Sage Journals: Discover world-class research

Abstract

Purpose:

Numerous studies have suggested that dyslipidemia is closely related to various cancers and the high-density lipoprotein cholesterol (HDL-C) levels are associated with the outcome of cancer patients. However, the predictive value of HDL-C in patients with renal cell carcinoma remains unclear. Our study aims to explore the relationship between the levels of serum HDL-C and the prognosis of renal cell carcinoma.

Methods:

A total of 308 patients diagnosed with clear cell renal cell carcinoma (CCRCC) who received surgical treatment were retrospectively enrolled in our study. The necessary clinical data of each enrolled patient were collected and the Kaplan–Meier method and the Cox proportional hazards regression model were used to calculate the overall survival and cancer-specific survival.

Results:

Kaplan–Meier and univariate analysis showed that a lower preoperative serum HDL-C level was a risk factor of CCRCC patients. Multivariate analyses demonstrated that a higher serum HDL-C level was closely associated with better overall survival (hazard ratio = 0.32; 95% confidence interval (0.13, 0.78); P=0.013) and cancer-specific survival (hazard ratio =0.42; 95% confidence interval (0.15, 0.99); P=0.048).

Conclusion:

Our findings suggest that an increased serum level of HDL-C might predict better overall survival and cancer-specific survival in patients with CCRCC.

Keywords

High density lipoprotein cholesterol clear cell renal cell cancer prognosis

Introduction

Renal cell carcinoma (RCC) is the most common tumor of the kidney in adults; it accounts for about 3% of adult malignancies,¹ with the highest mortality rate at over 40%.² Clear cell renal cell carcinoma (CCRCC) accounts for 70%–80% of all RCC cases, which is the most common pathological subtype. Since both chemotherapy and radiotherapy are not effective methods to treat RCC, the only effective treatment is surgical resection.³ However, after resection, 20%–40% patients will have neoplasm recurrence.⁴ Currently, clinical physicians often use Fuhrman grade⁵ and tumor-node-metastasis (TNM) stage⁶ to predict the prognosis of patients with RCC. Additionally, other prognostic models (e.g. such as pretreatment neutrophil-to-lymphocyte ratio,⁷ C-reactive protein to albumin ratio,⁸ albumin to globulin ratio,⁹ and positive surgical parenchymal margin¹⁰) have been established to evaluate the risks. Due to the insufficiency of these prognostic variables, a novel clinical and laboratory marker is urgently needed to predict the prognosis of these patients.

A metabolic disorder is widely considered to be an important hallmark in many cancers, and the lipid metabolism disorder has been confirmed to play a crucial role in the pathogenesis of cancers.^11-13 Numerous studies have found the correlation between serum lipids and various carcinoma, such as esophageal squamous cell carcinoma,¹⁴ breast cancer,¹⁵ renal cell carcinoma,¹⁶ non-small cell lung cancer,¹⁷ and gastric cancer.¹⁸ Wolfe et al.¹⁹ found that in breast cancer patients decreased serum high-density lipoprotein cholesterol (HDL-C) levels predicted a lower 5-year overall survival (OS) rate, and HDL-C can improve the radio sensitivity in breast cancer cells. In addition, serum HDL-C is significantly decreased in various cancer patients,^20-23 which suggests a lipid disorder associated with cancers. The accumulation of studies has found that decreased serum HDL-C levels are closely related to the poor outcome of cancer patients, such as hepatocellular carcinoma,²⁴ gastric cancer,²⁵ breast cancer,¹⁹ and colorectal cancer.²⁶ However, a study by Liu et al.²⁷ reported that pre-treatment high serum HDL-C levels were associated with the poor outcome of nasopharyngeal carcinoma patients via promoting the proliferation and migration of nasopharyngeal carcinoma cells.

However, the association between serum HDL and the outcome of CCRCC have not yet been evaluated. Our study aims to investigate the potential role of the HDL-C on OS and cancer-specific survival (CSS) in CCRCC patients after surgical resections.

Methods

Patients

We retrospectively enrolled 308 patients diagnosed with CCRCC who received surgical treatment in The Third Affiliated Hospital of Soochow University from 2003 to 2012.The inclusion criteria were: (i) CCRCC was histopathologically diagnosed; (ii) patients with radical nephrectomy; (iii) patients without preoperative neoadjuvant therapy; (iv) patients without cancer history; and (v) preoperative laboratory tests were obtained before treatment. After excluding 106 patients, the remaining 308 patients were finally included for analysis. Clinical data were obtained from the patients’ medical records. Written informed consent was obtained from each patient and the study was approved by the local Institute Research Ethics Committee.

Data collection

The necessary clinical data of each enrolled patient were collected as follows: age, sex, Fuhrman grade, tumor size, lymph node metastasis, necrosis of tumor, and lymphovascular invasion. Relevant laboratory indicators were collected before surgery.

Radical nephrectomy

Surgery was performed by several surgeons according to the standard criteria for radical nephrectomy. Open nephrectomy was performed in 147 patients, whereas the remaining 159 patients underwent laparoscopic nephrectomy. This procedure involves the removal of the whole kidney, along with a section of the tube leading to the bladder (ureter), the gland which sits at the top the kidney (adrenal gland), and the fatty tissue around the kidney.

Follow-up assessment

Follow-up was performed through telephone interviews and review of the medical records. The primary endpoint was OS, which was defined as the interval between the date of the resection and the last follow-up or death. The secondary endpoint was CSS, which was calculated from the date of the resection. The survival endpoint was CCRCC-related death or the date of last follow-up. Data for patients who died from other than metastatic disease were censored at the time of death. Patients with locally advanced CCRCC underwent laboratory tests and physical examinations every 6 months in the first 3 years, then annually thereafter. For patients with localized CCRCC, physical examinations and laboratory tests were performed twice in the first year and annually thereafter.

Statistical analysis

The survival curves were established by the Kaplan–Meier method and the log-rank test, which were used to test statistically significant differences. There is no validated cut-off value for HDL-C, and therefore receiver operating characteristic (ROC) curve analysis was performed with OS and CSS as the outcome, and the Youden index was then estimated.²⁸ The optimal cut-off value is that which allows the prediction of outcome with the best sensitivity and specificity.²⁹ Thus, the optimal cut-off value of the HDL-C was estimated by ROC curve statistical analyses. The chi-square test was used to evaluate the association between HDL-C levels and clinic pathological parameters. Univariate analysis and multivariate analysis were performed by the Cox proportional hazards model. A P < 0.05 was considered statistically significant in all statistical tests. All statistical analyses were performed using SPSS 22.0 software (IBM Corporation, Armonk, NY, USA).

Results

Patient characteristics

After the eligibility review, 308 patients with CCRCC who underwent radical nephrectomy were enrolled in the present study. The screening process and detailed information are presented in Figure 1. The clinicopathologic information of patients is presented in Table 1. Of the patients, 193 were male and 115 were female; 128 were older than 60 years, and 180 were younger; the median age was 59 (range 27–79) years; the median follow-up time was 60 (range: 1–149) months; by the last follow-up date, 40 patients had died, and 268 were alive. Upon performing ROC analysis, 1.23 mmol/L was used as the cut-off value for HDL-C. The cut-off value for alkaline phosphatase (AKP) was 125 U/L (upper limit of the normal range) and for lactate dehydrogenase (LDH) was 245 U/L (upper limit of the normal range).

Figure 1.

Flow diagram of the study selection process.

Table 1.

The clinicopathological characteristics of patients, and correlation of HDL-C with the clinicopathological characteristics.

Variable	No. of Patients (%)
	308	HDL⩽ 1.235	HDL> 1.235	P value
	308	(n=175)	(n=133)	P value
Age (years)				< 0.03*
⩽ 60	180 (58.4)	93 (53.1)	87 (65.4)
> 60	128 (41.6)	82 (46.9)	46 (34.6)
Sex				<0.001*
Male	193 (62.2)	126 (72.0)	67 (50.3)
Female	115 (37.8)	49 (28.0)	66 (49.7)
T stage				<0.549
1	252 (81.8)	140 (80.0)	112 (84.2)
2	32 (10.3)	19 (10.9)	13 (9.8)
3	24 (7.9)	16 (9.1)	8 (6.0)
N stage				0.836
0	298 (97.6)	169 (97.0)	129 (97.0)
1	10 (2.4)	6 (3.0)	4 (3.0)
Fuhrman grade				0.416
1	64 (20.7)	32 (18.3)	32 (24.1)
2	154 (50.0)	88 (50.3)	66 (49.6)
3	63 (20.5)	38 (21.7)	25 (18.8)
4	27 (8.8)	17 (9.7)	10 (7.5)
Tumor size, cm				0.222
⩽ 5	195 (63.3)	113 (64.6)	82 (61.7)
> 5	113 (36.7)	62 (35.4)	51 (38.3)
Tumor necrosis				0.279
Absent	281 (90.4)	157 (89.7)	124 (93.2)
Present	27 (9.6)	18 (10.3)	9 (6.8)
LVI				0.064
Absent	290 (94.2)	161 (92.0)	129 (97.0)
Present	18 (5.8)	14 (8.0)	4 (3.0)
LDH (U/L)				0.988
⩽245	299 (97.1)	170 (97.1)	129 (97.0)
>245	9 (2.9)	5 (2.9)	4 (3.0)
AKP (U/L)				0.198
⩽130	297 (96.4)	167 (95.4)	130 (97.7)
>130	11 (3.6)	8 (4.6)	3 (2.3)

indicates that the difference was statistically significant.

AKP: Alkaline phosphatase; LDH: lactate dehydrogenase; LVI: lymphovascular invasion.

Relationship between the HDL-C levels and other clinical characteristics

Correlations between HDL-C levels and clinicopathological characteristics of the 308 enrolled patients are shown in Table 1. The lower HDL-C level was significantly associated with males (P<0.001) and order age (P=0.03). Other clinicopathological indicators were not closely related to HDL-C levels, including tumor size, tumor stage, lymph node stage, Fuhrman grade, the presence of Tumor necrosis, lymphovascular invasion, LDH, and AKP levels (Table 1).

Prognostic value of HDL-C and clinical characteristics for OS

Among the 308 patients, there were 30 of 175 (17.1%) patients with an HDL-C level ⩽1.23 mmol/L and 10 of 133 (7.5%) patients with an HDL-C level >1.23 mmol/L who had died before the last follow-up date. A higher HDL-C level was closely associated with a better OS (hazard ratio [HR] 0.39; 95% confidence interval [CI] 0.19, 0.80; P=0.01; Table 2). The Kaplan–Meier survival analysis suggested that patients with a high HDL level had a better OS (P=0.008) (Figure 2). As shown in Table 2, according to the results of univariate analysis, other clinicopathological factors—including an order age (P=0.001), an advanced lymph node stage (P<0.001), an advanced T stage (P<0.001), an advanced Fuhrman grade (P<0.001), a larger tumor size (P<0.001), the presence of tumor necrosis (P<0.001), the presence of lymphovascular invasion (P<0.001), a high AKP level (>130 U/L) (P<0.001), and a high LDH level (>245 U/L) (P=0.025)—were significantly associated with poor OS. Based on the results of multivariate analysis, a decreased HDL-C level yielded a worse outcome (HR 0.32; 95% CI 0.13, 0.78; P=0.013; Table 2), which suggested that the level of HDL-C was an independent prognostic factor in patients with CCRCC. In addition, we observed significant associations of age, T stage, and lymph node stage with OS.

Table 2.

Univariate and multivariate Cox proportional analysis with overall survival.

Variable	Univariate analysis		Multivariate analysis
Variable	HR (95% CI)	P value	HR (95% CI)	P value
Age (years)		0.001*		0.016*
>60 vs. ⩽60	2.82 (1.46, 5.42)		2.63 (1.20, 5.75)
Sex		0.109
Male vs. female	1.80 (0.88, 3.67)
T stage		< 0.001*		0.045*
1	reference		reference
2	4.67 (2.06,10.59)	< 0.001*	1.34 (0.49, 3.67)	0.568
3	18.53 (9.05, 37.94)	< 0.001*	3.96 (1.32, 11.92)	0.014*
N stage		< 0.001*		0.014*
1 vs. 0	7.84 (3.26, 18.90)		5.19 (1.39, 19.33)
Fuhrman grade		< 0.001*		0.122
1	reference		reference
2	6.37 (0.84, 48.09)	0.073	7.99 (0.96, 66.69)	0.055
3	12.66 (1.65,97.15)	0.015*	10.27 (1.18, 89.74)	0.035*
4	16.70 (4.42, 283.20)	0.001*	3.26 (0.32, 33.59)	0.320
Tumor size, cm		< 0.001*		0.062
> 5 vs. ⩽ 5	4.99 (2.48, 10.06)		2.46 (0.96, 6.33)
Tumor necrosis		< 0.001*		0.055
Present vs. absent	3.95 (1.96, 7.98)		2.42 (0.98, 5.99)
LVI		< 0.001*		0.155
Present vs. absent	7.64 (3.60, 16.20)		2.27 (0.73, 7.05)
HDL-C (mmol/L)		0.01*		0.013*
>1.23 vs. ⩽1.23	0.39 (0.19, 0.80)		0.32 (0.13, 0.78)
LDH (U/L)		0.025*		0.071
>245 vs. ⩽245	3.87 (1.18, 12.68)		4.22 (0.89, 20.08)
AKP (U/L)		<0.001*		0.376
>130 vs. ⩽130	10.28 (4.23, 24.99)		1.75 (0.50, 6.04)

indicates that the difference was statistically significant.

AKP: alkaline phosphatase; CI: confidence interval; HR: hazard ratio; LDH: lactate dehydrogenase; LVI lymphovascular invasion.

Figure 2.

Kaplan–Meier curve for overall survival regarding high versus low HDL-C levels.

Prognostic value of HDL-C and clinical characteristics for CSS

Among the 308 patients, death due to CCRCC occurred in 26 of 175 (14.9%) patients with an HDL-C level ⩽1.23 mmol/L, and 10 of 133 (7.5%) patients with an HDL-C level >1.23 mmol/L at last follow-up date. Regarding CSS, univariate analyses showed that a low HDL-C level was closely associated with a worse CSS (HR 0.37; 95% CI 0.22, 0.95; P=0.017; Table 3). The Kaplan–Meier survival analysis suggested that a decreased HDL level had a better CSS in patients with CCRCC (P=0.03) (Figure 3). In univariate analysis, clinicopathological factors—such as order age (P=0.004), an advanced T stage (P<0.001), an advanced lymph node stage (P<0.001), a larger tumor size (P<0.001), the presence of tumor necrosis (P<0.001), the presence of lymphovascular invasion (P<0.001), a high AKP level (>130 U/L) (P<0.001), and a high LDH level (>245 U/L) (P=0.025)—were related to worse CSS. Multivariate analyses showed that a decreased HDL-C level was statistically linked with decreased CSS (HR 0.42; 95% CI 0.15, 0.99; P=0.048; Table 3). Moreover, age, T stage, N stage, tumor size, and tumor necrosis were independent risk factors for CSS (Table 3).

Table 3.

Univariate and multivariate Cox proportional analysis with cancer-specific survival.

Variable	Univariate analysis		Multivariate analysis
Variable	HR (95% CI)	P value	HR (95% CI)	P value
Age (years)		0.004*		0.034*
>60 vs. ⩽60	2.73 (1.38, 5.39)		2.42 (1.07, 5.48)
Sex		0.223
Male vs. female	1.55 (0.82, 2.95)
T stage		< 0.001*		0.023*
1	reference		reference
2	6.35 (2.67, 15.07)	< 0.001*	1.57 (0.55, 4.50)	0.401
3	23.27 (10.80, 50.17)	< 0.001*	4.73 (1.55, 14.49)	0.006*
N stage		< 0.001*		0.022*
1 vs. 0	7.84 (3.26, 18.90)		4.53 (1.25, 16.43)
Fuhrman grade		< 0.001*		0.192
1	reference		reference
2	5.95 (0.78, 45.27)	0.085	6.86 (0.82, 57.14)	0.075
3	11.96 (1.54, 92.64)	0.018*	7.97 (0.91,69.75)	0.061
4	36.76 (4.60, 293.98)	0.001*	2.87 (0.28, 29.37)	0.374
Tumor size, cm		< 0.001*		0.047*
> 5 vs. ⩽ 5	6.37 (2.89, 14.02)		2.88 (1.01, 8.15)
Tumor necrosis		< 0.001*		0.030*
Present vs. absent	4.71 (2.32, 9.60)		2.69 (1.10, 6.60)
LVI		< 0.001*		0.132
Present vs. absent	8.36 (3.90, 17.92)		2.37 (0.77, 7.31)
HDL-C (mmol/L)		0.017*		0.048*
>1.23 vs. ⩽ 1.23	0.37 (0.22, 0.95)		0.42 (0.15, 0.99)
LDH (U/L)		0.010*		0.077
>245 vs. ⩽245	4.72 (1.44, 15.43)		4.13 (0.86, 19.89)
AKP (U/L)		<0.001*		0.469
>130 vs. ⩽ 130	10.28 (4.23, 24.99)		1.58 (0.46, 5.46)

indicates that the difference was statistically significant.

AKP: alkaline phosphatase; CI: confidence interval; HR: hazard ratio; LDH: lactate dehydrogenase; LVI: lymphovascular invasion.

Figure 3.

Kaplan–Meier curve for cancer-specific survival regarding high versus low HDL-C levels.

Discussion

Although recent progress has been made in the epigenetic alterations and identification of genetics,³⁰ the frequently used prognostic evaluation of CCRCC currently depends on pathological factors and traditional clinicopathological prognostic variables.³¹ It has been reported that a significant association between a lower HDL-C level and a higher risk of incident cancer in a patient, which was independent of age, gender, body max index, serum LDL-C levels, and the presence of diabetes and smoking.³² Zhang et al.²² reported that, compared with the healthy controls, serum HDL-C levels tended to decrease in patients with renal cell cancer. The accumulation of studies has revealed that there was an inverse association between high HDL-C levels and the epidemiology of cancers, including breast cancer,³³ lung cancer,²⁰ non-Hodgkin lymphoma,³⁴ and overall cancer risk.³⁵

To clarify the relationship between HDL-C levels and CCRCC, pathological characteristics and outcome were examined in RCC patients. In the present study, we explored the effect of serum HDL-C levels on the prognosis of patients with CCRCC. Based on the cut-off value of HDL-C before treatment, both the univariate and multivariate analyses showed that a high serum HDL-C was associated with better OS. We showed that high serum HDL-C levels were also a favorable predictor for CSS in CCRCC patients. Consistent with our study, a high serumHDL also can be a favorable predictor in various cancers, such as breast cancer,³³ hepatocellular carcinoma,²⁴ and gastric cancer.²⁵

Recently, emerging evidence has shown that HDL-C was linked with the tumorigenesis and progression of cancers.^36,³⁷ Su et al.³⁷ found that HDL-C reduced the viability and proliferation of colon cancer cells, and HDL-C mimetics could reduce the tumor size of colon cancer in a mouse model. Wolfe et al.¹⁹ held that HDL-C decreased mammosphere formation and suppressed the survival via inhibiting the phosphorylation of epidermal growth factor receptor (EGFR), Akt, and forkhead box O3 (FOXO3a) in breast cancer cell lines. Sekine et al.³⁸ demonstrated that HDL-C promoted cell proliferation and migration in androgen-independent prostate cancer cells through the ABCA1/ERK1/2/Akt signaling pathway, whereas HDL-C had no such effects on androgen-dependent prostate cancer cells. A study by Liu et al.²⁷ reported that HDL-C could increase proliferation, invasion, and colony formation of nasopharyngeal carcinoma cells by a mechanism involving scavenger receptor class B member I. Thus, the exact mechanisms underlying the role that HDL-C plays in the development of tumors remain unclear. HDL-C may exert diverse effects on different tumors depending on the receptors that the tumors express.

Cholesterol plays a crucial role in membrane integrity and accumulates in discrete regions of the membrane (termed lipid rafts), which are involved in signaling cascades that were associated with cancer development.^39,⁴⁰ EGFR, which is known to be closely related to tumorigenesis, can be activated by cholesterol-mediated lipid rafts in the membrane.⁴¹ HDL-C, which plays a key role in the process of reverse cholesterol transport, removed cholesterol from peripheral tissues and transported it to the liver for metabolism.⁴² HDL-C removed excess cholesterol from an intracellular pool to maintain the cholesterol homeostasis in normal cells.⁴³ The explanation for the reduced HDL-C level in RCC patients may be that an increasing consumption and storage of cholesterol within tumor tissues during growth resulted in lower HDL levels in proliferating tissues. We speculated that HDL-C may remove the cholesterol from tumor cells to inhibit the formation of tumor cell membranes and cholesterol-mediated lipid rafts. Numerous studies have demonstrated that high serum HDL levels are associated with a decreased risk of cancers, including colon cancer,⁴⁴ breast cancer,⁴⁵ and prostate cancer.⁴⁶ As a result, lipoprotein treatment may be a promising anti-tumor agent in patients with a low serum HDL-C level; therefore, combination therapies of HDL-C-increasing agents with targeted therapies may improve the clinical outcome of RCC patients.

There are some limitations in the present study. First, the number of patients enrolled in our study was relatively small; second, our research was a retrospective study from a single center; third, our findings need to be confirmed in future studies from multiple centers.

Conclusion

Our results suggest that the serum HDL-C levels are a significant predictive factor in patients with CCRCC. Furthermore, the development of HDL-C-increasing agents may be a promising therapeutic strategy for RCC patients.

Footnotes

Author contributions

Bo Hao, Xufeng Peng, and Baochen Bi contributed equally to this work.

Declaration of conflicting interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics approval and consent to participate

Written informed consent was obtained from each patient and the study was approved by the Ethics Committee of the Third Affiliated Hospital of Soochow University.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Siegel

Miller

Jemal

Cancer statistics, 2015. CA Cancer J Clin 2015; 65 (1): 5–29.

van Spronsen

de Weijer

Mulders

et al . Novel treatment strategies in clear-cell metastatic renal cell carcinoma. Anticancer Drugs 2005; 16 (7): 709–717.

Chow

Youssef

Lianidou

et al . Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis. Clin Biochem 2010; 43 (1–2): 150–158.

Janzen

Kim

Figlin

et al . Surveillance after radical or partial nephrectomy for localized renal cell carcinoma and management of recurrent disease. Urol Clin North Am 2003; 30 (4): 843–852.

Erdogan

Demirel

Polat

Prognostic significance of morphologic parameters in renal cell carcinoma. Int J Clin Pract 2004; 58 (4): 333–336.

Edge

Compton

CC.

The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 2010; 17 (6): 1471–1474.

Ohno

Nakashima

Ohori

et al . Pretreatment neutrophil-to-lymphocyte ratio as an independent predictor of recurrence in patients with nonmetastatic renal cell carcinoma. J Urol 2010; 184 (3): 873–878.

Chen

Shao

Fan

et al . Prognostic significance of preoperative C-reactive protein: albumin ratio in patients with clear cell renal cell carcinoma. Int J Clin Exp Pathol 2015; 8 (11): 14893–14900.

Chen

Shao

Yao

et al . Preoperative albumin to globulin ratio predicts survival in clear cell renal cell carcinoma patients. Oncotarget 2017; 8 (29): 48291–48302.

10.

Permpongkosol

Colombo

Jr Gill

et al . Positive surgical parenchymal margin after laparoscopic partial nephrectomy for renal cell carcinoma: oncological outcomes. J Urol 2006; 176 (6 Pt 1): 2401–2404.

11.

Currie

Schulze

Zechner

et al . Cellular fatty acid metabolism and cancer. Cell Metab 2013; 18 (2): 153–161.

12.

Swierczynski

Hebanowska

Sledzinski

Role of abnormal lipid metabolism in development, progression, diagnosis and therapy of pancreatic cancer. World J Gastroenterol 2014; 20 (9): 2279–2303.

13.

Yue

Lee

et al . Cholesteryl ester accumulation induced by PTEN loss and PI3K/AKT activation underlies human prostate cancer aggressiveness. Cell Metab 2014; 19 (3): 393–406.

14.

Chen

Han

Wang

et al . Preoperative serum lipids as prognostic predictors in esophageal squamous cell carcinoma patients with esophagectomy. Oncotarget 2017; 8 (25): 41605–41619.

15.

Fan

Ding

Wang

et al . Decreased serum HDL at initial diagnosis correlates with worse outcomes for triple-negative breast cancer but not non-TNBCs. Int J Biol Markers 2015; 30 (2): e200–e207.

16.

Guo

Chen

et al . The effect of preoperative apolipoprotein A-I on the prognosis of surgical renal cell carcinoma: A retrospective large sample study. Medicine 2016; 95 (12): e3147.

17.

Chi

Liu

Chen

et al . High-density lipoprotein cholesterol is a favorable prognostic factor and negatively correlated with C-reactive protein level in non-small cell lung carcinoma. PLoS One 2014; 9 (3): e91080.

18.

Liu

Tao

Chen

et al . Preoperative body mass index, blood albumin and triglycerides predict survival for patients with gastric cancer. PLoS One 2016; 11 (6): e0157401.

19.

Wolfe

Atkinson

Reddy

et al . High-density and very-low-density lipoprotein have opposing roles in regulating tumor-initiating cells and sensitivity to radiation in inflammatory breast cancer. Int J Radiat Oncol Biol Phys 2015; 91 (5): 1072–1080.

20.

Siemianowicz

Gminski

Stajszczyk

et al . Serum HDL cholesterol concentration in patients with squamous cell and small cell lung cancer. Int J Mol Med 2000; 6 (3): 307–311.

21.

Franky Dhaval

Shilin Nandubhai

et al . Significance of alterations in plasma lipid profile levels in breast cancer. Integr Cancer Ther 2008; 7 (1): 33–41.

22.

Zhang

et al . Association of dyslipidemia with renal cell carcinoma: a 1ratio2 matched case-control study. PLoS One 2013; 8 (3): e59796.

23.

Muntoni

Atzori

Mereu

et al . Serum lipoproteins and cancer. Nutr Metab Cardiovasc Dis. 2009; 19 (3): 218–225.

24.

Jiang

Weng

Jiang

et al . The clinical significance of preoperative serum cholesterol and high-density lipoprotein-cholesterol levels in hepatocellular carcinoma. J Cancer 2016; 7 (6): 626–632.

25.

Tamura

Inagawa

Hisakura

et al . Evaluation of serum high-density lipoprotein cholesterol levels as a prognostic factor in gastric cancer patients. J Gastroenterol Hepatol 2012; 27 (10): 1635–1640.

26.

Cespedes Feliciano

Kroenke

Meyerhardt

et al . Metabolic dysfunction, obesity, and survival among patients with early-stage colorectal cancer. J Clin Oncol 2016; 34 (30): 3664–3671.

27.

Liu

Lin

Chen

et al . High-density lipoprotein cholesterol as a predictor of poor survival in patients with nasopharyngeal carcinoma. Oncotarget 2016; 7 (28): 42978–42987.

28.

Perkins

Schisterman

EF.

The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 2006; 163 (7): 670–675.

29.

Fan

Upadhye

Worster

Understanding receiver operating characteristic (ROC) curves. CJEM 2006; 8 (1): 19–20.

30.

Gerlinger

Rowan

Horswell

et al . Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366 (10): 883–892.

31.

Ficarra

Brunelli

Cheng

et al . Prognostic and therapeutic impact of the histopathologic definition of parenchymal epithelial renal tumors. Eur Urol 2010; 58 (5): 655–668.

32.

Jafri

Alsheikh-Ali

Karas

RH.

Baseline and on-treatment high-density lipoprotein cholesterol and the risk of cancer in randomized controlled trials of lipid-altering therapy. J Am Coll Cardiol 2010; 55 (25): 2846–2854.

33.

Tang

Wang

et al . The effect of preoperative serum triglycerides and high-density lipoprotein-cholesterol levels on the prognosis of breast cancer. Breast 2017; 32: 1–6.

34.

Lim

Gayles

Katki

et al . Serum high-density lipoprotein cholesterol and risk of non-hodgkin lymphoma. Cancer Res 2007; 67 (11): 5569–5574.

35.

Ahn

Lim

Weinstein

et al . Prediagnostic total and high-density lipoprotein cholesterol and risk of cancer. Cancer Epidemiol Biomarkers Prev 2009; 18 (11): 2814–2821.

36.

Inamdar

Mehta

Correlation between obesity and high-density lipoprotein cholesterol (HDL-C) in breast cancer patients of Southern Rajasthan. Indian J Surg Oncol 2011; 2 (2): 118–121.

37.

Grijalva

Navab

et al . HDL mimetics inhibit tumor development in both induced and spontaneous mouse models of colon cancer. Mol Cancer Ther 2012; 11 (6): 1311–1319.

38.

Sekine

Demosky

Stonik

et al . High-density lipoprotein induces proliferation and migration of human prostate androgen-independent cancer cells by an ABCA1-dependent mechanism. Mol Cancer Res 2010; 8 (9): 1284–1294.

39.

Brown

AJ.

Cholesterol, statins and cancer. Clin Exp Pharmacol Physiol 2007; 34 (3): 135–141.

40.

Simons

Ikonen

Functional rafts in cell membranes. Nature 1997; 387 (6633): 569–572.

41.

Varma

Mayor

GPI-anchored proteins are organized in submicron domains at the cell surface. Nature 1998; 394 (6695): 798–801.

42.

Slotte

JP.

HDL receptors and cholesterol efflux from parenchymal cells. Eur Heart J 1990; 11 (Suppl E): 212–217.

43.

Eisenberg

High density lipoprotein metabolism. J Lipid Res 1984; 25 (10): 1017–1058.

44.

van Duijnhoven

Bueno-De-Mesquita

Calligaro

et al . Blood lipid and lipoprotein concentrations and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition. Gut 2011; 60 (8): 1094–1102.

45.

Llanos

Makambi

Tucker

et al . Cholesterol, lipoproteins, and breast cancer risk in African American women. Ethn Dis 2012; 22 (3): 281–287.

46.

Mondul

Weinstein

Virtamo

et al . Serum total and HDL cholesterol and risk of prostate cancer. Canc Causes Contr 2011; 22 (11): 1545–1552.

Preoperative serum high-density lipoprotein cholesterol as a predictor of poor survival in patients with clear cell renal cell cancer

Abstract

Purpose:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Patients

Data collection

Radical nephrectomy

Follow-up assessment

Statistical analysis

Results

Patient characteristics

Relationship between the HDL-C levels and other clinical characteristics

Prognostic value of HDL-C and clinical characteristics for OS

Prognostic value of HDL-C and clinical characteristics for CSS

Discussion

Conclusion

Footnotes

Author contributions

Declaration of conflicting interest

Ethics approval and consent to participate

Funding

References