We provide a systematic literature review on tissue miRNAs in patients with RCC to evaluate and summarize their usefulness as prognostic markers. We undertook a systematic search for articles in English using the PubMed-Medline database from January 2010 to December 2020. Studies were identified and selected according to the PRISMA criteria and the PICO methodology. The population consisted of RCC patients undergoing nephrectomy and the main outcome of interest was recurrence-free survival (RFS). Only studies providing hazard ratios (HRs) from multivariate or univariate analyzes with corresponding 95% confidence intervals (CI) and/or area under the curve (AUC) were considered.
All nine included studies (1,541 patients) analyzed the relationship between tissue miRNA expression levels (up or downregulated) and RFS. Some of these found that the methylation status of miR-9-1, miR-9-3 and miR-124 was associated with a high risk of relapse. Moreover, miR-200b overexpression was associated with OS. MiR-210 overexpression indicated a shorter OS than those who were miR-210 negative. Finally, patients with high miR-125b expression had shorter cancer-specific survival (CSS) than those with low expression; similarly, patients with low miR-126 expression also had shorter CSS time.
Summary and outlook
Several studies tested the usefulness of specific miRNAs to predict RCC recurrence. Some of them showed a fair accuracy and strong relationship between specific miRNA over or under-expression and survival outcomes. However, results from these studies are preliminary and miRNAs use in routine clinical practice is still far to come.
We thank Prof. Luigi Schips and Dr. Marchioni Michele (Department of Medical, Oral and Biotechnological Sciences, G. d’Annunzio University of Chieti, SS Annunziata Hospital, Chieti, Italy) for their support in this study.
Research funding: None declared.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors declare no conflict of interest.
Informed consent: Not applicable.
Ethical approval: Not applicable.
2. Dabestani, S, Marconi, L, Kuusk, T, Bex, A. Follow-up after curative treatment of localised renal cell carcinoma. World J Urol 2018;36:1953–9. https://doi.org/10.1007/s00345-018-2338-z.Search in Google Scholar PubMed
3. Wang, G, Chen, L, Meng, J, Chen, M, Zhuang, L, Zhang, L. Overexpression of microRNA-100 predicts an unfavorable prognosis in renal cell carcinoma. Int Urol Nephrol 2013;45:373–9. https://doi.org/10.1007/s11255-012-0374-y.Search in Google Scholar PubMed
4. 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;67:913–24. https://doi.org/10.1016/j.eururo.2015.01.005.Search in Google Scholar PubMed
5. Marchioni, M, Martel, T, Bandini, M, Pompe, RS, Tian, Z, Kapoor, A, et al.. Marital status and gender affect stage, tumor grade, treatment type and cancer specific mortality in T1–2 N0 M0 renal cell carcinoma. World J Urol 2017;35:1899–905. https://doi.org/10.1007/s00345-017-2082-9.Search in Google Scholar PubMed
6. Ficarra, V, Guillè, F, Schips, L, de la Taille, A, Galetti, TP, Tostain, J, et al.. Proposal for revision of the TNM classification system for renal cell carcinoma. Cancer 2005;104:2116–23. https://doi.org/10.1002/cncr.21465.Search in Google Scholar PubMed
7. Bensalah, K, Pantuck, AJ, Crepel, M, Verhoest, G, Méjean, A, Valéri, A, et al.. Prognostic variables to predict cancer-related death in incidental renal tumours. BJU Int 2008;102:1376–80. https://doi.org/10.1111/j.1464-410X.2008.07847.x.Search in Google Scholar PubMed
8. Delahunt, B, Srigley, JR, Montironi, R, Egevad, L. Advances in renal neoplasia: recommendations from the 2012 international society of urological pathology consensus conference. Urology 2014;83:969–74. https://doi.org/10.1016/j.urology.2014.02.004.Search in Google Scholar PubMed
9. Marchioni, M, Bandini, M, Pompe, RS, Martel, T, Tian, Z, Shariat, SF, et al.. The impact of lymph node dissection and positive lymph nodes on cancer-specific mortality in contemporary pT2-3 non-metastatic renal cell carcinoma treated with radical nephrectomy. BJU Int 2018;121:383–92. https://doi.org/10.1111/bju.14024.Search in Google Scholar PubMed
10. Fu, Q, Liu, Z, Pan, D, Zhang, W, Xu, L, Zhu, Y, et al.. Tumor miR-125b predicts recurrence and survival of patients with clear-cell renal cell carcinoma after surgical resection. Cancer Sci 2014;105:1427–34. https://doi.org/10.1111/cas.12507.Search in Google Scholar PubMed PubMed Central
11. Saleeb, R, Kim, SS, Ding, Q, Scorilas, A, Lin, S, Khella, HW, et al.. The miR-200 family as prognostic markers in clear cell renal cell carcinoma. Urol Oncol Semin Orig Invest 2019;37:955–63. https://doi.org/10.1016/j.urolonc.2019.08.008.Search in Google Scholar PubMed
12. Shu, X, Hildebrandt, MA, Gu, J, Tannir, NM, Matin, SF, Karam, JA, et al.. MicroRNA profiling in clear cell renal cell carcinoma tissues potentially links tumorigenesis and recurrence with obesity. Br J Cancer 2017;116:77–84. https://doi.org/10.1038/bjc.2016.392.Search in Google Scholar PubMed PubMed Central
13. Zhang, J, Ye, Y, Chang, DW, Lin, S-H, Huang, M, Tannir, NM, et al.. Global and targeted miRNA expression profiling in clear cell renal cell carcinoma tissues potentially links miR-155-5p and miR-210-3p to both tumorigenesis and recurrence. Am J Pathol 2018;188:2487–96. https://doi.org/10.1016/j.ajpath.2018.07.026.Search in Google Scholar PubMed PubMed Central
14. Hildebrandt, MAT, Gu, J, Lin, J, Ye, Y, Tan, W, Tamboli, P, et al.. Hsa-miR-9 methylation status is associated with cancer development and metastatic recurrence in patients with clear cell renal cell carcinoma. Oncogene 2010;29:5724–8. https://doi.org/10.1038/onc.2010.305.Search in Google Scholar PubMed
15. Gebauer, K, Peters, I, Dubrowinskaja, N, Hennenlotter, J, Abbas, M, Scherer, R, et al.. Hsa-mir-124-3 CpG island methylation is associated with advanced tumours and disease recurrence of patients with clear cell renal cell carcinoma. Br J Cancer 2013;108:131–8. https://doi.org/10.1038/bjc.2012.537.Search in Google Scholar PubMed PubMed Central
16. Samaan, S, Khella, HWZ, Girgis, A, Scorilas, A, Lianidou, E, Gabril, M, et al.. miR-210 is a prognostic marker in clear cell renal cell carcinoma. J Mol Diagn 2015;17:136–44. https://doi.org/10.1016/j.jmoldx.2014.10.005.Search in Google Scholar PubMed
17. Wotschofsky, Z, Busch, J, Jung, M, Kempkensteffen, C, Weikert, S, Schaser, KD, et al.. Diagnostic and prognostic potential of differentially expressed miRNAs between metastatic and non-metastatic renal cell carcinoma at the time of nephrectomy. Clin Chim Acta 2013;416:5–10. https://doi.org/10.1016/j.cca.2012.11.010.Search in Google Scholar PubMed
18. Carlsson, J, Christiansen, J, Davidsson, S, Giunchi, F, Fiorentino, M, Sundqvist, P. The potential role of miR-126, miR-21 and miR-10b as prognostic biomarkers in renal cell carcinoma. Oncol Lett 2019;17:4566–74. https://doi.org/10.3892/ol.2019.10142.Search in Google Scholar PubMed PubMed Central
19. Marchioni, M, Rivas, JG, Autran, A, Socarras, M, Albisinni, S, Ferro, M, et al.. Biomarkers for renal cell carcinoma recurrence: state of the art. Curr Urol Rep 2021;22:31. https://doi.org/10.1007/s11934-021-01050-0.Search in Google Scholar PubMed PubMed Central
20. Mytsyk, Y, Dosenko, V, Skrzypczyk, MA, Borys, Y, Diychuk, Y, Kucher, A, et al.. Potential clinical applications of microRNAs as biomarkers for renal cell carcinoma. Cent Eur J Urol 2018;71:295–303. https://doi.org/10.5173/ceju.2018.1618.Search in Google Scholar PubMed PubMed Central
21. Di Meo, A, Brown, MD, Finelli, A, Jewett, MAS, Diamandis, EP, Yousef, GM. Prognostic urinary miRNAs for the assessment of small renal masses. Clin Biochem 2020;75:15–22. https://doi.org/10.1016/j.clinbiochem.2019.10.002.Search in Google Scholar
22. Shamseer, L, Moher, D, Clarke, M, Ghersi, D, Liberati, A, Petticrew, M, et al.. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 2015;350:g7647. https://doi.org/10.1136/bmj.g7647.Search in Google Scholar
23. Linares-Espinós, E, Hernández, V, Domínguez-Escrig, JL, Fernández-Pello, S, Hevia, V, Mayor, J, et al.. Methodology of a systematic review. Actas Urol Esp 2018;42:499–506. https://doi.org/10.1016/j.acuroe.2018.07.002.Search in Google Scholar
24. Nakata, W, Uemura, M, Sato, M, Fujita, K, Jingushi, K, Ueda, Y, et al.. Expression of miR-27a-3p is an independent predictive factor for recurrence in clear cell renal cell carcinoma. Oncotarget 2015;6:21645–54. https://doi.org/10.18632/oncotarget.4064.Search in Google Scholar
25. Kim, SP, Alt, AL, Weight, CJ, Costello, BA, Cheville, JC, Lohse, C, et al.. Independent validation of the 2010 American Joint Committee on Cancer TNM classification for renal cell carcinoma: results from a large, single institution cohort. J Urol 2011;185:2035–9. https://doi.org/10.1016/j.juro.2011.02.059.Search in Google Scholar
26. Kattan, MW, Reuter, V, Motzer, RJ, Katz, J, Russo, P. A postoperative prognostic nomogram for renal cell carcinoma. J Urol 2001;166:63–7. https://doi.org/10.1016/s0022-5347(05)66077-6.Search in Google Scholar
27. Sorbellini, M, Kattan, MW, Snyder, ME, Reuter, V, Motzer, R, Goetzl, M, et al.. A postoperative prognostic nomogram predicting recurrence for patients with conventional clear cell renal cell carcinoma. J Urol 2005;173:48–51. https://doi.org/10.1097/01.ju.0000148261.19532.2c.Search in Google Scholar
28. Frank, I, Blute, ML, Cheville, JC, Lohse, CM, Weaver, AL, Zincke, H. An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol 2002;168:2395–400. https://doi.org/10.1016/s0022-5347(05)64153-5.Search in Google Scholar
29. Karakiewicz, PI, Briganti, A, Chun, FK-H, Trinh, Q-D, Perrotte, P, Ficarra, V, et al.. Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol 2007;25:1316–22. https://doi.org/10.1200/jco.2006.06.1218.Search in Google Scholar
30. Yaycioglu, O, Roberts, WW, Chan, T, Epstein, JI, Marshall, FF, Kavoussi, LR. Prognostic assessment of nonmetastatic renal cell carcinoma: a clinically based model. Urology 2001;58:141–5. https://doi.org/10.1016/s0090-4295(01)01207-9.Search in Google Scholar
31. Karakiewicz, PI, Suardi, N, Capitanio, U, Jeldres, C, Ficarra, V, Cindolo, L, et al.. A preoperative prognostic model for patients treated with nephrectomy for renal cell carcinoma. Eur Urol 2009;55:287–95. https://doi.org/10.1016/j.eururo.2008.07.037.Search in Google Scholar
32. Isbarn, H, Karakiewicz, PI. Predicting cancer-control outcomes in patients with renal cell carcinoma. Curr Opin Urol 2009;19:247–57. https://doi.org/10.1097/mou.0b013e32832a0814.Search in Google Scholar
33. Patard, J-J, Kim, HL, Lam, JS, Dorey, FJ, Pantuck, AJ, Zisman, A, et al.. Use of the University of California Los Angeles integrated staging system to predict survival in renal cell carcinoma: an international multicenter study. J Clin Oncol 2004;22:3316–22. https://doi.org/10.1200/jco.2004.09.104.Search in Google Scholar
34. Cindolo, L, Patard, J-J, Chiodini, P, Schips, L, Ficarra, V, Tostain, J, et al.. Comparison of predictive accuracy of four prognostic models for nonmetastatic renal cell carcinoma after nephrectomy: a multicenter European study. Cancer 2005;104:1362–71. https://doi.org/10.1002/cncr.21331.Search in Google Scholar
35. Sun, M, Shariat, SF, Cheng, C, Ficarra, V, Murai, M, Oudard, S, et al.. Prognostic factors and predictive models in renal cell carcinoma: a contemporary review. Eur Urol 2011;60:644–61. https://doi.org/10.1016/j.eururo.2011.06.041.Search in Google Scholar
36. Sim, SH, Messenger, MP, Gregory, WM, Wind, TC, Vasudev, NS, Cartledge, J, et al.. Prognostic utility of pre-operative circulating osteopontin, carbonic anhydrase IX and CRP in renal cell carcinoma. Br J Cancer 2012;107:1131–7. https://doi.org/10.1038/bjc.2012.360.Search in Google Scholar
37. Sabatino, M, Kim-Schulze, S, Panelli, MC, Stroncek, D, Wang, E, Taback, B, et al.. Serum vascular endothelial growth factor and fibronectin predict clinical response to high-dose interleukin-2 therapy. J Clin Oncol 2009;27:2645–52. https://doi.org/10.1200/jco.2008.19.1106.Search in Google Scholar
38. Li, G, Feng, G, Gentil-Perret, A, Genin, C, Tostain, J. Serum carbonic anhydrase 9 level is associated with postoperative recurrence of conventional renal cell cancer. J Urol 2008;180:510–4. https://doi.org/10.1016/j.juro.2008.04.024.Search in Google Scholar
39. Ljungberg, B, Albiges, L, Abu-Ghanem, Y, Bensalah, K, Dabestani, S, Fernández-Pello, S, et al.. European Association of Urology Guidelines on renal cell carcinoma: the 2019 update. Eur Urol 2019;75:799–810. https://doi.org/10.1016/j.eururo.2019.02.011.Search in Google Scholar
40. Lee, RC, Feinbaum, RL, Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993;75:843–54. https://doi.org/10.1016/0092-8674(93)90529-y.Search in Google Scholar
42. Butz, H, Szabó, PM, Nofech-Mozes, R, Rotondo, F, Kovacs, K, Mirham, L, et al.. Integrative bioinformatics analysis reveals new prognostic biomarkers of clear cell renal cell carcinoma. Clin Chem 2014;60:1314–26. https://doi.org/10.1373/clinchem.2014.225854.Search in Google Scholar PubMed
43. Slaby, O, Redova, M, Poprach, A, Nekvindova, J, Iliev, R, Radova, L, et al.. Identification of MicroRNAs associated with early relapse after nephrectomy in renal cell carcinoma patients. Gene Chromosome Cancer 2012;51:707–16. https://doi.org/10.1002/gcc.21957.Search in Google Scholar PubMed
44. Wu, X, Weng, L, Li, X, Guo, C, Pal, SK, Jin, JM, et al.. Identification of a 4-microRNA signature for clear cell renal cell carcinoma metastasis and prognosis. PLoS One 2012;7:e35661. https://doi.org/10.1371/journal.pone.0035661.Search in Google Scholar PubMed PubMed Central
45. Osanto, S, Qin, Y, Buermans, HP, Berkers, J, Lerut, E, Goeman, JJ, et al.. Genome-wide MicroRNA expression analysis of clear cell renal cell carcinoma by next generation deep sequencing. PLoS One 2012;7:e38298. https://doi.org/10.1371/journal.pone.0038298.Search in Google Scholar PubMed PubMed Central
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