代谢综合征与肾透明细胞癌患者预后的相关性

doi:10.19723/j.issn.1671-167X.2022.04.009

Abstract

Abstract:

Objective: To investigate the effects of MetS on the prognosis of patients with clear cell renal cell carcinoma (ccRCC). Methods: Clinical and pathological data and the laboratory test of ccRCC 342 patients with diverticular stones who underwent ccRCC who underwent radical or partial nephrectomy were retrospectively collected and analyzed.The patients were divided into MetS group and non-MetS group, and the subgroups were defined according to the tumor size. The overall survival (OS), cancer-specific survival (CSS), and progression-free survival (PFS) of the two groups were analyzed by univariate Cox analysis, and the subgroup analyses were also performed. Kaplan-Meier survival curve and survival analysis for OS, CSS, and PFS of the two groups and the subgroups were conducted. Results: Univariate Cox analysis showed that MetS was a protective factor of postoperative OS [hazard ratio (HR)=0.551, 95%CI: 0.321-0.949, P=0.031], CSS (HR=0.460, 95%CI: 0.234-0.905, P=0.025), and PFS (HR 0.585, 95%CI: 0.343-0.998, P=0.049) in the patients with ccRCC. In the subgroup with tumor size≤4 cm, MetS was not associated with postoperative OS (HR=0.857, 95%CI: 0.389-1.890, P=0.702), CSS (HR=1.129, 95%CI: 0.364-3.502, P=0.833), and PFS (HR=1.554, 95%CI: 0.625-3.864, P=0.343). In the subgroup with tumor size>4 cm, Mets was a protective factor of postoperative OS (HR=0.377, 95%CI: 0.175-0.812, P=0.013), CSS (HR=0.280, 95%CI: 0.113-0.690, P=0.006), and PFS (HR=0.332, 95%CI: 0.157-0.659, P=0.002); Obesity was a protective factor of postoperative CSS (HR=0.464, 95%CI: 0.219-0.981, P=0.044), and PFS (HR=0.445, 95%CI: 0.238-0.833, P=0.011). Kaplan-Meier survival analysis showed that the long-term survival of patients with MetS was better than those without MetS in OS (P=0.029), CSS (P=0.021), and PFS (P=0.046); for the subgroup with tumor size≤4 cm, there was no significant difference in postoperative OS (P=0.702), CSS (P=0.833), and PFS (P=0.339) between patients with and without MetS; For the subgroup with tumor size>4 cm, the OS (P=0.010), CSS (P=0.003), and PFS (P=0.001) of patients with MetS were better than those without MetS. Conclusion: MetS was a protective factor of postoperative OS, CSS, and PFS in the patients with ccRCC, which was more obvious in subgroup with tumor size>4 cm. And obesity, the component of MetS, was correlated with postoperative OS and CSS.

Key words: Metabolic syndrome, Clear cell renal cell carcinoma, Overall survival, Cancer-specific survival, Progression-free survival

CLC Number:

R737

Mei-ni ZUO,Yi-qing DU,Lu-ping YU,Xiang DAI,Tao XU. Correlation between metabolic syndrome and prognosis of patients with clear cell renal cell carcinoma[J].Journal of Peking University (Health Sciences), 2022, 54(4): 636-643.

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References 27

1	Bray F , Ferlay J , Soerjomataram I , et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68 (6): 394- 424. doi: 10.3322/caac.21492
2	Ozbek E , Otunctemur A , Sahin S , et al. Renal cell carcinoma is more aggressive in Turkish patients with the metabolic syndrome[J]. Asian Pac J Cancer Prev, 2013, 14 (12): 7351- 7354. doi: 10.7314/APJCP.2013.14.12.7351
3	Chakiryan NH, Jiang DD, Gillis KA, et al. Real-world survival outcomes associated with first-line immunotherapy, targeted therapy, and combination therapy for metastatic clear cell renal cell carcinoma[J]. JAMA Netw Open, 2021, 4(5): e2111329[2022-03-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150693/.
4	Jonasch E , Walker CL , Rathmell WK . Clear cell renal cell carcinoma ontogeny and mechanisms of lethality[J]. Nat Rev Nephrol, 2021, 17 (4): 245- 261. doi: 10.1038/s41581-020-00359-2
5	Zhang GM , Zhu Y , Ye DW . Metabolic syndrome and renal cell carcinoma[J]. World J Surg Oncol, 2014, 12, 236. doi: 10.1186/1477-7819-12-236
6	Yao F , Bo Y , Zhao L , et al. Prevalence and influencing factors of metabolic syndrome among adults in China from 2015 to 2017[J]. Nutrients, 2021, 13 (12): 4475. doi: 10.3390/nu13124475
7	Bovolini A , Garcia J , Andrade MA , et al. Metabolic syndrome pathophysiology and predisposing factors[J]. Int J Sports Med, 2021, 42 (3): 199- 214. doi: 10.1055/a-1263-0898
8	Ewertz M , Jensen MB , Gunnarsdóttir K , et al. Effect of obesity on prognosis after early-stage breast cancer[J]. J Clin Oncol, 2011, 29 (1): 25- 31. doi: 10.1200/JCO.2010.29.7614
9	Qi J , An R , Bhatti P , et al. Anti-hypertensive medications and risk of colorectal cancer: A systematic review and meta-analysis[J]. Cancer Causes Control, 2022, 33 (6): 801- 812. doi: 10.1007/s10552-022-01570-1
10	Yang X, Li X, Dong Y, et al. Effects of metabolic syndrome and its components on the prognosis of endometrial cancer[J]. Front Endocrinol (Lausanne), 2021, 12: 780769[2022-03-01]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8717682/.
11	Bulut S , Aktas BK , Erkmen AE , et al. Metabolic syndrome prevalence in renal cell cancer patients[J]. Asian Pac J Cancer Prev, 2014, 15 (18): 7925- 7928. doi: 10.7314/APJCP.2014.15.18.7925
12	Yu Y , Gong L , Ye J . The role of aberrant metabolism in cancer: Insights into the interplay between cell metabolic reprogramming, metabolic syndrome, and cancer[J]. Front Oncol, 2020, 10, 942. doi: 10.3389/fonc.2020.00942
13	Luzzago S , Palumbo C , Rosiello G , et al. Metabolic syndrome predicts worse perioperative outcomes in patients treated with partial nephrectomy for renal cell carcinoma[J]. Urology, 2020, 140, 91- 97. doi: 10.1016/j.urology.2020.02.019
14	Zhang Y , Wu T , Xie J , et al. Effects of metabolic syndrome on renal function after radical nephrectomy in patients with renal cell carcinoma[J]. Int Urol Nephrol, 2021, 53 (10): 2127- 2135. doi: 10.1007/s11255-020-02759-6
15	Kriegmair MC , Mandel P , Porubsky S , et al. Metabolic syndrome negatively impacts the outcome of localized renal cell carcinoma[J]. Horm Cancer, 2017, 8 (2): 127- 134. doi: 10.1007/s12672-017-0289-2
16	Liu Z , Wang H , Zhang L , et al. Metabolic syndrome is associated with improved cancer-specific survival in patients with localized clear cell renal cell carcinoma[J]. Transl Androl Urol, 2019, 8 (5): 507- 518. doi: 10.21037/tau.2019.10.04
17	Eskelinen TJ , Kotsar A , Tammela TLJ , et al. Components of metabolic syndrome and prognosis of renal cell cancer[J]. Scand J Urol, 2017, 51 (6): 435- 441. doi: 10.1080/21681805.2017.1352616
18	Du Y , Yang W , Liu H , et al. Perirenal fat as a new independent prognostic factor in patients with surgically treated clear cell renal cell carcinoma[J]. Clin Genitourin Cancer, 2022, 20 (1): e75- e80. doi: 10.1016/j.clgc.2021.10.006
19	Ford E S . Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U.S[J]. Diabetes Care, 2005, 28 (11): 2745- 2749. doi: 10.2337/diacare.28.11.2745
20	Liu M , Wang J , Jiang B , et al. Increasing prevalence of metabolic syndrome in a Chinese elderly population: 2001-2010[J]. PLoS One, 2013, 8 (6): e66233. doi: 10.1371/journal.pone.0066233
21	Capitanio U , Bensalah K , Bex A , et al. Epidemiology of renal cell carcinoma[J]. Eur Urol, 2019, 75 (1): 74- 84. doi: 10.1016/j.eururo.2018.08.036
22	Miricescu D , Balan DG , Tulin A , et al. PI3K/AKT/mTOR signalling pathway involvement in renal cell carcinoma pathogenesis (Review)[J]. Exp Ther Med, 2021, 21 (5): 540. doi: 10.3892/etm.2021.9972
23	Silva A , Pereira SS , Monteiro MP , et al. Effect of metabolic syndrome and individual components on colon cancer characteristics and prognosis[J]. Front Oncol, 2021, 11, 631257. doi: 10.3389/fonc.2021.631257
24	Mallik R , Chowdhury TA . Metformin in cancer[J]. Diabetes Res Clin Pract, 2018, 143, 409- 419. doi: 10.1016/j.diabres.2018.05.023
25	Parker AS , Lohse CM , Cheville JC , et al. Greater body mass index is associated with better pathologic features and improved outcome among patients treated surgically for clear cell renal cell carcinoma[J]. Urology, 2006, 68 (4): 741- 746. doi: 10.1016/j.urology.2006.05.024
26	Wu X , Wang Q , Wang Z , et al. Association of extrarenal invasion patterns and tumor size with the differences in survival outcomes of t3a renal cell carcinoma: a proposal modified T3a stage system is needed[J]. Int J Gen Med, 2022, 15, 367- 378. doi: 10.2147/IJGM.S344215
27	Dinatale RG , Xie W , Becerra MF , et al. The association between small primary tumor size and prognosis in metastatic renal cell carcinoma: Insights from two independent cohorts of patients who underwent cytoreductive nephrectomy[J]. Eur Urol Oncol, 2020, 3 (1): 47- 56. doi: 10.1016/j.euo.2019.10.002

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Variable	non-MetS (n=184)	MetS (n=158)	P
Age/years, n(%)			0.009
< 60	111 (60.33)	73 (46.20)
≥60	73 (39.67)	85 (53.80)
Gender, n(%)			< 0.001
Male	146 (79.35)	81 (51.27)
Female	38 (20.65)	77 (48.73)
Smoking, n(%)			0.097
No	116 (63.04)	113 (71.52)
Yes	68 (36.96)	45 (28.48)
Clinical manifestation, n(%)			0.790
Incidental	128 (69.57)	112 (70.89)
Symptomatic	56 (30.43)	46 (29.11)
Tumor size, n(%)			0.846
≤4 cm	116 (63.04)	98 (62.03)
>4 cm	68 (36.96)	60 (37.97)
T stage, n(%)			0.083
T1-T2	167 (90.76)	151 (95.57)
T3-T4	17(9.24)	7(4.43)
Tumor grade, n(%)			0.536
1-2	164 (89.13)	144 (91.14)
3-4	20 (10.87)	14 (8.86)

Covariates	OS		CSS		PFS
Covariates	HR(95%CI)	P	HR(95%CI)	P	HR(95%CI)	P
Age (≥ 60 years vs. < 60 years)	1.709 (1.013-2.885)	0.045	1.061 (0.570-1.973)	0.852	0.983 (0.589-1.640)	0.947
Gender (male vs. female)	2.075 (1.099-3.918)	0.024	1.634 (0.798-3.342)	0.179	1.754 (0.962-3.195)	0.067
Smoking (yes vs.no)	1.249 (0.735-2.124)	0.411	0.999 (0.515-1.936)	0.998	1.059 (0.617-1.816)	0.836
Clinical manifestation (symptomatic vs. incidental)	2.127 (1.267-3.571)	0.004	2.142 (1.149-3.996)	0.017	2.307 (1.381-3.853)	0.001
Tumor size (>4 cm vs.≤4 cm)	2.354 (1.399-3.961)	0.001	4.055 (2.062-7.975)	< 0.001	3.895 (2.255-6.730)	< 0.001
T stage (T3-T4 vs. T1-T2)	2.156 (0.977-4.759)	0.057	2.183 (0.854-5.582)	0.103	2.014 (0.914-4.440)	0.083
Tumor grade (3-4 vs. 1-2)	2.166 (1.122-4.182)	0.021	2.780 (1.323-5.840)	0.007	2.246 (1.166-4.326)	0.016
MetS (yes vs. no)	0.551 (0.321-0.949)	0.031	0.460 (0.234-0.905)	0.025	0.585 (0.343-0.998)	0.049
Obesity (yes vs. no)	0.511 (0.304-0.859)	0.011	0.532 (0.285-0.993)	0.048	0.629 (0.377-1.049)	0.075
Hypertension (yes vs. no)	0.887 (0.512-1.535)	0.667	0.820 (0.428-1.572)	0.550	0.949 (0.549-1.639)	0.851
Diabetes mellitus (yes vs. no)	1.262 (0.754-2.114)	0.376	1.045 (0.562-1.944)	0.890	0.839 (0.501-1.407)	0.506
Hyper TG (yes vs. no)	0.727 (0.357-1.481)	0.380	0.574 (0.225-1.465)	0.246	0.805 (0.408-1.589)	0.532
Low HDL-C (yes vs. no)	1.021 (0.579-1.799)	0.943	0.969 (0.492-1.908)	0.928	1.211 (0.673-2.177)	0.523

Covariates	Tumor size≤4 cm		Tumor size>4 cm
Covariates	HR(95%CI)	P	HR(95%CI)	P
Age (≥ 60 years vs. < 60 years)	3.129 (1.306-7.494)	0.010	1.099 (0.553-2.182)	0.788
Gender (male vs. female)	2.943 (1.010-8.575)	0.048	1.596 (0.719-3.545)	0.251
Smoking (yes vs.no)	1.398 (0.628-3.113)	0.412	1.123 (0.551-2.287)	0.750
Clinical manifestation(symptomatic vs.incidental)	2.064 (0.890-4.787)	0.091	1.534 (0.770-3.054)	0.223
T stage (T3-T4 vs. T1-T2)	2.305 (0.541-9.815)	0.259	1.649 (0.634-4.288)	0.305
Tumor grade (3-4 vs. 1-2)	2.503 (0.855-7.327)	0.094	1.618 (0.701-3.736)	0.259
MetS (yes vs. no)	0.857 (0.389-1.890)	0.702	0.377 (0.175-0.812)	0.013
Obesity (yes vs. no)	0.517 (0.234-1.142)	0.103	0.506 (0.255-1.004)	0.051
Hypertension (yes vs. no)	0.905 (0.399-2.051)	0.811	0.734 (0.349-1.545)	0.416
Diabetes mellitus (yes vs. no)	1.879 (0.829-4.260)	0.131	1.069 (0.535-2.133)	0.851
Hyper TG (yes vs. no)	0.460 (0.138-1.539)	0.208	1.299 (0.534-3.157)	0.564
Low HDL-C (yes vs. no)	0.993 (0.428-2.305)	0.988	0.936 (0.434-2.019)	0.866

Covariates	Tumor size≤4 cm		Tumor size>4 cm
Covariates	HR(95%CI)	P	HR(95%CI)	P
Age (≥ 60 years vs. < 60 years)	2.388 (0.719-7.930)	0.155	0.726 (0.340-1.550)	0.408
Gender (male vs. female)	1.113 (0.335-3.698)	0.861	1.906 (0.772-4.704)	0.162
Smoking (yes vs.no)	0.420 (0.092-1.919)	0.263	1.316 (0.616-2.811)	0.478
Clinical manifestation(symptomatic vs.incidental)	0.882 (0.193-4.027)	0.871	1.752 (0.828-3.707)	0.143
T stage (T3-T4 vs. T1-T2)	0.047 (0.000-24607)	0.649	2.011 (0.760-3.321)	0.159
Tumor grade (3-4 vs. 1-2)	2.853 (0.624-13.032)	0.176	2.054 (0.872-4.840)	0.100
MetS (yes vs. no)	1.129 (0.364-3.502)	0.833	0.280 (0.113-0.690)	0.006
Obesity (yes vs. no)	0.692 (0.223-2.148)	0.524	0.464 (0.219-0.981)	0.044
Hypertension (yes vs. no)	1.054 (0.317-3.505)	0.931	0.597 (0.275-1.294)	0.191
Diabetes mellitus (yes vs. no)	3.067 (0.830-11.335)	0.093	0.798 (0.369-1.730)	0.568
Hyper TG (yes vs. no)	0.317 (0.041-2.457)	0.272	0.947 (0.328-2.739)	0.921
Low HDL-C (yes vs. no)	1.356 (0.366-5.018)	0.648	0.727 (0.328-1.610)	0.431

Covariates	Tumor size≤4 cm		Tumor size>4 cm
Covariates	HR(95%CI)	P	HR(95%CI)	P
Age (≥ 60 years vs. < 60 years)	1.642 (0.660-4.082)	0.286	0.750 (0.398-1.413)	0.374
Gender (male vs. female)	1.601 (0.576-4.445)	0.367	1.716 (0.817-3.606)	0.154
Smoking (yes vs.no)	0.393 (0.115-1.349)	0.138	1.483 (0.792-2.779)	0.218
Clinical manifestation(symptomatic vs.incidental)	1.586 (0.571-4.408)	0.376	1.656 (0.887-3.091)	0.113
T stage (T3-T4 vs. T1-T2)	0.047 (0.000-1409)	0.561	1.826 (0.806-4.141)	0.149
Tumor grade (3-4 vs. 1-2)	0.767 (0.102-5.750)	0.796	2.115 (1.033-4.330)	0.041
MetS (yes vs. no)	1.554 (0.625-3.864)	0.343	0.332 (0.157-0.659)	0.002
Obesity (yes vs. no)	1.182 (0.465-3.004)	0.726	0.445 (0.238-0.833)	0.011
Hypertension (yes vs. no)	1.137 (0.432-2.996)	0.794	0.691 (0.356-1.339)	0.273
Diabetes mellitus (yes vs. no)	2.229 (0.847-5.867)	0.104	0.586 (0.298-1.154)	0.122
Hyper TG (yes vs. no)	0.933 (0.310-2.811)	0.902	0.953 (0.399-2.276)	0.913
Low HDL-C (yes vs. no)	3.925 (0.906-17.004)	0.068	0.676 (0.349-1.312)	0.248

Correlation between metabolic syndrome and prognosis of patients with clear cell renal cell carcinoma

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