Recently, novel mechanisms underlying the pro-tumorigenic effects of cancer-associated fibroblasts (CAFs) have been identified in several cancers, including breast cancer. CAFs can secrete exosomes that are loaded with proteins, lipids, and RNAs to affect tumor microenvironment. Herein, we identify CAF-derived exosomes that can transfer miR-181d-5p to enhance the aggressiveness of breast cancer. Cancerous tissues and matched paracancerous tissues were surgically resected from 122 patients with breast cancer. Chromatin immunoprecipitation (ChIP) and dual luciferase reporter assays were employed to identify interaction between homeobox A5 (HOXA5) and caudal-related homeobox 2 (CDX2), as well as between CDX2 and miR-181d-5p, respectively. Human breast cancer Michigan Cancer Foundation-7 (MCF-7) cells were cocultured with CAF-derived exosomes. 5-Ethynyl-2'-deoxyuridine (EdU) assay, TUNEL staining, Transwell invasion assays, and scratch tests were carried out to evaluate MCF-7 cell functions. Nude mice bearing xenografted MCF-7 cells were injected with CAF-derived exosomes, and the tumor formation was evaluated. HOXA5 expressed at a poor level in breast cancer tissues, and its overexpression retarded MCF-7 cell proliferation, invasion, migration, and epithelial-mesenchymal transition (EMT) and facilitated its apoptosis in vitro. miR-181d-5p targets CDX2, a transcription factor binding to HOXA5 promoter. Coculture of CAFs and MCF-7 cells showed that CAFs prolonged proliferation and antagonized apoptosis of MCF-7 cells via release of exosomes. Coculture of MCF-7 cells and exosomes derived from CAFs identified miR-181d-5p as a mediator of the exosomal effects on MCF-7 cells, in part, via downregulation of CDX2 and HOXA5. CAF-derived exosomes containing miR-181d-5p promoted the tumor growth of nude mice bearing xenografted MCF-7 cells. In conclusion, exosomal miR-181d-5p plays a key role in CAF-mediated effects on tumor environment in breast cancer, likely via CDX2 and HOXA5.

Background: microRNA Let-7 serves as a tumor suppressor by targeting various oncogenic pathways in cancer cells. However, the underlying mechanism of its involvement in breast cancer remains largely unknown. With our research, our endeavor is to explore the role of the CDX2/let-7b/COL11A1 axis in breast cancer cell activities. Methods: Tumor tissues and adjacent normal tissues were collected from 86 patients with breast cancer. Human breast cancer epithelial cell line MCF-7 was treated with over-expressed CDX2, let-7b mimic, shRNA against COL11A1 and their negative controls. The expression of CDX2, let-7b, and COL11A1 in the tissues and cells was determined by RT-qPCR. Interactions among CDX2, let-7b, and COL11A1 were detected by ChIP and dual-luciferase reporter assay, respectively. After different transfections, cell invasion, migration, and proliferation abilities were determined by Transwell and EdU assays. Lastly, tumor xenografts in nude mice were established and hematoxylin and eosin staining was performed to assess the tumor growth and lymph node metastasis. Results: CDX2 and let-7b were poorly expressed in breast cancer cells and tissues. CDX2 bound to let-7b and promoted the expression of let-7b, which contrarily inhibited the expression of COL11A1. Cancer cell proliferation, invasion, migration, and metastasis were stimulated when CDX2 and let-7b were depleted or COL11A1 was over-expressed. Xenograft tumors growth and metastasis were in accordance with the results of cellular experiments. Conclusion: In agreement with these observations, we could reach a conclusion that CDX2 could promote let-7b expression, which may exert an inhibitory effect on the proliferation, migration, and metastasis of breast cancer cells via repressing the expression of COL11A1, providing a novel therapeutic strategy for the treatment of metastatic breast cancer.

Background: The 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. Methods: Transcriptome 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. Results: Using 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)⁺ fibroblasts were specifically linked to the C2 phenotype, while complement factor D (CFD)⁺ fibroblasts characterized the other ECM clusters. CellChat analysis indicated that ANGPTL4⁺ and CFD⁺ fibroblasts regulate B cell lineage via distinct signaling pathways. Additionally, analysis using the Kaplan–Meier Plotter website showed that CFD was favorable for immunotherapy response, whereas ANGPTL4 negatively impacted the outcomes of cancer patients receiving immunotherapy. Conclusion: We 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⁺ and CFD⁺ fibroblasts as potential biomarkers for immunotherapy in breast cancer.

Background: Breast cancer (BC) is a heterogeneous disease, with the ductal subtype exhibiting significant cellular diversity that influences prognosis and response to treatment. Single-cell RNA sequencing data from the GEO database were utilized in this study to investigate the underlying mechanisms of cellular heterogeneity and to identify potential prognostic markers and therapeutic targets. Methods: Bioinformatics analysis was conducted using R packages to analyze the single-cell sequencing data. The presence of highly variable genes and differences in malignant potency within the same BC samples were examined. Differential gene expression and biological function between Type 1 and Type 2 ductal epithelial cells were identified. Lasso regression and Cox proportional hazards regression analyses were employed to identify genes associated with patient prognosis. Experimental validation was performed in vitro and in vivo to confirm the functional relevance of the identified genes. Results: The analysis revealed notable heterogeneity among BC cells, with the presence of highly variable genes and differences in malignant behavior within the same samples. Significant disparities in gene expression and biological function were identified between Type 1 and Type 2 ductal epithelial cells. Through regression analyses, CYP24A1 and TFPI2 were identified as pivotal genes associated with patient prognosis. Kaplan-Meier curves demonstrated their prognostic significance, and experimental validation confirmed their inhibitory effects on malignant behaviors of ductal BC cells. Conclusion: This study highlights the cellular heterogeneity in ductal subtype breast cancer and delineates the differential gene expressions and biological functions between Type 1 and Type 2 ductal epithelial cells. The genes CYP24A1 and TFPI2 emerged as promising prognostic markers and therapeutic targets, exhibiting inhibitory effects on BC cell malignancy in vitro and in vivo. These findings offer the potential for improved BC management and the development of targeted treatment strategies.