Front. Immunol. Frontiers in Immunology Front. Immunol. 1664-3224 Frontiers Media S.A. 10.3389/fimmu.2021.778746 Immunology Original Research Multicenter Study of Controlling Nutritional Status (CONUT) Score as a Prognostic Factor in Patients With HIV-Related Renal Cell Carcinoma XueWenrui 1 ZhangYu 2 WangHua 3 ZhangYu 1 * HuXiaopeng 4 * 1Beijing Youan Hospital, Capital Medical University, Beijing, China 2Beijing Ditan Hospital, Capital Medical University, Beijing, China 3Chengdu Public Health Clinical Medical Center, Sichuan, China 4Beijing Chaoyang Hospital, Capital Medical University, Beijing, China

Edited by: Richard Skipworth, University of Edinburgh, United Kingdom

Reviewed by: Markus Xie, Genentech, United States; Yaqing Qie, University of Texas MD Anderson Cancer Center, United States

*Correspondence: Xiaopeng Hu, selihebe2019@163.com; Yu Zhang, wenruixue@hotmail.com

This article was submitted to Nutritional Immunology, a section of the journal Frontiers in Immunology

 30 11 2021 2021 12 778746 17 09 2021 09 11 2021 Copyright © 2021 Xue, Zhang, Wang, Zhang and Hu 2021 Xue, Zhang, Wang, Zhang and Hu

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

 Objective

In recent years, the controlled nutritional status (CONUT) score has been widely recognized as a new indicator for assessing survival in patients with urological neoplasms, including renal, ureteral, and bladder cancer. However, the CONUT score has not been analyzed in patients with HIV-related urological neoplasms. Therefore, we aimed to evaluate the prognostic significance of the CONUT score in patients with HIV-related renal cell carcinoma (RCC).

 Methods

A total of 106 patients with HIV-related RCC were recruited from four hospitals between 2012 and 2021, and all included patients received radical nephrectomy or partial nephrectomy. The CONUT score was calculated by serum albumin, total lymphocyte counts, and total cholesterol concentrations. Patients with RCC were divided into two groups according to the optimal cutoff value of the CONUT score. Survival analysis of different CONUT groups was performed by the Kaplan–Meier method and a log rank test. A Cox proportional risk model was used to test for correlations between clinical variables and cancer-specific survival (CSS), overall survival (OS), and disease-free survival (DFS). Clinical variables included age, sex, hypertension, diabetes, tumor grade, Fuhrman grade, histology, surgery, and CD4+ T lymphocyte count.

 Result

The median age was 51 years, with 93 males and 13 females. At a median follow-up of 41 months, 25 patients (23.6%) had died or had tumor recurrence and metastasis. The optimal cutoff value for the CONUT score was 3, and a lower CONUT score was associated with the Fuhrman grade (P=0.024). Patients with lower CONUT scores had better CSS (HR 0.197, 95% CI 0.077-0.502, P=0.001), OS (HR 0.177, 95% CI 0.070-0.446, P<0.001) and DFS (HR 0.176, 95% CI 0.070-0.444, P<0.001). Multivariate Cox regression analysis indicated that a low CONUT score was an independent predictor of CSS, OS and DFS (CSS: HR=0.225, 95% CI 0.067-0.749, P=0.015; OS: HR=0.201, 95% CI 0.061-0.661, P=0.008; DFS: HR=0.227, 95% CI 0.078-0.664, P=0.007). In addition, a low Fuhrman grade was an independent predictor of CSS (HR 0.192, 95% CI 0.045-0.810, P=0.025), OS (HR 0.203, 95% CI 0.049-0.842, P=0.028), and DFS (HR 0.180, 95% CI 0.048-0.669, P=0.010), while other factors, such as age, sex, hypertension, diabetes, tumor grade, histology, surgery, and CD4+ T lymphocyte count, were not associated with survival outcome.

 Conclusion

The CONUT score, an easily measurable immune-nutritional biomarker, may provide useful prognostic information in HIV-related RCC.

 controlling nutritional status score HIV-related renal cell carcinoma prognostic factor cancer-specific survival (CSS) overall survival (OS) disease-free survival (DFS) highly active antiretroviral therapy (HAART) Introduction

RCC is the most common pathologic type of renal cancer and the seventh most common tumor, accounting for 2% to 3% of all cancers (1). Twenty percent of newly diagnosed RCC patients have advanced disease, and approximately 30% experience local or distant disease recurrence after surgery for localized RCC (2). In recent years, relatively few infection cases of HIV-related RCC have been reported worldwide. Patients with such RCC have concurrent immune infection. Human immunodeficiency virus (HIV) infects human dendritic cells and macrophages and activates CD4+ T lymphocytes, resulting in disruption of the immune system, so the tumor incidence and mortality differ from those in ordinary RCC patients (3). RCC is more common in HIV-infected individuals than in age-matched non-HIV-infected individuals and is a common cause of morbidity and mortality. Possible mechanisms for this increased risk include reduced immune surveillance, direct effects of viral proteins, or cytokine dysregulation (4, 5). With the widespread application of and tremendous progress in early activation of highly active antiretroviral therapy (HAART), both virological suppression and immune recovery in patients with HIV-related RCC have been maintained at a good level (6). However, non-AIDS-defining cancers (non-ADCs), including urinary cancers, anal cancers, lung cancers, breast cancers and skin cancers, are still three times more frequent (7). If patients with HIV-related RCC can be assessed early, their survival could be significantly improved. Therefore, it is important to develop off-the-shelf biomarkers that can predict and even modify tumor outcomes based on risk stratification (8).

Immunological status comprising inflammatory and nutritional status, remains an important predictor of prognosis in patients with malignant tumors (9). Several biomarkers, such as the prognostic nutritional index (PNI) and the neutrophil to lymphocyte ratio (NLR), have been reported to be independent prognostic factors (10–12). Recently, the CONUT score, which is calculated from serum albumin, total lymphocyte counts, and total cholesterol concentration, has gained attention as a biomarker for predicting survival in patients with multiple cancers. A high CONUT score means lower levels of albumin, lymphocytes, and cholesterol, which are often associated with poorer nutritional and immune status in patients and may lead to poorer survival (13). Maintaining optimal nutritional status can greatly improve quality of life while reducing comorbidities, progression of HIV infection, and HIV-related mortality (14, 15). In addition, good nutrition also helps HIV-infected patients absorb HIV drugs (16). The effects of poor nutritional status and HIV are synergistic and interrelated, thus amplifying their respective harmful effects (15, 17). Increasing evidence suggests that, in addition to the genetic basis, host nutritional status and inflammatory responses also play an important role in cancer development and progression (18). At present, some articles suggest that the CSS, OS and DFS of patients with 5-year ordinary RCC (non-HIV related) in the low-CONUT group are significantly higher than those in the high-CONUT group (8, 19–24), but some articles suggest that a high CONUT score is not related to the prognosis of patients with ordinary RCC (25). However, there are no articles about the relationship between CONUT score and HIV-related RCC. To the best of our knowledge, this is the first multicenter study to evaluate the prognostic value of the CONUT score in HIV-related RCC.

 Materials and Methods Patients

We performed an open-label, retrospective, multicenter, cohort study. A total of 106 patients with HIV-related RCC who underwent radical nephrectomy or partial nephrectomy were included. All participants underwent preoperative urological CT examination showing a renal carcinoma volume ≤7 cm between 2012 and 2021. We excluded patients with ordinary RCC without HIV infection, patients with lymph node metastasis or distant metastasis, and patients with no follow-up results. All enrolled patients had provided blood samples with results for serum albumin, total lymphocyte counts, and total cholesterol concentration one week before surgery and were treated with HAART and monitored for associated CD4+ T lymphocyte count.

Pathological stage was determined according to the 2010 TNM grade and tumor grade according to the Fuhrman grading system. This study was in accordance with the Helsinki Declaration and approved by the Ethics Review Committee of all included hospitals. During follow-up, patients or their next of kin were informed of the study in detail, and verbal consent was obtained. All data are kept confidential.

 Follow-Up

Every three months within the first 3 years after surgery, the patient was admitted to the outpatient department of the hospital for routine blood examination, blood biochemistry, chest X-ray, abdominal color Doppler ultrasound and enhanced urinary CT examination. After 3 years, the above review was performed every 6 months until tumor recurrence, metastasis or death. Relapse is equal to the first detection of local recurrence, and metastasis is equal to the first discovery of lymph node or distant organ metastases (lung metastasis, brain metastasis, liver metastasis, etc.). Death was confirmed by relevant information from the hospitals or notification by the patient’s family during telephone follow-up.

 Study Endpoints

We considered CSS, OS, and DFS as the end points of the study (in months). CSS was defined as the time from the date of surgery to cancer-related death. OS was defined as the time from the date of surgery to the death of the individual from any cause. DFS was defined as the time from the date of surgery to radiologically or histologically confirmed recurrence or metastasis.

 CONUT Score, PNI and NLR

The CONUT score was calculated by serum albumin, total lymphocyte counts, and total cholesterol concentration (Table 1). The optimal cutoff value of the CONUT score was determined using the receiver operating characteristic (ROC) curve and the maximum Youden index value. The PNI was calculated as 10 × serum albumin (g/dl) + 0.005 × total lymphocyte count (per mm3). The NLR was calculated as the ratio of the number of neutrophils to the number of lymphocytes.

Definition of CONUT score.

Parameters CONUT
Normal Light Moderate Severe
Serum albumin (g/dL) ≥3.50 3.00-3.49 2.50-2.99 <2.50
Score 0 2 4 6
Total lymphocyte (/mm3) ≥1600 1200-1599 800-1199 <800
Score 0 1 2 3
Total cholesterol (mg/dL) ≥180 140-179 100-139 <100
Score 0 1 2 3
CONUT score (total) 0-1 2-4 5-8 9-12
Statistics

A chi-square test was used to analyze the correlations between the CONUT score and variables including age, sex, hypertension, diabetes, tumor grade, Fuhrman grade, histology, surgery, and CD4+ T lymphocyte count. Kaplan–Meier survival curves were plotted to estimate CSS, OS, and DFS. The predictors of CSS, OS and DFS were determined by univariate analysis, a Cox proportional risk model was used for multivariate analysis evaluation, and variables with P<0.05 in univariate analysis were included in subsequent multivariate analysis. GraphPad Prism Version 9 (GraphPad Software, La Jolla California USA, www.graphpad.com) was used to generate survival curves. Statistical analysis and ROC curves mapping were performed using SPSS version 23 (SPSS Inc., Chicago, IL, USA).

 Results CONUT Score and Its Cutoff Value

According to ROC analysis, the Youden index was used to determine the optimal cutoff value of the CONUT score as 3 (AUC: 0.746, 95% CI: 0.638-0.855, P<0.001, Figure 1. The area under the receiver operating characteristics curve, AUC). The CONUT score was assessed by dichotomous variables (low: <3, high: ≥3).

ROC curve for CONUT, PNI and NLR. ROC, receiver operating characteristic; CONUT, controlling nutritional status; PNI, prognostic nutritional index; NLR, neutrophil–lymphocyte ratio.

 Clinicopathological Features

Of the 106 patients enrolled, with a median age of 51 (IQR 27-75) years at the time of surgery, 93 were males, 13 were females, 80 underwent radical nephrectomy, and 26 underwent partial nephrectomy. Among them, 104 cases were clear cell carcinoma, 87 cases were T1N0M0, and 19 cases were T3N0M0. For the Fuhrman classification, 65 were grades I-II, and 41 were grades III-IV. The median CD4+ T lymphocyte count value was 435 (IQR 48-1536) cells/µl. The highest preoperative viral load was 1,018,049 copies/mL, and the lowest was undetectable (Table 2). The CONUT score was high in 45 cases (42%) and low in 61 cases (58%), and was closely correlated with the Fuhrman grade. A low CONUT score was significantly associated with lower Fuhrman grade (I-II vs III-IV, 66.2% vs 43.9%, respectively, p=0.024). The CONUT score had no significant correlation with age, sex, hypertension, diabetes, tumor grade, histology, surgery, or CD4+ T lymphocyte count (P>0.05) (Table 3).

Basic information of the cases and HIV- related data.

Case Age Gender Fuhr-man Tumor grade Histology Comorbidity CD4 count (cells/ul) Viral load (copies/ml) Surgery CONUT PNI NLR
1 59 Male III-IV T1N0M0 Clear cell Hypertension 614 NT RN 1 54.75 0.9628
2 74 Male I-II T1N0M0 Clear cell Hypertension 356 NT RN 2 41.65 2.7287
3 43 Male I-II T1N0M0 Clear cell None 400 TND RN 2 47.65 1.6163
4 58 Male I-II T1N0M0 Clear cell None 1536 341 RN 5 45.75 2.3498
5 54 Male III-IV T1N0M0 Clear cell Hypertension 628 NT RN 1 52.75 1.7355
6 51 Male I-II T1N0M0 Clear cell Diabetes 291 TND RN 1 52.3 2.5505
7 54 Male I-II T1N0M0 Clear cell Hypertension+ Diabetes 880 TND PN 0 58.05 1.0433
8 51 Male III-IV T3N0M0 Clear cell None 190 6495 RN 2 49.4 1.9253
9 34 Male III-IV T3N0M0 Clear cell None 462 TND RN 3 49.55 2.9802
10 72 Male III-IV T1N0M0 Clear cell None 378 NT RN 2 47.65 3.6067
11 54 Female III-IV T1N0M0 Clear cell None 535 63188 RN 1 48.65 1.4571
12 51 Male I-II T1N0M0 Clear cell None 142 409601 RN 1 47.25 0.8667
13 53 Male I-II T1N0M0 Non-clear cell Hypertension 328 31164 RN 0 52.85 2.0675
14 55 Male III-IV T1N0M0 Clear cell None 420 <40 RN 1 49.1 2.8654
15 39 Male I-II T1N0M0 Clear cell Hypertension 190 TND RN 0 47.65 1.5698
16 46 Male I-II T1N0M0 Clear cell Hypertension 267 61 RN 0 52.5 3.0571
17 67 Male III-IV T1N0M0 Clear cell Diabetes 821 TND RN 2 43.6 4.2982
18 59 Male III-IV T1N0M0 Clear cell None 375 TND RN 3 52.25 2.8956
19 56 Male I-II T3N0M0 Clear cell None 495 TND PN 2 51.5 3.0782
20 54 Female I-II T1N0M0 Clear cell None 687 552 RN 0 55.25 1.9875
21 62 Male III-IV T1N0M0 Clear cell Hypertension 325 NT RN 3 40.5 3.8593
22 49 Male III-IV T1N0M0 Clear cell None 641 <40 RN 1 51.25 0.9889
23 66 Male I-II T1N0M0 Clear cell Diabetes 531 180 RN 2 44.25 2.9136
24 30 Female I-II T1N0M0 Clear cell None 604 TND PN 2 51.25 1.0404
25 39 Male I-II T1N0M0 Clear cell None 296 NT RN 1 53.8 0.9825
26 51 Male III-IV T1N0M0 Clear cell None 151 73907 RN 3 45.85 1.2980
27 50 Male I-II T1N0M0 Clear cell None 327 4909 RN 2 48.95 2.7248
28 50 Male III-IV T1N0M0 Clear cell None 665 TND RN 2 55.1 1.1713
29 48 Male III-IV T1N0M0 Clear cell None 589 TND PN 2 52.15 1.2731
30 57 Male III-IV T1N0M0 Clear cell Diabetes 1062 <40 RN 0 60.9 0.8381
31 52 Male I-II T1N0M0 Clear cell Hypertension 268 TND RN 0 56.9 1.1234
32 36 Male I-II T1N0M0 Clear cell Diabetes 140 174 RN 3 46.75 3.7321
33 32 Male I-II T1N0M0 Clear cell None 1229 TND RN 3 47.95 6.4360
34 51 Male III-IV T3N0M0 Clear cell None 589 TND RN 2 54.9 3.0312
35 65 Male III-IV T1N0M0 Clear cell None 237 2241 RN 5 41.3 3.7697
36 50 Male III-IV T3N0M0 Clear cell None 179 57922 RN 2 41.4 3.3089
37 27 Male I-II T1N0M0 Clear cell None 1082 TND PN 2 44.3 4.0905
38 43 Female I-II T1N0M0 Clear cell None 538 TND PN 6 37 5.0417
39 46 Male I-II T3N0M0 Clear cell None 567 <40 RN 2 45.65 1.1357
40 63 Male III-IV T3N0M0 Clear cell None 73 1018049 RN 8 32.6 5.0417
41 51 Male I-II T1N0M0 Clear cell None 537 77565 RN 1 54.6 1.2692
42 35 Male I-II T1N0M0 Clear cell None 223 TND PN 2 46.15 1.3043
43 52 Male I-II T1N0M0 Clear cell None 321 14317 PN 3 52.3 1.6026
44 61 Male III-IV T3N0M0 Clear cell None 148 34900 RN 7 36.28 2.5641
45 41 Male I-II T1N0M0 Clear cell None 48 59900 RN 4 44.55 1.5039
46 55 Male I-II T1N0M0 Clear cell Diabetes 438 TND PN 4 43.25 2.4747
47 47 Male I-II T1N0M0 Clear cell None 530 TND PN 2 45.1 1.2083
48 54 Female I-II T1N0M0 Clear cell None 234 TND RN 6 38.1 2.6764
49 61 Female I-II T1N0M0 Clear cell None 311 TND RN 4 40.85 3.0689
50 75 Male I-II T1N0M0 Clear cell Hypertension 180 TND PN 5 45.55 3.0093
51 67 Male I-II T1N0M0 Non-clear cell None 356 TND RN 4 40.25 3.3926
52 57 Male III-IV T1N0M0 Clear cell Hypertension+ Diabetes 1399 TND RN 1 49.05 1.4150
53 45 Male III-IV T3N0M0 Clear cell None 530 TND RN 6 38.7 5.0176
54 39 Male I-II T1N0M0 Clear cell None 335 TND PN 3 39.3 4.6872
55 63 Male I-II T1N0M0 Clear cell None 423 4528 RN 2 46.5 2.9630
56 41 Male III-IV T1N0M0 Clear cell None 741 TND RN 5 37.27 4.9724
57 48 Male I-II T1N0M0 Clear cell None 552 <40 RN 1 47.5 2.5657
58 68 Male III-IV T1N0M0 Clear cell None 269 TND PN 2 50.9 3.0171
59 46 Female III-IV T3N0M0 Clear cell None 412 TND RN 2 50.25 1.5623
60 36 Male III-IV T1N0M0 Clear cell None 637 380 RN 6 32.28 3.9743
61 55 Male I-II T3N0M0 Clear cell None 190 TND RN 2 43.9 3.0587
62 38 Male III-IV T1N0M0 Clear cell None 423 TND RN 3 46.9 3.2568
63 64 Male III-IV T1N0M0 Clear cell None 449 900 RN 5 41.7 3.4102
64 69 Male I-II T1N0M0 Clear cell None 432 TND RN 1 52.15 3.8576
65 51 Male III-IV T3N0M0 Clear cell None 547 82080 RN 2 45.2 1.4568
66 49 Female I-II T1N0M0 Clear cell None 734 TND RN 3 41.15 3.7596
67 38 Male I-II T1N0M0 Clear cell None 353 TND PN 2 43.9 4.3264
68 55 Male I-II T1N0M0 Clear cell None 229 24611 PN 2 50.4 2.6384
69 46 Male I-II T1N0M0 Clear cell None 431 TND PN 3 39.9 4.7919
70 61 Female I-II T1N0M0 Clear cell None 524 TND RN 5 39.4 4.5244
71 61 Male III-IV T1N0M0 Clear cell None 421 <40 RN 5 38.43 3.1028
72 49 Male I-II T3N0M0 Clear cell None 778 <40 PN 3 49.8 2.0970
73 33 Male I-II T1N0M0 Clear cell Diabetes 567 TND PN 4 36.8 4.5654
74 64 Male I-II T3N0M0 Clear cell None 258 3600 RN 6 38.75 4.1971
75 57 Male I-II T1N0M0 Clear cell None 655 TND RN 4 46.05 3.2347
76 54 Male I-II T1N0M0 Clear cell None 356 TND RN 1 48.1 1.6593
77 42 Male I-II T1N0M0 Clear cell None 551 <40 RN 2 45.6 2.6567
78 45 Male I-II T1N0M0 Clear cell None 332 TND RN 1 46.65 3.0105
79 53 Male III-IV T3N0M0 Clear cell None 533 TND RN 6 40.6 3.5971
80 66 Male I-II T1N0M0 Clear cell None 550 TND PN 3 42.9 3.1775
81 41 Female I-II T1N0M0 Clear cell Diabetes 477 TND RN 2 45.4 2.8861
82 45 Male I-II T1N0M0 Clear cell None 377 TND RN 3 48.05 1.2693
83 55 Male III-IV T1N0M0 Clear cell None 636 5352 RN 2 43.75 2.2327
84 70 Male III-IV T3N0M0 Clear cell Hypertension 359 TND RN 3 48.1 2.4647
85 48 Male III-IV T3N0M0 Clear cell None 678 TND RN 2 45.15 3.6537
86 50 Male I-II T1N0M0 Clear cell None 790 TND RN 1 48.2 2.6974
87 44 Male I-II T1N0M0 Clear cell None 358 TND RN 2 48 1.2387
88 29 Male I-II T1N0M0 Clear cell None 559 NT PN 2 48.3 3.4697
89 68 Male I-II T1N0M0 Clear cell None 243 TND RN 5 39.15 4.6874
90 49 Female I-II T1N0M0 Clear cell None 438 TND PN 1 44.55 2.5813
91 38 Male III-IV T1N0M0 Clear cell None 457 <40 RN 7 36.6 4.6687
92 51 Male I-II T1N0M0 Clear cell None 221 TND RN 4 39.45 4.4367
93 33 Male I-II T1N0M0 Clear cell Hypertension 573 56781 RN 3 45.95 2.1187
94 67 Male III-IV T1N0M0 Clear cell None 329 TND RN 3 39.45 4.7652
95 56 Male I-II T1N0M0 Clear cell None 343 2538 RN 2 47.25 1.1495
96 48 Male I-II T1N0M0 Clear cell None 431 TND PN 1 49.45 2.7465
97 31 Male III-IV T1N0M0 Clear cell None 415 TND RN 5 39.75 3.9853
98 44 Male I-II T1N0M0 Clear cell None 227 <40 PN 1 50.3 1.0147
99 61 Male I-II T3N0M0 Clear cell None 621 44090 RN 2 45.95 2.6584
100 56 Female I-II T1N0M0 Clear cell None 591 TND PN 4 38.9 4.3251
101 52 Male III-IV T1N0M0 Clear cell None 790 NT RN 2 45.45 2.7892
102 44 Male I-II T1N0M0 Clear cell None 544 TND PN 3 40.3 3.4663
103 51 Male III-IV T1N0M0 Clear cell Diabetes 980 <40 RN 2 44.3 2.6835
104 38 Male III-IV T1N0M0 Clear cell None 555 399 RN 5 41.15 3.4678
105 57 Male III-IV T3N0M0 Clear cell None 288 TND RN 2 49.15 0.9785
106 37 Female I-II T1N0M0 Clear cell Hypertension 543 TND PN 1 48 2.4318

NT, not tested; TND, target not detected; RN, radical nephrectomy; PN, partial nephrectomy; CONUT, controlling nutritional status; PNI, prognostic nutritional index; NLR, neutrophil–lymphocyte ratio.

Clinicopathological characteristics of the 106 patients according to different CONUT groups.

Factors Total (n = 106) CONUT<3 (n = 61) CONUT≥3(n = 45) P-value*
Age (years)
 ≤65 94 55 39 P=0.574
 >65 12 6 6
Gender
 Male 93 54 39 P=0.773
 Female 13 7 6
Hypertention
 Yes 14 10 4 P=0.259
 No 92 51 41
Diabetes
 Yes 11 8 3 P=0.451
 No 95 53 42
Tumor grade
 T1N0M0 87 50 37 P=0.973
 T3N0M0 19 11 8
Fuhrman grade
 I-II 65 43 22 P=0.024
 III-IV 41 18 23
Histology
 Clear cell 104 60 44 P=0.671
 Non-clear cell 2 1 1
Surgery
 RN 80 46 34 P=0.986
 PN 26 15 11
CD4+ T lymphocyte count (cells/ul)
 ≥200 95 56 39 P=0.593
 <200 11 5 6

*Chi-square test. Bold value indicates statistical significance in univariate and multivariate analysis which had been detailed in the “Results” section.

RN, radical nephrectomy; PN, partial nephrectomy.

 Survival Outcome

The median postoperative follow-up time of CSS and OS was 41 (IQR 6-105) months, and that of DFS was 41 (IQR 4-105) months. In the high- and low-CONUT groups, the 5-year CSS rates were 47.79% and 85.54% (P<0.001) (Figure 2A), the 5-year OS rates were 44.47% and 85.54% (P<0.001) (Figure 2B), and the 5-year DFS rates were 44.86% and 86.16% (P<0.001) (Figure 2C), respectively.

Kaplan–Meier survival curves for HIV-related RCC patients treated with surgery. Survival curves set at cutoff value 3 for CSS (A), OS (B) and DFS (C). RCC, renal cell carcinoma; CSS, cancer-specific survival; OS, overall survival; DFS, disease-free survival; CONUT, controlling nutritional status.

As shown in Table 4, patients with lower CONUT scores had better CSS (HR 0.197, 95% CI 0.077-0.502, P=0.001), OS (HR 0.177, 95% CI 0.070-0.446, P<0.001) and DFS (HR 0.176, 95% CI 0.070-0.444, P<0.001). In addition, Fuhrman grade was also significantly correlated with CSS, OS and DFS (P<0.01). Multivariate Cox regression analysis indicated that a low CONUT score was an independent predictor of CSS, OS and DFS (CSS: HR=0.225, 95% CI 0.067-0.749, P=0.015; OS: HR=0.201, 95% CI 0.061-0.661, P=0.008; DFS: HR=0.227, 95% CI 0.078-0.664, P=0.007). A low Fuhrman grade was an independent predictor of CSS (HR 0.192, 95% CI 0.045-0.810, P=0.025), OS (HR 0.203, 95% CI 0.049-0.842, P=0.028), and DFS (HR 0.180, 95% CI 0.048-0.669, P=0.010) (Table 4), while other factors, such as age, sex, hypertension, diabetes, tumor grade, histology, surgery, and CD4+ T lymphocyte count, were not associated with survival outcome.

Univariate and multivariate analyses of clinicopathological parameters to predict CSS, OS and DFS in patients with HIV-related RCC.

Factors CSS OS DFS
Univariate Multivariate Univariate Multivariate Univariate Multivariate
HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value HR (95% CI) P-value
Age
 >65 1.612 (0.548-4.745) 0.386 1.473 (0.505-4.96) 0.479 1.517 (0.520-4.423) 0.446
 ≤65 1.00 (ref) 1.00 (ref) 1.00 (ref)
Gender
 Male 23.809 (0.057-9932.7) 0.303 23.826 (0.075-7603.4) 0.281 2.959 (0.399-21.933) 0.289
 Female 1.00 (ref) 1.00 (ref) 1.00 (ref)
Fuhrman grade
 I-II 0.086 (0.028-0.257) <0.01 0.192 (0.045-0.810) 0.025 0.079 (0.026-0.234) <0.01 0.203 (0.049-0.842) 0.028 0.104 (0.038-0.282) <0.01 0.180 (0.048-0.669) 0.010
 III-IV 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)
Tumor grade
 T1N0M0 0.757 (0.281-2.042) 0.583 0.673 (0.268-1.687) 0.398 0.823 (0.308-2.195) 0.697
 T3N0M0 1.00 (ref) 1.00 (ref) 1.00 (ref)
Histology
 Clear cell 20.919 (0.000-2325088) 0.608 20.917 (0.000-1434880) 0.593 20.908 (0.000-1589742) 0.596
 Non-clear cell 1.00 (ref) 1.00 (ref) 1.00 (ref)
Hypertension
 Yes 1.213 (0.360-4.087) 0.755 1.109 (0.332-3.709) 0.867 1.044 (0.312-3.491) 0.945
 No 1.00 (ref) 1.00 (ref) 1.00 (ref)
Diabetes
 Yes 0.862 (0.202-3.680) 0.842 0.790 (0.186-3.352) 0.749 0.782 (0.184-3.317) 0.738
 No 1.00 (ref) 1.00 (ref) 1.00 (ref)
Surgery
 RN 2.247 (0.763-6.615) 0.142 2.478 (0.849-7.229) 0.097 1.760 (0.660-4.694) 0.259
 PN 1.00 (ref) 1.00 (ref) 1.00 (ref)
CD4 count (cells/ul)
 ≥200 0.663 (0.225-1.951) 0.455 0.736 (0.252-2.147) 0.575 0.720 (0.247-2.099) 0.547
 <200 1.00 (ref) 1.00 (ref) 1.00 (ref)
CONUT score
 <3 0.197 (0.077-0.502) 0.001 0.225 (0.067-0.749) 0.015 0.177 (0.070-0.446) <0.001 0.201 (0.061-0.661) 0.008 0.176 (0.070-0.444) <0.001 0.227 (0.078-0.664) 0.007
 ≥3 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref)

Bold values indicate statistical significance in univariate and multivariate analysis which had been detailed in the “Results” section.

RCC, renal cell carcinoma; OS, overall survival; CSS, cancer-specific survival; DFS, disease-free survival; HR, hazard ratio; CONUT, controlling nutritional status; RN, radical nephrectomy; PN, partial nephrectomy.

 Compare CONUT Score With Other Biomarkers in Patients With HIV-Related RCC for Survival Prediction

The PNI and NLR values of 106 patients are also shown in Table 2. The ROC curve with the most sensitive and specific cutoff values of PNI and NLR is also shown in Figure 1. We compared the AUCs for predicting 5-year OS by CONUT, PNI and NLR. Among the prognostic factors, the CONUT has the highest AUC (0.746). The AUC score of PNI and NLR in relation to 5-year OS was 0.682 (95% CI 0.553–0.811) and 0.674 (95% CI 0.545–0.803), respectively.

 Discussion

In this study, patients with surgically treated HIV-related RCC with high CONUT scores had significantly shorter CSS, OS, and DFS than patients with low CONUT scores. Multivariate analysis further showed that the CONUT score was an independent factor influencing these survival outcomes. In addition, a low Fuhrman grade was significantly associated with survival outcomes. This study is the first to study the prognostic factors of patients with HIV-related RCC, which has certain reference significance for the surgical treatment selection of patients with HIV infection and the prognosis of patients with HIV-related RCC.

Among people living with HIV, non-HIV-related morbidity and mortality are becoming more common, and non-HIV hypotoxicity is becoming an important source of mortality (26). In recent years, there has been a significant increase in the incidence of malignant tumors in HIV-infected people because of the increased life expectancy associated with HAART, and urologists are increasingly likely to encounter HIV-infected patients with the same urinary problems as the general population (4). Strictly evaluating the surgical indications of patients with HIV-related RCC and early surgical treatment are crucial for patient prognosis. HIV mainly invades human CD4+ T lymphocytes, causing a reduction in their number and functional defects, thereby resulting in low immune function and an increasing incidence of various opportunistic infections. Although early literature on surgical outcomes in HIV-positive patients suggested an increased risk of perioperative complications (27), recent studies have shown that most procedures can be performed safely in HIV-positive patients with appropriate preoperative evaluation of CD4+ T lymphocyte count and viral load (28). Therefore, preoperative routine examination of CD4+ T lymphocytes in patients with HIV-related RCC is an aspect of evaluating whether patients can tolerate surgery, but CD4+ T lymphocytes do not serve as a good predictor of the survival prognosis of these patients; therefore, it is necessary to find prognostic predictors for patients with HIV-related RCC.

The CONUT score was determined by serum albumin, total lymphocyte counts, and total cholesterol concentration. Serum albumin is an indicator that can reflect patients’ nutritional status and is closely related to patients’ surgical tolerance and postoperative recovery. A lower level of serum albumin means the loss of immunity (29). HIV RNA levels and CD4+ T lymphocyte counts provide some prognostic information about HIV disease progression, but data published by Shruti H Mehta suggest that serum albumin levels provide more prognostic information than RNA levels and CD4 counts. Low levels of serum albumin not only reflect the general health status of HIV-infected patients but also reflect the effects of HIV on the host (30). Several studies of HIV seroepidemic cohorts have shown that low serum albumin is associated with all-cause mortality, even among individuals receiving HAART (31, 32). Sabin et al. demonstrated an independent role of serum albumin detected shortly after HIV serotransformation in all-cause mortality and a smaller but still significant role in AIDS progression. These associations were independent of CD4+ T lymphocyte count and HIV viral load (33).

Lymphocytes are believed to have antitumor ability by affecting the growth, migration, and apoptosis and inducing the cytotoxicity of tumor cells. The high density of lymphocytes reflects the immune response of tumors (34). F A Post et al. found that total lymphocyte count and CD4+ T lymphocyte count were equally good predictors of HIV infection disease progression, and severe lymphocytopenia (total lymphocyte counts <750/µl) predicted low survival and may reflect high susceptibility to opportunistic infections (35, 36). Moses R Kamya’s results showed a strong correlation between total lymphocyte counts and CD4+ T lymphocytes. Similar correlations between total lymphocyte counts and CD4+ T lymphocytes have been reported in North America, England and India (37).

Low cholesterol levels are associated with cancer outcomes. Cholesterol affects the structure and function of the membrane, such as membrane protein activity and membrane fluidity, thus affecting the ability of immunoactive cells to fight cancer cells (38). Dyslipidemia has also been observed in untreated HIV-infected patients, suggesting that HIV infection itself has deleterious metabolic effects (39). In the Swiss HIV cohort study, the use of HIV protease inhibitors was found to be associated with an increase in plasma total cholesterol (40). In the SMART study, discontinuation of HAART led to a reduction in total cholesterol concentration (41). After the initiation of HAART, lipid abnormalities in HIV patients become more obvious, and hypercholesterolemia is the most related disease (42–44). Serum total cholesterol concentration was a correlated and independent predictor of HIV RNA load, CD4+ T lymphocyte count and WHO clinical stage. In this era of testing and treatment, it is possible to use low serum total cholesterol concentration as a marker to predict the efficacy of HAART (45).

CONUT is mainly associated with malignant tumors and survival prognosis through the nutritional immune pathway. For HIV-infected patients, nutritional and immune functions are already low, and these patients have an increased risk of RCC, which is largely due to the loss of control of the oncogenic genome and the high prevalence of exposure to other carcinogens (46). Adam B Murphy et al. found that the frequency of metastatic disease in an HIV-related cohort was 2.5 times that observed in a non-HIV-related cohort, although this difference did not reach statistical significance (47). Wee Loon ONG’s study showed that a total of five HIV-related RCC patients in Australia’s statewide HIV centers underwent surgery without any perioperative complications (48). In metastatic clear cell RCC, targeted therapy or immunotherapy may interact with antiretroviral drugs to some extent (49). Annah B Layman et al. found a similar incidence, clinical presentation and outcome of RCC in HIV-infected and non-HIV-infected populations and no association between CD4+ T lymphocyte count and RCC risk at the onset of AIDS (50).

Our study has some limitations. First, the follow-up time of some patients was short, at only half a year, so it would be more meaningful to extend the follow-up time to ensure the accuracy of the results. Furthermore, the duration of treatment with antiviral drugs may also be a prognostic factor, but since most of the included patients were uncertain about the duration of HAART, no reliable data were obtained.

 Conclusion

HIV-infected patients eligible for HAART have a potentially normal life expectancy. Therefore, diseases such as RCC and other malignancies should be treated in the same way as those in non-HIV-infected patients. As it becomes increasingly possible to operate on HIV-infected patients undergoing HAART, CONUT’s role in predicting survival for HIV-related RCC is becoming increasingly important. The preoperative CONUT score not only objectively reflects the nutritional and immune statuses of the host but also is an independent predictor of CSS, OS and DFS in patients with HIV-related RCC.

 Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

 Ethics Statement

The studies involving human participants were reviewed and approved by Beijing You’an Hospital Affiliated to Capital Medical University. The patients/participants provided their written informed consent to participate in this study.

 Author Contributions

WX participated in manuscript preparation and writing. XH provided suggestion and edits. YZ (4th author) conceptualized, wrote, and revised manuscript. YZ (2nd author) and HW provided relevant patients data of their hospitals and offered suggestions for revising the article. All authors contributed to the article and approved the submitted version.

 Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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