Breast cancer has become a severe threat to human health, making it imperative to identify effective drugs and therapeutic targets.

Various molecular biology experiments, such as western blot analysis, cytologic effect, co‐immunoprecipitation, and immunofluorescence assays, as well as a nude mouse xenograft tumor model, were used to comprehensively analyze the impact of gamma‐interferon‐inducible lysosomal thiol reductase (GILT) on the malignant phenotype of breast cancer cells. This work was performed to examine GILT expression levels and explore the potential mechanism in breast cancer.

GILT protein expression levels were significantly lower in breast cancer cells than in normal breast epithelial cells. Overexpressing GILT inhibited breast cancer cell proliferation and migration and slowed tumor growth. GILT inhibited the interaction between the MYC and WDR5 transcription complex and played a tumor‐suppressive role. The MYC/WDR5 transcription complex inhibitor OICR‐9429 could synergize with GILT to inhibit breast cancer cell proliferation.

This study reveals a potential mechanism by which GILT can slow breast cancer growth, as well as identifying the possible clinical application value of small molecule inhibitor OICR‐9429. These data collectively provide novel treatment strategies for breast cancer therapy.

Increased glycolytic activity and lactate production are characteristic features of triple‐negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate‐responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.

Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA‐MB‐231 cells treated with 2‐Deoxy‐d‐glucose or l‐lactate were subjected to RNA sequencing (RNA‐seq). The gene set variation analysis algorithm was utilized to calculate the lactate‐responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single‐sample gene set enrichment analysis, and EPIC, were used to compare the tumor‐infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.

We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA‐seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin‐17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.

We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.