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A risk stratification model based on four novel biomarkers predicts prognosis for patients with renal cell carcinoma.

KUBOTA Shigehisa 80759118 Yoshida Tetsuya 60510310 KAGEYAMA Susumu 50378452 0000-0001-7150-647X ISONO Takahiro 20176259 0000-0003-2383-0667 YUASA Takeshi YONESE Junji KUSHIMA Ryoji 40252382 KAWAUCHI Akihiro 90240952 CHANO Tokuhiro 40346028 0000-0002-9959-1183 滋賀医科大学

2020.10.22

概要

Background:
Accurate prediction of the prognosis of RCC using a single biomarker is challenging due to the genetic heterogeneity of the disease. However, it is essential to develop an accurate system to allow better patient selection for optimal treatment strategies. ARL4C, ECT2, SOD2, and STEAP3 are novel molecular biomarkers identified in earlier studies as survival-related genes by comprehensive analyses of 43 primary RCC tissues and RCC cell lines.
Methods:
To develop a prognostic model based on these multiple biomarkers, the expression of four biomarkers ARL4C, ECT2, SOD2, and STEAP3 in primary RCC tissue were semi-quantitatively investigated by immunohistochemical analysis in an independent cohort of 97 patients who underwent nephrectomy, and the clinical significance of these biomarkers were analyzed by survival analysis using Kaplan-Meier curves. The prognostic model was constructed by calculation of the contribution score to prognosis of each biomarker on Cox regression analysis, and its prognostic performance was validated.
Results:
Patients whose tumors had high expression of the individual biomarkers had shorter cancer-specific survival (CSS) from the time of primary nephrectomy. The prognostic model based on four biomarkers segregated the patients into a high- and low-risk scored group according to defined cut-off value. This approach was more robust in predicting CSS compared to each single biomarker alone in the total of 97 patients with RCC. Especially in the 36 metastatic RCC patients, our prognostic model could more accurately predict early events within 2 years of diagnosis of metastasis. In addition, high risk-scored patients with particular strong SOD2 expression had a much worse prognosis in 25 patients with metastatic RCC who were treated with molecular targeting agents.
Conclusions:
Our findings indicate that a prognostic model based on four novel biomarkers provides valuable data for prediction of clinical prognosis and useful information for considering the follow-up conditions and therapeutic strategies for patients with primary and metastatic RCC.

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参考文献

1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;

68:7–30.

2. Vallet S, Pahernik S, Hofner T, Tosev G, Hadaschik B, Duensing S, et al.

Efficacy of targeted treatment beyond third-line therapy in metastatic

kidney cancer: retrospective analysis from a large-volume cancer center. Clin

Genitourin Cancer. 2015;13:E145–52.

3. Lin P, Ren H, Zhang Y, Zhou Z. Fifteen-gene expression based model

predicts the survival of clear cell renal cell carcinoma. Medicine 2018;97:e

11839.

4. Chang W, Gao X, Han Y, Du Y, Liu Q, Wang L, et al. Gene expression

profiling-derived immunohistochemistry signature with high prognostic

value in colorectal carcinoma. Gut. 2014;63:1457–67.

5. Brockman JA, Alanee S, Vickers AJ, Scardino PT, Wood DP, Kibel AS, et al.

Nomogram predicting prostate cancer-specific mortality for men with

biochemical recurrence after radical prostatectomy. Eur Urol. 2015;67:1160–7.

6. Massari F, Bria E, Ciccarese C, Munari E, Modena A, Zambonin V, et al.

Prognostic value of beta-tubulin-3 and c-Myc in muscle invasive urothelial

carcinoma of the bladder. Plos One. 2015;10:e0127908.

7. Fan C, Oh DS, Wessels L, Weigelt B, Nuyten DS, Nobel AB, et al.

Concordance among Gene-Expression– Based Predictors for Breast Cancer.

N Engl J Med. 2006;355:560–9.

8. Isono T, Chano T, Kitamura A, Yuasa T. Glucose deprivation induces G2/M

transition-arrest and cell death in N-GlcNAc2-modified protein-producing

renal carcinoma cells. PLoS One. 2014;9:e96168.

9. Isono T, Chano T, Yonese J, Yuasa T. Therapeutic inhibition of mitochondrial

function induces cell death in starvation-resistant renal cell carcinomas. Sci

Rep. 2016;6:25669.

Page 11 of 11

10. Yoshida T, Kageyama S, Isono T, Yuasa T, Kushima R, Kawauchi A, et al.

Superoxide dismutase 2 expression can predict prognosis of renal cell

carcinoma patients. Cancer Biomark. 2018;22:755–61.

11. Isono T, Chano T, Yoshida T, Makino A, Ishida S, Suzaki M, et al. ADPribosylation factor-like 4C is a predictive biomarker of poor prognosis in

patients with renal cell carcinoma. Am J Cancer Res. 2019;9:415–23.

12. Pu Z, Wang Q, Xie H, Wang G, Hao H. Clinicalpathological and prognostic

significance of survivin expression in renal cell carcinoma: a meta-analysis.

Oncotarget. 2017;8:19825–33.

13. Gao Z, Zhang D, Duan Y, Yan L, Fan Y, Fang Z, et al. A five-gene signature

predicts overall survival of patients with papillary renal cell carcinoma. PLoS

One. 2019;14:e0211491.

14. Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM. The 2016 WHO

classification of tumours of the urinary system and male genital organs-part

a: renal, penile, and testicular tumours. Eur Urol. 2016;70:93–105.

15. James DB. TNM Classification of Malignant Tumours. 8th ed. New Jersey:

Wiley-Blackwell; 2016.

16. Fujii S, Matsumoto S, Nojima S, Morii E, Kikuchi A. Arl4c expression in

colorectal and lung cancers promotes tumorigenesis and may represent a

novel therapeutic target. Oncogene. 2015;34:4834–44.

17. Hu Q, Masuda T, Sato K, Tobo T, Nambara S, Kidogami S, et al. Identification

of ARL4C as a peritoneal dissemination-associated gene and its clinical

significance in gastric cancer. Ann Surg Oncol. 2018;25:745–53.

18. Wakinoue S, Chano T, Amano T, Isono T, Kimura F, Kushima R, et al. ADPribosylation factor-like 4C predicts worse prognosis in endometriosisassociated ovarian cancers. Cancer Biomark. 2019;24:223–9.

19. Wondergem B, Zhang Z, Huang D, Ong CK, Koeman J, Hof DV, et al.

Expression of the PTTG1 oncogene is associated with aggressive clear cell

renal cell carcinoma. Cancer Res. 2012;72:4361–7431.

20. Loo SY, Hirpara JL, Pandey V, Tan TZ, Yap CT, Lobie PE, et al. Manganese

superoxide dismutase expression regulates the switch between an epithelial

and a mesenchymal-like phenotype in breast carcinoma. Antioxid Redox

Signal. 2016;25:283–99.

21. Han M, Xu R, Wang S, Yang N, Ni S, Zhang Q, et al. Six-transmembrane

epithelial antigen of prostate 3 predicts poor prognosis and promotes

glioblastoma growth and invasion. Neoplasia. 2018;20:543–54.

22. Isobe T, Baba E, Arita S, Komoda M, Tamura S, Shirakawa T, et al. Human

STEAP3 maintains tumor growth under hypoferric condition. Exp Cell Res.

2011;317:2582–91.

23. Kimura K, Matsumoto S, Harada T, Morii E, Nagatomo I, Shintani Y, et al.

ARL4C is associated with initiation and progression of lung adenocarcinoma

and represents a therapeutic target. Cancer Sci. 2019;111:951–3.

24. Harada T, Matsumoto S, Hirota S, Kimura H, Fujii S, Kasahara Y, et al.

Chemically modified antisense oligonucleotide against ARL4C inhibits

primary and metastatic liver tumor growth. Mol Cancer Ther. 2019;18:602–12.

25. 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.

26. Bhaoighill MN, Dunlop EA. Mechanistic target of rapamycin inhibitors: successes

and challenges as cancer therapeutics. Cancer Drug Resist. 2019;2:1069–85.

27. Wu H, Ding Z, Hu D, Sun F, Dai C, Xie J, et al. Central role of lactic acidosis

in cancer cell resistance to glucose deprivation-induced cell death. J Pathol.

2012;227:189–99.

28. Emami Riedmaier A, Fisel P, Nies AT, Schaeffeler E, Schwab M. Metformin

and cancer: from the old medicine cabinet to pharmacological pitfalls and

prospects. Trends Pharmacol Sci. 2013;34:126–35.

29. Coyle C, Cafferty FH, Vale C, Langley RE. Metformin as an adjuvant treatment

for cancer: a systematic review and meta-analysis. Ann Oncol. 2016;27:2184–95.

30. Sadeghi N, Abbruzzese JL, Yeung SC, Hassan M, Li D. Metformin use is

associated with better survival of diabetic patients with pancreatic cancer.

Clin Cancer Res. 2012;18:2905–12.

31. Cao X, Wen ZS, Wang XD, Li Y, Liu KY, Wang X. The clinical effect of

metformin on the survival of lung cancer patients with diabetes: a

comprehensive systematic review and meta-analysis of retrospective

studies. J Cancer. 2017;8:2532–41.

32. Xiao Y, Zheng L, Mei Z, Xu C, Liu C, Chu X, et al. The impact of metformin

use on survival in prostate cancer: a systematic review and meta-analysis.

Oncotarget. 2017;8:100449–58.

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