The impact of lipid metabolism on breast cancer: a review about its role in tumorigenesis and immune escape

Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer

BACKGROUND

Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.

METHODS

Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC (n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.

RESULTS

The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.

CONCLUSION

We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.

Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma

BACKGROUND

Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors.

METHODS

This study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.

RESULTS

After rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model's performance, increasing the validation cohort AUC to 0.87.

CONCLUSION

In this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.

CLINICAL RELEVANCE STATEMENT

This integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.

Apolipoprotein E is a prognostic factor for pancreatic cancer and associates with immune infiltration

BACKGROUND

The expression level of apolipoprotein E (APOE) in pancreatic ductal adenocarcinoma (PDAC) and its effect on the prognosis of PDAC patients are not clear. The effect of APOE on the immune status of patients with PDAC has not been elucidated.

METHODS

We obtained pancreatic cancer data from the TCGA and GETx databases. Patients with PDAC who underwent pancreatic surgery at the Second Affiliated Hospital of Jiaxing University between 2012 and 2021 were included. Clinical pathological data were recorded, plasma APOE levels were measured, and tissue samples were collected. A tissue microarray was generated using the collected tissue samples. APOE and CD4 staining was performed to determine immunoreactive scores (IRSs). The expression of APOE in the plasma and tumour tissues of pancreatic cancer patients was analysed and compared. The correlations between plasma APOE levels, tissue APOE levels and clinicopathological characteristics were analysed. Survival prognosis was analysed using Kaplan-Meier survival analysis and Cox multivariate regression analysis. The correlations between APOE expression levels and immune biomarkers and immune cells were further analysed. Single-cell analysis of APOE distribution in various cells was performed on the TISCH website.

RESULTS

APOE was highly expressed in the tumour tissue of pancreatic cancer patients, and high plasma APOE levels were associated with poor prognosis. Females, patients with high-grade disease and patients with pancreatic head carcinoma had high plasma APOE levels. High APOE expression in tumour tissues was associated with good prognosis. Mononuclear macrophages in the pancreatic cancer microenvironment primarily expressed APOE. APOE levels positively correlated with immune biomarkers, such as CD8A, PDCD1, GZMA, CXCL10, and CXCL9, in the tumour microenvironment. APOE promoted CD4 + T cell or dendritic cell infiltration in the tumour microenvironment.

CONCLUSIONS

APOE may affect the occurrence and development of pancreatic cancer by regulating the infiltration of immune cells in the tumour microenvironment.

SPDYC serves as a prognostic biomarker related to lipid metabolism and the immune microenvironment in breast cancer

Breast cancer remains the most common malignant carcinoma among women globally and is resistant to several therapeutic agents. There is a need for novel targets to improve the prognosis of patients with breast cancer. Bioinformatics analyses were conducted to explore potentially relevant prognostic genes in breast cancer using The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) databases. Gene subtypes were categorized by machine learning algorithms. The machine learning-related breast cancer (MLBC) score was evaluated through principal component analysis (PCA) of clinical patients' pathological statuses and subtypes. Immune cell infiltration was analyzed using the xCell and CIBERSORT algorithms. Kyoto Encyclopedia of Genes and Genomes enrichment analysis elucidated regulatory pathways related to speedy/RINGO cell cycle regulator family member C (SPDYC) in breast cancer. The biological functions and lipid metabolic status of breast cancer cell lines were validated via quantitative real-time polymerase chain reaction (RT‒qPCR) assays, western blotting, CCK-8 assays, PI‒Annexin V fluorescence staining, transwell assays, wound healing assays, and Oil Red O staining. Key differentially expressed genes (DEGs) in breast cancer from the TCGA and GEO databases were screened and utilized to establish the MLBC score. Moreover, the MLBC score we established was negatively correlated with poor prognosis in breast cancer patients. Furthermore, the impacts of SPDYC on the tumor immune microenvironment and lipid metabolism in breast cancer were revealed and validated. SPDYC is closely related to activated dendritic cells and macrophages and is simultaneously correlated with the immune checkpoints CD47, cytotoxic T lymphocyte antigen-4 (CTLA-4), and poliovirus receptor (PVR). SPDYC strongly correlated with C-C motif chemokine ligand 7 (CCL7), a chemokine that influences breast cancer patient prognosis. A significant relationship was discovered between key genes involved in lipid metabolism and SPDYC, such as ELOVL fatty acid elongase 2 (ELOVL2), malic enzyme 1 (ME1), and squalene epoxidase (SQLE). Potent inhibitors targeting SPDYC in breast cancer were also discovered, including JNK inhibitor VIII, AICAR, and JW-7-52-1. Downregulation of SPDYC expression in vitro decreased proliferation, increased the apoptotic rate, decreased migration, and reduced lipid droplets. SPDYC possibly influences the tumor immune microenvironment and regulates lipid metabolism in breast cancer. Hence, this study identified SPDYC as a pivotal biomarker for developing therapeutic strategies for breast cancer.

A prismatic view of the epigenetic-metabolic regulatory axis in breast cancer therapy resistance

Epigenetic regulation established during development to maintain patterns of transcriptional expression and silencing for metabolism and other fundamental cell processes can be reprogrammed in cancer, providing a molecular mechanism for persistent alterations in phenotype. Metabolic deregulation and reprogramming are thus an emerging hallmark of cancer with opportunities for molecular classification as a critical preliminary step for precision therapeutic intervention. Yet, acquisition of therapy resistance against most conventional treatment regimens coupled with tumor relapse, continue to pose unsolved problems for precision healthcare, as exemplified in breast cancer where existing data informs both cancer genotype and phenotype. Furthermore, epigenetic reprograming of the metabolic milieu of cancer cells is among the most crucial determinants of therapeutic resistance and cancer relapse. Importantly, subtype-specific epigenetic-metabolic interplay profoundly affects malignant transformation, resistance to chemotherapy, and response to targeted therapies. In this review, we therefore prismatically dissect interconnected epigenetic and metabolic regulatory pathways and then integrate them into an observable cancer metabolism-therapy-resistance axis that may inform clinical intervention. Optimally coupling genome-wide analysis with an understanding of metabolic elements, epigenetic reprogramming, and their integration by metabolic profiling may decode missing molecular mechanisms at the level of individual tumors. The proposed approach of linking metabolic biochemistry back to genotype, epigenetics, and phenotype for specific tumors and their microenvironment may thus enable successful mechanistic targeting of epigenetic modifiers and oncometabolites despite tumor metabolic heterogeneity.

In vitro and in silico evaluation of Andrographis paniculata ethanolic crude extracts on fatty acid synthase expression on breast cancer cells

Background

Fatty acid synthase (FASN), a key rate-limiting enzyme in the fatty acid biosynthesis pathway has been identified to be overexpressed in breast cancer. This overexpression has been affiliated with poor prognosis and resistance to chemotherapeutics. Consequently, FASN has come into focus as an appealing potential target for breast cancer treatment. Available FASN inhibitors, however, are unstable and have been correlated with adverse side effects.

Objective

This present study aims to investigate the potential of Andrographis paniculata ethanolic crude extract (AP) as a potent FASN inhibitor in breast cancer cells.

Materials & methods

This study used MTT assay and flow cytometry analysis to measure cell viability and apoptosis following AP treatment (0-500 μg/mL). Furthermore, FASN protein expression was evaluated using immunocytochemistry whereas lipid droplet formation was quantified using Oil Red O staining. Literature-based identified AP phytochemicals were subjected to the prediction of molecular docking and ADMET properties.

Results

This study demonstrated that AP significantly reduced cell viability while inducing apoptosis in breast cancer cells. In addition, for the first time, exposure to AP was demonstrated to drastically reduce intracellular FASN protein expression and lipid droplet accumulation in EMT6 and MCF-7 breast cancer cells. Docking simulation analysis demonstrated AP phytochemicals may have exerted an inhibitory effect by targeting the FASN Thioesterase (TE) domain similarly to the known FASN inhibitor, Orlistat. Moreover, all AP phytochemicals also possessed drug-likeness properties which are in accordance with Lipinski's rule of five.

Conclusions

These results highlight the potential of A. paniculata ethanolic crude extract as a FASN inhibitor and hence might have the potential to be further developed as a potent chemotherapeutic drug for breast cancer treatment.

Association between pathologic complete response and biochemical indicators after neoadjuvant therapy for HER2-positive breast cancer

PURPOSE

This study investigated the changes in the fasting blood glucose (FBG), fasting triglyceride (FTG), and fasting total cholesterol (FTC) levels during neoadjuvant therapy (NAT) for human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) and the association with pathologic complete response (pCR).

METHODS

Relevant data from Sichuan Cancer Hospital from June 2019 to June 2022 were collected and analyzed, and FBG, FTG, and FTC were divided into baseline, change, and process groups, which were grouped to analyze the changes after receiving NAT and the association with pCR.

RESULTS

In the estrogen receptor (ER)-negative subgroup, patients with low levels of FTG in the process group were more likely to achieve pCR compared to high levels, and in the progesterone receptor (PR)-negative subgroup, patients with lower FTG compared to higher FTG after receiving NAT was more likely to achieve pCR.

CONCLUSIONS

Patients with HER2-positive BC undergoing NAT develop varying degrees of abnormalities (elevated or decreased) in FBG, FTG, and FTC; moreover, the status of FTG levels during NAT may predict pCR in ER-negative or PR-negative HER2-positive BC.Early monitoring and timely intervention for FTG abnormalities may enable this subset of patients to increase the likelihood of obtaining a pCR along with management of abnormal markers.

Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples

An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.

High linoleic acid levels in red blood cells predict a poor response to neoadjuvant chemotherapy in human epidermal growth factor receptor type 2-positive breast cancer patients

OBJECTIVE

Polyunsaturated fatty acids are categorized as ω-3 or ⍵-6. Previous studies demonstrate that breast cancers display a high expression of fatty acid synthase and high fatty acid levels. Our study sought to determine if changes in plasma or red blood cell membrane fatty acid levels were associated with the response to preoperative (neoadjuvant) chemotherapy in non-metastatic breast cancer patients.

METHODS

Our prospective study assessed fatty acid levels in plasma and red blood cell membrane. Response to neoadjuvant chemotherapy was evaluated by the presence or absence of pathologic complete response and/or residual cancer burden.

RESULTS

A total of 28 patients were included. First, patients who achieved pathologic complete response had significantly higher neutrophil-to-lymphocyte ratio versus no pathologic complete response (P = 0.003). Second, total red blood cell membrane polyunsaturated fatty acids were higher in the absence of pathologic complete response (P = 0.0028). Third, total red blood cell membrane ⍵-6 polyunsaturated fatty acids were also higher in no pathologic complete response (P < 0.01). Among ⍵-6 polyunsaturated fatty acids, red blood cell membrane linoleic acid was higher in the absence of pathologic complete response (P < 0.01). Notably, plasma polyunsaturated fatty acid, ⍵-6, and linoleic acid levels did not have significant differences. A multivariate analysis confirmed red blood cell membrane linoleic acid was associated with no pathologic complete response; this was further confirmed by receiver operating characteristic analysis (specificity = 92.3%, sensitivity = 76.9%, and area under the curve = 0.855).

CONCLUSIONS

Pending further validation, red blood cell membrane linoleic acid might serve as a predictor biomarker of poorer response to neoadjuvant chemotherapy in non-metastatic human epidermal growth factor receptor type 2-positive breast cancer. Measuring fatty acids in red blood cell membrane could offer a convenient, minimally invasive strategy to identifying patients more likely to respond or those with chemoresistance.

Single-Cell Unsaturated Lipid Profiling for Studying Chemoresistance Heterogeneity of Triple-Negative Breast Cancer Cells

Chemoresistance to triple-negative breast cancer (TNBC) is a critical issue in clinical practice. Lipid metabolism takes a unique role in breast cancer cells; especially, unsaturated lipids involving cell membrane fluidity and peroxidation are highly remarked. At present, for the lack of a high-resolution molecular recognition platform at the single-cell level, it is still hard to systematically study chemoresistance heterogeneity based on lipid unsaturation proportion. By designing a single-cell mass spectrometry workflow based on CyESI-MS, we profiled the unsaturated lipids of TNBC cells to evaluate lipidomic remodeling under platinum stress. Profiling revealed the heterogeneity of the polyunsaturated lipid proportion of TNBC cells under cisplatin treatment. A cluster of cells identified by polyunsaturated lipid accumulation was found to be involved in platinum sensitivity. Furthermore, we found that the chemoresistance of TNBC cells could be regulated by fatty acid supplementation, which determinates the composition of unsaturated lipids. These discoveries provide insights for monitoring and controlling cellular unsaturated lipid proportions to overcome chemoresistance in breast cancer.

The lipid metabolism remodeling: A hurdle in breast cancer therapy

Lipids, as one of the three primary energy sources, provide energy for all cellular life activities. Lipids are also known to be involved in the formation of cell membranes and play an important role as signaling molecules in the intracellular and microenvironment. Tumor cells actively or passively remodel lipid metabolism, using the function of lipids in various important cellular life activities to evade therapeutic attack. Breast cancer has become the leading cause of cancer-related deaths in women, which is partly due to therapeutic resistance. It is necessary to fully elucidate the formation and mechanisms of chemoresistance to improve breast cancer patient survival rates. Altered lipid metabolism has been observed in breast cancer with therapeutic resistance, indicating that targeting lipid reprogramming is a promising anticancer strategy.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

Comprehensive multi-level expression profiling of key biomarkers in breast cancer patients

OBJECTIVES

In this comprehensive breast cancer (BC) study, we aimed to identify, validate, and characterize key biomarkers with significant implications in BC diagnosis, prognosis, and as therapeutic targets.

METHODS

Our research strategy involved a multi-level methodology, combining bioinformatic analysis with experimental validation.

RESULTS

Initially, we conducted an extensive literature search to identify BC biomarkers, selecting those with reported accuracies exceeding 20% in specificity and sensitivity. This yielded nine candidate biomarkers, which we subsequently analyzed using Cytoscape to identify a few key biomarkers. Based on the degree method, we denoted four key biomarkers, including progesterone receptor (PGR), epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), and Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2). Expression analysis using The Cancer Genome Atlas (TCGA) dataset revealed that PGR and EGFR exhibited significant (p-value < 0.05) down-regulation in BC samples when compared to controls, while ESR1 and ERBB2 showed up-regulation. To strengthen our findings, we collected clinical BC tissue samples from Pakistani patients and performed expression verification using real-time quantitative polymerase chain reaction (RT-qPCR). The results aligned with our initial TCGA dataset analysis, further validating the differential expression of these key biomarkers in BC. Furthermore, we utilized receiver operating characteristic (ROC) curves to demonstrate the diagnostic use of these biomarkers. Our analysis underscored their accuracy and sensitivity as diagnostic markers for BC. Survival analysis using the Kaplan-Meier Plotter tool revealed a prognostic significance of PGR, ESR1, EGFR, and ERBB2. Their expression levels were associated with poor overall survival (OS) of BC patients, shedding light on their roles as prognostic indicators in BC. Lastly, we explored DrugBank to identify drugs that may reverse the expression patterns , and estradiol, decitabine, and carbamazepine were singled out.

CONCLUSION

Our study gives valuable insight into BC biomarkers, for diagnosis and prognosis. These findings have implications for BC management using personalized and targeted therapeutic approaches for BC patients.

Intra-tumoral microbial community profiling and associated metabolites alterations of TNBC

Triple-negative breast cancer (TNBC) presents significant challenges to female health owing to the lack of therapeutic targets and its poor prognosis. In recent years, in the field of molecular pathology, there has been a growing focus on the role of intra-tumoral microbial communities and metabolic alterations in tumor cells. However, the precise mechanism through which microbiota and their metabolites influence TNBC remains unclear and warrants further investigation. In this study, we analyzed the microbial community composition in various subtypes of breast cancer through 16S rRNA MiSeq sequencing of formalin-fixed, paraffin-embedded (FFPE) tissue samples. Notably, Turicibacter, a microbe associated with cancer response, exhibited a significantly higher abundance in TNBC. Similarly, mass spectrometry-based metabolomic analysis revealed substantial differences in specific metabolites, such as nutriacholic, pregnanetriol, and cortol. Furthermore, we observed significant correlations between the intra-tumoral microbiome, clinicopathological characteristics, and human epidermal growth factor receptor-2 expression(HER2). Three microbial taxa (Cytophagaceae, Conexibacteraceae, and Flavobacteriaceae) were associated with tumor-infiltrating lymphocytes(TILs), which are indicative of antitumor immunity. This study creatively utilized FFPE tissue samples to assess intra-tumoral microbial communities and their related metabolic correlations, presenting avenues for the identification of novel diagnostic biomarkers, the development of therapeutic strategies, and the early clinical diagnosis of TNBC.