Aberrant lipid metabolism is a well-recognized hallmark of cancer. Notably, breast cancer (BC) arises from a lipid-rich microenvironment and depends significantly on lipid metabolic reprogramming to fulfill its developmental requirements. In this review, we revisit the pivotal role of lipid metabolism in BC, underscoring its impact on the progression and tumor microenvironment. Firstly, we delineate the overall landscape of lipid metabolism in BC, highlighting its roles in tumor progression and patient prognosis. Given that lipids can also act as signaling molecules, we next describe the lipid signaling exchanges between BC cells and other cellular components in the tumor microenvironment. Additionally, we summarize the therapeutic potential of targeting lipid metabolism from the aspects of lipid metabolism processes, lipid-related transcription factors and immunotherapy in BC. Finally, we discuss the possibilities and problems associated with clinical applications of lipid‑targeted therapy in BC, and propose new research directions with advances in spatiotemporal multi-omics.

Objective The neutrophil-to-lymphocyte ratio (NLR) is recognised as a suitable prognostic biomarker in patients with breast cancer. Nevertheless, the efficacy of this biomarker in predicting the pathological complete response (pCR) and survival in patients with breast cancer receiving neoadjuvant chemotherapy (NACT) is still controversial. This meta-analysis aimed to identify the association between baseline NLR and the prognosis of patients with breast cancer treated with NACT. Design Meta-analysis. Data sources Relevant literature published before 1 May 2021 was searched using the Cochrane Library, Embase, PubMed and the Web of Science databases. Eligibility criteria All studies involving patients with breast cancer treated with NACT and peripheral blood pretreatment NLR recorded as a dichotomous variable were included. Data extraction and synthesis Two researchers independently extracted and evaluated OR/HR and its 95% CIs of survival outcomes and clinicopathological parameters. Results A total of 19 studies were identified. From each study, the impact of NLR on the pCR, OR and HR, with their 95% CIs were extracted and combined using either a random or fixed-effects model. The results indicate that a higher pCR in patients with a low NLR (OR 1.620, 95% CI 1.209 to 2.169, p＜0.001). In addition, an elevated NLR predicted lower disease-free survival (HR 2.269, 95% CI 1.557 to 3.307, p<0.001) and overall survival (HR 1.691, 95% CI 1.365 to 2.096, p<0.001) in patients with breast cancer treated with NACT. Conclusions NLR is a suitable biomarker for predicting pCR and survival in patients with breast cancer receiving NACT.

Background: Inflammation plays an important role in tumor proliferation, metastasis, and resistance to chemotherapy. The systemic inflammation response index (SIRI), has been reported to be closely related to prognosis in many tumors, such as breast and gastric cancers. However, the predictive value of pretreatment SIRI on pathological complete response (pCR) rates in patients with breast cancer treated with neoadjuvant chemotherapy (NAC) is unknown. This study examined the correlation between SIRI and pCR in patients with breast cancer receiving NAC and identified convenient and accurate predictive indicators for pCR. Methods: We retrospectively analyzed the clinicopathological parameters and pretreatment peripheral blood characteristics of the 241 patients with breast cancer who received NAC between June 2015 and June 2020. Receiver operating characteristic (ROC) curves were used to determine the optimal cutoff of SIRI. ROC curves were also plotted to verify the accuracy of inflammatory markers for pCR prediction. The chi-squared test was used to explore the relationships of SIRI with pCR and other clinicopathological parameters. Multivariate analyses were performed using a logistic regression model. Results: Among the 241 patients, 48 (19.92%) achieved pCR. pCR was significantly related to SIRI, the neutrophil-lymphocyte ratio (NLR), the lymphocyte-monocyte ratio (LMR), molecular subtypes and other clinicopathological parameters, such as BMI, clinical T and N staging, and histological grade. Multivariate analyses indicated that the clinical T and N staging, SIRI, and NLR were independent prognostic factors for pCR in patients with breast cancer. The area under the ROC curve for SIRI was larger than that for NLR. Compared to patients with SIRI ≥0.72, patients with SIRI < 0.72 had a nearly 5-fold higher chance of obtaining pCR (odds ratio = 4.999, 95% confidence interval = 1.510–16.551, p = 0.000). Conclusions: Pretreatment SIRI is predictive of pCR in patients with breast cancer receiving NAC, and the index can assist physicians in formulating personalized treatment strategies.

Breast cancer (BC) is the most common malignancy worldwide and neoadjuvant therapy (NAT) plays an important role in the treatment of patients with early BC. However, only a subset of BC patients can achieve pathological complete response (pCR) and benefit from NAT. It is therefore necessary to predict the responses to NAT. Although many models to predict the response to NAT based on gene expression determined by the microarray platform have been proposed, their applications in clinical practice are limited due to the data normalization methods during model building and the disadvantages of the microarray platform compared with the RNA-seq platform. In this study, we first reconfirmed the correlation between immune profiles and pCR in an RNA-seq dataset. Then, we employed multiple machine learning algorithms and a model stacking strategy to build an immunological gene based model (Ipredictor model) and an immunological gene and receptor status based model (ICpredictor model) in the RNA-seq dataset. The areas under the receiver operator characteristic curves for the Ipredictor model and ICpredictor models were 0.745 and 0.769 in an independent external test set based on the RNA-seq platform, and were 0.716 and 0.752 in another independent external test set based on the microarray platform. Furthermore, we found that the predictive score of the Ipredictor model was correlated with immune microenvironment and genomic aberration markers. These results demonstrated that the models can accurately predict the response to NAT for BC patients and will contribute to individualized therapy.

Breast cancer (BC) is the most common malignancy among females. Chemotherapy drugs remain the cornerstone of treatment of BC and undergo significant shifts over the past 100 years. The advent of immunotherapy presents promising opportunities and constitutes a significant complementary to existing therapeutic strategies for BC. Chemotherapy as a cytotoxic treatment that targets proliferation malignant cells has recently been shown as an effective immune-stimulus in multiple ways. Chemotherapeutic drugs can cause the release of damage-associated molecular patterns (DAMPs) from dying tumor cells, which result in long-lasting antitumor immunity by the key process of immunogenic cell death (ICD). Furthermore, Off-target effects of chemotherapy on immune cell subsets mainly involve activation of immune effector cells including natural killer (NK) cells, dendritic cells (DCs), and cytotoxic T cells, and depletion of immunosuppressive cells including Treg cells, M2 macrophages and myeloid-derived suppressor cells (MDSCs). Current mini-review summarized recent large clinical trials regarding the combination of chemotherapy and immunotherapy in BC and addressed the molecular mechanisms of immunostimulatory properties of chemotherapy in BC. The purpose of our work was to explore the immune-stimulating effects of chemotherapy at the molecular level based on the evidence from clinical trials, which might be a rationale for combinations of chemotherapy and immunotherapy in BC.

Although DNA damage repair plays a critical role in cancer chemotherapy, the function of lncRNAs in this process remains largely unclear. In this study, in silico screening identified H19 as an lncRNA that potentially plays a role in DNA damage response and sensitivity to PARP inhibitors. Increased expression of H19 is correlated with disease progression and with a poor prognosis in breast cancer. In breast cancer cells, forced expression of H19 promotes DNA damage repair and resistance to PARP inhibition, whereas H19 depletion diminishes DNA damage repair and increases sensitivity to PARP inhibitors. H19 exerted its functional roles via direct interaction with ILF2 in the cell nucleus. H19 and ILF2 increased BRCA1 stability via the ubiquitin-proteasome proteolytic pathway via the H19- and ILF2-regulated BRCA1 ubiquitin ligases HUWE1 and UBE2T. In summary, this study has identified a novel mechanism to promote BRCA1-deficiency in breast cancer cells. Therefore, targeting the H19/ILF2/BRCA1 axis might modulate therapeutic approaches in breast cancer.