Prognostic Value of Neutrophil-to-Lymphocyte Ratio Across BCLC Stages in Hepatocellular Carcinoma: A Multicenter Retrospective Analysis

Prognostic Value of Neutrophil-to-Lymphocyte Ratio Across BCLC Stages in Hepatocellular Carcinoma: A Multicenter Retrospective Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value of Neutrophil-to-Lymphocyte Ratio Across BCLC Stages in Hepatocellular Carcinoma: A Multicenter Retrospective Analysis Dominik Šafčák, Sylvia Dražilová, Jakub Gazda, Svetlana Adamcová-Selčanová, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8360322/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Hepatocellular carcinoma (HCC) is the 6th most common malignancy worldwide with variable prognostic outcomes. The neutrophil-to-lymphocyte ratio (NLR) has emerged as a potential prognostic marker, but its applicability across BCLC stages remains unclear. This study aims to assess the prognostic value of NLR in patients with HCC across different BCLC stages. We conducted a multicenter retrospective analysis of 312 HCC patients diagnosed between 2010 and 2016. Patients were divided into BCLC stages 0-A, B, and C and further stratified by NLR < 1.5 and NLR ≥ 1.5. Overall survival (OS) and progression-free survival (PFS) were analyzed, with comparisons across stages and NLR subgroups. Patients with NLR < 1.5 were trending toward improved OS in each BCLC stage, with significant differences observed in stages B and C. In BCLC stage B, NLR < 1.5 was associated with a twofold increase in PFS compared to NLR ≥ 1.5. BCLC stage C patients with NLR < 1.5 achieved OS outcomes comparable to BCLC stage B patients with NLR ≥ 1.5. Although limited by sample size and heterogeneity in treatment modalities, these findings suggest NLR as a relevant prognostic factor across HCC stages. In conclusion, lower NLR was associated with better survival outcomes across BCLC stages, supporting its use as a prognostic marker in HCC. Future prospective studies are needed to confirm these findings and to determine optimal NLR cut-offs for clinical practice. hepatocellular carcinoma prognosis NLR neutrophile-to-lymphocyte ratio Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction In 2020, liver cancer was the sixth most frequently diagnosed cancer and the third leading cause of cancer-related mortality worldwide. Hepatocellular carcinoma (HCC) accounts for 75–85% of all liver cancer cases across histological subtypes 1 . Data from the Global Burden of Disease Liver Cancer Collaboration indicate that the number of new cases rose by over 75% between 1990 and 2015 2 . Numerous publications support this rising trend in global incidence. A study by Rumgay (2022) projects that, between 2020 and 2040, the incidence will increase by 55%, resulting in 1.4 million new cases and 1.3 million deaths globally 3 . The primary risk factors for hepatocellular carcinoma (HCC) include chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), consumption of aflatoxin-contaminated foods, heavy alcohol intake, and metabolic syndrome. These risk factors vary significantly by region 1 . Both systemic and microenvironmental inflammation contribute to the development and progression of HCC. The immune microenvironment in the liver is dominated by immunosuppressive cells—including tissue-resident macrophages (Kupffer cells), M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells—which enable immune evasion. This immunosuppressive milieu is counterbalanced by cells that promote antitumor immune responses, primarily CD8 + T cells 4 . Neutrophils, the most abundant circulating cells of the myeloid lineage, play a key role in the early immune response against bacterial and fungal pathogens 5 . Tumor-associated neutrophils exhibit two distinct phenotypes: N1 (antitumor) and N2 (pro-tumor) 6 . Additionally, inflammation and cancer proliferation both lead to changes in neutrophil and lymphocyte counts in peripheral blood. The neutrophil-to-lymphocyte ratio (NLR) is now widely recognized as a prognostic factor for overall survival across many cancer types, including HCC 7 . Methods We conducted a multicenter retrospective study involving patients from eight centers in Slovakia, diagnosed and treated between January 2010 and December 2016. Patient selection The key inclusion criterion was a diagnosis of HCC in subjects over 18 years of age, confirmed through either histopathological examination or magnetic resonance imaging, in accordance with EASL-EORTC guidelines. Exclusion criteria included: patients not meeting EASL-EORTC criteria, subjects undergoing treatment with any medication potentially altering the results of inflammatory indexes (i.e. corticosteroids, immunosuppressive drugs), patient with active infection at the time of diagnosis of HCC, patients with stage BCLC D, and patients with incomplete data. Data Collection All data were collected retrospectively from patients' medical records, including baseline blood test results (hematology, biochemistry, and hemocoagulation) and findings from imaging assessments at the time of the diagnosis. If any condition that might have influenced baseline values occured (e.g., acute bacterial infection), laboratory results from a later time were used. The Child-Pugh score was applied to estimate the severity of cirrhosis, and performance status was evaluated using the Eastern Cooperative Oncology Group (ECOG) scale. CT scans of the thorax, abdomen, and pelvis were used to identify extrahepatic spread. The Barcelona Clinic Liver Cancer (BCLC) classification was chosen for final staging and to guide patient treatment. The neutrophil–lymphocyte ratio (NLR) was calculated as follows: N/L, where N and L represent the peripheral absolute neutrophil and absolute lymphocyte counts per liter. Patients were managed according to local standards in place at the time of diagnosis. CT and MRI scans were used to assess treatment response and were evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 for patients treated with curative strategies, transarterial chemoembolization (TACE), and chemotherapy. For patients treated with tyrosine kinase inhibitors, response was evaluated using modified RECIST (mRECIST) criteria. Case report forms (CRFs) also included the date of death, extracted from either the patients' medical records or the Slovak Health Care Surveillance Authority database. The study protocol complies with the 1964 Helsinki Declaration, its subsequent amendments, and the principles of good clinical practice. The protocol was approved by the Ethics Committee of the East Slovakia Oncological Institute on May 27, 2021 (approval code: EK/2/05/2021). The committee waived the requirement for specific patient informed consent due to the retrospective nature of the data collection, as the data were pre-existing and not generated through research activity, and only anonymous data were used. Statistical analysis Continuous variables are described by medians and interquartile ranges (IQRs). Categorical variables are described by absolute counts and percentages. Mann–Whitney tests were used to compare differences in continuous variables and χ2tests/Fisher’s exact tests were used to compare differences in categorical variables. We also performed survival analyses by estimating the survival distributions using the Kaplan–Meier method and evaluated the significance using the log-rank test. All tests were performed at a 0.05 significance level. The NLR cut-off level was chosen based on current literature and to be clinically convenient.Data were analyzed using RStudio (RStudio Team; Boston, MA, USA). Results Initially, we screened all patients diagnosed with hepatocellular carcinoma (ICD-10-CM C22) and identified 483 cases that met the EASL-EORTC diagnostic criteria. Patients with BCLC stage D (n=98) and those with incomplete data (n=73) were excluded. Among the included patients with HCC (n=312), 18% were diagnosed at an early stage (BCLC 0-A, n=57), 37% at an intermediate stage (BCLC B, n=114), and 45% at an advanced stage (BCLC C, n=141). Across all BCLC stages, 86.8% of patients had an NLR ≥ 1.5 (n=270), while 13.2% had an NLR < 1.5 (n=42). The most common etiologies for HCC in the overall study population were alcohol abuse (126 patients; 40%), chronic hepatitis C (50 patients; 16%), and NAFLD (28 patients; 9%). In 33 patients, the etiology was unknown and classified as cryptogenic (33%). Further characteristics of the cohort are presented in Table 1. Regardless of BCLC stage, patients with the NLR ≥ 1.5 had significantly worse median Child-Pugh scores at diagnosis (6.0 vs. 5.0; p=0.010), shorter progression-free survival (18months vs. 6 months; p<0.0001), and shorter overall survival (31.0 months vs. 11.0 months; p<0.0001). Baseline comparisons showed that patients with an NLR ≥ 1.5 had significantly higher baseline levels of alkaline phosphatase (2.27 vs. 1.88; p=0.005), serum CRP (11.0 vs. 4.0; p<0.001), and platelet count (152 vs. 114; p<0.001) at the time of diagnosis. The median NLR at the time of HCC diagnosis increased with BCLC stage (0-A = 2.46, B = 2.86, and C = 3.86; p<0.001). Further laboratory characteristics of the cohort are presented in Table 2. Table 1 Overall characteristics of patients Characteristic Overall, n=312 NLR <1.5, n=42 NLR≥1.5, n=270 p-value Age 66 (60, 72) 62 (58, 69) 66 (60, 72) 0.11 Sex (male) 220 (71%) 34 (81%) 186 (69%) 0.11 BCLC 0-A B C 57 (18%) 114 (37%) 141 (45%) 12 (29%) 21 (50%) 9 (21%) 45 (17%) 93 (34%) 132 (49%) 0.004 ECOG 0 1 2 10 (3.2%) 299 (96%) 3 (1%) 1 (2.4%) 41 (98%) 0 (0%) 9 (3.3%) 258 (96%) 3 (1.1%) >0.9 Child-Pugh 6.00 (5.00, 7.25) 5.00 (5.00-7.00) 6.00 (5.00-8.00) 0.01 MELD 6.19 (5.91, 6.49) 6.25 (5.96, 6.38) 6.18 (5.91, 6.51) >0.9 NLR 3.18 (2.05, 5.06) 1.23 (1.00, 1.38) 3.58 (2.58, 5.31) <0.001 PFsurvival 7 (3, 18) 18 (9, 23) 6 (3, 16) <0.001 Overall survival 13 (5, 32) 31 (19, 85) 11 (4, 25) <0.001 BCLC – Barcelona Clinic Liver Cancer, ECOG - Eastern Cooperative Oncology Group, NLR – Neutrophil-to-Lymphocyte Ratio, PF survival – progression-free survival (months), Overall survival (months) Table 2 Laboratory characteristics of patients Characteristic Overall, n=312 NLR<1.5, n=42 NLR ≥1.5, n=270 p-value TB 21 (14, 34) 21 (14, 30) 21 (14, 34) 0.5 AST 1.04 (0.63, 1.86) 0.97 (0.63, 1.75) 1.06 (0.63, 1.86) 0.5 ALT 0.69 (0.44, 1.08) 0.65 (0.42, 1.20) 0.69 (0.44, 1.06) 0.8 GGT 2.47 (1.16, 4.98) 2.02 (1.07, 2.99) 2.59 (1.19, 5.09) 0.062 ALP 2.20 (1.62, 3.53) 1.88 (1.44, 2.47) 2.27 (1.66, 3.69) 0.005 Albumin 35 (30, 40) 37 (32, 41) 34 (29, 39) 0.076 PT 1.20 (1.11, 1.32) 1.19 (1.13, 1.27) 1.21 (1.10, 1.32) 0.7 PLT 145 (96, 239) 114 (86, 180) 152 (96, 244) 0.049 TC 4.10 (3.34, 4.96) 4.59 (3.58, 4.98) 3.95 (3.34, 4.91) 0.5 Na 138.5 (136.0, 140.0) 139.0 (137.2, 141.0) 138.0 (135.1, 140.0) 0.2 Urea 5.10 (4.20, 6.65) 4.80 (4.18, 5.80) 5.10 (4.20, 6.82) 0.4 Creatinine 78 (66, 95) 80 (67, 97) 77 (66, 93) 0.6 CRP 8 (3, 23) 4 (2, 8) 11 (3, 26) <0.001 NLR 3.18 (2.05, 5.06) 1.23 (1.00, 1.38) 3.58 (2.58, 5.31) <0.001 AFP 33 (6, 431) 16 (6, 84) 50 (5, 544) 0.2 AFP – alpha fetoprotein (µg/L), ALT – alanine aminotransferase (µkat/L), ALP – alkaline phosphatase (µkat/L), AST – aspartate aminotransferase (µkat/L), CRP – C-reactive protein (mg/L), GGT – gamma glutamyl transferase (µkat/L), NLR – neutrophile-to-lymphocyte ratio, PLT – platelet count (x10 9 ), PT – prothrombin time (INR), TB – total bilirubin (µmol/L), TC – total cholesterol (mmol/L) Univariate logistic regression shows, that NLR ≥1,5 is strong predictor of overall survival (HR=2.16, 95%CI: 1.50-3.11; p< 0.001). This outcome was confirmed by multivariable analysis (HR=1.87, 95%CI: 1.24-2.80; p=0,003). The median overall survival of subjects with BCLC 0-A was numerically higher in the subgroup with NLR < 1.5, but this difference was not statistically significant (56.9 months vs. 65.6 months; p=0.82). However, subjects with BCLC B and NLR < 1.5 had a significantly longer overall survival (40.1 vs. 16.9 months; p=0.002). A similar outcome was observed in patients with BCLC stage C (18.93 vs. 5.78 months; p=0.0028) (Figure 1). Univariate logistic regression shows, that NLR ≥1,5 is strong predictor of progression-free survival (HR=1.65, 95%CI: 1.18-2.31; p= 0.004). This outcome was not confirmed by multivariable analysis (HR=1.44, 95%CI: 0.99-2.09; p=0,055). Progression-free survival was numerically longer in subjects with BCLC stage 0-A and NLR < 1.5 (33.2 months vs. 17.4 months), though this difference was not statistically significant (p=0.23). A similar trend was observed in patients with BCLC stage B (40.1 vs. 16.9 months; p=0.061). However, patients with BCLC stage C and NLR < 1.5 had significantly longer progression-free survival (17.0 vs. 4.22 months; p=0.0028) (Figure 2). In addition, we compared overall survival across BCLC stages based on NLR prognostic subgroups. We found that the overall survival of patients with BCLC stage 0-A and NLR ≥ 1.5 was not significantly longer than that of patients with BCLC stage B and NLR < 1.5 (40.1 vs. 56.9 months; p=0.71), despite undergoing curative treatment. A similar outcome was observed when comparing patients with BCLC stage B and NLR ≥ 1.5 to those with BCLC stage C and NLR < 1.5 (18.9 vs. 16.9 months; p=0.71) (Figure 3). Building on these encouraging results, we progression-free survival across BCLC stages. In our cohort, progression-free survival in subjects with BCLC stage 0-A and NLR ≥ 1.5 was longer than the progression-free survival of subjects with BCLC stage B and NLR < 1.5 (33.2 months vs. 17.9 months; p=0.037). The progression-free survival of subjects with BCLC stage C and NLR < 1.5 was numerically longer than that of subjects with BCLC stage B and NLR ≥ 1.5 (17.0 months vs. 8.0 months), though this difference was not statistically significant (p=0.69), likely due to the low number of subjects with BCLC stage C and low NLR (n=9) (Figure 4). Discussion BCLC staging classification remains the most effective method for stratifying patients and guiding treatment based on disease stage, liver function, and patient performance status. The latest update in 2022 further stratifies BCLC stage B into subgroups, each with distinct treatment options, including transplant, TACE, and systemic therapy. It also recommends incorporating prognostic factors such as the ALBI score and AFP levels at diagnosis into the decision-making process 8 . Subclinical inflammation plays a critical role in the pathogenesis, progression, and metastasis of HCC. In an initial study by Sia et al. (2017), patients were divided into two groups, one of which was termed the "Immune Class." This group, representing 25% of all analyzed HCC cases, was characterized by high PD-L1 expression, enhanced cytolytic activity, and a low number of chromosomal alterations. The Immune Class can be further divided into subtypes: one with a depleted immune response (marked by elevated TGF-β expression and stromal activation) and another with an active T-cell response (featuring a higher presence of CD8 + T-lymphocytes and IFN signatures), based on immune cell infiltration 13 . Current research highlights etiological differences in HCC pathogenesis. Hepatitis B virus (HBV) promotes a tolerant immune microenvironment through depletion of effector CD8 + T-lymphocytes and the recruitment of immunosuppressive cells (MDSCs, Tregs, Bregs). In contrast, hepatitis C virus (HCV) contributes to CD8 + T-lymphocyte depletion and immune evasion through viral escape mutations and interference with MHC-independent antigen presentation. Alcohol-related liver disease (ALD)–associated HCC involves the translocation of bacterial PAMPs (pathogen-associated molecular patterns) from the intestine, resulting in an attenuated immune response (CD8 + T-lymphocytes) and an increase in suppressor cells within tumor tissue, such as tumor-associated macrophages (M2 phenotype) and MDSCs. In NAFLD-related HCC, chronic dyslipidemia activates dysfunctional cells—such as NK T cells, Th17 cells, IgA + plasma cells, and CD8 + PD1 + T lymphocytes—that disrupt immune surveillance and promote carcinogenesis 4 . Neutrophils are a critical component of circulating myeloid cells, responsible for initiating early immune responses against bacterial and fungal pathogens. Their primary mechanisms of action include phagocytosis, the release of reactive oxygen species (ROS) through granule secretion into the extracellular space, and the formation of neutrophil extracellular traps (NETs). Neutrophils also influence adaptive immunity through chemokine production 5 , 15 .Neutrophils from the bone marrow are recruited into tumor tissue via chemokines such as IL-8, IL-17, and G-CSF 6 . In HCC, tumor cells exhibit increased expression of chemokines including CXCL1, CXCL2, CXCL3, CXCL5, CXCL8, CXCL12, CXCL16, and CC motif ligand 5 (CCL5). This chemokine activity is further supported by tumor stromal cells, tumor-associated monocytes (which secrete CXCL2 and CXCL8), tumor-associated fibroblasts, and hepatic stellate cells 16 . Additionally, increased production of IL-17 and γδ T cells leads to higher recruitment of neutrophils 17 . In the tumor microenvironment, neutrophils, like most cells of the myeloid lineage, exhibit various phenotypic variations. N1 and N2 neutrophils represent two main polarities. N1 neutrophils are short-lived, highly cytotoxic, display a mature phenotype, and have strong immunostimulatory activity. In contrast, N2 neutrophils are long-lived, less cytotoxic, exhibit an immature phenotype, and possess pro-angiogenic, pro-metastatic, and immunosuppressive properties 6 .The phenotypic transformation of neutrophils is a dynamic process, regulated by their maturation and the specific tissue environment. Over time, this transformation forms a continuum between pro-tumoral and anti-tumoral phenotypes, with possible variations influenced primarily by the surrounding tissue 16 . Key factors promoting the transformation to the N2 phenotype include TGF-β, IL-6, GM-CSF, PGE2, hyaluronic acid fragments, and hypoxia 16 . Changes in the neutrophil-to-lymphocyte ratio (NLR), resulting from inflammation and tumor processes, can be detected in peripheral blood. The NLR index was first introduced by one of our study’s co-authors, Zahorec, in a study of ICU patients with oncological diseases 18 .In oncology, the NLR index has been evaluated in numerous publications as a significant prognostic factor for overall survival in various cancers, including lung cancer 19 – 22 , malignant melanoma 23 – 27 , and gastrointestinal tumors 28 , 29 , 29 – 33 . The NLR index has been investigated in numerous studies on the treatment of hepatocellular carcinoma. Early publications identified an NLR cut-off value of ≥ 5 as an independent negative prognostic factor for progression-free survival and overall survival in patients who underwent resection 34 and liver transplantation 35 . The first retrospective study analyzing the impact of NLR on survival parameters in patients treated with TACE was published by Huang et al. (2011), demonstrating significantly shorter overall survival in patients with NLR ≥ 3.3 36 . Similarly, the first study to evaluate survival in HCC patients treated with RFA based on NLR was a retrospective analysis by Chen et al. (2011). It showed that patients with an NLR ≥ 2.42 (cut-off selected based on the average value) had worse overall survival and a higher recurrence rate 37 . Currently, numerous studies support the NLR index as an effective prognostic factor for overall survival 38 – 43 and as a prognostic indicator for recurrence when using ablation techniques, resection, transplantation, or for progression-free survival in cases of TACE or systemic treatment 39 , 40 , 44 , 45 . Cut-off values used for dichotomization in these analyses range widely, from 1.62 to 5.0, and are typically defined by either the median or Youden’s index. This broad range of cut-offs, often optimized for the specific dataset, combined with a lack of prospective studies, complicates interpretation in routine clinical practice and limits NLR's utility as a predictive factor. Additionally, high NLR values are commonly associated with poorer survival outcomes, rather than emphasizing subgroups with a better prognosis. In our cohort, progression-free survival for curative treatments was numerically higher in the subgroup with NLR < 1.5. However, a statistically significant difference was not observed, likely due to the small sample size and the heterogeneity of curative treatment options used (RFA, resection, and liver transplantation). Similarly, in BCLC stage B, despite a 2.5-fold longer progression-free survival in the NLR < 1.5 subgroup, the difference was not statistically significant. In the BCLC stage C subgroup, the difference approached statistical significance, aligning with findings in several published studies 39 , 40 , 44 – 46 . For the assessment of overall survival, we applied a range of cut-off values for dichotomization, from 1.6 to 5.0 47 . The closest values to ours were used in studies by Hong et al. (2020) 47 and Cao et al. (2017) 48 . In Hong et al.'s study, 441 HCC patients who underwent either resection (n = 368) or a combination of TACE followed by resection (n = 73) were retrospectively analyzed, with no significant difference in recurrence-free survival (p = 0.489) or overall survival (p = 0.751) between the groups. However, the 5-year overall survival was significantly worse in patients with NLR ≥ 1.6 (78.4% vs. 100%, p = 0.027), with the cut-off value determined based on the average value before treatment initiation 47 . In the study by Cao et al. (2017), 426 patients with HBV-associated HCC who underwent resection were analyzed retrospectively. Multivariate analysis indicated significantly worse overall survival in patients with NLR > 1.62 (HR: 1.69; 95% CI: 1.13–2.53, p = 0.011), with the cut-off value determined using the Youden index 48 . In contrast, the cut-off value used in our patient group has not been previously published nor determined through ROC analysis. Our objective was to identify a value that could define a subgroup of patients with significantly longer overall survival within each BCLC stage. Curative-intent treatments may confound prognosis when using NLR alone. Nonetheless, NLR < 1.5 identified subgroups within advanced BCLC stages with survival comparable to earlier stages. Therefore, by selecting an appropriate NLR cut-off value, it is possible to identify a subgroup of patients in more advanced BCLC stages who may achieve survival rates comparable to those diagnosed and treated at earlier stages. These findings support future applications of NLR in identifying high-risk subgroups who may benefit from intensified or alternative therapies (e.g., adjuvant or neoadjuvant strategies), avoiding overtreatment in poor prognosis patients. An NLR < 1.5 as a prognostic factor was associated with a 2-year overall survival increase in BCLC stage B, a 1-year overall survival increase in BCLC stage C, and a 16-month overall survival increase in BCLC stage 0-A, compared to subjects with NLR ≥ 1.5.Patients with hepatocellular carcinoma at BCLC stage B and NLR ≥ 1.5 had comparable overall survival to those with more advanced BCLC stage C and NLR < 1.5. Additionally, the overall survival of patients with BCLC stage 0-A and NLR ≥ 1.5 was similar to that of patients with BCLC stage B and NLR < 1.5. These findings are significant in light of anticipated results from ongoing clinical trials on systemic adjuvant therapy (KEYNOTE-937, EMERALD-2, and CheckMate 9DX) and overall survival analysis in the IMbrave 050 study (NCT03867084, NCT03847428, NCT03383458, NCT04102098, NCT03867084). Such studies may aid in defining high-risk patients in the treatment of locally advanced disease and in identifying the benefits of combination therapy. For instance, the EMERALD-1 study combines TACE, durvalumab, and bevacizumab ( 10.1200/JCO.2024.42.3_suppl.LBA4 ), while ongoing studies include LEAP-012, CheckMate 74W, ABC-HCC, and RENOTACE (NCT04246177, NCT04340193, NCT04803994, NCT047778)]. Importantly, NLR should be interpreted cautiously when applying cut-offs derived from different populations or treatment contexts. Additionally, dichotomizing continuous variables like NLR may oversimplify prognosis. ROC curve optimization and modeling NLR as a continuous variable may enhance its utility. Conclusion NLR is a promising prognostic biomarker in HCC across BCLC stages. Lower NLR (< 1.5) is associated with improved survival, particularly in stages B and C. Further prospective studies are needed to validate findings and establish clinically meaningful NLR thresholds. Declarations Ethical considerations The study protocol complies with the 1964 Helsinki Declaration, its subsequent amendments, and the principles of good clinical practice. The protocol was approved by the Ethics Committee of the East Slovakia Oncological Institute on May 27, 2021 (approval code: EK/2/05/2021). Conflicts of Interest The authors declare that they have no conflicts of interest. Funding This research did not receive any particular funding. It was performed as part of the employment of the authors at their respective universities. Author Contribution Conceptualization:DŠ, and PJDatacuration:DŠ, JG, LB, SKFormalanalysis:JG, MJInvestigation:DŠ, IA, SAS, RB, MM, MR, ĽS, MŽMethodology:DŠ, RZ, PJSupervision: SD, PJWriting—original draft:DŠ, JGWriting—review&editing:SD, IA, SAA, RB, MM, MR, ĽSAllauthorshaveread and agreed to thepublishedversion of themanuscript. References Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin .71, 209–249 (2021). Akinyemiju, T. et al. The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the global burden of disease study 2015. JAMA Oncol .3, 1683–1691 (2017). Rumgay, H. et al. Global burden of primary liver cancer in 2020 and predictions to 2040. J. Hepatol .77, 1598–1606 (2022). Llovet, J. M. et al. Immunotherapies for hepatocellular carcinoma. Nat. Rev. Clin. Oncol .19, 151–172 (2022). Borregaard, N. 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Neutrophil-lymphocyte ratio predicts the therapeutic benefit of neoadjuvant transarterial chemoembolization in patients with resectable hepatocellular carcinoma. Eur. J. Gastroenterol. Hepatol .32, 1186–1191 (2020). Cao, Y. et al. Prediction of long–term survival rates in patients undergoing curative resection for solitary hepatocellular carcinoma. Oncol. Lett. (2017) doi: 10.3892/ol.2017.7612 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers agreed at journal 26 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 15 Dec, 2025 Submission checks completed at journal 15 Dec, 2025 First submitted to journal 14 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8360322","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561612905,"identity":"afe1545d-912f-4ec6-a71d-ef7a2ef3e727","order_by":0,"name":"Dominik Šafčák","email":"","orcid":"","institution":"East Slovakia Institute of Oncology","correspondingAuthor":false,"prefix":"","firstName":"Dominik","middleName":"","lastName":"Šafčák","suffix":""},{"id":561612911,"identity":"891f32d6-4c44-40bb-8f8b-5e904d4d6769","order_by":1,"name":"Sylvia Dražilová","email":"","orcid":"","institution":"Pavol Jozef Safarik University, Pasteur University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sylvia","middleName":"","lastName":"Dražilová","suffix":""},{"id":561612912,"identity":"2ae1bed8-3cc8-400c-b3c5-3258e4cc7496","order_by":2,"name":"Jakub Gazda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACxgY2BmYIM4HxAQMDGwMEE6mF2YAoLSAFMC1sEjARvIC5/ViadAFDrbx8e/Kzat4dfPYM/McS8DusJ+2Y9AyG44aNPc/MbvOeYUtskEg7QMAv6W3SPAzHGJslEoBa2tgSGCTYG/Br6X8O1mLfJpH+rRioBeiw4wS0zAA6jIehJrFHIseMGaiFsYGBkMNmPEu2nmFwIHkGz5tiyblAv7RJpCXg1WLYn2Z4u6CiznZ+e/rGD293HLPn5z9mgF8L2N0Gh6F2NhwjHJHyEKoOpqWGkIZRMApGwSgYgQAAxd1BPdiK9UEAAAAASUVORK5CYII=","orcid":"","institution":"Pavol Jozef Safarik University, Pasteur University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jakub","middleName":"","lastName":"Gazda","suffix":""},{"id":561612914,"identity":"ab0bd60c-d059-4f44-8108-a0aabba8ea03","order_by":3,"name":"Svetlana Adamcová-Selčanová","email":"","orcid":"","institution":"F.D. 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10:00:48","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132713,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/11c20083644710fd69d95944.html"},{"id":98778629,"identity":"d846242e-e481-4b75-9fbd-e75f4157d4f2","added_by":"auto","created_at":"2025-12-22 12:29:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21647,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival of HCC stratified by BCLC stage and NLR (cut-off 1.5)\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/dcc4a1b1ba43fd338ae06869.png"},{"id":98780816,"identity":"83994922-7abd-4222-9a44-b64893453c97","added_by":"auto","created_at":"2025-12-22 12:31:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19932,"visible":true,"origin":"","legend":"\u003cp\u003eProgression-free survival of HCC stratified by BCLC stage and NLR (cut-off 1.5)\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/a8ec1880c93ebe0b021f8b9e.png"},{"id":98777828,"identity":"d77fc133-94ed-45ab-bd2b-350c1c756f00","added_by":"auto","created_at":"2025-12-22 12:28:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18230,"visible":true,"origin":"","legend":"\u003cp\u003eComparative overall survival analysis across mixed BCLC stages and NLR subgroups (cut-off 1.5) in HCC\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/19c0f27876112230342af667.png"},{"id":98779106,"identity":"14271c8e-8c24-4997-825a-a7dcf4fb0364","added_by":"auto","created_at":"2025-12-22 12:29:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29276,"visible":true,"origin":"","legend":"\u003cp\u003eComparative progression-free survival analysis across mixed BCLC stages and NLR subgroups (cut-off 1.5) in HCC\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/41196c08a5b7ee3799dc55bb.png"},{"id":98783348,"identity":"7cc58427-8e82-4b96-8180-47bb4f53e445","added_by":"auto","created_at":"2025-12-22 12:41:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":789731,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8360322/v1/fa851f56-616c-4ae4-be5d-4058ef8c388a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of Neutrophil-to-Lymphocyte Ratio Across BCLC Stages in Hepatocellular Carcinoma: A Multicenter Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2020, liver cancer was the sixth most frequently diagnosed cancer and the third leading cause of cancer-related mortality worldwide. Hepatocellular carcinoma (HCC) accounts for 75\u0026ndash;85% of all liver cancer cases across histological subtypes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Data from the Global Burden of Disease Liver Cancer Collaboration indicate that the number of new cases rose by over 75% between 1990 and 2015\u003csup\u003e2\u003c/sup\u003e. Numerous publications support this rising trend in global incidence. A study by Rumgay (2022) projects that, between 2020 and 2040, the incidence will increase by 55%, resulting in 1.4\u0026nbsp;million new cases and 1.3\u0026nbsp;million deaths globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary risk factors for hepatocellular carcinoma (HCC) include chronic infection with hepatitis B virus (HBV) or hepatitis C virus (HCV), consumption of aflatoxin-contaminated foods, heavy alcohol intake, and metabolic syndrome. These risk factors vary significantly by region\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBoth systemic and microenvironmental inflammation contribute to the development and progression of HCC. The immune microenvironment in the liver is dominated by immunosuppressive cells\u0026mdash;including tissue-resident macrophages (Kupffer cells), M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells\u0026mdash;which enable immune evasion. This immunosuppressive milieu is counterbalanced by cells that promote antitumor immune responses, primarily CD8\u0026thinsp;+\u0026thinsp;T cells\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Neutrophils, the most abundant circulating cells of the myeloid lineage, play a key role in the early immune response against bacterial and fungal pathogens\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Tumor-associated neutrophils exhibit two distinct phenotypes: N1 (antitumor) and N2 (pro-tumor)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, inflammation and cancer proliferation both lead to changes in neutrophil and lymphocyte counts in peripheral blood. The neutrophil-to-lymphocyte ratio (NLR) is now widely recognized as a prognostic factor for overall survival across many cancer types, including HCC\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eWe conducted a multicenter retrospective study involving patients from eight centers in Slovakia, diagnosed and treated between January 2010 and December 2016.\u003c/p\u003e\u003ch2\u003ePatient selection\u003c/h2\u003e\u003cp\u003e The key inclusion criterion was a diagnosis of HCC in subjects over 18 years of age, confirmed through either histopathological examination or magnetic resonance imaging, in accordance with EASL-EORTC guidelines. Exclusion criteria included: patients not meeting EASL-EORTC criteria, subjects undergoing treatment with any medication potentially altering the results of inflammatory indexes (i.e. corticosteroids, immunosuppressive drugs), patient with active infection at the time of diagnosis of HCC, patients with stage BCLC D, and patients with incomplete data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eAll data were collected retrospectively from patients' medical records, including baseline blood test results (hematology, biochemistry, and hemocoagulation) and findings from imaging assessments at the time of the diagnosis. If any condition that might have influenced baseline values occured (e.g., acute bacterial infection), laboratory results from a later time were used. The Child-Pugh score was applied to estimate the severity of cirrhosis, and performance status was evaluated using the Eastern Cooperative Oncology Group (ECOG) scale. CT scans of the thorax, abdomen, and pelvis were used to identify extrahepatic spread. The Barcelona Clinic Liver Cancer (BCLC) classification was chosen for final staging and to guide patient treatment. The neutrophil\u0026ndash;lymphocyte ratio (NLR) was calculated as follows: N/L, where N and L represent the peripheral absolute neutrophil and absolute lymphocyte counts per liter.\u003c/p\u003e \u003cp\u003ePatients were managed according to local standards in place at the time of diagnosis. CT and MRI scans were used to assess treatment response and were evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) v. 1.1 for patients treated with curative strategies, transarterial chemoembolization (TACE), and chemotherapy. For patients treated with tyrosine kinase inhibitors, response was evaluated using modified RECIST (mRECIST) criteria. Case report forms (CRFs) also included the date of death, extracted from either the patients' medical records or the Slovak Health Care Surveillance Authority database.\u003c/p\u003e \u003cp\u003eThe study protocol complies with the 1964 Helsinki Declaration, its subsequent amendments, and the principles of good clinical practice. The protocol was approved by the Ethics Committee of the East Slovakia Oncological Institute on May 27, 2021 (approval code: EK/2/05/2021). The committee waived the requirement for specific patient informed consent due to the retrospective nature of the data collection, as the data were pre-existing and not generated through research activity, and only anonymous data were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are described by medians and interquartile ranges (IQRs). Categorical variables are described by absolute counts and percentages. Mann\u0026ndash;Whitney tests were used to compare differences in continuous variables and χ2tests/Fisher\u0026rsquo;s exact tests were used to compare differences in categorical variables. We also performed survival analyses by estimating the survival distributions using the Kaplan\u0026ndash;Meier method and evaluated the significance using the log-rank test. All tests were performed at a 0.05 significance level. The NLR cut-off level was chosen based on current literature and to be clinically convenient.Data were analyzed using RStudio (RStudio Team; Boston, MA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eInitially, we screened all patients diagnosed with hepatocellular carcinoma (ICD-10-CM C22) and identified 483 cases that met the EASL-EORTC diagnostic criteria. Patients with BCLC stage D (n=98) and those with incomplete data (n=73) were excluded.\u003c/p\u003e\n\u003cp\u003eAmong the included patients with HCC (n=312), 18% were diagnosed at an early stage (BCLC 0-A, n=57), 37% at an intermediate stage (BCLC B, n=114), and 45% at an advanced stage (BCLC C, n=141). Across all BCLC stages, 86.8% of patients had an NLR \u0026ge; 1.5 (n=270), while 13.2% had an NLR \u0026lt; 1.5 (n=42). The most common etiologies for HCC in the overall study population were alcohol abuse (126 patients; 40%), chronic hepatitis C (50 patients; 16%), and NAFLD (28 patients; 9%). In 33 patients, the etiology was unknown and classified as cryptogenic (33%). Further characteristics of the cohort are presented in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegardless of BCLC stage, patients with the NLR \u0026ge; 1.5 had significantly worse median Child-Pugh scores at diagnosis (6.0 vs. 5.0; p=0.010), shorter progression-free survival (18months vs. 6 months; p\u0026lt;0.0001), and shorter overall survival (31.0 months vs. 11.0 months; p\u0026lt;0.0001).\u003c/p\u003e\n\u003cp\u003eBaseline comparisons showed that patients with an NLR \u0026ge; 1.5 had significantly higher baseline levels of alkaline phosphatase (2.27 vs. 1.88; p=0.005), serum CRP (11.0 vs. 4.0; p\u0026lt;0.001), and platelet count (152 vs. 114; p\u0026lt;0.001) at the time of diagnosis. The median NLR at the time of HCC diagnosis increased with BCLC stage (0-A = 2.46, B = 2.86, and C = 3.86; p\u0026lt;0.001).\u0026nbsp;Further laboratory characteristics of the cohort are presented in Table 2.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eOverall characteristics of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall, n=312\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR \u0026lt;1.5, n=42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u0026ge;1.5, n=270\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e66 (60, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e62 (58, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e66 (60, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e220 (71%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e34 (81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e186 (69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eBCLC\u003cbr\u003e\u0026nbsp;0-A\u003c/p\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (18%)\u003c/p\u003e\n \u003cp\u003e114 (37%)\u003c/p\u003e\n \u003cp\u003e141 (45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12 (29%)\u003c/p\u003e\n \u003cp\u003e21 (50%)\u003c/p\u003e\n \u003cp\u003e9 (21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45 (17%)\u003c/p\u003e\n \u003cp\u003e93 (34%)\u003c/p\u003e\n \u003cp\u003e132 (49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (3.2%)\u003c/p\u003e\n \u003cp\u003e299 (96%)\u003c/p\u003e\n \u003cp\u003e3 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (2.4%)\u003c/p\u003e\n \u003cp\u003e41 (98%)\u003c/p\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9 (3.3%)\u003c/p\u003e\n \u003cp\u003e258 (96%)\u003c/p\u003e\n \u003cp\u003e3 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eChild-Pugh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.00 (5.00, 7.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e5.00 (5.00-7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.00 (5.00-8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eMELD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.19 (5.91, 6.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.25 (5.96, 6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6.18 (5.91, 6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.18 (2.05, 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1.23 (1.00, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e3.58 (2.58, 5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003ePFsurvival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e7 (3, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e18 (9, 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e6 (3, 16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eOverall survival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e13 (5, 32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e31 (19, 85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e11 (4, 25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 606px;\"\u003e\n \u003cp\u003eBCLC \u0026ndash; Barcelona Clinic Liver Cancer, ECOG - Eastern Cooperative Oncology Group, NLR \u0026ndash; Neutrophil-to-Lymphocyte Ratio, PF survival \u0026ndash; progression-free survival (months), Overall survival (months)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Laboratory characteristics of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall, n=312\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u0026lt;1.5, n=42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNLR\u003c/strong\u003e\u003cstrong\u003e\u0026ge;1.5, n=270\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eTB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e21 (14, 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e21 (14, 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e21 (14, 34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e1.04 (0.63, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.97 (0.63, 1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.06 (0.63, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e0.69 (0.44, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.65 (0.42, 1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e0.69 (0.44, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e2.47 (1.16, 4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e2.02 (1.07, 2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e2.59 (1.19, 5.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e2.20 (1.62, 3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.88 (1.44, 2.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e2.27 (1.66, 3.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e35 (30, 40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e37 (32, 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e34 (29, 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e1.20 (1.11, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.19 (1.13, 1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e1.21 (1.10, 1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e145 (96, 239)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e114 (86, 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e152 (96, 244)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e4.10 (3.34, 4.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4.59 (3.58, 4.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e3.95 (3.34, 4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eNa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e138.5 (136.0, 140.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e139.0 (137.2, 141.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e138.0 (135.1, 140.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e5.10 (4.20, 6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4.80 (4.18, 5.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e5.10 (4.20, 6.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e78 (66, 95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e80 (67, 97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e77 (66, 93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e8 (3, 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e4 (2, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e11 (3, 26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e3.18 (2.05, 5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.23 (1.00, 1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e3.58 (2.58, 5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eAFP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e33 (6, 431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e16 (6, 84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e50 (5, 544)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 605px;\"\u003e\n \u003cp\u003eAFP \u0026ndash; alpha fetoprotein (\u0026micro;g/L), ALT \u0026ndash; alanine aminotransferase (\u0026micro;kat/L), ALP \u0026ndash; alkaline phosphatase (\u0026micro;kat/L), AST \u0026ndash; aspartate aminotransferase (\u0026micro;kat/L), CRP \u0026ndash; C-reactive protein (mg/L), GGT \u0026ndash; gamma glutamyl transferase (\u0026micro;kat/L), NLR \u0026ndash; neutrophile-to-lymphocyte ratio, PLT \u0026ndash; platelet count (x10\u003csup\u003e9\u003c/sup\u003e), PT \u0026ndash; prothrombin time (INR), TB \u0026ndash; total bilirubin (\u0026micro;mol/L), TC \u0026ndash; total cholesterol (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 3px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 4px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUnivariate logistic regression shows, that NLR \u0026ge;1,5 is strong predictor of overall survival (HR=2.16, 95%CI: 1.50-3.11; p\u0026lt; 0.001). This outcome was confirmed by multivariable analysis (HR=1.87, 95%CI: 1.24-2.80; p=0,003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median overall survival of subjects with BCLC 0-A was numerically higher in the subgroup with NLR \u0026lt; 1.5, but this difference was not statistically significant (56.9 months vs. 65.6 months; p=0.82). However, subjects with BCLC B and NLR \u0026lt; 1.5 had a significantly longer overall survival (40.1 vs. 16.9 months; p=0.002). A similar outcome was observed in patients with BCLC stage C (18.93 vs. 5.78 months; p=0.0028) (Figure 1).\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression shows, that NLR \u0026ge;1,5 is strong predictor of progression-free survival (HR=1.65, 95%CI: 1.18-2.31; p= 0.004). This outcome was not confirmed by multivariable analysis (HR=1.44, 95%CI: 0.99-2.09; p=0,055).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eProgression-free survival was numerically longer in subjects with BCLC stage 0-A and NLR \u0026lt; 1.5 (33.2 months vs. 17.4 months), though this difference was not statistically significant (p=0.23). A similar trend was observed in patients with BCLC stage B (40.1 vs. 16.9 months; p=0.061). However, patients with BCLC stage C and NLR \u0026lt; 1.5 had significantly longer progression-free survival (17.0 vs. 4.22 months; p=0.0028) (Figure 2).\u003c/p\u003e\n\u003cp\u003eIn addition, we compared overall survival across BCLC stages based on NLR prognostic subgroups. We found that the overall survival of patients with BCLC stage 0-A and NLR \u0026ge; 1.5 was not significantly longer than that of patients with BCLC stage B and NLR \u0026lt; 1.5 (40.1 vs. 56.9 months; p=0.71), despite undergoing curative treatment. A similar outcome was observed when comparing patients with BCLC stage B and NLR \u0026ge; 1.5 to those with BCLC stage C and NLR \u0026lt; 1.5 (18.9 vs. 16.9 months; p=0.71) (Figure 3).\u003c/p\u003e\n\u003cp\u003eBuilding on these encouraging results, we progression-free survival across BCLC stages. In our cohort, progression-free survival in subjects with BCLC stage 0-A and NLR \u0026ge; 1.5 was longer than the progression-free survival of subjects with BCLC stage B and NLR \u0026lt; 1.5 (33.2 months vs. 17.9 months; p=0.037). The progression-free survival of subjects with BCLC stage C and NLR \u0026lt; 1.5 was numerically longer than that of subjects with BCLC stage B and NLR \u0026ge; 1.5 (17.0 months vs. 8.0 months), though this difference was not statistically significant (p=0.69), likely due to the low number of subjects with BCLC stage C and low NLR (n=9) (Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBCLC staging classification remains the most effective method for stratifying patients and guiding treatment based on disease stage, liver function, and patient performance status. The latest update in 2022 further stratifies BCLC stage B into subgroups, each with distinct treatment options, including transplant, TACE, and systemic therapy. It also recommends incorporating prognostic factors such as the ALBI score and AFP levels at diagnosis into the decision-making process\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubclinical inflammation plays a critical role in the pathogenesis, progression, and metastasis of HCC. In an initial study by Sia et al. (2017), patients were divided into two groups, one of which was termed the \"Immune Class.\" This group, representing 25% of all analyzed HCC cases, was characterized by high PD-L1 expression, enhanced cytolytic activity, and a low number of chromosomal alterations. The Immune Class can be further divided into subtypes: one with a depleted immune response (marked by elevated TGF-β expression and stromal activation) and another with an active T-cell response (featuring a higher presence of CD8\u0026thinsp;+\u0026thinsp;T-lymphocytes and IFN signatures), based on immune cell infiltration\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrent research highlights etiological differences in HCC pathogenesis. Hepatitis B virus (HBV) promotes a tolerant immune microenvironment through depletion of effector CD8\u0026thinsp;+\u0026thinsp;T-lymphocytes and the recruitment of immunosuppressive cells (MDSCs, Tregs, Bregs). In contrast, hepatitis C virus (HCV) contributes to CD8\u0026thinsp;+\u0026thinsp;T-lymphocyte depletion and immune evasion through viral escape mutations and interference with MHC-independent antigen presentation. Alcohol-related liver disease (ALD)\u0026ndash;associated HCC involves the translocation of bacterial PAMPs (pathogen-associated molecular patterns) from the intestine, resulting in an attenuated immune response (CD8\u0026thinsp;+\u0026thinsp;T-lymphocytes) and an increase in suppressor cells within tumor tissue, such as tumor-associated macrophages (M2 phenotype) and MDSCs. In NAFLD-related HCC, chronic dyslipidemia activates dysfunctional cells\u0026mdash;such as NK T cells, Th17 cells, IgA\u0026thinsp;+\u0026thinsp;plasma cells, and CD8\u0026thinsp;+\u0026thinsp;PD1\u0026thinsp;+\u0026thinsp;T lymphocytes\u0026mdash;that disrupt immune surveillance and promote carcinogenesis \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNeutrophils are a critical component of circulating myeloid cells, responsible for initiating early immune responses against bacterial and fungal pathogens. Their primary mechanisms of action include phagocytosis, the release of reactive oxygen species (ROS) through granule secretion into the extracellular space, and the formation of neutrophil extracellular traps (NETs). Neutrophils also influence adaptive immunity through chemokine production\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.Neutrophils from the bone marrow are recruited into tumor tissue via chemokines such as IL-8, IL-17, and G-CSF\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In HCC, tumor cells exhibit increased expression of chemokines including CXCL1, CXCL2, CXCL3, CXCL5, CXCL8, CXCL12, CXCL16, and CC motif ligand 5 (CCL5). This chemokine activity is further supported by tumor stromal cells, tumor-associated monocytes (which secrete CXCL2 and CXCL8), tumor-associated fibroblasts, and hepatic stellate cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, increased production of IL-17 and γδ T cells leads to higher recruitment of neutrophils\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In the tumor microenvironment, neutrophils, like most cells of the myeloid lineage, exhibit various phenotypic variations. N1 and N2 neutrophils represent two main polarities. N1 neutrophils are short-lived, highly cytotoxic, display a mature phenotype, and have strong immunostimulatory activity. In contrast, N2 neutrophils are long-lived, less cytotoxic, exhibit an immature phenotype, and possess pro-angiogenic, pro-metastatic, and immunosuppressive properties\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.The phenotypic transformation of neutrophils is a dynamic process, regulated by their maturation and the specific tissue environment. Over time, this transformation forms a continuum between pro-tumoral and anti-tumoral phenotypes, with possible variations influenced primarily by the surrounding tissue\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Key factors promoting the transformation to the N2 phenotype include TGF-β, IL-6, GM-CSF, PGE2, hyaluronic acid fragments, and hypoxia\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChanges in the neutrophil-to-lymphocyte ratio (NLR), resulting from inflammation and tumor processes, can be detected in peripheral blood. The NLR index was first introduced by one of our study\u0026rsquo;s co-authors, Zahorec, in a study of ICU patients with oncological diseases\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.In oncology, the NLR index has been evaluated in numerous publications as a significant prognostic factor for overall survival in various cancers, including lung cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, malignant melanoma\u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and gastrointestinal tumors\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan additionalcitationids=\"CR30 CR31 CR32\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe NLR index has been investigated in numerous studies on the treatment of hepatocellular carcinoma. Early publications identified an NLR cut-off value of \u0026ge;\u0026thinsp;5 as an independent negative prognostic factor for progression-free survival and overall survival in patients who underwent resection \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003eand liver transplantation\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The first retrospective study analyzing the impact of NLR on survival parameters in patients treated with TACE was published by Huang et al. (2011), demonstrating significantly shorter overall survival in patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;3.3\u003csup\u003e36\u003c/sup\u003e. Similarly, the first study to evaluate survival in HCC patients treated with RFA based on NLR was a retrospective analysis by Chen et al. (2011). It showed that patients with an NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.42 (cut-off selected based on the average value) had worse overall survival and a higher recurrence rate\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCurrently, numerous studies support the NLR index as an effective prognostic factor for overall survival\u003csup\u003e\u003cspan additionalcitationids=\"CR39 CR40 CR41 CR42\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and as a prognostic indicator for recurrence when using ablation techniques, resection, transplantation, or for progression-free survival in cases of TACE or systemic treatment\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Cut-off values used for dichotomization in these analyses range widely, from 1.62 to 5.0, and are typically defined by either the median or Youden\u0026rsquo;s index. This broad range of cut-offs, often optimized for the specific dataset, combined with a lack of prospective studies, complicates interpretation in routine clinical practice and limits NLR's utility as a predictive factor. Additionally, high NLR values are commonly associated with poorer survival outcomes, rather than emphasizing subgroups with a better prognosis.\u003c/p\u003e \u003cp\u003eIn our cohort, progression-free survival for curative treatments was numerically higher in the subgroup with NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5. However, a statistically significant difference was not observed, likely due to the small sample size and the heterogeneity of curative treatment options used (RFA, resection, and liver transplantation). Similarly, in BCLC stage B, despite a 2.5-fold longer progression-free survival in the NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 subgroup, the difference was not statistically significant. In the BCLC stage C subgroup, the difference approached statistical significance, aligning with findings in several published studies\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor the assessment of overall survival, we applied a range of cut-off values for dichotomization, from 1.6 to 5.0 \u003csup\u003e47\u003c/sup\u003e. The closest values to ours were used in studies by Hong et al. (2020) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and Cao et al. (2017) \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In Hong et al.'s study, 441 HCC patients who underwent either resection (n\u0026thinsp;=\u0026thinsp;368) or a combination of TACE followed by resection (n\u0026thinsp;=\u0026thinsp;73) were retrospectively analyzed, with no significant difference in recurrence-free survival (p\u0026thinsp;=\u0026thinsp;0.489) or overall survival (p\u0026thinsp;=\u0026thinsp;0.751) between the groups. However, the 5-year overall survival was significantly worse in patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.6 (78.4% vs. 100%, p\u0026thinsp;=\u0026thinsp;0.027), with the cut-off value determined based on the average value before treatment initiation \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the study by Cao et al. (2017), 426 patients with HBV-associated HCC who underwent resection were analyzed retrospectively. Multivariate analysis indicated significantly worse overall survival in patients with NLR\u0026thinsp;\u0026gt;\u0026thinsp;1.62 (HR: 1.69; 95% CI: 1.13\u0026ndash;2.53, p\u0026thinsp;=\u0026thinsp;0.011), with the cut-off value determined using the Youden index \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In contrast, the cut-off value used in our patient group has not been previously published nor determined through ROC analysis. Our objective was to identify a value that could define a subgroup of patients with significantly longer overall survival within each BCLC stage.\u003c/p\u003e \u003cp\u003eCurative-intent treatments may confound prognosis when using NLR alone. Nonetheless, NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 identified subgroups within advanced BCLC stages with survival comparable to earlier stages. Therefore, by selecting an appropriate NLR cut-off value, it is possible to identify a subgroup of patients in more advanced BCLC stages who may achieve survival rates comparable to those diagnosed and treated at earlier stages. These findings support future applications of NLR in identifying high-risk subgroups who may benefit from intensified or alternative therapies (e.g., adjuvant or neoadjuvant strategies), avoiding overtreatment in poor prognosis patients.\u003c/p\u003e \u003cp\u003eAn NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 as a prognostic factor was associated with a 2-year overall survival increase in BCLC stage B, a 1-year overall survival increase in BCLC stage C, and a 16-month overall survival increase in BCLC stage 0-A, compared to subjects with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5.Patients with hepatocellular carcinoma at BCLC stage B and NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5 had comparable overall survival to those with more advanced BCLC stage C and NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5. Additionally, the overall survival of patients with BCLC stage 0-A and NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5 was similar to that of patients with BCLC stage B and NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5.\u003c/p\u003e \u003cp\u003eThese findings are significant in light of anticipated results from ongoing clinical trials on systemic adjuvant therapy (KEYNOTE-937, EMERALD-2, and CheckMate 9DX) and overall survival analysis in the IMbrave 050 study (NCT03867084, NCT03847428, NCT03383458, NCT04102098, NCT03867084). Such studies may aid in defining high-risk patients in the treatment of locally advanced disease and in identifying the benefits of combination therapy. For instance, the EMERALD-1 study combines TACE, durvalumab, and bevacizumab (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2024.42.3_suppl.LBA4\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2024.42.3_suppl.LBA4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while ongoing studies include LEAP-012, CheckMate 74W, ABC-HCC, and RENOTACE (NCT04246177, NCT04340193, NCT04803994, NCT047778)].\u003c/p\u003e \u003cp\u003eImportantly, NLR should be interpreted cautiously when applying cut-offs derived from different populations or treatment contexts. Additionally, dichotomizing continuous variables like NLR may oversimplify prognosis. ROC curve optimization and modeling NLR as a continuous variable may enhance its utility.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNLR is a promising prognostic biomarker in HCC across BCLC stages. Lower NLR (\u0026lt;\u0026thinsp;1.5) is associated with improved survival, particularly in stages B and C. Further prospective studies are needed to validate findings and establish clinically meaningful NLR thresholds.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol complies with the 1964 Helsinki Declaration, its subsequent amendments, and the principles of good clinical practice. The protocol was approved by the Ethics Committee of the East Slovakia Oncological Institute on May 27, 2021 (approval code: EK/2/05/2021).\u003c/p\u003e \u003ch2\u003eConflicts of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any particular funding. It was performed as part of the employment of the authors at their respective universities.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization:DŠ, and PJDatacuration:DŠ, JG, LB, SKFormalanalysis:JG, MJInvestigation:DŠ, IA, SAS, RB, MM, MR, ĽS, MŽMethodology:DŠ, RZ, PJSupervision: SD, PJWriting\u0026mdash;original draft:DŠ, JGWriting\u0026mdash;review\u0026amp;amp;editing:SD, IA, SAA, RB, MM, MR, ĽSAllauthorshaveread and agreed to thepublishedversion of themanuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung, H. \u003cem\u003eet al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA. Cancer J. 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Lett.\u003c/em\u003e (2017) doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ol.2017.7612\u003c/span\u003e\u003cspan address=\"10.3892/ol.2017.7612\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"hepatocellular carcinoma, prognosis, NLR, neutrophile-to-lymphocyte ratio","lastPublishedDoi":"10.21203/rs.3.rs-8360322/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8360322/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma (HCC) is the 6th most common malignancy worldwide with variable prognostic outcomes. The neutrophil-to-lymphocyte ratio (NLR) has emerged as a potential prognostic marker, but its applicability across BCLC stages remains unclear. This study aims to assess the prognostic value of NLR in patients with HCC across different BCLC stages. We conducted a multicenter retrospective analysis of 312 HCC patients diagnosed between 2010 and 2016. Patients were divided into BCLC stages 0-A, B, and C and further stratified by NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 and NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5. Overall survival (OS) and progression-free survival (PFS) were analyzed, with comparisons across stages and NLR subgroups. Patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 were trending toward improved OS in each BCLC stage, with significant differences observed in stages B and C. In BCLC stage B, NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 was associated with a twofold increase in PFS compared to NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5. BCLC stage C patients with NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.5 achieved OS outcomes comparable to BCLC stage B patients with NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.5. Although limited by sample size and heterogeneity in treatment modalities, these findings suggest NLR as a relevant prognostic factor across HCC stages. In conclusion, lower NLR was associated with better survival outcomes across BCLC stages, supporting its use as a prognostic marker in HCC. Future prospective studies are needed to confirm these findings and to determine optimal NLR cut-offs for clinical practice.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Neutrophil-to-Lymphocyte Ratio Across BCLC Stages in Hepatocellular Carcinoma: A Multicenter Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:00:43","doi":"10.21203/rs.3.rs-8360322/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-20T18:42:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T22:00:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245880949529606994014733202391345654237","date":"2025-12-29T09:00:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177020040384925512301314608213537272425","date":"2025-12-26T10:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318293007446268820509166660119379966295","date":"2025-12-22T13:58:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T04:10:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T14:18:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-15T14:12:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bratislava Medical Journal","date":"2025-12-14T21:29:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bratislava-medical-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bratislava Medical Journal](https://link.springer.com/journal/44411)","snPcode":"44411","submissionUrl":"https://submission.springernature.com/new-submission/44411/3","title":"Bratislava Medical Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b6da8dd1-b475-4c79-89f6-f8be0d7fb843","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T05:24:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:00:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8360322","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8360322","identity":"rs-8360322","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}