Metabolic alterations, a hallmark of cancer, enable tumor cells to adapt to their environment by modulating glucose, lipid, and amino acid metabolism, which fuels rapid growth and contributes to treatment resistance. In primary breast cancer, metabolic shifts such as the Warburg effect and enhanced lipid synthesis are closely linked to chemotherapy failure. Similarly, metastatic lesions often display distinct metabolic profiles that not only sustain tumor growth but also confer resistance to targeted therapies and immunotherapies. The review emphasizes two major aspects: the mechanisms driving metabolic resistance in both primary and metastatic breast cancer, and how the unique metabolic environments in metastatic sites further complicate treatment. By targeting distinct metabolic vulnerabilities at both the primary and metastatic stages, new strategies could improve the efficacy of existing therapies and provide better outcomes for breast cancer patients.

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.

Breast cancer (BC) heterogeneity necessitates prognostic and therapeutic biomarkers. PANoptosis remains incompletely understood with regard to its role in BC immunomodulation and clinical outcomes. Here, we integrated transcriptomic data from 7067 patients with BC across multiple cohorts. Patients were stratified via non-negative matrix factorization based on PANoptosis-related gene expression, revealing three distinct clusters. Cluster C3 exhibited the poorest overall survival, characterized by upregulated lipid metabolism and suppressed immunity. A prognostic signature was constructed using a random survival forest model and served as an independent prognostic factor. Single-cell RNA sequencing of 31 tumors identified IL-33-expressing endothelial subclusters (ACKR1⁺ and FBLN5⁺) as key regulators of the tumor immune microenvironment. Reduced IL-33 expression correlated with increased M2 macrophages and CD4⁺ T cell depletion, while sex-specific analysis revealed IL-33 as a predictor of immunotherapy response. Our findings underscore the role of PANoptosis and IL-33⁺ endothelial cells in shaping BC immunity and prognosis.

Breast cancer patients with estrogen receptor-negative (ERneg) status, encompassing triple negative breast cancer (TNBC) and human epidermal growth factor receptor 2 positive breast cancer, are confronted with a heightened risk of drug resistance, often leading to early recurrence; the biomarkers and biological processes associated with recurrence is still unclear. In this study, we analyzed bulk RNA sequencing (RNA-seq) data from 285 cancer and paracancerous samples from 155 TNBC patients, along with transcriptome data from 11 independent public cohorts comprising 7,449 breast cancer patients and 26 single-cell RNA-seq datasets. Our results revealed differential enrichment of nerve-related pathways between TNBC patients with and without 10-year recurrence-free survival. We developed an early recurrence index (ERI) using a machine learning model and constructed a nomogram that accurately predicts the 10-year survival of ERneg patients (area under the curve [AUC]_Training = 0.79; AUC_Test = 0.796). Further analysis linked ERI to enhanced neural function and immunosuppression. Additionally, we identified EN1, the most significant ERI gene, as a potential biomarker that may regulate the tumor microenvironment and sensitize patients to immunotherapy.