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Abstract

Hepatocellular carcinoma (HCC) is a highly lethal disease that exhibits a rising global incidence, and most HCC patients are at an advanced stage at the time of diagnosis. Although lenvatinib has become the first-line option for HCC and its overall survival is not inferior to sorafenib, not all HCC patients could derive benefits from lenvatinib. Therefore, it was urgent to predict which group of HCC patients could derive benefits from lenvatinib and monitor its therapeutic effectiveness during treatment. This review summarized various biomarkers that could be used for prediction of lenvatinib response, including 7 biomarkers that are applied in clinical practice, 11 biomarkers that are applied in clinical practice, and 6 biomarkers that are under exploratory stage. By conducting this first comprehensive analysis of predictive biomarkers for lenvatinib in HCC patients, this review aims to help doctors to assess the response of lenvatinib treatment more accurately and, thus, develop more effective individualized treatment plans.

Plain language summary

Biomarkers that contribute to predicting the efficacy of lenvatinib treatment for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related death. Lenvatinib is a widely used treatment for advanced HCC, but not all patients benefit equally from it. This review summarizes the current biomarkers that could be used to predict treatment response to lenvatinib, and categorized the biomarkers into three groups based on their clinical relevance: (1) biomarkers which are applied in clinical practice (e.g., A-fetoprotein, systemic inflammatory indicators, etc.), (2) biomarkers which are highly relevant to clinical practice (e.g., serological biomarkers, changes in body composition), and (3) biomarkers which are under exploratory stage (e.g., non-coding RNA, Single nucleotide polymorphisms, etc.), aiming to provide theoretical support for individualized treatment and future translational research.

Keywords

biomarkers hepatocellular carcinoma lenvatinib predict

Background

Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent cancer globally and stands as the third major cause to cancer-related fatalities, with approximately 906,000 new cases and 830,000 deaths annually.^1
–3 The prognosis for HCC is unfavorable, with a mere 12.7% 3-year survival rate and a median survival of just 9 months.⁴ Although radical treatments, such as surgery, radiofrequency ablation, and liver transplantation (LT), are available for early-stage HCC patients, the majority of patients are typically diagnosed at an advanced stage.^5,6 In such cases, lenvatinib shows a survival benefit for patients with preserved liver function who have HCC.⁵

Lenvatinib is a small molecule tyrosine kinase inhibitor (TKI) that has shown therapeutic efficacy as a first-line targeted drug in HCC.⁷ According to the REFLECT trial, lenvatinib was non-inferior to sorafenib in terms of overall survival (OS) and showed superior outcomes in progression-free survival (PFS) and objective response rate (ORR) in patients with unresectable HCC (u-HCC).⁸ However, some patients are unable to take long-term lenvatinib due to the advancement of tumor and the occurrence of adverse events (AEs) like diarrhea, fatigue, and decreased appetite.⁹ Moreover, ORR of lenvatinib was generally less than 30% according to response evaluation criteria in solid tumors (RECIST) 1.1, which is mainly because some HCC patients are insensitive to lenvatinib.¹⁰ Therefore, it is an urgent need to discover effective predictive markers that could identify which group of patients with HCC is most likely to derive benefits from lenvatinib.

Biomarkers have greater potential in the prediction of disease progression after HCC treatment compared to imaging assessments like RECIST and modified RECIST (mRECIST).^11,12 Moreover, liquid biopsy has become a valuable strategy for obtaining tumor biomarkers, providing a less or non-invasive alternative to traditional tissue biopsies.^13,14 Importantly, liquid biopsy-derived biomarkers could reduce intratumoral heterogeneity compared to tissue biopsy, improving diagnostic sensitivity and accuracy and enhancing patient prognosis.^11,15 Currently, many trials have reported biomarkers that could be used to predict the efficacy and survival benefit in HCC patients treated with lenvatinib.

This review is the first to summarize various biomarkers used to predict the efficacy of lenvatinib in HCC, including 7 biomarkers that are applied in clinical practice, 11 biomarkers that are applied in clinical practice, and 6 biomarkers that are under exploratory stage. This review provides a comprehensive analysis of predictive biomarkers for lenvatinib treatment, aiming to help doctors to evaluate the response of lenvatinib treatment in HCC patients more accurately and, thus, develop more effective individualized treatment plans.

Methods

Through searching four databases (PubMed, Web of Science, Embase, and the Cochrane Library) from inception to June 2025, we selected studies about biomarkers that could predict the efficacy of lenvatinib in HCC and conducted a narrative review on this topic. The search strategy included combinations of the following terms: “hepatocellular carcinoma,” “HCC,” “lenvatinib,” “biomarkers,” “predictive markers,” and “response markers,” without restriction on publication year. Studies that did not report the source of the biomarker were excluded, as well as prognostic biomarker studies. Letters, editorials, expert opinions, case reports, and non-English language publications were excluded. Full-text reviews were conducted when abstract was insufficient for eligibility assessment. References of all included articles were also screened for relevant studies.

Biomarkers that are applied in clinical practice

Currently, several biomarkers that are already widely used in clinical practice have been identified as predictors of the therapeutic efficacy of lenvatinib in HCC, as summarized in Table 1.

Table 1.

Baseline characteristics biomarkers that are applied in clinical practice.

Study	Biomarker	Study type	Number of patients (M/F)	Liver function	Stage	Biomarker response(cutoff level)	Time of assessment
Issei et al., 2020 (Saeki, Yamasaki and Yamashita)	AFP, ALBI score	Retro.	70 (50/20)	Child-Pugh A (73.0%) Child-Pugh B (27.0%)	BCLC: A (4.3%) BCLC: B (41.4%) BCLC: C (54.3%)	AFP: 200 ng/mL DCP: 40 mAU/mL ALBI score: −2.44	From baseline to 1 month
Kodama et al., 2019²⁰	AFP, DCP	Retro.	20 (14/6)	Child-Pugh A (100%)	BCLC: B (35.0%) BCLC: C (65.0%)	Down regulation	At 2 and 4 weeks
Tada et al., 2020²²	NLR	Retro.	237 (181/56)	Child-Pugh A (86.1%) Child-Pugh B (13.1%) Child-Pugh C (0.8%)	BCLC: A (5.1%) BCLC: B (35.0%) BCLC: C (59.1%) BCLC: D (0.8%)	TLR: 4	At 100, 200, and 300 days
Sugama et al., 2021²³	NLR, PMI	Retro.	87 (61/26)	Not available	BCLC: A (33.3%) BCLC: B (44.8%) BCLC: C (20.7%) BCLC: D (1.2%)	NLR: 2.473 PMI: 5.66 cm²/m² (male); 4.61 cm²/m² (female)	Every 500 days
Tada et al., (Tada, Kumada and Hiraoka, 2021)	PLR	Retro.	283 (218/65)	Child-Pugh A (85.5%) Child-Pugh B (13.8%) Child-Pugh C (0.7%)	BCLC: 0 (0.7%) BCLC: A (11.0%) BCLC: B (37.8%) BCLC: C (49.5%) BCLC: D (1.1%)	PLR: 150	At 100, 200, 300, 400, and 500 days
Tada et al., 2022³⁰	CAR	Retro.	522 (410/112)	Child-Pugh A (97.4%) Child-Pugh B (2.6%)	BCLC: 0 (0.8%) BCLC: A (2.3%) BCLC: B (41.8%) BCLC: C (55.0%) BCLC: D (0.2%)	CAR: 0.108	At 4 or 12 weeks
Hayashi et al., 2020²⁹	CRP	Retro.	53 (42/11)	Child-Pugh A (100%)	BCLC: A (9.4%) BCLC: B (43.4%) BCLC: C (47.2%)	CRP: 1.0 mg/dL	At 207 days after treatment with lenvatinib
Ueshima et al., 2019³⁴	ALBI score	Retro.	82 (59/23)	Child-Pugh A (92.7%) Child-Pugh B (7.3%)	BCLC: A (8.6%) BCLC: B (32.9%) BCLC: C (58.5%)	Child-Pugh score of 5 ALBI grade 1	Every 4–8 weeks
Tada et al., 2021³⁶	ALBI score	Retro.	524 (398/126)	Child-Pugh A (85.5%) Child-Pugh B (14.0%) Child-Pugh C (0.6%)	BCLC: 0 (0.8%) BCLC: A (1.5%) BCLC: B (42.9%) BCLC: C (54.4%) BCLC: D (0.4%)	At 6 months: −2.19 At 12 months: −2.24 At 18 months: −2.14 At 24 months: −2.31	At 6, 12, 18, and 24 months

AFP, alpha-fetoprotein; ALBI, albumin-bilirubin; BCLC, Barcelona Clinic Liver Cancer; CAR, C-reactive protein to albumin ratio; CRP, c-reactive protein; DCP, des-gamma-carboxy prothrombin; NLR, neutrophil-to-lymphocyte ratio; TLR, Toll-like receptor; PLR, platelet-lymphocyte ratio; PMI, psoas muscle mass index; Pro., prospective; Retro., retrospective.

Alpha-fetoprotein

Approximately 60%–70% of patients diagnosed with primary liver cancer exhibit elevated plasma levels of alpha-fetoprotein (AFP).¹⁶ Many studies have demonstrated that AFP is closely related to the diagnosis and prognosis of HCC patients.^17,18

AFP response was defined as a decrease of 40% from the baseline (sensitivity: 1.000, specificity: 0.778, AUC: 0.396), which was a significant independent predictor of high ORR in the high AFP group (AFP ⩾ 10 ng/mL) in HCC patients treated with lenvatinib (odds ratio (OR): 51.389, 95% confidence interval (CI): 4.888–540.281, p = 0.001).¹⁹ Similarly, it was reported that levels of AFP decreased at weeks 2 in most of lenvatinib-treated HCC patients, and the patients with sustained reduction in levels of AFP at weeks 4 was associated with a higher ORR.²⁰ The study also noted that levels of AFP increased when lenvatinib was reduced or discontinued, suggesting some correlation between levels of AFP and efficacy of lenvatinib.²⁰ Therefore, the anti-tumor effect of lenvatinib is closely related to AFP levels. However, all the above studies have the limitations of retrospective study design and sample limitation, and lack the consistency of cutoff value; more prospective studies with large sample sizes were needed to further verified.

Systemic inflammatory indicators

Neutrophil/lymphocyte ratio

Neutrophil/lymphocyte ratio (NLR) is a systemic inflammatory index that is inexpensive and easy to measure, reflecting a potential equilibrium between inflammation caused by neutrophils and the immune function against tumors mediated by lymphocytes.²¹

NLR was independently associated with PFS (hazard ratio (HR): 1.897, 95% CI: 1.268–2.837) and OS (HR: 1.874, 95% CI: 1.097–3.119) in lenvatinib-treated HCC patients, and it was shown that patients who had high levels of NLR (⩾4) had significantly worse prognosis than those with low levels of NLR (<4; p = 0.007).²² Additionally, the use of splines in the HR analysis clarified an appropriate cutoff of approximately 3.0–4.5 for the NLR to predict OS in HCC patients.²² Conversely, it was reported that NLR did not show significant predictive value for lenvatinib treatment, but was a significant predictor of transarterial chemoembolization (TACE) in a retrospective study.²³ Furthermore, it was reported that the combined application of NLR and psoas muscle mass index (PMI) could play a predictive role.²³ They created a formula (=0.555 × [up-to-seven criteria out: 1, in: 0] + 0.161 × NLR − 0.191 × PMI), which showed that the low-level group in the lenvatinib group had better prognosis compared with the high-level group and could predict the 1 year survival rate better than Barcelona Clinic Liver Cancer (BCLC) alone, making it a promising predictive tool.²³ In conclusion, NLR could be a prognostic biomarker for u-HCC patients treated with lenvatinib, and the formula combined with PMI and NLR could help to predict the efficacy of lenvatinib in HCC. However, the relationship between NLR and efficacy of lenvatinib needs to be further analyzed and discussed in a large number of prospective studies.

Platelet-lymphocyte ratio

Platelet-lymphocyte ratio (PLR) is an indicator of systemic inflammation, which is cheap, easy to calculate, and was reported as a prognostic biomarker in HCC.^24,25 It was shown that high PLR (⩾150) was significantly correlated with shorter OS (p = 0.021) and PFS (p = 0.035) in u-HCC patients treated with lenvatinib, but not with lenvatinib treatment response.²⁶ Therefore, PLR could predict the survival of lenvatinib-treated u-HCC patients. However, due to the selection bias caused by the limitation of the study population, future studies need to be further verified.

C-reactive protein

C-reactive protein (CRP), a common inflammation marker, and elevated levels indicate systemic inflammation.²⁷ It was reported that elevated CRP levels (cutoff value: 1.0 mg/dL) were related to poor prognosis in HCC patients, while also being closely related to HCC stage.²⁸

CRP could be used to predict OS in lenvatinib-treated u-HCC patients in a retrospective study, indicating that high levels of CRP (>1.0 mg/dL) are significantly related to worse OS (HR: 10.9, 95% CI: 3.68–32.0, p < 0.001).²⁹ Therefore, it could screen patients who would benefit from lenvatinib. In addition, CRP is simple and easily accessible, and its baseline levels also correlate with time to treatment failure (TTF), relative dose intensity (RDI), and response to lenvatinib.²⁹ Therefore, CRP holds promise as a practical and effective biomarker to support personalized treatment strategies and optimize therapeutic decision-making in HCC.

C-reactive protein-to-albumin ratio

It was found that C-reactive protein-to-albumin ratio (CAR) is an easily measurable and inexpensive biomarker that could be used as a biomarker to reflect liver function and HCC stage.³⁰ It was reported that lenvatinib-treated u-HCC patients with high CAR (⩾0.108) had significantly shorter OS and PFS than those with low CAR (p < 0.001).³⁰ Therefore, CAR could be used as a biomarker to predict OS and PFS in u-HCC patients treated with lenvatinib. However, due to the lack of a standardized cutoff value, further studies should validate the cutoff value to improve the accuracy of CAR in predicting the response to lenvatinib treatment.

Biomarker based on comprehensive risk scores

The albumin-bilirubin score

Albumin-bilirubin (ALBI) score, an evidence-based scoring system, was designed for the evaluation of liver function in HCC patients in 2014.³¹ Moreover, it was reported that ALBI was classified into three grades and was independently correlated with OS of liver diseases such as HCC and cirrhosis.^32,33

The baseline ALBI score and the variation from baseline to 1 month in ALBI score are predictors of ORR in lenvatinib-treated HCC patients, especially in the low-AFP group (OR: 6.866, p = 0.039).¹⁹ Similarly, it was reported that ALBI grade 1 was an independent predictor of ORR in HCC patients receiving lenvatinib (p < 0.05).³⁴ Additionally, compared with ALBI scores ⩾ −2.44, HCC patients with ALBI scores <−2.44 had a lower rate of discontinuation, resulting in a longer lenvatinib treatment duration (6.1 vs 13.3 months, p = 0.014).¹⁹

It was reported that ALBI grading has an impact on prognosis in a post hoc analysis of lenvatinib-treated u-HCC patients.⁸ Besides, ALBI grade 1 and ALBI grade 2 HCC patients had longer median OS, higher ORR, and a lower incidence of grade ⩾ 3 treatment-related AEs in the lenvatinib group.³⁵ Moreover, mALBI grade 2b or 3 were independently associated with OS in patients with u-HCC who received lenvatinib therapy (HR: 2.123, 95% CI: 1.267–3.555, p = 0.004).²² Similarly, it was found that the ALBI score is a more predictive measure of OS than the Child-Pugh score in treating u-HCC patients with lenvatinib, and the mALBI classification could forecast outcomes in patients with Child-Pugh scores of 5.³⁶ Furthermore, it was reported that ALBI grade 3 could be used to independently predict OS in HCC patients treated with lenvatinib (adjusted HR: 6.699, p = 0.0039).³⁷ Additionally, mALBI grade 1 is the most favorable indication for the use of lenvatinib in a retrospective study of 375 HCC patients.³⁸

In conclusion, ALBI grading, including mALBI classification, could serve as prognostic indicators and predict lenvatinib treatment outcomes in HCC patients. However, ALBI score is only a static indicator and could not reflect the dynamic changes during the treatment process with lenvatinib, which could not comprehensively and accurately predict the efficacy of lenvatinib in HCC patients when used alone.

AFP-GGT-Hangzhou (AGH) scoring system

The AGH scoring system included three factors: preoperative AFP levels above 200 µg/L, gamma-glutamyl transferase (GGT exceeding 96 U/L, and surpassing the Hangzhou criteria.³⁹ It was shown that these three factors were independently related to poor disease-free survival (DFS) in patients with HCC undergoing LT in multivariate Cox analysis (Li, Chen and Xing, 2023). Moreover, HCC patients were divided into high-, intermediate-, low-risk cohorts using the AGH scoring system, and the differences in OS and PFS among the three groups were statistically significant (p < 0.05), while patients with high-risk AGH score could be more effective with lenvatinib adjuvant therapy after LT.39 Therefore, the AGH scoring system could be a simple and effective method to screen patients who derive benefits from lenvatinib after LT, and its integration of biomarkers and clinical criteria is worthy of further exploration and promotion. However, due to the small sample size of patients and the lack of mention of the application effect in other populations, its universality needs to be verified in a large number of studies.

Biomarkers that are highly relevant to clinical practice

There is growing evidence that several biomarkers, though not yet widely used in clinical practice, are highly clinically relevant and possess strong predictive and translational potential. Relevant studies are summarized in Table 2.

Table 2.

Baseline characteristics biomarkers that are highly relevant to clinical practice.

Study	Biomarker	Research type	Number of patients (M/F)	Liver function	Stage	Biomarker response(cutoff level)	Time of assessment
Shigesawa et al., 2020⁴⁵	ANG2, FGF19	Retro.	27 (25/2)	Child-Pugh A (80.8%) Child-Pugh B (19.2%)	BCLC: B (38.5%) BCLC: C (61.5%)	ANG2: 3108 pg/mL FGF19: 194 pg/mL	Baseline
Chuma et al., 2020⁴⁶	ANG2, FGF19	Pro.	74 (49/25)	Child-Pugh A (100%)	BCLC: B (51.4%) BCLC: C (48.6%)	ANG2/baseline: 0.672 FGF19/baseline: 1.51	ANG2: at 2, 4, and 8 weeks FGF19: at 4 and 8 weeks
Shigesawa et al., 2021⁴⁹	ANG2, VEGF	Pro.	37 (35/2)	Child-Pugh A (73.0%) Child-Pugh B (27.0%)	BCLC: B (29.7%) BCLC: C (70.3%)	ANG2: 3108 pg/mL VEGF: 514.9 pg/mL	Baseline
Goh et al. 2022⁶⁶	CXI	Retro.	116 (98/18)	Child-Pugh A (100%)	BCLC: B (9.5%) BCLC: C (90.5%)	CXI: 53	Every 1–3 months
Rapposelli et al., 2021⁶⁷	LEP	Pro.	404	Child-Pugh A (88.6%) Child-Pugh B (11.4%)	BCLC: B (58.2%) BCLC: C (41.8%)	PNI: 43.3	Every 5 months
Yamamoto et al., 2022⁶⁸	ΔSMA	Retro.	65 (50/15)	Child-Pugh A (86.2%) Child-Pugh B (13.8%)	BCLC: B (43.1%) BCLC: C (56.9%)	ΔSMA: 14%	Before the lenvatinib treatment, after 2 weeks, and after 14 weeks of lenvatinib administration.
Haruki et al., (Uojima, Chuma and Tanaka)	SMI	Retro.	100 (75/25)	Child-Pugh A (72%) Child-Pugh B (28%)	BCLC: B (49%) BCLC: C (51%)	SMI: 42 cm²/m² (male) 38 cm²/m² (female)	In the first 2 months
Yamaoka et al., 2022⁷⁰	ECW/TBW	Retro.	81 (64/17)	Child-Pugh A (100%)	BCLC: B (46.9%) BCLC: C (53.1%)	ECW/TBW: 0.40	Within 1 month of starting lenvatinib treatment, and 1 month after the end of lenvatinib treatment.
Nakatsuka et al., 2021⁶²	cfDNA	Pro.	100 (80/20)	Child-Pugh A (86.0%) Child-Pugh B (14.0%)	BCLC: A (32.0%) BCLC: B (39.0%) BCLC: C (29.0%)	cfDNA: 66.5 ng/mL	One day after treatment for patients treated with RFA or TACE; several days after treatment for those with MTAs
Fujii et al., 2021⁶³	cfDNA, ctDNA (VAF mean)	Retro.	24 (18/6)	Child-Pugh A (100.0%)	BCLC: B (41.2%) BCLC: C (58.8%)	Median value	Before and at weeks 4 of lenvatinib treatment
Tamai et al., 2020⁷¹	Cu/Zn	Retro.	175 (139/36)	Child-Pugh A (80.6%) Child-Pugh B (17.1%) Child-Pugh C (2.3%)	BCLC: 0 (21.7%) BCLC: A (32.0%) BCLC: B (22.3%) BCLC: C (21.7%) BCLC: D (2.3%)	Cu/Zn: 0.999	At 4–24 weeks after the initiation of treatment with lenvatinib
Eso et al., 2021⁸¹	M2BPGi	Retro.	80 (68/12)	Child-Pugh A (87.5%) Child-Pugh B (12.5%)	BCLC: B (37.5%) BCLC: C (62.5%)	M2BPGi: 1.5	Every 8–12 weeks

Ang2, angiopoietin 2; BCLC, Barcelona Clinic Liver Cancer; cfDNA, cell-free DNA; ctDNA, circulating tumor DNA; Cu/Zn: Copper/zinc; CXI, cachexia index; ECW/TBW, extracellular water to total body water ratio; FGF, fibroblast growth factor; LEP, lenvatinib prognostic index; M2BPGi, Mac-2 binding protein glycosylation isomer; Pro., prospective; Retro., retrospective; SMA, skeletal muscle attenuation; SMI, skeletal muscle index; TACE, transarterial chemoembolization; VAF, variant allele frequency; VEGF, vascular endothelial growth factor; MTA, molecular-targeted agent; PNI, prognostic nutritional index; RFA, radiofrequency ablation.

Biomarkers in serology

The predictive effect of biomarkers in serology on lenvatinib treatment for HCC has received extensive attention.

Angiopoietin 2

Angiopoietin 2 (Ang2), a ligand of the tyrosine kinase receptor Tie2, which exhibits widespread expression in embryonic tissues and promotes angiogenesis and tumor growth.^40
–42 It was reported that levels of Ang2 were increased in 131 HCC patients compared with healthy controls in a retrospective study (p < 0.0001).⁴³ Besides, Llovet et al.⁴⁴ showed that elevated levels of Ang2 were related to worse OS in HCC patients in a randomized controlled trial.

There was a significant correlation between lenvatinib response and baseline serum levels of Ang2 (p = 0.017) and all HCC patients (9/9) receiving lenvatinib who had low baseline levels of Ang2 achieved an objective response (cutoff level: 3108 pg/mL).⁴⁵ Similarly, it was shown that levels of Ang2 were significantly lower in the objective response group compared with the lenvatinib non-objective response group with HCC in a prospective cohort study (p = 0.0002).⁴⁶ Besides, it was reported that baseline levels of Ang2 were significantly negatively associated with treatment response to lenvatinib in a retrospective study (p = 0.002).⁴⁷ Additionally, it was identified that Ang2 as an independent biomarker associated with poor prognosis in HCC patients and high levels of Ang2 correlates with a shorter OS in 279 HCC patients treated with lenvatinib in a randomized controlled trial (HR: 1.436; p < 0.0001).⁴⁸ Moreover, it was reported that increased levels of Ang2 could be associated with worse liver function reserve (cutoff level: 3108 pg/mL), predicting poor PFS and OS in lenvatinib-treated HCC patients in a retrospective study.⁴⁹ Furthermore, high baseline Ang2 was found to be linked with a shorter median OS in the subgroup with hepatitis B virus etiological of lenvatinib-treated HCC patients, but not in the hepatitis C virus etiological subgroup.⁴⁸ Therefore, Ang2 could be used as a predictive marker to predict the efficacy of lenvatinib treatment and could determine whether u-HCC patients are suitable for systemic therapy. However, the expression of Ang2 is affected by many factors, and the use of Ang2 alone could not accurately predict the efficacy of HCC patients. Therefore, the combination of other predictors can improve the accuracy of predicting the treatment effect of lenvatinib.

Fibroblast growth factors

Fibroblast growth factor 19 (FGF19) plays a necessary role in glycogen synthesis, gluconeogenesis, and protein synthesis.^50,51 The objective response group had a significantly higher levels of FGF19 at 4 and 8 weeks compared with the non-objective response group in lenvatinib-treated HCC patients in a retrospective study (cutoff value: FGF19/baseline: 1.51).⁴⁶ Besides, it was reported that there was a significant negative relationship between baseline FGF19 and the response of lenvatinib in HCC patients in another retrospective study (cutoff value: 194 pg/mL, p < 0.001).⁴⁵ Besides, it was reported that the combination of increased FGF19 levels and decreased Ang2 levels is not only an indicator of favorable response to lenvatinib therapy (p = 0.0012) but is also associated with longer PFS in HCC patients (p = 0.0240; cutoff value: FGF19/baseline: 1.51, Ang2/baseline: 0.67).⁴⁶ Similarly, it was found that the combination of low baseline levels of Ang2 and FGF19 could predict favorable efficacy of lenvatinib in u-HCC patients in a retrospective study (cutoff value: Ang2: 3108 pg/mL, FGF19: 194 pg/mL).⁴⁵ However, due to inconsistent cutoff values, small sample sizes, and retrospective design, further studies with larger samples are needed.

FGF21 could be a biomarker for assessing the functional status of hepatocellular injury, which is significantly correlated with prognosis in HCC patients.⁵² It has been shown that a higher baseline level of FGF21 is correlated with shorter OS in lenvatinib-treated HCC patients (HR: 2.475, 95% CI: 1.565–3.922, p = 0.0001).⁴⁸ However, it was reported that FGF21 levels were higher in lenvatinib-treated HCC patients with an objective response than in those with non-objective response in a retrospective study, but there was no statistical significance (p = 0.147).⁴⁵

FGF23 is produced and released into the blood primarily by bone cells and acts as a hormone in the kidney, stimulating the excretion of phosphate and inhibiting the synthesis of 1,25(OH)₂D₃.^53
–55 It was found that changes in the increased levels of FGF23 were correlated with the favorable efficacy of lenvatinib treatment in HCC patients (p = 0.0022).⁴⁸ Furthermore, high baseline levels of FGF23 are one of the factors that constitute the signature of cytokines and angiogenic factors (CAFs), which are correlated with low RDI and shorter PFS in 41 lenvatinib-treated HCC patients.⁵⁶

In conclusion, FGFs could be important candidate biomarkers to predict the response to treatment with lenvatinib. However, more large-sample prospective studies are needed to confirm them as predictors of lenvatinib efficacy in HCC patients. Furthermore, whether the combination of FGFs with other biomarkers offers better predictive value is worth further investigation in future research.

Vascular endothelial growth factor

Lenvatinib, an inhibitor of tyrosine kinase receptors, effectively inhibits the phosphorylation of vascular endothelial growth factor (VEGF) receptors (VEGFR1, VEGFR2, and VEGFR3), which leads to an increase in VEGF levels compared to baseline after treatment with lenvatinib.⁵⁷ A significant correlation has been reported between higher baseline VEGF and shorter OS in 279 lenvatinib-treated HCC patients (HR: 1.181, p = 0.0037).⁴⁸ Additionally, low serum levels of VEGF (cutoff value: 514.9 pg/mL) were significantly correlated with worsening hepatic functional reserve in HCC patients at 8 weeks from the initiation of lenvatinib treatment (p = 0.0259).⁴⁹ However, VEGF levels were not significantly different between the objective response group and the non-objective response group in HCC patients at 4 weeks of lenvatinib treatment compared with baseline in a prospective cohort study (p = 0.540).⁴⁶ Therefore, VEGF has great potential in predicting HCC survival with lenvatinib. However, whether serum VEGF can predict the efficacy of lenvatinib in HCC still needs to be confirmed by further prospective studies.

Combination of Ang2 and VEGF levels

Ang2 and VEGF were significantly correlated with survival after lenvatinib treatment, so the combination of Ang2 and VEGF levels was further investigated in HCC.^48,49 It was shown that most HCC patients (8/9) with low baseline levels of VEGF and high levels of Ang2 showed worse Child-Pugh scores at 8 weeks from the initiation of lenvatinib treatment (cutoff value: Ang2: 3108 pg/mL, VEGF: 514.9 pg/mL), whereas patients with high baseline VEGF and low levels of Ang2 (0/9) did not showed worse Child-Pugh scores.⁴⁹ Therefore, the combination of Ang2 and VEGF could serve as a potential predictive biomarker of survival in u-HCC patients receiving lenvatinib. Previous study reported that the combination of Ang2 and VEGF could predict liver function after lenvatinib treatment; considering that their combination was also associated with survival after lenvatinib in HCC patients, whether it could be used as a biomarker for predicting the effectiveness of lenvatinib needs to be verified in future prospective studies.

Other serum growth factors

It was reported that patients with increased growth factors were significantly associated with shorter post-progression survival (PPS) in lenvatinib-treated HCC patients (p = 0.032).⁴⁷ Additionally, it was reported that lenvatinib-treated HCC patients who displayed favorable 9-CAFs characteristics are significantly associated with longer PFS (HR: 0.42, 95% CI: 0.18–0.96, p = 0.040).⁵⁶ Therefore, serum growth factors could serve as predictors to predict lenvatinib treatment response; more studies are needed due to the retrospective design and small sample size of current research.

Cell-free DNA

Circulating cell-free DNA (cfDNA) is a fragment of DNA released into the bloodstream by apoptosis, necrosis, or living cells, including circulating tumor DNA (ctDNA).⁵⁸ Moreover, previous studies reported that cfDNA could be used to reflect the progression of HCC, and the higher the concentration of cfDNA, the larger the tumor size and the higher tumor grade.^59
–61

It was reported that in patients with HCC who received lenvatinib, cfDNA levels generally increased after therapy, and the magnitude of this increase was significantly greater in those achieving an objective tumor response (OTR) compared to non-OTR group (p = 0.048).⁶² In another study involving 24 HCC patients, monitoring the changes in the mean variant allele frequency (VAFmean) of single-nucleotide variants in ctDNA revealed that a decline in VAFmean at week 4 could accurately predict partial response, with high sensitivity (1.0) and moderate specificity (0.67).⁶³

Although cfDNA elevation and ctDNA VAF reduction may appear contradictory, they in fact reflect distinct biological processes in response to treatment: an increase in total cfDNA likely indicates apoptosis or necrosis of tumor cells induced by therapy, while a decrease in ctDNA VAF reflects a reduction in the proportion of tumor-derived DNA. These two biomarkers thus provide complementary insights into therapeutic efficacy. Therefore, cfDNA holds promise as a noninvasive and dynamically monitorable predictive biomarker in clinical practice. However, its predictive reliability may be limited when used alone due to its susceptibility to non-tumor-related effects such as liver injury or inflammation. It is recommended to comprehensively evaluate cfDNA in combination with other biological indicators to improve the predictive performance.

Cachexia index

Cancer cachexia negatively impacts quality of life and survival in many patients with advanced cancer.⁶⁴ A new cachexia index (CXI) has been developed and calculated using (L3 skeletal muscle index) × (serum albumin)/NLR, which was originally proposed for non-small cell lung cancer.⁶⁵ It was shown that low CXI (<53) was independently related to shorter PFS (HR: 1.84, 95% CI: 1.09–3.09, p = 0.02) and OS (HR: 2.07, 95% CI: 1.17–3.65, p = 0.01) in lenvatinib-treated HCC patients.⁶⁶ Furthermore, disease control rate (DCR) and anorexia rates were also significantly lower in HCC patients with low CXI (<53) compared to patients with high CXI (⩾53) during the first 2 months of lenvatinib treatment.⁶⁶ Therefore, CXI could be a potential biomarker to predict the efficacy of lenvatinib in the treatment of patients with HCC. The weight and optimal cutoff value of each component of CXI need to be further explored in large-sample prospective studies.

Lenvatinib prognostic index

Lenvatinib prognostic index (LEP) is a new prognostic score that incorporates five factors, including lymphocytes, bilirubin, albumin, BCLC stage, and previous TACE (yes/no).⁶⁷ The LEP index categorizes HCC patients receiving lenvatinib treatment into low-, intermediate-, and high-risk cohorts, with significant differences observed in OS among them (29.8, 17.0, and 8.9 months; p < 0.0001).⁶⁷ Moreover, it was shown that lenvatinib has a positive predictive effect in the low-risk HCC group (p < 0.0001).⁶⁷ Therefore, LEP could be a potential indicator for predicting the efficacy of lenvatinib in the treatment of HCC patients. However, further investigation is needed because of retrospective design of current research, and a small number of low-risk patients were included.

Biomarkers based on changes in body composition

Changes in body composition play a crucial role in predicting outcomes in HCC treatment.⁶⁸ It was reported that there was a significant association between changes in body composition and TTF along with prognosis after 2 weeks in lenvatinib-treated HCC patients.⁶⁸ Besides, lenvatinib treatment led to a reduction in mean skeletal muscle attenuation within 2 weeks (p = 0.004), which was proved to be a reliable predictor of TTF in lenvatinib-treated HCC patients.⁶⁸ Similarly, decreased skeletal muscle index (SMI) is related to the worse TTF and OS in lenvatinib-treated HCC patients (TTF, p = 0.010; OS, p = 0.021).⁶⁹ Moreover, it was revealed that SMI of arm before lenvatinib treatment could reflect longer PFS in HCC patients (HR: 2.12, 95% CI: 1.23–3.64, p = 0.0069).⁷⁰ Furthermore, extracellular water to total body water ratio ⩽ 0.400 before lenvatinib treatment was significantly related to longer PPS (HR: 3.08, 95% CI: 1.32–7.18, p = 0.0093), OS (HR: 4.72, 95% CI: 12.03–11.00, p < 0.001), and PFS (HR: 2.66, 95% CI: 1.33–5.34, p = 0.0057) in HCC patients.⁷⁰ Therefore, assessment of body composition before and after lenvatinib treatment could be helpful to predict the efficacy of lenvatinib in HCC patients. However, changes in body composition are a complex process, and further large-scale studies and clinical trials are still needed to validate and confirm the effectiveness and practicality of using changes in body composition to predict lenvatinib efficacy.

Copper/zinc and metallothionein

Copper (Cu) and zinc (Zn) are basic nutritional metals related to metabolism in human cells.⁷¹ Cu has a close relationship with cell proliferation and tumor angiogenesis.³¹ Impaired zinc and/or copper function could occur at different stages of liver disease and could correlate with the severity of liver disease.^72
–77 Additionally, metallothionein (MT) is a protein of low molecular weight containing large amounts of cysteine, which could be highly induced by metal ions like Cu and Zn.⁷⁸

A higher survival rate was shown in HCC patients receiving lenvatinib with Cu/Zn <0.999 compared to Cu/Zn ⩾ 0.999 (p < 0.001).⁷¹ They also observed that lenvatinib could induce MT in HCC patients, and there was a significant positive relationship between MT levels and Cu/Zn in HCC patients.⁷¹ Furthermore, it was reported that elevated levels of MT could be related to decreased survival in lenvatinib-treated HCC patients, and this needs to be further investigated.⁷¹ Therefore, Cu/Zn and/or MT could be useful in assessing lenvatinib treatment resistance and survival outcomes of HCC patient. However, serum MT was not correlated with serum Cu or Zn levels measured separately, so the relationship between MT and other heavy metals in HCC and alterations in MT expression during Zn supplementation could be further explored in future studies.

Mac-2 binding protein glycosylation isomer

Wisteria sinensis agglutinin positive Mac-2 binding protein glycosylation isomer (M2BPGi) could serve as a serum biomarker to predict the stage of liver fibrosis and was found to be associated with nutritional status for HCC patients (p < 0.001).^79,80 Pretreatment levels of M2BPGi could be used as predictive markers for PFS (HR: 0.52, p = 0.0358) and also predict the tolerance and response to lenvatinib in HCC patients.⁸¹ Moreover, low serum levels of M2BPGi (<1.5) were correlated with longer cumulative PFS (p = 0.0003), higher RDI (p < 0.0001), ORR (p = 0.0004), DCR (p < 0.0001), and longer duration of lenvatinib treatment (p < 0.0001) compared with high levels of M2BPGi in HCC patients.⁸¹ Therefore, M2BPGi could be used as a potential biomarker in HCC patients treated with lenvatinib, and HCC patients with low levels of M2BPGi could benefit from lenvatinib treatment. However, further research is needed due to the retrospective design, small sample size of current researches, and lack of external validation.

Biomarkers that are under exploratory stage

Circular RNA

Circular RNAs (circRNAs) are formed by a 5′-3′-phosphodiester bond, regulating biological processes and transcription, altering epigenetic modifications, and acting as competing endogenous RNAs.^82
–84 Also, circRNAs were reported to be used as a prognostic biomarker to predict the prognosis of HCC and the therapeutic effectiveness of targeted therapy.⁸⁵

The expression of circKCNN2 significantly downregulated in HCC tissues compared to normal tissues (p < 0.001), and elevated levels of circKCNN2 significantly predicted longer OS (p = 0.013) and recurrence-free survival (p = 0.011) in lenvatinib-treated HCC.⁸⁶ However, the presence of high levels of circKCNN2 in HCC cells counteracts the anti-tumor effects of lenvatinib (possibly because both of them inhibit FGFR4 expression) and confers resistance to lenvatinib.⁸⁶ Moreover, it was reported that HCC cells that express circKCNN2 ectopically or have low intrinsic levels of circKCNN2 are more sensitive to lenvatinib and have a better treatment effect with lenvatinib in HCC patients.⁸⁶ Therefore, circKCNN2 could be used as a potential biomarker for the efficacy of lenvatinib in HCC patients, but it requires further translational research before conducting clinical trials.

MicroRNA

MicroRNAs (MiRNAs) are a group of non-coding, single-stranded RNA molecules with a length of around 22 nucleotides encoded by endogenous genes. MiRNAs are closely associated with the proliferation, invasion, and metastasis of HCC, and some miRNAs were already used as potential targets to develop novel anticancer drugs.^87,88

It was reported that miR-3154 was increased in HCC and liver cancer stem cells compared with adjacent normal tissue, and upregulation of miR-3154 was associated with short OS and DFS in HCC patients (p < 0.05).⁸⁹ Furthermore, miR-3154 could inhibit HNF4α (a tumor suppressor), promote HCC progression, and determine the response of HCC cells after lenvatinib treatment (as knockdown of miR-3154 makes HCC cells more sensitive to treatment with lenvatinib).⁸⁹ Additionally, the combination of low expression of miR-3154 and high expression of HNF4α was related to better prognosis and predicted better DFS in lenvatinib-treated HCC patients.⁸⁹ Therefore, miR-3154 had the potential to serve as a new biomarker for the individualized treatment of lenvatinib in HCC and improve the accuracy of prediction when combined with HNF4α. However, it should be noted that this result is based on laboratory studies, and the existing evidence may have false positives and biological heterogeneity. Therefore, its predictive potential urgently requires further validation through rigorous preclinical studies, large-scale clinical trials, and standardized detection methods to confirm its effectiveness.

Single-nucleotide polymorphisms

Previous studies have reported that single-nucleotide polymorphisms (SNPs) were related to the risk of liver cancer, which is important for understanding the genetic risk and pathogenesis of HCC.^72,90
–95 It was reported that the TC/CC genotypes of NOS3 rs2070744 correspond to poor outcomes in u-HCC patients treated with lenvatinib, but the results showed no statistical significance.⁹⁶ Furthermore, the combined SNP pattern of FGFR4 rs351855 and NOS3 rs2070744 genotype could be useful as predictors of treatment response and prognostic factors in lenvatinib-treated HCC patients (PFS: (HR = 2.56, p = 0.006); OS: (HR = 3.36, p = 0.013)).⁹⁶ Additionally, ABCG2 421C > A C/A or A/A was significantly related to the occurrence of anorexia (OR: 9.009, 95% CI: 1.750–46.383, p = 0.009) and could affect exposure to lenvatinib in HCC patients in a prospective study.⁹⁷ Moreover, a retrospective study found that a reduction in VAFmean of single-nucleotide after 4 weeks of lenvatinib treatment was associated with partial response in 24 HCC patients (specificity: 0.67, sensitivity: 1.0).⁶³ Therefore, SNPs could be a potential biomarker in predicting the efficacy of lenvatinib in HCC. However, the combination and interaction of genetic variations have a more significant impact on lenvatinib efficacy compared with individual SNPs.

Mucin 15

Mucin 15 (MUC15) is a member of the transmembrane mucin family, which acts primarily to hydrate, lubricate, and protect epithelial surfaces.⁹⁸ It was reported that downregulation of MUC15 is associated with poor late differentiation and metastasis of HCC.⁹⁹ It was reported that MUC15 expression is significantly lower in lenvatinib-resistant HCC patients and recurrent HCC, compared to primary lesions.¹⁰⁰ Besides, lenvatinib efficacy was upregulated in HCC cells that overexpress MUC15, which could predict the benefit of lenvatinib in HCC patients.¹⁰⁰ Therefore, MUC15 could serve as a potential biomarker for lenvatinib-treated HCC patients, which deserve further research in biomarker-driven basic and clinical trials.

ST6GAL1

ST6GAL1, a secreted protein derived from tumors, is positively regulated by FGF19 in hepatoma cells, while FGF19 could be a potential tumor biomarker for lenvatinib-susceptible HCC.¹⁰¹ Additionally, lenvatinib was significantly related to better survival than sorafenib in HCC patients with high serum levels of ST6GAL1 (p < 0.05).¹⁰¹ Therefore, serum ST6GAL1 could be used to identify patients with FGF19-driven invasive HCC who benefit from lenvatinib treatment and could be a novel and potential predictor. However, as the current evidence mainly comes from laboratory studies and has not been verified in clinical cohorts, more preclinical and translational studies are needed to further evaluate its application potential in the future.

Ongoing trials of biomarkers for lenvatinib in HCC

After consulting the website (http://apps.who.int/trialsearch), Table 3 provides a summary of the ongoing trials of biomarkers that predicted the effect of lenvatinib treatment in HCC patients, which could guide clinical practice in future.

Table 3.

Overview of ongoing trials of biomarkers for lenvatinib in HCC.

Sponsor	ClinicalTrials.govidentifier	Number of patients	Stage of disease	Treatment	Time of assessment	Biomarker
Third Affiliated Hospital, Sun Yat-Sen University	NCT05803928	70	Early recurrence after radical resection/ablation of HCC	Experimental: radiofrequency ablation + lenvatinib + sintilimab Control group: radiofrequency ablation	RFS: at 2, 3 years Safety: 18 months OS: 18 months	Biomarkers in tumor and peripheral blood such as AFP, CEA, CA199, CA125, RBC, Alb
RenJi Hospital	NCT04425226	192	HCC before LT	Experimental group: pembrolizumab + lenvatinib; Control group: no intervention	RFS: up to 4 years	Immune biomarkers
RenJi Hospital	NCT04443322	20	Locally advanced and metastatic HCC	Single group: durvalumab and lenvatinib	PFS: up to 3 years RFS: up to 4 years	Immune biomarkers
Shanghai Zhongshan Hospital	NCT04241523	50	Resectable HCC	Single group: lenvatinib	ORR: 1 year after LPI OS: 3 years	Serum biomarkers: AFP, antagonist-II
Beijing Ditan Hospital	NCT04127396	72	HCC with PVTT	Group 1: lenvatinib and TACE Group 2: Sorafenib and TACE	TTP: up to 18 months	VEGFR, FGFR, PDGF-α, IL-2
Peking Union Medical College Hospital	NCT04443309	53	Advanced HCC	Single group: lenvatinib + camrelizumab	ORR: 1 year	AFP, PD-L1 expression, CD8 T cell immunohistochemistry, and biomarkers from RNA-sequencing
National Taiwan University Hospital	NCT05286320	27	HCC with portal vein thrombosis	Single group: lenvatinib/pembrolizumab + SBRT combinations	Safety rate: the period to evaluate DLT is for 28 days after completion of SBRT ORR: 2.5 years	Immune biomarkers derived from tumor tissue and blood
Eisai Limited Company	NCT01761266	954	Unresectable HCC	Group 1: lenvatinib Group 1: sorafenib	OS: every 8 weeks	Serum biomarkers: ANG2, FGF19, FGF21, FGF23, VEGF Tumor markers: protein induced by vitamin K absence or antagonist-II
Sun Yat-sen University	NCT04044651	216	Advanced HCC	Experimental group: nivolumab + lenvatinib Control group: lenvatinib	ORR, PFS, DCR, TTP: 18 months	Immune biomarkers: PD-L1
Humanity and Health Medical Group Limited Company	NCT03899428	30	HBsAg positive advanced stage HCC	Group 1: durvalumab Group 2: sorafenib Group 3: lenvatinib Group 4: regorafenib Group 5: cabozantinib	HBsAg: assessed up to 2 years	Serum biomarkers: HBsAg
The Methodist Hospital Research Institute	NCT05171335	50	Transplant-eligible large HCC	Single group: lenvatinib + TACE Control group: matched historical control patients	Percent tumor necrosis: at time of transplant surgery	Serum biomarkers: VEGF, VEGFR-2, ANG2, FGF19, and FGF23
Tianjin Medical University Cancer Institute and Hospital	NCT04834986	30	Resectable HCC	Single group: tislelizumab + lenvatinib	Safety: 36 months	Genomic biomarkers: TMB, TNB, ITH, HLA subtype, HLA-LOH; Tumor microenvironmental biomarkers: tumor Infiltrating lymphocytes, biomarkers expressed on T cells; PD-L1 expression
Institut für Klinische Krebsforschung IKF GmbH at Krankenhaus Nordwest	NCT03841201	50	Advanced HCC	Single group: nivolumab + lenvatinib	ORR: 6 months	Immune biomarkers: PD-1, PD-L1 and PD-L2; immune cell infiltrates (IGHM, CD3, CD8, FOXP3, CD68, CD205); chemokines (CXCL9, CXCL10, CXCL13) and invasion markers (MMP7, MMP9)
National University Hospital, Singapore	NCT03499626	22	Unresectable or metastatic HCC with Childs Pugh status A	Single group: lenvatinib	ORR, PFS: up to 1 year since the start of treatment	Tumor EGFR/HER2/HER3/HER4

AFP, alpha-fetoprotein; ANG2, Angiopoietin 2; DCR, disease control rate; DLT, dual liver transplantation; FGF, fibroblast growth factor; HBsAg, hepatitis B surface antigen; HCC, hepatocellular carcinoma; IV, intravenous; LPI, liver perfusion imaging; LT, liver transplant; ORR, objective response rate; OS, overall survival; PFS, progression-free survival; PVTT, portal vein tumor thrombosis; RBC, red blood cell; RFS, recurrence-free survival; SBRT, stereotactic body radiotherapy; TACE, transarterial chemoembolization; TTP, time to progression; VEGF, vascular endothelial growth factor; CEA, carcinoembryonic antigen.

Conclusion

Lenvatinib shows great promise as a treatment option for HCC. Numerous predictive biomarkers are currently being identified and studied, which serve as crucial tools to provide HCC patients with a more effective and individualized treatment plan (Figure 1). Based on clinical relevance of biomarkers, we classified the predictive biomarkers for lenvatinib in HCC into three groups: (1) biomarkers that are applied in clinical practice, (2) biomarkers that are highly relevant to clinical practice, and (3) biomarkers that are under exploratory stage. Although some biomarkers are still in the exploratory stage, this review includes them with the aim of providing promising directions for future research. As future trials continue to shed light on predictive biomarkers for the efficacy of lenvatinib in HCC, these biomarkers promise to help doctors to evaluate the response of lenvatinib treatment more accurately and, thus, improve prognosis of HCC patients.

Figure 1.

Biomarkers for prediction of lenvatinib treatment in hepatocellular carcinoma.

It is noteworthy that the superior adaptability of these predictive biomarkers in lenvatinib treatment is closely associated with its unique target inhibition characteristics. Therefore, it is essential to further investigate the differences in biomarker responsiveness between lenvatinib and other TKIs based on their molecular mechanisms of action. Lenvatinib possesses a unique multi-target inhibition profile (VEGFR1–3, FGFR1–4, PDGFR-α/β, RET, KIT) which distinguishes it from other TKIs.¹⁰² For instance, FGF19/21 is more likely to predict the efficacy of lenvatinib due to its potent FGFR inhibition, whereas sorafenib—primarily targeting VEGFR2/PDGFR—shows greater sensitivity to VEGF-A but weaker FGFR suppression.¹⁰³ Similarly, Ang-2 synergistically promotes angiogenesis with VEGF, and the potent VEGFR inhibition of lenvatinib may make it more sensitive to Ang-2 levels, whereas other TKIs may have different effects on vascular remodeling.¹⁰⁴ These mechanistic differences underscore the need for biomarker selection tailored to specific drugs.

Except for mechanism-driven biomarkers, general indicators like AFP and liver function metrics (e.g., ALBI score) exhibit predictive value across TKIs. AFP regulates angiogenesis, and high AFP levels may be associated with VEGF signaling pathway activation, which have shown significant predictive value in the non-lenvatinib TKI treatment of liver cancer.¹⁰⁵ Moreover, the liver function evaluation index ALBI score was confirmed as an important predictor of TKIs (such as lenvatinib and sorafenib), reflecting the effect of hepatic metabolic function on drug effectiveness.¹⁰⁶ Furthermore, factors such as tumor heterogeneity, dynamic changes during tumor growth, the degree of affinity between biomarkers and drugs, and the sensitivity of detection methods may also influence the predictive performance of different biomarkers for various TKIs. These findings collectively suggest that although some markers have broad predictive value, the predictive efficacy of biomarkers is often modulated by the mechanism of action of specific TKIs, and the most appropriate predictor should be selected in combination with specific drugs in clinical application.

Although this review provides a comprehensive summary of candidate biomarkers for predicting lenvatinib response in HCC, the majority of studies are retrospective currently, which limited by small sample sizes and inconsistent cutoff values, which hinder their direct translation into clinical practice. Moreover, most studies lack detailed descriptions of pre-analytical technical variables, such as sample collection timing, assay platform consistency, and data normalization procedures, which further impedes comparability across studies.

In our opinion, not all biomarkers could be recommended for future clinical trials. Biomarkers that are applied in clinical practice and biomarkers that are highly relevant to clinical practice in our review are promising for clinical applications and should be prioritized for validation in future large-scale, prospective, multicenter clinical trials. Moreover, although certain markers, such as circKCNN2, miR-3154, SNPs (e.g., NOS3 rs2070744), MUC15, and M2BPGi, have so far been proposed in preclinical experiments, they should require further translational researches before conducting clinical trials.

In conclusion, whether to advance a specific biomarker to subsequent clinical trials should not only depend on technical feasibility but also take into account its clinical convenience and patient acceptability. In particular, as there are limitations in single indicator, it is suggested to carry out clinical trials in future that focus on comprehensive scoring of multiple indicators in HCC patients.

Footnotes

Acknowledgements

The graphical is created with Figdraw and Flaticon.com.

Declarations

ORCID iDs

Xiangyong Hao

Zihan Li

Xin Su

Jian Li

Youbao Ye

Cailiu Wang

Xiao Xu

Hao Song

Baoling Song

Xiang Lan

Tian Luo

References

Sung

Ferlay

Siegel

, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71(3): 209–249.

Ahn

Lee

Y-T

Agopian

, et al. Hepatocellular carcinoma surveillance: current practice and future directions. Hepatoma Res 2022; 8(0): 10.

Song

Bai

, et al. Early predictive value of circulating biomarkers for sorafenib in advanced hepatocellular carcinoma. Expert Rev Mol Diagn 2022; 22(3): 361–378.

Giannini

Farinati

Ciccarese

, et al. Prognosis of untreated hepatocellular carcinoma. Hepatology 2015; 61(1): 184–190.

European Association for the Study of the Liver. EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma. J Hepatol 2018; 69(1): 182–236.

Chen

, et al. Transarterial intervention therapy combined with systemic therapy for HCC: a review of recent five-year articles. Hepatoma Res 2024; 10(0): 42.

Suyama

Iwase

Lenvatinib: a promising molecular targeted agent for multiple cancers. Cancer Control 2018; 25(1): 1073274818789361.

Kudo

Finn

Qin

, et al. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet 2018; 391(10126): 1163–1173.

Rapposelli

Tada

Shimose

, et al. Adverse events as potential predictive factors of activity in patients with advanced hepatocellular carcinoma treated with lenvatinib. Liver Int 2021; 41(12): 2997–3008.

10.

Zhu

X-D

Sun

H-C.

Emerging agents and regimens for hepatocellular carcinoma. J Hematol Oncol 2019; 12(1): 110.

11.

Kim

Jain

Liquid biopsies for hepatocellular carcinoma. Transl Res 2018; 201: 84–97.

12.

Di Tommaso

Spadaccini

Donadon

, et al. Role of liver biopsy in hepatocellular carcinoma. World J Gastroenterol 2019; 25(40): 6041–6052.

13.

De Stefano

Chacon

Turcios

, et al. Novel biomarkers in hepatocellular carcinoma. Dig Liver Dis 2018; 50(11): 1115–1123.

14.

Bao

Min

, et al. Recent advances of liquid biopsy: interdisciplinary strategies toward clinical decision-making. Interdiscip Med 2023; 1(4): e20230021.

15.

Ocker

Biomarkers for hepatocellular carcinoma: what’s new on the horizon?

World J Gastroenterol 2018; 24(35): 3974–3979.

16.

Luo

Rong

Chen

, et al. A logistic regression model for noninvasive prediction of AFP-negative hepatocellular carcinoma. Technol Cancer Res Treat 2019; 18: 1533033819846632.

17.

Trevisani

Garuti

Neri

Alpha-fetoprotein for diagnosis, prognosis, and transplant selection. Semin Liver Dis 2019; 39(2): 163–177.

18.

Berhane

Fox

García-Fiñana

, et al. Using prognostic and predictive clinical features to make personalised survival prediction in advanced hepatocellular carcinoma patients undergoing sorafenib treatment. Br J Cancer 2019; 121(2): 117–124.

19.

Saeki

Yamasaki

Yamashita

, et al. Early predictors of objective response in patients with hepatocellular carcinoma undergoing lenvatinib treatment. Cancers (Basel) 2020; 12(4): 779.

20.

Kodama

Kawaoka

Namba

, et al. Correlation between early tumor marker response and imaging response in patients with advanced hepatocellular carcinoma treated with lenvatinib. Oncology 2019; 97(2): 75–81.

21.

Deng

, et al. Neutrophil-to-lymphocyte ratio for the prognostic assessment of hepatocellular carcinoma: a systematic review and meta-analysis of observational studies. Oncotarget 2016; 7(29): 45283–45301.

22.

Tada

Kumada

Hiraoka

, et al. Neutrophil-to-lymphocyte ratio is associated with survival in patients with unresectable hepatocellular carcinoma treated with lenvatinib. Liver Int 2020; 40(4): 968–976.

23.

Sugama

Miyanishi

Osuga

, et al. Combination of psoas muscle mass index and neutrophil/lymphocyte ratio as a prognostic predictor for patients undergoing nonsurgical hepatocellular carcinoma therapy. JGH Open 2021; 5(12): 1335–1343.

24.

Wang

Bai

, et al. Preoperative inflammatory markers of NLR and PLR as indicators of poor prognosis in resectable HCC. PeerJ 2019; 7: e7132.

25.

Yuan

Zhang

, et al. Prognostic value of inflammatory and nutritional markers for hepatocellular carcinoma. Medicine 2021; 100(25): e26506.

26.

Tada

Kumada

Hiraoka

, et al. Platelet-lymphocyte ratio predicts survival in patients with hepatocellular carcinoma who receive lenvatinib: an inverse probability weighting analysis. Eur J Gastroenterol Hepatol 2021; 32(2): 261–268.

27.

Asama

Suzuki

Takagi

, et al. Evaluation of inflammation-based markers for predicting the prognosis of unresectable pancreatic ductal adenocarcinoma treated with chemotherapy. Mol Clin Oncol 2018; 9(4): 408–414.

28.

Sieghart

Pinter

Hucke

, et al. Single determination of C-reactive protein at the time of diagnosis predicts long-term outcome of patients with hepatocellular carcinoma. Hepatology 2013; 57(6): 2224–2234.

29.

Hayashi

Shibata

, et al. C-reactive protein can predict dose intensity, time to treatment failure and overall survival in HCC treated with lenvatinib. PLoS One 2020; 15(12): e0244370.

30.

Tada

Kumada

Hiraoka

, et al. C-reactive protein to albumin ratio predicts survival in patients with unresectable hepatocellular carcinoma treated with lenvatinib. Sci Rep 2022; 12(1): 8421.

31.

Johnson

Berhane

Kagebayashi

, et al. Assessment of liver function in patients with hepatocellular carcinoma: a new evidence-based approach-the ALBI grade. J Clin Oncol 2015; 33(6): 550–558.

32.

D’Amico

Garcia-Tsao

Pagliaro

Natural history and prognostic indicators of survival in cirrhosis: a systematic review of 118 studies. J Hepatol 2006; 44(1): 217–231.

33.

Liu

Hsu

, et al. Comparison of twelve liver functional reserve models for outcome prediction in patients with hepatocellular carcinoma undergoing surgical resection. Sci Rep 2018; 8(1): 4773.

34.

Ueshima

Nishida

Hagiwara

, et al. Impact of baseline ALBI grade on the outcomes of hepatocellular carcinoma patients treated with lenvatinib: a multicenter study. Cancers (Basel) 2019; 11(7): 952.

35.

Vogel

Frenette

Sung

, et al. Baseline liver function and subsequent outcomes in the phase 3 REFLECT study of patients with unresectable hepatocellular carcinoma. Liver Cancer 2021; 10(5): 510–521.

36.

Tada

Kumada

Hiraoka

, et al. Impact of modified albumin-bilirubin grade on survival in patients with HCC who received lenvatinib. Sci Rep 2021; 11(1): 14474.

37.

Lin

Teng

Jeng

, et al. Combining immune checkpoint inhibitor with lenvatinib prolongs survival than lenvatinib alone in sorafenib-experienced hepatocellular carcinoma patients. Eur J Gastroenterol Hepatol 2022; 34(2): 213–219.

38.

Hiraoka

Kumada

Tada

, et al. Nutritional index as prognostic indicator in patients receiving lenvatinib treatment for unresectable hepatocellular carcinoma. Oncology 2020; 98(5): 295–302.

39.

Chen

Xing

, et al. The AGH score is a predictor of disease-free survival and targeted therapy efficacy after liver transplantation in patients with hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int 2023; 22(3): 245–252.

40.

Parmar

Apte

Angiopoietin inhibitors: a review on targeting tumor angiogenesis. Eur J Pharmacol 2021; 899: 174021.

41.

Maisonpierre

Suri

Jones

, et al. Angiopoietin-2, a natural antagonist for Tie2 that disrupts in vivo angiogenesis. Science 1997; 277(5322): 55–60.

42.

Stamenkovic

Angiopoietin-2 is implicated in the regulation of tumor angiogenesis. Am J Pathol 2001; 158(2): 563–570.

43.

Scholz

Rehm

Rieke

, et al. Angiopoietin-2 serum levels are elevated in patients with liver cirrhosis and hepatocellular carcinoma. Am J Gastroenterol 2007; 102(11): 2471–2481.

44.

Llovet

Peña

CEA

Lathia

, et al. Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma. Clin Cancer Res 2012; 18(8): 2290–2300.

45.

Shigesawa

Suda

Kimura

, et al. Baseline angiopoietin-2 and FGF19 levels predict treatment response in patients receiving multikinase inhibitors for hepatocellular carcinoma. JGH Open 2020; 4(5): 880–888.

46.

Chuma

Uojima

Numata

, et al. Early changes in circulating FGF19 and Ang-2 levels as possible predictive biomarkers of clinical response to lenvatinib therapy in hepatocellular carcinoma. Cancers (Basel) 2020; 12(2): 293.

47.

Yang

Suda

Maehara

, et al. Changes in serum growth factors during lenvatinib predict the post progressive survival in patients with unresectable hepatocellular carcinoma. Cancers (Basel) 2022; 14(1): 232.

48.

Finn

Kudo

Cheng

, et al. Pharmacodynamic biomarkers predictive of survival benefit with lenvatinib in unresectable hepatocellular carcinoma: from the phase III REFLECT study. Clin Cancer Res 2021; 27(17): 4848–4858.

49.

Shigesawa

Suda

Kimura

, et al. Baseline serum angiopoietin-2 and VEGF levels predict the deterioration of the liver functional reserve during lenvatinib treatment for hepatocellular carcinoma. PLoS One 2021; 16(3): e0247728.

50.

Somm

Jornayvaz

FR.

Fibroblast growth factor 15/19: from basic functions to therapeutic perspectives. Endocr Rev 2018; 39(6): 960–989.

51.

Kir

Kliewer

Mangelsdorf

DJ.

Roles of FGF19 in liver metabolism. Cold Spring Harb Symp Quant Biol 2011; 76: 139–144.

52.

Lee

Nga

Tian

, et al. Mitochondrial metabolic signatures in hepatocellular carcinoma. Cells 2021; 10(8): 1901.

53.

Damrath

Chen

Metzger

, et al. Non-additive effects of combined NOX1/4 inhibition and calcimimetic treatment on a rat model of chronic kidney disease-mineral and bone disorder (CKD-MBD). JBMR Plus 2022; 6(3): e10600.

54.

Yoshiko

Wang

Minamizaki

, et al. Mineralized tissue cells are a principal source of FGF23. Bone 2007; 40(6): 1565–1573.

55.

Chanakul

Zhang

Louw

, et al. FGF-23 regulates CYP27B1 transcription in the kidney and in extra-renal tissues. PLoS One 2013; 8(9): e72816.

56.

Ono

Aikata

Yamauchi

, et al. Circulating cytokines and angiogenic factors based signature associated with the relative dose intensity during treatment in patients with advanced hepatocellular carcinoma receiving lenvatinib. Ther Adv Med Oncol 2020; 12: 1758835920922051.

57.

Sharifi-Rad

Ozleyen

Boyunegmez Tumer

, et al. Natural products and synthetic analogs as a source of antitumor drugs. Biomolecules 2019; 9(11): 679.

58.

Lakatos

Hockings

Mossner

, et al. LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations. iScience 2021; 24(8): 102889.

59.

Tokuhisa

Iizuka

Sakaida

, et al. Circulating cell-free DNA as a predictive marker for distant metastasis of hepatitis C virus-related hepatocellular carcinoma. Br J Cancer 2007; 97(10): 1399–1403.

60.

Ren

Qin

, et al. The prognostic value of circulating plasma DNA level and its allelic imbalance on chromosome 8p in patients with hepatocellular carcinoma. J Cancer Res Clin Oncol 2006; 132(6): 399–407.

61.

Howell

Atkinson

Pinato

, et al. Identification of mutations in circulating cell-free tumour DNA as a biomarker in hepatocellular carcinoma. Eur J Cancer (Oxford, UK: 1990) 2019; 116: 56–66.

62.

Nakatsuka

Nakagawa

Hayata

, et al. Post-treatment cell-free DNA as a predictive biomarker in molecular-targeted therapy of hepatocellular carcinoma. J Gastroenterol 2021; 56(5): 456–469.

63.

Fujii

Ono

Hayes

, et al. Identification and monitoring of mutations in circulating cell-free tumor DNA in hepatocellular carcinoma treated with lenvatinib. J Exp Clin Cancer Res 2021; 40(1): 215.

64.

Currow

Temel

Abernethy

, et al. ROMANA 3: a phase 3 safety extension study of anamorelin in advanced non-small-cell lung cancer (NSCLC) patients with cachexia. Ann Oncol 2017; 28(8): 1949–1956.

65.

Jafri

Previgliano

Khandelwal

, et al. Cachexia index in advanced non-small-cell lung cancer patients. Clin Med Insights Oncol 2015; 9: 87–93.

66.

Goh

Kang

Jeong

, et al. Prognostic significance of cachexia index in patients with advanced hepatocellular carcinoma treated with systemic chemotherapy. Sci Rep 2022; 12(1): 7647.

67.

Rapposelli

Shimose

Kumada

, et al. Identification of lenvatinib prognostic index via recursive partitioning analysis in advanced hepatocellular carcinoma. ESMO Open 2021; 6(4): 100190.

68.

Yamamoto

Imai

Kuzuya

, et al. Changes in body composition predict the time to treatment failure of lenvatinib in patients with advanced hepatocellular carcinoma: a pilot retrospective study. Nutr Cancer 2022; 74(9): 3118–3127.

69.

Uojima

Chuma

Tanaka

, et al. Skeletal muscle mass influences tolerability and prognosis in hepatocellular carcinoma patients treated with lenvatinib. Liver Cancer 2020; 9(2): 193–206.

70.

Yamaoka

Kodama

Kawaoka

, et al. The importance of body composition assessment for patients with advanced hepatocellular carcinoma by bioelectrical impedance analysis in lenvatinib treatment. PLoS One 2022; 17(1): e0262675.

71.

Tamai

Iwasa

Eguchi

, et al. Serum copper, zinc and metallothionein serve as potential biomarkers for hepatocellular carcinoma. PLoS One 2020; 15(8): e0237370.

72.

Bischoff

Bernal

Dasarathy

, et al. ESPEN practical guideline: clinical nutrition in liver disease. Clin Nutr 2020; 39(12): 3533–3562.

73.

Zemrani

McCallum

Bines

JE.

Trace element provision in parenteral nutrition in children: one size does not fit all. Nutrients 2018; 10(11): 1819.

74.

Lin

Huang

Tsai

, et al. Selenium, iron, copper, and zinc levels and copper-to-zinc ratios in serum of patients at different stages of viral hepatic diseases. Biol Trace Element Res 2006; 109(1): 15.

75.

Nakayama

Fukuda

Ebara

A new diagnostic method for chronic hepatitis, liver cirrhosis and hepatocellular carcinoma based on serum metallothionein, copper and zinc level. Biol Pharm Bull 2002; 25(4): 426–431.

76.

Poo

Rosas-Romero

Montemayor

, et al. Diagnostic value of the copper/zinc ratio in hepatocellular carcinoma: a case control study. J Gastroenterol 2003; 38(1): 45.

77.

Plauth

Bernal

Dasarathy

, et al. ESPEN guideline on clinical nutrition in liver disease. Clin Nutr (Edinburgh, Scotland) 2019; 38(2): 485–521.

78.

Sun

Niu

Chen

, et al. Metallothionein-1G facilitates sorafenib resistance through inhibition of ferroptosis. Hepatology 2016; 64: 488–500.

79.

Narimatsu

Development of M2BPGi: a novel fibrosis serum glyco-biomarker for chronic hepatitis/cirrhosis diagnostics. Expert Rev Proteomics 2015; 12(6): 683–693.

80.

Eso

Takai

Taura

, et al. Association of Mac-2-binding protein glycosylation isomer level with nutritional status in chronic liver disease. J Gastroenterol Hepatol. February 2018. DOI: 10.1111/jgh.14130.

81.

Eso

Nakano

Mishima

, et al. Mac-2 binding protein glycosylation isomer predicts tolerability and clinical outcome of lenvatinib therapy for hepatocellular carcinoma. J Hepatobiliary Pancreat Sci 2021; 28(6): 498–507.

82.

Jeck

Sharpless

NE.

Detecting and characterizing circular RNAs. Nat Biotechnol 2014; 32(5): 453–461.

83.

Chen

LL.

The expanding regulatory mechanisms and cellular functions of circular RNAs. Nat Rev Mol Cell Biol 2020; 21(8): 475–490.

84.

, et al. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol 2023; 190: 104107.

85.

Sun

Zhang

Cao

, et al. Diagnostic and prognostic value of circular RNAs in hepatocellular carcinoma. J Cell Mol Med 2020; 24(10): 5438–5445.

86.

Liu

Chen

, et al. CircKCNN2 suppresses the recurrence of hepatocellular carcinoma at least partially via regulating miR-520c-3p/methyl-DNA-binding domain protein 2 axis. Clin Transl Med 2022; 12(1): e662.

87.

Wang

Mackowiak

, et al. MicroRNAs as regulators, biomarkers and therapeutic targets in liver diseases. Gut 2021; 70(4): 784–795.

88.

Garzon

Calin

Croce

CM.

MicroRNAs in cancer. Annu Rev Med 2009; 60(1): 167–179.

89.

Wei

Han

, et al. MiR-3154 promotes hepatocellular carcinoma progression via suppressing HNF4alpha. Carcinogenesis 2022; 43(10): 1002–1014.

90.

Hsieh

Chiang

, et al. Association of lncRNA CCAT2 and CASC8 gene polymorphisms with hepatocellular carcinoma. Int J Environ Res Public Health 2019; 16(16): 2833.

91.

De Re

Tornesello

De Zorzi

, et al. Clinical significance of polymorphisms in immune response genes in hepatitis C-related hepatocellular carcinoma. Front Microbiol 2019; 10: 475.

92.

Luo

, et al. Genetic polymorphisms in CD35 gene contribute to the susceptibility and prognosis of hepatocellular carcinoma. Front Oncol 2021; 11: 700711.

93.

Yuan

Van Den Berg

, et al. Genetic polymorphisms in the methylenetetrahydrofolate reductase and thymidylate synthase genes and risk of hepatocellular carcinoma. Hepatology 2007; 46(3): 749–758.

94.

Tanabe

Lemoine

Finkelstein

, et al. Epidermal growth factor gene functional polymorphism and the risk of hepatocellular carcinoma in patients with cirrhosis. JAMA 2008; 299(1): 53–60.

95.

Imaizumi

Higaki

Hara

, et al. Interaction between cytochrome P450 1A2 genetic polymorphism and cigarette smoking on the risk of hepatocellular carcinoma in a Japanese population. Carcinogenesis 2009; 30(10): 1729–1734.

96.

Azuma

Uojima

Chuma

, et al. Influence of NOS3 rs2070744 genotypes on hepatocellular carcinoma patients treated with lenvatinib. Sci Rep 2020; 10(1): 17054.

97.

Okubo

Ando

Ishizuka

, et al. Impact of genetic polymorphisms on the pharmacokinetics and pharmacodynamics of lenvatinib in patients with hepatocellular carcinoma. J Pharmacol Sci 2022; 148(1): 6–13.

98.

Pallesen

Berglund

Rasmussen

, et al. Isolation and characterization of MUC15, a novel cell membrane-associated mucin. Eur J Biochem 2000; 269(11): 2755–2763.

99.

Wang

R-Y

Chen

H-Y

, et al. MUC15 inhibits dimerization of EGFR and PI3K–AKT signaling and is associated with aggressive hepatocellular carcinomas in patients. Gastroenterology 2013; 145(6): 1436–1448.e12.

100.

Han

Zheng

Zhang

, et al. Downregulation of MUC15 by miR-183-5p.1 promotes liver tumor-initiating cells properties and tumorigenesis via regulating c-MET/PI3K/AKT/SOX2 axis. Cell Death Dis 2022; 13(3): 200.

101.

Myojin

Kodama

Maesaka

, et al. ST6GAL1 is a novel serum biomarker for lenvatinib-susceptible FGF19-driven hepatocellular carcinoma. Clin Cancer Res 2021; 27(4): 1150–1161.

102.

Fallahi

Ferrari

Galdiero

, et al. Molecular targets of tyrosine kinase inhibitors in thyroid cancer. Semin Cancer Biol 2022; 79: 180–196.

103.

Chen

Wang

Mechanisms of hepatocellular carcinoma and challenges and opportunities for molecular targeted therapy. World J Hepatol 2015; 7(15): 1964–1970.

104.

Giobbie-Hurder

Liao

, et al. Angiopoietin-2 as a biomarker and target for immune checkpoint therapy. Cancer Immunol Res 2017; 5(1): 17–28.

105.

Shan

Huang

Xie

, et al. Angiogenesis and clinicopathologic characteristics in different hepatocellular carcinoma subtypes defined by EpCAM and α-fetoprotein expression status. Med Oncol (Northwood, UK) 2011; 28(4): 1012–1016.

106.

Hiraoka

Kumada

Kudo

, et al. Albumin-bilirubin (ALBI) grade as part of the evidence-based clinical practice guideline for HCC of the Japan Society of Hepatology: a comparison with the liver damage and Child-Pugh classifications. Liver Cancer 2017; 6(3): 204–215.

Predictive value of biomarkers for lenvatinib in hepatocellular carcinoma

Abstract

Plain language summary

Keywords

Background

Methods

Biomarkers that are applied in clinical practice

Alpha-fetoprotein

Systemic inflammatory indicators

Neutrophil/lymphocyte ratio

Platelet-lymphocyte ratio

C-reactive protein

C-reactive protein-to-albumin ratio

Biomarker based on comprehensive risk scores

The albumin-bilirubin score

AFP-GGT-Hangzhou (AGH) scoring system

Biomarkers that are highly relevant to clinical practice

Biomarkers in serology

Angiopoietin 2

Fibroblast growth factors

Vascular endothelial growth factor

Combination of Ang2 and VEGF levels

Other serum growth factors

Cell-free DNA

Cachexia index

Lenvatinib prognostic index

Biomarkers based on changes in body composition

Copper/zinc and metallothionein

Mac-2 binding protein glycosylation isomer

Biomarkers that are under exploratory stage

Circular RNA

MicroRNA

Single-nucleotide polymorphisms

Mucin 15

ST6GAL1

Ongoing trials of biomarkers for lenvatinib in HCC

Conclusion

Footnotes

Acknowledgements

Declarations

ORCID iDs

References