Skip to main content

Advertisement

Log in

Genomic subtypes of cutaneous melanoma have distinct metabolic profiles: A single-cell transcriptomic analysis

  • SHORT REPORT
  • Published:
Archives of Dermatological Research Aims and scope Submit manuscript

Abstract

Objective

Genomic profiling previously classified melanoma into distinct subtypes based on the presence or absence of mutations in driver genes, but metabolic differences between and within these groups have yet to be thoroughly analyzed. Thus, the objective of the present study is to provide the first effort to holistically characterize the metabolic landscape of qualified melanoma genomic subtypes at single-cell resolution.

Methods

Expression data for a total of 1145 malignant cells sourced from NRAS(Q61L), BRAF(V600E), and NRAS/BRAF WT melanomas were retrieved from the Broad Single Cell Portal. Metabolic activity was interrogated by pathway scoring and gene set enrichment analysis.

Results

A total of 53 metabolic pathways were differentially regulated in at least one melanoma genomic subtype. Some notable findings include: BRAF/NRAS WT cells were enriched for fatty acid biosynthesis and depleted for metabolism of alanine, aspartate, and glutamate; BRAF(V600E) melanoma cells were enriched for beta-alanine metabolism and depleted for phenylalanine metabolism; NRAS(Q61L) melanoma cells were enriched for steroid biosynthesis and depleted for linoleic acid metabolism.

Conclusion

Primary limitations include the total quantity of single cells and breadth of available genomic subtypes plus inherent noisiness of the applied methodologies. Nonetheless, these findings nominate novel, testable therapeutic targets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Abbreviations

scRNA:

Single-cell RNA sequencing

TPM:

Transcripts per million

UMAP:

Uniform Manifold Approximation and Projection

PCA:

Principal component analysis

GSEA:

Gene set enrichment analysis

References

  1. Aggarwal P, Knabel P, Fleischer AB (2021) United States burden of melanoma and non-melanoma skin cancer from 1990 to 2019. J Am Acad Dermatol 85(2):388–395. https://doi.org/10.1016/j.jaad.2021.03.109

    Article  PubMed  Google Scholar 

  2. Kim SY, Kim SN, Hahn HJ, Lee YW, Choe YB, Ahn KJ (2015) Metaanalysis of BRAF mutations and clinicopathologic characteristics in primary melanoma. J Am Acad Dermatol 72(6):1036-1046.e2. https://doi.org/10.1016/j.jaad.2015.02.1113

    Article  CAS  PubMed  Google Scholar 

  3. Akbani R, Akdemir KC, Aksoy BA et al (2015) Genomic classification of cutaneous melanoma. Cell 161(7):1681–1696. https://doi.org/10.1016/j.cell.2015.05.044

    Article  CAS  Google Scholar 

  4. Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467):338–345. https://doi.org/10.1038/nature12625

    Article  CAS  PubMed  Google Scholar 

  5. Levitin HM, Yuan J, Sims PA (2018) Single-Cell transcriptomic analysis of tumor heterogeneity. Trends Cancer 4(4):264–268. https://doi.org/10.1016/j.trecan.2018.02.003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lee D, Smallbone K, Dunn WB et al (2012) Improving metabolic flux predictions using absolute gene expression data. BMC Syst Biol 6(1):73. https://doi.org/10.1186/1752-0509-6-73

    Article  PubMed  PubMed Central  Google Scholar 

  7. Xiao Z, Dai Z, Locasale JW (2019) Metabolic landscape of the tumor microenvironment at single cell resolution. Nat Commun 10(1):3763. https://doi.org/10.1038/s41467-019-11738-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Tirosh I, Izar B, Prakadan SM et al (2016) Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352(6282):189–196. https://doi.org/10.1126/science.aad0501

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lun ATL, McCarthy DJ, Marioni JC (2016) A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Res 5:2122. https://doi.org/10.12688/f1000research.9501.2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Subramanian A, Tamayo P, Mootha VK et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci 102(43):15545–15550. https://doi.org/10.1073/pnas.0506580102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sweeney JG, Liang J, Antonopoulos A et al (2018) Loss of GCNT2/I-branched glycans enhances melanoma growth and survival. Nat Commun 9(1):3368. https://doi.org/10.1038/s41467-018-05795-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. De Vellis C, Pietrobono S, Stecca B (2021) The role of glycosylation in melanoma progression. Cells 10(8):2136. https://doi.org/10.3390/cells10082136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mey A, Berthier-Vergnes O, Apoil PA, Doré JF, Revillard JP (1994) Expression of the galactose binding protein Mac-2 by human melanoma cell-lines. Cancer Lett 81(2):155–163. https://doi.org/10.1016/0304-3835(94)90197-x

    Article  CAS  PubMed  Google Scholar 

  14. Benjamin DI, Louie SM, Mulvihill MM et al (2014) Inositol phosphate recycling regulates glycolytic and lipid metabolism that drives cancer aggressiveness. ACS Chem Biol 9(6):1340–1350. https://doi.org/10.1021/cb5001907

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gaggioli C, Buscà R, Abbe P, Ortonne JP, Ballotti R (2003) Microphthalmia-associated transcription factor (MITF) is required but is not sufficient to induce the expression of melanogenic genes. Pigment Cell Res 16(4):374–382. https://doi.org/10.1034/j.1600-0749.2003.00057.x

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

None.

Funding

The present study had no funding source.

Author information

Authors and Affiliations

Authors

Contributions

MJD: Conceptualization, Methodology, Writing—Original draft preparation, Writing—Review & Editing, Software, Data curation, Validation.: JTT: Conceptualization, Methodology, Writing—Original draft preparation.: Z-NC: Methodology, Validation, Writing—Review & Editing.: KTR: Validation, Writing—Original draft preparation, Writing—Review & Editing.: SB: Methodology, Software.: SM: Writing—Original draft preparation, Writing—Review & Editing.: LL: Writing—Original draft preparation, Writing—Review & Editing.: AF: Writing—Original draft preparation.: SRL: Writing—Review & Editing, Supervision.

Corresponding author

Correspondence to Michael J. Diaz.

Ethics declarations

Conflict of interest

Dr. Shari Lipner has served as a consultant for Ortho-dermatologics, Hoth Therapeutics, Moberg Pharmaceuticals and BelleTorus Corporation.

Ethical approval

The present study is exempt from IRB approval requirements.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (XLSX 13 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Diaz, M.J., Tran, J.T., Choo, ZN. et al. Genomic subtypes of cutaneous melanoma have distinct metabolic profiles: A single-cell transcriptomic analysis. Arch Dermatol Res 315, 2961–2965 (2023). https://doi.org/10.1007/s00403-023-02690-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00403-023-02690-7

Keywords

Navigation