ArticleMolecular medicine (Cambridge, Mass.)2025
Transcriptome-wide analysis reveals potential roles of CFD and ANGPTL4 in fibroblasts regulating B cell lineage for extracellular matrix-driven clustering and novel avenues for immunotherapy in breast cancer.
Article in Molecular medicine (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- [Development of a 36-gene signature for predicting recurrence in non-muscle-invasive bladder cancer and validation in a Chinese cohort].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026Article
- Endothelial-derived PANoptosis factor IL33 is a potential immunotherapy in breast cancer.iScience · 2026Article
- B-cells in breast cancer: current insights and challenges.Frontiers in oncology · 2026Review
- Molecular remodeling of cancer-associated fibroblasts in breast cancer patients receiving anti-PD-1 immunotherapy.Frontiers in oncology · 2026Article
- Saliva from oral squamous cell carcinoma patients promotes tumor progression via Inflammation, stromal remodeling, and metabolic reprogramming in a mouse model.BMC oral health · 2025Article
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Abstract
backgroundThe remodeling of the extracellular matrix (ECM) plays a pivotal role in tumor progression and drug resistance. However, the compositional patterns of ECM in breast cancer and their underlying biological functions remain elusive.
methodsTranscriptome and genome data of breast cancer patients from TCGA database was downloaded. Patients were classified into different clusters by using non-negative matrix factorization (NMF) based on signatures of ECM components and regulators. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify core genes related to ECM clusters. Additional 10 independent public cohorts including Metabric, SCAN_B, GSE12276, GSE16446, GSE19615, GSE20685, GSE21653, GSE58644, GSE58812, and GSE88770 were collected to construct Training or Testing cohort, following machine learning calculating ECM correlated index (ECI) for survival analysis. Pathway enrichment and correlation analysis were used to explore the relationship among ECM clusters, ECI and TME. Single-cell transcriptome data from GSE161529 was processed for uncovering the differences among ECM clusters.
resultsUsing NMF, we identified three ECM clusters in the TCGA database: C1 (Neuron), C2 (ECM), and C3 (Immune). Subsequently, WGCNA was employed to pinpoint cluster-specific genes and develop a prognostic model. This model demonstrated robust predictive power for breast cancer patient survival in both the Training cohort (n = 5,392, AUC = 0.861) and the Testing cohort (n = 1,344, AUC = 0.711). Upon analyzing the tumor microenvironment (TME), we discovered that fibroblasts and B cell lineage were the core cell types associated with the ECM cluster phenotypes. Single-cell RNA sequencing data further revealed that angiopoietin like 4 (ANGPTL4)
conclusionWe identified distinct ECM clusters in breast cancer patients, irrespective of molecular subtypes. Additionally, we constructed an effective prognostic model based on these ECM clusters and recognized ANGPTL4
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