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. 2019 Dec 17:9:1422.
doi: 10.3389/fonc.2019.01422. eCollection 2019.

Dysregulation of Ketone Body Metabolism Is Associated With Poor Prognosis for Clear Cell Renal Cell Carcinoma Patients

Affiliations

Dysregulation of Ketone Body Metabolism Is Associated With Poor Prognosis for Clear Cell Renal Cell Carcinoma Patients

Wanmeng Cui et al. Front Oncol. .

Abstract

Kidney is an important organ for ketone body metabolism. However, the role of abnormal ketone metabolism and its possible function in tumorigenesis of clear cell renal cell carcinoma (ccRCC) have not yet been elucidated. Three differentially expressed key enzymes involved in ketone body metabolism, ACAT1, BDH2, and HMGCL, were screened out between ccRCC and normal kidney tissues using the GEO and TCGA databases.We confirmed that the transcription and protein expression of ACAT1, BDH2, and HMGCL were significantly lower in ccRCC by real-time RT-PCR and IHC assays. Those patients with lower expression of these three genes have a worse outcome. In addition, we demonstrated that ectopic expression of each of these genes inhibited the proliferation of ccRCC cells. The overexpressed ACAT1 and BDH2 genes remarkably impeded the migratory and invasive capacity of ccRCC cells. Furthermore, exogenous β-hydroxybutyrate suppressed the growth of ccRCC cells in vitro in a dose-dependent manner. Our findings suggest that ACAT1, BDH2, and HMGCL are potential tumor suppressor genes, and constitute effective prognostic biomarkers for ccRCC. Ketone body metabolism might thus be a promising target in a process for developing novel therapeutic approaches to treat ccRCC.

Keywords: bioinformatic analysis; clear cell renal cell carcinoma; ketone metabolism; prognosis; tumor suppressor.

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Figures

Figure 1
Figure 1
mRNA expression of ACAT1, BDH2, and HMGCL is downregulated in ccRCC in contrast to normal kidney samples. (A) A heatmap of the hierarchical clustering of 12 differentially expressed key genes involved in ketone body metabolism. Input data are the log2 ratios of mRNA expression values for ccRCCs over normal kidney tissues. Genes that are upregulated designated with red color, green color indicate downregulated genes. (B) The mRNA expression analysis of ACAT1, BDH2, and HMGCL genes in 533 cases of ccRCC and 72 cases of normal kidney tissues based on TCGA database. (C) The mRNA expression analysis of ACAT1, BDH2, and HMGCL genes by RT-qPCR in 14 primary renal cell carcinoma tissues and in the matched adjacent tissues, normalized to mRNA GAPDH a house-keeping gene expression level. ***p < 0.001.
Figure 2
Figure 2
Detection of ACAT1, BDH2, and HMGCL protein expression in 85 cases of primary ccRCC tissues and matched adjacent kidney tissues. (A) Immunohistochemical staining. Representative slides of ccRCC and matched adjacent tissues samples were stained by ACAT1, BDH2, and HMGCL antibodies, (B) Analysis of IHC staining. Scores of IHC staining of all the investigated ccRCCs and corresponding adjacent non-cancerous stromal tissue samples. ***p < 0.001.
Figure 3
Figure 3
The expression of ACAT1, BDH2, and HMGCL is significantly inter-correlated in ccRCCs. The pair-wise inter-correlation of transcriptional levels of ACAT1 and BDH2, ACAT1 and HMGCL, BDH2 and HMGCL based on TCGA database (A) and RT-qPCR data (B). (C) The pair-wise inter-correlation of protein levels of ACAT1 and BDH2, ACAT1 and HMGCL, BDH2 and HMGCL based on IHC staining scores.
Figure 4
Figure 4
ROC curve for ACAT1, BDH2, and HMGCL genes in ccRCC patients based on TCGA RNA-seq data. The ROC curves to evaluate the diagnostic value of ACAT1, BDH2, and HMGCL expression, either independently in 533 cases of RCC and 72 cases of normal kidney tissues, or combining two genes or three genes.
Figure 5
Figure 5
The lower expression of ACAT1, BDH2, and HMGCL predicts the worse prognosis for ccRCC patients. Kaplan-Meier survival curves of overall survival (A,C,E) and disease-free survival (B,D,F) were plotted based on the mRNA level of ACAT1, BDH2, and HMGCL, respectively. (G) Kaplan-Meier survival curves for ccRCC patients with combined values of ACAT1, BDH2, and HMGCL expression based on TCGA mRNA-seq data.
Figure 6
Figure 6
The tumor-suppressive effects of ACAT1, BDH2, and HMGCL expression in ccRCC. (A) The mRNA levels of ACAT1, BDH2, and HMGCL were determined by a real-time fluorescent quantitative PCR assay after transient transfection of 24 h. A bar chart shows a ratio of an mRNA expression value in cells transfected with a gene-coding vector to an mRNA value in cells transfected with an empty vector pCMV6. (B) The proliferation of 786-0 cells restored the expression of ACAT1, BDH2, and HMGCL genes was measured by a CCK8 assay. (C) A transwell assay. The violet color dots represent cells penetrating through matrix gels. (D) Wound healing assay. Images were taken at 0 and 12 h after introducing a scratch in 786-0 cells. Gap closure was measured as mean ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 7
Figure 7
The effect of β-hydroxybutyrate on the proliferation and migration of 768-0 cells. The relative concentration of extracellular (A) and intracellular (B) β-hydroxybutyrate, when restoring the expression of ACAT1, BDH2, and HMGCL, respectively. (C) The proliferation of 786-0 cells was measured by a CCK8 assay upon the treatment of β-hydroxybutyrate with concentration of 0, 2.5, 5, and 10 mM. (D) Wound healing assay. Images were taken at 0 and 24 h after introducing a scratch in 786-0 cells. Gap closure was measured as mean ± SD of three independent experiments. *p < 0.05; **p < 0.01; ***p < 0.001.

References

    1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. (2011) 144:646–74. 10.1016/j.cell.2011.02.013 - DOI - PubMed
    1. Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. (2004) 4:891–9. 10.1038/nrc1478 - DOI - PubMed
    1. Shiraishi T, Verdone JE, Huang J, Kahlert UD, Hernandez JR, Torga G, et al. Glycolysis is the primary bioenergetic pathway for cell motility and cytoskeletal remodeling in human prostate and breast cancer cells. Oncotarget. (2015) 6:130–43. 10.18632/oncotarget.2766 - DOI - PMC - PubMed
    1. Dang CV. Links between metabolism and cancer. Genes Dev. (2012) 26:877–90. 10.1101/gad.189365.112 - DOI - PMC - PubMed
    1. Williams KJ, Argus JP, Zhu Y, Wilks MQ, Marbois BN, York AG, et al. An essential requirement for the SCAP/SREBP signaling axis to protect cancer cells from lipotoxicity. Cancer Res. (2013) 73:2850–62. 10.1158/0008-5472.can-13-0382-t - DOI - PMC - PubMed