VHL-dependence of EHHADH Expression in a Human Renal Cell Carcinoma Cell Line

Main Article Content

Julia Felicitas Pilz
Marinella Klein
Elke Neumann-Haefelin
Athina Ganner

Keywords

ccRCC, EHHADH, fatty acid metabolism, RCC4, VHL

Abstract

The von Hippel-Lindau tumor suppressor gene (VHL) is mutated in up to 90% of clear cell renal cell carcinoma (ccRCC) cases, thus playing a key role in ccRCC pathogenesis. ccRCC can be classified as a metabolic disease in which alterations in fatty acid metabolism facilitate cancer cell proliferation. Enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH) is an enzyme involved in peroxisomal fatty acid degradation. It is primarily expressed in renal proximal tubule cells, presumably the origin of ccRCC. Although EHHADH is still a relatively unexplored gene, it is known to be differentially expressed in several tumors. In this study, analysis of several databases revealed that EHHADH expression is downregulated in ccRCC samples compared to healthy kidney samples. Moreover, cell culture experiments were performed to investigate the relationship between EHHADH and VHL at the gene and protein level. qPCR and Western blot analyses using the human ccRCC cell line RCC4 revealed that EHHADH is expressed in a VHL-dependent manner. RCC4 cells reconstituted with VHL show significantly higher EHHADH mRNA and protein levels than VHL-deficient RCC4 control cells. These results indicate that the downregulation of EHHADH in ccRCC reported may be due to the loss of VHL function. This study is the first to molecularly characterize EHHADH, a key enzyme in peroxisomal ß-oxidation, in relation to VHL, suggesting a potential pathogenic interaction that is worthy of further investigation.

Abstract 127 | PDF Downloads 127 HTML Downloads 0 XML Downloads 2

References

1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J Clin. 2021 Feb 4;71(3):209–49. 10.3322/caac.21660

2. Motzer RJ, Jonasch E, Agarwal N, Bhayani S, Bro WP, Chang SS, et al. Kidney cancer, Version 2.2017-NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017 Jun 1;15(6):804–34. 10.6004/jnccn.2017.0100

3. Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet. 2013 Jun 24;45(8):860–9. 10.1038/ng.2699

4. Frew IJ, Moch H. A clearer view of the molecular complexity of clear cell renal cell carcinoma. Annu Rev Pathol Mech Dis. 2015 Jan;10(1):263–89. 10.1146/annurev-pathol-012414-040306

5. Broadfield LA, Pane AA, Talebi A, Swinnen JV, Fendt SM. Lipid metabolism in cancer: New perspectives and emerging mechanisms. Dev Cell. 2021 May 17;56(10):1363–93. 10.1016/j.devcel.2021.04.013

6. Röhrig F, Schulze A. The multifaceted roles of fatty acid synthesis in cancer. Nat Rev Cancer. 2016 Nov 23;16(11):732–49. 10.1038/nrc.2016.89

7. Koundouros N, Poulogiannis G. Reprogramming of fatty acid metabolism in cancer. Br J Cancer. 2019 Dec 10;122(1):4–22. 10.1038/s41416-019-0650-z

8. Wettersten HI, Aboud OA, Lara PN, Weiss RH. Metabolic reprogramming in clear cell renal cell carcinoma. Nat Rev Nephrol. 2017 May 8;13(7):410–19. 10.1038/nrneph.2017.59

9. Gebhard RL, Clayman RV, Prigge WF, Figenshau R, Staley NA, Reesey C, et al. Abnormal cholesterol metabolism in renal clear cell carcinoma. J Lipid Res. 1987 Oct;28(10):1177–84. 10.1016/S0022-2275(20)38606-5

10. The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013 Jul 4;499(7456):43–9. 10.1038/nature12222

11. Horiguchi A, Asano T, Asano T, Ito K, Sumitomo M, Hayakawa M. Fatty acid synthase over expression is an indicator of tumor aggressiveness and poor prognosis in renal cell carcinoma. J Urol. 2008 Jul 18;180(3):1137–40. 10.1016/j.juro.2008.04.135

12. Reddy JK, Hashimoto T. Peroxisomal beta-oxidation and peroxisome proliferator-activated receptor alpha: An adaptive metabolic system. Annu Rev Nutr. 2001;21:193–230. 10.1146/annurev.nutr.21.1.193

13. Klootwijk ED, Reichold M, Helip-Wooley A, Tolaymat A, Broeker C, Robinette SL, et al. Mistargeting of peroxisomal EHHADH and inherited renal Fanconi’s syndrome. N Engl J Med. 2014 Jan 9;370(2):129–38. 10.1056/NEJMoa1307581

14. The Human Protein Atlas. Tissue expression of EHHADH [Internet]. 2023 [cited 2023 Aug 24]. Available from: https://www.proteinatlas.org/ENSG00000113790-EHHADH/tissue

15. Raghubar AM, Roberts MJ, Wood S, Healy HG, Kassianos AJ, Mallett AJ. Cellular milieu in clear cell renal cell carcinoma. Front Oncol. 2022 Oct 14;12:1–10. 10.3389/fonc.2022.943583

16. Klootwijk ED, Reichold M, Unwin RJ, Kleta R, Warth R, Bockenhauer D. Renal Fanconi syndrome: Taking a proximal look at the nephron. Nephrol Dial Transplant. 2015 Sep;30(9):1456–60. 10.1093/ndt/gfu377

17. Cui J, Yi G, Li J, Li Y, Qian D. Increased EHHADH expression predicting poor survival of osteosarcoma by integrating weighted gene coexpression network analysis and experimental validation. Biomed Res Int. 2021 May 3;2021:9917060. 10.1155/2021/9917060

18. Ren H, Li W, Liu X, Li S, Guo H, Wang W, et al. Identification and validation of an 6-metabolism-related gene signature and its correlation with immune checkpoint in hepatocellular carcinoma. Front Oncol. 2021 Nov 15;11:783934. 10.3389/fonc.2021.78393419

19. Yeh CS, Wang JY, Cheng TL, Juan CH, Wu CH, Lin SR. Fatty acid metabolism pathway play an important role in carcinogenesis of human colorectal cancers by microarray-bioinformatics analysis. Cancer Lett. 2006 Feb 28;233(2):297–308. 10.1016/j.canlet.2005.03.050

20. Peng H, Deng Y, Wang L, Cheng Y, Xu Y, Liao J, et al. Identification of potential biomarkers with diagnostic value in pituitary adenomas using prediction analysis for microarrays method. J Mol Neurosci. 2019 Nov 1;69(3):399–410. 10.1007/s12031-019-01369-x

21. Jiang W, Zhang L, Guo Q, Wang H, Ma M, Sun J, et al. Identification of the pathogenic biomarkers for hepatocellular carcinoma based on RNA-seq analyses. Pathol Oncol Res. 2019;25(3):1207–13. 10.1007/s12253-019-00596-2

22. Suto K, Kajihara-Kano H, Yokoyama Y, Hayakari M, Kimura J, Kumano T, et al. Decreased expression of the peroxisomal bifunctional enzyme and carbonyl reductase in human hepatocellular carcinomas. J Cancer Res Clin Oncol. 1999 Mar 1;125(2):83–8. 10.1007/s004320050246

23. Wu D, Pan Y, Zheng X. Identification of hub genes-based predictive model in hepatocellular carcinoma by robust rank aggregation and regression analysis. J Caner. 2021 Jan 30;12(7):1884–93. 10.7150/jca.52089

24. Lee H, Choi JY, Joung JG, Joh JW, Kim JM, Hyun SH. Metabolism-associated gene signatures for FDG avidity on PET/CT and prognostic validation in hepatocellular carcinoma. Front Oncol. 2022 Jan 31;12:845900. 10.3389/fonc.2022.845900

25. Chen P, Wang F, Feng J, Zhou R, Chang Y, Liu J, et al. Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma. Oncotarget. 2017 Apr 6;8(30):48948–58. 10.18632/oncotarget.16896

26. The University of Alabama at Birmingham. UALCAN: Analyze, Integrate, Discover [Internet]. 2023 [cited 2023 Oct 30]. Available from: https://ualcan.path.uab.edu/

27. The Human Protein Atlas. Tissue expression of EHHADH – staining kidney [Internet]. 2023 [cited 2023 Oct 30]. Available from: https://www.proteinatlas.org/ENSG00000113790-EHHADH/tissue/kidney#

28. The Human Protein Atlas. Tissue expression of EHHADH – staining renal cancer [Internet]. 2023 [cited 2023 Oct 30]. Available from: https://www.proteinatlas.org/ENSG00000113790-EHHADH/pathology/renal+cancer#ihc

29. Thoma CR, Frew IJ, Hoerner CR, Montani M, Moch H, Krek W. pVHL and GSK3β are components of a primary cilium-maintenance signalling network. Nat Cell Biol. 2007 Apr 22;9(5):588–95. 10.1038/ncb1579

30. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017 Jul 18;19(8):649–58. 10.1016/j.neo.2017.05.002

31. Wettersten HI, Hakimi AA, Morin D, Bianchi C, Johnstone ME, Donohoe DR, et al. Grade-dependent metabolic reprogramming in kidney cancer revealed by combined proteomics and metabolomics analysis. Cancer Res. 2015 Jun 15;75(12):2541–52. 10.1158/0008-5472.CAN-14-1703

32. Xiao H, Chen P, Zeng G, Xu D, Wang X, Zhang X. Three novel hub genes and their clinical significance in clear cell renal cell carcinoma. J Cancer. 2019 Nov 1;10(27):6779–91. 10.7150/jca.35223

33. Litwin JA, Beier K, Völkl A, Hofmann WJ, Fahimi HD. Immunocytochemical investigation of catalase and peroxisomal lipid β-oxidation enzymes in human hepatocellular tumors and liver cirrhosis. Virchows Archiv. 1999 Nov 1;435(5):486–95. 10.1007/s004280050432

34. Tanaka M, Masaki Y, Tanaka K, Miyazaki M, Kato M, Sugimoto R, et al. Reduction of fatty acid oxidation and responses to hypoxia correlate with the progression of de-differentiation in HCC. Molecular Medicine Reports. 2013 Feb 1;7(2):365–70. 10.3892/mmr.2012.1201

35. Walter KM, Schönenberger MJ, Trötzmüller M, Horn M, Elsässer HP, Moser AB, et al. Hif-2α promotes degradation of mammalian peroxisomes by selective autophagy. Cell Metab. 2014 Nov 4;20(5):882–97. 10.1016/j.cmet.2014.09.017

36. Kim JA. Peroxisome metabolism in cancer. Cells. 2020 Jul 14;9(7):1692. 10.3390/cells9071692

37. Zhao J, Zhou X, Chen B, Lu M, Wang G, Elumalai N, et al. p53 promotes peroxisomal fatty acid β-oxidation to repress purine biosynthesis and mediate tumor suppression. Cell Death Dis. 2023 Feb 7;14(2):1–15. 10.1038/s41419-023-05625-2