metricas
covid
Buscar en
Annals of Hepatology
Toda la web
Inicio Annals of Hepatology Nomogram for prediction of long-term survival with hepatocellular carcinoma base...
Información de la revista
Vol. 27. Núm. 2.
(marzo - abril 2022)
Compartir
Compartir
Descargar PDF
Más opciones de artículo
Visitas
1227
Vol. 27. Núm. 2.
(marzo - abril 2022)
Original article
Open Access
Nomogram for prediction of long-term survival with hepatocellular carcinoma based on NK cell counts
Visitas
1227
Lihua Yua,1, Xiaoli Liua,1, Xinhui Wanga, Dongdong Zhoua, Huiwen Yana, Yuqing Xiea,b, Qing Pua, Ke Zhangc,*, Zhiyun Yanga,
Autor para correspondencia
yangzhiyun2016@163.com

Corresponding authors.
a Center of Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
b First Clinical Medical College, Beijing University of Chinese Medicine, Beijing 100029, P.R. China
c Department of Hepatobiliary Surgery, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, P.R. China
Este artículo ha recibido

Under a Creative Commons license
Información del artículo
Resumen
Texto completo
Bibliografía
Descargar PDF
Estadísticas
Figuras (4)
Mostrar másMostrar menos
Tablas (3)
Table 1. Characteristics of patients with hepatocellular carcinoma
Table 2. Univariate and multivariate Cox regression analyses for 5-year OS in patients with HCC
Table 3. Comparison of the discriminative ability among the nomogram and other models
Mostrar másMostrar menos
Material adicional (1)
Abstract
Introduction

Among all immune cells, natural killer (NK) cells play an important role as the first line of defense against tumor. The purpose of our study is to observe whether the NK cell counts can predict the overall survival of patients with hepatocellular carcinoma (HCC).

Methods

To develop a novel model, from January 2010 to June 2015, HCC patients enrolled in Beijing Ditan hospital were divided into training and validation cohort. Cox multiple regression analysis was used to analyze the independent risk factors for 1-year, 3-year and 5-year overall survival (OS) of patients with HCC, and the nomogram was used to establish the prediction model. In addition, the decision tree was established to verify the contribution of NK cell counts to the survival of patients with HCC.

Results

The model used in predicting overall survival of HCC included six variables (namely, NK cell counts, albumin (ALB) level, alpha-fetoprotein (AFP) level, portal vein tumor thrombus (PVTT), tumor number and treatment). The C-index of nomogram model in HCC patients predicting 1-year, 3-year and 5-year overall survival was 0.858, 0.788 and 0.782 respectively, which was higher than tumor–lymph node–metastasis (TNM) staging system, Okuda, model for end-stage liver disease (MELD), MELD-Na, the Chinese University Prognostic Index (CUPI) and Japan Integrated Staging (JIS) scores (p < 0.001). The decision tree showed the specific 5-year OS probability of HCC patients under different risk factors, and found that NK cell counts were the third in the column contribution.

Conclusions

Our study emphasizes the utility of NK cell counts for exploring interactions between long-term survival of HCC patients and predictor variables.

Keywords:
Hepatocellular carcinoma
NK cells
nomogram
overall survival
prognosis
Abbreviations:
AFP
ALB
ALT
BCLC
CLIP
CUPI
HCC
JIS
MELD
MWA
NK cells
NLR
OS
PFS
PLR
PTA
PVTT
RFA
TACE
TBIL
TNM
γ-GGT
Texto completo
1Introduction

According to the 2020 global cancer statistics [1], the mortality rate of hepatocellular carcinoma (HCC) ranks third in the world. Every year, there are about 841,000 new cases of HCC, and the number of deaths is approximately 782,000, while the mortality rate of HCC in China accounts for half of the global population.

Immunotherapy is a major breakthrough in the field of cancer therapy in recent years, and the potential of natural killer (NK) cells for cancer immunotherapy has gradually become well known. NK cells play an important role in the innate immune response. When infected with a virus and the tumor microenvironment, NK cells play a particularly important role as the first line of defense [2, 3]. In the tumor microenvironment, the immune surveillance function of NK cells is impaired. Furthermore, the failure of NK cells is mainly manifested as a decrease in NK cell counts and function [4, 5]. A number of previous studies have shown that the number of NK cells in patients with HCC is reduced [6, 7], and a decrease in the proportion of NK cell subsets is associated with poor prognosis [8, 9]. However, the effect of reduced NK cell count on long-term prognosis in patients with HCC is unclear. Therefore, we aimed to analyze the correlation between NK cell counts detected by clinical objective laboratory indicators and the survival outcome of patients with HCC.

Currently, several well-known tumor prediction models are commonly used clinical prediction tools, including the Barcelona Clinic Liver Cancer (BCLC) [10], tumor–lymph node–metastasis (TNM) staging system [11], Okuda staging system [12], model for end-stage liver disease (MELD) [13], Cancer of the Liver Italian Program (CLIP) [14], the Chinese University Prognostic Index (CUPI) [15], and Japan Integrated Staging (JIS) [16]. These classic and recognized models only consider clinically relevant indicators (including liver function and tumor-related indicators), but have not yet involved immune indicators and the tumor microenvironment [17–19].

Nomograms are currently considered a simple visual and reliable tool for predicting risk by incorporating important related factors [20, 21]. The aim of the current study was to combine clinical and immunological indicators that are associated with the overall survival of patients with HCC into a prediction model. It also aimed to determine the value of immunological indicators (i.e., NK cell counts) in predicting the survival of patients with HCC.

2Patients and methods2.1Study design and population

The retrospective cohort (n = 190) was divided into training and validation cohorts. The training cohort comprised patients with HCC from Beijing Ditan Hospital between January 1, 2010, and December 31, 2013, and the validation cohort included patients with HCC from Beijing Ditan Hospital between January 1, 2014, and June 30, 2015. Overall survival (OS) was defined as the interval between the date of diagnosis and the occurrence of death or the end of follow-up. The survival group was defined as having no deaths during the follow-up period. The death group was defined as death from any cause between the date of diagnosis and the end of the follow-up period. Our follow-up method was to follow up every three months by telephone and consult the medical record system. The inclusion criteria were as follows: (1) patients with HCC who were diagnosed according to the 2019 China guidelines (based on biopsy, radiology, and alpha-fetoprotein [AFP level ≥ 400 ng/mL] serology results) [22]; (2) those with complete clinical data, including immunoassays (NK cell counts); and (3) those with more than 5 years of follow-up in the retrospective cohort study. The exclusion criteria were as follows: (1) pregnant women, (2) those younger than 18 years, (3) those with incomplete data, and (4) those with <5 years of follow-up. The study was approved by the Ethics Committee of Beijing Ditan Hospital.

2.2Calculation of NK cell counts

Before treatment, 5ml fasting venous blood were collected from the patients and were placed in EDTA anticoagulant blood collection tubes. Absolute cell counts were determined using TruCOUNT Tubes (BD) and a lyse-no-wash method according to the manufacturer's instructions by FACS Calibur flow cytometer (BD Biosciences). NK cell counts were detected using a MulitiTEST IMK kit (CD3/CD16/+CD56/CD45/CD19 Reagent, BD FACS Iysing solution) and TruCOUNT tubes, and analyzed automatically using FACSMULTISET software. NK cell-surface markers were detected using the following reagents for flow cytometry: anti-human CD3-BV786, anti-CD56-BV510, and anti-CD16-BV711 antibodies. NK cells were CD3CD16+ and/or CD56+ lymphocytes.

2.3Demographic and clinical data

We included important variables related to OS of patients with HCC, including age, sex, etiology of HCC, HBV DNA, antiviral therapy, portal vein tumor thrombus at baseline, cirrhosis, metastasis, Child-Pugh stage, BCLC stage, tumor number, tumor size, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), γ-glutamyl transferase (γ-GGT), total bilirubin (TBIL), albumin (ALB), prothrombin time activity (PTA), alanine aminotransferase (ALT), alpha-fetoproteinlevel (AFP), NK/lymphocytes, and NK absolute count. Consistent with our previous research, the treatment methods were classified as follows [23]: palliative treatment, minimally invasive treatment, and resection treatment. Palliative treatment was defined as supportive treatment. Minimally invasive treatment was considered to be local treatment of cancer cells or areas surrounding the cancer cells, which mainly included radiofrequency ablation (RFA), transarterial chemoembolization (TACE), and microwave ablation (MWA). Resection treatment was defined ascurative resection, including partial resection and liver transplantation. Baseline data were obtained at the first HCC diagnosis in the Beijing Ditan Hospital.

2.4Statistical analysis

All statistical analyses were performed using SPSS 22.0. Categorical variables were analyzed using the χ2 test or Fisher's exact probability test. Cox proportional regression analysis was performed to identify the independent prognostic factors of HCC. The 5-year OS rates were compared using the Kaplan–Meier method. Based on the multivariate analysis results, the nomogram model was established using rms in the R project version 3.4.2 package (http://www.rproject.org/). A calibration curve was used to evaluate the calibration degree between the predicted probability and actual probability of the nomogram model. The concordance index (C-index) was used to test the degree of discrimination of the model.

Subsequently, the decision tree was drawn with JMP pro version 15.2, as a method of machine learning, which can directly show the mapping relationship between predictors and outcomes. Decision trees visually demonstrate the 5-year OS rate of patients affected by different risk factors for HCC.

3Results3.1Characteristics of the retrospective cohort of patients with HCC

The retrospective cohort (n = 190) was divided into training (n = 139) and validation cohorts (n = 51). The characteristics of patients with HCC are presented in detail in Table 1. All demographic and clinical values of the two groups were balanced and comparable (p > 0.05). Among all patients with HCC, hepatitis B-related HCC accounted for 82.1%, and 143 (75.3%) received antiviral therapy. Among the different treatment methods, 68.9% of patients with HCC received local minimally invasive treatment, the remaining 10.6% received resection treatment, and 20.5% received palliative treatment. In the training cohort, of the 139 patients whose age ranged from 20 to 89 years, 81.3% were over 50 years and 106 (76.3%) were male, which showed male and elderly dominance. Overall, 114 (82.0%) patients with HCC were caused by HBV infection, and 104 (74.8%) patients were treated with antiviral therapy. The treatment method was minimally invasive (71.2%). According to the clinical outcome, they were divided into the survival group (n = 55) and the death group (n = 84). In the death group, 28.6% of HCC patients received conservative treatment, 21 (25%) had PVTT at baseline, 38 (45.2%) had metastasis, 12 (14.3%) had Child-Pugh C, 31 (36.9%) had BCLC stage C-D, and 31 (36.9%) had tumor size>5 cm. Meanwhile, compared with patients in the survival group, patients in the death group had higher levels of NLR, TBIL, γ-GGT, and AFP, while the levels of ALB and NK cell counts were lower than those in the survival group (Table S1, p < 0.05).

Table 1.

Characteristics of patients with hepatocellular carcinoma

Demographic and clinical valuesTotal  Training Cohort  Validation Cohort  P Value
n=190(%)  n=139(%)  n=51(%) 
Gender  0.232 
Male  149(78.4)  106(76.3)  43(84.3)   
Female  41(21.6)  33(23.7)  8(15.7)   
Age(y)        0.461 
≤50  38(20.0)  26(18.7)  12(23.5)   
>50  152(80.0)  113(81.3)  39(76.5)   
Etiology        0.872 
HBV  156(82.1)  114(82.0)  42(82.3)   
HCV  12(6.3)  9(6.5)  3(5.9)   
Alcohol  12(6.3)  9(6.5)  3(5.9)   
NAFLD  2(1.1)  2(1.4)  0(0.0)   
Not available  8(4.2)  5(3.6)  3(5.9)   
HBV-DNA(IU/mL)    0.729 
<5.0E+2  110(57.9)  82(59.0)  28(54.9)   
>5.0E+2  69(36.3)  50(36.0)  19(37.3)   
Not available  11(5.8)  7(5.0)  4(7.8)   
Treatment        0.151 
Palliative  39(20.5)  29(20.9)  10(19.6)   
Minimally invasive  131(68.9)  99(71.2)  32(62.7)   
Resection  20(10.6)  11(7.9)  9(17.7)   
Antiviral therapy(yes)  143(75.3)  104(74.8)  39(76.5)  0.815 
PVTT at baseline(yes)  27(14.2)  22(15.8)  5(9.8)  0.292 
Cirrhosis(yes)  181(95.3)  132(95.0)  49(96.1)  0.749 
Metastasis(yes)  69(36.3)  50(36.0)  19(37.3)  0.870 
Child-Pugh Stage    0.284 
109(57.4)  75(54.0)  34(66.7)   
65(34.2)  51(36.7)  14(27.5)   
16(8.4)  13(9.3)  3(5.8)   
BCLC stage    0.546 
0-B  147(77.4)  106(76.3)  41(80.4)   
C-D  43(22.6)  33(23.7)  10(19.6)   
Tumor multiplicity    0.398 
Solitary  125(65.8)  89(64.0)  36(70.6)   
Multiple  65(34.2)  50(36.0)  15(29.4)   
Tumor size(cm)    0.611 
≤5  132(69.5)  98(70.5)  34(66.7)   
>5  58(30.5)  41(29.5)  17(33.3)   
NLR        0.752 
≤2.41  97(51.1)  70(50.4)  27(52.9)   
>2.41  93(48.9)  69(49.6)  24(47.1)   
PLR        0.377 
≤83.84  92(48.4)  70(50.4)  22(43.1)   
>83.84  98(51.6)  69(49.6)  29(56.9)   
ALT (U/L)        0.607 
Normal ≤50  144(75.8)  104(74.8)  40(78.4)   
High>50  46(24.2)  35(25.2)  11(21.6)   
TBIL(umol/L)    0.141 
Normal ≤18.8  95(50.0)  65(46.8)  30(58.8)   
High>18.8  95(50.0)  74(53.2)  21(41.2)   
ALB(g/L)        0.204 
Normal ≤40  129(67.9)  98(70.5)  31(60.8)   
High>40  61(32.1)  41(29.5)  20(39.2)   
γ-GGT(IU/L)    0.651 
Normal≤60  102(53.7)  76(54.7)  26(51.0)   
High>60  88(46.3)  63(45.3)  25(49.0)   
PTA(%)        0.274 
Low≤70  60(31.6)  47(33.8)  13(25.5)   
High>70  130(68.4)  92(66.2)  38(74.5)   
AFP(ng/mL)    0.054 
Low≤400  158(83.2)  120(86.3)  38(74.5)   
High>400  32(16.8)  19(13.7)  13(25.5)   
NK/lymphocytes(%)    0.506 
Low≤12  82(43.2)  62(44.6)  20(39.2)   
High>12  108(56.8)  77(55.4)  31(60.8)   
NK absolute count(cells/uL)  0.520 
Low≤118  93(48.9)  70(50.4)  23(45.1)   
High>118  97(51.1)  69(49.6)  28(54.9)   

Abbreviations: HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease; BCLC, Barcelona Clinic Liver Cancer classification system; PVTT, portal vein tumor thrombus; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; ALT, alanine aminotransferase; TBIL, total bilirubin; ALB, albumin; γ-GGT, γ-glutamyl transferase; PTA, prothrombin time activity; AFP, alpha-fetoprotein.

3.2Risk factors and derivation of the prognostic nomogram

Cox proportional regression analysis was performed to evaluate the effect of clinical variables on the outcomes of patients with HCC. Univariate analysis showed that treatment, PVTT at baseline, antiviral therapy, metastasis, tumor number, tumor size, NLR, ALB, TBIL, γ-GGT, PTA, AFP, and NK cell absolute counts were significantly associated with survival in the training cohort (p< 0.1, Table 2). The above variables were entered into Cox multivariate analysis. This analysis revealed that treatment (minimally invasive, hazard ratio [HR] = 0.48, 95% confidence interval [CI]: 0.29-0.81, p = 0.006; resection treatment, HR = 0.05; 95% CI: 0.006-0.34, p = 0.003), PVTT at baseline (HR = 2.71, 95% CI: 1.51-4.86, p = 0.001), tumor multiplicity (HR = 2.00, 95% CI: 1.26-3.17, p = 0.003), ALB level (>40g/L; HR = 0.39, 95% CI: 0.22-0.71, p = 0.002), AFP level (> 400 ng/mL, HR = 2.73, 95% CI: 1.44-5.15, p = 0.002), and NK cell absolute count (>118 cells/μL, HR = 0.58, 95% CI: 0.37-0.92, p = 0.021) were the independent prognostic predictors of HCC (Table 2).

Table 2.

Univariate and multivariate Cox regression analyses for 5-year OS in patients with HCC

  Univariate analysisMultivariate analysis
Variables  HR(95%CI)  P values  HR(95%CI)  P values 
Gender, male vs.female  0.89(0.54-1.49)  0.660     
Age(y),≤50 vs.>50  1.25(0.69-2.27)  0.455     
Etiology         
HBV  Ref       
HCV  0.79(0.32-1.97)  0.616     
Alcohol  1.03(0.45-2.37)  0.949     
NAFLD  0.64(0.09-4.62)  0.658     
Not available  0.88(0.28-2.78)  0.821     
Treatment         
Palliative    Ref     
Minimally invasive  0.41(0.26-0.66)  <0.0001  0.48(0.29-0.81)  0.006 
Resection  0.04(0.006-0.32)  0.002  0.05(0.006-0.34)  0.003 
HBV-DNA(IU/mL)         
<5.0E+2  Ref       
>5.0E+2  1.31(0.83-2.07)  0.248     
Not available  1.87(0.80-4.39)  0.148     
Antiviral therapy,no vs.yes  0.67(0.42-1.07)  0.094     
PVTT at baseline,no vs.yes  4.89(2.93-8.17)  <0.0001  2.71(1.51-4.86)  0.001 
Cirrhosis,no vs.yes  1.76(0.56-5.57)  0.337     
Metastasis,no vs.yes  1.70(1.11-2.63)  0.016     
Tumor multiplicity,solitary vs.multiple  1.52(0.98-2.35)  0.062  2.00(1.26-3.17)  0.003 
Tumor size(cm),≤5 vs.>5  2.12(1.36-3.31)  0.001     
NLR,≤2.41 vs.>2.41  1.95(1.27-3.02)  0.002     
PLR,≤83.84 vs.>83.84  1.32(0.86-2.03)  0.200     
ALT(IU/L),≤50 vs.>50  1.19(0.73-1.93)  0.491     
TBIL(umol/L),≤18.8 vs.>18.8  2.03(1.30-3.18)  0.002     
ALB(g/L),≤40 vs.>40  0.34(0.19-0.61)  <0.0001  0.39(0.22-0.71)  0.002 
γ-GGT(IU/L),≤60 vs.>60  1.76(1.15-2.70)  0.010     
PTA(%),≤70 vs.>70  0.68(0.44-1.06)  0.088     
AFP(ng/mL),≤400 vs.>400  3.60(2.07-6.25)  <0.0001  2.73(1.44-5.15)  0.002 
NK/lymphocytes(%),12%  1.34(0.86-2.07)  0.194     
NK absolute count,118cells/ul  0.57(0.37-0.88)  0.011  0.58(0.37-0.92)  0.021 

Abbreviations: HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease; PVTT, portal vein tumor thrombus; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; ALT, alanine aminotransferase; TBIL, total bilirubin; ALB, albumin; γ-GGT, γ-glutamyl transferase; PTA, prothrombin time activity; AFP, alpha-fetoprotein.

The model was further established using a nomogram for 1-year, 3-year, and 5-year OS by combining NK cell counts, ALB level, AFP level, PVTT, tumor number, and treatment identified via the Cox multivariate analyses (Figure 1A). The nomogram included five parameters related to liver function and the tumor (ALB level, AFP level, PVTT, tumor number, and treatment) and one immunological parameter (NK cell counts).

Figure 1.

Nomogram prediction model and calibration curve. (A) Nomogram predicted overall survival (OS) for HCC patients. To use the nomogram, the value of an individual patient is located on each variable axis, and a line is drawn upward to determine the number of points received for the value of each variable. The sum of these numbers is located on the total point axis, and a line is drawn downward to OS axes to determine the likelihood of 1-year OS, 3-year OS and 5-year OS. The calibration curves for 1-year OS, 3-year OS and 5-year OS were identified in the training cohort (B) and in the validation cohort (C).

(0.38MB).
3.3Discrimination and calibration of the nomogram

In the training cohort, the C-indexes of the nomogram model predicting 1-year, 3-year, and 5-year OS were 0.858 (95% CI: 0.802-0.914), 0.788 (95% CI: 0.736-0.840) and 0.782 (95% CI: 0.735-0.829), respectively, which were higher than those of TNM, Okuda, MELD, MELD-Na, CUPI, and JIS scores (p < 0.001). The C-indexes predicting the 1-year, 3-year, and 5-year OS in the validation cohort were 0.861, 0.796, and 0.807, respectively, and were found to be better than the other models (Table 3).

Table 3.

Comparison of the discriminative ability among the nomogram and other models

CohortModels1-year overall survival3-year overall survival5-year overall survival
C-index  95% CI  C-index  95% CI  C-index  95% CI 
Training  Nomogram  0.858  0.802-0.914  0.788  0.736-0.840  0.782  0.735-0.829 
  BCLC stage  0.742  0.671-0.813  0.686  0.628-0.744  0.668  0.620-0.716 
  TNM stage  0.564  0.478-0.650  0.594  0.535-0.653  0.595  0.543-0.647 
  Okuda stage  0.649  0.557-0.741  0.606  0.541-0.671  0.620  0.565-0.675 
  MELD  0.617  0.515-0.719  0.617  0.548-0.686  0.612  0.552-0.672 
  MELD-Na  0.627  0.527-0.727  0.624  0.554-0.694  0.620  0.558-0.682 
  CLIP  0.749  0.658-0.840  0.696  0.633-0.759  0.691  0.635-0.747 
  CUPI  0.660  0.562-0.758  0.643  0.575-0.711  0.653  0.593-0.713 
  JIS  0.567  0.473-0.661  0.602  0.537-0.667  0.622  0.561-0.683 
Validation  Nomogram  0.861  0.746-0.976  0.796  0.693-0.899  0.807  0.712-0.902 
  BCLC stage  0.756  0.629-0.883  0.753  0.653-0.853  0.759  0.664-0.854 
  TNM stage  0.578  0.428-0.728  0.666  0.549-0.783  0.663  0.553-0.773 
  Okuda stage  0.706  0.552-0.860  0.659  0.539-0.779  0.674  0.560-0.788 
  MELD  0.516  0.318-0.714  0.554  0.406-0.702  0.535  0.395-0.675 
  MELD-Na  0.482  0.306-0.658  0.500  0.363-0.637  0.499  0.365-0.633 
  CLIP  0.835  0.748-0.922  0.777  0.691-0.863  0.778  0.695-0.861 
  CUPI  0.682  0.530-0.834  0.709  0.591-0.827  0.721  0.610-0.832 
  JIS  0.639  0.475-0.803  0.704  0.580-0.828  0.714  0.597-0.831 

Abbreviations: BCLC, Barcelona Clinic Liver Cancer classification system; TNM, tumor–lymph node–metastasis staging system; MELD, model for end-stage liver disease; CLIP, Cancer of the Liver Italian Program; CUPI, Chinese University Prognostic Index; JIS, Japan Integrated Staging.

The calibration plot for the probabilities of 1-year, 3-year, 5-year OS fitted well between the actual observation and the prediction of the model using a nomogram in the training and validation cohorts (Figure 1B and C). According to the nomogram scores patients were divided into, and low-risk (≤ 10.2 scores), medium-risk (10.2-14.4 scores), and high-risk (≥ 14.4 scores) groups. The Kaplan–Meier survival curve showed that the nomogram could distinguish the low-, medium-, and high-risk groups well in both the training and validation cohorts (p< 0.0001) (Figure 2A and B).

Figure 2.

The nomogram score is divided into low-, medium-, and high-risk groups according to the interquartile spacing (low risk group, ≤ 10.2 point; medium risk group, 10.2-14.4 point; high risk group, ≥ 14.4 point) in the training cohort (A) and validation cohort (B).

(0.18MB).
3.4Survival probability of decision tree under different risk factors

Six independent risk factors, including NK cell counts (≤ 118 cells/μL vs. >118 cells/μL), ALB level (≤ 40g/L vs. > 40g/L), AFP level (≤ 400ng/mL vs. > 400ng/mL), PVTT at baseline (No vs. Yes), tumor number (solitary vs. multiple) and treatment (palliative vs. minimally invasive, resection) were put into the decision tree, which was divided into seven layers according to the column contribution of variables to the outcome (Figure 3). The decision tree showed that the 5-year OS rate of patients with HCC in the PVTT (No) and the PVTT (Yes) groups were 46.2% and 4.5%, respectively. In addition, the 5-year OS rate of patients with NK cell counts≤118 cells/μL and those with NK cell counts > 118 cells/μL were 0.0% and 14.3% in the PVTT (Yes) group. As can be seen intuitively, the PVTT was in the first layer, and its column contribution was the largest. The treatment ranks second regarding column contribution. As an important immune index, NK cell count ranks third in column contribution, higher than the AFP level, tumor number, and ALB level (Table S2).

Figure 3.

Decision tree for model shows 5-year overall survival probability of patients with Hepatocellular carcinoma

(0.44MB).
3.5Prognostic value of NK cell counts in patients with HCC

We used the median NK cell counts of 118 as the cut-off value, and divided all patients into two groups. The 5-year OS of patients with HCC were compared using the Kaplan–Meier curves. We found that the 5-year OS rate of the high NK cell group (NK > 118 cells/μL) was significantly higher than that of the low NK cell group (NK ≤118 cells/μL) in both the training and validation cohorts (Figure 4 A and B). We further examined whether the low or the high NK cell group is closely related to the OS of patients with HCC. We found that in BCLC stage C-D, Child-Pugh B+C, and tumor multiple subgroups, HCC patients with high NK cell counts had higher survival rates (p = 0.004, p = 0.024, p = 0.025, respectively) (Figure 4D, F, and H). In BCLC stage 0-B, Child-Pugh A, and tumor solitary subgroups, there was no statistically significant difference in survival between patients with high NK cell counts and those with low NK cell counts (Figure 4C, E, and G).

Figure 4.

The 5-year overall survival analysis between HCC patients with NK cell counts > 118 cells/μL and NK cell counts ≤118 cells/μL in the retrospective cohort. (A) Nomogram in the Training Cohort; (B) Nomogram in the Validation Cohort; (C)BCLC stage 0-B; (D)BCLC stage C-D; (E) Child-Pugh A; (F)Child-Pugh B+C; (G) Tumor solitary; (H) Tumor multiple.

(0.24MB).
4Discussion

Research has been focused on the prognosis of patients with HCC, especially for long-term survival. In previous studies, models related to the prognosis of HCC have been reported, and the factors included in the models were related more to the liver function and the tumor [17-19, 24]. The influence of liver function and tumor-related factors on the occurrence and prognosis of patients with HCC is obvious. However, and the effect of other non-hepatic and non-tumor factors affecting the prognosis of patients with HCC should also be considered and confirmed.

In this study, the model included five liver function- and tumor-related factors, namely ALB level, AFP level, PVTT, tumor number, and treatment, as well as an immune-related factor, that is, NK cell counts. The effect of NK cell count on the OS rate of patients with HCC is worthy of attention. In addition, all the above factors are objective indicators obtained directly through clinical laboratory examinations.

NK cells are enriched in liver lymphocytes and have specific phenotypes and functional characteristics. As an important part of the liver innate immune system, NK cells play a vital role in tumor development through natural cytotoxicity and cytokine production [25]. Previous studies have shown that NK cells play an important role in the control of viral hepatitis, liver fibrosis, and liver cancer [26–28]. In patients with HCC, NK cells can inhibit the occurrence and development of tumors through different pathways and mechanisms, which are mainly mediated by perforin, granzyme, and IFN-γ. However, the function of NK cells is often inhibited in conditions of inflammation, fibrosis, and cirrhosis, even in the tumor microenvironment. Previous studies have found that compared with healthy subjects, NK cell counts in peripheral blood of patients with HCC are decreased, especially the number of CD56dimNK cell killing subsets is significantly reduced [6], which is consistent with the findings of this study. Zhang et al. found that CD11bCD27 (DN) NK cells accumulate in the tumor tissues of patients with HCC. Immunosuppressive factors (IL-10 and TGF-β) change the phenotype of NK cells and inhibit the immune surveillance function, resulting in immune escape of tumors [29]. Sun et al. found that the expression of CD96+NK cells increased in the tumor tissues of patients with HCC, and their functions were exhausted. Blocking TGF-β can restore NK cell function, which indicates that CD96 and other immunosuppressive receptors can play a more powerful role in anti-tumor immune response [30]. We found that the decrease in NK cell counts detected by clinical laboratory tests is an independent risk factor affecting the long-term prognosis of patients with HCC that cannot be ignored. Therefore, it is of great significance to improve the immune monitoring function of patients with HCC by increasing NK cell counts and activity.

A nomogram is a prediction model widely used in clinics; it is simple and visual. It can directly predict the survival rate of a single clinical variable and the entire model and promote clinical decision-making [31]. In recent years, the nomogram model has become a reliable clinical tool for quantifying the risk and evaluating the prognosis of lung cancer, breast cancer, prostate cancer, rectal cancer, and gastric cancer [32–36]. In this study, a nomogram was used to synthesize the best predictors of prognosis of patients with HCC, so that the 1-year, 3-year, and 5-year OS rates can be accurately predicted. At the same time, a calibration curve was used to evaluate the correlation between the predicted and actual risks.

As a method of machine learning, decision trees can directly show the mapping relationship between predictors and outcomes [37]. The algorithm of the decision tree is complex, and the software can provide it directly. We only need to pay attention to the levels and nodes displayed by the decision tree. The level is the contribution of different predictors to the outcome, and the node is the best way to divide the variables. Our previous study found that high numbers of CD4 T lymphocytes associate with longer progression-free survival (PFS) and OS in hepatocellular carcinoma [23]. CD4 T cells not only enhance the function of CD8 T cells, but also recruit NK cells to the tumor microenvironment. Therefore, immune cells play an important role in the survival of HCC [38, 39]. In this study, the decision tree intuitively shows the influence of different risk factors on the 5-year OS rate of patients with HCC. It can be seen that the NK cell count is in the third place of the column contribution of the decision tree. We consider that the impact of the reduction in NK cell counts on the outcome is still significant.

This study has the following limitations: First, the sample size is small. However, we used two modeling methods to prove the predictive ability of NK cell counts for the survival of patients with HCC. Second, the lack of external verification may affect the applicability of the model. Third, the decision tree lacks stability.

5Conclusions

A visual nomogram was used to establish a prediction model based on NK cell counts to predict the OS of patients with HCC, providing a simple and quick decision-making tool for clinical practice. In the future, NK cell count is worthy of the attention as clinical index, and it needs further verification regarding its clinical value.

DeclarationsEthics approval and consent to participate

This study is in accordance with the Declaration of Helsinki and has been approved by the ethical committee of Beijing Ditan Hospital, Capital Medical University. This study is a retrospective research. The ethics committee approved it to be used in this study while ensuring patient privacy, not interfering with the patient's treatment methods, and not bringing risks to the patient's physiology. Retrospective researches are unable to obtain written informed consent. The main reasons are: (1) we only collect the clinical laboratory data from patients, and the risks are manageable; (2) some patients have lost contact or died and are unable to track their informed consent signatures. This study was based on the approval of the ethics committee and the oral informed consent of the patients or their families who survived and could be contacted during the regular follow-up period.

Authors' contributions

Lihua Yu: data curation, methodology, formal analysis, writing-original draft, writing-review and editing. Xiaoli Liu: study design, data curation. Xinhui Wang, Dongdong Zhou, Huiwen Yan, Yuqing Xie, and Qing Pu: data acquisition. Ke Zhang: study design, corrected the final version of the manuscript. Zhiyun Yang: final approval of the version to be submitted. The definitive version has been read and approved by all authors.

Funding

This study was supported by the Special Fund of Capital Health Research and Development (No. 2020-2-2173), the National Natural Science Foundation of China (No. 81874435), Dengfeng Talent Support Program of Beijing Municipal Administration of Hospitals (No. DFL20191803), Fund for Beijing Science & Technology Development of TCM (No. JJ-2020-52), Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (ZYLX202127).

References
[1]
H Sung, J Ferlay, RL Siegel, M Laversanne, I Soerjomataram, A Jemal, et al.
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.
CA Cancer J Clin, 71 (2021), pp. 209-249
[2]
J Pahl, A. Cerwenka.
Tricking the balance: NK cells in anti-cancer immunity.
Immunobiology, 222 (2017), pp. 11-20
[3]
H Ljunggren, K. Malmberg.
Prospects for the use of NK cells in immunotherapy of human cancer.
Nat Rev Immunol, 7 (2007), pp. 329-339
[4]
X Liu, L He, J Han, L Wang, M Li, Y Jiang, et al.
Association of neutrophil-lymphocyte ratio and T lymphocytes with the pathogenesis and progression of HBV-associated primary liver cancer.
[5]
X Liu, M Li, X Wang, Z Dang, Y Jiang, X Wang, et al.
PD-1+ TIGIT+ CD8+ T cells are associated with pathogenesis and progression of patients with hepatitis B virus-related hepatocellular carcinoma.
Cancer Immunol Immunother, 68 (2019), pp. 2041-2054
[6]
L Cai, Z Zhang, L Zhou, H Wang, J Fu, S Zhang, et al.
Functional impairment in circulating and intrahepatic NK cells and relative mechanism in hepatocellular carcinoma patients.
Clin Immunol, 129 (2008), pp. 428-437
[7]
JH Jiang, ZF Zhou, JY Li, YB Ye, Q. Chen.
Expression and significance of the NK cell receptors in primary hepatocellular carcinoma and paracancerous tissues.
J Cell Mol Immunol, 28 (2012), pp. 529-532
[8]
V Chew, J Chen, D Lee, E Loh, J Lee, KH Lim, et al.
Chemokine-driven lymphocyte infiltration: an early intratumoural event determining long-term survival in resectable hepatocellular carcinoma.
[9]
C Sun, J Xu, Q Huang, M Huang, H Wen, C Zhang, et al.
High NKG2A expression contributes to NK cell exhaustion and predicts a poor prognosis of patients with liver cancer.
Oncoimmunology, 6 (2016),
[10]
JM Llovet, C Brú, J. Bruix.
Prognosis of hepatocellular carcinoma: the BCLC staging classification.
Semin Liver Dis, 19 (1999), pp. 329-338
[11]
SB Edge, CC. Compton.
The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM.
Ann Surg Oncol., 17 (2010), pp. 1471-1474
[12]
K Okuda, T Ohtsuki, H Obata, M Tomimatsu, N Okazaki, H Hasegawa, et al.
Natural history of hepatocellular carcinoma and prognosis in relation to treatment study of 850 patients.
[13]
R H Wiesner, R B Freeman, D C Mulligan.
Liver transplantation for hepatocellular cancer: The impact of the MELD allocation policy.
Gastroenterology, 127 (2004), pp. S261-S267
[14]
J M Llovet, J. Bruix.
Prospective validation of the Cancer of the Liver Italian Program (CLIP) score: a new prognostic system for patients with cirrhosis and hepatocellular carcinoma.
Hepatology, 32 (2000), pp. 678-680
[15]
SL Chan, FK Mo, PJ Johnson, GS Liem, TC Chan, MC Poon, et al.
Prospective validation of the Chinese University Prognostic Index and comparison with other staging systems for hepatocellular carcinoma in an Asian population.
J Gastroenterol Hepatol, 26 (2011), pp. 340-347
[16]
KZ Luo, T Itamoto, H Amano, A Oshita, Y Ushitora, Y Tanimoto, et al.
Comparative study of the Japan Integrated Stage (JIS) and modified JIS score as a predictor of survival after hepatectomy for hepatocellular carcinoma.
J Gastroenterol, 43 (2008), pp. 369-377
[17]
N. Listed.
A new prognostic system for hepatocellular carcinoma: A retrospective study of 435 patients.
Hepatology, 28 (1998), pp. 751-755
[18]
R. Tateishi.
Proposal of a new prognostic model for hepatocellular carcinoma: an analysis of 403 patients.
[19]
Z Weng, J Ertle, S Zheng, T Lauenstein, S Mueller, A Bockisch, et al.
A New Model to Estimate Prognosis in Patients with Hepatocellular Carcinoma after Yttrium-90 Radioembolization.
[20]
V Valentini, RG van Stiphout, G Lammering, MA Gambacorta, MC Barba, M Bebenek, et al.
Nomograms for predicting local recurrence, distant metastases, and overall survival for patients with locally advanced rectal cancer on the basis of European randomized clinical trials.
J Clin Oncol, 29 (2011), pp. 3163-3172
[21]
DS Han, YS Suh, SH Kong, HJ Lee, Y Choi, S Aikou, et al.
Nomogram Predicting Long-Term Survival After D2 Gastrectomy for Gastric Cancer.
J Clin Oncol, 30 (2012), pp. 3834-3840
[22]
Department of Medical Administration, National Health and Health Commission of the People's Republic of China. [Guidelines for diagnosis and treatment of primary liver cancer in China (2019 edition)].
Zhonghua Gan Zang Bing Za Zhi, 28 (2020), pp. 112-128
[23]
X Liu, Y Hou, X Wang, L Yu, X Wang, L Jiang, et al.
Machine learning-based development and validation of a scoring system for progression-free survival in liver cancer.
Hepatol Int, 14 (2020), pp. 567-576
[24]
RX Chen, YH Gan, NL Ge, Y Chen, M Ma, BH Zhang, et al.
A new prediction model for prognosis of patients with intermediate-stage HCC after conventional transarterial chemoembolization: an internally validated study.
J Cancer, 10 (2019), pp. 6535-6542
[25]
V Male, KA Stegmann, NJ Easom, MK. Maini.
Natural Killer Cells in Liver Disease.
Semin Liver Dis, 37 (2017), pp. 198-209
[26]
Z Zhang, S Zhang, Z Zou, J Shi, J Zhao, R Fan, et al.
Hypercytolytic activity of hepatic natural killer cells correlates with liver injury in chronic hepatitis B patients.
Hepatology, 53 (2011), pp. 73-85
[27]
K Cheent, SI. Khakoo.
Natural killer cells and hepatitis C:action andreaction.
[28]
DY Zhang, SL. Friedman.
Fibrosis-dependent mechanisms of hepatocarcinogenesis.
Hepatology, 56 (2012), pp. 769-775
[29]
QF Zhang, WW Yin, Y Xia, YY Yi, QF He, X Wang, et al.
Liver-infiltrating CD11b-CD27 NK subsets account for NK-cell dysfunction in patients with hepatocellular carcinoma and are associated with tumor progression.
Cell Mol Immunol, 14 (2017), pp. 819-829
[30]
H Sun, Q Huang, M Huang, H Wen, R Lin, M Zheng, et al.
Human CD96 Correlates to Natural Killer Cell Exhaustion and Predicts the Prognosis of Human Hepatocellular Carcinoma.
Hepatology, 70 (2019), pp. 168-183
[31]
A Iasonos, D Schrag, GV Raj, KS. Panageas.
How to build and interpret a nomogram for cancer prognosis.
J Clin Oncol, 26 (2008), pp. 1364-1370
[32]
W Liang, L Zhang, G Jiang, Q Wang, L Liu, D Liu, et al.
Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non-Small-Cell Lung Cancer.
J Clin Oncol, 33 (2015), pp. 861-869
[33]
JM Timmers, AL Verbeek, J IntHout, RM Pijnappel, MJ Broeders, GJ. den Heeten.
Breast cancer risk prediction model:a nomogram based on common mammographic screening findings.
Eur Radiol, 23 (2013), pp. 2413-2419
[34]
K Kawai, S Ishihara, H Yamaguchi, E Sunami, J Kitayama, H Miyata, et al.
Nomograms for predicting the prognosis of stage IV colorectal cancer after curative resection: a multicenter retrospective study.
Eur J Surg Oncol, 41 (2015), pp. 457-465
[35]
Z A Dotan, J. Ramon.
Nomograms as a Tool in Predicting Prostate Cancer Prognosis.
European Urology Supplements, 8 (2009), pp. 721-724
[36]
M Roberto, A Botticelli, L Strigari, M Ghidini, CE Onesti, M Ratti, et al.
Prognosis of elderly gastric cancer patients after surgery: a nomogram to predict survival.
[37]
M. Sahinoglu.
Security meter:a practical decision-tree model to quantify risk.
IEEE Security & Privacy, 3 (2005), pp. 18-24
[38]
TW Phares, SA Stohlman, M Hwang, B Min, DR Hinton, CC. Bergmann.
CD4 T cells promote CD8 T cell immunity at the priming and effector site during viral encephalitis.
J Virol, 86 (2012), pp. 2416-2427
[39]
HJ Kim, H. Cantor.
CD4 T-cell subsets and tumor immunity: the helpful and the not-so-helpful.
Cancer Immunol Res, 2 (2014), pp. 91-98

These authors contributed equally to this work.

Copyright © 2022. Fundación Clínica Médica Sur, A.C.
Descargar PDF
Opciones de artículo
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos