Secreted sphingomyelins modulate low mammary cancer incidence observed in certain mammals

Ledet, Melissa M.; Harman, Rebecca M.; Fan, Jiwei; Schmitt-Matzen, Emily; Díaz-Rubio, M. Elena; Zhang, Sheng; Walle, Gerlinde R. Van de

doi:10.1038/s41598-020-77639-1

Cited by 9 publications

(12 citation statements)

References 45 publications

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“…A recent study identified SM as a key secreted bioactive factor from mammary cells that caused triple-negative breast cancer cell death in vitro and reduced tumorigenicity in a xenograft mouse model in vivo. In addition, SM induced significant downregulation of genes associated with breast cancer progression [38]. These findings are consistent with our experimental results, showing that SM and a CM of M2 macrophages treated with SM decreased breast cancer cell viability, proliferation and migration.…”

Section: Discussionsupporting

confidence: 92%

“…Sphingolipid levels have been reported to be higher in breast cancer compared to normal breast tissue [37]. In contrast, a more recent study identified SMs as key secreted factors that possess natural cancer-suppressing mechanisms [38]. In vivo, Cer is converted to SM by the addition of a phosphorylcholine headgroup by SM synthase [39], while sphingomyelinase can reduce the levels of SM by conversion of SM to ceramide (Cer) [40,41].…”

Section: Introductionmentioning

confidence: 99%

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Multifunctional Role of Lipids in Modulating the Tumorigenic Properties of 4T1 Breast Cancer Cells

Rezaei

Júnior

et al. 2022

IJMS

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Tumor growth and progression are linked to an altered lipid metabolism in the tumor microenvironment (TME), including tumor cells and tumor-associated macrophages (TAMs). A growing number of lipid metabolism targeting drugs have shown efficacy in anti-tumor therapy. In addition, exogenously applied lipids and lipid analogues have demonstrated anti-tumor activities in several cancers, including breast cancer. In this study, we investigated the anti-tumor efficacies of the natural lipids palmitic acid (PA), sphingomyelin (SM), ceramide (Cer) and docosahexaenoic acid (DHA) on breast cancer cells. All tested lipids reduced the malignancy of breast cancer cells in vitro by impairing cell proliferation, migration and invasiveness. PA showed superior anti-tumor properties, as it additionally impaired cancer cell viability by inducing apoptosis, without affecting healthy cells. Co-culture experiments further demonstrated that Cer and PA reduced the immunosuppressive phenotype of M2 macrophages and the M2 macrophage-promoted the epithelial–mesenchymal transition (EMT) and migration of breast cancer cells. At the molecular level, this coincided with the up-regulation of E-cadherin. Our results highlight a powerful role for exogenously applied PA and Cer in reducing breast cancer tumorigenicity by simultaneously targeting cancer cells and M2 macrophages. Our findings support the notion that lipids represent alternative biocompatible therapeutic agents for breast cancer.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Multifunctional Role of Lipids in Modulating the Tumorigenic Properties of 4T1 Breast Cancer Cells

Rezaei

Júnior

et al. 2022

IJMS

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“…ISTD mixture (30 µL) was added to each 30 µL serum sample, followed by 190 µL of MeOH. Samples were then processed and analyzed using C30 column, using the same gradient in similar way as mentioned in the literature [20]. Specifically, chromatographic separation was performed on the same Vanquish UHPLC/QE-HF system with an Accucore C30, 2.6 µm column (2.1 mm id × 150 mm).…”

Section: Untargeted Lipidomics Analysis Of Serum Samplesmentioning

confidence: 99%

“…Lipidomics, a subset of the metabolomics field, is another liquid chromatography (LC)-MS-based analytical approach that focuses on identifying and profiling the full complement of lipid classes, subclasses, and lipid species in complex biological samples. An integration of metabolomics with lipidomics provides global overview of changes in metabolites and lipids and their associated metabolic pathways [19,20]. The proximity of lipidomics and metabolomics to the biological phenotypes integrates the cascading effects of the environment, gene expression, and regulatory processes.…”

Section: Introductionmentioning

confidence: 99%

Serum Metabolomic and Lipidomic Profiling Reveals Novel Biomarkers of Efficacy for Benfotiamine in Alzheimer’s Disease

Bhawal

Anderson

et al. 2021

IJMS

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Serum metabolomics and lipidomics are powerful approaches for discovering unique biomarkers in various diseases and associated therapeutics and for revealing metabolic mechanisms of both. Treatment with Benfotiamine (BFT), a thiamine prodrug, for one year produced encouraging results for patients with mild cognitive impairment and mild Alzheimer’s disease (AD). In this study, a parallel metabolomics and lipidomics approach was applied for the first exploratory investigation on the serum metabolome and lipidome of patients treated with BFT. A total of 315 unique metabolites and 417 lipids species were confidently identified and relatively quantified. Rigorous statistical analyses revealed significant differences between the placebo and BFT treatment groups in 25 metabolites, including thiamine, tyrosine, tryptophan, lysine, and 22 lipid species, mostly belonging to phosphatidylcholines. Additionally, 10 of 11 metabolites and 14 of 15 lipid species reported in previous literature to follow AD progression changed in the opposite direction to those reported to reflect AD progression. Enrichment and pathway analyses show that significantly altered metabolites by BFT are involved in glucose metabolism and biosynthesis of aromatic amino acids. Our study discovered that multiple novel biomarkers and multiple mechanisms that may underlie the benefit of BFT are potential therapeutic targets in AD and should be validated in studies with larger sample sizes.

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“…In recent years, the incidence of breast carcinoma increases significantly [ 1 ]. Breast carcinoma is a malignant tumor that occurs in breast ducts and epithelium and a highly heterogeneous cancer [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

Wang

2022

Computational and Mathematical Methods in Medicine

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The application effect of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on the improved fuzzy C-mean clustering (GA-PFCM) algorithm in analyzing premenopausal and postmenopausal invasive breast carcinoma was discussed. 159 patients with breast carcinoma were selected and divided into the postmenopausal group (71 patients) and the premenopausal group (88 patients) according to their menstrual status. The magnetic resonance images of the two groups were processed and analyzed using GA-PFCM algorithm, and the imaging characteristics and relevant parameters of DCE-MRI examination of the two groups were analyzed. Besides, the sensitivity, specificity, and accuracy of the diagnosis of invasive breast carcinoma by DCE-MRI examination were investigated. The results showed that the percentage of patients with lobulated lumps, patients with burrs on lesion edge, and patients with uniformly enhanced tumors in the premenopausal group was larger than that in the postmenopausal group ( P < 0.05 ). In the postmenopausal group, TCI of 33 patients was shown as platform curves, and that of 34 patients was shown as outflow curves. In the premenopausal group, TCI of 39 patients was shown as platform curves, and that of 41 patients was shown as outflow curves with a high proportion. The number of patients with peak height time ranging between 130 s and 260 s and of patients with early signal enhancement rate ranging between 100% and 200% was large. In contrast, the number of patients with ADC value higher than 1.2 × 10 − 3 was the least. In this research, there were 128 patients with positive invasive breast carcinoma and 31 with negative invasive breast carcinoma by pathological examination. Based on DCE-MRI examination, there were 121 patients with positive invasive breast carcinoma and 38 with negative invasive breast carcinoma. The sensitivity, specificity, and accuracy of invasive breast carcinoma by DCE-MRI were 91.41%, 87.1%, and 90.57%, respectively. In addition, the positive and negative predictive values reached 96.69% and 71.05%, respectively. In summary, GA-PFCM algorithm can be applied in the processing and segmentation of DCE-MRI images, and DCE-MRI could better diagnose invasive breast carcinoma with positive guiding value.

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Secreted sphingomyelins modulate low mammary cancer incidence observed in certain mammals

Cited by 9 publications

References 45 publications

Multifunctional Role of Lipids in Modulating the Tumorigenic Properties of 4T1 Breast Cancer Cells

Multifunctional Role of Lipids in Modulating the Tumorigenic Properties of 4T1 Breast Cancer Cells

Serum Metabolomic and Lipidomic Profiling Reveals Novel Biomarkers of Efficacy for Benfotiamine in Alzheimer’s Disease

Improved Fuzzy C-Means Clustering Algorithm-Based Dynamic Contrast-Enhanced Magnetic Resonance Imaging Features in the Diagnosis of Invasive Breast Carcinoma before and after Menopause

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