System for predicting liver cancer prognosis based on soluble immune checkpoints

Abstract

The invention provides a COX regression prognosis prediction model for HBV-HCC patients, and the prediction model combines clinical indexes and soluble immune checkpoint indexes. The prediction model provided by the invention can accurately predict the total survival rate of the patient for different event distributions (such as different intervention modes) and different clinical stages (BCLC stages 0-B and C-D), thereby providing direct and accurate auxiliary information of disease diagnosis and treatment for clinic.

Description

Technical Field

[0001] The present invention belongs to the field of liver cancer diagnosis, and in particular, the present invention relates to soluble immune checkpoints and their application in predicting the prognosis of liver cancer. Background Art

[0002] According to the 2020 Global Cancer Statistics, liver cancer is the sixth most common cancer in the world, with the fifth highest incidence and the third highest mortality rate. [1] Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, accounting for 90% of all primary liver cancer cases. [2] In my country, hepatitis B virus (HBV) infection is one of the most important causes of liver cancer. [3] , hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) accounts for more than 80% of HCC patients in China.

[0003] Liver cancer is characterized by high recurrence and mortality rates. Most liver cancer patients are already in the advanced stage when diagnosed, which limits treatment options. Currently, revolutionary advances in immune checkpoint inhibitor therapy have extended the survival of HCC patients, such as anti-PD-1 / PD-L1 antibodies (Anti-Programmed Death-1antibody / Anti-Programmed Death1Ligand 1antibody) and anti-CTLA4 antibodies (Anti-Cytotoxic T Lymphocyte-associatedAntigen-4antibody). However, although researchers have conducted a large number of studies to improve the clinical efficacy of HCC immunotherapy, patients' response to immune checkpoint blockade (ICB) is still limited, and there are also events such as drug resistance and adverse reactions. [4] Therefore, there is an urgent need to gain a deeper understanding of the immune mechanisms of HBV-HCC and explore new biomarkers and potential therapeutic targets.

[0004] In recent years, soluble immune checkpoints (sICs) have gradually attracted attention. Studies have shown that soluble immune checkpoints are associated with local CD8 + T cell exhaustion is closely related to the prognosis of HBV-HCC patients [5-8] In addition, they can bind to immune checkpoint inhibitors and affect their efficacy [9]Soluble immune checkpoints are mainly produced by mRNA translation or membrane-bound protein shedding and bind to receptors or ligands on the cell membrane, which increases the diversity of immune signaling pathways and the complexity of immune regulatory functions.

[10] Many clinical studies have evaluated the prognostic value of soluble immune checkpoints in cancer patients and explored the relationship between soluble immune checkpoint levels and clinical pathological factors. [11,12] However, few studies have been conducted on sICs in HBV-HCC patients. Therefore, it is necessary to explore the clinical significance of soluble immune checkpoints in HBV-HCC patients. Summary of the invention

[0005] The technical problem to be solved by the present invention is how to predict the prognosis of liver cancer patients based on immune checkpoints, especially soluble immune checkpoints, in combination with clinical indicators.

[0006] Based on the clinical and immune characteristics of a large number of HBV-HCC patients, the inventors of the present invention screened out the risk factors associated with the 3-year overall survival (OS) of HBV-HCC patients by using univariate and multivariate COX proportional risk analysis, and screened out the independent related factors of 3-year OS of HBV-HCC patients by multivariate COX regression analysis, and further constructed a risk scoring model. Validation experiments have shown that the risk scoring model can be used for risk stratification analysis of the prognosis of HBV-HCC patients; and because the model covers multiple soluble immune checkpoints, it can be seen that when clinicians choose a more favorable treatment regimen related to immune checkpoint inhibitor therapy for liver cancer patients, the model can provide useful auxiliary information.

[0007] Based on the above findings, the present invention provides the following technical solutions.

[0008] In one aspect, the present invention provides a method for grouping prognostic conditions of liver cancer patients, the method comprising:

[0009] (1) Assign values ​​to the clinical characteristics and soluble immune checkpoint characteristics of the patient:

[0010] The clinical features are hepatic encephalopathy, C-reactive protein (CRP) level, CD8+T cell count, and tumor diameter:

[0011] If the patient has hepatic encephalopathy, assign 1 point; if not, assign 0 points;

[0012] If the patient's CRP level is ≥5 mg / L, 1 point is assigned; if not, 0 point is assigned;

[0013] If the patient's CD8+ T cell count is ≥320 cells / μl, 1 point is assigned; if not, 0 point is assigned;

[0014] If the patient's tumor diameter is ≥5 cm, 1 point is assigned; otherwise, 0 point is assigned;

[0015] The soluble immune checkpoints are glucocorticoid-induced TNF receptor (GITR) and programmed death ligand 1 (PD-L1):

[0016] If the patient's GITR level is ≥75.89 pg / ml, 1 point is assigned; if not, 0 point is assigned;

[0017] If the patient's PD-L1 level is ≥7.32 pg / ml, 1 point is assigned; if not, 0 point is assigned;

[0018] (2) Substitute the values ​​of each feature in step (1) into the following risk scoring formula:

[0019] Riskscore = 1.1925413 × (hepatic encephalopathy) + 0.6751818 × (CRP) + (-0.6964451) × (CD8 + T cells) + 1.2505047 × (tumor diameter) + 0.6793419 × (PD-L1) + 0.8117957 × (GITR), the risk score was calculated;

[0020] (3) Comparing the risk score of step (2) with a reference value, if the risk score is ≥ the reference value, the patient is determined to be in a high-risk group with a poor prognosis; if not, the patient is determined to be in a low-risk group with a poor prognosis.

[0021] The methods provided by the present invention are not used to diagnose diseases.

[0022] In the method provided by the present invention, the liver cancer is preferably hepatocellular carcinoma (HCC), more preferably hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

[0023] According to a specific embodiment of the present invention, the liver cancer is HBV-HCC. In the context of the present invention, the term "HBV-HCC" is defined as a patient with positive serum of hepatitis B surface antigen (HBsAg ≥ 6 months) that meets the diagnostic requirements of HCC.

[0024] In the method provided by the present invention, the expression level of the soluble immune checkpoint is detected in the patient's blood, preferably in the plasma.

[0025] Optionally, the method provided by the present invention further comprises detecting the clinical characteristics and soluble immune checkpoint characteristics in step (1). The clinical characteristics and soluble immune checkpoint characteristics are judged or detected by methods generally recognized in the art. For example, Luminex multiple immunofluorescence analysis is used to detect soluble immune checkpoints in plasma, and data analysis uses ProcartaPlex Analyst 1.0 software to determine the level of soluble immune checkpoints by fitting a standard curve of mean fluorescence intensity and concentration.

[0026] In the method provided by the present invention, the reference value used in step (3) is the optimal cutoff value of the risk score calculated by the formula using the HBV-HCC patient group. According to a specific embodiment of the present invention, the reference value obtained by using the surv_cutpoint function of the R software during calculation is 2.74.

[0027] In the method provided by the present invention, the prognosis is reflected by the overall survival of the patient after treatment. The treatment method can be one or more of liver resection, minimally invasive treatment and palliative treatment. The overall survival can be 1, 2 or 3 years after treatment.

[0028] In the method provided by the present invention, the patient may be a patient with BCLC stage 0-B or CD; and / or, the patient may be a patient with different alpha fetal protein (AFP) levels.

[0029] In another aspect, the present invention provides a system for grouping prognostic conditions of liver cancer patients, the system comprising:

[0030] (1) Assignment module of the clinical characteristics of the patient and the characteristics of soluble immune checkpoints:

[0031] The clinical features are hepatic encephalopathy, C-reactive protein (CRP) level, CD8+T cell count, and tumor diameter:

[0032] If the patient has hepatic encephalopathy, assign 1 point; if not, assign 0 points;

[0033] If the patient's CRP level is ≥5 mg / L, 1 point is assigned; if not, 0 point is assigned;

[0034] If the patient's CD8+ T cell count is ≥320 cells / μl, 1 point is assigned; if not, 0 point is assigned;

[0035] If the patient's tumor diameter is ≥5 cm, 1 point is assigned; otherwise, 0 point is assigned;

[0036] The soluble immune checkpoints are glucocorticoid-induced TNF receptor (GITR) and programmed death ligand 1 (PD-L1):

[0037] If the patient's GITR level is ≥75.89 pg / ml, 1 point is assigned; if not, 0 point is assigned;

[0038] If the patient's PD-L1 level is ≥7.32 pg / ml, 1 point is assigned; if not, 0 point is assigned;

[0039] (2) The patient's risk score calculation module:

[0040] Substitute the values ​​of each feature obtained by the assignment module into the following risk scoring formula:

[0041] Riskscore = 1.1925413 × (hepatic encephalopathy) + 0.6751818 × (CRP) + (-0.6964451) × (CD8 + T cells) + 1.2505047 × (tumor diameter) + 0.6793419 × (PD-L1) + 0.8117957 × (GITR), the risk score was calculated;

[0042] (3) Grouping module:

[0043] The risk score obtained by the calculation module is compared with the reference value. If it is ≥ the reference value, the patient is determined to be in the high-risk group with poor prognosis; if not, the patient is determined to be in the low-risk group with poor prognosis.

[0044] In the system provided by the present invention, the liver cancer is preferably hepatocellular carcinoma (HCC), more preferably hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

[0045] According to a specific embodiment of the present invention, the liver cancer is HBV-HCC.

[0046] In the system provided by the present invention, the expression level of the soluble immune checkpoint is detected in the patient's blood, preferably in the plasma.

[0047] Optionally, the assignment module of the system provided by the present invention also detects the clinical characteristics and soluble immune checkpoint characteristics. In this detection module, the clinical characteristics and soluble immune checkpoint characteristics are judged or detected by methods generally recognized in the art. For example, Luminex multiple immunofluorescence analysis is used to detect soluble immune checkpoints in plasma, and ProcartaPlex Analyst 1.0 software is used for data analysis to determine the level of soluble immune checkpoints by fitting a standard curve of mean fluorescence intensity and concentration.

[0048] In the system provided by the present invention, the reference value used in the grouping module is the optimal cutoff value of the risk score calculated by the formula using the HBV-HCC patient group. According to a specific embodiment of the present invention, the reference value obtained by using the surv_cutpoint function of the R software during calculation is 2.74.

[0049] In the method provided by the present invention, the prognosis is reflected by the overall survival of the patient after treatment. The treatment method can be one or more of liver resection, minimally invasive treatment and palliative treatment. The overall survival can be 1, 2 or 3 years after treatment.

[0050] In the method provided by the present invention, the patient may be a patient with BCLC stage 0-B or CD; and / or, the patient may be a patient with different alpha fetal protein (AFP) levels.

[0051] In another aspect, the present invention provides a product for grouping the prognosis of liver cancer patients, and the product can be used in the system provided by the present invention.

[0052] Preferably, the product includes tools and reagents used in detecting the clinical characteristics and soluble immune checkpoint characteristics.

[0053] Compared with the prior art, the inventors of the present invention provide a new COX regression model, which can be used to predict the prognosis of HBV-HCC patients.

[0054] In the art, there are few prognostic prediction models for HBV-HCC patients that combine immune indicators. The prognostic prediction model of the present invention is based on clinical indicators and soluble ICs (sICs). It is known that sICs are an important component of immune regulation. Although their exact mechanism of action has not yet been determined, they have the advantages of minimally invasive, low-cost and rapid detection, and have great potential in the era of liquid biopsy. As soluble molecules, they are easily obtained from the blood and can be repeatedly detected, which is of great significance for evaluating the immune status and severity of liver cancer.

[0055] Specifically, the prognostic prediction model of the present invention includes two soluble ICs. One is GITR and the other is PD-L1. GITR, originally described as a glucocorticoid-induced factor, is reported to be expressed in regulatory T (Treg) cells.

[13] , NK cells and tumor infiltrating lymphocytes (TIL)

[14] , which promotes the activation and proliferation of effector T cells and reduces the generation of Treg cells

[15] The cell sources of soluble GITR are macrophages and Treg cells

[16] sGITR triggers inflammatory response in mice

[17] , causing cell cycle arrest and apoptosis of mouse macrophages and inducing various immune responses

[18] In our study, high levels of sGITR were significantly associated with shorter survival in HBV-HCC patients, which may be due to the fact that sGITR mediates immunosuppression by inhibiting the GITR / GITRL pathway, leading to poor prognosis in cancer patients. Regarding sPD-L1, we believe that sPD-L1 is a risk factor for HBV-HCC prognosis. In fact, soluble PD-L1 binds to PD-I and inhibits T cell responses.

[19] , which suggests that sPD-L1 is associated with impaired immune function and poor prognosis.

[0056] In terms of clinical characteristics, the prognostic prediction model of the present invention selected hepatic encephalopathy, C-reactive protein (CRP) level, CD8+T cell count, and tumor diameter, which are closely related to the patient's prognosis in clinical practice and are easily obtained clinical data. The prognostic prediction model of the present invention combines the patient's immune characteristics to improve the accuracy of survival prediction.

[0057] In conclusion, we developed a COX regression prognostic prediction model for HBV-HCC patients based on soluble immune checkpoints. This prediction model combines two major categories of features. For different event distributions (e.g., different intervention methods) and different clinical stages (BCLC stage 0-B, CD, various AFP levels), it can accurately predict the overall survival of patients. In practical applications, this model can be used to divide liver cancer patients, especially HBV-HCC patients, into high-risk and low-risk groups with poor prognosis, which can provide direct and accurate auxiliary information for disease diagnosis and treatment in clinics. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, wherein:

[0059] Figure 1 : Nomogram for predicting 1-, 2-, and 3-year survival of HBV-HCC patients.

[0060] Figure 2 : AB: ROC curves of 1-year, 2-year, and 3-year OS in the modeling group (A) and validation group (B); CD: area under the time-dependent ROC curve of the modeling group (C) and validation group (D).

[0061] Figure 3 : AC: calibration curves of 1 year (A), 2 years (B), and 3 years (C) in the modeling group; DF: calibration curves of 1 year (D), 2 years (E), and 3 years (F) in the validation group.

[0062] Figure 4 : A: KM survival curves of the total cohort divided into high-risk group and low-risk group based on risk score; BC: KM analysis of the survival differences between high-risk and low-risk groups in patients with different BCLC stages; DE: KM analysis of the survival differences between high-risk and low-risk groups in patients with different AFP levels. DETAILED DESCRIPTION

[0063] The present invention is described below with reference to specific examples. It will be appreciated by those skilled in the art that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention in any way.

[0064] The experimental methods in the following examples are conventional methods unless otherwise specified. The raw materials, reagents, etc. used in the following examples are commercially available products unless otherwise specified.

[0065] The following study adopted the following methods.

[0066] 1. Study Design and Data Collection

[0067] The study included 196 HBV-HCC patients hospitalized in the Department of Integrated Traditional Chinese and Western Medicine of Beijing Ditan Hospital between May 2017 and June 2020. Their baseline clinical data and blood samples were collected. Luminex multiplex immunofluorescence analysis was used to detect soluble immune checkpoints in plasma. Data analysis used ProcartaPlex Analyst 1.0 software, and the levels of soluble immune checkpoints were determined by fitting the standard curve of mean fluorescence intensity and concentration.

[0068] The inclusion criteria for HBV-HCC patients were as follows: (i) clinical diagnosis of HCC, (ii) diagnosis of chronic HBV infection, and (iii) age between 18 and 75 years. The exclusion criteria were as follows: (i) cholangiocarcinoma, (ii) metastatic HCC, (iii) combined with other types of cancer, (iv) lost to follow-up, (v) multiple organ failure, and (vi) incomplete clinical data.

[0069] The patients' survival status and disease progression were evaluated through electronic medical records and telephone follow-up every three months, and the time from enrollment to death was defined as overall survival (OS).

[0070] 2. Statistical Analysis

[0071] T test or Wilcoxon test was used to compare continuous variables, and chi-square test or Fisher's exact test was used to compare categorical variables; COX regression analysis was used to screen prognostic factors and build a prediction model. The risk score was calculated according to the COX regression model, and the surv_cutpoint function of R software was used to obtain the optimal cut-off value. Kaplan-Meier analysis, log-rank test, and COX analysis were used to compare OS between different risk groups. IBM SPSS Statistics 26.0 and R 4.3.1 were used for analysis. P < 0.05 was considered statistically significant.

[0072] Example 1 Baseline characteristics of HBV-HCC patients included in the study

[0073] According to the 7:3 randomization principle, 196 HBV-HCC patients were divided into a modeling group and a validation group, resulting in 138 cases (70%) in the modeling group, which were used to construct a survival model for predicting HBV-HCC patients; and 58 cases (30%) in the validation group, which were used to evaluate the predictive ability of the model.

[0074] The clinical and immune characteristics of the 196 HBV-HCC patients are shown in Tables 1 and 2 .

[0075] Table 1 Clinical baseline characteristics of HBV-HCC patients

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] *NLR: Neutrophil-to-lymphocyte ratio, using the median of 2.61 as the cut-off value

[0082] As shown in Tables 1 and 2 , the cutoff values ​​of laboratory data were clinical normal values, and the median values ​​were used to group the neutrophil-lymphocyte ratio (neutrophil number / lymphocyte number) and soluble immune checkpoint levels. All patients in this study were of Han nationality. Among the enrolled patients, 152 were male, accounting for 77.6%, and 44 were female, accounting for 22.4%, with a male-to-female ratio of 3.5:1. 88 HBV-HCC patients had a history of smoking, accounting for 44.9%, 73 patients had a history of drinking, accounting for 37.2%, and 29 patients had a family history of liver cancer, accounting for 14.8%. In terms of complications, fewer patients had hepatic encephalopathy and gastrointestinal bleeding complications, accounting for less than 10%, and 88 patients had ascites complications, accounting for 44.9%. In terms of virology, 86.2% of HBV-HCC patients had HBV-DNA below 500 IU / ml, and 134 patients were HBeAg negative, accounting for 70.9%. In terms of oncological characteristics, 74.5% of HBV-HCC patients were in the early stage of HCC (BCLC 0-B stage), and 25.5% of HBV-HCC patients were in the late stage of HCC (BCLC CD stage), 102 patients with HBV-HCC had a tumor diameter less than 5 cm, accounting for 52%, and 104 patients with ≥2 tumors, accounting for 53.1%. 20.4% of HBV-HCC patients had AFP ≥ 400ng / ml. In terms of treatment, treatment methods included liver resection, minimally invasive treatment and palliative treatment, among which minimally invasive treatment included TACE and radiofrequency ablation. 58.1% of HBV-HCC patients underwent minimally invasive treatment, and fewer patients underwent liver resection, accounting for 6 cases, accounting for 3.1%. 76 patients underwent palliative treatment, accounting for 38.8%. In terms of blood routine, 58.7% of HBV-HCC patients had high white blood cell counts, 35.7% of patients had anemia, and 105 patients (53.6%) had PLT < 100×10 9 / L; in terms of liver function, 80.6% of HBV-HCC patients had ALT < 40U / L, 57.7% of patients had AST < 40U / L, 45.9% of patients had total bilirubin levels < 18.8μmol / L, 71.4% of patients had albumin levels < 40g / L, and nearly 80% of patients had liver function tests of Child-Pugh A or B; in terms of coagulation, 58 HBV-HCC patients (29.6%) had PTA < 70%.

[0083] Table 2. Baseline soluble immune checkpoint levels in HBV-HCC patients

[0084]

[0085]

[0086] *Soluble immune checkpoints (pg / ml): The corresponding median is used as the cut-off value

[0087] Example 2 Screening of variables affecting the prognosis of HBV-HCC patients

[0088] Univariate COX analysis was used in the training group to screen risk factors associated with 3-year OS in patients with HBV-HCC. It was found that hepatic encephalopathy, ascites, HBV-DNA ≥ 500 IU / ml, AST ≥ 40 U / L, GGT ≥ 50 U / L, LDH ≥ 300 U / L, CRP ≥ 5 mg / L, CD8+ T cells ≥ 320 cells / μl, AFP ≥ 400 ng / ml, tumor diameter ≥ 5 cm, number of tumors ≥ 2, sBTLA ≥ 665.94 pg / ml, sGITR ≥ 75.89 pg / ml, sPD-L1 ≥ 7.32 pg / ml, and sTIM-3 ≥ 900.24 pg / ml were risk factors for 3-year OS in patients (all P < 0.05). Further multivariate COX regression analysis showed that hepatic encephalopathy, CRP ≥ 5 mg / L, CD8+ T cells ≥ 320 cells / μl, tumor diameter ≥ 5 cm, sGITR ≥ 75.89 pg / ml, and sPD-L1 ≥ 7.32 pg / ml were independent factors associated with the patient's 3-year OS.

[0089] Table 3. Independent risk factors affecting the prognosis of HBV-HCC patients in COX univariate and multivariate regression analysis

[0090]

[0091]

[0092] Example 3 Construction of a prognostic prediction model for HBV-HCC patients

[0093] Through COX regression analysis, we have screened out the influencing factors of HBV-HCC patients' prognosis. We used these six factors to construct a nomogram ( Figure 1 ). When using a nomogram, find the position of each variable on the axis and the corresponding point score on the Points axis in the vertical direction, calculate the total score of all variables, and determine the predicted probability of HBV-HCC survival on the axis. Our risk score formula is as follows:

[0094] Riskscore = 1.1925413 × (hepatic encephalopathy) + 0.6751818 × (CRP) + (-0.6964451) × (CD8 + T cells)+1.2505047×(tumor diameter)+0.6793419×(PD-L1)+0.8117957×(GITR)

[0095] Among them, if the patient has hepatic encephalopathy, 1 point is assigned; if not, 0 point is assigned;

[0096] If the patient's CRP level is ≥5 mg / L, 1 point is assigned; if not, 0 point is assigned;

[0097] If the patient's CD8+ T cell count is ≥320 cells / μl, 1 point is assigned; if not, 0 point is assigned;

[0098] If the patient's tumor diameter is ≥5 cm, 1 point is assigned; otherwise, 0 point is assigned;

[0099] The soluble immune checkpoints are glucocorticoid-induced TNF receptor (GITR) and programmed death ligand 1 (PD-L1):

[0100] If the patient's GITR level is ≥75.89 pg / ml, 1 point is assigned; if not, 0 point is assigned;

[0101] If the patient's PD-L1 level is ≥7.32pg / ml, 1 point is assigned; otherwise, 0 point is assigned.

[0102] Example 4 Verification and evaluation of COX regression model

[0103] We used the ROC curve to evaluate the discrimination of the model. We found that the areas under the ROC curve (AUC) of the 1-year, 2-year, and 3-year survival of HBV-HCC patients predicted by the COX regression model were 0.94, 0.83, and 0.78 in the modeling group, and 0.87, 0.85, and 0.79 in the validation group ( Figure 2 A and B in Figure 3); Considering the continuity of liver cancer survival time, we found through time-dependent AUROC analysis that the AUC value of the COX regression model in predicting any survival time point of 0-36 months in HBV-HCC patients in both the training group and the validation group was higher than 0.7 ( Figure 2 C and D), indicating that the model has good discrimination.

[0104] In addition, the calibration of the prediction model is an important indicator for evaluating the accuracy of the clinical prediction model. The calibration curve drawn according to the COX regression model shows the calibration between the actual probability of death and the predicted probability of HBV-HCC patients. We drew the calibration curves of the prediction model in the modeling group and the validation group at 1, 2, and 3 years ( Figure 3), both of which can well fit the actual survival rate of HBV-HCC patients, indicating that the COX regression model has good calibration.

[0105] Example 5 Risk stratification analysis of COX regression model in predicting the prognosis of HBV-HCC patients

[0106] Risk stratification of patients is very important for guiding patient management. We calculated the risk score of the COX regression model and used the surv_cutpoint() function of the R language software to find the optimal cut point, and finally divided HBV-HCC patients into a high-risk group (risk score ≥ 2.74 points) and a low-risk group (risk score < 2.74 points).

[0107] In the total cohort, the results of Kaplan-Meier survival analysis and log-rank test for the high-risk and low-risk groups were as follows Figure 4 As shown in A in Figure 2, there was a significant difference between the two groups (P<0.0001). In addition, we showed that there were significant survival differences between high-risk and low-risk patients in different subgroups. For example, in the total cohort, in the BCLC 0-B and CD stage subgroups, the 3-year OS survival of the low-risk group was significantly better than that of the high-risk group ( Figure 4 Similarly, the 3-year OS survival of the low-risk group was significantly better than that of the high-risk group in patients with different AFP levels ( Figure 4 DE in ).

[0108] Together, these results suggest that the model is able to accommodate different event distributions and clinical stages.

[0109] The above description of the specific embodiments of the present invention does not limit the present invention. Those skilled in the art may make various changes or modifications based on the present invention. As long as they do not depart from the spirit of the present invention, they should all fall within the scope of the claims attached to the present invention.

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Claims

1. A method for grouping prognostic conditions of liver cancer patients, the method comprising: (1) Assign values ​​to the clinical characteristics and soluble immune checkpoint characteristics of the patient: The clinical features are hepatic encephalopathy, C-reactive protein (CRP) level, CD8+T cell count, and tumor diameter: If the patient has hepatic encephalopathy, assign 1 point; if not, assign 0 points; If the patient's CRP level is ≥5 mg / L, 1 point is assigned; if not, 0 point is assigned; If the patient's CD8+ T cell count is ≥320 cells / μl, 1 point is assigned; if not, 0 point is assigned; If the patient's tumor diameter is ≥5 cm, 1 point is assigned; otherwise, 0 point is assigned; The soluble immune checkpoints are glucocorticoid-induced TNF receptor (GITR) and programmed death ligand 1 (PD-L1): If the patient's GITR level is ≥75.89 pg / ml, 1 point is assigned; if not, 0 point is assigned; If the patient's PD-L1 level is ≥7.32 pg / ml, 1 point is assigned; if not, 0 point is assigned; (2) Substitute the values ​​of each feature in step (1) into the following risk scoring formula: Riskscore = 1.1925413 × (hepatic encephalopathy) + 0.6751818 × (CRP) + (-0.6964451) × (CD8 + T cells) + 1.2505047 × (tumor diameter) + 0.6793419 × (PD-L1) + 0.8117957 × (GITR), the risk score was calculated; (3) comparing the risk score of step (2) with a reference value, and if the risk score is ≥ the reference value, the patient is identified as a high-risk group with a poor prognosis; If not, the patient is identified as a low-risk group with a poor prognosis; The methods described are not intended for use in diagnosing disease.

2. The method according to claim 1, characterized in that The liver cancer is hepatocellular carcinoma (HCC), preferably hepatitis B virus-related hepatocellular carcinoma (HBV-HCC); and / or The expression level of the soluble immune checkpoint is detected in the patient's blood, preferably in the plasma.

3. The method according to claim 1 or 2, characterized in that: Step (1) further comprises detecting the clinical characteristics and soluble immune checkpoint characteristics; and / or The reference value used in step (3) is the optimal cutoff value of the risk score calculated by the formula using the HBV-HCC patient group; preferably, the reference value is 2.

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4. The method according to any one of claims 1 to 3, characterized in that The prognosis is reflected by the patient's overall survival after treatment; and / or, The patients are patients with BCLC stage 0-B or CD; and / or, the patients are patients with different alpha fetal protein (AFP) levels.

5. A system for grouping prognostic conditions of liver cancer patients, the system comprising: (1) Assignment module of clinical characteristics and soluble immune checkpoint characteristics of the patient: The clinical features are hepatic encephalopathy, C-reactive protein (CRP) level, CD8+T cell count, and tumor diameter: If the patient has hepatic encephalopathy, assign 1 point; if not, assign 0 points; If the patient's CRP level is ≥5 mg / L, 1 point is assigned; if not, 0 point is assigned; If the patient's CD8+ T cell count is ≥320 cells / μl, 1 point is assigned; if not, 0 point is assigned; If the patient's tumor diameter is ≥5 cm, 1 point is assigned; otherwise, 0 point is assigned; The soluble immune checkpoints are glucocorticoid-induced TNF receptor (GITR) and programmed death ligand 1 (PD-L1): If the patient's GITR level is ≥75.89 pg / ml, 1 point is assigned; if not, 0 point is assigned; If the patient's PD-L1 level is ≥7.32 pg / ml, 1 point is assigned; if not, 0 point is assigned; (2) The patient's risk score calculation module: Substitute the values ​​of each feature obtained by the assignment module into the following risk scoring formula: Riskscore = 1.1925413 × (hepatic encephalopathy) + 0.6751818 × (CRP) + (-0.6964451) × (CD8 + T cells) + 1.2505047 × (tumor diameter) + 0.6793419 × (PD-L1) + 0.8117957 × (GITR), the risk score was calculated; (3) Grouping module: The risk score obtained by the calculation module is compared with the reference value. If it is ≥ the reference value, the patient is determined to be in the high-risk group with poor prognosis; if not, the patient is determined to be in the low-risk group with poor prognosis.

6. The system according to claim 5, characterized in that The liver cancer is hepatocellular carcinoma (HCC), more preferably hepatitis B virus-related hepatocellular carcinoma (HBV-HCC); and / or The expression level of the soluble immune checkpoint is detected in the patient's blood, preferably in the plasma.

7. The system according to claim 5 or 6, characterized in that: The assignment module further includes detecting the clinical characteristics and soluble immune checkpoint characteristics; and / or The reference value used in the grouping module is the optimal cutoff value of the risk score calculated by the formula using the HBV-HCC patient group; preferably, the reference value is 2.

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8. The system according to any one of claims 5 to 7, characterized in that The prognosis is reflected by the patient's overall survival after treatment; and / or The patients are patients with BCLC stage 0-B or CD; and / or, the patients are patients with different alpha fetal protein (AFP) levels.

9. A product for grouping the prognosis of liver cancer patients, the product being used in the method of any one of claims 1 to 4, or in the system of any one of claims 5 to 8.

10. The product according to claim 9, characterized in that The products include tools and reagents used to detect the clinical characteristics and soluble immune checkpoint characteristics.

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