Comparison of the prognostic value of different inflammation-based scores in patients with hepatocellular carcinoma after Lenvatinib therapy

Abstract

BACKGROUND

Inflammation is closely related to survival and disease progression in patients with cancer. However, the predictive value of inflammation-based scores for survival in patients with hepatocellular carcinoma (HCC) treated with Lenvatinib has not been fully elucidated.

AIM

To compare different inflammation scores' prognostic values, and establish novel nomogram for predicting overall survival (OS) in HCC patients on Lenvatinib.

METHODS

In total, 144 patients with HCC treated with Lenvatinib were enrolled in this study. The prognostic value of pre-treatment inflammation-based scores was retrospectively analyzed, including the platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, lymphocyte-to-C-reactive protein ratio, lymphocyte-to-monocyte ratio, systemic immune-inflammation index, C-reactive protein-to-albumin ratio, and prognostic nutritional index (PNI). Kaplan-Meier survival curves and time-dependent receiver operating characteristic analysis were used to assess predictive accuracy. Univariate and multivariate Cox regression analyses were conducted to identify prognostic factors predicting OS and construct a prognostic nomogram.

RESULTS

All the inflammation-based scores demonstrated good discrimination in terms of OS (all P < 0.05), and the PNI emerged as an independent predictor of OS in multivariate analysis (hazard ratio = 4.097; 95% confidence interval: 1.405-11.944; P = 0.01). We selected three independent prognostic factors (macrovascular invasion, metastasis, and PNI) to generate a nomogram for OS.

CONCLUSION

The PNI is a prognostic indicator for assessing OS in patients with HCC treated with Lenvatinib and is superior to other inflammation-based scores in predicting OS.

Key Words: Inflammation-based score; Hepatocellular carcinoma; Lenvatinib; Overall survival; Prognostic index; Nomogram

Core Tip: This study compared the prognostic value of multiple inflammation-based scores in patients with hepatocellular carcinoma treated with Lenvatinib. The prognostic nutritional index (PNI) emerged as an independent predictor of overall survival and was superior to the other scores. A nomogram incorporating the PNI was established, facilitating personalized clinical decisions.

INTRODUCTION

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and ranks as the third leading cause of cancer-related deaths worldwide[1]. Owing to the relatively insidious and rapid progression of the early-onset stage of HCC, most patients do not receive radical treatment at the time of diagnosis[2]. In recent years, there has been a surge in research on targeted drugs, offering a novel treatment approach for patients with HCC. Lenvatinib is an oral small molecule multireceptor tyrosine kinase inhibitor approved as first-line treatment for patients with unresectable HCC in the United States, the European Union, Japan, and China. In the REFLECT study, Lenvatinib demonstrated a higher objective response rate (24%) and longer progression-free survival than sorafenib[3]. However, the treatment efficacy of Lenvatinib varies among individuals, and practical and reliable prognostic predictors are required to assess its efficacy.

Inflammation is a hallmark of cancer, promoting the induction of angiogenesis, the activation of invasion, and metastasis[4]. Recent studies have shown a close association between systemic inflammation and tumor progression as well as the survival of patients with cancer[5,6]. Recently, a range of inflammation-based prognostic scores based on systemic inflammatory response factors have emerged, including the platelet-to-lymphocyte ratio (PLR)[7], neutrophil-to-lymphocyte ratio (NLR)[8], lymphocyte-to-C-reactive protein ratio (LCR)[9], lymphocyte-to-monocyte ratio (LMR)[10], systemic immune-inflammation index (SII)[11], C-reactive protein-to-albumin ratio (CAR)[12], and prognostic nutritional index (PNI)[13], all of which have shown robust power in predicting cancer prognosis. However, the predictive value of inflammation-based scores for survival in patients with HCC treated with Lenvatinib has not been fully elucidated.

This study aimed to directly compare the prognostic efficacy of various inflammation-based scores in patients with HCC following treatment with Lenvatinib.

MATERIALS AND METHODS

Patients

This study included 144 patients diagnosed with HCC who received Lenvatinib therapy between June 2020 and September 2022 at Sun Yat-sen University Cancer Center. Patients were selected based on the following criteria: (1) Aged 18 to 75 years; (2) Diagnosed with HCC through imaging or pathology according to the American Association for the Study of Liver Diseases practice guidelines[14]; (3) Confirmed records of receiving Lenvatinib; (4) Performance status < 2; (5) No other malignant tumors; and (6) Complete medical and follow-up data. The study used retrospective anonymous clinical data obtained after each patient agreed to treatment.

Treatment

Lenvatinib was administered according to the following regimen: The recommended daily dose was 8 mg once daily in patients who weighed < 60 kg and 12 mg once daily in patients who weighed ≥ 60 kg. Treatment was continued until disease progression or the occurrence of intolerable toxic effects.

Data collection

Blood samples were collected 5 days before the initiation of Lenvatinib treatment. Serum biomarker levels were measured through centrifugation, and serum albumin (ALB) concentrations were determined by colorimetry using a Roche Cobas 702 automated biochemical analyzer. Neutrophil, lymphocyte, and platelet levels were assessed using a Sysmex XN-2000 automated hemocytometer. The coefficient of variation between the two laboratory tests at our center was ≤ 5%.

Table 1 summarizes the basic statistical indicators and clinical characteristics of the patients selected in this study, including age, gender, hepatitis status, levels of alanine aminotransferase (U/L), aspartate aminotransferase (AST) (U/L), ALB (g/L), and total bilirubin (μmol/L), maximum tumor size (cm), number of tumors, presence of macrovascular invasion and lymph node metastasis, tumor-node–metastasis stage, C-reactive protein (CRP), PLR, NLR, LCR, LMR, SII, CAR, and PNI.

Table 1 Baseline characteristics of the enrolled patients.

Variables	n = 144
Age, year	52 (21-75)
Gender (male/female)	126/18 (87.5/12.5)
Hepatitis (yes/no)	128/16 (88.9/11.1)
ALT, U/L, (>/≤ 50)	64/80 (44.4/55.6)
AST, U/L, (>/≤ 40)	117/27 (81.3/18.7)
ALB, g/L, (>/≤ 35)	125/19 (86.8/13.2)
TBIL, µmol/L, (>/≤ 17.1)	62/82 (43/57)
Largest tumor size, cm	11.65 (1.2-23.1)
Tumor number (> 1/1)	109/35 (75.7/24.3)
Macrovascular invasion (yes/no)	102/42 (70.8/29.2)
Lymph node metastasis (yes/no)	39/105 (27/73)
Metastasis (yes/no)	27/117 (18.7/81.3)
TNM stage (III-IV/II)	125/19 (86.8/13.2)
CRP, mg/L (> 7.9/≤ 7.9)	100/44 (69.4/30.6)
PLR (1/0)	19/125 (13.2/86.8)
NLR (1/0)	73/71 (50.7/49.3)
LCR (1/0)	110/34 (76.4/23.6)
LMR (1/0)	89/55 (61.8/38.2)
SII (1/0)	78/66 (54.2/45.8)
CAR (1/0)	101/43 (70.1/29.9)
PNI (1/0)	71/73 (49.3/50.7)

Values are presented as median (range) or n (%). ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; ALB: Albumin; TBIL: Total bilirubin; TNM: Tumor-node-metastasis; CRP: C-reactive protein; PLR: Platelet-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; LCR: Lymphocyte-to-C-reactive protein ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic immune-inflammation index; CAR: C-reactive protein-to-albumin ratio; PNI: Prognostic nutritional index.

The inflammation-based scores are shown in Table 2. The radiological response was evaluated according to the Response Evaluation Criteria in Solid Tumors, v1.1, with computed tomography or magnetic resonance imaging conducted before the initiation of treatment and at 6-12 weeks after treatment or every 3 months thereafter[15].

Table 2 Systemic inflammation-based prognostic scores.

Variable	Score
C-reactive protein, mg/L
≤ 7.9	0
> 7.9	1
Platelet-to-lymphocyte ratio
Platelet count (× 109/L): Lymphocyte count (× 109/L) ≤ 269.51	0
Platelet count (× 109/L): Lymphocyte count (× 109/L) > 269.51	1
Neutrophil-to-lymphocyte ratio
Neutrophil count (× 109/L): Lymphocyte count (× 109/L) ≤ 3.28	0
Neutrophil count (× 109/L): Lymphocyte count (× 109/L) > 3.28	1
Lymphocyte-to-C-reactive protein ratio
104 × lymphocyte count (× 109/L): CRP (mg/L) > 2500	0
104 × lymphocyte count (× 109/L): CRP (mg/L) ≤ 2500	1
Lymphocyte-to-monocyte ratio
Lymphocyte count (× 109/L): Monocyte count (× 109/L) ≤ 2.41	0
Lymphocyte count (× 109/L): Monocyte count (× 109/L) > 2.41	1
Systemic immune-inflammation index
Platelet count (× 109/L) × neutrophil count (× 109/L)/Lymphocyte count (× 109/L) ≤ 768.03	0
Platelet count (× 109/L) × neutrophil count (× 109/L)/Lymphocyte count (× 109/L) > 768.03	1
C-reactive protein-to-albumin ratio
C-reactive protein (mg/L): Albumin (g/L) ≤ 0.19	0
C-reactive protein (mg/L): Albumin (g/L) > 0.19	1
Prognostic nutritional index
Albumin (g/L) + 5 × lymphocyte count (× 109/L) > 49.1	0
Albumin (g/L) + 5 × lymphocyte count (× 109/L) ≤ 49.1	1

CRP: C-reactive protein.

Overall survival (OS) was defined as the interval from the initiation of Lenvatinib treatment to cancer-related death.

Statistical analysis

The results are presented as medians and ranges, as the data were non-normally distributed. Continuous data were compared using the Mann-Whitney U test, and categorical data were assessed using the χ² test. Independent predictors of OS were identified by univariate and multivariate Cox regression analyses using the forward likelihood ratio method. Risk-stratified survival was represented by Kaplan-Meier curves and analyzed using the log-rank test. For single-value indicators, to avoid bias associated with the different criteria for the prognostic score cutoff values in this cohort, R software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria) was used to calculate the best cutoff scores for CRP, PLR, NLR, LCR, LMR, SII, CAR, and PNI (Supplementary Figure 1). Time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC) values at 6, 12, 18, and 24 months were calculated to compare the predictive ability of the eight inflammation-based scores. All data analyses were performed using SPSS software (version 25.0; IBM Corp., Armonk, NY, United States) and R (version 4.0.2).

RESULTS

Patient characteristics

A total of 144 patients with HCC who were treated with Lenvatinib at Sun Yat-sen University Cancer Center were enrolled in this study. A total of 126 (87.5%) of these patients were male, in contrast to 18 (12.5%) who were female, and the age distribution of the cohort covered 21 to 75 years. In the same group, 128 (88.9%) patients were hepatitis B virus (HBV) carriers, and 109 (75.7%) had multiple tumors. Tumor size ranged from 1.2 cm to 23.1 cm, with a median size of 11.65 cm. A total of 102 (70.8%) patients had macrovascular invasion, and 39 (27%) had lymph node metastasis. Furthermore, 27 (18.7%) patients had metastasis. Most of these patients (n = 138) received combined treatment with Programmed Death-1 inhibitors (Supplementary Table 1). Table 1 summarizes the clinical characteristics of the patients, including the eight inflammation-based scores.

OS

All inflammation-based scores were associated with OS in patients who received Lenvatinib (Figure 1). Low CRP, CAR, LCR, LMR, NLR, PLR, PNI, and SII scores suggested a good prognosis (all P < 0.05). In multivariate analysis, the PNI remained a significant and independent predictor of OS. The PNI was used to divide patients with HCC into two groups with different prognoses (median OS times of 12.5 months and 9.17 months, respectively).

Figure 1 Kaplan-Meier curves of the overall survival of hepatocellular carcinoma patients after Lenvatinib therapy. A: C-reactive protein; B: C-reactive protein-to-albumin ratio; C: Lymphocyte-to-C-reactive protein ratio; D: Lymphocyte-to-monocyte ratio; E: Neutrophil-to-lymphocyte ratio; F: Platelet-to-lymphocyte ratio; G: Prognostic nutritional index; H: Systemic immune-inflammation index. CRP: C-reactive protein; PLR: Platelet-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; LCR: Lymphocyte-to-C-reactive protein ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic immune-inflammation index; CAR: C-reactive protein-to-albumin ratio; PNI: Prognostic nutritional index.

Univariate and multivariate Cox regression analyses

Univariate analyses involved prognostic factors associated with clinical characteristics, liver function, tumor burden, and the eight inflammation-based scores. All inflammation scores were significant prognostic factors for OS, in addition to liver function and tumor burden. Multivariate Cox proportional analysis showed that macrovascular invasion (P = 0.023), metastasis (P = 0.012), and PNI (P = 0.01) were significant and independent prognostic factors for OS (Table 3).

Table 3 Univariate and multivariate Cox regression analyses of risk factors for overall survival.

Variables	Univariate			Multivariate
	HR	95%CI	P value	HR	95%CI	P value
Age, year (>/≤ 50)	0.821	0.325-2.075	0.677
Gender (female/male)	0.273	0.036-2.057	0.207
Largest tumor size, cm (>/≤ 10)	1.982	0.702-5.6	0.197
Tumor number (> 1/1)	8.086	1.073-60.944	0.043
Macrovascular invasion (yes/no)	10.399	1.380-78.368	0.023	10.594	1.394-80.525	0.023
Lymph node metastasis (yes/no)	1.383	0.490-3.908	0.540
Metastasis (yes/no)	4.393	1.47-13.128	0.008	4.431	1.395-14.073	0.012
PT, s (>/≤ 13.5)	1.098	0.251-4.799	0.901
HBsAg, IU/mL (>/≤ 0.05)	2.386	0.317-17.942	0.398
ALB, g/L (>/≤ 35)	1.322	0.382-4.569	0.659
ALT, U/L (>/≤ 50)	1.118	0.443-2.821	0.813
APOB, g/L (>/≤ 1.10)	0.822	0.308-2.196	0.697
APOA1, g/L (>/≤ 1.60)	1.166	0.267-5.094	0.838
AST, U/L (>/≤ 40)	27.327	0.145-5143.118	0.216
CHO, mmol/L (>/≤ 5.69)	1.038	0.402-2.683	0.938
CRE, µmol/L (>/≤ 97)	1.969	0.452-8.573	0.367
CRP, mg/L (>/≤ 7.9)	4.494	1.031-19.585	0.045
GGT, U/L (>/≤ 60)	27.116	0.115-6392.722	0.236
TBIL, µmol/L (>/≤ 20.5)	2.628	1.027-6.727	0.044
MO, × 109/L (>/≤ 0.6)	1.204	0.477-3.042	0.694
NE, × 109/L (>/≤ 6.3)	1.084	0.386-3.047	0.878
WBC, × 109/L (>/≤ 9.5)	1.067	0.351-3.246	0.909
PLR (>/≤ 269.51)	2.843	1.009-8.008	0.048
NLR (>/≤ 3.28)	3.268	1.161-9.195	0.025
LCR (>/≤ 3185.19)	7.315	0.971-55.084	0.053
LMR (>/≤ 2.41)	0.276	0.105-0.725	0.009
SII (>/≤ 768.03)	2.888	1.021-8.166	0.046
CAR (>/≤ 0.19)	2.708	0.782-9.374	0.116
PNI (>/≤ 49.1)	3.924	1.384-11.124	0.01	4.097	1.405-11.944	0.01

P < 0.05 is statistically significant in both univariate and multivariate analyses. PT: Prothrombin time; HBsAg: Hepatitis B surface antigen; ALB: Albumin; ALT: Alanine aminotransferase; APOB: Apolipoprotein B; APOA1: Apolipoprotein A1; AST: Aspartate aminotransferase; CHO: Cholesterol; CRE: Creatinine; CRP: C-reactive protein; GGT: Γ-Glutamyl transferase; TBIL: Total bilirubin; PLR: Platelet-to-lymphocyte ratio; NLR: Neutrophil-to-lymphocyte ratio; LCR: Lymphocyte-to-C-reactive protein ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic immune-inflammation index; CAR: C-reactive protein-to-albumin ratio; PNI: Prognostic nutritional index; MO: Monocyte; NE: Neutrophil; WBC: White blood cell.

Prognostic nomogram for prediction of OS

Variables derived from Cox proportional analysis were used to establish a prognostic nomogram for OS. The prognostic factors of the nomogram included three risk factors: Macrovascular invasion (yes vs no), metastasis (yes vs no), and the PNI (> 49.1 vs ≤ 49.1). Drawing a vertical line along the axis labeled “1- and 2-year OS probability” enabled us to determine the probability of the outcomes by summing the total scores of all factors and placing them on the total score scale (Figure 2A). Calibration plots showed satisfactory consistency between the nomogram-predicted OS and actual survival outcomes (Figure 2B and C). Supplementary Figure 2 illustrates the predictive performance of the nomogram when using the specific values of each variable derived from the individual study subjects. Additionally, the figure shows the scoring pathway and predicted outcomes of the subjects presented in the nomogram.

Figure 2 Nomogram and calibration curves for predicting 1- and 2-year overall survival in hepatocellular carcinoma patients treated with Lenvatinib. A: Nomogram for predicting the probability of 1- and 2-year overall survival (OS) in hepatocellular carcinoma patients after Lenvatinib therapy; B: Calibration curve for predicting patient OS at 1 year; C: Calibration curve for predicting patient OS at 2 years. OS: Overall survival; PNI: Prognostic nutritional index.

Efficacy of the nomogram: Comparison with inflammation-based scores

Time-dependent ROC curve analysis was performed to evaluate the predictive accuracy of the nomogram for OS. When the nomogram was used to predict 1- and 2-year survival rates, the AUC was 0.853 and 0.947, respectively (Figure 3A). Time-dependent ROC curves for 1- and 2-year OS were constructed to compare the efficacy of the nomogram and inflammation-based scores (Supplementary Figure 3), and the nomogram outperformed the other measures. The nomogram was more accurate in predicting OS based on the AUC of the time-dependent ROC curve (Figure 3B). The AUC values were calculated (Supplementary Table 2). The nomograms had consistently higher values than the other measures.

Figure 3 Time-dependent area under the curve analyses by receiver operating characteristic curves and plots to validate prognostic accuracy of the nomogram and inflammation-based scores in hepatocellular carcinoma patients treated with Lenvatinib. A: The area under the curve (AUC) values for time-dependent receiver operating characteristic curves verified the prognostic accuracy of the nomogram; B: Time-dependent AUC plot of survival predicted by the inflammation-based scores and the nomogram. AUC: Area under the curve.

Relationships between the PNI and clinical characteristics

The correlations between the PNI and clinical characteristics are shown in Table 4. A higher PNI was associated with higher AST levels (P = 0.002), lower ALB levels (P < 0.001), larger tumor size (P = 0.003), an increased number of tumors (P = 0.041), and the presence of macrovascular invasion (P = 0.036).

Table 4 Baseline characteristics of patients grouped by prognostic nutritional index, n (%).

Variables	PNI ≤ 49.1 (n = 71)	PNI > 49.1 (n = 73)	P value
Age, year	54 (29-74)	50 (21-75)	0.018
Gender (male/female)	64/7 (90.1/9.9)	62/11 (84.9/15.1)	0.345
Hepatitis (yes/no)	58/13 (81.7/18.3)	54/19 (74.0/26.0)	0.265
ALT, U/L	48.1 (10-177.1)	42.9 (0.5-320.3)	0.248
AST, U/L	73.1 (27.1-506.7)	69.6 (21.8-290.5)	0.002
ALB, g/L	37.7 (28.3-46.4)	43.9 (35.2-50.9)	< 0.001
TBIL, µmol/L	17.2 (5.3-60.9)	15.8 (5.5-31.1)	0.068
Largest tumor size, cm	12.4 (2.8-23.1)	10.4 (1.2-21.5)	0.003
Tumor number (> 1/1)	59/12 (83.1/16.9)	50/23 (68.5/31.5)	0.041
Macrovascular invasion (yes/no)	56/15 (78.9/21.1)	46/27 (63.0/37.0)	0.036
Lymph node metastasis (yes/no)	18/53 (25.4/74.6)	21/52 (28.8/71.2)	0.645
TNM stage (III-IV/II)	64/7 (90.1/9.9)	61/12 (83.6/16.4)	0.243
Best tumor response
CR	0 (0)	0 (0)
PR	19 (26.8)	26 (35.6)
SD	34 (47.9)	33 (45.2)
PD	18 (25.4)	14 (19.2)
ORR	19 (26.8)	26 (35.6)	0.252
DCR	53 (74.6)	59 (80.8)	0.373

Values are presented as median (range). P < 0.05 is statistically significant. PNI: Prognostic nutritional index; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; ALB: Albumin; TBIL: Total bilirubin; TNM: Tumor-node-metastasis; CR: Complete response; PR: Partial response; SD: Stable disease; PD: Progressive disease; ORR: Objective response rate; DCR: Disease control rate.

DISCUSSION

Numerous studies have highlighted the associations between inflammation-based scores and cancer-specific survival. However, the inflammation-related markers that can best predict the prognosis of patients with HCC treated with Lenvatinib remain unclear. Therefore, this study comprehensively explored the correlations between inflammation-based scores and OS in patients with HCC and concluded that the PNI is superior to other inflammation-based scores in predicting OS.

HCC, which is closely related to inflammation, is a malignant tumor mainly caused by chronic HBV and/or hepatitis C virus infection. Host inflammation-related factors are important predictors of the prognosis of HCC after treatment[16]. Most cases of HCC occur because of chronic liver inflammation, fibrosis, and cirrhosis[17-21]. This study shows that the systemic inflammatory response, as indicated by the PNI, which is calculated from a combination of the serum ALB level and total peripheral lymphocyte count, is a superior tool for assessing survival in patients with HCC treated with Lenvatinib compared with other inflammation-based measures. The PNI can be measured clinically by simple, readily available, and inexpensive means to determine prognosis by dividing patients treated with Lenvatinib into different risk groups.

Inflammation is closely related to the occurrence and development of tumors. Various cells in the tumor immune microenvironment play a major role in the processes of inflammation, tumor progression, and metastasis[22]. In the inflammatory process associated with cancers, continuous angiogenesis is considered to be one of the key mechanisms of tumorigenesis and progression[4]. The vascular endothelial growth factor (VEGF) receptor pathway is one of the most important regulatory pathways of tumor angiogenesis, in which VEGF-A plays a more important role. VEGF can also directly participate in the immune escape mechanism of tumors, inhibit the extravasation of immune cells into tumor tissue, and reduce the presentation of tumor antigens by inhibiting the maturation of dendritic cells. Lenvatinib inhibits tumor angiogenesis and regulates the tumor immune microenvironment by comprehensively blocking the VEGF signaling pathway, resulting in the inhibition or death of tumor cells[23]. Moreover, Lenvatinib plays an immunomodulatory role by inhibiting the fibroblast growth factor/fibroblast growth factor receptor pathway, thereby reducing tumor Programmed Death-Ligand 1 expression and inhibiting regulatory T cell differentiation[24]. We established a connection between Lenvatinib treatment and inflammation-based scores and then explored their interrelationship.

Numerous studies have demonstrated the efficacy of the PNI as a predictor for various digestive system tumors, including HCC[25], although the specific mechanism has not been fully elucidated. The PNI was originally proposed to assess perioperative immune-nutrition status and surgical risk in patients undergoing gastrointestinal surgery. However, with the increase in studies, the PNI has gradually been used to evaluate the immune nutrition of patients with different types of cancer. Liver cancer are closely related to nutritional status and potential cirrhosis.

Malnutrition and cirrhosis may compromise the anti-tumor and anti-metastatic responses of the body. A low PNI indicates relatively poor nutritional status and lymphopenia. Many studies have demonstrated the close association between PNI and the occurrence and progression of various malignant tumors[26-28]. Lymphopenia indicates a weakened immune response in the body[29]. In contrast, serum ALB levels reflect liver function, which greatly affects the survival and prognosis of patients with HCC undergoing similar treatments. Serum ALB is an indicator of nutritional status. Low level of serum ALB indicates malnutrition, which compromises the cellular and humoral immune response, phagocytosis, and other host defense mechanisms among patients with cancers[30].

Several limitations exist in this study. Firstly, as a retrospective study based on a cohort from a single-center in China, its findings cannot be extrapolated to other regions or countries. Although many cases of liver cancer in China are associated with HBV infection, the predominant causes in the United States, Japan, and other countries include hepatitis C virus infection, excessive alcohol consumption, and an imbalanced diet. Therefore, the findings of this study need to be validated in other patients with diverse disease backgrounds. Second, we enrolled patients who received combination therapy in addition to Lenvatinib during treatment, which inevitably introduced bias. Finally, the potential regulatory mechanisms of serum ALB and peripheral lymphocyte count in the context of Lenvatinib therapy remain incompletely understood and require further research efforts.

CONCLUSION

Our study demonstrates that the PNI is an independent prognostic indicator for patients with HCC treated with Lenvatinib and performs better compared to other inflammation-based scores. It is an easy-to-use risk stratification tool that allows physicians to make more case-specific decisions about Lenvatinib use in patients with HCC.

ACKNOWLEDGEMENTS

We thank Dr. Teng Long for critical evaluation of the manuscript and work.