Advances in Analyzing the Breast Cancer Lipidome and Its Relevance to Disease Progression and Treatment

Hyperlipidemia drives tumor growth in a mouse model of obesity-accelerated breast cancer growth

Obesity is an established risk factor for breast cancer (BC), yet the specific mechanisms driving this association remain unclear. Dysregulated lipid metabolism has emerged as a key factor in cancer cell biology. While obesity is often accompanied by hyperlipidemia, the isolated impact of elevated lipid levels on BC growth has not been experimentally tested. Using the E0771 orthotopic model of obesity-accelerated BC growth in immune-competent mice, we investigated the direct role of systemic lipids in tumor growth. Combining dietary and genetic mouse models, we show that elevated circulating lipids are sufficient to accelerate BC tumor growth even in the absence of obesity or alterations in blood glucose and/or insulin levels. Pharmacological lowering of systemic lipid levels attenuates BC growth in obese mice, suggesting a direct role for lipids in fueling tumor expansion. Notably, we also show that weight loss alone, without a corresponding reduction in lipid levels such as that induced by a ketogenic diet, fails to protect against BC, highlighting the necessity of targeting lipid metabolism in obesity-associated BC. Our findings establish hyperlipidemia as a critical driver of BC progression and suggest that lipid-lowering interventions may be a promising strategy to mitigate BC risk in obese individuals.

Targeting lipid metabolism: novel insights and therapeutic advances in pancreatic cancer treatment

Lipid metabolism in cancer is characterized by dysregulated lipid regulation and utilization, critical for promoting tumor growth, survival, and resistance to therapy. Pancreatic cancer (PC) is a highly aggressive malignancy of the gastrointestinal tract that has a dismal 5-year survival rate of less than 10%. Given the essential function of the pancreas in digestion, cancer progression severely disrupts its function. Standard treatments for PC such as surgical resection, chemotherapy, and radiotherapy. However, these therapies often face significant challenges, including biochemical recurrence and drug resistance.Given these limitations, new therapeutic approaches are being developed to target tumor metabolism. Dysregulation of cholesterol biosynthesis and alterations in fatty acids (FAs), such as palmitate, stearate, omega-3, and omega-6, have been observed in pancreatic cancer. These lipids serve as energy sources, signaling molecules, and essential components of cell membranes. Their accumulation fosters an immunosuppressive tumor microenvironment that supports cancer cell proliferation and metastasis.Moreover, lipid metabolism dysregulation within immune cells, particularly T cells, impairs immune surveillance and weakens the body's defenses against cancer. Abnormal lipid metabolism also contributes to drug resistance in PC. Despite these challenges, targeting lipid metabolism may offer a promising therapeutic strategy. By enhancing lipid peroxidation, the induction of ferroptosis-a form of regulated cell death-could impair the survival of PC cells and hinder disease progression.

Exploring lipidomic profiles and their correlation with hormone receptor and HER2 status in breast cancer

Dysregulated lipid metabolism promotes the progression of various cancer types, including breast cancer. The present study aimed to explore the lipidomic profiles of patients with breast cancer, providing insights into the correlation between lipid compositions and tumor subtypes characterized by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. Briefly, 30 patients with breast cancer were categorized into four groups based on their HR and HER2 status: HR+ no HER2 expression (HER2-0), HR+ HER2-low; HR+ HER2-positive (pos) and HR- HER2-pos. The lipidomic profiles of these patients were analyzed using high-throughput liquid chromatography-mass spectrometry. The data were processed through principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and random forest (RF) classification to assess the lipidomic variations and significant lipid features among these groups. The profiles of the lipids, particularly triglycerides (TG) such as TG(16:0-18:1-18:1)+NH4, were significantly different across the groups. PCA and PLS-DA identified unique lipid profiles in the HR+ HER2-pos and HR+ HER2-0 groups, while RF highlighted phosphatidylinositol-3,4,5-trisphosphate(21:2)+NH4 as a crucial lipid feature for accurate patient grouping. Advanced statistical analysis showed significant correlations between lipid carbon chain length and the number of double bonds within the classifications, providing insights into the role of structural lipid properties in tumor biology. Additionally, a clustering heatmap and network analysis indicated significant lipid-lipid interactions. Pathway enrichment analysis showed critical biological pathways, such as the 'Assembly of active LPL and LIPC lipase complexes', which has high enrichment ratio and statistical significance. In conclusion, the present study underscored that lipidomic profiling is crucial in understanding the metabolic alterations associated with different breast cancer subtypes. These findings highlighted specific lipid features and interactions that may serve as potential biomarkers for breast cancer classification and target pathways for therapeutic intervention. Furthermore, advanced lipidomic analyses can be integrated to decipher complex biological data, offering a foundation for further research into the role of lipid metabolism in cancer progression.

Enhanced Lipidomics Analysis of Breast Cancer Cells Using Three-phase Liquid Extraction and Ultra High-performance Liquid Chromatography Coupled With Quadrupole Time-of-Flight Tandem Mass Spectrometry

Lipid extraction of complex biological samples is essential for high-quality data in liquid chromatography-mass spectrometry (LC-MS)-based lipidomics. This study introduces a three-phase liquid extraction (3PLE)-ultra-high-performance LC coupled with quadrupole time-of-flight tandem MS method. This method was successfully applied to lipidomics analysis of breast cancer cells, including highly metastatic MDA-MB-231 and slightly metastatic MCF7 cells. The 3PLE method employed an n-hexane/methyl tert-butyl ether/acetonitrile/water solvent system that formed one aqueous and two organic phases. Neutral and polar lipids were enriched in the upper and middle organic phases, respectively, and combined for detection, thereby reducing analysis time. Compared with the Bligh and Dyer method, 3PLE achieved higher sensitivity and detected more features, with over a 50% increase in the relative abundance of nearly 50% of the differential lipids. In total, 21 differential lipids were identified in the MDA-MB-231 group and 22 in the MCF7 group compared to normal breast epithelial cells (MCF10A). Pathway analysis suggested that lipid changes in breast cancer cells were associated with glycerophospholipid metabolism, arachidonic acid metabolism, sphingolipid metabolism, and linoleic acid metabolism. The study presents a highly efficient lipidomics method, providing a scientific foundation for understanding breast cancer pathogenesis and aiding in diagnosis.

Knock-out of CD73 delays the onset of HR-negative breast cancer by reprogramming lipid metabolism and is associated with increased tumor mutational burden

OBJECTIVE

CD73 (ecto-5'-nucleotidase, NT5E), a cell-surface enzyme converting 5'-AMP to adenosine, is crucial for cancer progression. However, its role in the tumorigenesis process remains mostly obscure. We aimed to demonstrate CD73's role in breast cancer (BC) tumorigenesis through metabolic rewiring of fatty acid metabolism, a process recently indicated to be regulated by BC major prognostic markers, hormone receptors (HR) for estrogen (ER), and progesterone (PR).

METHODS

A murine model of chemically induced mammary gland tumorigenesis was applied to analyze CD73 knock-out (KO)-induced changes at the transcriptome (RNA-seq), proteome (IHC, WB), and lipidome (GC-EI-MS) levels. CD73 KO-induced changes were correlated with scRNA-seq and bulk RNA-seq data for human breast tissues and BCs from public collections and confirmed at the proteome level with IHC or WB analysis of BC tissue microarrays and cell lines.

RESULTS

CD73 KO delayed the onset of HR/PR-negative mammary tumors in a murine model. This delay correlated with increased expression of genes related to biosynthesis and β-oxidation of fatty acids (FAs) in the CD73 KO group at the initiation stage. STRING analysis based on RNA-seq data indicated an interplay between CD73 KO, up-regulated expression of PR-coding gene, and DEGs involved in FA metabolism, with PPARγ, a main regulator of FA synthesis, as a main connective node. In epithelial cells of mammary glands, PPARγ expression correlated with CD73 at the RNA level. With cancer progression, CD73 KO increased the levels of PUFAn3/6 (polyunsaturated omega 3/6 FAs), known ligands of PPARγ and target for lipid peroxidation, which may lead to oxidative DNA damage. It correlated with the downregulation of genes involved in cellular stress response (Mlh1, Gsta3), PR-or CD73-dependent changes in the intracellular ROS levels and expression or activation of proteins involved in DNA repair or oxidative stress response in mammary tumor or human BC cell lines, increased tumor mutational burden (TMB) and genomic instability markers in CD73 low HR-negative human BCs, and the prolonged onset of tumors in the CD73 KO HR/PR-negative group.

CONCLUSIONS

CD73 has a significant role in tumorigenesis driving the reprogramming of lipid metabolism through the regulatory loop with PR and PPARγ in epithelial cells of mammary glands. Low CD73 expression/CD73 KO might enhance mutational burden by disrupting this regulatory loop, delaying the onset of HR-negative tumors. Our results support combining therapy targeting the CD73-adenosine axis and tumor lipidome against HR-negative tumors, especially at their earliest developmental stage.

Assessing Breast Cancer through Tumor Microenvironment Mapping of Collagen and Other Biomolecule Spectral Fingerprints─A Review

Breast cancer is a major challenge in the field of oncology, with around 2.3 million cases and around 670,000 deaths globally based on the GLOBOCAN 2022 data. Despite having advanced technologies, breast cancer remains the major type of cancer among women. This review highlights various collagen signatures and the role of different collagen types in breast tumor development, progression, and metastasis, along with the use of photoacoustic spectroscopy to offer insights into future cancer diagnostic applications without the need for surgery or other invasive techniques. Through mapping of the tumor microenvironment and spotlighting key components and their absorption wavelengths, we emphasize the need for extensive preclinical and clinical investigations.

Tumor Lipid Signatures Are Descriptive of Acquisition of Therapy Resistance in an Endocrine-Related Breast Cancer Mouse Model

The lipid metabolism adaptations of estrogen and progesterone receptor-positive breast cancer tumors from a mouse syngeneic model are investigated in relation to differences across the transition from hormone-dependent (HD) to hormone-independent (HI) tumor growth and the acquisition of endocrine therapy (ET) resistance (HIR tumors). Results are articulated with reported polar metabolome results to complete a metabolic picture of the above transitions and suggest markers of tumor progression and aggressiveness. Untargeted nuclear magnetic resonance metabolomics was used to analyze tumor and mammary tissue lipid extracts. Tumor progression (HD-HI-HIR) was accompanied by increased nonesterified cholesterol forms and phospholipids (phosphatidylcholine, phosphatidylethanolamine, sphingomyelins, and plasmalogens) and decreased relative contents of triglycerides and fatty acids. Predominating fatty acids became shorter and more saturated on average. These results were consistent with gradually more activated cholesterol synthesis, β-oxidation, and phospholipid biosynthesis to sustain tumor growth, as well as an increase in cholesterol (possibly oxysterol) forms. Particular compound levels and ratios were identified as potential endocrine tumor HD-HI-HIR progression markers, supporting new hypotheses to explain acquired ET resistance.

Characterisation of Canine and Feline Breast Tumours, Their Metastases, and Corresponding Primary Cell Lines Using LA-REIMS and DESI-MS Imaging

Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

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.

Adaptations of membrane trafficking in cancer and tumorigenesis

Membrane trafficking, a fundamental cellular process encompassing the transport of molecules to specific organelles, endocytosis at the plasma membrane and protein secretion, is crucial for cellular homeostasis and signalling. Cancer cells adapt membrane trafficking to enhance their survival and metabolism, and understanding these adaptations is vital for improving patient responses to therapy and identifying therapeutic targets. In this Review, we provide a concise overview of major membrane trafficking pathways and detail adaptations in these pathways, including COPII-dependent endoplasmic reticulum (ER)-to-Golgi vesicle trafficking, COPI-dependent retrograde Golgi-to-ER trafficking and endocytosis, that have been found in cancer. We explore how these adaptations confer growth advantages or resistance to cell death and conclude by discussing the potential for utilising this knowledge in developing new treatment strategies and overcoming drug resistance for cancer patients.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Lipidome of mammographic breast density in premenopausal women

BACKGROUND

High mammographic breast density (MBD) is a strong risk factor for breast cancer development, but the biological mechanisms underlying MBD are unclear. Lipids play important roles in cell differentiation, and perturbations in lipid metabolism are implicated in cancer development. Nevertheless, no study has applied untargeted lipidomics to profile the lipidome of MBD. Through this study, our goal is to characterize the lipidome of MBD in premenopausal women.

METHODS

Premenopausal women were recruited during their annual screening mammogram at the Washington University School of Medicine in St. Louis, MO. Untargeted lipidomic profiling for 982 lipid species was performed at Metabolon (Durham, NC®), and volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) was assessed using Volpara 1.5 (Volpara Health®). We performed multivariable linear regression models to investigate the associations of lipid species with MBD and calculated the covariate-adjusted least square mean of MBD by quartiles of lipid species. MBD measures were log10 transformed, and lipid species were standardized. Linear coefficients of MBD were back-transformed and considered significant if the Bonferroni corrected p-value was < 0.05.

RESULTS

Of the 705 premenopausal women, 72% were non-Hispanic white, and 23% were non-Hispanic black. Mean age, and BMI were 46 years and 30 kg/m2, respectively. Fifty-six lipid species were significantly associated with VPD (52 inversely and 4 positively). The lipid species with positive associations were phosphatidylcholine (PC)(18:1/18:1), lysophosphatidylcholine (LPC)(18:1), lactosylceramide (LCER)(14:0), and phosphatidylinositol (PI)(18:1/18:1). VPD increased across quartiles of PI(18:1/18:1): (Q1 = 7.5%, Q2 = 7.7%, Q3 = 8.4%, Q4 = 9.4%, Bonferroni p-trend = 0.02). The lipid species that were inversely associated with VPD were mostly from the triacylglycerol (N = 43) and diacylglycerol (N = 7) sub-pathways. Lipid species explained some of the variation in VPD. The inclusion of lipid species increased the adjusted R2 from 0.45, for a model that includes known determinants of VPD, to 0.59.

CONCLUSIONS

We report novel lipid species that are associated with MBD in premenopausal women. Studies are needed to validate our results and the translational potential.

GPD1 inhibits the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid metabolism signaling pathway

Glycerol 3-phosphate dehydrogenase 1 (GPD1) acts as a tumor suppressor in various types of cancer. However, the mechanisms of GPD1 anti-tumor remain unclear in breast cancer. This study aims to explore the function and clinical relevance of GPD1 in breast cancer. We confirmed that GPD1 inhibited the ability of proliferation, migration, and invasion in GPD1 overexpression breast cancer cells by CCK-8, wound healing, and Transwell assays, respectively. We found that GPD1 overexpression activated the lipid synthesis pathway and PI3K/AKT signaling pathway. The inhibitory effect of GPD1 on breast cancer cells was also weakened after treatment with LY294002, a PI3K/AKT pathway inhibitor. These results indicated that GPD1 suppressed the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid signaling pathways. Meanwhile, we detected that the relationship between GPD1 level and survival rate presents a positive correlation in breast cancer patients from the Cancer Genome Atlas (TCGA) database. Therefore, GPD1 can be a prognostic biomarker and target in developing therapeutic strategies for breast cancer patients.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

DDIT4 Downregulation by siRNA Approach Increases the Activity of Proteins Regulating Fatty Acid Metabolism upon Aspirin Treatment in Human Breast Cancer Cells

Repositioning of aspirin for a more effective breast cancer (BC) treatment requires identification of predictive biomarkers. However, the molecular mechanism underlying the anticancer activity of aspirin remains fully undefined. Cancer cells enhance de novo fatty acid (FA) synthesis and FA oxidation to maintain a malignant phenotype, and the mechanistic target of rapamycin (mTORC1) is required for lipogenesis. We, therefore, aimed to test if the expression of mTORC1 suppressor DNA damage-inducible transcript (DDIT4) affects the activity of main enzymes in FA metabolism after aspirin treatment. MCF-7 and MDA-MB-468 human BC cell lines were transfected with siRNA to downregulate DDIT4. The expression of carnitine palmitoyltransferase 1 A (CPT1A) and serine 79-phosphorylated acetyl-CoA carboxylase 1 (ACC1) were analyzed by Western Blotting. Aspirin enhanced ACC1 phosphorylation by two-fold in MCF-7 cells and had no effect in MDA-MB-468 cells. Aspirin did not change the expression of CPT1A in either cell line. We have recently reported DDIT4 itself to be upregulated by aspirin. DDIT4 knockdown resulted in 1.5-fold decreased ACC1 phosphorylation (dephosphorylation activates the enzyme), 2-fold increased CPT1A expression in MCF-7 cells, and 2.8-fold reduced phosphorylation of ACC1 following aspirin exposure in MDA-MB-468 cells. Thus, DDIT4 downregulation raised the activity of main lipid metabolism enzymes upon aspirin exposure which is an undesired effect as FA synthesis and oxidation are linked to malignant phenotype. This finding may be clinically relevant as DDIT4 expression has been shown to vary in breast tumors. Our findings justify further, more extensive investigation of the role of DDIT4 in aspirin's effect on fatty acid metabolism in BC cells.

Scientometric analysis of lipid metabolism in breast neoplasm: 2012-2021

Introduction: In recent years, more and more studies have proved that lipid metabolism plays an essential role in breast cancer's proliferation and metastasisand also has a specific significance in predicting survival. Methods: This paper collected data from 725 publications related to lipid metabolism in breast neoplasm from 2012 to 2021 through the Web of Science Core Collection database. Bibliometrix, VOSviewer, and CiteSpace were used for the scientometrics analysis of countries, institutions, journals, authors, keywords, etc. Results: The number of documents published showed an increasing trend, with an average annual growth rate of 14.49%. The United States was the most productive country (n = 223, 30.76%). The journals with the largest number of publications are mostly from developed countries. Except for the retrieved topics, "lipid metabolism" (n = 272) and "breast cancer" (n = 175), the keywords that appeared most frequently were "expression" (n = 151), "fatty-acid synthase" (n = 78), "growth" (n = 72), "metabolism" (n = 67) and "cells" (n = 66). Discussion: These findings and summaries help reveal the current research status and clarify the hot spots in this field.

Comprehensive serum lipidomic analyses reveal potential biomarkers for malignant breast cancer: A case-control study

BACKGROUND

Breast cancer is the most worldwide commonly found malignancy among women. The evidence for lipidomic studies of breast cancer in the Chinese population is relatively limited.

OBJECTIVE

Our current study aimed to identify peripheral lipids capable of distinguishing adults with and without malignant breast cancer in a Chinese population and to explore the potential lipid metabolism pathways implicated in breast cancer.

METHODS

Lipidomics was performed with an Ultimate 3000 UHPLC system coupled with a Q-Exactive HF MS platform by using the serum of 71 female patients with malignant breast cancer and 92 age-matched (± 2 years) healthy women. The data were uploaded to and processed by the specialized online software Metaboanalyst 5.0. Both univariate and multivariate analyses were carried out for potential biomarker screening. Areas under the receiver-operating characteristic (ROC) curves (AUCs) of identified differential lipids were obtained for evaluating their classification capacity.

RESULTS

A total of 47 significantly different lipids were identified by applying the following criteria: false discovery rate-adjusted P < 0.05, variable importance in projection ⩾ 1.0, and fold change ⩾ 2.0 or ⩽ 0.5. Among them, 13 lipids were identified as diagnostic biomarkers with the area under curve (AUC) greater than 0.7. Multivariate ROC curves indicated that AUCs greater than 0.8 could be achieved with 2-47 lipids.

CONCLUSIONS

Using an untargeted LC-MS-based metabolic profiling approach, our study provides preliminary evidence that extensive dysregulations of OxPCs, PCs, SMs and TAGs were involved in the pathological processes of breast cancer. We provided clues for furtherly investigating the role of lipid alterations in the pathoetiology of breast cancer.

Lipid Metabolism Heterogeneity and Crosstalk with Mitochondria Functions Drive Breast Cancer Progression and Drug Resistance

Breast cancer (BC) is a heterogeneous disease that can be triggered by genetic alterations in mammary epithelial cells, leading to diverse disease outcomes in individual patients. The metabolic heterogeneity of BC enhances its ability to adapt to changes in the tumor microenvironment and metabolic stress, but unfavorably affects the patient's therapy response, prognosis and clinical effect. Extrinsic factors from the tumor microenvironment and the intrinsic parameters of cancer cells influence their mitochondrial functions, which consequently alter their lipid metabolism and their ability to proliferate, migrate and survive in a harsh environment. The balanced interplay between mitochondria and fatty acid synthesis or fatty acid oxidation has been attributed to a combination of environmental factors and to the genetic makeup, oncogenic signaling and activities of different transcription factors. Hence, understanding the mechanisms underlying lipid metabolic heterogeneity and alterations in BC is gaining interest as a major target for drug resistance. Here we review the major recent reports on lipid metabolism heterogeneity and bring to light knowledge on the functional contribution of diverse lipid metabolic pathways to breast tumorigenesis and therapy resistance.