Introduction: Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide. Lack of biomarkers for follow-up after treatment is a clinical challenge. DNA methylation has been proposed to be a potential biomarker in HCC. However, there is still a lack of evidence of its clinical use. This study aimed to evaluate the value of using plasma Adenomatous Polyposis Coli promoter methylation level (APC-MET) as a potential biomarker in HCC treatment. Method: A total of 96 patients with HCC at BCLC stage B who underwent local tumor ablation treatment were prospectively included in this study. APC-MET was examined in the plasma of each patient before and 1 month after treatment. The prediction value of APC-MET for survival outcome and disease status after treatment was analyzed and adjusted with alpha-fetoprotein and protein induced by vitamin K absence-II using Cox regression analysis. Results: Univariate Cox regression analysis showed preoperative APC-MET >0 (HR, 2.9, 95% CI: 1.05–8.05, p = 0.041) and postoperative APC-MET >0 (HR, 3.47, 95% CI: 1.16–10.4, p = 0.026) were both predictors of death, and preoperative APC-MET >0 was a predictor of disease progression after treatment (HR, 2.04, 95% CI: 1.21–3.44, p = 0.007). In multivariate models, preoperative APC-MET >0 was a significant predictor of disease progression after adjusting with the other two traditional biomarkers (HR, 1.82, 95% CI: 1.05–3.17, p = 0.034). Conclusions: Hypermethylation of APC promoter appears to be a potential biomarker that could predict patient survival and disease progression outcomes in patients with intermediate-stage HCC after local ablation treatment.

Hepatocellular carcinoma (HCC) is a major global health problem, accounting for approximately 90% of primary liver cancers worldwide [1]. The incidence of liver cancer has been increasing steadily over the past few decades, particularly in Asia, where chronic hepatitis B virus infection is endemic. Despite recent advances in HCC treatment, the prognosis of HCC patients remains poor, with a 5-year overall survival rate of only 10–20% [2, 3]. Early diagnosis and treatment are crucial for improving the prognosis of HCC patients.

Biomarkers for cancer are essential in clinical medicine as they serve multiple functions. They can assist in determining the risk of cancer, screening, and diagnosis before a cancer diagnosis, while also having a significant role in predicting prognosis, evaluating treatment response, monitoring pharmacokinetics during treatment, assessing treatment response, and detecting recurrence after treatment. These biomarkers are valuable tools that help physicians in providing personalized treatment to their patients, thereby improving patient outcomes. In the era of precision medicine and the progress of multidiscipline therapy for HCC, discovering more effective biomarkers is important to help clinicians deal with HCC. Over the past few decades, extensive research has been conducted to identify useful biomarkers for HCC. Several biomarkers, such as alpha-fetoprotein (AFP) and protein induced by vitamin K absence-II (PIVKA-II), have been widely used in clinical practice for HCC diagnosis and monitoring. However, these biomarkers have limitations in sensitivity and specificity, about 40% of HCC patients have normal AFP, and 26% of patients have normal PIVKA-II [4‒6]. Elevation of AFP level may be due to causes other than HCC, such as cirrhosis, chronic hepatitis, and other malignancies [7‒9]. Therefore, new biomarkers are still an unmet need.

The emergence of new advanced genomics and proteomics technologies has paved the way for exploring new diagnostic and prognostic biomarkers for HCC. These biomarkers include various biomolecules such as proteins, DNA, mRNA, microRNAs, metabolites, and abnormal DNA methylation. DNA methylation can arise from physiological processes such as embryo development, genomic imprinting, X-inactivation, and chromosome stability, as well as pathological processes including metabolic disorders, mental disorders, autoimmune diseases, asthma, aging, and cancer [10]. DNA methylation involves adding a methyl group to the carbon-5 position of cytosine residues in CpG dinucleotides, which is catalyzed by a family of DNA methyltransferases. Methylation in the promoter or 5’ region of CpG islands has been shown to lead to the transcriptional repression of downstream genes [11]. Recently, emerging evidence has suggested that DNA methylation plays a crucial role in downregulating tumor suppressor genes and tumor suppressor miRNAs in many cancer cells [12]. Studies have revealed that methylation markers could be detected in patient blood and associated with poor disease prognosis [13]. Studies also reported that Adenomatous Polyposis Coli (APC) promoter genes that are silenced by DNA methylation may be served as a potential useful biomarker for HCC patients [14].

Most previous studies focusing on DNA methylation have mostly been conducted on tumor tissue, which is invasive and challenging to obtain, limiting its practical use in clinical settings. In this particular study, our objective was to assess the prognostic potential of the methylation level of APC promoter (APC-MET), a tumor suppressor gene, present in peripheral blood before and after treatment of HCC.

Patients and Study Design

This is a prospective study, which was approved by the Ethics Committee of the Institute Review Board of National Taiwan University Hospital (NTUH REC: 202009079DIPB; ClinicalTrials: NCT03267290) and was carried out in accordance with the approved guidelines. All participants provided informed consent before joining this study. A total of 96 patients with HCC with BCLC stage B who underwent radiofrequency ablation from January 2019 to June 2021 at National Taiwan University Hospital were included in the study. The diagnosis of HCC was made by a dynamic image (typical image pattern) or tumor biopsy (atypical image pattern). Perioperative characteristics including age, sex, hepatitis profile, and biochemical data were collected preoperatively.

Interventional Procedure

All patients underwent moderate intravenous conscious sedation and local infiltration of 2% lidocaine throughout the procedure. Percutaneous local ablation was performed on all tumors under ultrasound guidance using RFA multi-electrodes with a 200-W generator (Cool-tip; Medtronic, Mansfield, MA, USA). The technical success of RFA was defined as the complete ablation of the HCC with a surrounding safety margin of 0.5–1.0 cm in immediate follow-up CT images.

Treatment Outcome and Follow-Up

All patients underwent curative radiofrequency ablation by a single physician. The blood tests of AFP, PIVKA-II, and APC-MET from all patients before treatment and 1 month after treatment were collected. Patients were followed up at the first month after initial treatment and then every 2–3 months for 1 year. Patients received regular follow-up with abdominal sonography or computed tomography scans carried out every 3–6 months. Disease progression was established through a typical dynamic imaging pattern of HCC, or histopathological tissue examination. Post-progression, patients were provided with and discussed appropriate treatments. The disease progression date or death date was documented and was censored on April 1, 2023.

Real-Time Quantitative Methylation Analysis

Cell-free DNA from plasma samples was bisulfite-converted using the EZ DNA Methylation Kit (Zymo Research) and amplified via real-time quantitative methylation-specific PCR (qMSP) using fluorescent probes. Each reaction was performed using ×1 Kapa Probe Fast qPCR Master Mix with 0.5 μm of each primer and 0.25 μm of the probe in a total volume of 20 μL. Amplification was performed on a QuantStudio 5 real-time PCR system (Thermo Fisher Scientific). Based on our previous work [14], we determined the methylation level of APC promoter (APC-MET) as the difference in Ct value between β-actin and APC using the following formula: 2^[Ct (β-actin) - Ct (APC)] ×1,000 in accordance with the protocol outlined in the previous study [15].

Biomarkers

Abnormal AFP was defined by AFP level >20 ng/mL. Abnormal PIVKA-II level was defined by PIVKA-II level >40 mAU/mL. Abnormal APC-MET was defined by APC-MET level >0 (detectable).

Statistical Analysis

Data were presented as mean ± standard deviation or median (interquartile range) for continuous variables and frequency (percentage, %) for categorical variables. In univariate analysis, the differences in the distributions of continuous variables and categorical variables between groups were examined using the Wilcoxon rank-sum test, χ2 test, Fisher’s exact test, and log-rank test as appropriate for the data type. Univariate and multivariate Cox regression analyses were conducted to estimate the adjusted effects of prognostic factors on the survival outcome and disease outcome. Statistical analysis was performed using the R 4.2.2 software (R Foundation for Statistical Computing, Vienna, Austria). Two-sided p value ≤0.05 was considered statistically significant.

Patient Demographic Characteristics

A total of 96 patients were included in the analysis, and the demographic characteristics are shown in Table 1. Of these patients, 69 were male (71.9%) and 27 were female (28.1%), with an average age of 69.2 ± 10.2 years. In terms of disease background, 50 patients (52.1%) had chronic hepatitis B, while 28 patients (29.2%) were diagnosed with chronic hepatitis C infection. Single tumors were observed in 62 patients, constituting 64.6% of the cohort. As for the liver function assessment, 87 patients (91.6%) were classified as Child-Pugh Class A, and 72 patients (75.8%) fell under ALBI grade 1. Upon a median follow-up period of 19 ± 7.8 months, disease progression was identified in 61 (63.5%) patients, and 15 (15.6%) patients had passed away.

Table 1.

Characteristics of patients with BCLC stage B HCC in this study cohort

CharacteristicAll patients (N = 96)
Age, years 69.2±10.2 
Gender (male), n (%) 69 (71.9) 
Hepatitis B carrier, n (%) 50 (52.1) 
Hepatitis C carrier, n (%) 28 (29.2) 
Tumor number, n (%) 
 1 62 (64.6) 
 2 17 (17.7) 
 3 16 (16.7) 
 >3 1 (1) 
Largest tumor size, cm 2.6±2.2 
Child-Pugh score, n (%) 
 A 87 (91.6) 
 B 8 (8.4) 
ALBI grade, n (%) 
 1 72 (75.8) 
 2 23 (24.2) 
Bilirubin-total, mg/dL 1.0±0.6 
AST, units/L 39.5±42.8 
ALT, units/L 30.3±27.8 
Platelet, k/μL 171.7±80.1 
Creatinine, mg/dL 0.9±0.3 
Fibroscan, kPa 14.3±12.3 
Preoperative APC-MET >0, n (%) 27 (28.1) 
Preoperative AFP >20, n (%) 37 (38.5) 
Preoperative PIVKA-II >40, n (%) 51 (56) 
Postoperative APC-MET >0, n (%) 26 (27.4) 
Postoperative AFP >20, n (%) 22 (22.9) 
Postoperative PIVKA-II >40, n (%) 33 (36.3) 
CharacteristicAll patients (N = 96)
Age, years 69.2±10.2 
Gender (male), n (%) 69 (71.9) 
Hepatitis B carrier, n (%) 50 (52.1) 
Hepatitis C carrier, n (%) 28 (29.2) 
Tumor number, n (%) 
 1 62 (64.6) 
 2 17 (17.7) 
 3 16 (16.7) 
 >3 1 (1) 
Largest tumor size, cm 2.6±2.2 
Child-Pugh score, n (%) 
 A 87 (91.6) 
 B 8 (8.4) 
ALBI grade, n (%) 
 1 72 (75.8) 
 2 23 (24.2) 
Bilirubin-total, mg/dL 1.0±0.6 
AST, units/L 39.5±42.8 
ALT, units/L 30.3±27.8 
Platelet, k/μL 171.7±80.1 
Creatinine, mg/dL 0.9±0.3 
Fibroscan, kPa 14.3±12.3 
Preoperative APC-MET >0, n (%) 27 (28.1) 
Preoperative AFP >20, n (%) 37 (38.5) 
Preoperative PIVKA-II >40, n (%) 51 (56) 
Postoperative APC-MET >0, n (%) 26 (27.4) 
Postoperative AFP >20, n (%) 22 (22.9) 
Postoperative PIVKA-II >40, n (%) 33 (36.3) 

Data are presented as mean ± standard deviation for continuous variables and frequency (%) for categorical variables.

AFP, alpha-fetoprotein; ALBI, albumin-bilirubin; ALT, alanine transaminase; APC-MET, the methylation level of Adenomatous Polyposis Coli promoter; AST, aspartate transaminase; HCC, hepatocellular carcinoma; PIVKA-II, protein induced by vitamin K absence-II.

Survival Outcome and Predictive Value of APC-MET

The biomarkers between patients with stable disease and progression disease before and after treatment are shown in Table 2. The mean value of preoperative APC-MET was significantly higher in patients with progression disease compared with patients with stable disease (6.4 ± 8.9 vs. 1.4 ± 4.1, p < 0.01). Univariate Cox regression analysis showed that preoperative APC-MET >0 (HR, 2.9, 95% CI: 1.05–8.05, p = 0.041) and postoperative APC-MET >0 (HR, 3.47, 95% CI: 1.16–10.4, p = 0.026) were both predictors of death after treatment. Other predictors of death include multiple tumors (HR, 4.02, 95% CI: 1.34–12.1, p = 0.013), child B (HR, 3.84, 95% CI: 1.08–13.7, p = 0.038), and ALBI grade 2 (HR, 5.11, 95% CI: 1.81–14.4, p = 0.002). As for traditional biomarkers, preoperative AFP >20 (HR, 3.34, 95% CI: 1.14–9.78, p = 0.028), postoperative AFP >20 (HR, 3.95, 95% CI: 1.42–10.9, p = 0.008), preoperative PIVKA-II >40 (HR, 3.87, 95% CI: 1.01–14.8, p = 0.048), and postoperative PIVKA-II >40 (HR, 6.18, 95% CI: 1.66–23.0, p = 0.007) were also predictors of death (Table 3). In multivariate models consisting of three biomarkers, postoperative PIVKA-II >40 (HR, 4.68, 95% CI: 1.16–19.0, p = 0.031) is the only significant predictor of death after adjusting with the other two biomarkers (Table 3). The Kaplan-Meier curve showed that patients with undetectable preoperative APC-MET had significantly better survival outcomes than those with preoperative APC-MET >0 (p = 0.032, Fig. 1). Patients with undetectable postoperative APC-MET also had significantly better survival outcomes than those with postoperative APC-MET >0 (p = 0.018, Fig. 2).

Table 2.

Comparison of biomarkers between patients with stable disease and progression disease before and after treatment

CharacteristicStable disease (N = 35)Progression disease (N = 61)p value
Preoperative, ng/mL 
 AFP level 136.2±423.3 1,824.4±10,510 0.35 
 AFP >20 11 (31.4) 26 (42.6) 0.28 
Postoperative, ng/mL 
 AFP level 11.5±18.5 2,554.3±11,662 0.20 
 AFP >20 4 (11.4) 18 (29.5) 0.04 
Preoperative PIVKA-II, mAU/mL 
 PIVKA-II level 326.5±1,067.4 4,082.7±14,837 0.14 
 PIVKA-II >40 15 (44.1) 36 (63.2) 0.08 
Postoperative PIVKA-II, mAU/mL 
 PIVKA-II level 32.0±26.2 4,926.7±17,071.4 0.10 
 PIVKA-II >40 4 (11.8) 29 (50.9) <0.01 
Preoperative APC-MET 
 APC-MET level 1.4±4.1 6.4±8.9 <0.01 
 APC-MET >0 4 (11.4) 23 (37.7) <0.01 
Postoperative APC-MET 
 APC-MET level 3.0±6.1 5.1±8.6 0.20 
 APC-MET >0 8 (22.9) 18 (30) 0.45 
CharacteristicStable disease (N = 35)Progression disease (N = 61)p value
Preoperative, ng/mL 
 AFP level 136.2±423.3 1,824.4±10,510 0.35 
 AFP >20 11 (31.4) 26 (42.6) 0.28 
Postoperative, ng/mL 
 AFP level 11.5±18.5 2,554.3±11,662 0.20 
 AFP >20 4 (11.4) 18 (29.5) 0.04 
Preoperative PIVKA-II, mAU/mL 
 PIVKA-II level 326.5±1,067.4 4,082.7±14,837 0.14 
 PIVKA-II >40 15 (44.1) 36 (63.2) 0.08 
Postoperative PIVKA-II, mAU/mL 
 PIVKA-II level 32.0±26.2 4,926.7±17,071.4 0.10 
 PIVKA-II >40 4 (11.8) 29 (50.9) <0.01 
Preoperative APC-MET 
 APC-MET level 1.4±4.1 6.4±8.9 <0.01 
 APC-MET >0 4 (11.4) 23 (37.7) <0.01 
Postoperative APC-MET 
 APC-MET level 3.0±6.1 5.1±8.6 0.20 
 APC-MET >0 8 (22.9) 18 (30) 0.45 

Data are presented as mean ± standard deviation for continuous variables and frequency (%) for categorical variables.

The p values of statistical tests were calculated using the Wilcoxon rank-sum test for continuous variables and the Pearson’s χ2 test for categorical variables.

AFP, alpha-fetoprotein; APC-MET, the methylation level of Adenomatous Polyposis Coli promoter; PIVKA-II, protein induced by vitamin K absence-II.

Table 3.

Cox regression analysis of predictors for death

a Univariate Cox regression analyses of predictors for death

HR95% CIp value
Male gender 1.977 0.556–7.035 0.293 
Multiple tumors 4.017 1.336–12.077 0.013 
Child-Pugh score B 3.835 1.077–13.653 0.038 
ALBI grade 2 5.113 1.814–14.416 0.002 
Hepatitis B carrier 0.675 0.239–1.905 0.458 
Hepatitis C carrier 1.509 0.535–4.257 0.436 
Preoperative APC-MET >0 2.902 1.046–8.054 0.041 
Preoperative AFP >20 3.337 1.139–9.778 0.028 
Preoperative PIVKA-II >40 3.867 1.011–14.791 0.048 
Postoperative APC-MET >0 3.473 1.160–10.399 0.026 
Postoperative AFP >20 3.947 1.423–10.945 0.008 
Postoperative PIVKA-II >40 6.181 1.662–22.986 0.007 
b Multivariate Cox regression analyses of preoperative biomarkers as predictors of death 
HR95% CIp value
Male gender 1.977 0.556–7.035 0.293 
Multiple tumors 4.017 1.336–12.077 0.013 
Child-Pugh score B 3.835 1.077–13.653 0.038 
ALBI grade 2 5.113 1.814–14.416 0.002 
Hepatitis B carrier 0.675 0.239–1.905 0.458 
Hepatitis C carrier 1.509 0.535–4.257 0.436 
Preoperative APC-MET >0 2.902 1.046–8.054 0.041 
Preoperative AFP >20 3.337 1.139–9.778 0.028 
Preoperative PIVKA-II >40 3.867 1.011–14.791 0.048 
Postoperative APC-MET >0 3.473 1.160–10.399 0.026 
Postoperative AFP >20 3.947 1.423–10.945 0.008 
Postoperative PIVKA-II >40 6.181 1.662–22.986 0.007 
b Multivariate Cox regression analyses of preoperative biomarkers as predictors of death 
HR95% CIp value
Preoperative APC-MET >0 1.439 0.431–4.809 0.554 
Preoperative AFP >20 2.441 0.740–8.055 0.143 
Preoperative PIVKA-II >40 3.492 0.912–13.371 0.068 
c Multivariate Cox regression analyses of postoperative biomarkers as predictors of death 
HR95% CIp value
Preoperative APC-MET >0 1.439 0.431–4.809 0.554 
Preoperative AFP >20 2.441 0.740–8.055 0.143 
Preoperative PIVKA-II >40 3.492 0.912–13.371 0.068 
c Multivariate Cox regression analyses of postoperative biomarkers as predictors of death 
HR95% CIp value
Postoperative APC-MET >0 2.278 0.579–8.966 0.239 
Postoperative AFP >20 2.331 0.685–7.930 0.176 
Postoperative PIVKA-II >40 4.683 1.155–18.986 0.031 
HR95% CIp value
Postoperative APC-MET >0 2.278 0.579–8.966 0.239 
Postoperative AFP >20 2.331 0.685–7.930 0.176 
Postoperative PIVKA-II >40 4.683 1.155–18.986 0.031 
Fig. 1.

Overall survival. Preoperative APC-MET = 0 versus APC-MET >0.

Fig. 1.

Overall survival. Preoperative APC-MET = 0 versus APC-MET >0.

Close modal
Fig. 2.

Overall survival. Postoperative APC-MET = 0 versus APC-MET >0.

Fig. 2.

Overall survival. Postoperative APC-MET = 0 versus APC-MET >0.

Close modal

Disease Outcome and Predictive Value of APC-MET

Univariate Cox regression analysis showed that preoperative APC-MET >0 was a predictor of disease progression after treatment (HR, 2.04, 95% CI: 1.21–3.44, p = 0.007). Although postoperative APC-MET >0 has an HR of 1.71 (95% CI: 1.21–3.44), the p value was not significant (p = 0.058). Other predictors of disease progression include ALBI grade 2 (HR, 1.98, 95% CI: 1.14–3.43, p = 0.015). As for traditional biomarkers, postoperative AFP >20 (HR, 2.8, 95% CI: 1.59–4.92, p < 0.001), preoperative PIVKA-II >40 (HR, 2.17, 95% CI: 1.25–3.78, p = 0.006), and postoperative PIVKA-II >40 (HR, 4.08, 95% CI: 2.37–7.02, p < 0.001) were also predictors of death (Table 4). In multivariate models consisting of three biomarkers, preoperative APC-MET >0 (HR, 1.82, 95% CI: 1.05–3.17, p = 0.034), preoperative PIVKA-II >40 (HR, 2.21, 95% CI: 1.27–3.86, p = 0.005), postoperative AFP >20 (HR, 2.56, 95% CI: 1.31–5.0, p = 0.006), and postoperative PIVKA-II >40 (HR, 3.58, 95% CI: 2.04–6.29, p < 0.001) were significant predictors of disease progression after adjusting with other biomarkers (Table 4). The Kaplan-Meier curve showed that patients with undetectable preoperative APC-MET had significantly better disease-free survival than those with preoperative APC-MET >0 (p = 0.006, Fig. 3). However, patients with undetectable postoperative APC-MET had broadline significantly better disease-free survival than those with postoperative APC-MET >0 (p = 0.055, Fig. 4).

Table 4.

Cox regression analysis of predictors for disease progression

a Univariate Cox regression analyses of predictors for progression

HR95% CIp value
Male gender 1.358 0.757–2.439 0.305 
Multiple tumors 1.553 0.787–3.068 0.205 
Child-Pugh score B 1.803 0.766–4.243 0.177 
ALBI grade 2 1.979 1.143–3.427 0.015 
Hepatitis B carrier 1.229 0.742–2.038 0.423 
Hepatitis C carrier 0.690 0.384–1.240 0.215 
Preoperative APC-MET >0 2.040 1.210–3.439 0.007 
Preoperative AFP >20 1.659 0.995–2.765 0.052 
Preoperative PIVKA-II >40 2.172 1.248–3.780 0.006 
Postoperative APC-MET >0 1.717 0.982–3.001 0.058 
Postoperative AFP >20 2.798 1.590–4.923 <0.001 
Postoperative PIVKA-II >40 4.075 2.366–7.021 <0.001 
b Multivariate Cox regression analyses of preoperative biomarkers as predictors of disease progression 
HR95% CIp value
Male gender 1.358 0.757–2.439 0.305 
Multiple tumors 1.553 0.787–3.068 0.205 
Child-Pugh score B 1.803 0.766–4.243 0.177 
ALBI grade 2 1.979 1.143–3.427 0.015 
Hepatitis B carrier 1.229 0.742–2.038 0.423 
Hepatitis C carrier 0.690 0.384–1.240 0.215 
Preoperative APC-MET >0 2.040 1.210–3.439 0.007 
Preoperative AFP >20 1.659 0.995–2.765 0.052 
Preoperative PIVKA-II >40 2.172 1.248–3.780 0.006 
Postoperative APC-MET >0 1.717 0.982–3.001 0.058 
Postoperative AFP >20 2.798 1.590–4.923 <0.001 
Postoperative PIVKA-II >40 4.075 2.366–7.021 <0.001 
b Multivariate Cox regression analyses of preoperative biomarkers as predictors of disease progression 
HR95% CIp value
Preoperative APC-MET >0 1.821 1.047–3.167 0.034 
Preoperative AFP >20 1.521 0.882–2.625 0.131 
Preoperative PIVKA-II >40 2.211 1.268–3.857 0.005 
c Multivariate Cox regression analyses of postoperative biomarkers as predictors of disease progression 
HR95% CIp value
Preoperative APC-MET >0 1.821 1.047–3.167 0.034 
Preoperative AFP >20 1.521 0.882–2.625 0.131 
Preoperative PIVKA-II >40 2.211 1.268–3.857 0.005 
c Multivariate Cox regression analyses of postoperative biomarkers as predictors of disease progression 
HR95% CIp value
Postoperative APC-MET >0 1.498 0.822–2.730 0.187 
Postoperative AFP >20 2.564 1.314–5.001 0.006 
Postoperative PIVKA-II >40 3.579 2.037–6.287 <0.001 
HR95% CIp value
Postoperative APC-MET >0 1.498 0.822–2.730 0.187 
Postoperative AFP >20 2.564 1.314–5.001 0.006 
Postoperative PIVKA-II >40 3.579 2.037–6.287 <0.001 
Fig. 3.

Progression-free survival. Preoperative APC-MET = 0 versus APC-MET >0.

Fig. 3.

Progression-free survival. Preoperative APC-MET = 0 versus APC-MET >0.

Close modal
Fig. 4.

Progression-free survival. Postoperative APC-MET = 0 versus APC-MET >0.

Fig. 4.

Progression-free survival. Postoperative APC-MET = 0 versus APC-MET >0.

Close modal

Our study demonstrated that perioperative abnormal APC-MET was associated with poor survival outcome after treatment, and abnormal APC-MET before locoregional treatment was associated with poor prognosis of disease progression. Hypermethylation of APC promoter (APC-MET) appears to be a potentially useful biomarker in clinical use that could predict survival outcome and disease progression in HCC patients with BCLC stage B after ablation treatment.

DNA methylation is considered a promising biomarker for cancer detection. Most studies on DNA methylation have focused on analyzing tumor tissue, which is invasive and sometimes difficult to obtain, limiting its clinical application. In this study, we examined the potential of cell-free methylated DNA from peripheral blood as a noninvasive and convenient technique to predict clinical outcomes in HCC patients. APC is a tumor suppressor gene that regulates WNT/β-catenin signaling activation and plays a crucial role in the development of various neoplasms [16‒18]. Recent research indicates that APC may serve as a promising biological marker for HCC and has the potential to become a therapeutic target [19‒21]. Some studies have reported an association between methylation of APC and HCC progression. A meta-analysis has shown that methylation of the APC promoter is significantly associated with HCC risk and might be a promising biomarker [22]. However, the role and clinical application of circulating derivatives of DNA methylation in the treatment response and disease progression of HCC after treatment remain unclear.

Previous studies have shown that APC promoters are methylated in HCC tissue [23, 24]. In our study, we detected APC-MET methylation in 28.1% of patients preoperatively and 27.4% of patients postoperatively, indicating a gap between the tissue level and clinical application of DNA methylation. Nevertheless, these perioperative APC-MET abnormalities showed consistent predictive value for survival outcome and disease progression outcome. After adjusting for AFP and PIVKA-II, an abnormal APC-MET result was independently associated with disease progression after treatment. The result suggests that APC-MET has a predictive value with a different mechanism from AFP and PIVKA-II. Hypermethylation of genes could be influenced by underlying liver conditions such as inflammation or fibrosis. However, based on the previous research [25], methylation levels of APC in patients without HCC do not show the significant increase observed in HCC patients, regardless of whether they are in a state of advanced inflammation such as cirrhosis or in an early fibrosis stage due to viral hepatitis. Consequently, the degree of APC methylation appears to be unaffected by the presence of background liver inflammation and fibrosis.

Our study has some limitations. Our sample size is small, and the study population is highly homogeneous, consisting only of BCLC stage B patients receiving ablation therapy, which limits our ability to conduct subgroup analysis to compare the association of APC-MET with different stages and tumor statuses. However, our highly homogeneous patient population reduces many confounding factors, enabling us to see the potential of APC-MET as a new biomarker in clinical practice. Further study with a larger number of cases with more diverse patients with longer follow-up is needed.

In conclusion, the current study provides evidence that APC promoter methylation (APC-MET) was detected in the plasma of nearly one-third of HCC patients, and it appears to be a valuable prognostic marker after treatment in predicting survival outcome and disease outcome. The use of circulating cell-free DNA methylation as a biomarker could improve patient outcomes by allowing for early detection of treatment response and disease progression and facilitating personalized medicine.

The research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki, all patients have given their written informed consent, and the study protocol was reviewed and approved by the Research Ethics Committee of National Taiwan University Hospital (NTUH REC: 202009079DIPB; ClinicalTrials: NCT03267290).

The authors have no conflicts of interest to declare.

No funding was received in support of this work.

C.‐Y.H. and K.‐W.H. performed most of the study and drafted the manuscript. C.‐Y.H., C.‐Y.L., H.‐J.S., and K.‐W.H. participated in the acquisition and statistical analysis of the data. C.‐Y.L. and K.‐W.H. contributed to the conception and design of this study. C.‐Y.H., C.‐Y.L., and H.‐K.W. assisted with the interpretation of the results. K.-W.H. revised the manuscript for intellectual content.

Additional Information

Registry and the Registration No. of the study/trial: ClinicalTrials: NCT03267290.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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