ArticleFrontiers in immunology2025
Multiomics profiling identifies the poor prognostic role of a tumor cluster with GNA15 overexpression in triple-negative breast cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Single-Cell-Derived Malignant Epithelial Programs Define Prognostic Risk and Therapeutic Vulnerability in Ovarian Cancer.Journal of Cancer · 2026Article
- Integrative bulk and single-cell transcriptomic analysis identify an ac4C-related signature in lung adenocarcinoma.Journal of Cancer · 2026Article
- Integrative machine learning and single-cell analysis identifies nicotine-related diagnostic genes and myeloid remodeling in COPD.Frontiers in immunology · 2026Article
- Identification of a novel inflammatory mast cell subpopulation in triple-negative breast cancer.Frontiers in immunology · 2026Article
- Subtype-Dependent Transcriptional Coupling Between DNMT1 and E2F-Linked Proliferative Programs in Breast Cancer: A Dual-Cohort Transcriptomic Analysis.Journal of Cancer · 2026Article
- Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.Frontiers in immunology · 2026Article
- GNA15 as a regulator of tumor-immune crosstalk: oncogenic signaling, microenvironmental remodeling, and implications for targeted immunotherapy.Frontiers in immunology · 2026Review
- Multi-omics Data Reveal SLC9A3R2 Promotes Breast Cancer Progression and an Immunosuppressive Microenvironment.Journal of Cancer · 2026Article
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Authors and funding
11 authors.
Funding
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Abstract
Background: Tumor heterogeneity impacts invasive behaviors, treatment response, and clinical outcomes in triple-negative breast cancer (TNBC). However, this heterogeneity remains incompletely characterized. This study aims to utilize multi-scale data to investigate inter-tumoral heterogeneity and identify potential TNBC biomarkers. Methods: Single-cell RNA expression profiles were analyzed using R packages. Specifically, the infercnv, Pyscenic, GeneNMF, SCP, Vector, CellChat, and hdWGCNA packages were employed to identify malignant cells and characterize heterogeneity in transcription factors, metaprograms, lineage evolution, developmental trajectories, cell-cell interactions, and co-expression networks. Bulk RNA datasets were incorporated to assess the prognostic value of cell clusters and candidate genes. G Protein Subunit Alpha 15 ( Results: A total of 14,335 malignant cells were isolated from epithelial cells across 15 single-cell RNA samples. Six tumor cell clusters were identified, which exhibited distinct prognoses, biological functions, driver transcription factors, and co-expression networks. Notably, the S2 cluster demonstrated association with multiple malignancy-related pathways and inferior survival outcomes. Conclusions: Our study comprehensively delineated TNBC tumor cell heterogeneity and established the critical role of
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