Single nucleotide polymorphism site marker composition for predicting postoperative recurrence and metastasis risk of liver cancer patient and application of single nucleotide polymorphism site marker composition

Abstract

The invention relates to an SNP (Single Nucleotide Polymorphism) marker composition for predicting postoperative recurrence and metastasis risks of a liver cancer patient and application of the SNP marker composition. The composition comprises any one or more of rs4925, rs3809875, rs1201559, rs2729835, rs3764795, rs3213837, rs17563, rs3213869, rs3750050, rs2066853, rs10983347, rs17319721, rs7815456, rs12001327, rs11955631, rs17827807, rs7640235, rs2472692, rs7506696 or The SNP marker composition provided by the invention can accurately predict the prognosis of a liver cancer patient, can evaluate the prognosis of the patient before treatment, and formulates a more positive and effective treatment scheme for the patient with poor prognosis, thereby realizing individualized treatment of the patient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of genetic engineering and tumor medicine, and particularly relates to a single nucleotide polymorphism site marker composition for predicting the risk of recurrence and metastasis of a liver cancer patient after surgery and application thereof. BACKGROUND

[0002] Globally, about 865,000 people are diagnosed with primary liver cancer each year, and about 75.8 million people die from liver cancer each year. The main histological subtype of liver cancer is hepatocellular carcinoma (HCC), which is referred to as liver cancer for convenience of description. Surgical resection and radiofrequency ablation are currently one of the main treatment methods for liver cancer. About 50-70% of liver cancer patients relapse after surgical resection and radiofrequency ablation. There is a subgroup of patients with more invasive tumors among liver cancer patients, and these patients can benefit from adjuvant targeted therapy after radical surgery. Therefore, accurate assessment of the risk of liver cancer recurrence and metastasis after surgery is the key to determining which patients need adjuvant therapy. Currently, TNM staging, pathological grading, tumor size, and the presence or absence of vascular invasion are commonly used to assess the risk of tumor recurrence and metastasis in liver cancer patients after surgery. However, patients with the same TNM stage, pathological grade, tumor size, and vascular invasion status may have very different prognoses, so the current prediction method for the postoperative recurrence staging system of liver cancer needs to be improved, which can be achieved by using validated specific tumor biomarkers. Several multi-gene classifiers have been reported to predict the risk of liver cancer recurrence and metastasis, but so far there is still no stable and reliable molecular marker applied in the prediction of the risk of liver cancer recurrence and metastasis in clinical practice. Single-nucleotide polymorphisms (SNP) refers to DNA sequence polymorphism caused by single-nucleotide variation at the genomic level. It is the most common type of human heritable variation, accounting for more than 80% of all known polymorphisms. With the continuous development of whole genome technology, research on single nucleotide polymorphisms related to disease outcomes, including cancer, has become more in-depth. As the third generation of genetic markers, it has been widely used in the diagnosis and prognosis prediction of major diseases such as malignant tumors in recent years, and has shown characteristics such as rapid, sensitive, and accurate, thus having broad application prospects. There is no SNP site that can accurately and effectively judge the prognosis of tumors applied in tumor prognosis prediction. However, some reports have explored the correlation between variations in related genes and tumor prognosis, involving DNA repair genes (ERCC1, XRCC1), chemokines (CXCL12, MCP-1), matrix metalloproteinases (MMP-9, MMP-1), interleukins (IL-8), tumor suppressor genes (TP53), cell cycle-related genes (CCNE1), key genes for prostaglandin synthesis (PTGS2), ubiquitin-protein ligase (MDM2), etc. However, due to the relatively small sample size involved in these studies, the results are still controversial, and the potential value is not sufficient to be further developed and applied. SUMMARY

[0003] The technical problem to be solved by the present application is to overcome the defects and deficiencies of single nucleotide polymorphism variation site molecular markers in predicting the risk of postoperative recurrence and metastasis of liver cancer, and to provide a set of SNP molecular marker compositions for predicting the risk of postoperative recurrence and metastasis of liver cancer, so as to assist in guiding individualized treatment and improving the prognosis of liver cancer patients.

[0004] To achieve the above-mentioned object, the technical scheme adopted by the present application is as follows:

[0005] In a first aspect, the present application provides a single nucleotide polymorphism site marker composition for predicting the risk of postoperative recurrence and metastasis of liver cancer patients, which comprises any one or several of rs4925, rs3809875, rs1201559, rs2729835, rs3764795, rs3213837, rs17563, rs3213869, rs3750050, rs2066853, rs10983347, rs17319721, rs7815456, rs12001327, rs11955631, rs17827807, rs7640235, rs2472692, rs7506696 or rs17254718. By detecting the genotypes of the above-mentioned SNP sites in liver cancer patients, the present application can predict the risk of recurrence and metastasis of liver cancer patients after radical treatment (surgery, radiofrequency ablation).

[0006] In a second aspect, the present application provides the use of the single nucleotide polymorphism site marker composition of the first aspect in the preparation of a kit for predicting the risk of postoperative recurrence and metastasis of liver cancer patients.

[0007] In a third aspect, the present application provides the use of the single nucleotide polymorphism site marker composition of the first aspect in constructing a scoring model for predicting the risk of postoperative recurrence and metastasis of liver cancer patients.

[0008] As a preferred embodiment of the third aspect, the scoring model is calculated by detecting the first aspect single nucleotide polymorphism site marker composition in DNA, and then calculating the recurrence risk score value by using a recurrence risk score formula; the recurrence risk score formula is as follows:

[0009] The recurrence risk score value = rs4925 x (0.16587136) + rs3809875 x (-0.07779507) + rs1201559 x (0.10928937) + rs2729835 x (0.17100035) + rs3764795 x (-0.07437999) + rs3213837 x (-0.29805032) + rs17563 x (-0.01873964) + rs3213869 x (0.52569608) + rs3750050 x (-0.25104883) + rs2066853 x (-0.17367663) + rs10983347 x (-0.11852972) + rs17319721 x (-0.12864303) + rs7815456 x (-0.34279256) + rs12001327 x (0.81795195) + rs11955631 x (0.45868298) + rs17827807 x (0.41434224) + rs7640235 x (-0.93421262) + rs2472692 x (0.33566485) + rs7506696 x (0.33566485) + rs17254718 x (-0.45879418);

[0010] Wherein, the subject with the recurrence risk score value ≥ 0.64 is classified as high recurrence metastasis risk, i.e. high risk group, and the subject with the recurrence risk score value < 0.64 is classified as low recurrence metastasis risk, i.e. low risk group.

[0011] In the fourth aspect, the application provides application of the recurrence risk score formula of the third aspect in preparation of a kit for predicting recurrence metastasis risk of a liver cancer patient after surgery.

[0012] In the fifth aspect, the application provides primers used for detecting the single nucleotide polymorphism site markers of the first aspect, wherein the primer set comprises nucleotide sequences as shown in SEQ ID NO: 1-40.

[0013] As a preferred embodiment of the fifth aspect, the primer sequence for detecting the rs4925 site is shown as SEQ ID NO: 1-2; the primer sequence for detecting the rs3809875 site is shown as SEQ ID NO: 3-4; the primer sequence for detecting the rs1201559 site is shown as SEQ ID NO: 5-6; the primer sequence for detecting the rs2729835 site is shown as SEQ ID NO: 7-8; the primer sequence for detecting the rs3764795 site is shown as SEQ ID NO: 9-10; the primer sequence for detecting the rs3213837 site is shown as SEQ ID NO: 11-12; the primer sequence for detecting the rs17563 site is shown as SEQ ID NO: 13-14; the primer sequence for detecting the rs3213869 site is shown as SEQ ID NO: 15-16; the primer sequence for detecting the rs3750050 site is shown as SEQ ID NO: 17-18; the primer sequence for detecting the rs2066853 site is shown as SEQ ID NO: 19-20; the primer sequence for detecting the rs10983347 site is shown as SEQ ID NO: 21-22; the primer sequence for detecting the rs17319721 site is shown as SEQ ID NO: 23-24; the primer sequence for detecting the rs7815456 site is shown as SEQ ID NO: 25-26; the primer sequence for detecting the rs12001327 site is shown as SEQ ID NO: 27-28; the primer sequence for detecting the rs11955631 site is shown as SEQ ID NO: 29-30; the primer sequence for detecting the rs17827807 site is shown as SEQ ID NO: 31-32; the primer sequence for detecting the rs7640235 site is shown as SEQ ID NO: 33-34; the primer sequence for detecting the rs2472692 site is shown as SEQ ID NO: 35-36; the primer sequence for detecting the rs7506696 site is shown as SEQ ID NO: 37-38; the primer sequence for detecting the rs17254718 site is shown as SEQ ID NO: 39-40.

[0014] In a sixth aspect, the present application provides application of the primer set of the fifth aspect in preparation of a kit for predicting the risk of postoperative recurrence and metastasis of a liver cancer patient.

[0015] In a seventh aspect, the present application provides a kit for predicting the risk of postoperative recurrence and metastasis of a liver cancer patient, which comprises the primer set of the fifth aspect.

[0016] As a preferred embodiment of the seventh aspect, the kit further comprises PCR buffer, dNTP, magnesium chloride, DNA polymerase and deionized water.

[0017] Compared with the prior art, the present application has the following beneficial effects:

[0018] The present application provides a single nucleotide polymorphism (SNP) marker related to the risk of recurrence and metastasis of liver cancer and an application thereof, and a detection kit for predicting the recurrence and metastasis of liver cancer, which is used for assisting in guiding individualized treatment and improving the prognosis of liver cancer patients. The detection kit provided by the present application can accurately predict the prognosis of liver cancer patients by detecting the SNP site on the peripheral blood DNA or liver cancer tissue DNA of the liver cancer patients; after the kit of the present application is applied to clinical detection, the prognosis of the patients can be evaluated before treatment, and a more active and effective treatment plan is formulated for the patients with poor prognosis, so as to realize the individualized treatment of the patients and provide the survival rate; in addition, the kit only needs to detect the peripheral blood, and has the characteristics of convenient sampling, simple operation, timeliness and the like. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 Fig. 1 is a schematic diagram of the total progression-free survival curve of high-risk patients and low-risk patients in the training set;

[0020] Figure 2 Fig. 2 is a schematic diagram of the time-dependent ROC curve of the model in the training set;

[0021] Figure 3 Fig. 3 is a schematic diagram of the total progression-free survival curve of high-risk patients and low-risk patients in verification set 1;

[0022] Figure 4 Fig. 4 is a schematic diagram of the time-dependent ROC curve of the model in verification set 1;

[0023] Figure 5 Fig. 5 is a schematic diagram of the total progression-free survival curve of high-risk patients and low-risk patients in verification set 2;

[0024] Figure 6 Fig. 6 is a schematic diagram of the time-dependent ROC curve of the model in verification set 2. DETAILED DESCRIPTION

[0025] In order to better illustrate the purpose, technical scheme and advantages of the present application, the present application will be further described below in combination with specific examples.

[0026] Example 1: Screening of SNP site for predicting the risk of recurrence and metastasis of liver cancer patients after operation

[0027] (1) Selection of research samples:

[0028] The training set for this example consists of 347 patients with liver cancer who were pathologically diagnosed at the Sun Yat-sen University Cancer Center and have complete medical records (including tumor stage, medical history, examination results, previous treatment plans, etc.). Patients must be newly diagnosed patients with no history of cancer and have not received any prior radiation therapy, chemotherapy, or other anti-tumor treatments related to the cancer before admission. All of the above patients were newly diagnosed patients with no history of cancer and had not received any prior radiation therapy, chemotherapy, or other anti-tumor treatments related to the cancer before admission.

[0029] All patients participated in the study voluntarily and signed informed consent. Patients included in the study were followed up regularly, every three months for two years after the end of treatment, and every six months thereafter.

[0030] (2) Genomic DNA extraction from peripheral blood or liver cancer tissue:

[0031] Before the first treatment, peripheral blood from the patients in the training set was collected using EDTA anticoagulant tubes or surgical resection samples were stored in a -80°C refrigerator. Genomic DNA from peripheral blood or liver cancer tissue was extracted using the phenol-chloroform method according to conventional operating procedures. The concentration of the obtained DNA samples was usually 20-50 ng / μl, and the purity (OD 260 / 280 ) is generally between 1.6-2.0.

[0032] (3) Screening of SNP sites with risk of recurrence and metastasis in patients with liver cancer after surgery:

[0033] Asian Screening Array (ASA) chip detection was used to detect SNPs associated with the risk of postoperative recurrence and metastasis in liver cancer patients. A chi-square test was used to screen for SNPs associated with recurrence (to ensure test power, P < 0.001 was set). Subsequently, the least absolute shrinkage and selection algorithm (LASSO) was used to screen for SNPs closely associated with recurrence-recurrence time, resulting in the following SNPs:

[0034] Table 1 SNP sites significantly associated with the risk of postoperative recurrence and metastasis in patients with liver cancer and their P values

[0035]

[0036] (4) Amplification of SNP sites:

[0037] Specific primers were used to amplify the following SNP sites by PCR. The primer sequences used are shown in Table 2. The PCR system is shown in Table 3. The PCR reaction program was set as follows: 94°C, 2 min; 98°C, 10 s, 56°C, 30 s, 68°C, 40 s, 33 cycles; 4°C, ∞.

[0038] Table 2 SNP sites and their amplification primers

[0039]

[0040] Table 3 amplification system

[0041]

[0042]

[0043] (4) Construction of prediction risk regression model

[0044] The SNP sites in Table 1 above were used to construct the model by the proportional hazards model (Cox model), and the hazard ratio and 95% confidence interval (95% CI) of SNP genotype for the prognosis of liver cancer patients were calculated. The recurrence risk prediction score was calculated by the multi-gene weighted scoring method, wherein each SNP site included in the prediction model was multiplied by the Cox regression risk coefficient of the site according to its additive genetic model (coded as 0, 1 or 2, see Table 2) and then summed to obtain a comprehensive score. The score cutoff value of high and low risk groups was determined by the default parameters of the survminer package. Statistical analysis was completed using R software, and the statistical significance level was P<0.05 for all tests except the chi-square test, all of which were two-sided tests.

[0045] Table 4 Score of each SNP site included in the prediction model according to its additive genetic model

[0046]

[0047]

[0048] According to the SNP sites in Table 4 above, the final prediction formula for the risk of postoperative recurrence and metastasis of liver cancer patients is as follows:

[0049] Recurrence risk score value = rs4925 x (0.16587136) + rs3809875 x (-0.07779507) + rs1201559 x (0.10928937) + rs2729835 x (0.17100035) + rs3764795 x (-0.07437999) + rs3213837 x (-0.29805032) + rs17563 x (-0.01873964) + rs3213869 x (0.52569608) + rs3750050 x (-0.25104883) + rs2066853 x (-0.17367663) + rs10983347 x (-0.11852972) + rs17319721 x (-0.12864303) + rs7815456 x (-0.34279256) + rs12001327 x (0.81795195) + rs11955631 x (0.45868298) + rs17827807 x (0.41434224) + rs7640235 x (-0.93421262) + rs2472692 x (0.33566485) + rs7506696 x (0.33566485) + rs17254718 x (-0.45879418);

[0050] The genotype information of each site is obtained by sequencing the DNA amplification product of the SNP site (see Table 2), and the score of the genotype of different SNP sites is obtained according to Table 4, for example, when the genotype of SNP site rs4925 is CC, the score of this genotype is 0, when the genotype of this site is CA (or AC), the score of this genotype is 1, and when the genotype of this site is AA, the score of this genotype is 2, and similarly, the score of different genotypes of each SNP site is obtained according to Table 2, and then the score of each SNP site is brought into the above recurrence risk score value formula, and finally the recurrence risk score value is obtained.

[0051] The ROC curve is drawn according to the above recurrence risk score formula to evaluate the prediction performance of the above SNP site, and the results show that the area under the ROC curve (AUC) of the above SNP site is greater than 0.7, as shown in Figure 2 The above SNP site has a certain accuracy and can be used as a marker for predicting the recurrence and metastasis risk of liver cancer patients after operation.

[0052] The subjects with a score value greater than or equal to 0.64 are classified as high recurrence and metastasis risk, i.e. high-risk group, and the subjects with a score value less than 0.64 are classified as low recurrence and metastasis risk, i.e. low-risk group. The patients are divided into low-risk group and high-risk group in the training set by the above prediction model; Figure 1The results showed that patients in the low-risk group in the training set had a longer progression-free survival (HR = 0.23, 95% CI = 0.16-0.34, P = 1.79 × 10 -14 ).

[0053] Example 2 Validation of the prediction model

[0054] (1) Validation set:

[0055] This example provides two validation sets:

[0056] Validation set 1 samples were collected from 174 patients with liver cancer diagnosed by pathology at the First Affiliated Hospital of Anhui Medical University; validation set 2 samples were collected from 146 patients with liver cancer diagnosed by pathology at Guangzhou Chest Hospital, all with complete medical records (including tumor stage, medical history, examination results, previous treatment plans, etc.); patients in the validation set of this example must be newly diagnosed patients with no history of tumor and have not received tumor-related radiotherapy, chemotherapy, or other anti-tumor treatments before admission. All of the above patients were newly diagnosed patients with no history of tumor and had not received tumor-related radiotherapy, chemotherapy, or other anti-tumor treatments before admission.

[0057] (2) Prediction model verification:

[0058] Extract genomic DNA from peripheral blood or liver cancer tissue of patients in validation set 1 and validation set 2 respectively:

[0059] Before the first treatment, peripheral blood from the patients in the training set was collected using EDTA anticoagulant tubes or surgical resection samples were stored in a -80°C refrigerator. Genomic DNA from peripheral blood or liver cancer tissue was extracted using the phenol-chloroform method according to conventional operating procedures. The concentration of the obtained DNA samples was usually 20-50 ng / μl, and the purity (OD 260 / 280 ) is generally between 1.6-2.0.

[0060] By sequencing the DNA amplification products of the above-mentioned SNP sites, the genotype information of each site is obtained, and the genotypes of different SNP sites are scored according to Table 4. The scores are then substituted into the recurrence risk scoring formula constructed in Example 1 above to obtain the recurrence risk score value.

[0061] The ROC curve was drawn based on the above recurrence risk score formula to evaluate the prediction efficiency of the above SNP sites. The results showed that Figure 4 、 6 As shown in the figure, the area under the ROC curve (AUC) of the above SNP site is greater than 0.7, which has a certain accuracy and can be used as a marker to predict the risk of recurrence and metastasis in patients with liver cancer after surgery. The prediction formula shows the ability to predict the risk of recurrence in the validation set.

[0062] The subjects with a score greater than or equal to 0.64 are classified as high recurrence and metastasis risk, i.e. high-risk group, and the subjects with a score less than 0.64 are classified as low recurrence and metastasis risk, i.e. low-risk group. Through the above prediction model, the patients in the validation set are divided into low-risk group and high-risk group, Figure 3 , 5 It is shown that the low-risk patients in the two validation sets also have longer progression-free survival (validation set 1: HR=0.413, 95% CI=0.268-0.636, P=6.01x10 -5 ; validation set 2: HR=0.263, 95% CI=0.137-0.498, P=4.15x10 -5 ;).

[0063] Example 3

[0064] This example provides the application of the marker combination of Example 1 in a kit for predicting the recurrence and metastasis risk of a liver cancer patient after surgery. The SNP marker combination obtained in Example 1 includes: rs4925, rs3809875, rs1201559, rs2729835, rs3764795, rs3213837, rs17563, rs3213869, rs3750050, rs2066853, rs10983347, rs17319721, rs7815456, rs12001327, rs11955631, rs17827807, rs7640235, rs2472692, rs7506696, rs17254718. The above markers are used to construct a score model formula for predicting the recurrence and metastasis risk of a liver cancer patient after surgery by using a proportional hazards regression model to calculate the recurrence risk score value, and the model formula is specifically as follows:

[0065] Recurrence risk score value = rs4925 x (0.16587136) + rs3809875 x (-0.07779507) + rs1201559 x (0.10928937) + rs2729835 x (0.17100035) + rs3764795 x (-0.07437999) + rs3213837 x (-0.29805032) + rs17563 x (-0.01873964) + rs3213869 x (0.52569608) + rs3750050 x (-0.25104883) + rs2066853 x (-0.17367663) + rs10983347 x (-0.11852972) + rs17319721 x (-0.12864303) + rs7815456 x (-0.34279256) + rs12001327 x (0.81795195) + rs11955631 x (0.45868298) + rs17827807 x (0.41434224) + rs7640235 x (-0.93421262) + rs2472692 x (0.33566485) + rs7506696 x (0.33566485) + rs17254718 x (-0.45879418);

[0066] The recurrence risk score value is calculated by the above model formula, and the subject with a recurrence risk score value greater than or equal to 0.64 is classified as a high recurrence and metastasis risk, i.e., a high-risk group, and the subject with a recurrence risk score value less than 0.64 is classified as a low recurrence and metastasis risk, i.e., a low-risk group.

[0067] The low-risk group patient can not be treated with adjuvant chemotherapy after radical surgery; the high-risk group patient should be treated with adjuvant therapy for preventing recurrence, such as sorafenib, bevacizumab, talimogene laherparepvec, or transhepatic arterial infusion chemotherapy.

[0068] Example 4

[0069] The present embodiment provides a kit for predicting the recurrence and metastasis risk of a liver cancer patient after surgery, which comprises specific primers (primer sequences are shown in Table 2) for detecting each SNP site in Example 1, and further comprises PCR buffer, dNTP, magnesium chloride, DNA polymerase, and deionized water.

[0070] The kit is used as follows: the peripheral blood or liver cancer tissue sample of a liver cancer patient is treated according to the standard procedure of the phenol-chloroform method, and the genomic DNA is extracted, with a DNA concentration of 30-60 ng / μL and a purity (OD 260 / 280PCR amplification was performed using specific primers (see Table 2) between 1.6-2.0, and the reaction system is shown in Table 3.

[0071] After the PCR reaction, DNA electrophoresis (1% agarose gel, voltage: 120V, time 30 minutes) was performed to detect the PCR products, and the PCR products with expected fragment size (673bp), single band and moderate brightness were selected for sequencing.

[0072] Table 5 amplification system

[0073]

[0074]

[0075] The PCR amplification reaction program was set as: 94℃, 2min; 98℃, 10s, 56℃, 30s, 68℃, 40s, 33 cycles; 4℃, ∞.

[0076] The risk score is calculated by the recurrence risk score formula of the application, and the subjects with a recurrence risk score value greater than or equal to 0.64 are classified as high recurrence and metastasis risk, i.e. high-risk group, and the subjects with a recurrence risk score value less than 0.64 are classified as low recurrence and metastasis risk, i.e. low-risk group; the low-risk group patients can not be given adjuvant chemotherapy and other treatments after radical surgery; the high-risk group patients should be given adjuvant treatment to prevent recurrence, such as sorafenib, bevacizumab plus talimogene laherparepvec or transhepatic arterial infusion chemotherapy.

[0077] Finally, it should be explained that the above examples are only used to illustrate the technical solutions of the application and not to limit the protection scope of the application. Although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the application can be modified or replaced equivalently without departing from the essence and scope of the technical solutions of the application.

Claims

1. A single nucleotide polymorphism site marker composition for predicting the risk of postoperative recurrence and metastasis in patients with liver cancer, characterized in that: The composition includes: any one or more of rs4925, rs3809875, rs1201559, rs2729835, rs3764795, rs3213837, rs17563, rs3213869, rs3750050, rs2066853, rs10983347, rs17319721, rs7815456, rs12001327, rs11955631, rs17827807, rs7640235, rs2472692, rs7506696 or rs17254718.

2. Use of the single nucleotide polymorphism site marker composition as claimed in claim 1 in the preparation of a kit for predicting the risk of postoperative recurrence and metastasis of liver cancer patients.

3. Use of the single nucleotide polymorphism site marker composition as described in claim 1 in constructing a scoring model for predicting the risk of postoperative recurrence and metastasis in patients with liver cancer.

4. The application scoring model according to claim 3, wherein: The scoring model is to calculate the recurrence risk score value by using a recurrence risk scoring formula after detecting the single nucleotide polymorphism site marker composition according to claim 1 in DNA; the recurrence risk scoring formula is as follows: Recurrence risk score = rs4925 × (0.16587136) + rs3809875 × (-0.07779507) + rs1201559 × (0.10928937) + rs2729835 × (0.17100035) + rs3764795 × (-0.07437999) + rs3213837 × (-0.29805032) + rs17563 × (-0.01873964) + rs3213869 × (0.52569608) + rs3750050 × (-0.25104883) + rs2066853 × (-0.17367663) + r s10983347×(-0.11852972)+rs17319721×(-0.12864303)+rs7815456×(-0.34279256)+rs12001327×(0.81795195)+rs11955631×(0.45868298)+rs17827807×(0.41434224)+rs7640235×(-0.93421262)+rs2472692×(0.33566485)+rs7506696×(0.33566485)+rs17254718×(-0.45879418); Among them, subjects with a recurrence risk score ≥ 0.64 were classified as having a high risk of recurrence and metastasis, i.e., a high-risk group, and subjects with a recurrence risk score < 0.64 were classified as having a low risk of recurrence and metastasis, i.e., a low-risk group.

5. The use according to claim 4, characterized in that The application of the recurrence risk scoring formula in the preparation of a kit for predicting the risk of postoperative recurrence and metastasis in patients with liver cancer.

6. A primer set for detecting the single nucleotide polymorphism site marker composition according to claim 1, characterized in that: The primer set includes nucleotide sequences as shown in SEQ ID NOs: 1 to 40.

7. The primer set according to claim 6, wherein The primer sequences for detecting the rs4925 site are shown in SEQ ID NOs: 1-2; The primer sequences for detecting the rs3809875 site are shown in SEQ ID NOs: 3-4; The primer sequences for detecting the rs1201559 site are shown in SEQ ID NOs: 5-6; The primer sequences for detecting the rs2729835 site are shown in SEQ ID NOs: 7-8; The primer sequences for detecting the rs3764795 site are shown in SEQ ID NOs: 9-10; The primer sequences for detecting the rs3213837 site are shown in SEQ ID Nos: 11-12; The primer sequences for detecting the rs17563 site are shown in SEQ ID NOs: 13-14; The primer sequences for detecting the rs3213869 site are shown in SEQ ID NOs: 15-16; The primer sequences for detecting the rs3750050 site are shown in SEQ ID NOs: 17-18; The primer sequences for detecting the rs2066853 site are shown in SEQ ID NOs: 19-20; The primer sequences for detecting the rs10983347 site are shown in SEQ ID NOs: 21-22; The primer sequences for detecting the rs17319721 site are shown in SEQ ID NOs: 23-24; The primer sequences for detecting the rs7815456 site are shown in SEQ ID NOs: 25-26; The primer sequences for detecting the rs12001327 site are shown in SEQ ID NOs: 27-28; The primer sequences for detecting the rs11955631 site are shown in SEQ ID NOs: 29-30; The primer sequences for detecting the rs17827807 site are shown in SEQ ID NOs: 31-32; The primer sequences for detecting the rs7640235 site are shown in SEQ ID NOs: 33-34; The primer sequences for detecting the rs2472692 site are shown in SEQ ID NOs: 35-36; The primer sequences for detecting the rs7506696 site are shown in SEQ ID NOs: 37-38; The primer sequences for detecting the rs17254718 site are shown in SEQ ID NOs: 39-40.

8. Use of the primer set according to claim 6 in preparing a kit for predicting the risk of postoperative recurrence and metastasis of liver cancer patients.

9. A kit for predicting the risk of recurrence and metastasis of liver cancer patients after surgery, characterized in that: The kit comprises the primer set according to claim 6.

10. The kit according to claim 9, wherein The kit also includes PCR buffer, dNTPs, magnesium chloride, DNA polymerase and deionized water.

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