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Single-cell analysis identifies PI3+S100A7+keratinocytes in early cervical squamous cell carcinoma with HPV infection.
BACKGROUND: Cervical squamous cell carcinoma (CESC), the most common subtype of cervical cancer, is primarily caused by the high-risk human papillomavirus (HPV) infection and genetic susceptibility. Single-cell RNA sequencing (scRNA-seq) has been widely used in CESC research to uncover the diversity of cell types and states within tumor tissues, enabling a detailed study of the tumor microenvironment (TME). This technology allows precise mapping of HPV infection in cervical tissues, providing valuable insights into the initiation and progression of HPV-mediated malignant transformation. METHODS: We performed the scRNA-seq to characterize gene expression in tumor tissues and paired adjacent para-cancerous tissues from four patients with early-stage CESC using the 10× Genomics platform. The HPV infection and its subtypes were identified using the scRNA data and viral sequence mapping, and trajectory analyses were performed using HPV+ or HPV- cells. Interactions between different types of keratinized cells and their interactions with other cell types were identified, and pathways and specificity markers were screened for proliferating keratinized cells. The Cancer Genome Atlas (TCGA) dataset was used to verify the prognostic correlation between tumor-specific PI3+S100A7+ keratinocyte infiltration and CESC, and the localization relationship between PI3+S100A7+ keratinocytes and macrophages was verified by immunofluorescence staining. RESULTS: Various types of keratinocytes and fibroblasts were the two cell types with the most significant differences in percentage between the tumor tissue samples and paired adjacent non-cancerous tissue samples in the early stages of CESC. We found that PI3+S100A7+ keratinocytes were associated with early HPV-positive CESC, and PI3+S100A7+ keratinocytes were more abundant in tumors than in adjacent normal tissues in the TCGA-CESC dataset. Analysis of clinical information revealed that the infiltration of PI3+S100A7+ keratinocytes was notably higher in tumors with poor prognosis than in those with good prognosis. Additionally, multiplex immunofluorescence analysis showed a specific increase in PI3+S100A7+ expression within tumor tissues, with PI3+S100A7+ keratinocytes and CD163+ macrophages being spatially very close to each other. In the analysis of cell-cell interactions, macrophages exhibited strong crosstalk with PI3+S100A7+ proliferating keratinocytes in HPV-positive CESC tumors, mediated by tumor necrosis factor (TNF), CCL2, CXCL8, and IL10, highlighting the dynamic and tumor-specific enhancement of macrophage-keratinocyte interactions, which are associated with poor prognosis and immune modulation. Using CIBERSORTx, we discovered that patients with high infiltration of both PI3+S100A7+ proliferating keratinocytes and macrophages had the shortest overall survival. In the analysis of cell-cell interactions, PI3+S100A7+ proliferating keratinocytes and macrophages were found to be involved in highly active pathways that promote differentiation and structure formation, including cytokine receptor interactions, the Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway, and TNF signaling pathway regulation. Further subtyping of fibroblast populations identified four subtypes. The C1 group, characterized by its predominance in tumor tissues, is a subtype enriched with cancer-associated fibroblasts (CAFs), whereas the C3 group is primarily enriched in adjacent non-cancerous tissues and consists of undifferentiated cells. Moreover, the distinct molecular and cellular differences between HPV16- and HPV66-associated tumors were demonstrated, emphasizing the unique tumor-promoting mechanisms and microenvironmental influences driven by each HPV subtype. CONCLUSIONS: We discovered a heterogeneous population of keratinocytes between tumor and adjacent non-cancerous tissues caused by HPV infection and identified macrophages and specific CAFs that play a crucial role during the early stage in promoting the inflammatory response and remodeling the cancer-promoting TME. Our findings provide new insights into the transcriptional landscape of early-stage CESC to understand the mechanism of HPV-mediated malignant transformation in cervical cancer.
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Combining single-cell and bulk RNA sequencing data to create a reliable prognostic model for predicting clear-cell renal cell carcinoma progression.
BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is the most common pathological type of kidney cancer and is characterized by a low survival rate. Accurate prediction of the occurrence and progression of ccRCC is crucial for diagnosis and treatment. This study aimed to integrate multiple publicly available bulk sequencing and single-cell datasets on ccRCC to establish a novel prognostic model for reliable and precise predictions of ccRCC development. METHODS: We used data from five ccRCC samples from the Gene Expression Omnibus (GEO) database to identify 1,303 overlapping differentially expressed genes (DEGs). Through pseudotime analysis of single-cell ccRCC data sourced from the GEO database, we identified 4,002 genes that were highly associated with cancer progression. By using machine learning to screen reliable prognostic genes, we constructed a prognostic model for renal cancer with The Cancer Genome Atlas-Kidney Renal Clear-Cell Carcinoma Collection (TCGA-KIRC) dataset and validated its effectiveness using a novel GEO renal cancer dataset. Finally, we explored the role of these prognostic genes in the progression of ccRCC through in vivo and in vitro experiments. RESULTS: The five ccRCC sequencing datasets exhibited significant heterogeneity. Therefore, we screened 211 DEGs that were highly associated with the development and prognosis of renal cancer. By exploring the biological functions of these genes, we found that they closely influenced the prognosis of patients with cancer. Upon screening, a reliable set of renal cancer-related DEGs was obtained from multiple samples. The prognostic model accurately identified renal cancer stages and predicted outcomes. In both in vivo and in vitro experimental results, we found that intervening in the expression of these prognostic genes can significantly slow down the progression of ccRCC. CONCLUSIONS: We developed a valuable predictive tool for ccRCC progression, which can help estimate the survival period of patients with renal cancer and aid in the clinical diagnosis and targeted therapy of tumors.
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Identification and validation of a signature based on myofibroblastic cancer-associated fibroblast marker genes for predicting prognosis, immune infiltration, and therapeutic response in bladder cancer.
PURPOSE: Myofibroblastic cancer-associated fibroblasts (myCAFs) are important components of the tumor microenvironment closely associated with tumor stromal remodeling and immunosuppression. This study aimed to explore myCAFs marker gene biomarkers for clinical diagnosis and therapy for patients with bladder cancer (BC). MATERIALS AND METHODS: BC single-cell RNA sequencing (scRNA-seq) data were obtained from the National Center for Biotechnology Information Sequence Read Archive. Transcriptome and clinical data were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Subsequently, univariate Cox and LASSO (Least Absolute Shrinkage and Selection Operator regression) regression analyses were performed to construct a prognostic signature. Immune cell activity was estimated using single-sample gene set enrichment analysis whilst the TIDE (tumor immune dysfunction and exclusion) method was employed to assess patient response to immunotherapy. The chemotherapy response of patients with BC was evaluated using genomics of drug sensitivity in cancer. Furthermore, Immunohistochemistry was used to verify the correlation between MAP1B expression and immunotherapy efficacy. The scRNA-seq data were analyzed to identify myCAFs marker genes. RESULTS: Combined with bulk RNA-sequencing data, we constructed a two-gene (COL6A1 and MAP1B) risk signature. In patients with BC, the signature demonstrated outstanding prognostic value, immune infiltration, and immunotherapy response. This signature served as a crucial guide for the selection of anti-tumor chemotherapy medications. Additionally, immunohistochemistry confirmed that MAP1B expression was significantly correlated with immunotherapy efficacy. CONCLUSIONS: Our findings revealed a typical prognostic signature based on myCAF marker genes, which offers patients with BC a novel treatment target alongside theoretical justification.
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Deciphering cell states and the cellular ecosystem to improve risk stratification in acute myeloid leukemia.
Acute myeloid leukemia (AML) demonstrates significant cellular heterogeneity in both leukemic and immune cells, providing valuable insights into clinical outcomes. Here, we constructed an AML single-cell transcriptome atlas and proposed sciNMF workflow to systematically dissect underlying cellular heterogeneity. Notably, sciNMF identified 26 leukemic and immune cell states that linked to clinical variables, mutations, and prognosis. By examining the co-existence patterns among these cell states, we highlighted a unique AML cellular ecosystem (ACE) that signifies aberrant tumor milieu and poor survival, which is confirmed by public RNA-seq cohorts. We further developed the ACE signature (ACEsig), comprising 12 genes, which accurately predicts AML prognosis, and outperforms existing signatures. When applied to cytogenetically normal AML or intensively treated patients, the ACEsig continues to demonstrate strong performance. Our results demonstrate that large-scale systematic characterization of cellular heterogeneity has the potential to enhance our understanding of AML heterogeneity and contribute to more precise risk stratification strategy.
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A single-cell atlas of the peripheral immune response in patients with severe COVID-19.
There is an urgent need to better understand the pathophysiology of Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, which has infected more than three million people worldwide1. Approximately 20% of patients with COVID-19 develop severe disease and 5% of patients require intensive care2. Severe disease has been associated with changes in peripheral immune activity, including increased levels of pro-inflammatory cytokines3,4 that may be produced by a subset of inflammatory monocytes5,6, lymphopenia7,8 and T cell exhaustion9,10. To elucidate pathways in peripheral immune cells that might lead to immunopathology or protective immunity in severe COVID-19, we applied single-cell RNA sequencing (scRNA-seq) to profile peripheral blood mononuclear cells (PBMCs) from seven patients hospitalized for COVID-19, four of whom had acute respiratory distress syndrome, and six healthy controls. We identify reconfiguration of peripheral immune cell phenotype in COVID-19, including a heterogeneous interferon-stimulated gene signature, HLA class II downregulation and a developing neutrophil population that appears closely related to plasmablasts appearing in patients with acute respiratory failure requiring mechanical ventilation. Importantly, we found that peripheral monocytes and lymphocytes do not express substantial amounts of pro-inflammatory cytokines. Collectively, we provide a cell atlas of the peripheral immune response to severe COVID-19.
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Integrating anoikis and ErbB signaling insights with machine learning and single-cell analysis for predicting prognosis and immune-targeted therapy outcomes in hepatocellular carcinoma.
BACKGROUND: Hepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored. METHODS: This study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated. RESULTS: The single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq). CONCLUSION: AERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.
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Diversity and intratumoral heterogeneity in human gallbladder cancer progression revealed by single-cell RNA sequencing.
BACKGROUND: Gallbladder cancer (GC) is a malignant disease characterized with highly cellular heterogeneity and poor prognosis. Determining the intratumoral heterogeneity and microenvironment (TME) can provide novel therapeutic strategies for GC. METHODS: We performed the single-cell RNA sequencing on the primary and lymph node metastatic gallbladder tumors and the adjacent normal tissues of five patients. The transcriptomic atlas and ligand-receptor-based intercellular communication networks of the single cells were characterized. RESULTS: The transcriptomic landscape of 24,887 single cells was obtained and characterized as 10 cellular clusters, including epithelial, neuroendocrine tumor cells, T&NK cells, B cells, RGS5+ fibroblasts, POSTN+ fibroblasts, PDGFRA+ fibroblasts, endothelial, myeloid cells, and mast cells. Different types of GC harbored distinct epithelial tumor subpopulations, and squamous cell carcinoma could be differentiated from adenocarcinoma cells. Abundant immune cells infiltrated into adenocarcinoma and squamous cell carcinoma, rather than neuroendocrine neoplasms, which showed significant enrichment of stromal cells. CD4+/FOXP3+ T-reg and CD4+/CXCL13+ T helper cells with higher exhausting biomarkers, as well as a dynamic lineage transition of tumor-associated macrophages from CCL20hi /CD163lo , CCL20lo /CD163hi to APOE+, were identified in GC tissues, suggesting the immunosuppressive and tumor-promoting status of immune cells in TME. Two distinct endothelial cells (KDR+ and ACKR1+), which were involved in angiogenesis and lymphangiogenesis, showed remarkable ligand-receptor interactions with primary GC cells and macrophages in gallbladder tumors. CONCLUSIONS: This study reveals a widespread reprogramming across multiple cell populations in GC progression, dissects the cellular heterogeneity and interactions in gallbladder TME, and provides potential therapeutic targets for GC.
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Neuroendocrine prostate cancer converges on a fetal pulmonary neuroendocrine-like program.
Advanced prostate cancers can relapse as neuroendocrine prostate cancer (NEPC), a treatment-resistant state that is independent of androgen receptor (AR) signaling and whose developmental identity remains unclear. We integrated single-cell, bulk, cistrome and spatial datasets across published cohorts and mapped malignant tumor states to human fetal atlases. In the discovery cohort (35,696 tumor cells, 20 patients), NEPC cells were malignant and shared copy-number architecture with adenocarcinoma (chromosome-level r = 0.64). NEPC most closely matched a fetal, lineage-restricted neuroendocrine program, best supported as pulmonary neuroendocrine-like among the alternatives tested (islet, chromaffin and sympathoblast), with the same direction across three cohorts; the pulmonary-versus-islet distinction was method-sensitive and remains provisional. Cross-sectional stage ordering and one illustrative single-tumor pseudotime analysis were consistent with luminal-program loss and neuroendocrine-program gain, while the specific ordering of individual transcription factors remained partly dataset-dependent. A regulatory screen using five methods nominated a circuit centered on ASCL1 whose convergent enhancers overlapped fetal pulmonary neuroendocrine chromatin and showed FOXA1 binding in cell-line and xenograft models (87.0% versus 33.8%; P = 0.016). In one NEPC Visium section, the program localized to malignant NEPC territory defined by copy number, with an area under the receiver operating characteristic curve (AUROC) of 0.85. Based on three NEPC patients with cross-cohort replication of direction, these data support NEPC as an adenocarcinoma-related malignant state converging on a fetal pulmonary neuroendocrine-like reference program and nominate regulatory and surface targets linked to ASCL1 for functional study.
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Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies.
Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets.
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Integrative genomics unveils basement membrane-related diagnostic markers and therapeutic targets in esophageal squamous cell carcinoma.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is often diagnosed at advanced stages due to the inherent limitations of current screening methodologies. Central to evaluating tumor invasion and prognostic assessment in ESCC is the integrity of the basement membrane (BM). However, current research on the implications of BM-related genes (BMRGs) in diagnosing ESCC remains sparse. METHODS: We performed a comprehensive analysis using single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database, alongside gene expression profiles acquired from GEO and The Cancer Genome Atlas (TCGA) databases. This identified differentially expressed BMRGs in ESCC. Employing LASSO, RF, and SVM-RFE, we selected potential BM biomarkers and crafted a diagnostic nomogram for ESCC, validated by ROC curves and AUC values. We also explored immune infiltration and biological mechanisms through consensus clustering and GSVA, and utilized single cell trajectory analysis and GSCALite to study gene distributions and pathways. In vitro experiments further elucidated the role of these genes in ESCC carcinogenesis. RESULTS: Here, we discovered that ESCC cell types exhibited markedly elevated BM-related scores. Our analysis pinpointed seven BM genes upregulated and linked to immune infiltration, showcasing unique gene expression profiles and varying immune cell densities across the BM-related subtypes. Furthermore, a robust positive correlation was observed between these genes expression and EMT activity. The knockdown of BGN significantly suppressed cell proliferation, migration, invasion, while also augmenting cell viability following chemotherapy drug treatment. CONCLUSION: Our study identified seven key BMRGs (BGN, LAMB3, SPARC, MMP1, LUM, COL4A1, and NELL2) and established a diagnostic nomogram for ESCC. Of noteworthy significance is the discovery of BGN as a promising drug target, indicating a novel strategy for future clinical combination therapies in ESCC.