Advertisement

Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features

  • Dongzhi Cen
  • Li XuEmail author
  • Siwei ZhangEmail author
  • Zhiguang Chen
  • Yan Huang
  • Ziqi Li
  • Bo Liang
Oncology
  • 18 Downloads

Abstract

Purpose

To investigate associations between CT imaging features, RUNX3 methylation level, and survival in clear cell renal cell carcinoma (ccRCC).

Materials and methods

Patients were divided into high RUNX3 methylation and low RUNX3 methylation groups according to RUNX3 methylation levels (the threshold was identified by using X-tile). The CT scanning data from 106 ccRCC patients were retrospectively analyzed. The relationship between RUNX3 methylation level and overall survivals was evaluated using the Kaplan-Meyer analysis and Cox regression analysis (univariate and multivariate). The relationship between RUNX3 methylation level and CT features was evaluated using chi-square test and logistic regression analysis (univariate and multivariate).

Results

β value cutoff of 0.53 to distinguish high methylation (N = 44) from low methylation tumors (N = 62). Patients with lower levels of methylation had longer median overall survival (49.3 vs. 28.4) months (low vs. high, adjusted hazard ratio [HR] 4.933, 95% CI 2.054–11.852, p < 0.001). On univariate logistic regression analysis, four risk factors (margin, side, long diameter, and intratumoral vascularity) were associated with RUNX3 methylation level (all p < 0.05). Multivariate logistic regression analysis found that three risk factors (side: left vs. right, odds ratio [OR] 2.696; p = 0.024; 95% CI 1.138–6.386; margin: ill-defined vs. well-defined, OR 2.685; p = 0.038; 95% CI 1.057–6.820; and intratumoral vascularity: yes vs. no, OR 3.286; p = 0.008; 95% CI 1.367–7.898) were significant independent predictors of high methylation tumors. This model had an area under the receiver operating characteristic curve (AUC) of 0.725 (95% CI 0.623–0.827).

Conclusions

Higher levels of RUNX3 methylation are associated with shorter survival in ccRCC patients. And presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

Key Points

RUNX3 methylation level is negatively associated with overall survival in ccRCC patients.

Presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

Keywords

Clear cell renal cell carcinoma Computed tomography Radiogenomics 

Abbreviations

AUC

Area under the curve

ccRCC

Clear cell RCC

CI

Confidence interval

HR

Hazard ratio

NCI

National Cancer Institute

OR

Odds ratio

RCC

Renal cell carcinoma

RUNX3

Runt-related transcription factor-3

TCIA

The Cancer Imaging Archive

Notes

Funding

Supported by Guangdong Science and Technology Project (2016ZC0142), the project for the Social Development Project of Dongguan City (2015108101032), and Medical Scientific Research Foundation of Guangdong Province (A2016391)

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Siwei Zhang.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was not required for this study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• prognostic study

• performed at one institution

Supplementary material

330_2019_6049_MOESM1_ESM.docx (19 kb)
ESM 1 (DOCX 19 kb)

References

  1. 1.
    Park M, Shim M, Kim M, Song C, Kim CS, Ahn H (2017) Prognostic heterogeneity in T3aN0M0 renal cell carcinoma according to the site of invasion. Urol Oncol 35:458 e417–458 e422CrossRefGoogle Scholar
  2. 2.
    Chen L, Li H, Gu L et al (2016) Prognostic role of urinary collecting system invasion in renal cell carcinoma: a systematic review and meta-analysis. Sci Rep 6:21325CrossRefGoogle Scholar
  3. 3.
    Wei JH, Haddad A, Wu KJ et al (2015) A CpG-methylation-based assay to predict survival in clear cell renal cell carcinoma. Nat Commun 6:8699CrossRefGoogle Scholar
  4. 4.
    Sanford T, Meng MV, Railkar R, Agarwal PK, Porten SP (2018) Integrative analysis of the epigenetic basis of muscle-invasive urothelial carcinoma. Clin Epigenetics 10:19CrossRefGoogle Scholar
  5. 5.
    Wang Z, Zhang Z, Zhang C, Xu Y (2018) Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma. Oncol Lett 15:8491–8499Google Scholar
  6. 6.
    Evelonn EA, Degerman S, Kohn L, Landfors M, Ljungberg B, Roos G (2016) DNA methylation status defines clinicopathological parameters including survival for patients with clear cell renal cell carcinoma (ccRCC). Tumour Biol 37:10219–10228CrossRefGoogle Scholar
  7. 7.
    Fisel P, Kruck S, Winter S et al (2013) DNA methylation of the SLC16A3 promoter regulates expression of the human lactate transporter MCT4 in renal cancer with consequences for clinical outcome. Clin Cancer Res 19:5170–5181CrossRefGoogle Scholar
  8. 8.
    Joosten SC, Deckers IA, Aarts MJ et al (2017) Prognostic DNA methylation markers for renal cell carcinoma: a systematic review. Epigenomics 9:1243–1257CrossRefGoogle Scholar
  9. 9.
    Lee YS, Lee JW, Jang JW et al (2013) Runx3 inactivation is a crucial early event in the development of lung adenocarcinoma. Cancer Cell 24:603–616CrossRefGoogle Scholar
  10. 10.
    Zheng J, Mei Y, Xiang P et al (2018) DNA methylation affects metastasis of renal cancer and is associated with TGF-beta/RUNX3 inhibition. Cancer Cell Int 18:56CrossRefGoogle Scholar
  11. 11.
    Pan C, Xiang L, Pan Z et al (2018) MiR-544 promotes immune escape through downregulation of NCR1/NKp46 via targeting RUNX3 in liver cancer. Cancer Cell Int 18:52CrossRefGoogle Scholar
  12. 12.
    Chen F, Liu X, Cheng Q, Zhu S, Bai J, Zheng J (2017) RUNX3 regulates renal cell carcinoma metastasis via targeting miR-6780a-5p/E-cadherin/EMT signaling axis. Oncotarget 8:101042–101056Google Scholar
  13. 13.
    Liu Z, Chen L, Zhang X et al (2014) RUNX3 regulates vimentin expression via miR-30a during epithelial-mesenchymal transition in gastric cancer cells. J Cell Mol Med 18:610–623CrossRefGoogle Scholar
  14. 14.
    Chen F, Bai J, Li W et al (2013) RUNX3 suppresses migration, invasion and angiogenesis of human renal cell carcinoma. PLoS One 8:e56241CrossRefGoogle Scholar
  15. 15.
    Seisenberger S, Popp C, Reik W (2010) Retrotransposons and germ cells: reproduction, death, and diversity. F1000 Biol Rep 2Google Scholar
  16. 16.
    Li C, Cen D, Liu Z, Liang C (2018) Presence of intratumoral calcifications and vasculature is associated with poor overall survival in clear cell renal cell carcinoma. J Comput Assist Tomogr 42:418–422CrossRefGoogle Scholar
  17. 17.
    Wang Y, Qin X, Wu J et al (2014) Association of promoter methylation of RUNX3 gene with the development of esophageal cancer: a meta analysis. PLoS One 9:e107598CrossRefGoogle Scholar
  18. 18.
    Yan C, Kim YW, Ha YS et al (2012) RUNX3 methylation as a predictor for disease progression in patients with non-muscle-invasive bladder cancer. J Surg Oncol 105:425–430CrossRefGoogle Scholar
  19. 19.
    Richiardi L, Fiano V, Vizzini L et al (2009) Promoter methylation in APC, RUNX3, and GSTP1 and mortality in prostate cancer patients. J Clin Oncol 27:3161–3168CrossRefGoogle Scholar
  20. 20.
    Liu Z, Zhang T, Jiang H, Xu W, Zhang J (2018) Conventional MR-based preoperative nomograms for prediction of IDH/1p19q subtype in low-grade glioma. Acad Radiol.  https://doi.org/10.1016/j.acra.2018.09.022
  21. 21.
    Dasgupta A, Gupta T, Pungavkar S et al (2018) Nomograms based on preoperative multiparametric magnetic resonance imaging for prediction of molecular subgrouping in medulloblastoma: results from a radiogenomics study of 111 patients. Neuro Oncol.  https://doi.org/10.1093/neuonc/noy093
  22. 22.
    Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology 287:732–747CrossRefGoogle Scholar
  23. 23.
    Pinker K, Shitano F, Sala E et al (2018) Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 47:604–620CrossRefGoogle Scholar
  24. 24.
    Ni D, Ma X, Li HZ et al (2018) Factors associated with postoperative renal sinus invasion and perinephric fat invasion in renal cell cancer: treatment planning implications. Oncotarget 9:10091–10099Google Scholar
  25. 25.
    Oh S, Sung DJ, Yang KS et al (2017) Correlation of CT imaging features and tumor size with Fuhrman grade of clear cell renal cell carcinoma. Acta Radiol 58:376–384CrossRefGoogle Scholar
  26. 26.
    Bowen L, Xiaojing L (2018) Radiogenomics of clear cell renal cell carcinoma: associations between mRNA-based subtyping and CT imaging features. Acad Radiol.  https://doi.org/10.1016/j.acra.2018.05.002
  27. 27.
    Karlo CA, Di Paolo PL, Chaim J et al (2014) Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology 270:464–471CrossRefGoogle Scholar
  28. 28.
    Wu L, Shi W, Li X et al (2016) High expression of the human equilibrative nucleoside transporter 1 gene predicts a good response to decitabine in patients with myelodysplastic syndrome. J Transl Med 14:66CrossRefGoogle Scholar
  29. 29.
    Jiang W, Liu N, Chen XZ et al (2015) Genome-wide identification of a methylation gene panel as a prognostic biomarker in nasopharyngeal carcinoma. Mol Cancer Ther 14:2864–2873CrossRefGoogle Scholar
  30. 30.
    Camp RL, Dolled-Filhart M, Rimm DL (2004) X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 10:7252–7259CrossRefGoogle Scholar
  31. 31.
    Tian YH, Zou WH, Xiao WW et al (2016) Oligometastases in AJCC stage IVc nasopharyngeal carcinoma: a subset with better overall survival. Head Neck 38:1152–1157CrossRefGoogle Scholar
  32. 32.
    Du Q, Li Q, Sun D, Chen X, Yu B, Ying Y (2016) Calibration of interphase fluorescence in situ hybridization cutoff by mathematical models. Cytometry A 89:239–245CrossRefGoogle Scholar
  33. 33.
    Zhang YG, Yang HL, Long Y, Li WL (2016) Circular RNA in blood corpuscles combined with plasma protein factor for early prediction of pre-eclampsia. BJOG 123:2113–2118CrossRefGoogle Scholar
  34. 34.
    Chen Y, Liu C, Lu W et al (2016) Clinical characteristics and risk factors of pulmonary hypertension associated with chronic respiratory diseases: a retrospective study. J Thorac Dis 8:350–358CrossRefGoogle Scholar
  35. 35.
    Shen L, Kantarjian H, Guo Y et al (2010) DNA methylation predicts survival and response to therapy in patients with myelodysplastic syndromes. J Clin Oncol 28:605–613CrossRefGoogle Scholar
  36. 36.
    Fleischer T, Frigessi A, Johnson KC et al (2014) Genome-wide DNA methylation profiles in progression to in situ and invasive carcinoma of the breast with impact on gene transcription and prognosis. Genome Biol 15:435Google Scholar
  37. 37.
    Coppede F, Lopomo A, Spisni R, Migliore L (2014) Genetic and epigenetic biomarkers for diagnosis, prognosis and treatment of colorectal cancer. World J Gastroenterol 20:943–956CrossRefGoogle Scholar
  38. 38.
    de Benedetti F, Massa M, Robbioni P, Ravelli A, Burgio GR, Martini A (1991) Correlation of serum interleukin-6 levels with joint involvement and thrombocytosis in systemic juvenile rheumatoid arthritis. Arthritis Rheum 34:1158–1163CrossRefGoogle Scholar
  39. 39.
    Jansen RW, van Amstel P, Martens RM et al (2018) Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis. Oncotarget 9:20134–20155CrossRefGoogle Scholar
  40. 40.
    Zhou M, Leung A, Echegaray S et al (2018) Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 286:307–315CrossRefGoogle Scholar
  41. 41.
    Gutman DA, Dunn WD Jr, Grossmann P et al (2015) Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology 57:1227–1237CrossRefGoogle Scholar
  42. 42.
    Chen X, Zhou Z, Hannan R et al (2018) Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model. Phys Med Biol.  https://doi.org/10.1088/1361-6560/aae5cd
  43. 43.
    Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefGoogle Scholar
  44. 44.
    Alessandrino F, Krajewski KM, Shinagare AB (2016) Update on radiogenomics of clear cell renal cell carcinoma. Eur Urol Focus 2:572–573CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of Radiation OncologyThe Third Affiliated Hospital of Guangzhou Medical UniversityGuangzhouPeople’s Republic of China
  2. 2.The Second Affiliated Hospital of Guangzhou University of Chinese Medicine & The Second Clinical College of Guangzhou University of Chinese Medicine & Guangdong Provincial Hospital of Chinese MedicineGuangzhouPeople’s Republic of China

Personalised recommendations