Zhang X, Liao MJ, Ren LY, Qin WY, Mu SW, She SP, Fei R, Cong X, Zhou YP, Chen DB, Chen HS. AADN score: Predicting response to transarterial chemoembolization, sintilimab and lenvatinib in patients with hepatocellular carcinoma. World J Gastroenterol 2025; 31(48): 113856 [DOI: 10.3748/wjg.v31.i48.113856]
Corresponding Author of This Article
Hong-Song Chen, Professor, Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China. chenhongsong2999@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Xue Zhang, Min-Jun Liao, Li-Ying Ren, Shao-Ping She, Ran Fei, Xu Cong, Dong-Bo Chen, Hong-Song Chen, Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, Beijing 100044, China
Min-Jun Liao, Yuan-Ping Zhou, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China
Wan-Ying Qin, Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541000, Guangxi Zhuang Autonomous Region, China
Shao-Wei Mu, Department of Hepatobiliary Surgery, Peking University People’s Hospital, Beijing 100044, China
Hong-Song Chen, Peking University Third Hospital, Beijing 100191, China
Co-corresponding authors: Dong-Bo Chen and Hong-Song Chen.
Author contributions: Zhang X and Liao MJ contributed to data analysis and manuscript writing; Ren LY, Qin WY, Mu SW, She SP, Fei R and Cong X were responsible for patient enrollment and data collection; Zhou YP provided patient care and study materials; Chen DB and Chen HS conceived and supervised the study; Zhang X and Liao MJ contributed equally to this manuscript and are co-first authors; Chen DB and Chen HS contributed equally to this manuscript and are co-corresponding authors. All authors reviewed and approved the final manuscript.
Supported by National Key Sci-Tech Special Project of China, No. 2018ZX10302207; Beijing Nova Program, No. 20250484965; Beijing Natural Science Foundation, No. 7222191 and No. 7244426; Fundamental Research Funds for the Central Universities, Peking University, No. PKU2024XGK005; Peking University Medicine Seed Fund for Interdisciplinary Research, No. BMU2021MX007 and No. BMU2022MX001; Fundamental Research Funds for the Central Universities, Peking University People’s Hospital Scientific Research Development Funds, No. RDX2020-06 and No. RDJ2022-14; and the Qi-Min Project.
Institutional review board statement: The study protocol, conducted in accordance with the principles of the Declaration of Helsinki, was approved by the Institutional Review Board of each participating center of Peking University People’s Hospital (Approval No. 2024PHB061-001), NanFang Hospital of Southern Medical University (Approval No. NFEC-202305-K32-01), and Affiliated Hospital of Guilin Medical University (Approval No. 2021WJWZC14).
Informed consent statement: Written informed consent was obtained from all participants prior to the initiation of combined therapy.
Conflict-of-interest statement: The authors declare no conflict of interest.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: If reasonably necessary, the datasets used and analyzed in this study can be obtained from the corresponding author.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hong-Song Chen, Professor, Peking University People’s Hospital, Peking University Hepatology Institute, Infectious Disease and Hepatology Center of Peking University People’s Hospital, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases, Beijing International Cooperation Base for Science and Technology on NAFLD Diagnosis, No. 11 Xizhimen South Street, Xicheng District, Beijing 100044, China. chenhongsong2999@163.com
Received: September 9, 2025 Revised: October 11, 2025 Accepted: November 10, 2025 Published online: December 28, 2025 Processing time: 110 Days and 2.6 Hours
Abstract
BACKGROUND
Although the triple therapy of transarterial chemoembolization (TACE) combined with immune checkpoint inhibitors and tyrosine kinase inhibitors is becoming an effective treatment for unresectable hepatocellular carcinoma (uHCC). However, there is still a lack of effective tools for predicting therapeutic effects at present.
AIM
To develop a predictive tool for the prognosis of uHCC patients treated with TACE, sintilimab and lenvatinib.
METHODS
Based on multicenter data, this study constructed and validated an AADN score as variables to predict overall survival in patients treated with this combination therapy. This study included 188 uHCC cases (training cohort: n = 101, validation cohort: n = 87) from three different hospitals. Who were treated with TACE, sintilimab and lenvatinib.
RESULTS
In multivariate analysis, alpha-fetoprotein ≥ 100 ng/mL [hazard ratio (HR) = 2.579, P = 0.010], alkaline phosphatase > 120 U/L, (HR = 2.234, P = 0.021), direct bilirubin > 7.3 μmol/L (HR = 2.931, P = 0.007) and neutrophil to lymphocyte ratio > 2.5 (HR = 3.127, P = 0.006) were identified as independent prognostic factors and were used to establish the AADN score. Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves were used to assess the accuracy of the AADN score, with area under receiver operating characteristic curve values of 0.827 (training cohort, 95% confidence interval: 0.743-0.911) and 0.832 (validation cohort, 95% confidence interval: 0.742-0.923). According to the score, the patients were divided into low-risk, intermediate-risk and high-risk groups. Overall survival and progression-free survival were significantly different between groups.
CONCLUSION
The AADN score can distinguish the prognostic risk of uHCC patients treated with TACE, sintilimab and lenvatinib, provides a basis for individualized treatment decision-making, and have clinical application prospect.
Core Tip: This multicenter study developed and validated a novel AADN score to predict survival in patients with unresectable hepatocellular carcinoma treated with triple therapy (transarterial chemoembolization, sintilimab, and lenvatinib). The AADN score effectively stratified patients into distinct low-risk, intermediate-risk, and high-risk groups with significantly different overall and progression-free survival outcomes, demonstrating high predictive accuracy. This tool provides a practical basis for prognostic assessment and individualized treatment decision-making in this patient population.
Citation: Zhang X, Liao MJ, Ren LY, Qin WY, Mu SW, She SP, Fei R, Cong X, Zhou YP, Chen DB, Chen HS. AADN score: Predicting response to transarterial chemoembolization, sintilimab and lenvatinib in patients with hepatocellular carcinoma. World J Gastroenterol 2025; 31(48): 113856
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide[1], and its treatment strategy is highly dependent on staging, tumor burden and liver function status[2]. For early-stage HCC, radical surgical resection or liver transplantation is the preferred treatment, with a 5-year survival rate of 50% to 70%[3,4]. For intermediate-stage HCC, transarterial chemoembolization (TACE) or systemic therapy is commonly adopted. In contrast, for advanced-stage HCC, targeted therapy (such as lenvatinib), immune checkpoint inhibitors [such as programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors], or combination regimens are the main treatment options[4]. For patients with advanced liver cirrhosis or multiple tumors (≥ 3) that cannot be surgical-removed, liver transplantation is a potential curative option[5]. Furthermore, for end-stage HCC (such as extensive metastasis, Child-Pugh grade C and refusal of active treatment), palliative care focuses on symptom control, with the goal of improving the quality of life. Radical resection remains the preferred treatment option for HCC patients, but more than 50% of HCC patients have lost the opportunity for surgical resection when diagnosed and are classified as unresectable HCC (uHCC)[2]. TACE is a well-established therapeutic approach for advanced HCC[6-8]. By obstructing the blood supply to the tumor tissue, TACE induces ischemic necrosis and is extensively employed in Asian nations[9-11]. The therapeutic effect of TACE shows significant heterogeneity, mainly influenced by factors such as tumor blood supply, liver function, operation techniques, and tumor biological characteristics. Importantly, integration of TACE with targeted therapy and immunotherapy has demonstrated notable enhancements in the antineoplastic efficacy, emerging as a pivotal treatment modality for uHCC[12-14].
The combination of TACE with immune checkpoint inhibitors like sintilimab and tyrosine kinase inhibitors such as lenvatinib has demonstrated notable efficacy in treating advanced HCC[15-17], which can provide survival benefits for patients with uHCC[18,19]. However, there is still a lack of effective tools for predicting therapeutic effects at present. Consequently, there is a pressing demand for pragmatic and robust prognostic models to prognosticate the outcomes of uHCC patients undergoing triple therapy.
To enhance the prognostic model for uHCC patients undergoing TACE with sintilimab and lenvatinib, this study developed and validated an AADN score. This model, utilizing alpha-fetoprotein (AFP), alkaline phosphatase (ALP), direct bilirubin (DBIL), and neutrophil to lymphocyte ratio (NLR) as parameters, was derived from multicenter real-world data. The aim was to predict survival outcomes in uHCC patients receiving TACE with sintilimab and lenvatinib, offering a standardized prognostic tool for clinical trials in this setting.
MATERIALS AND METHODS
Patient enrollment
This multicenter retrospective cohort study included 188 patients with uHCC (advanced disease or predicted residual liver volume after surgery) who were admitted to Peking University People’s Hospital, Hepatobiliary Surgery, Nanfang Hospital (Southern Medical University) and Affiliated Hospital of Guilin Medical University. All patients received TACE in combination with sintilimab (200 mg, intravenous injection every 3 weeks) and lenvatinib (12 mg/day for those weighing ≥ 60 kg and 8 mg/day for those weighing < 60 kg). Before each TACE treatment, lenvatinib and sintilimab were suspended for 3 days. If no serious TACE-related adverse events occurred, they resumed after 3 days. Antiviral treatment with entecavir or tenofovir is provided to all patients with hepatitis B virus infection. The study protocol, conducted in accordance with the principles of the Declaration of Helsinki[20], was approved by the Institutional Review Board of each participating center of Peking University People’s Hospital (Approval No. 2024PHB061-001), NanFang Hospital of Southern Medical University (Approval No. NFEC-202305-K32-01), and Affiliated Hospital of Guilin Medical University (Approval No. 2021WJWZC14). All patients signed written informed consent prior to combination therapy. A total of 188 patients were enrolled. The training cohort and validation cohort were divided randomly: 101 in the training cohort and 87 in the validation cohort (Figure 1).
Figure 1 Flowchart of the research cohort.
uHCC: Unresectable hepatocellular carcinoma; BCLC: Barcelona Clinic Liver Cancer; TACE: Transarterial chemoembolization; mRECIST: Modified Response Evaluation Criteria for Solid Tumors; HCC: Hepatocellular carcinoma.
Inclusion criteria: (1) Age ≥ 18 years; (2) uHCC (BCLC stage B/C) diagnosed by imaging (enhanced computed tomography/magnetic resonance imaging meeting the diagnostic criteria for HCC); (3) Treated with TACE, sintilimab and lenvatinib; (4) Clinical data recording at baseline (within 1 week before treatment); and (5) At least one measurable lesion that meets the modified Response Evaluation Criteria for Solid Tumors (mRECIST) exists.
Exclusion criteria: (1) With other malignant tumors; (2) With severe liver and kidney dysfunction (Child-Pugh C); (3) With previous systemic treatment for HCC; (4) With missing follow-up data; and (5) Active autoimmune diseases or severe hematological diseases.
Treatment and follow-up
All patients received TACE (tumor response was evaluated according to mRECIST criteria) combined with sintilimab plus lenvatinib. They underwent enhanced computed tomography/magnetic resonance imaging examinations and laboratory evaluations every 4-8 weeks. The therapeutic efficacy was assessed by two independent radiologists in accordance with the mRECIST criteria. 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. The secondary endpoint was progression-free survival (PFS), defined as the time interval from the start of treatment to disease progression or death.
Model construction and validation
Training cohort analysis: (1) Predictors with P < 0.05 in univariate Cox regression entered multivariate modeling. The AADN score was constructed based on Cox proportional hazards regression to determine prognostic factors, time-dependent receiver operating characteristic was used to assess discrimination between training and validation cohorts, and X-tile index was used to determine the optimal cutoff value. Kaplan-Meier survival curves were plotted according to risk stratification of AADN score, and log-rank test was used for comparison between groups; (2) Building AADN score based on regression coefficients: Binomial threshold setting for AFP, ALP, DBIL, NLR (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), each variable was assigned an approximate integer value of its regression coefficient β (AFP: 0.947, ALP: 0.804, DBIL: 1.075, NLR: 1.14, the weighted sum of original coefficients is retained in actual modeling, AADN score = 0.947 × AFP (ng/mL) (0: ≤ 100 ng/mL; 1: > 100 ng/mL) + 0.804 × ALP (U/L) (0: ≤ 120 U/L; 1: > 120 U/L) + 1.075 × DBIL (μmol/L) (0: ≤ 7.3 μmol/L; 1: > 7.3 μmol/L) + 1.14 × NLR (0: ≤ 2.5; 1: > 2.5); and (3) Risk groups were divided according to the score distribution: The optimal cutoff value was determined (≤ 1.10 as low risk, > 1.10 to ≤ 3.10 as intermediate risk, > 3.10 as high risk).
Validation cohort analysis: The AADN score constructed by the training group was applied to the validation group, and the AADN score of each patient were calculated and grouped, and the OS differences and model consistency between the groups were compared.
Statistical analysis
The SPSS version 26.0 software was used for analysis and R version 4.3.1 or GraphPad version 10 was used for plotting. Normal distribution data were expressed as mean ± SD, and group comparisons were performed by t-test; non-normal distribution data were expressed as median (interquartile range), and were performed by Mann-Whitney U test. Categorical variables were expressed as n (%) using χ2 test or Fisher’s exact test. Univariate/multivariate Cox regression analysis was performed to analyze OS-related factors, and the hazard ratio (HR) and 95% confidence interval (CI) were calculated. Differences in survival between risk groups were compared by Kaplan-Meier curves and Log-rank test. P < 0.05 was statistically significant.
RESULTS
Patient baseline characteristics
A multicenter cohort of 188 patients with uHCC treated with TACE in combination with sintilimab and lenvatinib (training cohort: n = 101, validation cohort: n = 87) was analyzed. Baseline characteristics are shown in Table 1. There were no differences between the two groups in terms of demographic and clinical characteristics (P > 0.05): Male (86.1% in training cohort vs 88.5% in validation cohort, P = 0.790), mean age (57.74 ± 12.44 years old in training cohort vs 57.78 ± 10.06 years old in validation cohort, P = 0.808), hepatitis B surface antigen (76.2% in training cohort vs 85.1% in validation cohort, P = 0.183). The disease characteristics, including portal vein tumor thrombus (PVTT) (27.7% in training cohort vs 40.2% in validation cohort, P = 0.098), AFP (training cohort: 96.5 (5.96-1210) vs validation cohort: 115.4 (7.16-1098.3), P = 0.808), and extrahepatic spread (training cohort: 26% vs validation cohort: 24%, P = 0.933), were also not statistically different between groups (P > 0.05). The balance of these baseline characteristics provided a reliable basis for subsequent cohort comparisons.
Table 1 Comparison of clinicopathological characteristics of two groups patients, mean ± SD/median (interquartile range).
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 and named AADN score. AADN score = 0.947 × AFP (0 or 1) + 0.804 × ALP (0 or 1) + 1.075 × DBIL (0 or 1) + 1.14 × NLR (0 or 1) (stratification thresholds: AFP ≤ 100 ng/mL = 0, > 100 ng/mL = 1; ALP ≤ 120 U/L = 0, > 120 U/L = 1; DBIL ≤ 7.3 μmol/L = 0, > 7.3 μmol/L = 1; NLR ≤ 2.5 = 0, > 2.5 = 1).
The training cohort determined the optimal cutoff values through 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 cut-off value (low-risk group: n = 23, intermediate -risk group: n = 47, high-risk group: n = 17).
Performance of the AADN score
The diagnostic efficacy of the AADN score in the training cohort (n = 101) and the validation cohort (n = 87) was evaluated through the area under the curve (AUC) and compared with the clinical indicators. The AADN score demonstrated excellent discriminative performance in both the training and validation cohorts. In the training cohort, the AUC value of the AADN score was higher than that of the established clinical parameters (AUC = 0.827, 95%CI: 0.743-0.911; AFP: 0.702, ALP: 0.685, DBIL: 0.701, NLR: 0.507; Figure 2A), and showed considerable 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; Figure 2B). The AADN score exhibited remarkable robustness and discrimination within the validation cohort, as evidenced by its AUC, which was substantially higher than that of other indicators. The calibration curve of the AADN score for predicting 3-year survival showed that in both the training cohort and validation cohort, the predicted survival probabilities were in good agreement with the observed ones, as the curves closely aligned with the ideal diagonal line (Figure 2C). In addition, the receiver operating characteristic curve compares the diagnostic performance of the AADN score, hepatic arterial infusion chemotherapy score, and albumin-bilirubin score. The AUC of the AADN score is 0.823 (95%CI: 0.743-0.911), which is higher than that of the hepatic arterial infusion chemotherapy score (AUC = 0.799, 95%CI: 0.712-0.887) and the albumin-bilirubin score (AUC = 0.741, 95%CI: 0.644-0.838; Figure 2D). This confirms the model’s strong clinical applicability.
Figure 2 Receiver operating characteristic curve analysis of the predictive performance of the AADN score in the training cohort and validation cohort.
A: Receiver operating characteristic (ROC) curve analysis of the predictive performance of the AADN score in the training cohort; B: ROC curve analysis of the predictive performance of the AADN score in the validation cohort; C: Calibration curve of the AADN score for predicting 3-year survival; D: ROC curve was used to compare the diagnostic efficacy of AADN score, hepatic arterial infusion chemotherapy score and albumin-bilirubin score. ROC: Receiver operating characteristic; NLR: Neutrophil to lymphocyte ratio; AFP: Alpha-fetoprotein; ALP: Alkaline phosphatase; DBIL: Direct bilirubin; AUC: Area under the curve; CI: Confidence interval; HAP: Hepatic arterial infusion chemotherapy; ALBI: Albumin-bilirubin.
Survival analysis of the AADN score
In the training cohort, more than 50% of patients in the low-risk and intermediate-risk AADN score group were still alive at 36 months. The median OS of the high-risk group was 10 months (95%CI: 7.48-12.53). In the low-risk AADN score group, more than 50% of patients still had no disease progression at 36 months. The median PFS in 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 that there were significant survival differences at all endpoints (log-rank P < 0.0001 for OS/PFS in the training cohort; Figure 3A and B).
Figure 3 The prognosis of both the training cohort and the validation cohort based on the AADN score.
A: The Kaplan-Meier overall survival curves for the training cohort classify patients into low-risk, intermediate-risk, and high-risk groups according to their AADN score (log-rank P < 0.0001); B: The Kaplan-Meier progression-free survival curves for the training cohort classify patients into low-risk, intermediate-risk, and high-risk groups according to their AADN score (log-rank P < 0.0001); C: The Kaplan-Meier overall survival curves for the validation cohort classify patients into low-risk, intermediate-risk, and high-risk groups according to their AADN score (log-rank P < 0.0001); D: The Kaplan-Meier progression-free survival curves for the validation cohort classify patients into low-risk, intermediate-risk, and high-risk groups according to their AADN score (log-rank P < 0.0001).
In the validation cohort, more than 50% of patients in the low-risk and intermediate-risk AADN score group were still alive at the end of the follow-up, while the median OS in 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 still had no disease progression at the end of the follow-up. The median PFS in 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 at all endpoints (log-rank P < 0.0001 for OS/PFS in the validation cohort) (Figure 3C and D).
The prognostic efficacy of AADN score in subgroup analyses
As illustrated in Figure 4, the AADN score demonstrated predictive efficacy across various subgroups, including PVTT (yes/no), AFP (> 100 ng/mL, ≤ 100 ng/mL), and BCLC stage (B/C). However, heterogeneity was observed in the subgroup with AFP ≤ 100 ng/mL (PFS: P = 0.2902), which may be attributed to limitations in sample size rather than instability of the predictive model.
Figure 4 Subgroup analysis of survival outcomes stratified by clinical and pathological characteristics.
A: Overall survival (OS) and progression-free survival (PFS) of patients without portal vein tumor thrombus in the training cohort based on the AADN score (log-rank, OS: P < 0.0001, PFS: P = 0.0004); B: OS and PFS of patients with portal vein tumor thrombus in the training cohort based on the AADN 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 the AADN 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 the AADN score (log-rank, OS: P = 0.0022, PFS: P = 0.0051); E: OS and PFS of Barcelona Clinic Liver Cancer stage B patients in the training cohort based on the AADN score (log-rank, OS: P = 0.0007, PFS: P = 0.0379); F: OS and PFS of Barcelona Clinic Liver Cancer stage C patients in the training cohort based on the AADN score (log-rank, OS: P = 0.0002, PFS: P = 0.0003). PVTT: Portal vein tumor thrombus; OS: Overall survival; AFP: Alpha-fetoprotein; PFS: Progression-free survival; BCLC: Barcelona Clinic Liver Cancer.
DISCUSSION
Multi-disciplinary team has been used in the clinical treatment of HCC, and the treatment plan should be selected based on the stage, liver function and the patient’s physical condition. For advanced-stage patients, systemic therapy is the main treatment. The triple therapy combining immune checkpoint inhibitors, tyrosine kinase inhibitors, and TACE significantly enhances anti-tumor efficacy through multiple mechanisms of synergy[19,21-23]. The combination of TACE and systemic therapy has achieved a synergistic effect through “local killing + systemic immune activation”, and the objective response rate has increased to 50%-70%[24,25]. TACE could release tumor antigens to boost immune response. Targeted drugs such as sorafenib and lenvatinib inhibit the vascular endothelial growth factor receptor pathway to block tumor angiogenesis[26]. Immune checkpoint inhibitors relieve T cell exhaustion and activate anti-tumor immunity by blocking the PD-1/PD-L1 or cytotoxic T-lymphocyte antigen-4 pathways. The combination can further reverse the immunosuppressive microenvironment[27]. However, there is still a lack of effective tools for predicting therapeutic effects at present.
This study utilized multi-center data to construct the AADN score by integrating four key indicators: AFP, ALP, DBIL, and NLR. Generally, AFP is a classic tumor marker for HCC. A high AFP level indicates active tumor proliferation and strong invasiveness[28,29]. As an indicator of hepatobiliary system function and bone metastasis, elevated ALP in HCC 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 HCC patients[30], which was confirmed in our research. DBIL reflects cholestasis and liver function impairment. Young et al’s research[31] found that compared with total bilirubin, DBIL concentration can more accurately predict patients’ tolerance to TACE. Elevated DBIL not only indicates bile excretion disorders but may also be associated with concurrent cholangitis, and biliary tract infection can exacerbate liver function impairment. Moreover, bacterial metabolites activate the Toll-like receptor 4 pathway, induce PD-L1 expression, and weaken the effect of immunotherapy[32,33]. As a biomarker of systemic inflammation, NLR is associated with the progression, metastasis and prognosis of various cancers. Both neutrophils and lymphocytes play significant roles in the “game” between the immune system and tumor cells[34,35]. High NLR reflects an increase in neutrophil infiltration and inhibition of lymphocyte function, suggesting an increase in pro-inflammatory factors (such as interleukin-6 and tumor necrosis factor-α) in the tumor microenvironment, which inhibits the activity of cluster of differentiation 8-positive T lymphocyte cells and promotes immune escape and indicates an imbalance between pro-inflammatory and anti-tumor immune systems, which is associated with a poor prognosis for various solid tumors[36,37]. In this study, the AADN score could predict OS and PFS in uHCC patients undergoing TACE in combination with sintilimab and lenvatinib, which has strong discriminatory power and clinical applicability, effectively distinguishing between low-risk, intermediate-risk, and high-risk groups. The AADN score established through “inflammation-immunity” and “liver function” can provide a more comprehensive assessment of the prognosis of patients receiving triple therapy.
Similarly, studies have shown that the C-reactive protein and AFP in immunotherapy (CRAFITY) score based on serum C-reactive protein and AFP levels has a significant effect in evaluating the prognosis of patients with HCC who receive TACE combined with immunotherapy and targeted therapy. However, the C-index for the CRAFITY score were 0.62-0.66[38-41]. GRAPHS-CRAFITY integrates gender and three magnetic resonance imaging features (gross growth type, intratumoral fat, and enhanced tumor capsule) on the basis of CRAFITY. Compared with the CRAFITY score alone, the GRAPHS-CRAFITY score shows better prognostic prediction and discrimination ability (C index: 0.74 vs 0.66, P < 0.001)[42]. However, gender as a prognostic factor may be questioned. Another study has shown that when OS is used as an outcome indicator to treat advanced cancer, there is no statistically significant association between the patient’s gender and the therapeutic effect[43]. Zeng et al[44] found that total bilirubin ≥ 17 μmol/L, AFP ≥ 400 ng/mL, and extrahepatic metastasis are independent predictors of survival. Further, they constructed the transcatheter arterial embolization score, which plays a role in predicting the acceptance of triple therapy before TACE treatment. The transcatheter arterial embolization score in evaluating the prognosis of patients with lenvatinib and PD-1 inhibitors showed certain efficacy (AUC = 0.807). In our study, the predictive efficacy of the AADN score (AUC = 0.823) has better performance than other scores[45-47].
There are several limitations in our study. Firstly, the retrospective design may have selection bias. Secondly, the sample size is limited, leading to a small size of high-risk group. In the future, we will conduct prospective studies, expand the sample size, and attempt to include more variables to enhance the accuracy and universality of the AADN score.
CONCLUSION
The AADN score precisely stratifies the prognosis of uHCC patients with low-cost and highly accessible blood indicators (based on AFP, ALP, DBIL, and NLR), providing a practical tool for individualized strategies of TACE combined with sintilimab and lenvatinib treatment. In the future, it is necessary to optimize the scoring algorithm through prospective research and expand its application scenarios, ultimately promoting the progress of precision medicine for uHCC.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Gastroenterology and hepatology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade A, Grade A, Grade B
Novelty: Grade A, Grade B, Grade C
Creativity or Innovation: Grade A, Grade A, Grade C
Scientific Significance: Grade A, Grade B, Grade C
P-Reviewer: Li DH, MD, Professor, China; Lucke-Wold B, PhD, United States S-Editor: Zuo Q L-Editor: A P-Editor: Xu J
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