Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling - PubMed (original) (raw)

Discovery of potential biomarkers in human melanoma cells with different metastatic potential by metabolic and lipidomic profiling

Hye-Youn Kim et al. Sci Rep. 2017.

Abstract

Malignant melanoma, characterized by its ability to metastasize to other organs, is responsible for 90% of skin cancer mortality. To investigate alterations in the cellular metabolome and lipidome related to melanoma metastasis, gas chromatography-mass spectrometry (GC-MS) and direct infusion-mass spectrometry (DI-MS)-based metabolic and lipidomic profiling were performed on extracts of normal human melanocyte (HEMn-LP), low metastatic melanoma (A375, G361), and highly metastatic melanoma (A2058, SK-MEL-28) cell lines. In this study, metabolomic analysis identified aminomalonic acid as a novel potential biomarker to discriminate between different stages of melanoma metastasis. Uptake and release of major metabolites as hallmarks of cancer were also measured between high and low metastatic melanoma cells. Lipid analysis showed a progressive increase in phosphatidylinositol (PI) species with saturated and monounsaturated fatty acyl chains, including 16:0/18:0, 16:0/18:1, 18:0/18:0, and 18:0/18:1, with increasing metastatic potential of melanoma cells, defining these lipids as possible biomarkers. In addition, a partial-least-squares projection to latent structure regression (PLSR) model for the prediction of metastatic properties of melanoma was established, and central metabolic and lipidomic pathways involved in the increased motility and metastatic potential of melanoma cells were identified as therapeutic targets. These results could be used to diagnose and control of melanoma metastasis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1

Figure 1

Heatmap representing the relative levels of metabolites and lipids in the three different cell types (HEMn-LP, A375, A2058).

Figure 2

Figure 2

PLS-DA analysis based on metabolic and lipidomic data. (A) PLS-DA-derived score plot from melanocytes and two melanoma cell lines (n = 10 for each group) and (B) important metabolites and lipids selected based on VIP scores.

Figure 3

Figure 3

Establishment and validation of the PLSR model for prediction of the metastatic stage of melanocyte and metastatic melanoma cells (calibration dataset, n = 10, black and white; validation dataset, n = 2, red).

Figure 4

Figure 4

The migratory and invasive potential of the melanoma cell lines (A375, G361, A2058, and SK-MEL-28). (A) Represented images of migrated and invaded cells and (B) quantified data (mean ± SEM) from three independent experiments.

Figure 5

Figure 5

Box plots depicting the relative levels of potential biomarkers, (A) aminomalonic acid, (B) PI 16:0/18:1, (C) PI 16:0/18:0, (D) PI 18:0/18:1 and (E) PI 18:0/18:0 of normal human melanocytes and melanoma cells with different metastatic potential. Normal human melanocyte (HEMn-Lp), low metastatic melanoma cell lines (A375, G361), and highly metastatic melanoma cell lines (A2058, SK-MEL-28).

Figure 6

Figure 6

Metabolic changes and associated pathways in melanocytes and melanoma cells with different metastatic potential (HEMn-LP, A375, A2058), as determined by gas chromatography-mass spectrometry. Major pathways associated with melanoma metastasis, (A) glycolysis, (B) glycine, serine and threonine metabolism, and cysteine and methionine metabolism, (C) alanine, aspartate and glutamate metabolism, arginine and proline metabolism, and β-alanine metabolism were identified. Metabolic pathways were proposed based on a comparison with data in the KEGG database (

http://www.genome.jp/kegg/

). ANOVA, followed by Tukey’s post-hoc test (p < 0.05), was conducted, and different letters indicate statistically significant differences between samples.

Figure 7

Figure 7

Schematic representation of the glycerophospholipid and sphingolipid pathways in human melanoma cells. Box plots show relative changes in lipid species with VIP > 1.0 in melanocytes and melanoma cells with different metastatic potential (HEMn-LP, A375, A2058) as determined by direct infusion-mass spectrometry. ANOVA, followed by Tukey’s post-hoc test (p < 0.05), was conducted, and different letters indicate statistically significant differences between samples. Proposed pathways were based on data in the KEGG database (

http://www.genome.jp/kegg/

). PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PS, phosphatidylserine; LPA, lysophosphatidic acid; PA, phosphatidic acid; DAG, diacylglycerol; CDP-DAG, cytidine diphosphate-diacylglycerol; Pm-CoA, palmitoyl coenzyme A; S1P, sphingosine-1-phosphate .

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References

    1. Gray-Schopfer V, Wellbrock C, Marais R. Melanoma biology and new targeted therapy. Nature. 2007;445:851–857. doi: 10.1038/nature05661. - DOI - PubMed
    1. Nikolaou V, Stratigos A. Emerging trends in the epidemiology of melanoma. Br. J. Dermatol. 2014;170:11–19. doi: 10.1111/bjd.12492. - DOI - PubMed
    1. Garbe C, et al. Diagnosis and treatment of melanoma: European consensus-based interdisciplinary guideline. Eur. J. Cancer. 2010;46:270–283. doi: 10.1016/j.ejca.2009.10.032. - DOI - PubMed
    1. Howlader, N. et al. SEER cancer statistics review, 1975–2012. Bethesda, MD: National Cancer Institute, http://seer.cancer.gov/csr/1975_2012/ (2015).
    1. Deichmann M, Kahle B, Moser K, Wacker J, Wüst K. Diagnosing melanoma patients entering American Joint Committee on Cancer stage IV, C-reactive protein in serum is superior to lactate dehydrogenase. Br. J. Cancer. 2004;91:699–702. - PMC - PubMed

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