Prognostic Value and Model Construction of Preoperative Inflammatory Markers in Patients with Metastatic Renal Cell Carcinoma

doi:10.21203/rs.3.rs-2502001/v1

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Prognostic Value and Model Construction of Preoperative Inflammatory Markers in Patients with Metastatic Renal Cell Carcinoma

https://doi.org/10.21203/rs.3.rs-2502001/v1

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Purpose: Inflammation is considered to be one of the driving factors of cancer, and chronic inflammation plays a crucial role in tumor growth and metastasis. In this study, the aim was to study the predictive value of preoperative inflammatory biomarkers including preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and aspartate aminotransferase-to-lymphocyte ratio (ALR) which was a novel inflammatory biomarker, for overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC).

Method: This study included a total of 198 patients with mRCC from a single center from 2006 to 2022. The optimal cut-off levels of the three biomarkers were calculated by using the receiver operating characteristic curve (ROC). The patients were divided into two groups (high and low values) according to the cut-off level, and the difference between them was analyzed and compared by the Kaplan-Meier curve. Cox univariate and multivariate analyzes were used to assess independent prognostic inflammatory biomarkers. Finally, independent prognostic inflammatory biomarkers were incorporated into the prognostic model to establish a nomogram to predict the postoperative survival of patients with mRCC.

Result: The optimal cut-off level of NLR evaluated by the ROC curve was 3.836, LMR was 3.106, and ALR was 68.056. Patients with NLR and ALR higher than the cut-off level and LMR lower than the cut-off level were significantly related to OS. Multivariate analysis showed that lower LMR and higher ALR were independent risk factors for OS. In addition, a nomogram can accurately predict the OS in patients with mRCC.

Conclusion：ALR and LMR are independent risk factors for the prognosis of patients with mRCC. By monitoring ALR and LMR after operation, the prognosis of patients with mRCC can be better evaluated.

inflammatory biomarkers

metastatic renal cell carcinoma

overall survival

prognostic model

Renal carcinoma is the second most common urological malignant tumor to bladder cancer, of which renal cell carcinoma accounts for 90%. The clear cell carcinoma is the most common subtype of renal cell carcinoma with high rates of metastasis and recurrence rate(Zhu et al., 2020). Studies have shown that people with metastasis when diagnosed with renal cell carcinoma account for approximately 30%(Zheng et al., 2019). Since metastatic renal cell carcinoma is a heterogeneous disease that is highly resistant to chemotherapy, the therapy managements are few to choose. Although the treatment of mRCC has improved dramatically in the past decade(Flaifel et al., 2019), it didn’t show a better 5-year survival rate of patients, which was only about 10%(Singh et al., 2017). Therefore, it is of great importance to explore the relationship between the indicators in the perioperative period and the high risk of mRCC. This is crucial in the treatment, intervention, and follow-up of patients.

Studies have demonstrated some risk factors for OS in patients with mRCC, such as BMI, hyponatremia, nephrectomy, baseline hemoglobin, baseline lactate dehydrogenase (LDH), and so on(Albiges et al., 2016; Jeppesen, Jensen, Donskov, Marcussen, & von der Maase, 2010; Vogl et al., 2006). More and more evidences have supported the fact that inflammation promotes tumors, and is even closely related to tumor metastasis and recurrence(Aran et al., 2016). In recent years, with the deepening understanding of the tumor microenvironment and the improvement of perioperative diagnosis and treatment capabilities, some inflammatory indicators have been proposed to predict the prognosis of various cancers. For example, the neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) have been reported as the prognostic biomarkers in lung, colorectal, gastric, esophagus, hepatocellular carcinoma, and kidney cancers(Boissier, Campagna, Branger, Karsenty, & Lechevallier, 2017; Deng et al., 2015; Ding et al., 2020; Grimes, Hannan, Tyson, & Thwaini, 2018; Peng et al., 2017; Song, Wu, & Wang, 2019). Aspartate aminotransferase (AST) is a kind of aminotransferase and is a common blood biomarker which is distributed in multiple organs such as the liver, heart, skeletal muscle, and kidney. Studies have confirmed that AST and ALT can effectively predict the outcome of patients with hepatocellular carcinoma, renal cell carcinoma and breast cancer(Bezan et al., 2015; Li et al., 2020). The aspartate aminotransferase to lymphocyte ratio (ALR), a new inflammatory biomarker, has only been used in hepatocellular carcinoma and the one has been proven prognostic value so far(Wu et al., 2021). However, inflammatory biomarkers especially inflammatory index combination has not been studied extensively in mRCC.

Therefore, in this study, we aimed to prove the prognostic significance of NLR and LMR for mRCC and evaluate the potential prognostic impact of ALR on mRCC patients; explored its relationship with OS. Finally, in order to explore the value of inflammatory biomarkers in the prognosis of mRCC patients. ALR and other independent prognostic factors were chosen to construct a nomogram to predict the survival status of mRCC patients at 1 and 2 years after surgery.

Patient

The medical records of 212 patients with mRCC from a single center from 2006 to 2021 were prospectively collected and analyzed retrospectively. Patients with incomplete data were excluded (n = 14), and the final cohort included 198 patients. The inclusion criteria are as follows: 1) Renal cell carcinoma patients with clear evidence of metastasis to other sites before surgery. 2) Unilateral kidney cancer. 3) No severe inflammation, infection, high fever, blood disease, and kidney rupture. 4) Informed consent was obtained from eligible patients, and all medical records were approved by the Ethics Committee of our hospital.

Collect routine examination data after admission: including demographic data and medical history, hematology and laboratory data 1 week before surgery, AST, surgical method, metastatic organs, postoperative pathological parameters, and targeted therapy drug. Surgical methods include open surgery and minimally invasive surgery which contains robotic and laparoscopic nephrectomy. Postoperative pathological parameters included pathological cell type, pathologic Fuhrman’s grade, and TNM grade based on the International Union Against Cancer (UICC). Targeted drugs are divided into PD1 and TK1 drugs. The calculation formulas of NLR, LMR, and ALR are as follows:

NLR = neutrophil count/lymphocyte count ratio; LMR = lymphocyte count/monocyte count ratio; ALR = aspartate aminotransferase/lymphocyte count ratio.

Follow up

After postoperative discharge, the patient underwent regular outpatient follow-up checks every three months for the first two years, every six months for the third to fifth years, and then every year thereafter. The inspection contents included blood routine examination, blood biochemical examination, and abdominal ultrasonography. The primary study outcome of this study was OS, defined as the time from the end of surgery to death or the last follow-up. The last follow-up date is December 31, 2021

Statistical analysis

Most statistical studies in this project were analyzed using R software 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were expressed as mean (interquartile range, (IQR)), and categorical variables were expressed as counts and percentages. The optimal cut-off levels of the inflammatory index were calculated according to the Receiver operating characteristic (ROC) curves and determined by the Youden index, and then the area under the curve(AUC) significance test was performed using DeLong's test. The correlation between NLR, LMR, and ALR and each clinical variable was determined using Pearson's chi-square test or Fisher's exact test. The inflammatory biomarkers were divided into two groups according to the optimal cutoff levels, the survival curves were drawn by Kaplan-Meier survival analysis, and then the log-rank test was used for significance comparison. The three inflammatory indicators and other Important clinicopathological parameters （Gender， Age， Hemoglobin， Targeted therapy， Tumor site， Number of metastatic site， Histology， Microvascular invasion， Tumor size， Nephrectomy， Furman grade， T stage， N stage， Tumor necrosis）were subjected to univariate Cox regression analysis, and the univariate analysis variables with P≤0.05 were included in the multivariate Cox regression analysis to clarify the independent prognostic factors of OS and used (Concordance, C- index) for consistency check analysis. The nomogram was jointly constructed using independent prognostic factors, and its predictive accuracy was evaluated. In this study, P≤0.05 was defined as a statistical difference.

Baseline characteristics of patients

Table 1 shows the clinical and pathological characteristics of the patients. A total of 198 patients were included in this study, with 154 (77.8%) males and 44 (22.2%) females. Eighty-two (41.4%) patients were deceased and 116 (58.6%) were still alive by the last follow-up.

Figure 1 shows the ROC curves of ALR, NLR, and LMR, of which the AUC areas are 0.75 (CI: 0.680-0.819), 0.71 (CI: 0.635-0.784) and 0.68 (CI: 0.604-0.755), respectively. According to the ROC curve, the best cut-off levels of the three indicators are calculated as 68.056, 3.836, and 3.106 respectively, and the Youden indices are 0.414, 0.366, and 0.311 respectively. In order to facilitate the analysis of the relationship between the three indicators and the disease, each of the three indicators was divided into two groups, including the high ALR (>68.056) group and low ALR (≤68.056) group, high NLR (>3.836) group and low NLR (≤3.836) group, and high LMR (>3.106) group and low LMR (≤3.106) group.

Table 2 shows the relationship between clinicopathological parameters and three inflammatory biomarkers. There were 80 (40.4%) cases in the high ALR group and 118 (59.6%) cases in the low ALR group, 42 (21.2%) cases in the high NLR group, and 156 (78.8%) cases in the low NLR group, and 123 (62.1%) cases in the low LMR group and 75 (37.9%) cases in the low LMR group. Overall, patients with higher ALR performed higher T stage (P≤0.05), and patients with higher NLR were more prone to Tumor necrosis (P≤0.05). But there was no statistical significance between LMR and Gender, Age, Furman grade, Targeted therapy, Tumor site, Number of metastatic sites, Histology, Microvascular invasion, Tumor size, Nephrectomy, T stage, N stage, and Tumor necrosis (p>0.05).

Associations of ALR, NLR, and LMR with OS

As shown in Figure 2, Kaplan-Meier survival analysis was performed according to the optimal cut-off level, and the results showed that patients with low levels of ALR (HR=(0.33 (0.21−0.52)) and NLR (HR= 0.23 (0.15−0.36)) had longer OS, while patients with high LMR (HR = 2.57 (1.66−3.99)) had a longer OS. Tested by the Log-rank test, all three indicators were statistically significant (P<0.001)

Univariate and multivariate Cox regression survival analysis

Table 3 shows the relationship between clinicopathological parameters and ALR, NLR, LMR, as well as OS. The results of the univariate analysis showed that Hemoglobin, Histology, Nephrectomy, T stage, Tumor necrosis, ALR, NLR, and LMR were prognostic factors for OS, while other variables had no statistical difference (P>0.05). Bring Important parameters from univariate analysis into multivariate analysis to assess their relationship with OS, we obtained the results that tumor necrosis (HR, 0.524; 95% CI, 0.328-0.838; P=0.007), LMR (HR, 0.783; 95% CI, 0.650-0.943; P=0.01) and ALR (HR, 1.005; 95% CI, 1.001-1.008; P=0.006) were independent risk factors for OS.

Prognostic nomogram for OS

In order to facilitate the evaluation of patients after mRCC surgery and make the results of the prediction model more readable, we combined independent risk factors with some clinically important pathological parameters (Hemoglobin, Number of metastatic sites, Histology, Microvascular invasion, Furman grade, T stage, N stage, Tumor necrosis, LMR and ALR) were included in the prognostic nomogram (Figure 3). The nomogram predicted the 1-year and 2-year survival of patients with mRCC after the operation, and the higher the total point, the worse the prognosis. Afterward, we carried out prognosis calibration on the constructed nomogram. This figure depicts the difference between the predicted probability and the actual probability corresponding to the model in 1 year and 2 years. It can be seen that the survival predicted by the nomogram is appropriate to the actual situation, proving that the forecast fit well (Figure 3). The C-index of the consistency test was 0.753 (0.727-0.779) (P<0.001).

Patients with renal cell carcinoma account for 90% of people with renal carcinoma2. Localized renal cell carcinoma are generally cured with surgery, while metastatic renal cell carcinoma often has poor treatment effects(Yin et al., 2016), and the 5-year survival rate is less than 10%5. Therefore, it is necessary to discover independent risk factors for these patients.

At present, studies have confirmed that the prognosis of patients with mRCC is related to many indicators. For example, in 2016, LaurenceAlbiges and others proved that there is a close relationship between BMI and the prognosis of patients with mRCC(Albiges et al., 2016); in 2010, ANJeppesen and others showed that hyponatremia is an independent prognosis and predictive factor for patients with mRCC(Jeppesen et al., 2010). In addition, some tumor-related parameters have been identified as prognostic factors for mRCC survival, such as nephrectomy, baseline hemoglobin, baseline lactate dehydrogenase (LDH), and alkaline phosphatase (aP)(Vogl et al., 2006). Interestingly, previous studies have confirmed that neutrophil count, has been incorporated into the International metastatic RCC Database Alliance (IMDC) model, which is one of the most common prognostic models for stratifying mRCC patients into risk groups(Patel et al., 2020). However, NLR as a more sensitive and accurate has not been taken into account at that time. In this study, we will further explore the relationship between inflammatory biomarkers and the prognosis of patients with mRCC so as to find complementary biomarkers.

In this study, 198 mRCC patients' clinicopathological indicators were included. The results of COX proportional hazards regression proved that LMR and ALR are independent risk factors for OS. In univariate analysis, NLR was an important indicator, while in multivariate analysis, NLR had no statistical significance. In addition, we also found that patients with higher ALR had higher T stage, and patients with higher NLR were more prone to Tumor necrosis. Thus we combined the two inflammatory indicators, ALR and LMR, with some conventional clinicopathological indicators to form a nomogram to predict the 1-year and 2-year survival rate of mRCC patients after surgery, with which the calibration map fits well.

In recent years, more and more evidences have shown that there is a close relationship between inflammation and cancer and the predictive value of the two common inflammatory biomarkers (NLR and LMR) in cancer has been confirmed(Eo, 2015; Teng, Zhang, Zhang, Zhang, & Li, 2016; Wang et al., 2016). For patients with non-metastatic renal cell carcinoma, studies have shown that NLR and LMR have important prognostic significance after surgery(Hutterer et al., 2014; Pichler et al., 2013). In addition, studies have also demonstrated the prognostic value of NLR on mRCC patients after surgery(Cetin et al., 2013; Lecot et al., 2019). As inflammatory immune cells, neutrophils alter the tumor microenvironment by expressing chemokine receptors CXCR1 and CXCR2(Long et al., 2021). Moreover, neutrophils also release reactive oxygen species (ROS) and proteases to promote cancer development(Antonio et al., 2015). Monocyte-derived macrophages secrete oncogenic factors or respond to cancer-associated cytokines to promote tumorigenesis. Lymphocytes are a key part of the immune response(Ham, Lima, Lek, & Moller, 2020). T-lymphocytes can directly kill cancer cells; B-lymphocytes can produce immunoglobulins and kill and inhibit cancer cells by producing IgM antibodies. A decline in LMR means a decrease in lymphocyte count or a relative or absolute increase in monocyte count, which also indirectly indicates the weakening of the body's anti-tumor function. This is consistent with our conclusion that a low LMR level corresponds to a bad prognosis.

Aminotransferases include AST and alanine aminotransferase (ALT), which are liver enzymes produced by malignant or non-malignant cells and have been proven to be biomarkers of various malignant tumors such as lung cancer, breast cancer, and pancreatic cancer(Rawson NS, 1990; Stocken et al., 2008; Thornburg et al., 2008). ALT is a liver-specific index. Compared with ALT, AST is more widely distributed in the body(Bezan et al., 2015), so it is more meaningful to explore the relationship between AST and non-hepatic diseases. Studies have shown that the ratio of AST/ALT is an important prognostic factor for tumors of the urinary system(Su, Liu, Li, Zhang, & Li, 2020), but there are few studies on the influence of the ratio of ALR on the prognosis of urinary tumors. The influence of liver metastases on the prediction accuracy of ALR indicators can basically be ruled out for only 12 patients had liver metastases among the 198 cases in this study.

In basic research, the "Warburg effect" was put forward by Otto Heinrich Warburg. He found that cancer tissue chooses glycolysis as the main way to synthesize ATP even in an aerobic environment and performs a higher aerobic glycolysis rate compared with normal tissue(Elf & Chen, 2014). As an important enzyme in the malate-aspartate shuttle pathway in glycolysis, AST is an essential part that increases the aerobic glycolysis and glutamine synthesis of cancer cells. In addition, about 80% of RCC patients have mutations in the VHL gene(Chan & Giaccia, 2010), which upregulates the hypoxia-inducible factor (HIF) and produces a pseudo-hypoxic state, which in turn increases glycolysis(Sudarshan, Linehan, & Neckers, 2007). All these have laid a potential and reasonable theoretical foundation for this article.

Again, there are also some limitations in this study. First, we only collected data related to some inflammatory markers while we did not collect other important markers such as serum albumin and C-reactive protein. These markers may play an important role in the occurrence and development of mRCC， but we will conduct further research on this point later. Second, although a single center is beneficial to control the confounding factors affecting prognosis such as surgical procedures, it is also prone to bias. Future larger sample and multicenter prospective study will further confirm the findings. Last, this study did not choose progression-free survival as a study outcome for lucubrating, and we will recruit more patients for further study in the future.

This study included a total of 198 patients with mRCC and the results demonstrated that NLR, LMR, and a new inflammatory marker-ALR can be used as predictors to evaluate the prognosis of mRCC patients. As independent prognostic factors, ALR and LMR are cheap and convenient to test. It may be considered to combine them with conventional pathological parameters to improve the prognosis assessment of mRCC patients.

Acknowledgments

The authors thank all the staff in the Senior Department of Urology of the Third Medical Center of PLA General Hospital for their support during the study.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

All authors contributed to the study conception and design. Data collection and analysis were performed by JW, XZ and JY. The first draft of the manuscript was written by JW. XM and XL commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Ethical approval

This study was approved by the Ethics Committee of the Chinese PLA General Hospital (internal registration no.S2013-065-01).

Consent to participate

All patients provided written informed consent.

Consent to publish

Not applicable.

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Table 1 Baseline characteristics of patients

Characteristics	overall
Gender, n (%)
Male	154(77.8%)
Female	44(22.2%)
Age, median (IQR)	57 (49, 63)
Furman grade, n (%)
G1+G2	108 (54.5%)
G3+G4	90 (45.5%)
Targeted therapy, n (%)
Absent	35 (17.7%)
TK1	25 (12.6%)
PD1	138 (69.7%)
Tumor site, n (%)
Left	120 (60.6%)
Right	78 (39.4%)
Number of metastatic site, n (%)
＜2	151 (76.3%)
≥2	47 (23.7%)
Histology, n (%)
Clear cell	176 (88.9%)
Non–clear cell	22 (11.1%)
Microvascular invasion, n (%)
Present	47 (23.7%)
Absent	151 (76.3%)
Tumor size (cm), n (%)
＞7	116 (58.6%)
≤7	82 (41.4%)
Nephrectomy, n (%)
Minimally invasive	176 (88.9%)
Open	22 (11.1%)
T stage, n (%)
T1 + T2	54 (27.3%)
T3 + T4	144 (72.7%)
N stage , n (%)
N0	147 (74.2%)
N1	51 (25.8%)
Tumor necrosis, n (%)
Present	98 (49.5%)
Absent	100 (50.5%)

Table 2 Baseline parameters of the study

Parameters		ALR＞68.056	ALR≤68.056	P value		NLR＞3.836	NLR≤3.836	P value		LMR＞3.106	LMR≤3.106	P value
All patients		80	118			42	156			75	123
Gender, n (%)	Male	60 (30.3%)	94 (47.5%)	0.549	Male	29 (14.6%)	125 (63.1%)	0.185	Male	59 (29.8%)	95 (48%)	0.953
	Female	20 (10.1%)	24 (12.1%)		Female	13 (6.6%)	31 (15.7%)		Female	16 (8.1%)	28 (14.1%)
Age n (%)	＞60	31 (15.7%)	42 (21.2%)	0.763	＞60	16 (8.1%)	57 (28.8%)	0.996	＞60	30 (15.2%)	43 (21.7%)	0.575
	≤60	49 (24.7%)	76 (38.4%)		≤60	26 (13.1%)	99 (50%)		≤60	45 (22.7%)	80 (40.4%)
Furman grade, n (%)	G1+G2	41 (20.7%)	67 (33.8%)	0.534	G1+G2	21 (10.6%)	87 (43.9%)	0.623	G1+G2	36 (18.2%)	72 (36.4%)	0.195
	G3+G4	39 (19.7%)	51 (25.8%)		G3+G4	21 (10.6%)	69 (34.8%)		G3+G4	39 (19.7%)	51 (25.8%)
Targeted therapy, n (%)	Absent	18 (9.1%)	17 (8.6%)	0.275	Absent	5 (2.5%)	30 (15.2%)	0.535	Absent	15 (7.6%)	20 (10.1%)	0.796
	TK1	11 (5.6%)	14 (7.1%)		TK1	6 (3%)	19 (9.6%)		TK1	9 (4.5%)	16 (8.1%)
	PD1	51 (25.8%)	87 (43.9%)		PD1	31 (15.7%)	107 (54%)		PD1	51 (25.8%)	87 (43.9%)
Tumor site, n (%)	Left	51 (25.8%)	69 (34.8%)	0.550	Left	28 (14.1%)	92 (46.5%)	0.467	Left	45 (22.7%)	75 (37.9%)	1.000
	Right	29 (14.6%)	49 (24.7%)		Right	14 (7.1%)	64 (32.3%)		Right	30 (15.2%)	48 (24.2%)
Number of metastatic site, n (%)	＜2	55 (27.8%)	96 (48.5%)	0.061	＜2	30 (15.2%)	121 (61.1%)	0.532	＜2	56 (28.3%)	95 (48%)	0.810
	≥2	25 (12.6%)	22 (11.1%)		≥2	12 (6.1%)	35 (17.7%)		≥2	19 (9.6%)	28 (14.1%)
Histology, n (%)	Clear cell	70 (35.4%)	106 (53.5%)	0.778	Clear cell	36 (18.2%)	140 (70.7%)	0.422	Clear cell	69 (34.8%)	107 (54%)	0.393
	Non–clear cell	10 (5.1%)	12 (6.1%)		Non–clear cell	6 (3%)	16 (8.1%)		Non–clear cell	6 (3%)	16 (8.1%)
Microvascular invasion, n (%)	Present	23 (11.6%)	24 (12.1%)	0.232	Present	14 (7.1%)	33 (16.7%)	0.149	Present	21 (10.6%)	26 (13.1%)	0.353
	Absent	57 (28.8%)	94 (47.5%)		Absent	28 (14.1%)	123 (62.1%)		Absent	54 (27.3%)	97 (49%)
Tumor size (cm), n (%)	＞7	41 (20.7%)	75 (37.9%)	0.114	＞7	21 (10.6%)	95 (48%)	0.273	＞7	39 (19.7%)	77 (38.9%)	0.187
	≤7	39 (19.7%)	43 (21.7%)		≤7	21 (10.6%)	61 (30.8%)		≤7	36 (18.2%)	46 (23.2%)
Nephrectomy, n (%)	Minimally invasive	73 (36.9%)	103 (52%)	0.522	Minimally invasive	39 (19.7%)	137 (69.2%)	0.579	Minimally invasive	68 (34.3%)	108 (54.5%)	0.698
	Open	7 (3.5%)	15 (7.6%)		Open	3 (1.5%)	19 (9.6%)		Open	7 (3.5%)	15 (7.6%)
T stage, n (%)	T1 + T2	13 (6.6%)	41 (20.7%)	0.007	T1 + T2	6 (3%)	48 (24.2%)	0.053	T1 + T2	19 (9.6%)	35 (17.7%)	0.754
	T3 + T4	67 (33.8%)	77 (38.9%)		T3 + T4	36 (18.2%)	108 (54.5%)		T3 + T4	56 (28.3%)	88 (44.4%)
N stage , n (%)	N0	57 (28.8%)	90 (45.5%)	0.531	N0	29 (14.6%)	118 (59.6%)	0.504	N0	50 (25.3%)	97 (49%)	0.083
	N1	23 (11.6%)	28 (14.1%)		N1	13 (6.6%)	38 (19.2%)		N1	25 (12.6%)	26 (13.1%)
Tumor necrosis, n (%)	Present	43 (21.7%)	55 (27.8%)	0.400	Present	27 (13.6%)	71 (35.9%)	0.047	Present	43 (21.7%)	55 (27.8%)	0.115
	Absent	37 (18.7%)	63 (31.8%)		Absent	15 (7.6%)	85 (42.9%)		Absent	32 (16.2%)	68 (34.3%)

Table2 Abbreviations: NLR: Neutrophil-to-Lymphocyte Ratio; ALR: aspartate aminotransferase-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio.

Difference between groups was tested by Chi-squared test

Table 3 Univariate and multivariate Cox regression survival analysis for the prediction of OS

Characteristics	Total(N)	Univariate analysis		Multivariate analysis
Characteristics	Total(N)	Hazard ratio (95% CI)	P value	Hazard ratio (95% CI)	P value
Gender	198
Male	154	Reference
Female	44	1.161 (0.687-1.962)	0.577
Age	198	1.004 (0.986-1.022)	0.687
Hemoglobin	198	0.987 (0.978-0.995)	0.002	0.991 (0.981-1.001)	0.065
Targeted therapy	198
Absent	35	Reference
TK1	25	0.747 (0.365-1.530)	0.425
PD1	138	0.680 (0.396-1.167)	0.162
Tumor site	198
Left	120	Reference
Right	78	0.951 (0.607-1.490)	0.828
Number of metastatic site	198
＜2	151	Reference
≥2	47	1.123 (0.683-1.848)	0.648
Histology	198
Clear cell	176	Reference
Non–clear cell	22	2.235 (1.216-4.108)	0.010	1.820 (0.964-3.436)	0.065
Microvascular invasion	198
Present	47	Reference
Absent	151	0.823 (0.491-1.379)	0.459
Tumor size (cm)	198
＞7	116	Reference
≤7	82	1.379 (0.891-2.133)	0.149
Nephrectomy	198
Minimally invasive	176	Reference
Open	22	0.321 (0.117-0.879)	0.027	0.397 (0.140-1.126)	0.082
Furman grade	198
G1+G2	108	Reference
G3+G4	90	1.407 (0.909-2.178)	0.126
T stage	198
T1 + T2	54	Reference
T3 + T4	144	1.917 (1.094-3.358)	0.023	1.263 (0.702-2.271)	0.436
N stage	198
N0	147	Reference
N1	51	1.021 (0.610-1.708)	0.937
Tumor necrosis	198
Present	98	Reference
Absent	100	0.542 (0.348-0.844)	0.007	0.524 (0.328-0.838)	0.007
LMR	198	0.736 (0.625-0.867)	<0.001	0.783 (0.650-0.943)	0.010
NLR	198	1.127 (1.049-1.211)	0.001	0.948 (0.823-1.091)	0.457
ALR	198	1.007 (1.005-1.010)	<0.001	1.005 (1.001-1.008)	0.006

Table 3 Statistically significant results were in bold

Abbreviations: NLR: Neutrophil-to-Lymphocyte Ratio; ALR: aspartate aminotransferase-to-lymphocyte ratio; LMR: lymphocyte-to-monocyte ratio.

Difference between groups was tested by Chi-squared test

No competing interests reported.

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Prognostic Value and Model Construction of Preoperative Inflammatory Markers in Patients with Metastatic Renal Cell Carcinoma

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