Yonsei Med J.  2019 Nov;60(11):1013-1020. 10.3349/ymj.2019.60.11.1013.

A Two-DNA Methylation Signature to Improve Prognosis Prediction of Clear Cell Renal Cell Carcinoma

• Shi S
• Ye S
• Wu X
• Xu M
• Zhuo R
• Liao Q
• Xi Y

• KMID: 2460217
• DOI: http://doi.org/10.3349/ymj.2019.60.11.1013

Abstract

PURPOSE
Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis.
MATERIALS AND METHODS
An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves.
RESULTS
This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2).
CONCLUSION
The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.

Keyword

Carcinoma; renal cell; biomarkers; DNA methylation; prognosis

MeSH Terms

Biomarkers
Carcinoma, Renal Cell*
Classification
DNA Methylation
Genome
Humans
Methods
Methylation*
Prognosis*
ROC Curve
Survival Rate
Biomarkers

Figure

• 

  Fig. 1 Flow-chart of the study. The order of analyses to develop the risk score model and to validate the efficiency of the signature to predict prognostic outcomes. TCGA, The Cancer Genome Atlas; ccRCC, clear cell renal cell carcinoma.

• 

  Fig. 2 Evaluation of the predictive performance of the two-DNA methylation signature. (A) Receiver operating characteristic analysis of the sensitivity and specificity for survival time by the two-DNA methylation signature in the training dataset. The black dot represents the optimal validation dataset using the two-DNA methylation signature. (B) The Kaplan-Meier analysis was used to visualize the survival probability for the low-risk versus high-risk groups of patients based on the optimal cut-off point. Rows represent survival time (days), and columns represent survival rate. TPR, true positive rate; FPR, false positive rate; AUC, area under the curve of receiver operating characteristics.

• 

  Fig. 3 Risk score analysis of the training set. The distribution of two-DNA methylation based risk core, patient survival, and methylation levels of two CpGs were analyzed in the training set (n=129). (A) Two-DNA methylation signature risk score distribution. ‘Time’ means ‘survival time’. (B) Heat-map of the DNA methylation profiles. Rows represent CpG sites, and columns represent patients.

• 

  Fig. 4 Stratified analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses of patients with clear cell renal cell carcinoma of different age. Grouping was based on age at initial diagnosis: ≤60 (n=106, 49.53%), >60 (n=108, 50.47%). (A) Kaplan-Meier analysis with two-sided log-rank test was performed to estimate the differences in survival time between the low-risk and high-risk patients. (B) ROC curves of the two-DNA methylation signature were used to demonstrate the sensitivity and specificity in predicting the survival time of patients. TPR, true positive rate; FPR, false positive rate; AUC, area under the curve of receiver operating characteristics.

Reference

1. Thiesen HJ, Steinbeck F, Maruschke M, Koczan D, Ziems B, Hakenberg OW. Stratification of clear cell renal cell carcinoma (ccRCC) genomes by gene-directed copy number alteration (CNA) analysis. PLoS One. 2017; 12:e0176659. PMID: 28486536.

2. Ljungberg B, Bensalah K, Canfield S, Dabestani S, Hofmann F, Hora M, et al. EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol. 2015; 67:913–924. PMID: 25616710.

3. Young JR, Coy H, Douek M, Lo P, Sayre J, Pantuck AJ, et al. Clear cell renal cell carcinoma: identifying the gain of chromosome 12 on multiphasic MDCT. Abdom Radiol (NY). 2017; 42:236–241. PMID: 27519835.

4. Mehdi A, Riazalhosseini Y. Epigenome aberrations: emerging driving factors of the clear cell renal cell carcinoma. Int J Mol Sci. 2017; 18:E1774. PMID: 28812986.

5. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017; 67:7–30. PMID: 28055103.

6. Drucker BJ. Renal cell carcinoma: current status and future prospects. Cancer Treat Rev. 2005; 31:536–545. PMID: 16236454.

7. Scélo G, Brennan P. The epidemiology of bladder and kidney cancer. Nat Clin Pract Urol. 2007; 4:205–217. PMID: 17415353.

8. Pan Y, Liu G, Zhou F, Su B, Li Y. DNA methylation profiles in cancer diagnosis and therapeutics. Clin Exp Med. 2018; 18:1–14. PMID: 28752221.

9. van der Kooi EL, de Greef JC, Wohlgemuth M, Frants RR, van Asseldonk RJ, Blom HJ, et al. No effect of folic acid and methionine supplementation on D4Z4 methylation in patients with facioscapulohumeral muscular dystrophy. Neuromuscul Disord. 2006; 16:766–769. PMID: 17005397.

10. Hao X, Luo H, Krawczyk M, Wei W, Wang W, Wang J, et al. DNA methylation markers for diagnosis and prognosis of common cancers. Proc Natl Acad Sci U S A. 2017; 114:7414–7419. PMID: 28652331.

11. Dubrowinskaja N, Gebauer K, Peters I, Hennenlotter J, Abbas M, Scherer R, et al. Neurofilament Heavy polypeptide CpG island methylation associates with prognosis of renal cell carcinoma and prediction of antivascular endothelial growth factor therapy response. Cancer Med. 2014; 3:300–309. PMID: 24464810.

12. Wang L, Gao W, Hu F, Xu Z, Wang F. MicroRNA-874 inhibits cell proliferation and induces apoptosis in human breast cancer by targeting CDK9. FEBS Lett. 2014; 588:4527–4535. PMID: 25281924.

13. Zhang L, Yan DL, Yang F, Wang DD, Chen X, Wu JZ, et al. DNA methylation mediated silencing of microRNA-874 is a promising diagnosis and prognostic marker in breast cancer. Oncotarget. 2017; 8:45496–45505. PMID: 28525377.

14. Li W, Zhang X, Lu X, You L, Song Y, Luo Z, et al. 5-Hydroxymethylcytosine signatures in circulating cell-free DNA as diagnostic biomarkers for human cancers. Cell Res. 2017; 27:1243–1257. PMID: 28925386.

15. Jeschke J, Bizet M, Desmedt C, Calonne E, Dedeurwaerder S, Garaud S, et al. DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. J Clin Invest. 2017; 127:3090–3102. PMID: 28714863.

16. Zhang W, Yan W, You G, Bao Z, Wang Y, Liu Y, et al. Genome-wide DNA methylation profiling identifies ALDH1A3 promoter methylation as a prognostic predictor in G-CIMP-primary glioblastoma. Cancer Lett. 2013; 328:120–125. PMID: 22960273.

17. Shukla S, Pia Patric IR, Thinagararjan S, Srinivasan S, Mondal B, Hegde AS, et al. A DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-κB nexus. Cancer Res. 2013; 73:6563–6573. PMID: 24078801.

18. Sandoval J, Mendez-Gonzalez J, Nadal E, Chen G, Carmona FJ, Sayols S, et al. A prognostic DNA methylation signature for stage I non-small-cell lung cancer. J Clin Oncol. 2013; 31:4140–4147. PMID: 24081945.

19. Evelönn EA, Landfors M, Haider Z, Köhn L, Ljungberg B, Roos G, et al. DNA methylation associates with survival in non-metastatic clear cell renal cell carcinoma. BMC Cancer. 2019; 19:65. PMID: 30642274.

20. Peters I, Dubrowinskaja N, Hennenlotter J, Antonopoulos WI, Von Klot CAJ, Tezval H, et al. DNA methylation of neural EGFL like 1 (NELL1) is associated with advanced disease and the metastatic state of renal cell cancer patients. Oncol Rep. 2018; 40:3861–3868. PMID: 30272321.

21. Dagher J, Delahunt B, Rioux-Leclercq N, Egevad L, Srigley JR, Coughlin G, et al. Clear cell renal cell carcinoma: validation of World Health Organization/International Society of Urological Pathology grading. Histopathology. 2017; 71:918–925. PMID: 28718911.

22. Delahunt B, Cheville C, Martignoni G, Humphrey PA, Magi-Galluzzi C, McKenney J, et al. The International Society of Urological Pathology (ISUP) grading system for renal cell carcinoma and other prognostic parameters. Am J Surg Pathol. 2013; 37:1490–1504. PMID: 24025520.

23. Cho HJ, Yu A, Kim S, Kang J, Hong SM. Robust likelihood-based survival modeling with microarray data. J Stat Softw. 2009; 29:1–16.

24. Jung EJ, Lee HJ, Kwak C, Ku JH, Moon KC. Young age is independent prognostic factor for cancer-specific survival of low-stage clear cell renal cell carcinoma. Urology. 2009; 73:137–141. PMID: 18950844.

25. Ricketts CJ, Linehan WM. Gender specific mutation incidence and survival associations in Clear Cell Renal Cell Carcinoma (ccRCC). PLoS One. 2015; 10:e0140257. PMID: 26484545.

26. Ebru T, Fulya OP, Hakan A, Vuslat YC, Necdet S, Nuray C, et al. Analysis of various potential prognostic markers and survival data in clear cell renal cell carcinoma. Int Braz J Urol. 2017; 43:440–454. PMID: 27583351.

27. Cheville JC, Blute ML, Zincke H, Lohse CM, Weaver AL. Stage pT1 conventional (clear cell) renal cell carcinoma: pathological features associated with cancer specific survival. J Urol. 2001; 166:453–456. PMID: 11458046.

28. Zhan Y, Guo W, Zhang Y, Wang Q, Xu XJ, Zhu L. A five-gene signature predicts prognosis in patients with kidney renal clear cell carcinoma. Comput Math Methods Med. 2015; 2015:842784. PMID: 26539246.

29. Liang B, Zhao J, Wang X. A three-microRNA signature as a diagnostic and prognostic marker in clear cell renal cancer: an In Silico analysis. PLoS One. 2017; 12:e0180660. PMID: 28662155.

30. Song J, Peng J, Zhu C, Bai G, Liu Y, Zhu J, et al. Identification and validation of two novel prognostic lncRNAs in kidney renal clear cell carcinoma. Cell Physiol Biochem. 2018; 48:2549–2562. PMID: 30121668.

31. Xu D, Zhang S, Zhang S, Liu H, Li P, Yu L, et al. NOD2 maybe a biomarker for the survival of kidney cancer patients. Oncotarget. 2017; 8:101489–101499. PMID: 29254180.

32. Dai W, Teodoridis JM, Zeller C, Graham J, Hersey J, Flanagan JM, et al. Systematic CpG islands methylation profiling of genes in the wnt pathway in epithelial ovarian cancer identifies biomarkers of progression-free survival. Clin Cancer Res. 2011; 17:4052–4062. PMID: 21459799.

33. Zuo S, Wang L, Wen Y, Dai G. Identification of a universal 6-lncRNA prognostic signature for three pathologic subtypes of renal cell carcinoma. J Cell Biochem. 2018; 10. 30. [Epub]. Available at: . DOI: 10.1002/jcb.28012.

34. Wu F, Kaczynski TJ, Sethuramanujam S, Li R, Jain V, Slaughter M, et al. Two transcription factors, Pou4f2 and Isl1, are sufficient to specify the retinal ganglion cell fate. Proc Natl Acad Sci U S A. 2015; 112:E1559–E1568. PMID: 25775587.

35. Maskell LJ, Mahadeo AV, Budhram-Mahadeo VS. POU4F2/Brn-3b transcription factor is associated with survival and drug resistance in human ovarian cancer cells. Oncotarget. 2018; 9:36770–36779. PMID: 30613365.

36. Reinert T, Borre M, Christiansen A, Hermann GG, Ørntoft TF, Dyrskjøt L. Diagnosis of bladder cancer recurrence based on urinary levels of EOMES, HOXA9, POU4F2, TWIST1, VIM, and ZNF154 hypermethylation. PLoS One. 2012; 7:e46297. PMID: 23056278.

37. Christie JD, Ma SF, Aplenc R, Li M, Lanken PN, Shah CV, et al. Variation in the myosin light chain kinase gene is associated with development of acute lung injury after major trauma. Crit Care Med. 2008; 36:2794–2800. PMID: 18828194.

38. Soung YH, Lee JW, Kim SY, Nam SW, Park WS, Lee JY, et al. Mutational analysis of the kinase domain of MYLK2 gene in common human cancers. Pathol Res Pract. 2006; 202:137–140. PMID: 16448786.