Display options
Share it on

JMIR Cancer. 2020 May 19;6(1):e16974. doi: 10.2196/16974.

Expression of Genes Related to Lipid Handling and the Obesity Paradox in Melanoma: Database Analysis.

JMIR cancer

Claudia Giampietri, Luana Tomaipitinca, Francesca Scatozza, Antonio Facchiano

Affiliations

  1. Department of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy.
  2. Istituto Dermopatico dell'Immacolata - Istituto di Ricovero e Cura a Carattere Scientifico, IDI-IRCCS, Rome, Italy.

PMID: 32209538 PMCID: PMC7267996 DOI: 10.2196/16974

Abstract

BACKGROUND: Publicly available genomic and transcriptomic data in searchable databases allow researchers to investigate specific medical issues in thousands of patients. Many studies have highlighted the role lipids play in cancer initiation and progression and reported nutritional interventions aimed at improving prognosis and survival. Therefore, there is an increasing interest in the role that fat intake may play in cancer. It is known that there is a relationship between BMI and survival in patients with cancer, and that there is an association between a high-fat diet and increased cancer risk. In some cancers, such as colorectal cancer, obesity and high fat intake are known to increase the risk of cancer initiation and progression. On the contrary, in patients undergoing treatment for melanoma, a higher BMI unexpectedly acts as a protective factor rather than a risk factor; this phenomenon is known as the obesity paradox.

OBJECTIVE: We aimed to identify the molecular mechanism underlying the obesity paradox, with the expectation that this could indicate new effective strategies to reduce risk factors and improve protective approaches.

METHODS: In order to determine the genes potentially involved in this process, we investigated the expression values of lipid-related genes in patients with melanoma or colorectal cancer. We used available data from 2990 patients from 3 public databases (IST [In Silico Transcriptomics] Online, GEO [Gene Expression Omnibus], and Oncomine) in an analysis that involved 3 consecutive validation steps. Of this group, data from 1410 individuals were analyzed in the IST Online database (208 patients with melanoma and 147 healthy controls, as well as 991 patients with colorectal cancer and 64 healthy controls). In addition, 45 melanoma, 18 nevi, and 7 healthy skin biopsies were analyzed in another database, GEO, to validate the IST Online data. Finally, using the Oncomine database, 318 patients with melanoma (312 controls) and 435 patients with colorectal cancer (445 controls) were analyzed.

RESULTS: In the first and second database investigated (IST Online and GEO, respectively), patients with melanoma consistently showed significantly (P<.001) lower expression levels of 4 genes compared to healthy controls: CD36, MARCO, FABP4, and FABP7. This strong reduction was not observed in patients with colorectal cancer. An additional analysis was carried out on a DNA-TCGA data set from the Oncomine database, further validating CD36 and FABP4.

CONCLUSIONS: The observed lower expression of genes such as CD36 and FABP4 in melanoma may reduce the cellular internalization of fat and therefore make patients with melanoma less sensitive to a high dietary fat intake, explaining in part the obesity paradox observed in patients with melanoma.

©Claudia Giampietri, Luana Tomaipitinca, Francesca Scatozza, Antonio Facchiano. Originally published in JMIR Cancer (http://cancer.jmir.org), 19.05.2020.

Keywords: CD36; FABPs; gene expression; melanoma, colorectal cancer; obesity paradox; public databases; transcriptomic analysis

References

  1. Int J Cancer. 2008 Sep 1;123(5):1160-5 - PubMed
  2. Stem Cells Int. 2017;2017:1656053 - PubMed
  3. Curr Pharmacol Rep. 2015 Oct;1(5):283-294 - PubMed
  4. J Immunother Cancer. 2019 Mar 29;7(1):89 - PubMed
  5. Curr Oncol Rep. 2019 Jul 1;21(8):72 - PubMed
  6. Oncotarget. 2015 Mar 30;6(9):7348-63 - PubMed
  7. J Immunother Cancer. 2019 Aug 19;7(1):222 - PubMed
  8. Eur J Cancer. 2013 Feb;49(3):642-57 - PubMed
  9. Cancers (Basel). 2019 Mar 29;11(4): - PubMed
  10. J Lipid Res. 1998 Apr;39(4):777-88 - PubMed
  11. Biochim Biophys Acta. 2015 Jul;1851(7):929-36 - PubMed
  12. FEBS Open Bio. 2019 Dec;9(12):2117-2125 - PubMed
  13. Nature. 2017 Jan 5;541(7635):41-45 - PubMed
  14. Am J Epidemiol. 2008 Jul 1;168(1):30-7 - PubMed
  15. Oxid Med Cell Longev. 2018 Dec 2;2018:1471682 - PubMed
  16. Oncotarget. 2016 Nov 22;7(47):77257-77275 - PubMed
  17. Cell Rep. 2016 May 31;15(9):2000-11 - PubMed
  18. Cell Death Differ. 2015 Jul;22(7):1131-43 - PubMed
  19. J Exp Clin Cancer Res. 2018 Jun 15;37(1):118 - PubMed
  20. Int J Mol Sci. 2017 Jun 15;18(6): - PubMed
  21. N Engl J Med. 2016 Aug 25;375(8):794-8 - PubMed
  22. Nat Rev Cancer. 2012 Feb 16;12(3):159-69 - PubMed
  23. Physiol Rev. 2015 Jul;95(3):727-48 - PubMed
  24. Cancer Epidemiol Biomarkers Prev. 2007 Dec;16(12):2533-47 - PubMed
  25. Clin Cancer Res. 2010 Mar 15;16(6):1884-93 - PubMed
  26. Biochim Biophys Acta. 2014 Oct;1842(10):1993-2009 - PubMed
  27. Nutrients. 2019 Apr 11;11(4): - PubMed
  28. Arteriosclerosis. 1984 May-Jun;4(3):270-5 - PubMed
  29. J Transl Med. 2016 Oct 4;14(1):285 - PubMed

Publication Types