Prognostic model for predicting curative effect of treating non-resectable hepatocellular carcinoma patient by combining transcatheter arterial chemoembolism with trinodilimab and lenvatinib

Abstract

The invention discloses a prognosis model for predicting the curative effect of treating a patient with non-resectable hepatocellular carcinoma by combining transcatheter arterial chemoembolism with truditinib and revatinib. The research finds that four indexes of AFP, ALP, DBIL and NLR are all independent prognosis factors of a uHCC patient which is treated by a TACE (triplex angiotensin converting enzyme) combined Czodimab and lenvatinib triplex therapy. By constructing the AADN scoring model, the prognosis of the patient can be comprehensively evaluated. The AADN score has the advantages that a baseline index before treatment is easy to obtain; the treatment response is directly related; the score is dynamic and can be adjusted according to index changes in the treatment process. According to the AADN scoring model, blood indexes (AFP, ALP, DBIL and NLR) with low cost and high accessibility are adopted, the prognosis of the uHCC patient can be accurately layered, and a practical tool is provided for formulating an individualized scheme for treatment by combining TACE with the Czodimab and the lenvatinib.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of disease prognosis evaluation and molecular biology, and particularly relates to a prognosis model for predicting the curative effect of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib on patients with unresectable hepatocellular carcinoma. BACKGROUND

[0002] Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide [1] Radical resection is still the preferred treatment for HCC patients, but more than 50% of HCC patients have lost the opportunity for surgical resection at the time of diagnosis and are classified as unresectable hepatocellular carcinoma (uHCC) [2] Transcatheter arterial chemoembolization (TACE) is a recognized treatment for advanced HCC [3-5] By blocking the blood supply of tumor tissue, it induces tumor ischemic necrosis and is widely used in Asian countries [6-8] TACE combined with targeted therapy and immunotherapy can significantly improve anti-tumor efficacy and has become a key treatment for uHCC [9-11] In recent years, the efficacy of TACE combined with immune checkpoint inhibitors such as sindilimab and tyrosine kinase inhibitors such as lenvatinib in the treatment of advanced HCC has been confirmed [12-14] This regimen can bring survival benefits to uHCC patients [15,16] However, there is still a lack of effective tools to predict the efficacy of this treatment. Therefore, there is an urgent need for practical and reliable prognosis models to predict the treatment outcome of uHCC patients receiving triple therapy. SUMMARY

[0003] The purpose of the present application is to provide a prognosis model for predicting the curative effect of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib on patients with unresectable hepatocellular carcinoma.

[0004] To improve the prognosis evaluation system for uHCC patients receiving TACE combined with sindilimab and lenvatinib, the present application develops and verifies the AADN score based on multi-center real-world data, with AFP, ALP, DBIL and NLR as parameters. The present application predicts the prognosis outcome of uHCC patients receiving the combined therapy and provides a standardized prediction tool for clinical trials in this treatment scenario.

[0005] To achieve the purpose of the present application, in the first aspect, the present application provides a set of prognosis indicators for patients with unresectable hepatocellular carcinoma treated by transcatheter arterial chemoembolization (TACE) combined with sindilimab and lenvatinib, which consists of four blood indicators: AFP, ALP, DBIL and NLR.

[0006] In a second aspect, the present application provides use of the indicators in constructing a prognostic model for predicting the efficacy of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib in treating patients with unresectable hepatocellular carcinoma.

[0007] In a third aspect, the present application provides a prognostic model for predicting the efficacy of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib in treating patients with unresectable hepatocellular carcinoma, which is an AADN scoring model established based on blood indicators AFP, ALP, DBIL and NLR as follows: AADN score = 0.947 x AFP (assigned value 0 or 1) + 0.804 x ALP (assigned value 0 or 1) + 1.075 x DBIL (assigned value 0 or 1) + 1.14 x NLR (assigned value 0 or 1) wherein AADN score ≤ 1.10 is a low-risk group; 1.10 < AADN score ≤ 3.10 is a medium-risk group; AADN score > 3.10 is a high-risk group; The stratification threshold values are as follows: AFP ≤ 100 ng / ml, assigned value 0, AFP > 100 ng / ml, assigned value 1; ALP ≤ 120 U / L, assigned value 0, ALP > 120 U / L, assigned value 1; DBIL ≤ 7.3 μmol / L, assigned value 0, DBIL > 7.3 μmol / L, assigned value 1; NLR ≤ 2.5, assigned value 0, NLR > 2.5, assigned value 1.

[0008] In a fourth aspect, the present application provides use of a reagent for detecting the expression levels of AFP, ALP, DBIL and NLR in the preparation of a prognostic reagent or kit for predicting the efficacy of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib in treating patients with unresectable hepatocellular carcinoma.

[0009] The prognosis includes overall survival (OS) and progression-free survival (PFS) of patients with unresectable hepatocellular carcinoma.

[0010] By the above technical solution, the present application has at least the following advantages and beneficial effects: This invention confirms through analysis that AFP, ALP, DBIL, and NLR are independent prognostic factors for uHCC patients receiving TACE combined with sintilimab and lenvatinib triple therapy. By constructing the AADN scoring model, patient prognosis can be assessed more comprehensively. The advantages of the AADN score include: (1) it is based on baseline indicators obtained before treatment, making it easy to obtain in clinical practice (requiring only routine blood tests and liver function assessments); (2) it is directly related to treatment response (in immunotherapy, inflammatory status and liver function are key influencing factors); and (3) the score is dynamic and can be adjusted according to changes in indicators during treatment (for example, changes in NLR before and after treatment can reflect immune activation status). The AADN scoring model of this invention uses low-cost and highly accessible blood indicators (based on AFP, ALP, DBIL, and NLR), which can accurately stratify the prognosis of uHCC patients, providing a practical tool for the development of individualized treatment plans for TACE combined with sintilimab and lenvatinib. Attached Figure Description

[0011] Figure 1 The flowchart for the research queue is provided for this invention.

[0012] Figure 2 Receiver operating characteristic (ROC) curve analysis of the AADN score prediction performance in the training and validation groups in a preferred embodiment of the present invention. (A) ROC curve analysis of the AADN score prediction performance in the training cohort. (B) ROC curve analysis of the AADN score prediction performance in the validation cohort.

[0013] Figure 3 The prognosis of the training and validation groups based on AAND scores in a preferred embodiment of the present invention is shown in Figure (A). The Kaplan-Meier OS curves of the training cohort are used to classify patients into low-risk, intermediate-risk, and high-risk groups based on their AAND scores (Log-rank). P <0.0001). (B) Kaplan-Meier OS curves for the validation cohort: Patients were categorized into low-risk, intermediate-risk, and high-risk groups based on their AADN scores (Log-rank). P <0.0001). (C) Kaplan-Meier PFS curves of the training cohort. Patients were divided into low-risk, intermediate-risk, and high-risk groups based on their AADN scores (Log-rank). P <0.0001). (D) Kaplan-Meier PFS curves for the validation cohort were used to categorize patients into low-risk, intermediate-risk, and high-risk groups based on their AADN scores (Log-rank). P <0.0001).

[0014] Figure 4Survival outcome subgroup analysis according to clinical and pathological characteristics in the preferred embodiment of the present application. (A) OS and PFS of patients without PVTT in the training cohort based on AAND score (Log-rank, OS: P <0.0001, PFS: P =0.0004). (B) OS and PFS of patients with PVTT in the training cohort based on AAND score (Log-rank, OS: P = 0.0161,PFS: P = 0.0262). (C) OS and PFS of patients with AFP <100 ng / ml in the training cohort based on AAND score (Log-rank, OS: P = 0.0486, PFS: P = 0.2902). (D) OS and PFS of patients with AFP ≥100 ng / ml in the training cohort based on AAND score (Log-rank, OS: P = 0.0022, PFS: P = 0.0051). (E) OS and PFS of patients with BCLC / B in the training cohort based on AAND score (Log-rank, OS: P = 0.0007, PFS: P = 0.0379). (F) OS and PFS of patients with BCLC / C in the training cohort based on AAND score (Log-rank, OS: P = 0.0002, PFS: P = 0.0003). DETAILED DESCRIPTION

[0015] Although the triple therapy of transcatheter arterial chemoembolization (TACE) combined with immune checkpoint inhibitors (such as sintilimab) and tyrosine kinase inhibitors (such as lenvatinib) has become an effective regimen for treating patients with unresectable hepatocellular carcinoma (uHCC), there is still a lack of effective tools to predict the efficacy of this therapy, thus the present application is proposed to predict the prognosis outcome of uHCC patients receiving this combined therapy, and to provide a standardized prediction tool for clinical trials in this treatment scenario.

[0016] The present application adopts the following technical solutions: The present application is based on multi-center data, constructs and verifies AADN score, which is used as a variable to predict the overall survival (OS) and progression-free survival (PFS) of patients receiving the combined therapy. 188 uHCC patients from 3 different hospitals (training cohort: 101 cases, validation cohort: 87 cases) were included in the study, and all patients received TACE combined with sindilimab and lenvatinib treatment.

[0017] Multivariate analysis showed that serum alpha-fetoprotein (AFP) ≥100 ng / ml ([HR] 2.579, P =0.010), alkaline phosphatase (ALP) >120 U / L ([HR] 2.234, P =0.021), direct bilirubin (DBIL) >7.3 μmol / L ([HR] 2.931, P =0.007) and neutrophil-to-lymphocyte ratio (NLR) >2.5 ([HR] 3.127, P =0.006) were independent prognostic factors, and AADN score was established accordingly. The accuracy of AADN score was evaluated using Kaplan-Meier survival curve and time-dependent receiver operating characteristic curve (ROC), and the area under the curve (AUC) of the training cohort and the validation cohort was 0.827 (95% confidence interval [CI]: 0.743-0.911) and 0.832 (95% CI: 0.742-0.923), respectively. According to the total score, patients were divided into low-risk group (≤1.10), medium-risk group (>1.10 to ≤3.10) and high-risk group (>3.10), and there were significant differences in overall survival (OS) and progression-free survival (PFS) among the groups.

[0018] It can be seen that the AADN score prognostic model can effectively distinguish the prognosis risk of uHCC patients receiving TACE combined with sindilimab and lenvatinib treatment, provide basis for individualized treatment decision-making, and has clinical application prospect.

[0019] Example 1 Construction of a prognostic model for predicting the efficacy of transcatheter arterial chemoembolization combined with sindilimab and lenvatinib in the treatment of patients with unresectable hepatocellular carcinoma I. Methods 1. Patients This multicenter retrospective cohort study included 188 patients with uHCC (disease progression to advanced stage or predicted postoperative residual liver volume less than 200 ml) from Peking University People's Hospital, Southern Hospital of Southern Medical University, and Affiliated Hospital of Guilin Medical College. All patients received TACE combined with sintilimab (200 mg, intravenous injection once every 3 weeks) and lenvatinib (12 mg / day for patients weighing ≥ 60 kg, 8 mg / day for patients weighing < 60 kg). Before each TACE treatment, sintilimab and lenvatinib were suspended for 3 days; if no serious TACE-related adverse events occurred, the drugs were resumed 3 days later. All hepatitis B virus (HBV)-infected patients received antiviral therapy with entecavir or tenofovir. The regimen has been approved by the institutional review board (IRB) of each center, in accordance with the principles of the Helsinki Declaration and the requirements of the ethics committee

[17] . All patients signed a written informed consent form before receiving combination therapy. A total of 188 patients were included and randomly divided into a training cohort (101 cases) and a validation cohort (87 cases) Figure 1 ].

[0020] Inclusion criteria ① Age ≥ 18 years; ② Diagnosed as uHCC (BCLC stage B / C) by imaging examination (enhanced CT / MRI consistent with HCC diagnostic criteria); ③ Received TACE combined with sintilimab and lenvatinib treatment; ④ Possessed baseline period (within 1 week before treatment) clinical data records; ⑤ Had at least one measurable lesion meeting the modified Response Evaluation Criteria in Solid Tumors (mRECIST). The present invention has been approved by the ethics committee of Peking University People's Hospital, Southern Hospital of Southern Medical University, and Affiliated Hospital of Guilin Medical College, and has been permitted by the responsible person of the institution, and the research process conforms to the Helsinki Declaration.

[0021] Exclusion criteria ① Combined with other malignancies; ② Presence of severe liver and kidney dysfunction (Child-Pugh class C); ③ Previously received systemic treatment for HCC; ④ Lack of follow-up data; ⑤ Presence of active autoimmune diseases or severe hematological diseases.

[0022] Treatment and follow-up All patients received TACE combined with sintilimab and lenvatinib treatment (tumor efficacy was evaluated according to the mRECIST standard). Enhanced MRI / CT imaging and laboratory evaluation were performed every 4-8 weeks, and the treatment efficacy was evaluated by two independent radiologists according to the mRECIST standard. Overall survival (OS) was defined as the time from the start of treatment to all-cause death or the last follow-up; the primary endpoint was OS, i.e., the time from meeting the inclusion criteria and starting combination therapy to all-cause death, data truncation, or the end of follow-up. Progression-free survival (PFS) was defined as the time interval from the start of treatment to disease progression or death, which was the secondary endpoint.

[0023] 2. Model construction and validation Training cohort analysis: (1) The predictors with P<0.05 in univariate Cox regression analysis were included in the multivariate model. The prognostic factors were determined based on Cox proportional hazards regression, and the AADN score was constructed; the time-dependent receiver operating characteristic curve (time-dependent ROC) was used to evaluate the discrimination of the training cohort and the validation cohort; the best cutoff value was determined by the X-tile index. Kaplan-Meier survival curves were drawn according to the risk stratification of the AADN score, and the log-rank test was used for comparison between groups.

[0024] (2) Based on the regression coefficient, the AADN score was constructed: the binary threshold of AFP, ALP, DBIL and NLR was set (AFP: ≤100 ng / ml vs. >100 ng / ml; ALP: ≤120 U / L vs. >120 U / L; DBIL: ≤7.3 μmol / L vs. >7.3 μmol / L; NLR: ≤2.5 vs. >2.5), and the regression coefficient β value of each variable (AFP was 0.947; ALP was 0.804; DBIL was 1.075; NLR was 1.14), and the weighted sum of the original coefficient was retained in the actual modeling. The calculation formula of AADN score is: AADN score = 0.947×AFP (ng / ml) (“≤100” is assigned 0; “>100” is assigned 1) + 0.804×ALP (U / L) (“≤120” is assigned 0; “>120” is assigned 1) + 1.075×DBIL (μmol / L) (“≤7.3” is assigned 0; “>7.3” is assigned 1) + 1.14×NLR (“≤2.5” is assigned 0; “>2.5” is assigned 1).

[0025] (3) According to the score distribution, the risk groups were divided: the best cutoff value was determined (≤1.10 points for low-risk group, >1.10 points to ≤3.10 points for medium-risk group, and >3.10 points for high-risk group).

[0026] Validation cohort analysis: The AADN score constructed by the training group was applied to the validation group, and the AADN score of each patient was calculated and grouped, and the differences in OS and PFS between groups and the consistency of the model were compared.

[0027] 3. Statistical methods SPSS 26.0 software was used for analysis, and GraphPad 10 was used for plotting. Normally distributed data were expressed as mean ± standard deviation (x ± s), and t-test was used for comparison between groups; non-normally distributed data were expressed as median (interquartile range IQR = Q3-Q1), and Mann-Whitney U test was used. Categorical variables were expressed as frequency (percentage) and were compared using chi-square test or Fisher's exact test. Single / multivariate Cox regression analysis was used to analyze the OS related factors, and HR and 95% confidence interval (CI) were calculated. The survival difference between the high-risk groups was compared by Kaplan-Meier curve and Log-rank test. 2 test or Fisher's exact test. Single / multivariate Cox regression analysis was used to analyze the OS related factors, and HR and 95% confidence interval (CI) were calculated. The survival difference between the high-risk groups was compared by Kaplan-Meier curve and Log-rank test. P <0.05, the difference was statistically significant.

[0028] II. Results 1. Baseline characteristics of patients A multicenter cohort analysis was performed on 188 uHCC patients who received TACE combined with sindibulin and lenvatinib (training cohort: n = 101, validation cohort: n = 87). The baseline characteristics are shown in Table 1. There were no differences in demographic and clinical characteristics between the two groups (P > 0.05) P <0.05): male (training group 89.1% vs validation group 88.5%, P = 0.790), mean age (training group 57.74 ± 12.44 years vs validation group 57.78 ± 10.06 years, P = 0.808), hepatitis B surface antigen (training group 76.2% vs validation group 85.1%, P = 0.183). Disease characteristics included PVTT (training group 27.7% vs validation group 40.2%, P = 0.097), AFP (training group: 96.5 (5.96-1210) vs validation group: 115.4 (7.16-1098.32), P = 0.808), extrahepatic spread (EHS) (training group: 26% vs validation group: 24%, P = 0.933) (P > 0.05). The balance of these baseline characteristics provided a reliable basis for subsequent cohort comparison. P <0.05). The balance of these baseline characteristics provided a reliable basis for subsequent cohort comparison.

[0029] Table 1. Clinical and pathological characteristics of patients

[0030] Abbreviations: n: Number of patients; BMI: Body Mass Index; HBsAg: Hepatitis B surface antigen; PVTT: Portal vein tumor thrombus; BCLC: Barcelona Clinical Stage of Liver Cancer; TBS: Tumor Burden Score; EHS: Extrahepatic metastasis; ECOG-PS: Eastern Cooperative Oncology Group Performance Status Score; WBC: White blood cell count; NEUT: Neutrophil count; LYMPH: Lymphocyte count; TBIL: Total bilirubin; IQR: Interquartile range; DBIL: Direct bilirubin; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; GGT: Gamma-glutamyl transferase; ALP: Alkaline phosphatase; AFP: Alpha-fetoprotein; ALBI: Albumin-bilirubin grade; BUN: Blood urea nitrogen; Cr: Creatinine; PT: Prothrombin time; INR: International Normalized Ratio; NLR: Neutrophil-to-lymphocyte ratio.

[0031] 2. AADN scoring model Univariate Cox regression analysis showed that PVTT (HR = 2.216, 95% CI: 1.205 ~ 4.077) P =0.011), Child stage (HR = 2.519, 95% CI: 1.345 ~ 4.718, P = 0.004), DBIL (>7.3 μmol / L: HR = 3.629, 95%CI: 1.875 ~ 7.025, P <0.001), ALP (>120 μmol / L: HR =2.892, 95%CI: 1.544 ~ 5.416, P = 0.001), AFP (>100 ng / ml: HR = 2.798, 95%CI: 1.451 ~ 5.395, P = 0.002), NLR (>2.5: HR = 3.363, 95%CI: 1.606 ~ 7.044, P = 0.001) is OS-related (Table 2).

[0032] Table 2 Univariate Cox regression analysis of overall survival (OS) in the training cohort

[0033] Abbreviations: HR, hazard ratio; CI, confidence interval; BMI, body mass index; HBsAg, hepatitis B surface antigen; PVTT, portal vein tumor thrombus; BCLC, Barcelona Clinic Liver Cancer; TBS, tumor burden score; EHS, extrahepatic metastasis; ECOG-PS, Eastern Cooperative Oncology Group performance status; DBIL, direct bilirubin; ALP, alkaline phosphatase; AFP, alpha-fetoprotein; NLR, neutrophil-to-lymphocyte ratio.

[0034] Multivariate Cox regression analysis was performed with these parameters, and four predictors significantly associated with OS were identified (Table 3): AFP (>100 ng / ml: HR = 2.579, 95% CI: 1.258 ~ 5.285, P = 0.010), ALP (>120 U / L: HR = 2.234, 95% CI: 1.127 ~ 4.430, P = 0.021), DBIL (>7.2 μmol / L: HR = 2.931, 95% CI: 1.346 ~ 6.382, P = 0.007), and NLR (>2.5: HR = 3.127, 95% CI: 1.384 ~ 7.063, P = 0.006).

[0035] Based on the regression coefficients of the above variables (AFP: 0.947, ALP: 0.804, DBIL: 1.075, NLR: 1.14), a scoring model was constructed, named AADN scoring model. AADN score = 0.947 x AFP (0 or 1) + 0.804 x ALP (0 or 1) + 1.075 x DBIL (0 or 1) + 1.14 x NLR (0 or 1) (categorical threshold: “AFP≤100 ng / ml” assigned 0, “AFP>100 ng / ml” assigned 1; “ALP≤120 u / L” assigned 0, “ALP>120 U / L” assigned 1; “DBIL≤7.3 μmol / L” assigned 0, “DBIL>7.3 μmol / L” assigned 1; “NLR≤2.5” assigned 0, “NLR>2.5” assigned 1).

[0036] The training cohort was used to determine the optimal cutoff value by X-tile: low-risk group (AADN score≤1.10, n = 35), intermediate-risk group (AADN score 1.10-3.10, n = 51), and high-risk group (AADN score>3.10, n = 15). The validation cohort was grouped according to the same cutoff value (low-risk group: n = 23, intermediate-risk group: n = 47, and high-risk group: n = 17).

[0037] Table 3 Multivariate Cox regression analysis of overall survival in the training cohort

[0038] Abbreviations PVTT: portal vein tumor thrombus; DBIL: direct bilirubin; ALP: alkaline phosphatase; AFP: alpha-fetoprotein; NLR: neutrophil-to-lymphocyte ratio.

[0039] 3. Performance of the AADN score model The diagnostic performance of the AADN score was assessed by AUC in the training cohort (n = 101) and validation cohort (n = 87) and compared with clinical indicators. The AADN score showed excellent discriminative performance in both the training cohort and the validation cohort. In the training cohort, the AUC value of the AADN score was higher than that of established clinical parameters (AUC = 0.827, 95% CI: 0.743-0.911; AFP 0.702, ALP 0.685, DBIL 0.701, NLR 0.507) (Fig. A), and showed a fairly high accuracy in the validation cohort (AUC = 0.832, 95% CI: 0.742-0.923; AFP 0.667, ALP 0.643, DBIL 0.701, NLR 0.670) (Fig. B). The AADN score showed significant robustness and discriminative performance in the validation cohort, with a 95% CI significantly higher than other indicators. This confirmed the strong clinical applicability of the model. Figure 2 Figure 2

[0040] 4. Survival analysis of the AADN score model In the training cohort, more than 50% of patients in the low-risk and intermediate-risk AADN score groups were still alive at 36 months. The median OS of the high-risk group was 10 months (95% CI: 7.48 ~ 12.53). More than 50% of patients in the low-risk AADN score group were still progression-free at 36 months. The median PFS of the intermediate-risk and high-risk groups was 16 months (95% CI: 13.55 ~ 18.45) and 7 months (95% CI: 5.14 ~ 8.86), respectively. Kaplan-Meier analysis showed significant survival differences in all endpoints (log-rank P < 0.0001 for OS / PFS in the training cohort) (Fig. A, B and C). Figure 3

[0041] ​​​In the validation cohort, more than 50% of patients in the low-risk and intermediate-risk AADN score groups were still alive at the end of follow-up, while the median overall survival of the high-risk group was 10 months (95% CI: 7.34-12.65). In the low-risk AADN score group, more than 50% of patients were still disease-free at the end of follow-up. The median PFS of the intermediate-risk and high-risk groups was 13 months (95% CI: 9.64 ~ 16.36) and 7 months (95% CI: 4.98 ~ 9.02), respectively. Kaplan-Meier analysis showed that there were significant survival differences in all endpoints (log-rank P < 0.0001 for OS / PFS in the validation cohort) Figure 3 , B and D).

[0042] 5. Subgroup analysis of the impact of AADN score on prognosis As shown in Figure 4 , the AADN score showed predictive effects in different subgroups, including PVTT (yes / no), AFP (>100 ng / ml, ≤100 ng / ml), and BCLC stage (B / C). However, heterogeneity was observed in the subgroup of AFP ≤100 ng / ml (PFS: P = 0.2902), which may be attributed to the limitation of sample size rather than the instability of the predictive model.

[0043] In recent years, transcatheter arterial chemoembolization (TACE) combined with regorafenib and sindilimab has been increasingly recognized as an effective treatment regimen for patients with unresectable hepatocellular carcinoma (uHCC) [16,18,19] . However, there is still a lack of a reliable scoring system to predict the survival prognosis of such patients. The present invention utilizes multi-center data to integrate four key indicators: alpha-fetoprotein (AFP), alkaline phosphatase (ALP), direct bilirubin (DBIL), and neutrophil-to-lymphocyte ratio (NLR), to construct and validate the AADN score model. The model aims to predict the overall survival (OS) and progression-free survival (PFS) of uHCC patients receiving TACE combined with regorafenib and sindilimab. The results show that the model has good discrimination ability and clinical applicability, and can effectively distinguish low-risk, medium-risk, and high-risk patient groups. Therefore, the model provides a highly practical tool for clinicians to optimize individualized treatment plans and assess patient prognosis.

[0044] The triple therapy of immune checkpoint inhibitors (such as PD-1 inhibitor sintilimab), tyrosine kinase inhibitors (such as lenvatinib) and TACE can significantly improve the anti-tumor effect through multiple synergistic mechanisms (such as TACE releasing tumor antigens to enhance immune response, lenvatinib inhibiting angiogenesis, and PD-1 inhibitor relieving immune suppression). A phase II prospective clinical trial showed that the objective response rate (ORR) of the triple therapy of TACE combined with lenvatinib and sintilimab reached 63.9%, and the median overall survival was prolonged to 17.9 months, which showed significant improvement in statistical and clinical significance compared with traditional systemic therapy

[20] . However, there is still a lack of effective tools to predict the efficacy of this therapy. The AADN score established in the present application integrates AFP, ALP, DBIL and NLR four variables, and the clinical significance of each variable is as follows: first, AFP is a classic tumor marker for hepatocellular carcinoma, and an elevated AFP level indicates active tumor proliferation and strong invasiveness

[21] . Even in the context of immunotherapy, it still has prognostic value

[22] . ALP can reflect the function of the liver and gallbladder system and bone metastasis, and elevated ALP in hepatocellular carcinoma patients is often accompanied by tumor vascular invasion or intrahepatic metastasis. Previous studies have shown that elevated serum ALP is an independent risk factor for the prognosis of hepatocellular carcinoma patients

[23] , and this conclusion is also confirmed in the present application. DBIL can reflect the degree of cholestasis and liver function damage, and Young S's study found that compared with total bilirubin, direct bilirubin concentration can more accurately predict the tolerance of patients to TACE treatment

[24] . In addition, cancer-related inflammation is also significantly associated with the survival prognosis of cancer patients

[25] . NLR, as a biomarker of systemic inflammatory state, is associated with the progression, metastasis and prognosis of various cancers. In the "game" between the immune system and tumor cells, neutrophils and lymphocytes play an important role [26, 27] . Elevated NLR indicates an imbalance between pro-inflammatory response and anti-tumor immunity, which is associated with poor prognosis of various solid tumors

[28] .

[0045] The present application confirms that AFP, ALP, DBIL and NLR are all independent prognostic factors. By constructing the AADN score model, the prognosis of patients can be more comprehensively evaluated.

[0046] The advantages of the AADN score include: (1) based on baseline indicators obtained before treatment, easy to obtain in clinical practice (only routine blood tests and liver function assessment are required); (2) directly related to treatment response (in immune-targeted therapy, inflammatory status and liver function are key influencing factors); (3) the score is dynamic and can be adjusted during treatment according to changes in indicators (for example, changes in NLR before and after treatment can reflect the state of immune activation).

[0047] In summary, the AADN score model of the present application uses blood indicators (based on AFP, ALP, DBIL and NLR) with low cost and high accessibility, which can accurately stratify the prognosis of uHCC patients, and provides a practical tool for individualized regimen of TACE combined with sintilimab and lenvatinib treatment. In the future, the scoring algorithm needs to be optimized through prospective studies, and its application scenarios need to be expanded, so as to ultimately promote the development of precision medicine for unresectable hepatocellular carcinoma.

[0048] Although the present application has been described in detail above with general description and specific embodiments, some modifications or improvements can be made on the basis of the present application, which is obvious to those skilled in the art. Therefore, these modifications or improvements made on the basis of not deviating from the spirit of the present application, all belong to the scope of the present application.

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Claims

1. A prognostic indicator for patients with unresectable hepatocellular carcinoma treated with transcatheter arterial chemoembolization combined with sintilimab and lenvatinib, characterized in that... It consists of four blood indicators, namely AFP, ALP, DBIL and NLR.

2. Use of the indicator according to claim 1 in constructing a prognostic model for predicting the efficacy of transcatheter arterial chemoembolization combined with sintilimab and lenvatinib in the treatment of unresectable hepatocellular carcinoma.

3. A prognostic model for predicting the efficacy of transcatheter arterial chemoembolization combined with sintilimab and lenvatinib in the treatment of unresectable hepatocellular carcinoma, characterized in that... It is the following AADN scoring model established based on the blood indicators AFP, ALP, DBIL and NLR; AADN score = 0.947 × AFP (assigned 0 or 1) + 0.804 × ALP (assigned 0 or 1) + 1.075 × DBIL (assigned 0 or 1) + 1.14 × NLR (assigned 0 or 1) Among them, AADN score ≤ 1.10 is the low-risk group; 1.10 < AADN score ≤ 3.10 is the medium-risk group; AADN score > 3.10 is the high-risk group; The stratification thresholds are as follows: AFP ≤ 100 ng / ml, assigned 0, AFP > 100 ng / ml, assigned 1; ALP ≤ 120 U / L, assigned 0, ALP > 120 U / L, assigned 1; DBIL ≤ 7.3 μmol / L, assigned 0, DBIL > 7.3 μmol / L, assigned 1; NLR ≤ 2.5, assigned 0, NLR > 2.5, assigned 1.

4. Use of a reagent for detecting the expression levels of AFP, ALP, DBIL and NLR in the preparation of a prognostic reagent or kit for the efficacy of transcatheter arterial chemoembolization combined with sintilimab and lenvatinib in the treatment of unresectable hepatocellular carcinoma.

5. The application according to claim 4, characterized in that, The prognosis includes the overall survival period and progression-free survival period of patients with unresectable hepatocellular carcinoma.

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