Single-cell transcriptomics deciphers cancer-associated fibroblast heterogeneity and immune regulatory mechanisms in the bladder cancer tumor microenvironment
Aug 2026· Frontiers in Cell and Developmental Biology· Vol 14· 0 citations· 31 references
Medicine
TL;DR
Single-cell transcriptomics of publicly available BLCA data identifies two transcriptionally distinct fibroblast states in adjacent tissue and supports an exploratory ligand–receptor interaction framework, warranting prospective validation with primary CAF populations and adequately powered multi-specimen cohorts.
Abstract
Background Bladder cancer (BLCA) is a common malignancy, with muscle-invasive bladder cancer (MIBC) associated with a 5-year survival rate below 50%. Cancer-associated fibroblasts (CAFs) are heterogeneous stromal components of the tumor microenvironment (TME) that contribute to tumor progression, therapy resistance, and immune evasion. However, the molecular basis of CAF diversity and immune regulation in BLCA remains incompletely understood. Methods We analyzed single-cell transcriptomic data from GEO dataset GSE135337 using dimensionality reduction, unsupervised clustering, differential expression analysis, GO/KEGG/GSEA, diffusion pseudotime inference, and LIANA ligand–receptor analysis. TCGA-BLCA bulk transcriptomic data (n = 408 total; n = 401 after stage filtering) were used for score validation. RT–qPCR was performed to assess selected myCAF- and iCAF-associated genes in RT4 and T24 bladder cancer cells. Results Analysis of 9,253 post-QC cells across two non-paired specimens (6,035 adjacent-tissue; 3,218 tumor) yielded 9 DE-marker-annotated cell populations. Among 1,923 retained adjacent-tissue fibroblasts, two transcriptionally distinct states were identified: Homeostatic fibroblasts (n = 1,197; enriched for COL1A1, DCN) and CCL2-high activated-like fibroblasts (n = 726; CCL2 log2FC = 0.806, FDR = 8.08 × 10−11). No fibroblast-like cells were retained in the tumor specimen. Diffusion pseudotime positioned CCL2-high cells at a later within-sample state (median DPT 0.813 vs. 0.672; FBLN1 rho = −0.808). Exploratory LIANA analysis identified 1,347 fibroblast-outgoing ligand–receptor interactions. A pre-specified six-gene myCAF score (ACTA2, COL1A1, MMP11, MYL9, TAGLN, TPM2) tracked pathological stage in TCGA-BLCA (P = 1.60 × 10−12) and showed an unadjusted overall survival association (HR = 1.21, 95% CI: 1.02–1.42, P = 0.025) that attenuated to non-significance after age and stage adjustment (HR = 1.03, P = 0.748); the association was not replicated in GSE31684 (n = 93; log-rank P = 0.443). RT–qPCR confirmed higher expression of ACTA2, POSTN, MMP11, FAP, IL6, and CXCL12 in T24 versus RT4 cells (4.33–16.56-fold; all BH-adjusted q < 0.01). Conclusion Single-cell transcriptomics of publicly available BLCA data identifies two transcriptionally distinct fibroblast states in adjacent tissue and supports an exploratory ligand–receptor interaction framework. A six-gene myCAF-associated score tracks pathological stage but does not independently predict overall survival after covariate adjustment and was not replicated in an independent cohort. These findings constitute an exploratory computational framework warranting prospective validation with primary CAF populations and adequately powered multi-specimen cohorts.
BACKGROUND
Prostate cancer (PCa) bone metastases cause significant morbidity and mortality in advanced disease. The tumor microenvironment (TME) of bone metastases drives disease progression and therapeutic resistance, yet comprehensive characterization of its cellular heterogeneity remains limited. This study aims to characterize cellular populations and molecular signatures of PCa bone metastases using single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database.
METHODS
scRNA-seq data from PCa bone metastasis samples were obtained from GEO. Quality control, normalization, dimensionality reduction, and cell type identification were performed using Seurat. Differential expression, pseudotime trajectory, pathway enrichment, gene regulatory network, and cell-cell communication analyses were conducted to investigate molecular mechanisms of bone metastasis progression.
RESULTS
Single-cell analysis identified distinct cellular populations within the bone metastatic TME, including malignant epithelial cells, fibroblasts, endothelial cells, osteoblasts, osteoclasts, and immune cells. Clustering revealed heterogeneous transcriptional signatures, while pseudotime analysis uncovered developmental transitions between cell states. Key transcription factors, enriched pathways related to bone remodeling, angiogenesis, and immune regulation, and critical signaling interactions between cancer and stromal cells were identified.
CONCLUSION
This study provides comprehensive insights into the cellular composition and molecular architecture of the PCa bone metastatic TME, revealing distinct cell populations, type-specific gene signatures, and cell-cell communication networks driving bone metastasis progression.
Jun Zhao, De-Sheng Wu· SLAS technology· 0 citations
This study identified UBE2C+ proliferative tumor cells as a functionally relevant malignant subpopulation in SCLC and links this state to immune-stromal communication networks within the tumor microenvironment and provides a systems-level framework for investigating the cancer-immunity regulome in SCLC.
Hong-Ling Jia, Yongxuan An, Bing Chen et al.· Frontiers in Immunology· 0 citations
ABSTRACT Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, its progression and treatment heterogeneity are mainly influenced by driver gene and tumor micro-environment (TME) interactions. Nevertheless, the mechanisms of this process at the single-cell level remain unclear. This study integrated TCGA and multi-center single-cell transcriptome data to identify a 575 genes HCC-specific core set, developing a single-cell “oncogene scoring” system to quantify individual carcinogenic activity. This score is significantly elevated in malignant and proliferative T cells and is closely associated with metabolic reprogramming, aberrant cell‒cell communication, and immunosuppressive phenotypes. Based on these characteristics, we constructed a machine learning-based Random Survival Forest (RSF) prognostic model validated in multiple independent cohorts, which classifies patients into distinct risk subtypes. The high-risk group exhibits genomic instability, increased tumor stemness, and immune evasion, while the low-risk group was more sensitive to drugs such as sorafenib. This study highlights the potential pathways by which high oncogenic activity is associated with HCC progression, suggesting a profound link with single-cell metabolic‒immune crosstalk. The constructed RSF model offers a promising computational framework for risk stratification and provides hypothesis-generating insights that may inform future personalized treatment strategies for HCC patients.
Colorectal cancer (CRC) ranks among the most prevalent malignancies globally, yet exhibits limited responsiveness to immunotherapy. This therapeutic challenge stems primarily from incomplete understanding of immune microenvironment regulation in CRC. Although Eomesodermin (EOMES) has been implicated in tumor immune checkpoint gene expression, its specific interaction and regulatory mechanisms within the CRC immune microenvironment require systematic investigation. RNA-sequencing expression profiles and corresponding clinical information for 33 types of tumors were downloaded from the TCGA dataset for Pan-Cancer analysis of gene EOMES. Single cell sequencing data of CRC from TISCH was used for single-gene analysis of EOMES. The primary human CRC tissue samples were obtained for tissue multiplexed immunofluorescence staining. The isolated CD8+ T cells were cocultured with MC38 cells, following by real-time PCR detection, immunoblotting assay, and flow cytometry analysis. Pan-cancer analysis demonstrated that EOMES expression was significantly elevated in tumor tissues from 33 cancer types (including COAD/READ) compared to normal tissues (BRCA, DLBC, ESCA et al.). Further immune microenvironment correlation analysis showed that EOMES expression strongly correlates with immune microenvironment modulation, particularly in COAD. Importantly, elevated EOMES levels were identified in exhausted CD8⁺ T cells within CRC tissues (COAD/READ subtypes). Moreover, EOMES expression demonstrates significant linkage to immune/inflammatory regulatory pathways in CRC. High EOMES⁺CD8⁺ T cell infiltration occurs in microsatellite-stable (MSS) CRC but not in microsatellite instability-high (MSI-H) CRC. Most importantly, EOMES expression positively correlates with exhausted CD8⁺ T cells exclusively in MSS CRC. Further in vitro experiments demonstrated the strong link between EOMES upregulation and CD8+ T cell exhaustion, thereby impairing their cytotoxic function against CRC cells. EOMES critically participates in CRC immune microenvironment regulation, with its upregulation positively associated with exhausted CD8⁺ T cell infiltration. This association is most pronounced in MSS CRC, revealing a subtype-specific immunomodulatory mechanism.
Background Laryngeal squamous cell carcinoma (LSCC) remains a major clinical challenge with limited therapeutic options and unsatisfactory outcomes. Hypoxia-induced stress responses are known to contribute to tumor progression, immune remodeling, and therapeutic resistance. However, the cell type-specific hypoxia-associated transcriptional programs within the immune and stromal microenvironment of LSCC remain incompletely understood. Methods We analyzed an immune-enriched LSCC single-cell RNA sequencing dataset (GSE150321, GSM4546858), generated by CD45-based magnetic bead sorting, to characterize hypoxia-associated stress response signatures across immune and stromal cell populations. The workflow included quality control, dimensionality reduction, unsupervised clustering, cell type annotation, pathway enrichment, hypoxia score assessment, robust rank aggregation, pseudotime trajectory inference, and cell–cell communication analysis. Representative hypoxia-associated genes were further validated experimentally in LSCC-related cell models. Results Nine major cell populations were identified, including T cells, epithelial cells, B cells, macrophages, dendritic cells, cycling cells, fibroblasts, mast cells, and endothelial cells. Hypoxia-associated pathway activity was broadly enriched, with particularly strong activation in myeloid populations. Slingshot analysis using macrophages as the trajectory root revealed a monocyte-to-macrophage maturation trajectory, along which hypoxia scores were highest in macrophages and decreased toward lymphoid cells (Spearman R = −0.271, P = 1.78 × 10-¹7). Myeloid-specific hypoxia-associated genes, including PLA2G7, CD163, and APOC1, were identified. Robust rank aggregation further highlighted candidate hypoxia-associated genes such as HIF1A, CXCL8, S100A8, and ENO1. Experimental validation in THP-1 cells confirmed hypoxia-induced upregulation of these genes, and ELISA showed increased CXCL8 secretion under hypoxia (985 ± 87 vs. 215 ± 34 pg/mL, P < 0.001). In contrast, the novel myeloid-specific genes showed negligible expression in Hep-2 epithelial cells, supporting their cell-type specificity. Conclusions This study characterizes hypoxia-associated transcriptional programs within the immune and stromal microenvironment of LSCC rather than providing a comprehensive malignant epithelial tumor atlas. The findings highlight myeloid-centered hypoxia adaptation as potential sources of biomarker candidates and therapeutic insight for LSCC. THP-1 monocytic cell validation provides functional support for myeloid-centered hypoxia adaptation.
Huihui Du, Yating Wang, Renyu Lin et al.· Frontiers in Immunology· 0 citations
Introduction Cervical cancer is a common gynecological malignancy, having high incidence and mortality rates, especially in developing regions. The tumor microenvironment (TME) plays a crucial role in disease progression, and cancer‐associated fibroblasts (CAFs) are key components of the TME. However, the roles of CAFs in tumor‐adjacent normal tissue remain unclear. Methods Herein, we used single‐cell RNA sequencing to generate high‐resolution cellular atlases of cervical cancer and tumor‐adjacent normal tissue, systematically profiling fibroblast–cancer cell interactions and comparing fibroblasts from distinct origins. Pseudotime analysis and CytoTRACE scoring were employed to investigate the developmental trajectory of fibroblasts. Immunohistochemistry validated the inflammatory CAF (iCAF) subpopulation of C7 fibroblasts and assessed its correlations with clinicopathological features. Results We found that C7 fibroblasts secrete CXCL12 to activate the ACKR3 (CXCR7) receptor in cancer cells, thereby promoting tumor cell proliferation, invasion, and metastasis. C7 fibroblasts also highly expressed multiple oncogenic genes, including IL6, CXCL3, CXCL2, CCL2, GPC3, PLAU, TNFAIP6, PTX3, and EIF4A3, and genes associated with poor prognosis, including CXCL3, TNFAIP6, PTX3, IGSF10, and LDLR. High C7 fibroblast abundance was associated with advanced International Federation of Gynecology and Obstetrics stage, lymph node metastasis, and postmenopausal status. Discussion Our findings suggest that C7 fibroblasts being predominantly distributed in tumor‐adjacent normal tissues is significantly associated with cervical cancer progression, and that C7 fibroblasts in these tissues may serve as potential biomarkers and therapeutic targets for tumor microenvironment modulation.
Yi Liu, D. Yin, Lu-guang Wu et al.· Frontiers in Cell and Develo...· 0 citations
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