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

  • Oncology
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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.

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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

References

  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 e422

    Article  Google Scholar 

  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 6:21325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 6:8699

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 10:19

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wang Z, Zhang Z, Zhang C, Xu Y (2018) Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma. Oncol Lett 15:8491–8499

    PubMed  PubMed Central  Google Scholar 

  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 37:10219–10228

    Article  CAS  PubMed  Google Scholar 

  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 19:5170–5181

    Article  CAS  PubMed  Google Scholar 

  8. Joosten SC, Deckers IA, Aarts MJ et al (2017) Prognostic DNA methylation markers for renal cell carcinoma: a systematic review. Epigenomics 9:1243–1257

    Article  CAS  PubMed  Google Scholar 

  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 24:603–616

    Article  CAS  PubMed  Google Scholar 

  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 18:56

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 18:52

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 8:101042–101056

    PubMed  PubMed Central  Google Scholar 

  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 18:610–623

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen F, Bai J, Li W et al (2013) RUNX3 suppresses migration, invasion and angiogenesis of human renal cell carcinoma. PLoS One 8:e56241

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Seisenberger S, Popp C, Reik W (2010) Retrotransposons and germ cells: reproduction, death, and diversity. F1000 Biol Rep 2

  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 42:418–422

    Article  PubMed  Google Scholar 

  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:e107598

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 105:425–430

    Article  CAS  PubMed  Google Scholar 

  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 27:3161–3168

    Article  CAS  PubMed  Google Scholar 

  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. 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. 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 287:732–747

    Article  PubMed  Google Scholar 

  23. Pinker K, Shitano F, Sala E et al (2018) Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 47:604–620

    Article  PubMed  Google Scholar 

  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 9:10091–10099

    PubMed  Google Scholar 

  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 58:376–384

    Article  PubMed  Google Scholar 

  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. 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 270:464–471

    Article  PubMed  Google Scholar 

  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 14:66

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 14:2864–2873

    Article  CAS  PubMed  Google Scholar 

  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 10:7252–7259

    Article  CAS  PubMed  Google Scholar 

  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 38:1152–1157

    Article  PubMed  Google Scholar 

  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 89:239–245

    Article  CAS  PubMed  Google Scholar 

  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 123:2113–2118

    Article  CAS  PubMed  Google Scholar 

  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 8:350–358

    Article  PubMed  PubMed Central  Google Scholar 

  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 28:605–613

    Article  CAS  PubMed  Google Scholar 

  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 15:435

    PubMed  PubMed Central  Google Scholar 

  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 20:943–956

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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 34:1158–1163

    Article  PubMed  Google Scholar 

  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 9:20134–20155

    Article  PubMed  PubMed Central  Google Scholar 

  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 286:307–315

    Article  PubMed  Google Scholar 

  41. Gutman DA, Dunn WD Jr, Grossmann P et al (2015) Somatic mutations associated with MRI-derived volumetric features in glioblastoma. Neuroradiology 57:1227–1237

    Article  PubMed  PubMed Central  Google Scholar 

  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. 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 34:2157–2164

    Article  PubMed  Google Scholar 

  44. Alessandrino F, Krajewski KM, Shinagare AB (2016) Update on radiogenomics of clear cell renal cell carcinoma. Eur Urol Focus 2:572–573

    Article  PubMed  Google Scholar 

Download references

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)

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Correspondence to Li Xu or Siwei Zhang.

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Guarantor

The scientific guarantor of this publication is Siwei Zhang.

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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.

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No complex statistical methods were necessary for this paper.

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Written informed consent was not required for this study.

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Institutional Review Board approval was obtained.

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• retrospective

• prognostic study

• performed at one institution

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Cen, D., Xu, L., Zhang, S. et al. Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features. Eur Radiol 29, 5415–5422 (2019). https://doi.org/10.1007/s00330-019-06049-3

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