Risk assessment model based on nucleotide metabolism-related genes highlights SLC27A2 as a potential therapeutic target in breast cancer.

PURPOSE: Breast cancer (BC) is the most prevalent malignant tumor worldwide among women, with the highest incidence rate. The mechanisms underlying nucleotide metabolism on biological functions in BC remain incompletely elucidated. MATERIALS AND METHODS: We harnessed differentially expressed nucleotide metabolism-related genes from The Cancer Genome Atlas-BRCA, constructing a prognostic risk model through univariate Cox regression and LASSO regression analyses. A validation set and the GSE7390 dataset were used to validate the risk model. Clinical relevance, survival and prognosis, immune infiltration, functional enrichment, and drug sensitivity analyses were conducted. RESULTS: Our findings identified four signature genes (DCTPP1, IFNG, SLC27A2, and MYH3) as nucleotide metabolism-related prognostic genes. Subsequently, patients were stratified into high- and low-risk groups, revealing the risk model's independence as a prognostic factor. Nomogram calibration underscored superior prediction accuracy. Gene Set Variation Analysis (GSVA) uncovered activated pathways in low-risk cohorts and mobilized pathways in high-risk cohorts. Distinctions in immune cells were noted between risk cohorts. Subsequent experiments validated that reducing SLC27A2 expression in BC cell lines or using the SLC27A2 inhibitor, Lipofermata, effectively inhibited tumor growth. CONCLUSIONS: We pinpointed four nucleotide metabolism-related prognostic genes, demonstrating promising accuracy as a risk prediction tool for patients with BC. SLC27A2 appears to be a potential therapeutic target for BC among these genes.

PRMT5 reduces immunotherapy efficacy in triple-negative breast cancer by methylating KEAP1 and inhibiting ferroptosis.

BACKGROUND: As an emerging treatment strategy for triple-negative breast cancer (TNBC), immunotherapy acts in part by inducing ferroptosis. Recent studies have shown that protein arginine methyltransferase 5 (PRMT5) has distinct roles in immunotherapy among multiple cancers by modulating the tumor microenvironment. However, the role of PRMT5 during ferroptosis, especially for TNBC immunotherapy, is unclear. METHODS: PRMT5 expression in TNBC was measured by IHC (immunohistochemistry) staining. To explore the function of PRMT5 in ferroptosis inducers and immunotherapy, functional experiments were conducted. A panel of biochemical assays was used to discover potential mechanisms. RESULTS: PRMT5 promoted ferroptosis resistance in TNBC but impaired ferroptosis resistance in non-TNBC. Mechanistically, PRMT5 selectively methylated KEAP1 and thereby downregulated NRF2 and its downstream targets which can be divided into two groups: pro-ferroptosis and anti-ferroptosis. We found that the cellular ferrous level might be a critical factor in determining cell fate as NRF2 changes. In the context of higher ferrous concentrations in TNBC cells, PRMT5 inhibited the NRF2/HMOX1 pathway and slowed the import of ferrous. In addition, a high PRMT5 protein level indicated strong resistance of TNBC to immunotherapy, and PRMT5 inhibitors potentiated the therapeutic efficacy of immunotherapy. CONCLUSIONS: Our results reveal that the activation of PRMT5 can modulate iron metabolism and drive resistance to ferroptosis inducers and immunotherapy. Accordingly, PRMT5 can be used as a target to change the immune resistance of TNBC.

Development and validation of an endoplasmic reticulum stress-related molecular prognostic model for breast cancer.

Background: Breast cancer is the most frequently diagnosed cancer and a leading cause of cancer-related death in women. Endoplasmic reticulum stress (ERS) plays a crucial role in the pathogenesis of several malignancies. However, the prognostic value of ERS-related genes in breast cancer has not been thoroughly investigated. Methods: We downloaded and analyzed expression profiling data for breast invasive carcinoma samples in The Cancer Genome Atlas-Breast Invasive Carcinoma (TCGA-BRCA) and identified 23 ERS-related genes differentially expressed between the normal breast tissue and primary breast tumor tissues. We constructed and validated risk models using external test datasets. We assessed the differences in sensitivity to common antitumor drugs between high- and low-scoring groups using the Genomics of Drug Sensitivity in Cancer (GDSC) database, evaluated the sensitivity of patients in high- and low-scoring groups to immunotherapy using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm, and assessed immune and stromal cell infiltration in the tumor microenvironment (TME) using the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. We also analyzed the expression of independent factors in the prognostic model using the Western-blot analysis for correlation in relation to breast cancer. Results: Using multivariate Cox analysis, FBXO6, PMAIP1, ERP27, and CHAC1 were identified as independent prognostic factors in patients with breast cancer. The risk score in our model was defined as the endoplasmic reticulum score (ERScore). ERScore had high predictive power for overall survival in patients with breast cancer. The high-ERScore group exhibited a worse prognosis, lower drug sensitivity, and lower immunotherapy response and immune infiltration than did the low-ERScore group. Conclusions based on ERScore were consistent with Western-blot results. Conclusion: We constructed and validated for the first time an endoplasmic reticulum stress-related molecular prognostic model for breast cancer with reliable predictive properties and good sensitivity, as an important addition to the prognostic prediction model for breast cancer.