of ERβ dominance and instead propose a dual-isoform and cell and tissue-specific framework for understanding estrogen receptor signaling in endometriosis. These findings hold important implications for future therapeutic strategies. Specifically, treatments that target ERβ alone may fail to account for the functional role and relative abundance of ERα. In the future, therapeutic approaches that consider isoform-specific, tissue-specific, and cell-specific expression patterns may prove most effective in reducing recurrence and improving outcomes for patients.
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Pan-cancer single-cell atlas of immunotherapy response identifies ZNF385A as a regulator of immune evasion in small cell lung cancer.
Although immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape of solid tumors, response rates in patients with small cell lung cancer (SCLC) remain limited, and acquired resistance is highly prevalent. The underlying mechanisms of this immunotherapy resistance remain to be fully elucidated. Clinically, SCLC typically manifests as an "immune-cold" tumor, characterized by a low abundance of CD8+ T cell infiltration and the rare formation of tertiary lymphoid structures (TLS). While DNA damage repair (DDR) is closely linked to innate immune responses, how DDR networks orchestrate the SCLC immune microenvironment remains obscure. In this study, we integrated single-cell transcriptomic data (comprising 344,447 high-quality cells) from six cancer types (BCC, CRC, HCC, HNSCC, iCCA, and SCLC). Our comparative analysis revealed a fundamental depletion of TLS-associated cellular subpopulations (e.g., CXCL13+ CD8+ T cells, HLA-DRB5+ B cells, and CXCL9+ dendritic cells) in SCLC, which was significantly correlated with aberrant DDR activity. Through high-dimensional weighted gene co-expression network analysis (hdWGCNA), we identified ZNF385A as the core hub gene within the DDR-associated module. ZNF385A is highly expressed in SCLC and is associated with poorer prognosis. In vitro, ZNF385A depletion suppressed SCLC cell proliferation and induced apoptosis, accompanied by R-loop accumulation and activation of cGAS-STING signaling, indicating a potential link between ZNF385A, genomic stability and tumor-intrinsic innate immune signaling. Collectively, these findings identify ZNF385A as a potential regulator associated with TLS deficiency and immune evasion in SCLC.
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FTSJ1-mediated IL1RN mRNA instability promotes inflammation-driven hepatocellular carcinoma.
BACKGROUND: The role of 2'-O-methyltransferases in hepatocellular carcinoma (HCC) progression, particularly concerning inflammation, remains unclear. This study investigated their prognostic significance, identified key prognostic target FTSJ1, and elucidated its inflammatory-related biological functions and molecular mechanisms. METHODS: Transcriptomic data from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) were integrated to data of subtype HCC patients via consensus clustering based on 2'-O-methyltransferase expression. Single-gene survival analysis linked FTSJ1, FTSJ2, FTSJ3, and FBL to prognosis. A four-gene risk model was built. Multivariate COX regression identified independent risk factors. FTSJ1 expression and prognostic value were validated clinically using quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Functional roles were assessed in vitro [Cell Counting Kit-8 (CCK-8), Transwell, apoptosis/cell cycle assays] and in vivo (xenograft models). Mechanisms were explored via RNA sequencing (RNA-seq), gene set enrichment analysis (GSEA), and inflammatory cytokine detection. RESULTS: Consensus clustering defined three subtypes. Subtype 1 (high 2'-O-methyltransferase expression) showed the worst prognosis. FTSJ1/2/3 and FBL correlated significantly with survival. The four-gene risk model predicted survival across cohorts [The Cancer Genome Atlas (TCGA)/Gene Expression Omnibus (GEO) datasets (GSE54236/GSE144269)]. FTSJ1 was an independent risk factor [multivariate Cox, hazard ratio (HR) =2.268, P=0.01] and significantly elevated in HCC tissues. Functionally, FTSJ1 knockdown inhibited proliferation and migration; induced G0/G1 arrest; promoted apoptosis in vitro; and suppressed tumor growth in vivo. Mechanistically, FTSJ1 drove HCC progression by reducing RNA stability of the key anti-inflammatory gene interleukin 1 receptor antagonist (IL1RN), leading to downregulated tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) and enhanced pro-inflammatory signaling. CONCLUSIONS: High 2'-O-methyltransferase abundance predicts poor HCC prognosis. FTSJ1 is a novel independent prognostic biomarker and oncogenic factor that promotes HCC progression by dysregulating the inflammatory response pathway, highlighting its translational potential in inflammation-driven HCC.
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Revelation of prognosis and tumor microenvironment of colorectal cancer based on genes related to antibody-dependent cellular phagocytosis and single-cell landscape.
BACKGROUND: Increasing evidence highlights the crucial role of antibody-dependent cellular phagocytosis (ADCP) in colorectal cancer (CRC). However, how to use ADCP-related genes to predict prognosis in CRC and guide treatment remains unelucidated. METHODS: Gene expression profiles and clinical data information on CRC were sourced from the Cancer Genome Atlas (TCGA) database. We obtained the validation set GSE29621 and CRC single-cell dataset GSE178341 from the Gene Expression Omnibus (GEO) database and the ADCP-related gene set from the literature. Based on the TCGA-CRC cohort, univariate Cox and LASSO Cox regression analyses were employed to screen for ADCP-related genes linked with prognosis. Then a prognostic model was set up through multivariate Cox regression analysis. We further graphed a nomogram based on clinical information and risk scoring and evaluated its prognostic value using Kaplan-Meier (K-M) survival curves and receiver operation characteristic (ROC) curves. Based on the single-cell data analysis model, the expression levels of genes in different cell clusters were evaluated by scoring individual cells using the AUCell R package. Finally, functional enrichment, immune infiltration, and somatic mutation analyses were performed on the high- and low-ADCP-related risk score (ADCPRS) groups clustered by the median value of the ADCPRS. In addition, small molecular drugs for the treatment of CRC patients were analyzed using drug sensitivity analysis of IC50 and molecular docking. RESULTS: This project created a prognostic model based on 7 feature genes using the TCGA training set. The K-M survival curves and ROC curves indicated that the model, as well as the nomogram, was capable of accurately predicting prognosis for CRC patients. Based on scRNA-seq data analysis, the 7 feature genes were examined to be expressed across 8 cell clusters (Monocytes, CD8 + T cells, Epithelial cells, B cells, Macrophages, HSC, Endothelial cells, and Fibroblasts). We scored individual cells and revealed that cells with higher scores were mainly concentrated in B cells and macrophages. Functional enrichment analysis manifested that the upregulated differentially expressed genes (DEGs) in the high-ADCPRS group were mainly enriched in signaling pathways such as the Drug metabolism cytochrome P450, Neuroactive ligand-receptor interaction, and Calcium signaling pathway. Immune infiltration analysis manifested that Th1 cells, iDCs, and Th2 cells had higher abundance in the low-ADCPRS group. Gene mutation analysis uncovered that both high- and low-ADCPRS groups had high mutation rates, with APC and TP53 being the top two genes with the highest mutation rates. Moreover, the drug sensitivity analysis and molecular docking uncovered that Dasatinib, Benzaldehyde, and Tegafur may aid in treating CRC patients. CONCLUSION: The prognostic model developed in this project functioned as a potential tool for risk assessment. The 7 model genes may serve as prognostic biomarkers for CRC, which can guide treatment decisions for CRC patients.
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A new perspective on endometriosis: Integrating eQTL mendelian randomization with transcriptomics and single-cell data analyses.
Endometriosis is caused by the migration of endometrial cells to locations outside the uterine lining. Despite the increasing prevalence of endometriosis, there has been limited research on genetic effects, and its molecular mechanisms remain unclear. This study aimed to investigate the mechanisms underlying the development of endometriosis and to identify new genetic targets for endometriosis by integrating data from gene chips, single-cell mapping, and genome-wide association study databases. Using the Gene Expression Omnibus database, we downloaded data on normal endometrium, eutopic endometrium, and ectopic lesion tissues to explore the differentially expressed genes (DEGs) between normal and eutopic endometrium, and between eutopic and ectopic endometrium. Assessment of the relationships between DEGs and endometriosis involved differential expression, expression quantitative trait loci (eQTL), and Mendelian randomization (MR) analyses. Two single-cell atlas datasets were then analyzed to explore the mechanisms underlying disease development and progression. Intersection of MR results with DEGs between normal and eutopic endometrium highlighted 28 candidate biomarker genes (17 upregulated and 11 downregulated). Similarly, we identified two additional candidate biomarker genes by intersecting the DEGs between eutopic and ectopic endometrium with MR results. Among these 30 candidates, further filtering using single-cell datasets revealed that the histamine N-methyltransferase (HNMT), coiled-coil domain containing 28 A (CCDC28A), fatty acid desaturase 1 (FADS1) and mahogunin ring finger 1 (MGRN1) genes were differentially expressed between the normal and eutopic groups, consistent with transcriptomic and MR results. Our findings suggested that eutopic endometrium exhibits epithelial-mesenchymal transition (EMT). Cell communication analysis focused on ciliated epithelial cells expressing CDH1 and KRT23 revealed that, in the eutopic endometrium, ciliated epithelial cells are strongly correlated and interact with natural killer cells, T cells, and B cells. We identified four novel biomarker genes and found evidence for EMT in the eutopic endometrium. The mechanism of endometriosis progression may be closely related to EMT and changes in the immune microenvironment triggered by damage to ciliated epithelial cells.