Ether lipid and sphingolipid expression patterns are estrogen receptor-dependently altered in breast cancer cells

Biochemical Pathways Delivering Distinct Glycosphingolipid Patterns in MDA-MB-231 and MCF-7 Breast Cancer Cells

The complex structure of glycosphingolipids (GSLs) supports their important role in cell function as modulators of growth factor receptors and glutamine transporters in plasma membranes. The aberrant composition of clustered GSLs within signaling platforms, so-called lipid rafts, inevitably leads to tumorigenesis due to disturbed growth factor signal transduction and excessive uptake of glutamine and other molecules needed for increased energy and structural molecule cell supply. GSLs are also involved in plasma membrane processes such as cell adhesion, and their transition converts cells from epithelial to mesenchymal with features required for cell migration and metastasis. Glutamine activates the mechanistic target of rapamycin complex 1 (mTORC1), resulting in nucleotide synthesis and proliferation. In addition, glutamine contributes to the cancer stem cell GD2 ganglioside-positive phenotype in the triple-negative breast cancer cell line MDA-MB-231. Thieno[2,3-b]pyridine derivative possesses higher cytotoxicity against MDA-MB-231 than against MCF-7 cells and induces a shift to aerobic metabolism and a decrease in S(6)nLc4Cer GSL-positive cancer stem cells in the MDA-MB-231 cell line. In this review, we discuss findings in MDA-MB-231, MCF-7, and other breast cancer cell lines concerning their differences in growth factor receptors and recent knowledge of the main biochemical pathways delivering distinct glycosphingolipid patterns during tumorigenesis and therapy.

Chronic inflammation, neuroglial dysfunction, and plasmalogen deficiency as a new pathobiological hypothesis addressing the overlap between post-COVID-19 symptoms and myalgic encephalomyelitis/chronic fatigue syndrome

After five waves of coronavirus disease 2019 (COVID-19) outbreaks, it has been recognized that a significant portion of the affected individuals developed long-term debilitating symptoms marked by chronic fatigue, cognitive difficulties ("brain fog"), post-exertional malaise, and autonomic dysfunction. The onset, progression, and clinical presentation of this condition, generically named post-COVID-19 syndrome, overlap significantly with another enigmatic condition, referred to as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Several pathobiological mechanisms have been proposed for ME/CFS, including redox imbalance, systemic and central nervous system inflammation, and mitochondrial dysfunction. Chronic inflammation and glial pathological reactivity are common hallmarks of several neurodegenerative and neuropsychiatric disorders and have been consistently associated with reduced central and peripheral levels of plasmalogens, one of the major phospholipid components of cell membranes with several homeostatic functions. Of great interest, recent evidence revealed a significant reduction of plasmalogen contents, biosynthesis, and metabolism in ME/CFS and acute COVID-19, with a strong association to symptom severity and other relevant clinical outcomes. These bioactive lipids have increasingly attracted attention due to their reduced levels representing a common pathophysiological manifestation between several disorders associated with aging and chronic inflammation. However, alterations in plasmalogen levels or their lipidic metabolism have not yet been examined in individuals suffering from post-COVID-19 symptoms. Here, we proposed a pathobiological model for post-COVID-19 and ME/CFS based on their common inflammation and dysfunctional glial reactivity, and highlighted the emerging implications of plasmalogen deficiency in the underlying mechanisms. Along with the promising outcomes of plasmalogen replacement therapy (PRT) for various neurodegenerative/neuropsychiatric disorders, we sought to propose PRT as a simple, effective, and safe strategy for the potential relief of the debilitating symptoms associated with ME/CFS and post-COVID-19 syndrome.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

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.

Novel lipometabolism biomarker for chemotherapy and immunotherapy response in breast cancer

Emerging proof shows that abnormal lipometabolism affects invasion, metastasis, stemness and tumor microenvironment in carcinoma cells. However, molecular markers related to lipometabolism have not been further established in breast cancer. In addition, numerous studies have been conducted to screen for prognostic features of breast cancer only with RNA sequencing profiles. Currently, there is no comprehensive analysis of multiomics data to extract better biomarkers. Therefore, we have downloaded the transcriptome, single nucleotide mutation and copy number variation dataset for breast cancer from the TCGA database, and constructed a riskScore of twelve genes by LASSO regression analysis. Patients with breast cancer were categorized into high and low risk groups based on the median riskScore. The high-risk group had a worse prognosis than the low-risk group. Next, we have observed the mutated frequencies and the copy number variation frequencies of twelve lipid metabolism related genes LMRGs and analyzed the association of copy number variation and riskScore with OS. Meanwhile, the ESTIMATE and CIBERSORT algorithms assessed tumor immune fraction and degree of immune cell infiltration. In immunotherapy, it is found that high-risk patients have better efficacy in TCIA analysis and the TIDE algorithm. Furthermore, the effectiveness of six common chemotherapy drugs was estimated. At last, high-risk patients were estimated to be sensitive to six chemotherapeutic agents and six small molecule drug candidates. Together, LMRGs could be utilized as a de novo tumor biomarker to anticipate better the prognosis of breast cancer patients and the therapeutic efficacy of immunotherapy and chemotherapy.

'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging

The molecular pathology of breast cancer is challenging due to the complex heterogeneity of cellular subtypes. The ability to directly identify and visualize cell subtype distribution at the single-cell level within a tissue section enables precise and rapid diagnosis and prognosis. Here, we applied mass spectrometry imaging (MSI) to acquire and visualize the molecular profiles at the single-cell and subcellular levels of 14 different breast cancer cell lines. We built a molecular library of genetically well-characterized cell lines. Multistep processing, including deep learning, resulted in a breast cancer subtype, the cancer's hormone status, and a genotypic recognition model based on metabolic phenotypes with cross-validation rates of up to 97%. Moreover, we applied our single-cell-based recognition models to complex tissue samples, identifying cell subtypes in tissue context within seconds during measurement. These data demonstrate "on the spot" digital pathology at the single-cell level using MSI, and they provide a framework for fast and accurate high spatial resolution diagnostics and prognostics.

Sex Differences in Cardiovascular Diseases: A Matter of Estrogens, Ceramides, and Sphingosine 1-Phosphate

The medical community recognizes sex-related differences in pathophysiology and cardiovascular disease outcomes (CVD), culminating with heart failure. In general, pre-menopausal women tend to have a better prognosis than men. Explaining why this occurs is not a simple matter. For decades, sex hormones like estrogens (Es) have been identified as one of the leading factors driving these sex differences. Indeed, Es seem protective in women as their decline, during and after menopause, coincides with an increased CV risk and HF development. However, clinical trials demonstrated that E replacement in post-menopause women results in adverse cardiac events and increased risk of breast cancer. Thus, a deeper understanding of E-related mechanisms is needed to provide a vital gateway toward better CVD prevention and treatment in women. Of note, sphingolipids (SLs) and their metabolism are strictly related to E activities. Among the SLs, ceramide and sphingosine 1-phosphate play essential roles in mammalian physiology, particularly in the CV system, and appear differently modulated in males and females. In keeping with this view, here we explore the most recent experimental and clinical observations about the role of E and SL metabolism, emphasizing how these factors impact the CV system.

Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences

Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.

Metabolomic Profiling of Blood-Derived Microvesicles in Breast Cancer Patients

Malignant cells differ from benign ones in their metabolome and it is largely unknown whether this difference is reflected in the metabolic profile of their microvesicles (MV), which are secreted into the blood of cancer patients. Here, they are present together with MV from the various blood and endothelial cells. Harvesting MV from 78 breast cancer patients (BC) and 30 controls, we characterized the whole blood MV metabolome using targeted and untargeted mass spectrometry. Especially (lyso)-phosphatidylcholines and sphingomyelins were detected in a relevant abundance. Eight metabolites showed a significant discriminatory power between BC and controls. High concentrations of lysoPCaC26:0 and PCaaC38:5 were associated with shorter overall survival. Comparing BC subtype-specific metabolome profiles, 24 metabolites were differentially expressed between luminal A and luminal B. Pathway analysis revealed alterations in the glycerophospholipid metabolism for the whole cancer cohort and in the ether lipid metabolism for the molecular subtype luminal B. Although this mixture of blood-derived MV contains only a minor number of tumor MV, a combination of metabolites was identified that distinguished between BC and controls as well as between molecular subtypes, and was predictive for overall survival. This suggests that these metabolites represent promising biomarkers and, moreover, that they may be functionally relevant for tumor progression.

Analysis of the Expression and Prognostic Potential of a Novel Metabolic Regulator ANGPTL8/Betatrophin in Human Cancers

The angiopoietin-like protein (ANGPTL) family members, except for the novel atypical member ANGPTL8/betatrophin, have been reported to participate in angiogenesis, inflammation and cancer. ANGPTL8/betatrophin is a metabolic regulator that is involved in lipid metabolism and glucose homeostasis. However, little is known about the expression and prognostic value of ANGPTL8/betatrophin in human cancers. In this study, we first conducted detailed analyses of ANGPTL8/betatrophin expression in cancer/normal samples via the Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), DriverDBv3, ENCORI and UALCAN databases. ANGPTL8/betatrophin showed high tissue specificity (enriched in the liver) and cell-type specificity (enriched in HepG2 and MCF7 cell lines). More than one databases demonstrated that the gene expression of ANGPTL8/betatrophin was significantly lower in cholangiocarcinoma (CHOL), breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), uterine corpus endometrial carcinoma (UCEC), and significantly higher in kidney renal clear cell carcinoma (KIRC) compared with that in normal samples. However, the protein expression of ANGPTL8/betatrophin displayed opposite results in clear cell renal cell carcinoma (ccRCC)/KIRC. Based on the expression profiles, the prognostic value was evaluated with the GEPIA, DriverDBv3, Kaplan Meier plotter and ENCORI databases. Two or more databases demonstrated that ANGPTL8/betatrophin significantly affected the survival of KIRC, uterine corpus endometrial carcinoma (UCEC), pheochromocytoma and paraganglioma (PCPG) and sarcoma (SARC); patients with PCPG and SARC may benifit from high ANGPTL8/betatrophin expression while high ANGPTL8/betatrophin expression was associated with poor prognosis in KIRC and UCEC. Functional analyses with the GeneMANIA, Metascape and STRING databases suggested that ANGPTL8/betatrophin was mainly involved in lipid homeostasis, especially triglyceride and cholesterol metabolism; glucose homeostasis, especially insulin resistance; AMPK signaling pathway; PI3K/Akt signaling pathway; PPAR signaling pathway; mTOR signaling pathway; HIF-1 signaling pathway; autophagy; regulation of inflammatory response. ANGPTL8/betatrophin may be a promising prognostic biomarker and therapeutic target, thus providing evidence to support further exploration of its role in defined human cancers.

Mass Spectrometry and Computer Simulation Predict the Interactions of AGPS and HNRNPK in Glioma

Ether lipids are overexpressed in malignant tumor and play an important role in tumor process. Glioma is the most common malignant central nervous system tumor, and the content of ether lipids is higher than that of normal tissues. Alkylglycerone phosphate synthase (AGPS) is a key enzyme in the synthesis of ether esters and plays a vital role in maintaining the morphology and pathogenic properties of tumor cells. The cell proliferation and the content of tumor-related lipid such as monoalkylglycerol ether (MAGe), lysophosphatidic acid ether (LPAe), lysophosphatidylcholine ether (LPCe), lysophosphatidylethanolamine ether (LPEe), phosphatidyl inositol (PI), phosphatidylcholine (PC), and phosphatidylserine (PS) were suppressed after AGPS silencing in U251, H4, and TJ905 cells; however, heterogeneous nuclear ribonucleoprotein K (HNRNPK) could reverse the above phenomenon such as cellar proliferation and ether lipid secretion. We found that HNRNPK was the target protein of AGPS by coimmunoprecipitation and mass spectrometry assay and verified by western blot assay in U251 cells. It confirmed that AGPS and HNRNPK are coexpressed in the cellular nucleus by a confocal laser microscope. The main protein-protein interaction mechanism between AGPS and HNRNPK is hydrogen bond, conjugation bond, hydrophobic bond, and electrostatic force by computer simulation prediction.