In this review, the advances in the study of breast cancer molecular classifications and the molecular signatures of the luminal subtypes A and B of breast cancer were summarized. Effective clinical outcomes depend mainly on successful preclinical diagnosis and therapeutic decisions. Over the last few years, the ever-expanding investigations focusing on breast cancer diagnosis and the clinical trials have provided accumulating information on the molecular characteristics of breast cancer. Specifically, among the estrogen receptor (ER)-positive types of breast cancer, the luminal subtype A breast cancer has been shown to exhibit good clinical outcomes with endocrine therapy, whereas the luminal subtype B breast cancer represents the more complicated type, diagnostically as well as therapeutically. Furthermore, even in luminal subtype A breast cancer, the resistance to treatment has become the major limitation for endocrine-based therapy. Accumulating molecular data and further clinical trials may enable more accurate diagnostic and therapeutic decisions. The molecular signatures have emerged as a powerful tool for future diagnosis and therapeutic decisions, although currently available data are limited.

Donkey milk is considered an ideal substitute for human milk and is considered a potential complementary dairy product for the treatment of a variety of human diseases, including cancer.

Pilose antler (PA) is a traditional Chinese functional food and 22 tumor-suppressing polypeptides was detected in its water extract. They inhibit the progression and metastasis of triple-negative 4T1 breast cancer at multiple key sites.

Background: In this study, we aimed to explore the tumour associated immune signature of breast cancer (BC) and conduct integrative analyses with immune infiltrates in BC. Methods: We downloaded the transcriptome profiling and clinical data of BC from The Cancer Genome Atlas (TCGA) database. The list of immune-related signatures was from the Innate database. The limma package was utilized to conduct the normalization, and we screened the differential immune signatures in BC. A univariate Cox regression model and the LASSO method were used to find the hub prognostic immune genes. The TAIG risk model was calculated based on the multivariate Cox regression results, and a receiver operating characteristic (ROC) curve was generated to assess the predictive power of TAIG. Moreover, we also conducted a correlation analysis between TAIG and the clinical characteristics. Additionally, we utilized the METABRIC cohort as the validation data set. The TIMER database is a comprehensive resource for performing systematic analyses of immune infiltrates across various malignancies. We evaluated the associations of immune signatures with several immune cells based on TIMER. Furthermore, we used the CIBERSORT algorithm to determine the fractions of immune cells in each sample and compared the differential distributions of immune infiltrates between two TAIG groups using the Wilcoxon rank-sum test. Results: A total of 1,178 samples were obtained from the TCGA-BRCA database, but only 1,045 breast tumour samples were matched with complete transcriptome expression data. Meanwhile, we collected a total of 1,094 BC patients from the METABRIC cohort. We found a list of 1,399 differential immune signatures associated with survival, and functional analysis revealed that these genes participated in cytokine-cytokine receptor interactions, Th1 and Th2 cell differentiation and the JAK-STAT signalling pathway. The TAIG risk model was established from the multivariate Cox analysis, and we observed that high TAIG levels correlated with poor survival outcomes based on Kaplan-Meier analysis. The Kruskal-Wallis test suggested that high TAIG levels correlated with high AJCC-TNM stages and advanced pathological stages (P<0.01). We validated the well robustness of TAIG in METABRIC cohort and 5-year AUC reached up to 0.829. Moreover, we further uncovered the associations of hub immune signatures with immune cells and calculated the immune cell fractions in specific tumour samples based on gene signature expression. Last, we used the Wilcoxon rank-sum test to compare the differential immune density in the two groups and found that several immune cells had a significantly lower infiltrating density in the high TAIG groups, including CD8⁺ T cells (P=0.031), memory resting CD4⁺ T cells (P=0.026), M0 macrophages (P=0.023), and M2 macrophages (P=0.048). Conclusions: In summary, we explored the immune signature of BC and constructed a TAIG risk model to predict prognosis. Moreover, we integrated the identified immune signature with tumour-infiltrating immune cells and found adverse associations between the TAIG levels and immune cell infiltrating density.

Myeloid‐derived suppressor cells (MDSCs) are responsible for antitumor immunodeficiency in tumor‐bearing hosts. Primarily, MDSCs are classified into 2 groups: monocytic (M)‐MDSCs and polymorphonuclear (PMN)‐MDSCs. In most cancers, PMN‐MDSCs (CD11b⁺Ly6C^lowLy6G⁺ cells) represent the most abundant MDSC subpopulation. However, the functional and phenotypic heterogeneities of PMN‐MDSC remain elusive, which delays clinical therapeutic targeting decisions. In the 4T1 murine tumor model, CD11b⁺Ly6G^low PMN‐MDSCs were sensitive to surgical and pharmacological interventions. By comprehensively analyzing 64 myeloid cell‐related surface molecule expression profiles, cell density, nuclear morphology, and immunosuppressive activity, the PMN‐MDSC population was further classified as CD11b⁺Ly6G^lowCD205⁺ and CD11b⁺Ly6G^highTLR2⁺ subpopulations. The dichotomy of PMN‐MDSCs based on CD205 and TLR2 is observed in 4T07 murine tumor models (but not in EMT6). Furthermore, CD11b⁺Ly6G^lowCD205⁺ cells massively accumulated at the spleen and liver of tumor‐bearing mice, and their abundance correlated with in situ tumor burdens (with or without intervention). Moreover, we demonstrated that CD11b⁺Ly6G^lowCD205⁺ cells were sensitive to glucose deficiency and 2‐deoxy‐d‐glucose (2DG) treatment. Glucose transporter 3 (GLUT3) knockdown by siRNA significantly triggered apoptosis and reduced glucose uptake in CD11b⁺Ly6G^lowCD205⁺ cells, demonstrating the dependence of CD205⁺ PMN‐MDSCs survival on both glucose uptake and GLUT3 overexpression. As GLUT3 has been recognized as a target for the rescue of host antitumor immunity, our results further directed the PMN‐MDSC subsets into the CD205⁺GLUT3⁺ subpopulation as future targeting therapy.

Triple-negative breast cancer (TNBC) has the characteristics of a complex molecular landscape, aggressive or high proliferation leading to poor prognosis, and behavioral heterogeneity. The purpose of the present study was to determine the spatiotemporal expression of α-smooth muscle actin (α-SMA)-positive cancer-associated fibroblasts (CAFs) at histological level in 4T1 tumors and to predict the sensitivity to 138 drugs in patients with TNBC according to α-SMA expression. The quantitative results of fibrosis showed that the volume of 4T1 tumors correlated positively with the area of tumor fibrosis. Furthermore, we divided 4T1 tumors according to the degree of fibrosis and characterized the molecular characteristics of the four regions. Finally, the difference in the signaling pathways and sensitivity to 138 drugs was analyzed in patients with TNBC according to α-SMA expression combined with The Cancer Genome Atlas (TCGA) database. The myogenesis, TGF-β, and Notch signaling pathways were upregulated and the patients with TNBC were significantly differentially sensitive to 25 drugs. The results of in vivo experiments showed that the inhibitory effect of embelin on 4T1 tumors with high α-SMA expression was greater than that on 4TO7 tumors with low α-SMA expression. At the same time, embelin significantly decreased α-SMA and PDGFRA expression in 4T1 tumors compared with the control group. Our findings add to understanding of CAF distribution in the 4T1 tumor microenvironment and its possible role in treating cancer.